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Characterization of Omega Dietary Supplements by combining FT-IR and
Pattern Recognition Tools Michael J. Wenstrup and Luis Rodriguez-Saona
Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210
Methodology
Introduction
Abstract
The objective of this research was to create a model
for rapid determination of fatty acid composition and
classification of fish oil supplements.
25 Samples of Fish,
Flaxseed and CLO oils
Derivatization and
methylation
Cary 630 portable benchtop FT
mid-infrared spectrometer
Agilent 6890 gas
chromatography system
CH
CH2
CH3
C-H
stretching
C=O
stretching
Fingerprint
region
IR Spectral Data
+ Internal Standard
+ External Standards
GC Fatty Acid Composition Data
CHEMOMETRICS
Quantification Model
Partial Least Square Regression
(PLSR)
Soft Independent Modeling of Class
Analogy (SIMCA)
Classification Model
+
IR
GC-FAME
Interest in omega-3 dietary supplements has increased due to their health and nutritional
benefits. Scientific studies on these products have uncovered anti-inflammatory effects,
decreased cardiovascular disease mortality, and the potential reduction in blood
pressure. The compounds primarily responsible for these effects, polyunsaturated fatty
acids (PUFA) such as eicosapentaenoic acid (EPA), 20:5, and docosahexaenoic acid
(DHA), 22:6, are almost exclusively found in seafood products and are essential fatty
acids for humans (de Leiris 2009). The concentration of EPA and DHA in fish depends
on the type of fish, with salmon, herring, mackerel, tuna, and sardines having the highest
concentrations. As each PUFA has a different role in health promotion and disease
prevention, it is important to understand the variability in composition within fish oil
products, and whether they are supplied as triglycerides, esters, or fatty acids. Alkyl
esters of omega-3 fatty acids are more prone to oxidation than their triglyceride
counterparts, and some evidence suggests that the bioavailability of alkyl esters is lower
than that of intact triglycerides. Modern, rapid techniques to determine composition,
authenticity, and quality of fish oils are not yet readily available, but development of
Fourier transform infrared (FT-IR) and attenuated total reflectance (ATR) spectrometry
equipment has opened the door to novel methods. FT-IR systems provide a more robust
alternative to older dispersive systems, and ATR-IR systems can accommodate a wide
Figure 1. ATR-IR systems offer
higher signal intensity and are
compatible with a wider array
of sample types than other
sampling accessories.
Objective
GC
Sa
mp
le
GC Determination Label
EPA (%) DHA (%) EPA
(%) DHA
(%)
Fis
h A
K1 41.8 3.5 25.2 3.3 30 20
K2 58.6 3.1 35.2 2.8
MF1 43.0 0.7 34.7 1.7 40 30
MF2 48.7 2.1 37.9 3.0
S 47.6 0.7 34.7 0.0 36 21.6
N 43.5 1.1 28.3 0.4 27 18
CE 42.1 4.8 30.9 3.7 32 24
CS 41.1 3.4 26.8 2.7 30 20
NF 23.9 1.2 13.5 1.5 18 12
Fis
h B
W1 24.3 3.6 7.4 1.9 30
W2 26.2 1.4 7.5 0.5
ON1 22.0 1.8 10.4 1.5 30
ON2 21.2 0.4 11.3 0.7
NM1 22.9 1.6 14.5 4.1 15 10
NM2 21.7 0.0 10.3 0.2
CN 21.3 0.2 10.6 0.2 18 12
U 21.7 1.5 11.2 1.2 16.5 11
Raw F 6.7 0.1 3.8 0.2 6 5
CLO
T 14.5 1.0 10.1 1.0 12.3 8.2
VC 13.1 0.3 11.7 0.8 7.7 11.4
CLO 13.5 0.2 14.6 0.4 -
Factors SECV r Val SEC r Cal RPD
EPA 4 2.73 0.98 2.65 0.98 5.1
DHA 5 1.76 0.99 1.66 0.99 8.0
Figure 4. PLS Regression models of EPA and DHA content. Reference EPA/DHA data from GC data in Table 1.
Table 2. PLSR fit and error parameters. SECV
is Standard Error of Cross-Validation, SEC is
Standard Error of Calibration, RPD is Relative
Percent Difference to reference data.
Table 1. GC-FAME fatty acid analysis with label fatty acid
estimates. Left side groups names are SIMCA classes.
Figure 2 (above). SIMCA classification plot from 900-
1300 cm-1 region, showing 5 classes and EPA content
trend (red arrows).
Figure 3. Spectral comparison of the 1700-
1800 cm-1 regions of Fish groups A & B,
showing the shift in the ester peak.
SIMCA
PLSR
Discussion SIMCA classification reveals relationships between samples and can be used to
classify unknown samples into groups. Fish oils clustered into two groups, A and B,
Cod Liver Oil (CLO), Virgin Fish Oils (Raw) and Flaxseed oils clustered into
distinct groups with significant interclass distances (Figure 2). The amount of
EPA/DHA increased down the PC3 axis, and the Fish A group was separated from
By correlating the MIR spectral data with
fatty acid composition information, a PLSR
model was built.. Table 2 shows the
parameters for evaluating the model, as
well as comparison to the reference data.
A separate SIMCA model that included only Fish A &Fish B groups was constructed,
with a similar separation to that seen in Figure 2. Inspection of the spectral overlays
revealed a downward shift of the triglyceride ester band, from 1745 cm-1 to
1737 cm-1 (Figure 3). This shift is associated with fatty acid methyl and ethyl esters.
the other groups down PC1.
The discriminating power plot
revealed that the band at 1038 cm-1
was primarily responsible for
classification in this model.
References
Figure 5. Sample
application during
spectral collection.
The ATR is skirted
by a temperature-
controlled collar,
ensuring a
homogenous liquid
phase. Required
sample volume is
between 60 and 100
μL.
• Analysis of fatty acid composition by gas chromatography revealed
a wide range of EPA (6-60%) and DHA (3-40%) concentrations.
These values are comparable to the label claims, with most
samples having more EPA/DHA than claimed.
• With correlation coefficients of cross-validation of ≥ 0.98 and
SECVs of 2.73% and 1.76%, for EPA and DHA respectively, PLSR
fatty acid determination is comparable to modern chromatography
techniques (Table 2). Ratio of performance deviation (RPD) of the
models versus the reference data shows that this method is
capable of quantifying EPA & DHA across a broad
compositional range.
• SIMCA classification revealed clustering by oil source, as well as
3D spacing with regards to EPA/DHA content. This region
responsible for this significant separation, 900-1300 cm-1, is
associated with C=C and C-O stretching. The band primarily
responsible for discrimination, 1038 cm-1, has been found to
correlate with the C-O stretching in esters.
• The Fish A group has a distinct carbonyl band shift (from 1745 cm-1
to 1737 cm-1) that is associated with alkyl esters. This may reflect
the delivery of these supplements as primarily fatty acid alkyl
esters instead of triglycerides.
Conclusions
We have shown a fast, simple, and reliable method for determining
the characterization of the fish oils in these supplements and for
quantification of the important omega-3 fatty acids present, to
support and validate labeling claims. The portable ATR-MIR method
not only provides precise fatty acid composition information, but can
be used to determine the state of these fatty acids; whether they be
methyl/ethyl esters, free fatty acids, or triglycerides.
Omega-3 dietary supplements have been linked with health benefits including
decreased cardiovascular disease mortality, anti-inflammatory effects, reduced
blood pressure, and antiatherogenic activity. The sources of these effects,
polyunsaturated fatty acids (PUFA) such as eicosapentaenoic acid (EPA)
docosahexaenoic acid (DHA), are almost exclusively found in seafood products and
are essential fatty acids for humans. The concentration of EPA and DHA in fish is
highly variable; depending on species, catch location, environment, season and
processing parameters in determining the ratio of fatty acids found in the oil. Our
objective was to characterize the composition of commercial Omega-3 dietary
supplements using mid-infrared spectroscopy, gas chromatography, and
chemometrics. Supplements were purchased from retailers that included a large
amount of source, processing, and compositional variability. Fatty acid composition
of oils was determined by fatty acid methyl ester gas chromatography. The
supplements encompassed a wide range of fatty acid profiles and delivery methods.
Mid-infrared spectral data was collected on portable infrared systems and analyzed
by pattern recognition (SIMCA and PLSR). SIMCA analysis using the 900-1300 cm-1
region allowed for tight clustering of samples into distinct classes, with significant
(>3) interclass distances. Discriminating power showed a strong influence of the
1038 cm-1 band, which is typically associated with C=C and C-O stretching
vibrations. PLSR generated multivariate models with high correlation (R ≥ 0.98)
between the infrared spectrum and fatty acid composition, and SECV of 2.73 and
1.76 for EPA and DHA, respectively. Our results indicate that ATR-IR
spectroscopy combined with pattern recognition analysis provides for robust
screening and determination of fatty acid composition of fish oil supplements.
variety of sample types, from gases, to liquids, to powders.
Although the food and supplement industries have utilized
near-infrared (NIR) spectroscopy for decades, the
applications of mid-infrared (MIR) spectroscopy have only
recently gained ground. ATR FT-MIR is a non-destructive and
rapid technique that allows for analysis of all the chemical
bonds in a system. When combined with multivariate
statistical analysis and modern computational power
(collectively known as chemometrics), the large amount of
information provided by MIR can be used to replace
traditional techniques (Bosque-Sendra et al., 2012). In oil
analysis, these traditional techniques are both time
consuming and expensive. Gas chromatography (GC) and
high-performance liquid chromatography (HPLC) are used in
the analysis of oils, but the extensive sample preparation,
long run-times, and destructive nature of the analysis has
piqued interest in spectroscopic alternatives.
Bosque-Sendra, JM; Cuadros-Rodriguez, L; Ruiz-Samblas, C; de la Mata, AP. Combining chromatography and
chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional
data-A review. ANALYTICA CHIMICA ACTA, v. 724, 2012, p. 1-11.
Cabo, N. Infrared spectroscopy in the study of edible oils and fats. JOURNAL OF THE SCIENCE OF FOOD
AND AGRICULTURE, v. 75 issue 1, 1997, p. 1-11.
De Leiris, J; de Lorgeril, M; Boucher, F. Fish Oil and Heart Health. JOURNAL OF CARDIOVASCULAR
PHARMACOLOGY, v. 54 issue 5, 2009, p. 378-384.