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Flow injection – ICPMS:
Optimization and applications for trace element analysis
INAUGURALDISSERTATION
zur Erlangung der Doktorwürde
an der Naturwissenschaftlichen Fakultät
der Karl – Franzens Universität Graz
vorgelegt von Irene B. Rodriguez
im Oktober 2008
ii
This work was performed at the Institute of Chemistry – Analytical
Chemistry, Karl – Franzens University Graz, under the supervision of Ao. Univ.
Prof. Mag. Dr. Walter Goessler in the period from November 2005 to October 2008.
iii
Abstract
Trace element analysis is required for the proper assessment of health risk
and environmental impact of the contaminant of interest. The ubiquitous nature of
arsenic and its well-known varying species-dependent toxicity demands for rapid
and accurate means of determination. The coupling of HPLC with ICPMS offers
an excellent way for the speciation analysis of arsenic. This set-up was optimized
for a flow injection system using 0.3% HNO3 with 10% methanol (v/v) and 20 mM
phosphate buffer with 10% methanol (v/v) as eluents. The detection limits
determined for the optimized method were 38 ng L-1 and 62 ng L-1 for the nitric
acid and phosphate buffer eluents, respectively. The method was validated using
different reference/control materials representative of different matrices. Results
from analysis of BCR 422, DOLT 3, DORM 2, IAEA 407, LUTS 1, TORT 2, NIST
certified reference water 1643e, NIES human urine 18 and SERONORM FE 1114,
compared against values determined from conventional ICPMS measurements
show good potential of the optimized method for trace arsenic determinations in
various samples.
A modification of the method which enables direct analysis of samples
without pretreatment was also optimized. The same HPLC-ICPMS set-up was
used and 0.3% HNO3 with 10% methanol (v/v) was employed as eluent. In this
modified method, the HPLC was configured to draw 15 µL of the internal
standard mixture (100 µg L-1 each of Ge and Te, prepared in 3% HNO3), and then 5
µL of the sample/standard prior to injection. This afforded a dilution which is
precise and completely automated. The determined detection limit was 40 ng L-1.
Validation was carried out using several ClinChek control materials and the
method was also applied to actual urine samples. The results show the advantage
of the flow injection-ICPMS method for rapid determination of arsenic in various
samples which entail less sampling handling and higher sample throughput.
iv
The flow injection ICPMS method was also used to determine arsenic
content in fish sauce samples. The fish sauce samples were found to contain
arsenic in the range 0.69 to 2.75 mg L-1. Speciation analysis performed on the
samples revealed that most of the arsenic was present as arsenobetaine with traces
of arsenocholine, trimethylarsine oxide, and trimethylarsenopropionate. These
results suggest that fish sauce is safe for consumption. Total arsenic content
determined by flow injection ICPMS showed good agreement with the sum of
species and the total values obtained by conventional ICPMS measurements
performed on digested samples. This highlights the possibility of applying the
flow injection-ICPMS method for column recovery determinations.
v
Acknowledgement
It has always been a pleasure to reach this part in the preparation of a manuscript. One thinks not of the obstacles passed, but one rather reminisces the joys of the hurdles, and the people who have been supportive along the way. It will be virtually impossible to name each and every person who had shared this path with me but nonetheless, I take this opportunity to thank those who come to mind. I am deeply grateful to my supervisor, Dr. Walter Goessler, for the chance to work with him and for giving me the opportunity to be his student. I am thankful to my examiner, Dr. Kurt Kalcher, for accommodating my schedule in such a short span of time and for his comments. I am indebted to Dr. Kevin Francesconi, for being the chairman of the Rigorosum committee and for his counsel in a lot of things that goes beyond this phase in my life. I am grateful to the Austrian Academic Exchange Services for the scholarship granted to me, without that support it would have been a different story. I am thankful to the Institute of Analytical Chemistry workgroup at the Karl-Franzens University Graz. The entire workgroup has been supportive in many ways, not just in the line of work but in integration to life in Graz as well. I thank Astrid for all the help ever since that first November morning when I had to fix my documents. I acknowledge Linda’s persistence in giving me those lessons with the bike – proving that it is not an impossible task. I thank Alice for that wonderful cartoon which never fails to give me a smile. To Sakda and Maria, we will make that greatest trip in the world someday, somehow. I am surely glad to have met Mojtaba and his family. I thank DJ and Georg for their votes of confidence. Theirs were the vote for sanity in my sometimes crazy world. I thank my Grazer brothers, Au and Pejmann. They were the voices of reason in a psychedelic life. I am glad to have met Yuy, Titar and Chanai. They have shown me the wonders of Thai food. I have also experienced Wiener schnitzel evenings and the like with Alex, Stefan, Dani and all those people who have come to dine in WE 13. To Mitch, for being my sounding board all these times. I thank Alison and Kevin for the wonderful Australian Christmas lunches in Graz. It surely was a pleasure to be part of that tradition.
vi
I am grateful to my entire family for believing in me, for allowing me to be who I am, and supporting my decisions – some of which are not easy to make at all. To the loving memory of my father, you wanted me to be a doctor, well, it’s a different one but perhaps you will agree with it anyway. To my sister, for always making me feel that indeed, no matter what, I am her one and only Han. To my brothers, for being there as always – the times we’ve spent together brings joy even when the day is grey and the clouds speak of gloomy weather. To my mother, her patience is beyond my understanding but I am sure glad that she has it – for her support in my dreams and my hopes – for her love which is truly only one mother can give. To Jo, I may be your compass but then, you are my anchor. It is impossible to write the contribution each and everyone had to my life, but to all those who I have met, know that the times I have spent with you, and the lessons you have imparted in me, will forever form part of me and my memory.
vii
It is not the magnitude of the waves we create which will measure our success.
It is the constant little ripples, the perseverance we have to make them,
enabling us to make the world a little better, that will make all the difference.
graz, october 2008
viii
Curriculum Vitae
Irene B. Rodriguez
39 Masaya St., Old Capitol Site
Diliman, Quezon City, 1101
Philippines
Email: [email protected]
Phone : +63 919 588 6175
Educational Background
PhD Chemistry, November 2005 to October 2008 Karl-Franzens University Graz, Austria Thesis : “Flow injection – ICPMS : Optimization and applications in trace
element analysis” MS Chemistry University of the Philippines Diliman, Quezon City, Philippines Thesis : “Analysis of Disinfection By-Products in Metro Manila Drinking Water” BS Chemistry University of the Philippines Los Baños, Laguna, Philippines Thesis : “Analysis of the Volatile Compounds of Six Scented Rice Varieties (Oryza
sativa L.)” Work Experience Faculty Institute of Chemistry, University of the Philippines Diliman, Quezon City, Philippines November 2000 to May 2005 University Research Associate Marine Science Institute, University of the Philippines Diliman, Quezon City,
Philippines October 1998 to October 2000 Science Research Specialist Philippine Rice Research Institute, University of the Philippines Los Baños,
Philippines November 1997 to May 1998
ix
Scientific Papers/Presentations: Publications:
Rodriguez, I. B., Raber, G. and Goessler, W. (2009). Arsenic speciation in fish sauce samples determined with HPLC coupled to inductively coupled plasma mass spectrometry. Food Chemistry, 112, 1084-1087.
Rodriguez, I. B., Francesconi, K. A. and Goessler, W. (2008). A rapid method
for the determination of total arsenic in biological digests and aqueous extracts by flow injection inductively coupled plasma mass spectrometry. Journal of Analytical Atomic Spectroscopy, 23, 235-239.
Rodriguez, I. B., Quibuyen, T.A.O. and Espino, M. P. B. (2006). Analysis of volatile
disinfection by-products in drinking water. Kimika, 22, 1-6.
Oral Presentations:
Rodriguez, I. B., Raber, G. and Goessler, W., Arsenic speciation analysis in fish sauce samples using HPLC coupled with ICPMS, 15th Young Investigators’ Seminar on Analytical Chemistry, Ljubljana, Slovenia, July 2-5, 2008.
Rodriguez, I. B., Francesconi, K. A.. and Goessler, W., Flow injection ICPMS:
an elegant way for trace arsenic analysis, 4th Austrian Society for Analytical Chemists Forum, Vienna, Austria, May 30-31, 2008.
Rodriguez, I. B., Francesconi, K. A. and Goessler, W., Rapid method for
arsenic analysis by flow injection inductively coupled plasma mass spectrometry, 14th Young Investigators’ Seminar on Analytical Chemistry, Pardubice, Czech Republic, June 25-28, 2007.
Rodriguez, I. B., Quibuyen, T.A.O. and Espino, M. P. B., Analysis of volatile
disinfection by-products in drinking water, 20th Philippine Chemistry Congress (PCC), Baguio City, Philippines, 11-13 April 2005.
Poster presentations:
Rodriguez, I. B., Quibuyen, T. A. O., and Espino, M. P. B., Analysis of trihalomethanes and haloacetonitriles in Drinking Water. 32nd KKP-ST Annual Convention. October 21-22, 2003, UPLB, Laguna, Philippines.
Bolivar, J. G., Rodriguez, I. B., and Hernandez, H. P., Comparison of Volatile
Compounds Emitted by Cooked and Base-Treated Milagrosa Brown Rice (Oryza sativa L.). Asia-Pacific Conference on Analytical Science and 18th Philippine Chemistry Congress (PCC), February 19-23, 2002, Mandaluyong City, Philippines.
Rodriguez, I. B., and Ganzon-Fortes, E. T., Laboratory Production of Gametophytes
from Tetraspores of the Agarophyte, Gelidiella acerosa (Forssk.) Feldmann & Hamel. 5th National Symposium in Marine Science, October 17-19, 1999, Quezon City, Philippines.
x
Seminars/Training
Participant, 3rd Austrian Society for Analytical Chemists Forum, Linz, Austria, June 1-2, 2007.
Participant, 2nd Austrian Society for Analytical Chemists Forum, Graz, Austria, June 9-10, 2006.
Participant, Seminar-Workshop for Teachers. Foundation for Upgrading the Standard
of Education, Inc., May 20–21, 2004. Makati City, Philippines. Technical staff, 3rd Asia- Pacific Marine Biotechnology Conference. November 28-December 1, 1999. UP-Marine Science Institute. Quezon City, Philippines.
Awards received
Best Presenter, 15th Young Investigators’ Seminar on Analytical Chemistry, Ljubljana, Slovenia, July 2-5, 2008. Best Presenter, 14th Young Investigators’ Seminar on Analytical Chemistry, Pardubice, Czech Republic, June 25-28, 2007.
Other eligibility/examinations taken Philippine Chemistry Licensure Examination, passed, September 2000. Philippine Career Service Professional Examination, passed, September 1998.
xi
Table of contents
Page
Abstract iii
Acknowledgement v
Curriculum Vitae viii
List of Abbreviations xii
1. Introduction 1
2. Review of related literature 7
3. Methodology 38
3.1 Chemicals and reagents 38
3.2 Reference/Control materials 38
3.3 Sample preparation 40
3.3.1 Arsenic analysis 40
3.3.1.1 Sample mineralization by microwave digestion 40
3.3.1.2 Total element determination 41
3.3.1.3 Arsenic speciation analysis by HPLC-ICPMS 41
3.3.1.4 Arsenic speciation analysis by HPLC-ESIMS 42
3.3.1.5 Flow injection – ICPMS analysis 42
3.3.2 Calcium analysis 43
3.4 Data treatment 44
4. Results and discussion 45
4.1 Flow injection ICPMS method for arsenic determination 45
4.2 Analysis of fish sauce samples 63
4.3 Calcium analysis by flow injection-ICPMS 75
5. Summary 82
References 85
List of Figures 94
List of Tables 96
xii
List of Abbreviations AAS Atomic absorption spectroscopy
AB Arsenobetaine
AC Arsenocholine
AFS Atomic fluorescence spectroscopy
APCI/MS Atmospheric pressure chemical ionization mass
spectrometry
p-ASA p-Aminophenylarsonic acid
As(III) Arsenite
As(V) Arsenate
ASE Accelerated solvent extraction
ATP Adenosine triphosphate
BCR Community Bureau of Reference
CE Capillary electrophoresis
DMA Dimethylarsinate
DCP Direct current plasma
DOLT Dogfish liver
DORM Dogfish muscle
EDTA Ethylenediaminetetraacetic acid
ESIMS Electrospray ionization mass spectrometry
EU European Union
FAAS Flame atomic absorption spectroscopy
FI-ICPMS Flow injection with inductively coupled plasma mass
spectrometry
GC Gas chromatography
GC-MS Gas chromatography mass spectrometry
GFAAS Graphite furnace atomic absorption spectroscopy
HG-AFS Hydride generation atomic fluorescence spectroscopy
HPLC High pressure liquid chromatography
IAEA International Atomic Energy Agency
xiii
IC Ion chromatography
ICPAES Inductively coupled plasma atomic emission spectroscopy
ICPMS Inductively coupled plasma mass spectrometry
IEC Ion-exchange chromatography
IUPAC International Union for Pure and Applied Chemistry
LC Liquid chromatography
LUTS Lobster hepatopancreas (non-defatted)
MA Methylarsonate
MCL Maximum contaminant level
MIP Microwave induced plasma
MIPOES Microwave induced plasma atomic emission spectroscopy
4-NPAA 4-Nitrophenylarsonic acid
NIES National Institute of Environmental Studies
NIST National Institute of Standards and Technology
PAAs Phenylarsonic acids
PEEK Polyetheretherketone
RPLC Reversed-phase liquid chromatography
SEC Size exclusion chromatography
SFC Supercritical fluid chromatography
TETRA Tetramethylarsonium ion
TMAO Trimethylarsine oxide
TORT Lobster hepatopancreas
USEPA United States Environmental Protection Agency
UV Ultra-violet
WHO World Health Organization
1
Chapter 1. Introduction
Take a book about poisons and there is a great probability that you will be
reading a chapter on arsenic. Arsenic has a long history associated with its toxic
properties that every time it is mentioned someone, who is not particularly
informed about the differences in the toxicities of a wide range of arsenic species,
will generally move away from the topic. Arsenic derives its name from the Greek
word arsenikon which means masculine or potent, a word which was originally
borrowed from the Persian word Zarnikh which means yellow orpiment. It was
discovered in 1250 A.D. by Albertus Magnus and was first prepared by Johann
Schroeder in 1649 (Polmear, 1998; Stoeppler, 2004).
Arsenic is a metalloid that occurs in nature in powder, amorphous or
crystalline forms. The wide distribution of arsenic is attributed to natural
processes and anthropogenic input. It can be released as a result of volcanic action,
erosion of rocks, forest fires and geothermal weathering (Cullen and Reimer, 1989;
B’Hymer and Caruso, 2004). Distribution of the metalloid is elevated by metal
mining and smelting, combustion of fossil fuels, and through use of pesticides and
animal feeds. These processes and activities lead to presence of trace arsenic
concentrations in the air, soil, water, and living organisms. Arsenic occurs in
different oxidation states: -3, 0, +3 and +5. The most common arsenic compounds
are present in the trivalent and pentavalent forms, usually referred to as arsenites
and arsenates, respectively.
Minerals that contain arsenic include realgar (As4S4), orpiment (As2S3),
arsenolite (As2O3), arsenopyrite (FeAsS), and loellingite (FeAs2). Other common
arsenic-containing minerals are cobaltite (CoAsS), skutterudite (CoAs3), mimetite
(Pb5(AsO4)3Cl), and erythrite (Co3(AsO4)2·8H2O). Processing of these materials
releases arsenic in the air in the form of arsenic trioxide. The properties of arsenic
closely resemble that of phosphorus. Both form colorless, odorless and crystalline
oxides (As2O3 and As2O5; P2O3 and P2O5) which readily dissolve in water. Both
2
also form unstable and gaseous hydrides (AsH3 and PH3). These similarities
between the two enable arsenic to substitute phosphorus in biochemical reactions.
When the less stable arsenate replaces the stable phosphorus anion in phosphate, a
rapid hydrolysis of high-energy bonds in compounds such as adenosine
triphosphate (ATP) occurs (B’Hymer and Caruso, 2004). Another mechanism
wherein arsenic impairs biological processes is when arsenite binds with sulfhydyl
groups and disrupts the function of sulfhydryl-containing enzymes. This leads to
the inhibition of the pyruvate and succinate oxidation pathways, and also the
tricarboxylic acid cycle (B’Hymer and Caruso, 2004).
The reputation of arsenic as a poison is so great that it was regarded in the
past as the poison of kings and the king of poisons. Recent popular notion about it
is still related to its toxicity as epitomized in mainstream television shows and
movies. However, arsenic has enjoyed its share of being beneficial. Prior to the
advent of antibiotics, arsenic was the popular cure for some diseases.
Arsphenamine (proposed by Paul Ehrlich) was the medicine of choice against
syphilis and trypanosomiasis (Cullen and Reimer, 1989). Fowler’s solution
(proposed by Thomas Fowler, hence named after him), which is 1% potassium
arsenite dissolved in water, has been used in various ways since its introduction as
a potent medicine. It was used as treatment against psoriasis. Until now, arsenic
medication in this form, used in conjunction with ascorbic acid and some other
agents such as melphalan and dexamethasone, is still the best drug against acute
promyelocytic leukaemia (APL) or multiple myeloma (Šlejkovec et al., 2008).
Arsenic compounds were used industrially as herbicide, pesticide and,
most commonly, as feed additive to improve growth of poultry although arsenic
use in these applications have been reduced in recent years because of health
concerns (Kumaresan and Riyazuddin, 2001; Brisbin et al., 2002; Sun et al., 2002).
One such insecticide is sodium methyl arsonate, which replaced lead arsenate, for
use in fruit-bearing trees. Roxarsone (4-hydroxy-3-nitrobenzenearsonic acid), p-
ASA ( p-aminophenylarsonic acid), 4-NPAA (4-nitrophenylarsonic acids) and
3
PAAs (phenylarsonic acids) are several arsenic-containing feed additives used to
control cecal coccidiosis, act as growth promoters, and also for better pigmentation
and increase in egg production. Use of these additives are prohibited days prior to
bringing these food products in the market since arsenic may still be present in the
tissues, e.g. meat and eggs.
Arsenic is also widely used in wood preservation, in the form of chromated
copper arsenate, which works effectively against rot or insect infestation. This was
very popular for structural and outdoor building materials but its use also saw
decline in recent years because of reported cases of poisoning. In some countries
though, arsenic use for wood preservation is still prevalent. Arsenic was also used
as a pigmentation agent (Cullen and Reimer, 1989). Scheele’s green (copper
arsenate) was used as a colouring agent in sweeteners. Another colouring agent is
Paris green (or Emerald green, copper acetoarsenite). Other industrial applications
of arsenic include use in bronzing, pyrotechny, and in the semiconductor industry.
Gallium arsenide is considered to be vital in the semiconductor industry because
integrated circuits made of this compound perform better compared to those
made with silicon (Dutov et al., 1997). This is typically found in discrete
microwave devices, light-emitting diodes, lasers, photoelectric devices and other
devices. Lately, the use of arsine gas as dopant in the production of
semiconductors has increased although the use of less toxic forms, such as
tributylarsine, is being considered (http://www.astdr.cdc.gov/csem/arsenic/
exposure_pathways.html, accessed 2.10.2008).
The status of arsenic as a major element of environmental and health
interest makes it an analyte of vital importance. This interest is even more
intensified by the complexity of its properties and its ubiquitous nature. Arsenic is
associated with skin diseases, peripheral vascular disease, increased risk of
cardiovascular disease, diabetes, and gastroenterological disease (Cullen and
Reimer, 1989; Mandal and Suzuki, 2002). Exposure to arsenic, mainly through
drinking water, has been reported to be linked to incidence of cancers in the skin,
4
bladder, lungs, liver, and kidney. Much has been learned about arsenic and its
species but still, improvements in the equipment used for analysis offer new
possibilities for a better understanding of this element. These advancements come
in an opportune moment since species information, specifically as regards arsenic,
is becoming to be more of concern rather than total arsenic concentration. Better
measurement techniques also entail easier and more reliable basis for drafting
regulations concerning arsenic and its compounds.
From the classical methods utilizing colorimetric and gravimetric methods,
the quantitative determination of arsenic has evolved to harness the advances of
technological improvements. Colorimetric and gravimetric methods lack the
sensitivity of newer techniques and were also semi-quantitative when used for
arsenic determinations. The spectroscopic methods, both absorption and
fluorescence, mainly replaced these classical techniques offering better sensitivity
and lower detection methods. Atomic absorption spectroscopy (AAS) was used
with flame (FAAS) or graphite furnace (GFAAS) as source with the latter
providing better performance. Improved detection limits and sensitivity were
reported for spectroscopic methods applied to samples which were allowed to
pass a hydride generator thus allowing selective determination of hydride-
forming arsenic species. The advent of inductively coupled plasma changed the
research picture even more. Inductively coupled plasma, with both mass
spectrometry (ICPMS) and atomic emission spectroscopy (ICPAES), offer even
lower detection limits and much better sensitivities. The wider linear range,
especially in the case of ICPMS, was also another advantage of the plasma
techniques.
The arsenic speciation in the environment and in living organisms is so rich
that an in-depth discussion would require more than what this work can cover.
For more information about the species and their distribution, several review
articles are then highly suggested (Cullen and Reimer, 1989; Edmonds and
Francesconi, 1993; Gong et al., 2002; Francesconi and Kuehnelt, 2004). In general,
5
arsenic is usually present in the trivalent forms under reducing conditions and in
the pentavalent forms under oxidizing conditions or in oxygenated environments.
The inter-conversion between the two redox states is governed by the redox
potential (Eh), pH and biological processes (Gao and Burau, 1997). In marine
organisms, most of the arsenic present is comprised of the organoarsenicals such
as arsenobetaine (AB), arsenocholine (AC), trimethyarsine oxide (TMAO),
tetramethylarsonium ion (TETRA), and arsenosugars. Marine organisms contain
higher concentrations of arsenic, but mostly in the organic forms, as opposed to
their terrestrial counterparts which can contain arsenic in the inorganic forms.
Thus, even with higher arsenic content, seafood is still deemed suitable for
consumption because organoarsenicals, specifically arsenobetaine, which
constitutes most of the arsenic in these food sources, are believed to be non-toxic.
One notable exception, though, is from a recently published work by Sloth and
Julshamn (2008). They studied blue mussels collected from various sampling sites
in Norway and reported the unusually high levels of inorganic arsenic in samples
from two counties included in the study, Sogn and Fjordane, and Hordaland. This
recent finding again raises questions as to safety of marine-derived food products
for consumption.
This research was undertaken with several objectives in mind. What was
foremost in the goals was to have a flow injection ICPMS method, which will
utilize the same high performance liquid chromatography (HPLC) coupled with
ICPMS set-up used for speciation analysis, for total arsenic determinations in
various samples. Also, the method should be applicable to the same samples that
are being subjected to speciation analysis thus allowing ease of switching from one
mode of measurement to the other. This means that samples will not be treated
further prior to total arsenic determination; thus the species information will be
intact, hence the column recoveries determined will be more reliable.
6
Specifically, the following are the objectives of this work:
1. To optimize a flow injection-ICPMS method suitable for arsenic
determinations in various samples with trace to high concentrations and
also samples which are available in limited quantities.
2. To validate this method using reference materials which are representative
of matrices usually subjected to arsenic analysis.
3. To apply the method to real samples and evaluate performance in
determination of arsenic in samples with simple to complex matrix.
4. To evaluate performance of the method in the determination of column
recoveries.
7
Chapter 2. Review of related literature
The recent decades have been a whirlwind of technological advancements
which have brought changes in immense proportions to almost everyone’s way of
life. Almost everything is touched by this technological advancement but the
changes are more visible in the sciences. Improvement is viewed not only because
of a novel idea but also due to the unique advantage it can offer in terms of quality
and ease of use. In the scientific field, changes brought about by technological
advances are very obvious with automation and speed of data acquisition the
driving points behind most of advancements made in recent years. Life sciences,
in general, are being pushed to be in the forefront of change not only because of
the capability of the technology available, but also because of the increasing
concern to improve the quality of life. Chemistry is in front of all of these
challenges playing a significant role not just in the essential industries as food and
health, but also in the environmental sector. Analytical chemistry, specifically, is
viewed as a significant aspect for quality assurance of products and also to ensure
that compounds that may pose health risks are monitored, evaluated, and
analyzed fast and accurately. The demand for more rapid and accurate analytical
methods has been great because of the expectations as a result of the technological
advancement, and also because of the increasing awareness about chemicals with
possible health and environmental risks.
In the field of analytical chemistry, all of these advances led to development
of methods for determination of various analytes of concern such as heavy metals,
poly-aromatic hydrocarbons, volatile organic compounds, and persistent organic
pollutants. Recent improvements in technical capabilities have greatly impacted
research in the field of trace metals analyses, in particular. A literature search
specifically meant for methods applicable to trace analysis, either total element
analysis or speciation analysis, would result to a varied mix of instrumentation
and also with much lower detection limits being reported as new techniques are
continuously employed in the method development. In metals analysis, the
8
challenge is not just determination of total concentrations but also information
about the species present. This is mainly because of the increasing awareness of
the species-related effect of heavy metals to humans and the environment in
general and hence, led to the introduction of the area of speciation analysis.
Knowledge of the species distribution has elevated to be of utmost concern
to better understand the environmental impact and for critical evaluation to assess
health effects. It is widely accepted that speciation is necessary for the proper
assessment of the toxicological impact of elements of interest since toxicities of
species, a phenomenon exemplified perfectly by the different arsenic species
widely available in nature. This led to the bombardment of the literature available
with methods for speciation analysis. Most of these methods have utilized a
separation step coupled with a detection step. The most popular of these coupled
techniques is high performance liquid chromatography (HPLC) with inductively
coupled plasma mass spectrometry (ICPMS) for selective detection because of the
ease of using these two techniques together. The use of HPLC with detection using
electrospray ionization mass spectrometry (ESIMS) has also been applied in
various methods because unlike ICPMS, detection with ESIMS gives molecular
information. However, ESIMS is more susceptible to matrix effects which entail a
major hindrance in the use of this technique. Separation by gas chromatography
(GC) has also been considered by researchers but the main drawback of using GC
is the need to convert the analytes to a volatile form which may be difficult for
some target compounds and virtually impossible for others. Also, conversion of
the analytes to a form suitable for GC entails an additional sample treatment step
which can lead to either loss of the analytes or introduction of possible
contaminants which is undesirable in trace element analysis.
Other techniques such as atomic absorption spectroscopy (AAS), atomic
fluorescence spectroscopy (AFS), inductively coupled plasma atomic emission
spectroscopy (ICPAES), and various electrochemical techniques have been
optimized for selective detection of metal-containing contaminants. Sample
9
preparations have also dealt with different extraction techniques from the simple
liquid extraction to more advanced ones like solid phase extraction and
supercritical fluid extraction. The extraction technique of choice is highly
dependent on the chemical and physical characteristics of the target analytes
which also dictate the choice of the solvent for the extraction. In some cases, the
detector of choice also limits the choice of solvents for extraction. One such
example would be in the use of ICPMS which is preferential to solvents with low
salt content. This review will cover instrumentation used in the field of trace metal
analysis with special focus on techniques used for the elements that are of
relevance to this dissertation work. Also included are the separation techniques,
basically chromatography, used prior to the selective detection of the elements.
Spectroscopic methods, such as AAS and AFS, became popular ways for
elemental determination because of the comparative ease of use of the
instrumentation involved. For arsenic analysis, though, use of these techniques
has declined in recent years because of newer technology available. These
spectroscopic techniques suffer from low sensitivity and are therefore not suitable
for ultra trace determinations. Moreover, high background noise is encountered
because of the complexity of the matrices being analyzed. The use of hydride
generation enhanced sensitivity and reduced the background noise but only up to
a certain extent. One more disadvantage as regards GFAAS is the difficulty of
coupling it online with separation techniques. Hence, for GFAAS detection, one
way to tackle this difficulty is doing a prerequisite fractionation of the sample and
then subsequent batch analysis which makes the entire analytical procedure
tedious and time-consuming. Fractionation, as defined by the IUPAC, is a process
of classification of an analyte or a group of analytes from a certain sample
according to physical (e.g., size, solubility) or chemical (e.g., bonding, reactivity)
properties (Templeton et al., 2000). Also, instrumental set-up may require
changing cathode lamps more often and it may require changing the operating
conditions depending on the element being analyzed. For AFS, the inherent
disadvantage is the observed light scattering when real samples are analyzed.
10
Despite these disadvantages, AAS and AFS are still the choice for routine
measurements because of the low operation and instrument costs.
The launch of instruments with ionization sources utilizing the argon-
supported inductively coupled plasma changed the scenario in trace element
determinations. Other plasma sources that are also available are the direct current
plasma (DCP) and the microwave induced plasma (MIP). Two inherent
characteristics of plasma are that they can conduct electricity and also affected by
a magnetic field. The very high temperatures afforded in argon-supported plasma
ionization sources enable almost simultaneous desolvation, vaporization,
atomization, and ionization of analytes. The emitted ions produced in the plasma
are very well-suited for emission measurements and also for introduction in mass
spectrometers. The high temperature involved also makes it easy to populate the
plasma with ions and this leads to different consequences depending on the mode
of detection. With ICPAES, a large number of ions from different elements from
the sample may lead to spectral interferences which can make quantification
difficult. As for ICPMS, the mass analyzer separates the ions according to their
m/e ratios thus making it more sensitive as a detection tool.
Coupling of the plasma-based techniques, ICPAES and ICPMS, to most
separation systems is easy because typical HPLC flow rates are suitable for use
with the introduction systems of these plasma-based instruments. However,
choosing the right solvent is dictated by the nature of the solvent because these
instruments are very sensitive as regards solvents. The use of solvents from Na
and K salts is not advisable because these may deposit in the cones and surfaces of
the sample introduction system and cause instability of the plasma, if not the
extinction of the plasma itself. Also, the use of organic modifiers can produce
instability, on one hand, and enhance response of some target analytes with low
first ionization potential compared to carbon, i.e., arsenic and selenium, on the
other hand (Larsen and Stürup, 1994; Larsen, 1998; Kovačevič and Goessler, 2005).
This enhanced response is already widely researched and it has been attributed to
11
charge transfer reactions occurring in the plasma between the charged carbon ions
and the atoms of elements with ionization potentials from 9 to 11 eV; i.e., 9.8 eV
and 9.7 eV for arsenic and selenium, respectively (Kovačevič and Goessler, 2005).
This signal enhancement is also caused by improvement in the nebulisation of the
sample and the shift of the zone of maximum ion density in the plasma.
ICPAES is a robust technique but compared to ICPMS, its application is
preferred for analysis of major elements. For ultra trace determinations, ICPMS is
without doubt the technique of choice because of high sensitivity that can be
achieved using these instruments. The wider linear range and better performance
for multi-elemental determination also bring ICPMS ahead of the other
techniques. These built-in characteristics plus the capability for isotope
measurements solidifies the position of ICPMS as the technique of choice for
elemental determination. Use of these plasma-based instruments is limited though
because of the instrument and operation costs that a low-budget laboratory would
resort to the spectroscopic instruments for needed routine measurements.
ICPMS usage also comes with great deal of difficulties. Aside from its low
tolerance to solvents with high organic and salt content, it also suffers from
isobaric and especially, polyatomic interferences. For the solvents, instead of using
buffers from Na and K salts, buffers from ammonium salts are then used as
alternative. To counteract the effect of organic modifiers, most of the recent ICPMS
models were then configured to accommodate these solvents by using cooler
spray chamber, decreasing solvent input by the use of micro-flow nebulizers, and
then through modifications in the instrumentation to allow introduction of oxygen
to help convert carbon to carbon dioxide. The main interferences in ICPMS are due
to isobaric and polyatomic interferences. Isobaric interferences are from elements
with the same nominal mass as that of the target analyte. Polyatomic interferences
come from the combination of species in the plasma. Another source of
interference is the formation of doubly-charged species resulting from the loss of 2
electrons instead of one. Mathematical corrections can be employed to account for
12
polyatomic interferences. Recent instruments though are equipped with
reaction/collision cells, employing hydrogen or helium as the reaction or collision
gas, to reduce these interferences more effectively. Other reaction gases that may
be used are NH3 and CH4. The use of these reactive gases may lead to polyatomic
ions which are then eventually ejected from the ICPMS system using effective
configuration in the kinetic energy bias voltage. The use of helium as collision gas
offers advantage of its wider applicability because it may be used for complex
matrices. For the use of reaction gas, however, applicability is limited to simple
matrices because of the probable formation of polyatomic interferences or prior
knowledge of the sample matrix so that the nature of the polyatomic interferences
that may be produced will be predicted beforehand.
Another detection technique that has gained popularity in recent years is
electrospray mass spectrometry. This technique is based on the formation of a
continuous spray from the solvent used where the ions are formed or, if already
present, extracted and directed towards the mass spectrometer. Stewart (1999)
tackled the progress in the development of the field as a tool for elemental
speciation. In his review, he covered application of electrospray MS in the
detection of the presence of metal ions and non-metallic inorganic species and
discussed the impact of the technique in gas phase chemistry as well as biological
mass spectrometry. He also provided an overview of the types of
species/complexes that were already studied using electrospray MS along with a
discussion of analytical aspects pertaining to the analyte type. In the end, Stewart
(1999) concluded that high backgrounds due to the chemical noise and
suppression due to matrix effects are the important factors limiting the application
of electrospray MS. Recently, there is also an increase in the use of tandem mass
spectrometry for molecular identification and more and more for application in
reaction monitoring (Francesconi and Pergantis, 2004; Nischwitz et al., 2006; Yuan
et al. 2008).
13
The current use and potential applications of electrospray (ESIMS) and
atmospheric pressure chemical ionization (APCI-MS) mass spectrometric
techniques to speciation analysis was discussed comprehensively by Rosenberg
(2003). The popularity of these techniques are primarily owed to the matching
solvent flow rates of the sample introduction system with HPLC flow rates which
makes coupling of the two techniques easy. These are also amenable to samples
with lower volatility and samples which are thermally labile, samples which are
otherwise not suitable for GCMS. Since both are soft ionization techniques, the gas
phase ions are representative of the ions present in the liquid phase; although ESI
is more likely to preserve the integrity of the analyte species compared to APCI.
Consequently, ESI is more widely applied to speciation analysis. ESI can be
operated in soft, medium, and hard ionization modes which enable simultaneous
determination of the molecular ion, the major fragment and the element of
interest. These fundamental characteristics are the reasons why ESIMS is usually
used to complement spectroscopic measurements. ESIMS is then used for the
species identity confirmations after analyte identification by spectroscopic
determination. ESIMS is also increasingly utilized to complement results from
ICPMS measurements, harnessing the strengths of both techniques makes the
results achieved more solid. This complementary use of ESIMS and ICPMS has
already been discussed by Houk (1998) wherein the author clearly pointed out the
advantages of each technique when used alone but highlighted the possibilities of
what can be acquired when these two are used to complement results when doing
speciation analysis.
Various chromatographic separation techniques have been employed for
speciation analysis. Ponce de León et al. (2002) did one comprehensive review of
chromatographic separation techniques that have been coupled with ICPMS. They
covered the liquid chromatographic separations such as reversed-phase, ion-
exchange, size-exclusion, and chiral chromatography, to the use of gas
chromatography, supercritical fluid chromatography and capillary
electrophoresis. They provided advantages of each technique and also the
14
requirements of each when interfaced with ICPMS, in particular. At the end of the
review, they noted that among all these chromatographic systems, liquid
chromatography, either with ion-exchange or with reversed-phase conditions, are
the commonly used. This was also the observation made by Francesconi and
Kuehnelt (2004) in their review of arsenic-relevant literature. This review provides
a thorough compilation of published works on arsenic speciation available from
2000 to 2003 and discussed extensively the status of the field as regards extraction
solvents, extraction systems, analytical difficulties and possibilities as well as
current uncertainties and problems in the field of arsenic speciation analysis.
Michalke wrote back to back reviews (Michalke, 2002a and 2002b) that dealt with
general aspects of the coupling and recent trends in application. Mora et al. (2003)
discussed the different sample introduction systems in plasma spectrometry with
emphasis on the main advantages and drawbacks associated with each type. This
article also gives an overview of the processes that affect the aerosol from the
moment it was generated until it reaches the plasma. A later paper authored by
Todoli and Mermet in 2006 also discussed introduction systems with special
emphasis on the analysis of liquid microsamples with ICPAES and ICPMS
detection.
Most of the reviews that have covered the coupling of liquid
chromatography (LC) present almost similar considerations as regards advantages
as well as disadvantages of the different modes used. The central advantage of
using LC is the extended range of separation mechanisms available to the analyst.
There are various mobile phases that may be employed plus the variety of
commercially available stationary phases; choices of which to use is, although,
dictated by the degree of species preservation desired by the analyst. The use of
stationary phases, however, is the main cause of the disadvantage in the use of LC
because this is where adsorption effects, contamination, or probable species inter-
conversion may occur (Michalke, 2002a). In the case of the use of buffers or
organic modifiers, these solvents may also cause denaturation of native species or
may induce complexation of free or labile-bound metal species. Thus, the analyst
15
should carefully weigh his options and his desired outcome before making the
choice of separation conditions.
The most commonly used liquid chromatographic conditions are size-
exclusion chromatography (SEC), adsorption chromatography, ion-exchange
chromatography (IEC), and reversed-phase liquid chromatography (RPLC). All of
these conditions have inherent advantages over others but also carries
accompanying disadvantages unique for each type. SEC offers possibility for site
characterization because retention of a solute depends on the molecular size and
not the molecular weight (Michalke, 2002a). The disadvantage arising from SEC is
also related to its dependence on the molecular size for the separation. Michalke
(2002a) noted that peaks must be sufficiently narrow to obtain an adequate
separation owing to the limited peak capacity of the columns used in this type of
chromatography separation. One more is the necessity for a difference of a factor
of 2 between molecular sizes of the analytes to enable baseline separation. Gong et
al. (2002) have noted in their review that both low-pressure and high-pressure SEC
techniques have been demonstrated for arsenic speciation. They also added that
low-pressure SEC is usually used to remove large matrix molecules prior to
analysis.
A related technique to SEC is ion-exclusion chromatography. It involves the
use of strong anion- or cation-exchange resins for the separation process (Gong et
al., 2002). The separation process is carried out by taking advantage of the
different charges on the analyte species. Three types of interactions are exhibited
in ion-exclusion chromatography: ion-exclusion, ion-exchange and hydrophobic
interactions; all of which are suitable to separate various arsenic species (Gong et
al., 2002).
Another separation technique taking advantage of the charge differences in
the analyte is ion-exchange chromatography. The main reason for the popularity
of this technique is owed to the high separation efficiency and it wide applicability
16
(Michalke, 2002a). It is based on the utilization of exchange equilibria between
charged solute ions and the oppositely charged surface of the stationary phase. In
this technique, the relative retention of the analyte ions is determined by pH, ionic
strength of the mobile phase and the nature of the ion exchanger. Also, the
diffusion rate is dependent on the size and porosity of the resin beads, and the
viscosity of the eluent. Gong et al. (2002) have reviewed several articles pertaining
to use of ion-exchange chromatography for separation of arsenic species.
Ion chromatography (IC), with ICPMS and ESIMS for detection, has been
applied to investigate the chemical stability of arsenosugars in simulated gastric
juice and acidic environments (Gamble et al., 2002), as well as basic environments
(Gamble et al., 2003). The group have chosen IC-ICPMS for the trace determination
of the arsenicals and IC-ESIMS for the structural identification. The work was
fundamentally based on the observation that arsenosugars are considerably more
chemically labile than arsenobetaine. Hence, they were interested to know how
these compounds will behave when subjected to simulated gastric type
environments, acidic, or basic environments. They concluded that arsenosugars
undergo acid hydrolysis (gastric juice pH of 1.1) at a rate of about 1.5% h-1 at 38°C,
a rate which increased to 12% h-1 at 60°C. As for the basic conditions, they
reported that the use of 0.83% tetramethylammonium hydroxide at 60°C produced
minimal degradation and hence can be a suitable extraction solvent to ensure
species-specific integrity over time. For the studies in basic conditions, they
adjusted the pH of the samples for analysis to an approximate pH of 9.
Adsorption chromatography is rarely used in speciation analysis because
irreversible changes in the analyte species may occur due to the high polarity of
stationary phases commonly used in this type of separation (Michalke, 2002a).
Michalke (2002a) also added that this separation mode is advantageous only in
circumstances wherein the target species is adsorbed selectively and enriched
from a matrix.
17
For general applications, reversed-phase liquid chromatography (RPLC) is
the most commonly used mode of separation. The separation in RPLC is achieved
using columns with stationary phase less polar than the mobile phase. The wider
applicability of RPLC is even more augmented by the possibility of using ion-
pairing reagents. RPLC is typically used in parallel or in combination with other
techniques as a part of orthogonal speciation concepts (Michalke, 2002a). One
problem encountered with the use of RPLC is the effect of pH stability of the
eluents to the reproducibility. Another is difficulty arising from the high salt
content of the buffers and need for organic modifiers, problems which are
significantly causing impediment when RPLC is interfaced with plasma detection
techniques (Michalke, 2002a). A variation of RPLC is micellar HPLC wherein a
relatively high concentration of a surfactant is used as counterions which enable
formation of the micelles that then act as the separation agent (B´Hymer and
Caruso, 2004).
Multiple chromatography is another mode being used recently. In this type,
multiple columns and separation modes are combined to bring about separation
of a range of arsenic species (Gong et al., 2002). The separation can either be
carried online or offline. Szpunar and Lobinski (2002) discussed the benefits of
using this multidimensional approach in the biochemical speciation analysis. They
highlighted that due to the complexity of the bioligand environment, a single
separation technique seldom offers sufficient separation efficiency. Hence, there is
a need for a consecutive use of two or more techniques with orthogonal separation
mechanisms.
The coupling of LC with ICPMS has radically changed knowledge about
arsenic species in nature. In the past years, more arsenicals were identified and
toxicological properties being thoroughly researched. However, water-soluble
species were almost always the topic of interest because of the ease of extraction
and sample preparation. Lately, though, lipid-soluble arsenicals are getting their
share of the limelight in research. This attention is mainly brought about by the
18
fact that properties of these arsenicals are largely unknown but they may be
present in fish oil capsules which are used as food supplements. Popularity of fish
oil capsules as supplements is mainly due to the rich fatty acid content which is
heavily advertised to be good for the heart and preventing heart ailments. The
pioneering work of Schmeisser et al. (2005), which was based on the coupling of
HPLC with ICPMS, led to identification of arsenolipids which paved the way for
more studies pertaining to these species. Kohlmeyer et al. (2005) also determined
arsenic species in fish oil samples. For this work, the authors did parallel
determinations on methanol/water extracts and acid digests of fish oil employing
ion chromatography-ICPMS. They reported presence of up to eight different
arsenic species with dimethylarsenate as the major component. A recently
published work by Rumpler et al. (2008) was focused on cod-liver oil. In thus
work, cod-liver oil was partitioned between hexane aqueous methanol. The polar
phase was subjected to preparative chromatography with size-exclusion and
anion-exchange media to yield a fraction enriched with the polar arsenolipids. The
subsequent analysis of the fraction by using HPLC-ICPMS showed presence of at
least 15 arsenolipids. This work has put forward interest in these compounds
which in the future will give light to still unknown chemistry of arsenic in these
substrates.
HPLC, operated with reversed-phase conditions, coupled to ICPMS was
also the method of choice employed by Raml et al. (2006) for better separation of
thio-arsenic compounds. Thio-arsenicals are a group of compounds which are
sulfur analogues of oxo-arsenicals and found in mollusks, algae and urine (Raml et
al., 2006). The first thio-arsenical was reported by Hansen et al. in 2004. They have
identified thio-dimethylarsenoacetate in the urine of sheep that feeds on algae.
The use of reversed-phase conditions is favorable for the separation of thio-
arsenicals owing to the less polar character of these compounds compared to their
oxo-analogues which are not well retained in similar separation conditions (Raml
et al., 2006).
19
Wuilloud et al. (2004) did a full review of the available literature on
coupling GC with ICPAES, ICPMS, and microwave-induced plasma atomic
emission spectroscopy (MIPOES). The review provided updates on the current
state of the methodologies involved and paid particular attention to the
applications for analysis of elemental species. The authors pointed out that
compared to the other plasma-based detection techniques, the coupling of GC to
ICPMS was viewed to be more viable because it combines the high resolving
power of the former with the high sensitivity capability of the latter. The interface
between the two systems is also straightforward with the minimum requirement
that the volatilized analytes from the GC column remain in the gas phase until it
reaches the plasma. This requirement is generally addressed by heating the entire
transfer line from GC to the ICPMS to avoid condensation of the analyte or by the
use of an aerosol carrier which mixes with the GC effluent prior to introduction to
the plasma. The first approach is more advantageous, primarily because of the
absence of the aerosol being introduced into the plasma, and leads to better
sensitivity, lower detection limits, and minimal polyatomic interferences. The
review also provides a comprehensive discussion of the recent advances in mass
analyzers and notes the advantages of using time of flight or double focusing
sector field mass analyzers. The advantage offered by these mass analyzers is
inherently connected to the mode of separation of target analytes from other ions.
In double focusing sector field analyzers, only ions that have the programmed
m/e ratio and equal centrifugal and centripetal forces are allowed to pass through
the flight tube and then subsequently analyzed using the difference in their kinetic
energies. Time of flight analyzers, as the name implies, uses the differences in the
transit time of the analytes, thus lighter ions are detected first because of their
relative higher velocity compared to heavier ions. In addition, the authors also
tackled classical and modern sample preparation methods including extraction
techniques and derivatization reactions.
Pantsar-Kallio and Korpela (2000) have reported a method utilizing GC-MS
for the analysis of gaseous arsenic species. They also used the method for the
20
stability studies of arsine and trimethylarsine. The method was based on the
volatility of arsine, methylarsine, dimethylarsine, and trimethylarsine generated
by allowing the corresponding arsenicals to react with tetrahydroborate(III) and
nitric acid. The reported detection limits were between 24-174 pg with analysis
time of less than 2 min. As regards stability, they reported that dimethylarsine was
stable in strongly acidic conditions and trimethylarsine was relatively stable in air.
The latter observation can possibly explain why this form is detected in the
ambient environment.
The use of supercritical fluid chromatography (SFC) combines the high
diffusion coefficients of GC and solubility properties of LC (Wai and Wang, 2000).
SFC also generally requires lower temperatures for separation and is therefore
suitable for analyzing thermally labile compounds including a number of
organometallics. Wai and Wang (2000) discussed detection systems that have been
used with SFC and also application of SFC in the determination of organometallic
compounds. They mentioned the use of SFC with ICPMS but only provided a few
insights into it. The article written by Vela and Caruso (2000) dealt more with the
issues regarding interfacing SFC with both ICPAES and ICPMS. This also
describes several applications of SFC-ICP coupled methods on the analysis of
organometallics. Vela and Caruso (2000) noted that the main factors to consider
when coupling these two techniques are analyte transport efficiency and plasma
response to supercritical fluids. They also discussed the pros and cons in using
carbon dioxide as mobile phase. The use of carbon dioxide is advantageous
because of several reasons: it is readily available commercially with very high
purity, it is non-toxic, non-flammable, and it has very convenient critical points
(31.1°C and 72.8 atm, critical temperature and pressure, respectively). However,
the main disadvantage is the deposition of carbon on the sampler and skimmer
cones when too much CO2 is introduced into the plasma. Thus, the use of capillary
columns which entails lower flow rates of the mobile phase is preferred. The
authors also commented on the limited popularity of using SFC with the plasma-
based detection techniques. They attributed this limited use to the “mismatching”
21
polarity of most organometallics to the non-polar characteristics of CO2 which is
the most common mobile phase employed in SFC.
The first work incorporating capillary electrophoresis (CE) with ICPMS was
reported by Olesik et al. (1995) which proved to be a pioneering work in this field.
In this work, the major goal of the group was to develop a technique that will
provide rapid quantitative elemental speciation while offering detection limits in
the low ppb to sub-ppb range. Moreover, they also intended to use the method to
determine the concentrations of free ions with different charge states, metal-ligand
complexes and organometallic species. Their first attempt had detection limits that
were more than a factor of 20 inferior to the available techniques at that time but it
heralded an interest in coupling of these two techniques. A follow-up work by
Olesik et al. (1998) focused on the issues as regards interfacing CE with ICPMS but
elaborated further on the advantages, as well as disadvantages, of laminar flow in
CE-ICPMS. The report also tackled difficulties in the measurements such as loss of
sample, chemical matrix effects and changes in speciation. The authors also noted
the reason why they chose CE over other chromatographic techniques. They
explained that the choice was made under the assumption that minimizing
chemical interaction, i.e. the analyte-stationary phase interaction during the
separation, may minimize changes in speciation during analysis. A report of
Majidi (2000) was also based on the same arguments. Majidi (2000) discussed
principles and reasons for interfacing and specifically paid attention to design
considerations for instrument interface as well as anticipated difficulties with
speciation experiments. The report also mentioned probable applications for
specific matrices and analytes with special emphasis on sample aging since kinetic
and thermodynamic factors will influence analyte distribution when it is removed
from the native environment.
Kannamkumarath et al. (2002) made a thorough review of the available
literature on CE-ICPMS until 2001. The review dealt with basic and practical
aspects in the coupling, advantages and limitations, and also applications. They
22
also enumerated critical aspects that anyone intending to use the coupled
technique should know. They noted the following as the challenges one may face
when using CE-ICPMS: maintaining effective electrical contact at the outlet end of
the CE capillary, countering or minimizing laminar flow generated from the
suction effect of the operating nebuliser, minimizing brand broadening, and
obtaining high transport efficiency. Álvarez-Llamas et al. (2005) surveyed the
published works on the same topic from 2002 to mid-2005 to complement the
review previously done by Kannamkumarath et al. (2002). The authors here noted
the changes in the interfaces being used with the introduction of micronebulizers
and interfaces that can allow generation of volatile species of analytes. They
pointed out, however, that recent literature on CE-ICPMS were no longer focused
entirely on the interface design but more directed on particular applications.
CE has been employed by Sun et al. (2002) for the separation of arsenic
species with direct ultra-violet (UV) detection. They have investigated normal and
reversed electroosmotic flow separation modes using 20 mM NaHCO3-Na2CO3
buffer at pH 10. They also systematically investigated the influence of electrolyte
pH and composition, applied voltage and reversal protocols on the method
performance. The authors reported recoveries in the range 78.3 – 108.3% for the
organic and inorganic species they studied. A recent work by Li et al. (2008)
compared the performance of CE coupled online with AAS, AFS, and ICPMS.
They reported that for speciation analysis of metal-biomolecule interactions, CE-
ICPMS promises substantial improvements in identification and quantification of
multi-species systems as opposed to CE-AAS and CE-AFS.
Another group of techniques that has been employed for the analysis and
speciation of arsenic is stripping potentiometry. Muñoz and Palmero (2005)
reviewed the available literature on the use of stripping potentiometry for
determination of arsenic between 1980 and 2003. They have enumerated the
advantages of electrochemical techniques over the others and foremost on the list
is the simple and cheaper cost for the instrumentation involved and the operation.
23
The technique is also considered to have excellent selectivity and high sensitivity
that allows diversifying the different oxidation states of arsenic. However, the
major disadvantage of the technique is its limited applicability to the
determination of arsenic in simple solutions. In more complex matrices, a
preliminary separation of the target analyte from the interfering matrix is a must.
Among electrochemical methods, stripping potentiometry has more advantages
compared to voltammetric stripping methods owing to the fact that in the former,
no current passes through the electrode which makes it less susceptible to
interferences as compared to the latter technique.
A prerequisite step prior to analysis which is critical in every measurement
is the extraction step. Various methods have been optimized and evaluated for the
extraction of metal-containing compounds from the classical liquid-liquid
extraction to more advanced extraction techniques utilizing ozonation and other
technologies. In general, the choice of extraction system and the solvent are
governed by the nature of the target analyte and in some cases also influenced by
the requirements of the detector used. Pizzaro et al. (2003) evaluated the efficiency
of consecutive extraction either using individual solvents or mixtures; water,
water/methanol (1+1, 1+9, or 9+1) and phosphoric acid. They used the various
combinations of the extraction solvents for the extraction of arsenic species in rice,
fish and chicken tissues, and soil samples. The study showed that the best
extraction efficiency and the easiest extraction solvent to handle was a mixture of
1+1 methanol/water for the rice samples and for the chicken and fish tissues. For
the extraction of soil samples, they reported that best results were achieved with
1M phosphoric acid. As regards stability of the arsenic species in the extracts, they
noted that the analytes were stable in the rice extracts for a period of three months.
For the fish and chicken tissues, however, they observed that AB was converted to
DMA over time, but this conversion was hindered up to some extent with
increasing methanol content in the extraction solvent which perhaps was due to
the higher protein content of the sample matrix. In the soil samples, DMA and MA
24
remained stable for the period of the study but As(III) was readily converted to
As(V).
A study focused on the evaluation of the influence of the sample dispersion
media used in accelerated solvent extraction (ASE) has been described by
Gallagher et al. (2002). Dispersion of the sample in a support matrix is a
prerequisite in ASE before the extraction step can be commenced. In this work,
three dispersion media were evaluated by the authors and they presented that
Teflon dispersion media had better extraction recoveries compared to Filter Aid
and Q-beads dispersion media. The authors have demonstrated the need for the
use of dispersion media by using the certified material DORM 2. They noted that
there is a 58% reduction in the extraction efficiency of arsenic species if the
material was not previously dispersed and homogeneously suspended in a
dispersion media.
An extraction technique utilizing the recently introduced technology of
ultrasound-assisted extraction was optimized by Balarama Krishna and
Arunachalam (2004). The authors applied this method for the multi-elemental
extraction of lichen and mussel samples with subsequent detection using both
ICPAES and ICPMS. The extraction solvent used was 1% HNO3 (v/v) with metal
solubilisation using 4 min sonication time at 40% amplitude for a 100 g sample.
The authors concluded that the method is a fast and simple way for the estimation
of most major, minor and trace elements in these particular samples. Another
extraction technique using this technology was reported by Sanz et al. (2005). In
this method, the authors used a focused sonication probe for the extraction in rice,
chicken and fish tissues, and soil samples. In the article, the authors also explained
the basic principles of sonochemistry. The extraction technique works due to the
cavitation provoked by the bubbles formed by a wave sound in a liquid that
compresses and decompresses continuously resulting to extreme temperatures
and pressures in the liquid. When an analyte present as a solid is subjected to
these generated conditions, the analyte then is extracted to the liquid medium.
25
Sanz et al. (2005) reported that for chicken tissues, an enzymatic treatment has to
be done prior to the extraction to aid in the liberation of the species which are
protein-linked. In fish tissues, quantitative extraction was achieved after
sonication time of 1 min with water as solvent. This was also the same time
required for soil and sediment samples but using phosphoric acid as solvent. The
relatively short extraction time was advantageous, as the authors remarked, to
limit if not totally avoid the species inter-conversion.
The most widely studied matrix and is still being continuously subjected to
analysis for arsenic content is drinking water. This is important since this source
comprises a major pathway of arsenic exposure. Milstein et al. (2002) studied
drinking water samples acquired through the National Human Exposure
Assessment Survey in the United States using ion-exchange chromatography with
ICPMS for detection and reported levels of As(III) ranging from 0.10 to 0.60 ng ml-
1. They also reported presence of As(V), DMA, and MA in their samples. These
values are very low compared to reported arsenic levels in water samples from
tube wells in Bangladesh. The exposure of a wide population in Bangladesh to
high levels of arsenic was an unexpected outcome of a campaign to provide clean
water to combat waterborne diseases arising from the use of surface waters. Most
of the tube wells erected were later found to contain arsenic at levels higher than
0.05 mg L-1, which is the country’s standard for the contaminant (Chaudhuri,
2004). The situation in Bangladesh put arsenic in the limelight and pushed
forward efforts to address this widespread exposure through development of
purification and remediation techniques. Hung et al. (2004) reviewed the analytical
methods used for determination of inorganic arsenic in water. In this review, an
exhaustive account of more than 100 papers about the topic is presented focussing
on which analytical methodology offers lower detection limits.
The study of Coelho et al. (2002) on river and rain water samples showed
that their samples contained mainly As(III) and levels of As(V) were below the
detection limits. Arsenic is usually found in water bodies as the inorganic forms
26
and the concentrations are largely dependent on the anthropogenic activity in the
area and also on the nature of the river bed. A study by Gault et al. (2003) revealed
that a contaminated river contained mainly the inorganic arsenic forms As(III) and
As(V). Upon mixing with a relatively not contaminated river, the species
distribution changed markedly with the dramatic decrease of As(V). They have
ascribed this observation to probable adsorption of As(V) to iron (oxyhydro)oxide
deposits native to the river bed downstream.
A similar pattern of speciation is apparent in seawater samples wherein
total arsenic concentration may be about 1-2 ng ml-1 (Cullen and Reimer, 1989).
These values entail difficulty in the determination because it often requires a pre-
concentration step before the analysis since the levels may be already near the
detection limits of most techniques. In their method utilizing ICPMS operated in
the reaction cell mode with prior hydride generation, Nakazato et al. (2002)
reported detection limits from 21 to 25 pg ml-1 for As(III), As(V), and MA. They
have applied the method to characterize seasonal variations and found that As(V)
in surface waters increased during the winter season while that for As(III) and MA
decreased during the same period. These observations were attributed to the
change in the bioconversion activity. Another study focusing on seawater looked
at the variation of the arsenic speciation before and after a phytoplankton bloom
(Cabon and Cabon, 2000). The authors utilized flow injection with hydride
generation before detection by AAS for their work and observed that before the
bloom, arsenic is practically present as As(V). The arsenic speciation changed
during the phytoplankton bloom with most of the arsenic present as As(III), about
20% of DMA and traces of MA. They noted that after the bloom, all the As(III)
were converted to As(V) but the levels of DMA and MA not affected after the
bloom suggesting that the latter two are relatively stable in seawater.
The presence of arsenic in soils and sediments are highly influenced by
agricultural run-off and geological activity. The average concentration in the
continental crust is estimated to be about 3 mg kg-1 (Cullen and Reimer, 1989), but
27
the anthropological input increases arsenic levels in these matrices as opposed to
the levels in the overlying water. Analysis of arsenic in these matrices will give
information on the bioavailability and mobility of the arsenic species. The most
likely arsenic species in soils and sediments are As(III) and As(V) with traces of
MA and DMA (Demesmay and Olle, 1997). Whalley et al. (1999) surveyed total
arsenic in sediments from the Western North Sea and the Humber Estuary. Their
work was basically guided by the knowledge that the inorganic species are the
predominant arsenicals in this sample matrix and the arsenic content in these
samples may contain from about 5 to 15 mg kg-1 arsenic based on dry mass. The
results from this study revealed arsenic concentrations ranging from less than 0.15
to 135 mg kg-1 (dry mass basis). Ellwood and Maher (2003) measured arsenic
species in marine sediments by coupling HPLC and ICPMS, and reported that
As(III) and arsenosugar concentrations were higher in samples that were not
freeze-dried and also when exposure to air was kept to a minimum.
A study by Shi et al. (2003) focused on the determination of As(III) and
As(V) in soils. This work which utilized flow injection hydride generation with
AFS detection also evaluated the efficiency of different solutions for extraction.
They reported that KH2PO4 and NaOH solutions have higher extraction efficiency
and that most of the arsenic mainly existed as As(V) forms. Analysis of soils and
sediments sometimes require labour intensive batch extractions, a fact exemplified
by this study. On this note, Dong and Yan (2005) optimized a method which also
used flow injection but with online sequential extraction prior to hydride
generation (HG) with AFS detection. They applied the method to soil reference
materials and reported good agreement between the certified arsenic values and
the values they derived. Another modification was reported by Matusiewicz and
Mroczkowska (2003) employing graphite furnace-AAS as detector after the
hydrides were formed from slurry samples. The slurry was prepared by mixing
the samples in hydrochloric acid with 0.1% Triton x-100 in water, subsequent
sonication and then a final ozonation step. For this work, they validated it using
28
reference materials and reported favourable recoveries (about 91%) for the arsenic
content in the reference sediments.
The arsenic cycling is also highly influenced by the biota metabolism. A
study on indigenous plant species in Thailand showed that several plants growing
in a contaminated area can accumulate arsenic in very high concentrations
(Visoottiviseth et al., 2002). In this study, the authors suggested the possible use of
hyperaccumulators, such as the fern species Pityrogramma calomelanos and Pteris
vittata, in the phytoremediation of arsenic contaminated soils. Their survey of
different plants local to the contaminated site also revealed very high arsenic
concentration in Mimosa pudica, a herb, and Melastoma malabrathricum, a shrub. The
latter two plant species though are not suitable for the phytoremediation because
of lower arsenic tolerance.
Arsenic accumulation in rice is also widely researched because it constitutes
the staple food of a large population (D`Ilio et al., 2002). Feedstuffs, such as rice
straw, are also widely used for cattle or other livestock and poultry (Yuan et al.,
2005). The analysis done by D`Ilio et al. (2002) on various types of rice showed that
arsenic content in the rice varieties studied do not pose serious threats both to
humans and animals. They also noted that the variety Ribe is suitable as a
candidate for reference material, should one be needed for arsenic analysis in rice,
owing to the higher arsenic content in this variety. The study of Yuan et al. (2005)
was directed towards evaluation of different sample pre-treatment for rice straw.
Their results suggested that extraction using a water-ethanol mixture with
microwave assistance gave the best extraction efficiency. Analysis of rice straw
revealed presence of As(III) and As(V) as main arsenic species and traces of MA
and DMA in the straw shoots. A similar study conducted on rice samples
performed by Kohlmeyer et al. (2003) reported that arsenite was the predominant
species in the samples studied. They also presented that higher arsenic amounts
were detected in raw rice and brown rice as opposed to polished rice, i.e. white
29
rice and parboiled rice. They speculated that the likely reason for these trends is
the difference in the composition of the bran and the starch core.
Some other samples that have been subjected to arsenic analysis are wines
(Wangkarn and Pergantis, 1999) and soft drinks (El-Hadri et al., 2007). The analysis
of wines by Wangkarn and Pergantis (1999) determined arsenic concentrations
between 7 and 13 ng mL-1 which were significantly lower than maximum
permissible limit (MPL) for this sample. The MPL of arsenic in wines is set at 200
ng mL-1 as defined by the Office Internationale de la Vigne et du Vin (Wangkarn
and Pergantis, 1999). Analysis of wines using ICPMS as detector faces difficulties
because of the matrix. To compensate for the organic content, the authors used
internal standardization and they pointed out that standardization using indium
performed favourably with recovery of 100 ± 2% for a sample spiked with arsenic.
They also looked at the feasibility of using selenium as internal standard but
recovery of the spiked sample was 76 ± 7% which can be ascribed to the effect of
other matrix constituents such as Ca, Na, K or Mg. The method optimized by El-
Hadri et al. (2007) employing hydride generation with AFS detection for total
arsenic analysis was used to determine the element in various soft drinks (colas,
teas and fruit juices). They reported recoveries of the spiked samples from 94 to
101% with detection limits ranging from 0.01 to 0.03 ng mL-1. Comparison of the
results from the HG-AFS method with results from dry ashing, a method used for
their in-house check, was also reported to be comparable.
A review of available data on arsenic in marine organisms by Francesconi
and Edmonds (1996) tackled mean concentrations of the element and also
discussed bio-transformations in the environment. From this review, data
available showed that mean arsenic concentrations for brown, red, and green
algae are in the ranges 10 – 62 mg kg-1, 1.4 – 19 mg kg-1, and 1.5 – 17 mg kg-1, for
each type respectively (values all in dry mass basis). For marine animals, the mean
values for finfish ranged from 6.5 – 60 mg kg-1 (dry mass) and 0.3 – 7.7 mg kg-1
(wet mass); for crustaceans, the reported values were from 7 – 91 mg kg-1 (dry
30
mass) and 3 – 50 mg kg-1 (wet mass); for bivalve molluscs, the mean values were
from 3.5 – 5.0 mg kg-1 (dry mass) and 2 – 20 mg kg-1 (wet mass); and for gastropod
molluscs, values ranged from 8.1 – 38 mg kg-1 (dry mass) and 1.6 – 107 mg kg-1
(wet mass). A summary showing results of some studies on marine animals
presented in the Environmental Health Criteria 224 (Ng et al., 2001) by a collective
body of organizations shows varied concentrations of arsenic in the samples. The
reported mean arsenic concentration in mussels from Dutch estuaries was 1 mg
kg-1 (wet mass basis). Clams and oysters collected from the coasts of the United
States had values ranging from 1.1 to 2.7 mg kg-1. Shellfish from the Arabian Gulf
reportedly had mean arsenic concentrations from 3 to 15.8 mg kg-1 (wet mass).
Molluscs from the Great Barrier Reef had arsenic content from 481 to 1025 mg kg-1
(dry mass). As for marine fish, fish muscle had values ranging from 0.59 to 17 mg
kg-1 (wet mass) while other studies also reported mean arsenic concentrations in
the liver and muscle tissue of marine animals to be generally less than 1 mg kg-1.
Typical values for these samples would be challenging to establish due to
differences in the level of anthropogenic input to the area where samples may be
taken.
Determination of arsenic in samples of marine origin has intrinsic
difficulties because of the matrix from which arsenicals may not be released easily
making it suitable for determination. Arsenobetaine, which is almost always the
major component, is very difficult to decompose and can remain intact when
using soft extraction techniques (Cullen and Reimer, 1989; Francesconi and
Edmonds, 1997). Entwistle and Hearn (2006) optimized a method for the
quantitative determination of arsenic in fish tissues. They utilized open vessel wet
digestion using a mixture of sulphuric and nitric acids at 300 °C and subsequent
analysis of the digests by ICPMS. Application of the method to certified reference
materials afforded them recoveries from 96.1 to 105.9% indicating suitability of the
method for total arsenic determinations.
31
Cava-Montesinos et al. (2005) had a different approach in their work. They
focused mainly on the analysis of toxic arsenicals in fish and mussel samples by
HG-AFS. For this undertaking, they extracted the arsenic species through
sonication with 3 M HNO3 and 0.1% (m/v) Triton x-100 and washing of the
residue with 0.1% (m/v) ethylenediaminetetraacetic acid (EDTA) solution. Their
recovery studies returned values greater than 93% for As(III), As(V), DMA, and
MA spiked in a lyophilized sardine tissue. Another method which focused on the
analysis of inorganic arsenic in fish was proposed by Larsen et al. (2005). In this
work, the group used microwave-assisted dissolution by sodium hydroxide in
ethanol. This step converted the arsenite to arsenate and enabled them to
determine the total inorganic content as a single species by anion-exchange HPLC
with ICPMS detection. They verified the method by spiking experiments, recovery
for As(V) corresponding to previously spiked form as As(III) was 104 ± 7%; and
also by comparing the total inorganic arsenic values they determined to the
guideline values provided in the certified reference material TORT-2. They have
successfully applied the method to various fish samples but they noted that it was
not suitable for mackerel, perhaps due to the fat-rich nature of the fish.
High arsenic in marine organisms, including species that are used for
human food, has been a concern for so long. Setting of maximum permissible
concentrations, and whether it should include speciation data, was the topic raised
by Francesconi (2005) in a Forum. In this Forum, the author pointed out that in
addition to arsenobetaine, a lot of arsenicals with unknown toxicities may be
present in seafood products. Thus, speciation data with appropriate toxicity
testing would provide scientifically grounded limits for these products which can
protect both consumers and the interests of seafood suppliers. The levels of arsenic
in drinking water and some food items are regulated in some countries and by the
World Health Organization (WHO). The WHO set the provisional guideline value
of 10 µg L-1 for arsenic in drinking water (http://www.who.int/mediacentre/
factsheets/fs210/en/index.html, accessed 6.10.2008). The European Union (EU)
also adopted this value for the allowable concentration of this element in drinking
32
water (http://ec.europa.eu/food/fs/sc/oldcomm7/out09_en.html, accessed
6.10.2008). The Environmental Protection Agency of the United States (USEPA) set
a maximum contaminant level (MCL) of 10 µg L-1 for arsenic
(http://www.epa.gov/OGWDW/standards.html, accessed 6.10.2008). In Canada,
the maximum acceptable concentration in drinking water is 25 µg L-1 for arsenic
(http://www.hc-sc.gc.ca/ewh-semt/pubs/water-eau/arsenic/guideline-
recommandation-eng.php, accessed 6.10.2008). These guideline values highlight
the need to further improve existing methods for the determination of arsenic and
arsenical species in various samples. These are also the driving force behind the
need to develop faster and accurate methods for arsenic analysis.
Measurement of arsenic in urine has become a common practice to assess
exposure to the element. Though determination in this matrix comes with
considerable difficulties, i.e. presence of dissolved salts and high organic content,
it is still the best sample for clinical applications because sample collection is non-
intrusive and most arsenicals are excreted via the urine. Aside from the difficulties
arising from the nature of the matrix, estimation of the exposure by monitoring
arsenic levels in urine may be confounded by the consumption of seafood and
other foodstuff containing high levels of arsenic, i.e. mushroom (Francesconi et al.,
2002). Amarasiriwardena et al. (1998) compared the accuracy of three analytical
methods for the determination of total arsenic in urine by ICPMS. They reported
that accurate and precise determinations can be achieved by the addition of 1% N2
to the plasma flow or 3% N2 to the nebulizer gas flow with parallel internal
standardization; or by the addition of ethanol to the standards and samples with
parallel use of tellurium as internal standard. From their conclusions, they
highlighted the need of choosing an internal standard for arsenic which resembles
it closely in terms of ionization behaviour in the sample matrix.
The group of Sloth et al. (2004) performed speciation analysis on various
urine materials (both reference and control materials) by ion-exchange HPLC with
ICPMS detection. Their work was carried out using gradient elution and they
33
were successful in the identification of dimethylarsinoyl-acetic acid and
trimethylarsoniopropionate in the samples for the first time. The results also
showed good agreement between the sum of species and the total arsenic content
in the certified reference material NIES human urine No. 18. A different approach
for analysis was used earlier by Wang et al. (2001). For this method optimization,
they used flow injection with on-line dilution of human urine before detection
with ICPMS. The method was evaluated for multi-elemental capability and they
reported a detection limit of 0.30 µg L-1 for arsenic. Though the method proved
useful for multi-elemental determination, they noted that the determined
concentration means for arsenic were higher, perhaps owing to the interference
from 40Ar35Cl.
Other human tissues or samples that have been subjected to arsenic analysis
include serum, plasma, blood, nail, hair, and breast milk (Hall et al., 2006; Adair et
al., 2006; Pandey et al., 2007). Arsenic analysis in these samples, as in the case of
urine, also suffers from interferences brought about by the matrix itself. Despite
the problems encountered in this type of analysis, these samples are still widely
considered because these are good diagnostic tools for a number of diseases,
workplace exposure to metal-containing contaminants, and also for assessment of
nutrient availability. Blood arsenic, specifically, is a very useful biomarker in case
of acute arsenic poisoning or continuous high-level exposure. Hall et al. (2006) also
reiterated that blood receives arsenic inputs from both exogenous exposure and
from tissue compartments which makes the blood samples representative of
internal arsenic burden. Hair and nails are useful indicators of past exposure and
may be used to estimate the relative length of time since the occurrence of acute
exposure to the element. Exposure assessment is increasingly gaining interest and
lately, these monitoring studies are focusing more on children as subjects because
it is believed that the younger population is more susceptible to health risks
associated with pollutants (Wilhelm et al., 2006).
34
Cornelis et al. (1998) opined that pre-treatment of clinical samples may be
familiar to biologists and biochemists but not to the inorganic trace element
analyst. In their article, the authors listed important points that have to be done to
get valuable results in the analysis of clinical samples. They noted that the release
of target species out of the cells must be accomplished foremost and suggested
that samples such as packed cells/red blood cells have to be lysed to free their
content; tissues should be homogenized first prior to a separation between soluble
species and those bound to insoluble compounds. Another thing to consider is the
sample pre-treatment to select a particular group of species such as the utmost
consideration of whether the low molecular mass or the high molecular mass will
be the target analytes. The separation between the two groups can then be
performed using centrifugal ultrafiltration which can produce filtrates that are
protein-free. The last crucial point that has to be done, as the authors pointed out,
is desalting the samples because the ionic strength of the samples may not comply
with the chromatographic conditions for optimum separation of the analytes. This
step is especially necessary for analysis of urine samples which can have very
variable electrolyte composition and other supernatants of tissue homogenates
may also have a high salt content.
Shibata et al. (1994) performed three chromatographic conditions, ion-pair
in both cation-exchange and anion-exchange conditions, and gel permeation
chromatography. These experiments have resulted to the identification and
verification of the presence of arsenobetaine as the major component of every
sample they studied. Arsenic in the samples was present in increasing
concentrations according to this trend: plasma < serum < blood cell fractions.
The use of collision or reaction cell system in recent models of ICPMS was
used to overcome matrix interferences in blood and serum samples by Wahlen et
al. (2004). The work was intended for multi-elemental determination, including
arsenic as one of the analytes. They noted that increased recovery was observed
for arsenic owing to the nature of the matrix. They compensated for the high
35
organic content by adding 3% butan-1-ol to the diluent, a solution containing 0.7
mM ammonia solution, 0.01 mM EDTA and 0.07% (v/v) Triton x-100.
Recent published work that dealt on arsenic speciation discussed the
analytical artefacts when performing analysis of clinical samples (Šlejkovec et al.,
2008). This work showed that no analytical artefacts were observable with proper
storage of urine samples, i.e. in liquid nitrogen. Serum and plasma samples,
however, proved to be a different case because they observed losses during the
speciation procedure. They speculated that the disappearance of the analyte may
have resulted from precipitation of As(III)-containing proteins/peptides during
the extraction step with methanol/water mixture as solvent. For the losses
encountered in whole blood samples, they assigned these to possible binding of
the As(III)-containing proteins/peptides on the column material during the
separation process. This work underscores the need to perform mass balance
checks after each critical step in the whole determination procedure because the
results can greatly affect metabolic and pharmacokinetic interpretations.
Methods for elemental analysis that are reported in the literature have
taken advantage of all available means of detection with corresponding sample
pre-concentration or online extraction. Some have utilized flow injection (FI) with
ICPMS (Beauchemin and Specht, 1998; Wangkarn and Pergantis, 1999; Wang et al.,
2001; Huang and Beauchemin, 2003), hydride generation AAS (Cabon and Cabon,
2000) or hydride generation AFS (Shi et al., 2003). Beauchemin and Specht (1998)
described a flow injection manifold for the analysis of several elements in river
water. The manifold incorporated a cleaning column on the buffer line to remove
trace contaminants from the buffer and features a preconcentration factor which
can be varied easily by changing the sample loading time. The method was
applied to certified river water, SLRS 2 and showed good results for V, Co, Cd and
Pb by standard addition method; and for V, Co, Cd and Sb by external calibration
method. They also reported that determination of Zn was still difficult owing to
high concentration of the element in the blank. In 1999, Wangkarn and Pergantis
36
also described use of a microscale flow injection system with ICPMS (FI-ICPMS)
for detection. They used the optimized method to determine arsenic in red and
white wines. The authors showed that the use of a microscale FI-ICPMS system
was able to reduce signal enhancement of arsenic caused by the organic solvents
by a factor of 2-3 compared to a conventional FI-ICPMS system.
FI-ICPMS was also the choice of Wang et al. (2001) and Huang and
Beauchemin (2003) when they optimized a method suitable for multi-element
determination. Wang et al. (2001) used it for the analysis of human urine and they
reported that matrix effects were minimized effectively when a dilution factor of
16.5 was employed. Their system also enabled online dilution and online standard
addition, and was validated by using NIST reference material SRM 2670 and
SeronormTM Trace Elements in Urine. They reported good correlation of the
experimental results with certified values. Huang and Beauchemin (2003)
employed their FI-ICPMS system, which also features possibility for online
standard addition, for the analysis of human serum. Validation was performed by
analyzing SeronormTM reference human serum and they got good agreement for
Al, Mn, Fe, Co, Cu and Zn.
Cabon and Cabon (2000) utilized flow injection with hydride generation
atomic absorption spectroscopy (FI-HG-AAS) for the speciation of major arsenic
species in seawater. The speciation was carried out by varying the experimental
hydride generation conditions which enabled determination of As(III), total
arsenic, hydride reactive species, and the non-hydride reactive species by
difference. This was done in combination with another method which included
cryogenic trapping of hydride reactive species on a chromatographic phase and
subsequent sequential release which enabled determination of inorganic As,
MMA, and DMA. The use of flow injection with hydride generation atomic
fluorescence spectroscopy (FI-HG-AFS) was employed by Shi et al. (2003) for the
analysis of soil samples with specific determination of As(III) and As(V). The
37
method was applied to real samples and detection limits reported were 0.11 and
0.07 µg L-1 for As(III) and As(V), respectively.
With the wide range of techniques that can be employed for trace analysis,
an analyst should therefore weigh and carefully consider his desired results when
choosing the appropriate one suited for the analytical considerations. For this
work, the primary objective was to optimize a method suitable for trace analysis in
various matrices which can provide rapid determination of total concentrations
and a convenient way to estimate column recoveries. To achieve this goal, the
advantages of flow injection were merged with the unparalleled selective
detection capabilities of the ICPMS.
38
Chapter 3. Methodology
3.1. Chemicals and reagents
All reagents and chemicals used throughout the study were of analytical
grade unless otherwise specified. All dilutions and sample/standard preparations
were done using deionized water (18.2 MΩ cm resistivity) prepared using a Milli-
Q system (Millipore, Bedford, MA, USA). Nitric acid and aqueous ammonia 25%
(suprapure) were purchased from Merck (Darmstadt, Germany). Methanol was
acquired from Carl Roth GmbH (Karlsruhe, Germany). Ammonium dihydrogen
phosphate (p.a.), ammonium formate (p.a.) and formic acid (p.a.) were obtained
from Fluka (Buchs, Switzerland). Single-element standard solutions of elements
listed in Table 3.1 were procured from CPI International (Santa Rosa, USA).
Table 3.1. List of element stock solutions used in this work.
Element Source Purity Matrix
Arsenic As metal 99.999+ 2% HNO3
Calcium CaCO3 99.999 2% HNO3
Germanium (NH4)2GeF6 99.998 2% HNO3
Indium In metal 99.999 2% HNO3
Lithium Li2CO3 99.999 1% HNO3
Rhenium Re metal 99.99 2% HNO3
Scandium Sc2O3 99.99 2% HNO3
Selenium Se metal 99.999 2% HNO3
Tellurium Te metal 99.999 2% HNO3 + 0.2% HF
Thallium Tl metal 99.999 2% HNO3
Yttrium Y2O3 99.999 2% HNO3
3.2. Reference/Control materials
Various reference and control materials representing different matrices
were used to validate the quality of the method. These materials are summarized
in Table 3.2 which includes information such as the content of the material itself
and from which supplier the materials were acquired.
39
Table 3.2. List of reference/control materials employed in this work.
Reference/control material Source
NIST reference water 1643e National Institute of Standards and
Technology, Gaithersburg, MD, USA
BCR-422 (cod muscle) Community Bureau of Reference,
Brussels, Belgium
IAEA-407 (fish tissue) International Atomic Energy Agency,
Vienna, Austria
DOLT-3 (dogfish liver) National Research Council Canada,
Ontario, Canada
DORM-2 (dogfish muscle) National Research Council Canada,
Ontario, Canada
LUTS-1 (non-defatted lobster
hepatopancreas)
National Research Council Canada,
Ontario, Canada
TORT-2 (lobster hepatopancreas) National Research Council Canada,
Ontario, Canada
NIES human urine No. 18 National Institute of Environmental
Studies, Tsukuba, Japan
Seronorm trace elements in urine FE
1114
SERO, Billingstad, Norway
ClinChek urine levels I and II (lot
number 607)
Recipe Chemicals + Instruments GmbH
Labortechnik, Munich, Germany
ClinChek plasma levels I and II (lot
number 417)
Recipe Chemicals + Instruments GmbH
Labortechnik, Munich, Germany
ClinChek serum level II (lot number
608 and 141)
Recipe Chemicals + Instruments GmbH
Labortechnik, Munich, Germany
ClinChek blood level II (lot number
545)
Recipe Chemicals + Instruments GmbH
Labortechnik, Munich, Germany
40
3.3. Sample preparation
All sample filtrations were done using 0.20 µm Nylon filters (Markus
Bruckner Analysentechnik, Linz, Austria). Vials (300 µL and 1 ml capacity;
Agilent, Waldbronn, Germany) were used as sample containers during
measurements. Polyethylene vials (15 ml; Cellstar, E&K Scientific, CA, USA) were
used in extraction of arsenic from solid reference materials. For centrifugation, a
Jouan C4-22 centrifuge (Jouan, Saint Mazaire, France) was employed and operated
at 4500 rpm for 15 min. Samples which needed drying were loaded into a Heto
MAXI dry (Heto-Holten A/s, Allerød, Denmark). Sonication was done using
Transsonic T700/H (Elma GmbH & Co KG, Singen, Germany) which may be
operated in different sonication times.
3.3.1 Arsenic analysis
The solid reference materials were subjected to extraction using 1+1
methanol/water for arsenic analysis. Representative samples (as per supplier’s
recommendation) were weighed in polyethylene vials and 10 ml of the extraction
solvent was added. The vials were shaken top over bottom for 18 hours and were
allowed to stand for a few minutes before centrifugation. The supernatants were
collected and aliquots were subjected to evaporation while another set of aliquots
were directly analysed either by flow injection ICPMS or digested for conventional
ICPMS analysis. The residues from the set of aliquots subjected to evaporation
were reconstituted in Milli-Q water and afterwards subjected to either flow
injection ICPMS or also digested for conventional ICPMS analysis. The liquid
reference materials were treated similar to the extracts both of which were diluted
as needed.
3.3.1.1. Sample mineralization by microwave digestion
For sample preparation prior to total element determination, representative
samples were subjected to complete mineralization using an ultraCLAVE®3
microwave heated autoclave (EMLS, Leutkirch, Germany). Prior to digestion, 2 ml
of HNO3 and 2 ml of water were added to the quartz vessels containing the
41
samples. These were then placed in the system which was loaded with argon (4 x
106 Pa) before initiating the microwave program. The program used was as
follows: heating to 75°C (ramped in 5 min), after which further heated to 150°C
(ramped in 20 min), then increased to the final temperature of 250°C (ramped in 20
min). The system was maintained at this final temperature for 30 min before
allowing it to cool. The digests were then diluted appropriately before total
element determination.
3.3.1.2. Total element determination
Conventional ICPMS measurements were carried out by taking advantage
of the integrated sample introduction system (ISIS) Agilent ASX-500 system
(Waldbronn, Germany) connected to an Agilent 7500ce ICPMS equipped with a
PFA microconcentric nebulizer and a Scott double pass spray chamber. The
octopole cell was operated with helium (3 ml min-1) as collision gas for total
arsenic determination. The carrier gas flows, cell voltages, torch alignment and
other ICPMS operating parameters were optimized to get a sensible signal to noise
ratio. Tuning for sensitivity was carried out by monitoring a solution containing 1
µg L-1 of Li, Y, and Tl monitored at amu 7, 89, and 205, respectively. The ICPMS
was tuned to have the maximum counts for these elements, specifically 89Y which
has the closest mass to arsenic. The counts corresponding to m/z 103 (for Rh, a
rare element) was also monitored and parameters were changed to get the lowest
value (lower than mean count of 3). But the most important values monitored
were for CeO/Ce ratio (156/140) and for the doubly-charged species (70/140). The
ICPMS tuning parameters were changed to get low values (less than 3%) for
optimum performance of the ICPMS instrument.
3.3.1.3. Arsenic speciation analysis by HPLC-ICPMS
Speciation analyses were carried out in fish sauce samples and were done
both in anion- and cation-exchange conditions using an Agilent 1100 HPLC
(equipped with a vacuum degasser, binary pump, and a variable injection loop or
100 mm3) series connected to the Agilent 7500ce ICPMS via a PEEK
42
(polyetheretherketone) tubing (0.125 cm i.d. x 100 cm). Anionic separation was
carried out using a PRP-X100 column (4.1 x 250 mm, 10 µm particle size;
Hamilton, Reno, Nevada, USA) operated at 40°C and using a 20 mM phosphate
buffer (pH 6.0, adjusted with aqueous NH3) supplied at 1.5 ml min-1. Cationic
separation was done using a Zorbax 300 SCX column (4.6 x 250 mm, 5 µm particle
size; Hewlett-Packard, Waldbronn, Germany) maintained at a temperature of 30°C
and using 10 mM pyridine (pH 2.3, adjusted with formic acid) supplied at a rate of
1.5 ml min-1. The volume of injection for both chromatographic conditions was 20
µL. For the speciation analyses, the ion intensities at m/z 75 and 77 were monitored
corresponding to arsenic and 40Ar35Cl, respectively.
3.3.1.4. Arsenic speciation analysis by HPLC-ESIMS
The fish sauce samples were also subjected to arsenic species identification
by coupling HPLC with electrospray ionization mass spectrometry. The
experiments were performed using an Agilent LC/MSD 1100 series system with
single quadruple MS of the SL type operated in positive mode. The separation was
carried out on a Shodex RSpak NN-614 column (6 x 150 mm, 10 µm particle size;
Shimadzu, Korneuburg, Austria) and using a 5 mM ammonium formate buffer
(pH 3.0) as eluent with a flow rate of 0.4 ml min-1. The column was maintained at
30°C and the volume of injection used was 5 µL.
3.3.1.5. Flow injection – ICPMS analysis
Diluted samples – arsenic determination. The flow injection ICPMS measurements
were carried out by directly connecting the injector port of an Agilent 1100 HPLC
system to the nebulizer of an Agilent 7500ce ICPMS via a PEEK tubing (0.125 cm
i.d. x 100 cm). The eluents used for arsenic determination in diluted samples were
either 0.3% nitric acid with 10% methanol (v/v, pH = 1.4) or 20 mM phosphate
buffer with 10% methanol (v/v, pH = 5.6) both supplied at 0.15 ml min-1.
Monitoring of the column temperature was not necessary since the configuration
by-passed the column entirely. The internal standards (prepared in a mixture)
were manually added to the samples/standards prior to dilution to have the
43
following concentrations in the final solution: 400 µg L-1 of Ge, 600 µg L-1 of Se,
and 1 mg L-1 of Te. Relatively high concentrations of the internal standards were
used to ensure stability of the signals. The volume of injection was varied to as low
as 2 µL to as high as 100 µL but for most of the method optimization, a 20 µL
volume of injection was utilized. The analysis was carried out using the time-
resolved analysis mode on the ICPMS, the same mode used when performing
chromatographic separation. This offered the advantage of having all the signals
for standards and samples representing one run on one chromatogram. But care
should be utmost when deciding the sequence because one chromatogram can
only have a maximum time of 10000 s. Thus the sequence should be designed in a
way that all will fall within this duration.
Undiluted clinical samples – arsenic determination. The flow injection ICPMS
method employed for arsenic analysis directly applied to clinical samples was
essentially the same as that discussed above except that addition of the internal
standards and subsequent dilution were done automatically using the injection
program in the HPLC system. The thermosttated autosampler of the Agilent 1100
HPLC system can be configured to draw specific amount of solutions from
different vials prior to loading of the sample. It can also be programmed to mix the
solutions in the needle, the effect of which was also evaluated. The HPLC was
programmed to draw 15 µL from a vial containing the internal standards (mixture
of 100 µg L-1 each of Ge and Te, prepared in 3% HNO3) and then 5 µl from the vial
containing sample/standard prior to injection. The eluent used was 0.3% nitric
acid with 10% methanol (v/v, pH = 1.4) operated at a flow rate of 0.15 ml min-1.
3.3.2 Calcium analysis
For calcium analysis, the same configuration of the HPLC-ICPMS set-up
was employed and the eluent used was 0.3% nitric acid (v/v, pH = 1.3), which
was kept at a flow rate of 0.15 ml min-1. The internal standards composed of a
mixture of scandium and germanium (200 µg L-1 of each standard) were manually
added to the solutions before final dilution. The monitored masses were 40Ca, 45Sc
44
and 74Ge. All measurements were carried out using hydrogen as a reaction gas (3.5
ml min-1) to enable monitoring of 40Ca with the lowest achievable interference
from 40Ar.
3.4 Data treatment
The evaluation of data was done using the software package (Agilent,
Waldbronn, Germany) bundled with the ICPMS. Quantification was based on
peak area (for non-normalized data) or peak ratio (for normalized data against an
internal standard). The detection limit was estimated using the standard with the
lowest concentration in the calibration solutions. The response from the blank was
subtracted prior to constructing a calibration curve. The detection limit was
determined by taking 3x the standard deviation of the standard with the lowest
concentration. Accuracy was evaluated by comparison of the determined values
with values in the certificate issued with the reference materials. For control
materials, accuracy was evaluated both against the information value listed in the
packet and against the results from conventional ICPMS measurements performed
on digested control materials. Precision was evaluated by monitoring drift
standards to gauge within a day’s variability. As for inter-day variability, the
determined values of reference materials (or extracts of the reference materials)
were used as the indicator of precision.
45
Chapter 4. Results and Discussion
4.1 Flow injection ICPMS method for arsenic determination
The guiding goal for this work was to have a flow injection ICPMS method
which would be applicable to the simultaneous analyses of several elements in one
run. This can be done without any difficulty using the integrated sample
introduction system (ISIS) coupled with the ICPMS. But the ultimate goal was to
directly analyze samples which were being subjected to speciation analysis, thus
enabling a direct correlation between elemental composition after
chromatographic separation and total element content derived from flow injection
ICPMS analysis. With this objective in mind, we then started by first looking at a
suitable eluent. Since 20 mM phosphate buffer is a typical solvent for HPLC
separation in most routine separation done in arsenic analysis, we considered
using it. Our initial results showed that for most of the elements we were looking
at, there was no linear relationship between the response and the concentration of
the multi-element standards. Switching to 1% HNO3 as eluent solved this problem
but the very low pH was too harsh for routine use with the HPLC although acidic
conditions are favored with ICPMS measurements. Acidic conditions in the
solutions being analyzed by ICPMS aids in the stability of the analytes in the
solution by hindering possible precipitation or adsorption.
We lowered the acid content and used 0.3% nitric acid (v/v, pH = 1.3) and
still got the linear correlation we wanted for most of the elements we were
monitoring. We also tried different flow rates (0.10, 0.15 and 0.20 ml min-1) and
0.15 ml min-1 gave the peak geometry that we desired. The stop time between
injections was set at 0.50 min corresponding to about 90 s for every injection. A
typical flow injection signal is shown in Fig. 4.1.1 and it clearly shows the linear
correlation between arsenic response and increase in the concentration. The
signals were monitored using the time-resolved mode which enabled monitoring
of the signals in one chromatogram.
46
Fig. 4.1.1. Typical flow injection signals for arsenic at different concentrations, the injection volume was 20 µL.
At this point of the method optimization, we focused our work on the
analysis of arsenic. Since the analyte of concern was limited to arsenic alone, we
also considered adding methanol in the eluent because presence of methanol is
widely known to increase sensitivity of arsenic (Larsen and Stürup, 1994; Larsen,
1998; Kovačevič and Goessler, 2005). Addition of methanol in the eluent also
compensates for the organic content of the samples being analyzed in a way that
the signal enhancement caused by the sample matrix will be negligible. We varied
the methanol content of the 0.3% HNO3 from 0 to 15% methanol (v/v) and the
corresponding signal for arsenic at various volumes of injections were examined
(results shown in Fig. 4.1.2).
47
0.00E+00
1.25E+07
2.50E+07
3.75E+07
5.00E+07
3 5 10 15
% Methanol in the eluent
Me
an
Are
a o
f A
s 10 µL
30 µL
70 µL
90 µL
Fig. 4.1.2. Influence of methanol concentration in the 0.3% HNO3 eluent on the arsenic signal at different injection volumes at an arsenic concentration of 100 µg L-1.
The results validated the well-documented signal enhancement for arsenic
caused by the addition of methanol. Addition of 3% methanol (v/v) resulted to a
6% increase in the counts as compared to plain nitric acid only. But the addition of
5% and 6% methanol (v/v) in the eluent produced even more dramatic increase in
the raw counts; 5% methanol produced an increase corresponding to about 300%
while 6% methanol in the eluent resulted to about 600% increase. However,
addition of too much methanol (exemplified by 15% methanol, v/v) eventually
resulted to a decrease in sensitivity when compared to that with 10% methanol
(increase corresponding to about 550% compared with plain nitric acid eluent
alone). This trend completely agrees with what is known from literature. For the
rest of the measurements, 10% methanol was added to the nitric acid eluent. Also,
the use of 20 mM phosphate buffer was again considered and 10% methanol was
also added to the phosphate buffer due to the same reasons. Further experiments
using 20 mM phosphate buffer also proved exemplary which was expected
because this eluent is a very common mobile phase for anion-exchange
chromatographic separation of arsenic species.
48
The experiments to check which would be an appropriate internal standard
were carried out using 0.3% HNO3 with 10% methanol and the suitable internal
standard found was also applied when the eluent was switched to 20 mM
phosphate buffer with 10% methanol. There are several basic requirements for
choosing a suitable internal standard with ICPMS for detection (Park and Song,
2005, Amarasiriwardena et al., 1998). The internal standard should have a nominal
mass close to that of the analyte and it should also have an ionization potential
close to that of the analyte. Another is that the internal standard should not be
present in the samples, a prerequisite which is a must for all internal standard
normalization procedures. For our purpose, we looked at the possibility of using
indium and germanium which are commonly used for internal standard
normalization. Results from normalization with indium showed erratic results and
so we considered probable use of germanium, selenium, and tellurium.
Germanium is commonly used for internal standard normalization for arsenic
because it has a mass very close to arsenic (Amarasiriwardena et al., 1998).
Tellurium is also a potential internal standard because it resembles the ionization
potential of arsenic (Amarasiriwardena et al., 1998). Selenium also satisfies the
nearness to the mass of arsenic and nearly resembles its ionization potential (Park
and Song, 2005).
The determined arsenic concentration in TORT 2 extract from
normalization against 74Ge, 78Se and 128Te are presented in Fig. 4.1.3. It should be
stressed however that, although the materials we used for method validation
contain selenium, the concentration we were adding for normalization was
sufficient to minimize contributions from the material itself (the highest would be
in DOLT 3 which would contribute about 3% in the counts for selenium assuming
a 100% extraction).
49
0
7
14
21
10 µL 30 µL 50 µL 70 µL 90 µL
Volume of injection, µL
As
con
cen
tra
tio
n (
mg
/kg
)
Ge 74
Se 78
Te 128
Fig. 4.1.3. Determined arsenic concentration (mg kg-1) in extracts of the reference material TORT 2 after internal standard normalization against 74Ge, 78Se, and 128Te.
The determined arsenic concentration clearly approximates the certified
arsenic content in the solid reference material (TORT 2, 21.6 ± 1.8 mg kg-1) which
indicates that normalization can be done with any one of the three internal
standards. Nonetheless, it would be practical to normalize results against either
74Ge or 128Te rather than with 78Se in the analysis of real samples because some
samples with unknown composition may contain selenium in appreciable
amounts. As for the results presented in the previous figure, it is worth noting that
the determined values were not affected by the volume of injection. This entails
that the method can be used to analyze arsenic in samples with limited availability
requiring very small volumes for injection. The actual arsenic concentrations in the
solutions being injected spans a range of arsenic concentration from as low as 15.0
± 0.5 µg L-1 (for LUTS 1) to as high as 99.2 ± 1.9 µg L-1 (for BCR 422). It should be
noted, though, that the analyst can always choose the order of dilution suitable for
the samples for analysis.
50
For further method validation, we applied the method to different certified
reference materials representative of different types of matrix encountered in
arsenic analysis. We considered materials such as NIST reference water 1643e and
NIES human urine 18 to an assortment of biological reference materials which
were mostly in solid form. For the solid reference materials, a prerequisite
extraction step was performed. A simplified schematic diagram is shown in Fig.
4.1.4. The solid reference materials were extracted with a methanol/water (1+1)
mixture by shaking top over bottom for 18 hours. The extracts were then
processed further as shown in the diagram.
Fig. 4.1.4. Schematic diagram of the procedure showing vital steps followed
in this work for arsenic determination.
51
Triplicate runs were performed on all measurements, i.e. triplicate samples
subjected to extraction which eventually corresponded to triplicate samples for
digestion prior to conventional ICPMS analysis or triplicate samples for flow
injection-ICPMS analysis. Liquid materials were treated and processed in the same
way as the reconstituted materials. Upon dilution, these samples were analyzed
by flow injection ICPMS and the results are presented separately for liquid
materials (Table 4.1.1) and for that of solid reference materials (Tables 4.1.2 and
4.1.3).
Table 4.1.1. Determined arsenic concentration in liquid reference/control
materials, (mean ± SD, n = 3).
Flow injection analysis (µg L-1) Reference/Control material
As concentration (µg L-1) in material
Total As analysis (µg L-1)
0.3% HNO3 with 10% methanol
20 mM Phosphate buffer with 10% methanol
Water 1643e 60.5 ± 0.7 59.2 ± 0.4 56.3 ± 2.0 56.6 ± 0.6
Human Urine 18 137 ± 11 146 ± 2 146 ± 3 137 ± 3
Seronorm FE 1114 100 ± 3 157 ± 2 144 ± 3 131 ± 3
Comparison of the total arsenic values obtained from conventional ICPMS
analysis on digested liquid materials showed that for both NIST reference water
1643e and NIES human urine 18, the total values from conventional ICPMS
measurements are in accordance with the reference values. The values returned
for NIES human urine are also in agreement with reported values by other groups.
Hata et al. (2007) reported a value of 131.5 ± 1.2 µg L-1 while Sloth et al. (2004)
determined 140 ± 5 µg L-1 using the urine reference material. For the water
reference material, Gil et al. (2007) reported 59.59 ± 0.25 µg L-1 while Kile et al.
(2007) accounted for 101.7 ± 5.8% of the arsenic content in the certified water
material.
52
In the case of the control material Seronorm FE 1114 however, there was a
large discrepancy in the values. The information packet that came with this urine
material shows that arsenic content was determined using hydride generation
atomic absorption spectroscopy. This instrumentation is a very efficient way for
measuring arsenic species which can form hydrides but is not suitable for total
arsenic determinations specifically when most of the arsenic is present as
arsenobetaine or other non-hydride forming arsenic species. We speculated that
Seronorm FE 1114 may contain arsenobetaine which could explain why we were
getting higher total arsenic value than what was contained in the information
packet. A quick check for arsenobetaine by cation-exchange separation revealed
presence of this arsenic species at a concentration equal to 46 µg L-1 in the material.
This arsenobetaine content when added to the information value in the packet
would give a number in close agreement to the total value we obtained by
conventional ICPMS measurements. This then entailed that the values we derived
from conventional ICPMS measurements can be used as the point of reference for
arsenic level in the materials. The results from flow injection ICPMS
measurements using either eluent showed good correlation with the values
derived from conventional ICPMS analysis. This denotes that the flow injection
method can approximate well the total arsenic content of liquid samples.
In most sample pretreatment procedures for arsenic analysis, the extraction
solvent is evaporated (after the centrifugation and decantation steps) and the
residue is subsequently reconstituted in Milli-Q water or oftentimes with the
eluent used for speciation analysis. This is commonly done to ensure that the
samples injected, i.e. in the HPLC for separation, will be in the same matrix as the
standards used for quantification. For the solid reference materials we used,
extraction was performed by shaking representative amount in 1+1 (v/v)
methanol/water mixture. Aliquots of these extracts were then taken, the solvent
was evaporated, and the residue was re-dissolved in Milli-Q water. Aliquots of
these reconstituted samples were then analyzed in different ways: conventional
ICPMS after acid-assisted microwave digestion and flow injection ICPMS using
53
either 0.3% HNO3 with 10% methanol or 20 mM phosphate buffer with 10%
methanol (results are presented in Table 4.1.2). The total arsenic values obtained
for conventional ICPMS measurements illustrate the good correlation of these
values with the certified arsenic content in the solid reference materials. This
relationship indicates that the total arsenic values can be used as a benchmark of
the arsenic content in the reconstituted extracts. Comparison of these values with
the values derived from flow injection analysis, using either eluent, shows that
these fit nicely with each other. Thus, the flow injection ICPMS method can be
used as an alternative method for total arsenic analysis in extracts which are
typically subjected to speciation analysis.
Table 4.1.2. Determined arsenic concentration in reconstituted extracts of certified
reference materials, (mean ± SD, n = 3).
Flow injection analysis (mg kg-1) Certified reference material
Certified As concentration (mg kg-1)
Total As analysis (mg kg-1)
0.3% HNO3 with 10% methanol
20 mM Phosphate buffer with 10% methanol
BCR 422 21.1 ± 0.5 20.9 ± 0.3 20.1 ± 0.5 20.8 ± 0.8
DOLT 3 10.2 ± 0.5 7.6 ± 0.2 6.9 ± 0.3 7.3 ± 0.4
DORM 2 18.0 ± 1 17.9 ± 0.2 16.5 ± 0.3 17.3 ± 0.8
IAEA 407 12.6 ± 0.3 12.9 ± 0.1 11.8 ± 0.3 12.3 ± 0.4
LUTS 1 2.8 ± 0.1 3.4 ± 0.1 2.8 ± 0.3 3.0 ± 0.4
TORT 2 21.6 ± 1.8 19.1 ± 0.4 17.9 ± 0.8 18.4 ± 0.8
The method performance was also evaluated as regards precision and inter-
day variability. For every measurement, a drift standard was injected every now
and then to monitor stability of the measurement run within the day. Typically,
the standard with the middle concentration (usually 10 µg L-1) was injected after
the series of standard solutions and right before the first sample. Afterwards, the
drift standard was injected again after 3 or 4 samples (depending on the number
of samples) have been injected. The variability of within day measurements, as
54
reflected from the measurement of the drift, were 2.5% using the nitric acid eluent
and 3.0% using the phosphate buffer eluent. As for inter-day variability, it was
evaluated using the returned values on the reference material DORM 2. The
values were found to be within 3.7% of each other using the nitric acid eluent and
3.1% using the phosphate buffer eluent. The detection limits determined for the
optimized method were 38 ng L-1 and 62 ng L-1 for the nitric acid and phosphate
buffer eluents, respectively.
Initially, only the reconstituted extracts were used for the comparison
between conventional ICPMS and flow injection measurements. In the course of
the experiments, one question was raised: what if the samples subjected to
speciation analysis were the direct methanol/water extracts and not the
reconstituted extracts? With this question in mind, similar measurements were
performed on the methanol/water extracts (results shown in Table 4.1.3). From
the results, it is evident that the flow injection ICPMS method employing either
eluent can approximate well the total arsenic content in the extracts as there is
good correlation between the values obtained.
Table 4.1.3. Determined arsenic concentration in methanol/water extracts of
certified reference materials, (mean ± SD, n = 3).
Flow injection analysis (mg kg-1) Certified reference material
Certified As concentration (mg kg-1)
Total As analysis (mg kg-1)
0.3% HNO3 with 10% methanol
20 mM Phosphate buffer with 10% methanol
BCR 422 21.1 ± 0.5 19.8 ± 0.3 19.4 ± 0.5 20.9 ± 0.9
DOLT 3 10.2 ± 0.5 7.3 ± 0.1 7.3 ± 0.3 7.5 ± 0.3
DORM 2 18.0 ± 1 17.2 ± 0.2 17.2 ± 0.3 16.9 ± 0.9
IAEA 407 12.6 ± 0.3 11.8 ± 0.1 11.7 ± 0.5 12.4 ± 0.6
LUTS 1 2.8 ± 0.1 3.0 ± 0.1 3.0 ± 0.3 3.3 ± 0.3
TORT 2 21.6 ± 1.8 18.4 ± 0.7 17.3 ± 0.5 18.2 ± 0.9
55
The values from conventional ICPMS analysis on digested methanol/water
extracts also show good approximation of the certified content in the solid
materials and hence indicate that the extraction step was efficient. The values we
derived were comparable to previous reported values by other researchers. Hirata
et al. (2006) used DORM 2 and TORT 2 for their method validation and reported
17.5 ± 1.1 mg kg-1 and 20.0 ± 0.5 mg kg-1 for total arsenic in DORM 2 and TORT 2,
respectively. Kirby and Maher (2002) also used these two certified reference
materials to evaluate their method for determination of water-soluble arsenic
species and reported extraction efficiencies of 103 ± 2% for DORM 2 and 92 ± 5%
for TORT 2. These recoveries were better compared to that reported by Brisbin et
al. (2002) for different extraction conditions for TORT 2 which had a mean value of
16.2 ± 3.9 mg kg-1 (corresponding to extraction recovery of 67.6 ± 2.1%). Pizarro et
al. (2004) reported total arsenic concentration of 2.7 ± 0.2 mg kg-1 in LUTS 1 when
they used it for method validation using bidimensional chromatography with
ICPMS detection. Most of the reference materials above have also been used by
Scriver et al. (2005) and they reported these total arsenic values: 9.9 ± 0.6 mg kg-1 in
DOLT 3, 18.9 ± 2.1 mg kg-1 in DORM 2, 21.2 ± 1.3 mg kg-1 in TORT 2, and 2.8 ± 0.1
mg kg-1 in LUTS 1. For BCR 422, Damkröger et al. (1997) reported a 25 ± 10%
recovery with high pressure ashing, a method which they said was not able to
decompose all the arsenic species in this material and was so far outperformed by
a dry-ashing procedure which can mineralize 95% of the arsenic content. They
however noted that further modifications with the high pressure ashing procedure
would be helpful because of less time required as compared to the dry-ashing
procedure which is not suitable for routine analysis because of the length of time
needed for this procedure. Entwistle and Hearn (2006) reported a recovery of 103.6
± 6.2% in BCR 422 using open vessel wet digestion with a mixture of sulfuric acid
and nitric acid.
The results presented in Tables 4.1.2 and 4.1.3 also show that the analyst has
the choice of which extracts can be subjected to speciation analysis and at the same
time, has the assurance that he can measure the total arsenic content of the
56
samples by just by-passing the column. This set-up, thus, paves the way for
straightforward determination of column recoveries. Column recovery is the
quantitative measure of how much of the original sample is being eluted from the
column after chromatographic separation. It is important to know this parameter
to have a mass-balance at the end of the measurements. Furthermore, the flow
injection method may be used to easily identify steps or stages in the procedure
where analyte loss may be occurring, i.e. in the extraction or in the
chromatographic separation step.
The optimized flow injection method offers rapid determination of arsenic
in various extracts and matrices. However, applicability of the method will be
tested when samples for analysis are of limited availability and arsenic content is
in ultra trace concentration. With these samples, dilution will put the arsenic
concentration near or below the limit of quantification of the ICPMS. Some
samples that can really make analysis difficult are most clinical samples like urine,
blood, plasma, and serum. Arsenic concentration in these samples can be in the
ultra trace levels that analysis without dilution of the samples is preferred.
However, the matrices of the samples offer difficulty for the determination. Even
faced with this hindrance, we thought it would be worth the while to optimize a
flow injection method suitable for these samples.
For preliminary studies, only urine reference materials were used for
method optimization of a direct flow injection ICPMS method. The same HPLC-
ICPMS set-up was used and the 0.3% HNO3 with 10% methanol supplied at 0.15
ml min-1 was employed as eluent. The major modification of this direct flow
injection ICPMS method with the original was that dilution was achieved using
the automated injection program of the HPLC used. This configuration allowed us
to introduce the internal standard without the need for manual spiking by using
the internal standard mixture as the diluent. The HPLC was programmed to take 5
µL of standards/samples and then 15 µL of a mixture of internal standards prior
57
to injection. This set-up takes advantage of the very precise sample uptake
capability of the liquid chromatograph used.
Our preliminary results showed that addition of the internal standards, 74Ge
and 128Te prepared in Milli-Q water, provided excellent data which was calculated
without normalization. But normalized data, calculated both against 74Ge and
128Te, resulted to over-estimation of arsenic content in the urine materials. These
were perhaps due to the matrix suppression of the germanium and tellurium
signals. Confronted with these results, the urine materials were analyzed again
but the internal standards were prepared in different nitric acid concentrations.
The results (Fig.4.1.5) indicate that addition of the internal standards prepared in
3% HNO3 best estimates the results from total arsenic analysis done by
conventional ICPMS on digested urine materials. Although the non-normalized
data is not affected by the acid content in the internal standard mixture and hence
may be used independently, we deemed it necessary to have an internal standard
suitable with the configuration we are using to validate results. Thus for other
measurements, we prepared the internal standards in 3% HNO3.
Determined arsenic concentration in urine
0
40
80
120
160
200
0% acid 1% acid 3% acid Total As
As
conc
entra
tion
NIES 18 (against Te)
Seronorm (against Te)
NIES 18
Seronorm
Fig. 4.1.5. Determined arsenic concentration in urine materials showing non-normalized data and normalized data against 128Te prepared in a mixture (74Ge and 128Te) with varying nitric acid concentration.
58
Four different urine reference/control materials were analyzed using
conventional ICPMS and flow injection ICPMS, both directly and with dilution of
materials. Again, triplicate runs of the samples were processed throughout. The
eluent used for both flow injection methods was 0.3% HNO3 with 10% methanol.
The results were in good agreement with each other as reflected in Table 4.1.4.
This connotes that the direct flow injection method can be used for total arsenic
determinations.
Table 4.1.4. Determined arsenic concentration in urine (µg L-1) using different methods of analysis, mean ± SD, n = 3.
Determined arsenic concentration (µg L-1) Urine material
Total Asa FI-ICPMS (1+9) FI-ICPMS (direct)
NIES human urine 18 149 ± 1 146 ± 3 147 ± 2
Seronorm FE 1114 149 ± 8 144 ± 3 143 ± 2
ClinChek Level I 38.6 ± 1.3 38.8 ± 1.3 40.8 ± 0.8
ClinChek Level II 79.2 ± 2.6 76.1 ± 1.3 82.6 ± 1.9
Urine A 286 ± 12 279 ± 2 283 ± 11
Urine B 8.3 ± 0.4 7.4 ± 0.3 8.3 ± 0.4
Urine C 127 ± 1 129 ± 2 129 ± 3
Urine D 25.9 ± 1.0 23.9 ± 1.3 24.8 ± 0.8
Urine E 25.5 ± 2.7 22.8 ± 0.9 24.3 ± 0.4
Urine F 4.8 ± 0.4 4.2 ± 0.2 5.1 ± 0.6
Urine G 33.5 ± 1.5 29.6 ± 0.5 32.3 ± 0.3
Urine H 7.7 ± 0.4 7.4 ± 0.6 7.8 ± 0.4
Urine I 23.1 ± 0.3 22.8 ± 0.5 25.1 ± 0.5
Urine J 9.7 ± 0.4 8.3 ± 0.6 9.5 ± 0.4
a Determined after acid-assisted microwave digestion.
59
A closer look at the concentrations of these urine reference/control
materials reveal concentrations which are high compared to arsenic levels in real
samples. Thus, actual urine samples were also subjected to the different methods
of analysis. Urine samples A to J were the midstream first morning urine of 10
volunteers. The volunteers were not asked to avoid any seafood or other food
known to contain high arsenic levels because these are just spot samples used for
comparison of the total arsenic values derived using the three different ways of
analyses. The results summarized in Table 4.1.4 show that there is a good
correlation between the three measurements which implies that the modified flow
injection ICPMS method is indeed suitable for the arsenic determination in urine
samples with trace to very high concentrations. This is specifically advantageous
because the method reduces sample handling wherein contamination may be
introduced, and also avoids the necessity for digestion which can hamper
determination when arsenic is diluted to the point of the limit of detection of the
instrument used.
The results gathered for the urine reference/control materials and actual
samples made us wonder if this modified flow injection ICPMS method would
also be suitable for other body fluids. We thought of widening applicability by
including other clinical samples such as blood, plasma, and serum. These clinical
samples are not routinely used for arsenic analysis but are very helpful to assess
grave exposure to the element. The most commonly analyzed body fluid is urine
because urinary arsenic is a good indicator of arsenic exposure and can be easily
acquired since sample collection is non-intrusive. Application of the direct flow
injection-ICPMS method on various ClinChek control materials showed
interesting results specifically as regards the tellurium signals when applied to
blood samples. We noticed splitting in the tellurium signals when used as internal
standard but this was noticed only with the blood samples (results presented in
Fig.4.1.6.
60
Fig. 4.1.6. Observed signal splitting for 128Te used as an internal standard (added in a mixture 100 µg L-1 each of 74Ge and 128Te).
This observation prompted further experiments: we looked at the effect of
varying the flow rate of the mobile phase, changing the stop time between sample
injections, sample preparation parameters (such as sonication and time of
stabilization after reconstitution of the lyophilized blood material in Milli-Q
water), and order of drawing from the vials (either sample first or the internal
standard mixture first) prior to injection. The splitting was affected by the order of
drawing the sample from the vials; it was observed when the blood samples were
drawn before the internal standard mixture prior to injection (results presented in
Fig.4.1.7). This was perhaps caused by the matrix itself which may be difficult to
flush out of the capillaries and onto the nebuliser.
61
Fig. 4.1.7. 128Te signals as influenced by the order of drawing the solutions from vials prior to injection.
We also considered mixing the solutions in the injector needle prior to
loading of the sample but as shown in Fig. 4.1.8, mixing in the needle did not
produce significant effect as long as the sample is drawn first. For most samples
therefore, we recommend drawing the internal standard first, and then the sample
prior to injection. We speculate that the acid content in the internal standard
mixture and the liquid positioning in the needle help to push the sample towards
the tubing and avoid the possibility of separation in the capillary tubing.
62
Fig. 4.1.8. 128Te signals as influenced by mixing of the sample and internal standards in the HPLC needle prior to injection.
After having the answer to what was causing the splitting of the tellurium
signals, the experiments were then focused again on the application of the method
to the various clinical control materials. Further measurements showed that within
day variability by monitoring a drift standard was within 2.2%. Inter-day
variability was determined to be within 3.1% based on the determined value of the
NIES urine reference material. The determined detection limit was 40 ng L-1.
The various ClinChek control materials were subjected to analyses (results
summarized in Table 4.1.5). The blood and serum control materials do not have
information values for arsenic thus arsenic values in these materials were based on
results from routine conventional ICPMS method. The plasma materials have
information values for arsenic derived using AAS. The blood and serum materials
have trace arsenic concentrations as suggested by total values from conventional
measurements. The plasma materials have high arsenic content as revealed by the
63
conventional ICPMS measurements and these values agree with the values in the
information packet. Results from the modified flow injection method agree well
with the values from both the conventional ICPMS and the original flow injection
ICPMS method for all control materials. These results imply that the modified
method may be used for the direct analysis of clinical samples without the
necessity of manual sample dilution. This increases sample throughput with lesser
risk of contamination because of the reduced sample handling.
Table 4.1.5. Determined arsenic concentration in clinical samples (µg L-1) using
different methods of analysis, mean ± SD, n = 3.
Determined arsenic concentration (µg L-1) Urine material
Total Asa FI-ICPMS (1+9) FI-ICPMS (direct)
ClinChek Blood II lot 545 3.8 ± 0.1 3.9 ± 0.2 3.8 ± 0.1
ClinChek Serum I lot 608 2.0 ± 0.3 2.0 ± 0.1 2.0 ± 0.2
ClinChek Plasma I lot 417 193 ± 4 184 ± 5 194 ± 14
ClinChek Plasma I lot 417 474 ± 12 447 ± 15 473 ± 21
a Determined after acid-assisted microwave digestion.
4.2. Analysis of fish sauce samples
While making a literature search related to the work on the flow injection
method, a search returned an article from a Japanese research group working with
the determination of arsenic in fish sauce samples. My curiosity was stimulated
with this article because of their finding that dimethylarsenic acid (DMA) was the
major arsenic compound present in the fish sauce samples they studied (Kato et
al., 2004). In the article, the authors have developed a method utilizing HPLC with
electrospray mass spectrometry (HPLC-ESIMS) for detection and applied it in the
analysis of various fish sauces and the source raw materials. The authors have
concluded that arsenobetaine, which is the major component in the raw materials,
was converted to dimethylarsenic acid and that the identification of DMA in the
64
samples was certain because of the ESIMS detection they have used. This ignited
my interest because dimethylarsenic acid is relatively toxic compared to
organoarsenicals which are widely known to be non-toxic at all. One more thing
that got me interested in it was the fact that fish sauce is virtually indispensable in
every South East Asian kitchen. Fish sauce is widely consumed that even health
experts are considering it as a medium to introduce iron in the diet to combat iron
deficiency anemia (Thuy et al., 2003; Mannar & Gallego, 2002; Fidler et al., 2003).
Fish sauce is used in Asia as an alternative to salt or soy sauce and it is
recently gaining popularity in other parts of the world as well. It is prepared by
allowing a salt/fish mixture to ferment for a period of 6 months or longer. After
fermentation, the brown liquid produced is collected, bottled and sold in the
market. It is known as nước mắm in Vietnam, nam pla in Thailand, ngan byar yay in
Myanmar, padaek in Laos, teuk trei in Cambodia, patis in the Philippines, trasi in
Indonesia and aek jeot in Korea. The salt content in the finished product can be
between 20 to 30% which explains why it is an alternative to salt. This high salt
content in the fish sauces poses a problem for arsenic determination with ICPMS
as there will be interferences from 40Ar35Cl on the arsenic signal. However,
comparing ICPMS with ESIMS, the latter would be more susceptible to matrix
interferences compared to ICPMS. The use of the latter instrumentation is
preferred because it is more robust and in this case, we thought that it would be
worth analyzing fish sauce samples. For this work, six fish sauces bought from an
Asian store in Graz, Austria were used (source material and country from which
the sauce was produced are listed in Table 4.2.1).
65
Table 4.2.1. Fish sauce samples analyzed for arsenic content by HPLC-ICPMS. Fish sauce sample Raw material Manufacturer
Fish Sauce 1 Oyster Pichai Fish Sauce Co., Ltd., Thailand
Fish Sauce 2 Squid Thai Fish Sauce Factory, Co., Ltd.,
Thailand
Fish Sauce 3 Fish Tang Sang Hah Co., Ltd., Thailand
Fish Sauce 4 Fish Húng Thành, Vietnam
Fish Sauce 5 Fish Tang Thai Chiang, Thailand
Fish Sauce 6 Fish Công Ty Tnhh Khai Thác, Vietnam
The fish sauce samples were filtered and diluted (1+99) prior to speciation
analysis. Anionic separation done using PRP X-100 column (4.1 x 250 mm, 10 µm
particle size) with 20 mM phosphate buffer (pH = 6.0) as eluent revealed the
absence of anionic species (or probable presence but below the detection limit of
0.01 µg L-1). Chromatographic separation in cationic conditions using Zorbax 300
SCX column (4.6 x 250 mm, 5 µm particle size) using 10 mM pyridine at pH = 2.3
showed that the major arsenic species is arsenobetaine with traces of
arsenocholine, trimethylarsine oxide, tetramethylarsonium ion, and
trimethylarsenopropionate (overlaid chromatograms are presented in Figure
4.2.1). The 40Ar35Cl interference from the matrix itself is clearly visible in the
signals at around 2 minutes. This was validated by monitoring the signal of 77Se in
parallel with 75As. A summary of all arsenic species present in all six fish sauce
samples is presented in Table 4.2.2.
66
Retention time, min
0 2 4 6 8
Abu
ndan
ce
0
200
400
600
800
1000
1200
1400
5 µg/L standardsFish sauce 4
AB
TMAOAC Tetra
40Ar35Cl TMAP
Fig. 4.2.1. Overlaid chromatograms of a standard mix of 5.0 µg L-1 each of AB, TMAO, AC and TETRA (solid line) and fish sauce sample 4 (dotted line). (Zorbax 300 SCX column, 4.6 x 250 mm, 5 µm particle size; 10 mM pyridine, pH 2.3; flow rate: 1.5 ml min-1; column temperature: 30°C; volume of injection: 20 µL).
Table 4.2.2. Determined arsenic species in fish sauce samples using HPLC-ICPMS,
mean ± SD, n = 3.
Concentration of arsenic species (mg L-1) Fish sauce
sample Arsenobetaine Arsenocholine TMAO TMAP
Fish Sauce 1 0.55 ± 0.05 0.046 ± 0.009 0.052 ± 0.013 0.014 ± 0.004
Fish Sauce 2 0.80 ± 0.07 0.062 ± 0.012 0.038 ± 0.012 0.012 ± 0.006
Fish Sauce 3 0.91 ± 0.08 0.080 ± 0.009 0.030 ± 0.016 0.019 ± 0.007
Fish Sauce 4 1.98 ± 0.17 0.16 ± 0.02 0.043 ± 0.011 0.011 ± 0.004
Fish Sauce 5 1.44 ± 0.12 0.11 ± 0.01 0.011 ± 0.006 0.016 ± 0.007
Fish Sauce 6 2.55 ± 0.06 0.13 ± 0.02 0.028 ± 0.012 < 0.01
67
Our results were in divergence to the findings of the Japanese group as
regards the major arsenic species present. Thus to further confirm our results, we
also did spiking experiments. Fig. 4.2.2 shows overlaid chromatograms of fish
sauce sample 4 and sample 4 spiked with DMA to have a final concentration of 5
µg L-1 in the solution subjected to analysis. The outcome of this experiment clearly
indicates that DMA is not the major arsenical in our samples. The disparity may
have been due to the different samples used in the studies. Their samples were
mainly from Japan and the samples in this study were from Thailand and
Vietnam. But as regards methodology employed, use of the ICPMS is more robust
and less susceptible to matrix effects compared to ESIMS. Thus, with the
widespread use of this condiment, it is imperative that the main arsenic
component in the material be known. Our results using ICPMS show that use of
this condiment is safe but since the results from the Japanese group differ from
ours, there is a need to resolve the differences. In this note, ESIMS experiments
were also performed.
Retention time, min
0 2 4 6 8
Abu
ndan
ce
0
200
400
600
800
Fish sauce 4Fish sauce 4 spiked with 5 µg/L DMA
DMA
Fig. 4.2.2. Overlaid chromatograms of fish sauce sample 4 spiked with DMA to a final concentration of 5.0 µg L-1 (solid line) and fish sauce sample 4 (dotted line). (PRP-X100 column, 4.1 x 250 mm, 10 µm particle size; 20 mM phosphate buffer, pH 6.0; flow rate: 1.5 ml min-1; column temperature: 40°C; volume of injection: 20 µL).
68
We further ascertained arsenic species identification by doing HPLC with
ESIMS measurements on the samples. HPLC-ESIMS validated the results we got
with ICPMS detection. Fig.4.2.3 shows the data from the arsenobetaine standard
showing the total ion chromatogram and the molecular ion fragment with m/z
equal to 179 which is characteristic for arsenobetaine. The m/z equal to 91
corresponds to AsO+ which is formed because of the presence of oxygen impurity
in the nitrogen gas. With the presence of oxygen, it was observed that monitoring
of m/z 91 is more viable because the detector is more sensitive in detecting this
rather than m/z 75 for As+ (Kuehnelt et al., 2003). The results when a DMA
standard was analyzed by HPLC-ESIMS are presented in Fig.4.2.4. The
characteristic m/z for DMA corresponding to the molecular ion was monitored at
m/z = 139. When an aliquot of fish sauce sample 4 was injected (results shown in
Fig.4.2.5), the results clearly validate the results from HPLC-ICPMS analysis. The
analysis of fish sauce sample 4 showed that AB is indeed the major component in
the sample.
69
Fig. 4.2.3. Chromatograms of AB standard (200 µg L-1) obtained by HPLC-ESIMS (Shodex RSpak NN-614 column, 6 x 150 mm, 10 µm particle size; 5 mM ammonium formate buffer, pH 3.0; flow rate: 0.4 ml min-1; column temperature: 30°C; volume of injection: 5 µL).
70
Fig. 4.2.4. Chromatograms of DMA standard (200 µg L-1) obtained by HPLC-
ESIMS (Shodex RSpak NN-614 column, 6 x 150 mm, 10 µm particle size; 5 mM ammonium formate buffer, pH 3.0; flow rate: 0.4 ml min-1; column temperature: 30°C; volume of injection: 5 µL).
71
Fig. 4.2.5. Chromatograms of fish sauce sample 4 (120 µg L-1) obtained by
HPLC-ESIMS (Shodex RSpak NN-614 column, 6 x 150 mm, 10 µm particle size; 5 mM ammonium formate buffer, pH 3.0; flow rate: 0.4 ml min-1; column temperature: 30°C; volume of injection: 5 µL).
72
Analysis of the fish sauce samples also gave us the opportunity to check
applicability of the flow injection method for column recovery determination. We
analyzed total arsenic content of the fish sauce samples by flow injection ICPMS
using 0.3% HNO3 with 10% methanol as eluent. A parallel analysis was also
carried out using conventional ICPMS. For validation, extracts of the reference
materials TORT 2 and DORM 2 were analyzed alongside the fish sauce samples.
The reference materials were extracted as in previous work and processed
similarly. As for the variability of the flow injection ICPMS measurements, the
drift standard monitored showed values within 1.5% during a day’s measurement
and the determined arsenic content on DORM 2 was within 3.0% for inter-day
measurements.
Results shown in Table 4.2.3 reveal the agreement between the flow
injection results with the sum of arsenic species accounted for after
chromatographic separation. The values were also in agreement with the results
from conventional ICPMS measurements. This highlights the suitability of the
method for column recovery determinations without the tedious prerequisite
sample preparation steps necessary for conventional ICPMS measurements.
Table 4.2.3. Determined arsenic concentration in various fish sauce samples using
different methods, mean ± SD, n = 3. Concentration of arsenic (mg L-1) Reference
material/ Fish sauce sample
Total Asa Total As (FI – ICPMS)
Sum of species
DORM 2 17.2 ± 0.2 17.2 ± 0.3 18.0 ± 1.0
TORT 2 18.4 ± 0.7 17.3 ± 0.6 21.6 ± 1.8
Fish Sauce 1 0.69 ± 0.02 0.69 ± 0.03 0.67 ± 0.03
Fish Sauce 2 0.92 ± 0.02 0.92 ± 0.06 0.91 ± 0.03
Fish Sauce 3 1.08 ± 0.02 1.06 ± 0.03 1.04 ± 0.09
Fish Sauce 4 2.20 ± 0.02 2.13 ± 0.06 2.19 ± 0.16
Fish Sauce 5 1.63 ± 0.08 1.66 ± 0.03 1.58 ± 0.08
Fish Sauce 6 2.79 ± 0.02 2.75 ± 0.03 2.71 ± 0.09
a Determined by conventional ICPMS measurements on digested samples.
73
In the course of the analysis of fish sauce samples, experiments to evaluate
the influence of the salt content on the signals of 75As, 78Se and 128Te were also
performed. Experiments were conducted wherein these three elements were
added in solutions containing increasing amounts of NaCl (from 10 to 250 mM). In
one experiment, all three were spiked to have a concentration of 100 µg L-1 in the
final solution and in another, the elements were spiked to simulate the
experimental conditions used in the flow injection ICPMS measurements. These
experiments were conducted using the flow injection ICPMS operating conditions
employed in the analysis of fish sauce samples. The results are presented in
Fig.4.2.6 and Fig.4.2.7.
Fig. 4.2.6. Effect of increasing NaCl concentration on the 75As, 78Se and 128Te
signals simulating real analysis conditions.
74
Fig. 4.2.7. Effect of increasing NaCl concentration on the 75As, 78Se and 128Te
with 100 µg L-1 of each element in the solutions.
From the previous results, it was clear that in both conditions, the signals
for selenium and tellurium were hindered somehow with the increase in the
NaCl concentration. This was expected because increase in salt content may
result to deposition in the cones that then causes decrease in the corresponding
signals. As for the arsenic signals, a decrease was also observed when arsenic
was added in a concentration of 100 µg L-1 which can be attributed to the same
reasons as above. However, a slight increase in the signals was perceivable
which is perhaps caused by contribution from 40Ar35Cl. Thus, ample dilution
was done for the analysis of the fish sauce samples to minimize the interference
from 40Ar35Cl.
75
4.3. Calcium analysis by flow injection-ICPMS
The first opportunity to widen applicability of the flow injection ICPMS
method came when researchers from the Joanneum Institute in Graz wanted a
method based on ICPMS detection for calcium in their perfusate samples.
Perfusate is a liquid used for the preservation of organs prior to implantation
(Baker et al., 1981; Collins et al, 2008). For this purpose, the same HPLC-ICPMS
set-up was used but some parameters were modified. The eluent was plain 0.3%
HNO3 supplied at 0.15 ml min-1. The ICPMS was tuned to give maximum
sensitivity to calcium monitored at m/z 40. This was specifically difficult
because of the interference from 40Ar but the use of hydrogen as a reaction gas
offered possibility of doing this. Hydrogen was supplied at 3.5 ml min-1 to have
the best signal to noise ratio. Before further discussion on the method
optimization, a short background on calcium is presented next.
Peterlik and Stoeppler (2004) wrote an entire chapter focusing entirely on
calcium in the book Elements and their Compounds in the Environment. They
presented the chemical and physical properties of the element, and discussed
available methodologies for the determination of its compounds. A brief
account of the discussion is hereby presented. Calcium was first discovered in
1808, almost simultaneously but otherwise independently, by Sir Humphry
Davy and the team of Berzelius and Pontin. It was first produced in its pure
form by Moissan in 1898. Calcium derives its name from the Latin word “calx”
which means lime. It constitutes 3.63% of the Earth’s crust which makes it the
fifth most abundant element both in the human body and the environment.
Calcium is an essential mineral ion and it plays an important role in the
regulation of a great number of molecular, cellular, and systematic processes in
the vertebrate organism. Specifically, calcium is required for growth,
development and maintenance of the integrity of the skeletal system. It also
determines the threshold of neuromuscular excitation via its plasma
76
concentration. Calcium also functions as an intracellular “second messenger” in
many processes such as cellular proliferation and differentiation. In vertebrates,
99% of the element is confined to the bone as crystalline phosphate salt
hydroxyapatite [Ca10(PO4)6(OH)2], and the remaining 1% is unevenly
distributed between the extra and intracellular fluids. The mean Ca
concentration in the plasma of healthy individuals is 2.5 x 10-3 M.
Calcium belongs to Group 2 in the periodic table of elements with atomic
number 20 and atomic weight of 40.078 amu. It has six natural isotopes: 40Ca,
42Ca, 43Ca, 44Ca, 46Ca and 48Ca with 40Ca being the most abundant with natural
occurrence of 96.947%. It is widely used in various industrial processes, such as
metallurgy, and its salts are components of building stones. Determination of
calcium may be done spectroscopically using the red resonance line at 622.0 nm
and the green line at 535.5 nm. Alternatively, it can be determined
gravimetrically by precipitation as CaC2O4 or through complexometric titration
with EDTA. Various colorimetric methods with arsenazo or methylthymol blue
can also be used. Other techniques such as flame AAS and ICPAES have also
been used for calcium determination.
As already mentioned, the purpose was to optimize a flow injection
ICPMS method suitable for calcium determination in the perfusate matrix. The
first that was performed was evaluation of the effect of the matrix itself on the
determination. To achieve this, samples provided were subjected to the
conditions previously discussed. Necessary dilutions were done and the diluted
samples were analyzed and quantified against an external calibration solution
prepared from a stock Ca solution. Table 4.3.1 shows calcium concentration in
the original samples, the dilution factor and the expected concentration in the
solutions subjected to the analysis. The external calibration solutions were in the
range of 0 to 500 µg L-1. Fig. 4.3.1 shows the initial results. The use of scandium
and germanium as internal standards were also studied.
77
Table 4.3.1. Calcium concentration in solutions subjected to preliminary analysis to evaluate effect of perfusate matrix.
Sample Dilution factor Expected concentration
in solution, µg L-1 A1 100 25.1
A2 20 125.3
A3 10 250.5
B1 100 200.4
B2 200 100.2
B3 1000 20.0
C1a 500 320.6
C1b 1000 160.3
C1c 5000 32.1
C2a 1000 160.3
C2b 2000 80.2
C2c 10000 16.0
E 25.0
Notes: A1 to A3 were prepared from a solution which contained 0.0625 mM Ca in perfusate, B1 to B3 from a solution which had 0.5 mM Ca, and C1a to C2c were from a solution of 4.0 mM Ca. E was prepared from a Ca working standard prepared from CPI Ca stock solution.
Preliminary results when the samples in Table 4.3.1 were subjected to
analysis showed that the matrix somehow influences the determination of
calcium in this matrix. It should be noted that calcium concentrations in these
solutions were calculated against calibration solutions that were prepared from
a commercially available stock standard, and therefore did not contain
perfusate. Results presented in Fig.4.3.1 show discrepancies between the
determined values as opposed to the expected Ca concentration, more
pronounced specifically in sample C1a. This sample had the lowest dilution
factor in this set. These results prompted a similar experiment wherein the
calibration solutions were spiked with 100 µL of a solution containing perfusate
only (perfusate blank). The results (also presented in Fig.4.3.1), however, show
78
that the presence of perfusate in the matrix was not crucial to the determination
and that it does not interfere in the analysis. The results when the calculation
was made against a set of calibration solutions containing perfusate were
essentially similar to those gathered against the set of calibration solutions
without perfusate. This entails that future measurements may be done against
calibration solutions prepared from stock standard.
Determined Ca concentration using different sets of calibration solutions
0
90
180
270
360
450
C1a C1b C1c C2a C2b C2c
Sample
Ca
conc
entra
tion
(µg/
L)
Expected
N - With perfusat
Ge - With perfusat
N - No perfusat
Ge - No perfusat
Fig. 4.3.1. Determined calcium concentration in samples measured against
different sets of calibration solutions (with or without perfusate).
The results summarized in Table 4.3.2 show that there is great potential
for flow injection-ICPMS for analysis of calcium in perfusate. Although, it
should be noted that some of the results were higher than the expected
concentrations in the solutions. This was primarily the reason why the effect of
the perfusate matrix was evaluated. But then again, as shown in the previous
figure, presence or absence of perfusate in the calibration solutions did not
produce significant difference in the results.
79
Table 4.3.2. Determined calcium concentration in solutions subjected to preliminary analysis calculated against a set of calibration solutions containing perfusate, n = 3.
Determined calcium concentration, µg L-1 Sample Expected
concentration in solution, µg L-1
Non-normalized data
Normalized against 74Ge
A1 25.1 21.6 ± 0.3 21.1 ± 0.2
A2 125.3 138 ± 5 147 ± 3
A3 250.5 287 ± 5 315 ± 6
B1 200.4 232 ± 2 227 ± 4
B2 100.2 132 ± 4 131 ± 3
B3 20.0 33.8 ± 2.0 31.5 ± 0.9
C1a 320.6 384 ± 2 370 ± 4
C1b 160.3 172 ± 5 165 ± 3
C1c 32.1 45.8 ± 1.4 43.0 ± 1.1
C2a 160.3 182 ± 5 179 ± 2
C2b 80.2 77.6 ± 1.4 74.4 ± 1.4
C2c 16.0 21.4 ± 0.7 19.4 ± 0.5
E 25.0 27.3 ± 0.9 26.7 ± 1.1
A new set of samples were given for analysis together with a set of
calibration solutions. The concentrations of the original calibration solutions
with the corresponding concentration in the solution subjected to analysis are
presented in Table 4.3.2. For a parallel check, a calibration set prepared from Ca
stock standard solution was also prepared with the corresponding
concentrations. The samples were then analyzed and quantified against the two
sets of calibration solutions.
80
Table 4.3.3. Calcium concentration in standard calibration solutions (mM) and corresponding concentration (µg L-1) in solution subjected to analysis.
Original concentration (mM)
Dilution factor Concentration in solution, µg L-1
0 200 0
0.1 200 20.0
0.25 200 25.1
0.5 200 50.2
1.0 200 100.4
1.5 200 150.3
2.0 200 200.8
The quantification using the calibration solution made from the perfusate-
containing solutions was only done against the first 4 points (0 to 50.2 µg L-1)
because there was no significant increase in the peak area corresponding to the
supposed increasing content of Ca in the last 3 standards (100 to 200 µg L-1). It was
assumed that perhaps these solutions were filtered prior to handing over which
can mean that the undissolved Ca may have been filtered out. Though there was
no obvious precipitation observed in the vials handed to us, it was thought to be
filtered because of the previous observation regarding the 4.0 mM Ca solution
previously given to us. Precipitation was observed when the 4.0 mM Ca solution
was allowed to stand for a time. The concentration of Ca in these last three
standards was determined using the calibration set from CPI stock standard. The
values determined were 0.372 ± 0.001, 0.362 ± 0.001, and 0.467 ± 0.001 mM instead
of 1.0, 1.5 and 2.0 mM, respectively. The results for the analysis of the samples,
against both calibration sets, are summarized in Table 4.3.4. Comparison of the
values determined shows potential application of the flow injection ICPMS
method for the analysis of calcium in this matrix. Good correlation of the values is
also apparent.
81
Table 4.3.4. Determined calcium concentration in samples (µg L-1) quantified against two different sets of calibration solutions, n=3.
Determined level against calibration set containing perfusate, µg L-1
Determined level against calibration set prepared from CPI stock standard, µg L-1
Sample
Not normalized Against 74Ge Not normalized Against 74Ge
GMD06_S6_md2_4 0.135 ± 0.012 0.132 ± 0.011 0.115 ± 0.001 0.120 ± 0.002
GMD06_S6_md1_4 0.056 ± 0.001 0.055 ± 0.001 0.055 ± 0.001 0.058 ± 0.001
GMD06_S4_md2_4 0.181 ± 0.003 0.181 ± 0.002 0.163 ± 0.002 0.174 ± 0.003
GMD06_S4_md1_2 0.047 ± 0.001 0.049 ± 0.001 0.047 ± 0.001 0.050 ± 0.001
82
Chapter 5. Summary
The ever-growing awareness about the health risk associated with arsenic
has fuelled the demand for faster and accurate means for detection and
quantification. Advances in technology also calls for more robust methods which
can tender reliable determination of the element in various matrices. A flow
injection ICPMS method suitable for determination of metal/metalloid
concentration in different matrices was optimized. The method employed an
Agilent HPLC 1100 system as the flow injector and was coupled to an Agilent
7500ce ICPMS. Agilent HPLC 1100 features a binary pump, a vacuum degasser,
thermosttated autosampler and a variable 100 mm3 injection loop. This HPLC
system offers very accurate introduction of chosen volume of the sample as well as
automated standard addition and dilution of samples. The outlet of the HPLC was
directly connected to the nebulizer of the ICPMS using a PEEK tubing (0.125 cm
i.d. x 100 cm). Agilent 7500ce ICPMS is equipped with a PFA microconcentric
nebulizer, a Scott double pass spray chamber and an octopole reaction cell. The
octopole cell of the mass spectrometer was operated accordingly for matrix
interference reduction.
The eluents used for selective determination of arsenic were 0.3% HNO3
with 10% methanol (v/v) and 20 mM phosphate buffer with 10% methanol (pH
6.0, v/v), both supplied at 0.15 ml min-1. The method was validated using several
reference materials (acquired as a solid): BCR 422, DOLT 3, DORM 2, IAEA 407,
LUTS 1 and TORT 2. Several liquid materials (NIES human urine 18, NIST
certified reference water 1643e and SERONORM FE 1114) were also used for
method validation. For the solid reference materials, extraction using
methanol/water mixture (1+1) was carried out. An aliquot of this methanol/water
extract was subjected to analysis directly. Another aliquot was evaporated and
reconstituted in Milli-Q water before subjecting to similar analysis. Liquid
materials were diluted using Milli-Q water prior to analysis. The results from
these measurements were correlated against values derived from conventional
83
ICPMS analysis done on digests after acid-assisted microwave digestion. Results
from conventional ICPMS measurements, specifically for the extracts, were in
good agreement with reference values for arsenic content in the materials and
hence were used as the benchmark of arsenic levels. Comparison of the
determined values from flow injection ICPMS measurement show good
correlation between these values and those obtained by conventional ICPMS
method. The quantification was done against peak area against an external
calibration as well as by internal standardization. The internal standards used
were 74Ge, 78Se and 128Te.
The applicability of the flow injection ICPMS method was pushed a little
further by analysis of samples of limited availability, i.e. biological fluids. Arsenic
in clinical samples, such as blood, serum, or plasma, is a good indicator of
exposure but these samples may come in very limited amount. The HPLC system
was then programmed to allow automated dilution of the samples by mixing it
with a solution containing the internal standards. Validation of the modified
method using several ClinChek control materials showed that this method can
approximate the arsenic content well as reflected by good agreement with total
arsenic values obtained by conventional ICPMS measurement on acid digests.
Analysis of the certified material NIES human urine 18 also revealed similar
trends.
The flow injection not only offers a rapid determination of arsenic in
samples but it also offers a fast and convenient way to determine column
recoveries. The system used gives the analyst the choice to by-pass the column, to
determine total arsenic content in the sample; or to allow the sample to pass the
column, to allow separation of the arsenic species and subsequently determine the
concentration of each species. This simple switching in the injection valve then
affords for rapid comparison between the total arsenic content and the sum of
species accounted for after chromatographic separation. This capability for column
recovery determination was demonstrated in the analysis of fish sauce samples.
84
Fish sauce is a condiment typically used in South East Asian countries. It is
the brown liquid derived from a salt/fish mixture which is allowed to undergo
fermentation for a period of 6 months or more. The sauce collected is then bottled
and sold in the market. Fish sauce is an alternative to salt or soy sauce and may
contain up to 30% salt content. The very high salt content in these samples is a
problem especially with ICPMS detection since formation of 40Ar35Cl is then
expected to interfere with 75As signal. Dilution of the samples and use of helium as
a collision gas were then employed to counter the interference from the matrix.
Speciation analysis was performed on these samples using both anion and cation-
exchange conditions. Total arsenic content was determined both by conventional
ICPMS and flow injection ICPMS. Comparison of the total arsenic values
determined using both methods reveal good agreement. These values were also in
good agreement with the sum of species accounted for after speciation analysis.
The fish sauce samples were found to contain arsenobetaine as the major arsenic
species with traces of arsenocholine, trimethylarsine oxide, and
trimethylarsenopropionate. The levels detected plus the fact that most of it is
present as the non-toxic arsenobetaine suggests that fish sauce (at least those
included in this study) is safe for human consumption.
A further application of the method was in the determination of calcium in
perfusate. For this work, the eluent was simply 0.3% HNO3 and the set-up was
employed similarly except that the ICPMS was operated with hydrogen as a
reaction gas. This modification enabled monitoring of 40Ca with minimum
interference from 40Ar. The use of 45Sc and 74Ge as internal standards were also
investigated. Analysis of the perfusate samples showed applicability of the
method for determination of other element of interest. Ultimately, simultaneous
multi-elemental determination by flow injection ICPMS may be done but
systematic and thorough experiments, i.e. as regards eluents, diluents, needs to be
done to achieve this goal.
85
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List of Figures
Figure Title Page
4.1.1 Typical flow injection signals for arsenic at different concentrations, the injection volume was 20 µL.
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4.1.2 Influence of methanol concentration in the 0.3% HNO3 eluent on the arsenic signal at different injection volumes at an arsenic concentration of 100 µg L-1.
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4.1.3 Determined arsenic concentration (mg kg-1) in extracts of the reference material TORT 2 after internal standard normalization against 74Ge, 78Se, and 128Te.
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4.1.4 Schematic diagram of the procedure showing vital steps followed in this work for arsenic determination.
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4.1.5 Determined arsenic concentration in urine materials showing non-normalized data and normalized data against 128Te prepared in a mixture (74Ge and 128Te) with varying nitric acid concentration.
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4.1.6 Observed signal splitting for 128Te used as an internal standard (added in a mixture, 100 µg L-1 each of 74Ge and 128Te).
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4.1.7 128Te signals as influenced by the order of drawing the solutions from vials prior to injection.
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4.1.8 128Te signals as influenced by mixing of the sample and internal standards in the HPLC needle prior to injection.
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4.2.1 Overlaid chromatograms of a standard mix of 5.0 µg L-1 each of AB, TMAO, AC and TETRA (solid line) and fish sauce sample 4 (dotted line). (Zorbax 300 SCX column, 4.6 x 250 mm, 5 µm particle size; 10 mM pyridine, pH 2.3; flow rate: 1.5 ml min-1; column temperature: 30ºC; volume of injection: 20 µL).
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4.2.2 Overlaid chromatograms of fish sauce sample 4 spiked with DMA to a final concentration of 5.0 µg L-1 of DMA (solid line) and fish sauce sample 4 (dotted line). (PRP-X100 column, 4.1 x 250 mm, 10 µm particle size; 20 mM phosphate buffer, pH 6.0; flow rate: 1.5 ml min-1; column temperature: 40ºC; volume of injection: 20 µL).
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Figure Title Page
4.2.3 Chromatograms of AB standard (200 µg L-1) obtained by HPLC/ESIMS (Shodex RSpak NN-614 column, 6 x 150 mm, 10 µm particle size; 5 mM ammonium formate buffer, pH 3.0; flow rate: 0.4 ml min-1; column temperature: 30ºC; volume of injection: 5 µL).
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4.2.4 Chromatograms of DMA standard (200 µg L-1) obtained by HPLC/ESIMS (Shodex RSpak NN-614 column, 6 x 150 mm, 10 µm particle size; 5 mM ammonium formate buffer, pH 3.0; flow rate: 0.4 ml min-1; column temperature: 30ºC; volume of injection: 5 µL).
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4.2.5 Chromatograms of fish sauce sample 4 (120 µg L-1) standard obtained by HPLC/ESIMS (Shodex RSpak NN-614 column, 6 x 150 mm, 10 µm particle size; 5 mM ammonium formate buffer, pH 3.0; flow rate: 0.4 ml min-1; column temperature: 30ºC; volume of injection: 5 µL).
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4.2.6 Effect of increasing NaCl concentration on the 75As, 78Se and 128Te signals simulating real analysis conditions.
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4.2.7 Effect of increasing NaCl concentration on the 75As, 78Se and 128Te with 100 µg L-1 of each element in the solutions.
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4.3.1 Determined calcium concentration in samples measured against different sets of calibration solutions (with or without perfusate).
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List of Tables
Table Title Page
3.1 List of element stock solutions used in this work. 38
3.2 List of reference/control materials employed in this work. 39
4.1.1 Determined arsenic concentration in liquid reference/control materials, (mean ± SD, n = 3).
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4.1.2 Determined arsenic concentration in reconstituted extracts of certified reference materials, (mean ± SD, n = 3).
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4.1.3 Determined arsenic concentration in methanol/water extracts of certified reference materials, (mean ± SD, n = 3).
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4.1.4 Determined arsenic concentration in urine (µg L-1) using different methods of analysis, mean ± SD, n = 3.
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4.1.5 Determined arsenic concentration in clinical samples (µg L-1) using different methods of analysis, mean ± SD, n = 3.
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4.2.1 Fish sauce samples analyzed for arsenic content by HPLC-ICPMS.
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4.2.2 Determined arsenic species in fish sauce samples using HPLC/ICPMS, mean ± SD, n = 3.
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4.2.3 Determined arsenic concentration in various fish sauce samples using different methods, mean ± SD, n = 3.
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4.3.1 Calcium concentration in solutions subjected to preliminary analysis to evaluate effect of perfusate matrix.
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4.3.2 Determined calcium concentration in solutions subjected to preliminary analysis calculated against a set of calibration solutions containing perfusate, n = 3.
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4.3.3 Calcium concentration in standard solutions (mM) and corresponding concentration (µg L-1) in solutions subjected to analysis.
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4.3.4 Determined calcium concentration in samples (µg L-1) quantified against two different sets of calibration solutions, n = 3.
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