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Brigham Young UniversityBYU ScholarsArchive
All Theses and Dissertations
2018-06-01
Evaluation of Pigments from a Purple Variety ofAtriplex hortensis L. for Use in Food ApplicationsEva Graciela Vila RoaBrigham Young University
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BYU ScholarsArchive CitationVila Roa, Eva Graciela, "Evaluation of Pigments from a Purple Variety of Atriplex hortensis L. for Use in Food Applications" (2018).All Theses and Dissertations. 7436.https://scholarsarchive.byu.edu/etd/7436
Evaluation of Pigments from a Purple Variety of Atriplex hortensis L.
for Use in Food Applications
Eva Graciela Vila Roa
A thesis submitted to the faculty of Brigham Young University
in partial fulfillment of the requirements for the degree of
Master of Science
Michael L. Dunn, Chair Oscar Pike Frost Steele Eric Jellen
Department of Nutrition, Dietetics, and Food Science
Brigham Young University
Copyright © 2018 Eva Graciela Vila Roa
All Rights Reserved
ABSTRACT
Evaluation of Pigments from a Purple Variety of Atriplex hortensis L. for Use in Food Applications
Eva Graciela Vila Roa Department of Nutrition, Dietetics, and Food Science, BYU
Master of Science
Atriplex hortensis L., also known as orach, is a leafy vegetable from the Amaranthaceae family, which has historically been consumed as a potherb, like spinach. The brightly colored leaves are a source of high quality protein, but may also be of interest as a potential source of natural food pigments. An aqueous extraction was obtained from the freshly harvested leaves of the ‘Triple Purple’ variety of A. hortensis. The extract was spray-dried into a powder, and individual pigments were analyzed using HPLC and LC-MS. The powder was also included as a color additive in a typical stabilizer/sweetener preparation and mixed into plain yogurt. Two batches of colored yogurt were held under light and dark conditions and tested for pH and color (L*a*b*) every 15 days. A visual sensory panel was performed on days 0, 45, and 90 to evaluate the color acceptance.
A total of three types of betacyanins and six types of anthocyanins were tentatively identified by HPLC and/or LC-MS. Orach pigments in yogurt were not stable under full light exposure. The color of samples exposed to light degraded within days. There were statistically significant differences found in L*a*b* scores in the dark treatment, beyond 30 days; but these modest changes in dark-stored samples were not found to be statistically significant in the consumer sensory panel. The tentative identification of both anthocyanins and betacyanins in orach is a novel finding in botanical research, as the literature indicates that these two pigment classes are mutually exclusive. The application of heat during pigment extraction, spray drying, and yogurt color additive preparation, did not appear to appreciably affect stability of orach pigments, indicating that orach extract could be used as a color in different process applications, if protected from light.
Keywords: red color, natural color additive, anthocyanin, betalain, yogurt
ACKNOWLEDGEMENTS
I would like to express my gratitude to my late father for teaching me to work hard and be
the best in everything I do. I also want to thank my mother and sister for their encouragement,
love, and support. I would like to give thanks to my husband for being patient and courageous to
follow me wherever my dreams take me.
I would like to thank Dr. Michael Dunn for being a big support not only in my project, but
also in my personal life. Finally, I want to express my gratitude to Dr. Oscar Pike, Dr. Frost Steele
and Dr. Eric Jellen for their guidance, assistance, and meaningful feedback.
iv
TABLE OF CONTENTS
TITLE .............................................................................................................................................. i
ABSTRACT .................................................................................................................................... ii
ACKNOWLEDGEMENTS ........................................................................................................... iii
TABLE OF CONTENTS ............................................................................................................... iv
LIST OF TABLES ......................................................................................................................... vi
LIST OF FIGURES ...................................................................................................................... vii
INTRODUCTION .......................................................................................................................... 1
MATERIALS AND METHODS .................................................................................................... 4
Orach Leaf Preparation ............................................................................................................... 4
Pigment Extraction ...................................................................................................................... 4
HPLC Analysis of Color Extract ................................................................................................ 4
Pigment Identification by LC-MS .............................................................................................. 5
Yogurt Color Base Preparation ................................................................................................... 6
Yogurt Preparation ...................................................................................................................... 6
Stability Test ............................................................................................................................... 7
pH ................................................................................................................................................ 7
Colorimeter ................................................................................................................................. 7
Visual Consumer Sensory Testing .............................................................................................. 8
v
Statistical Analysis ...................................................................................................................... 9
RESULTS AND DISCUSSION ................................................................................................... 10
Pigment Identification ............................................................................................................... 10
Color stability in yogurt ............................................................................................................ 12
CONCLUSION ............................................................................................................................. 15
REFERENCES ............................................................................................................................. 16
TABLES ....................................................................................................................................... 18
FIGURES ...................................................................................................................................... 22
APPENDIX A. Review of Literature ........................................................................................... 23
APPENDIX B. Color Base Formulation ..................................................................................... 32
APPENDIX C. Color JMP Output ............................................................................................... 33
APPENDIX D. pH JMP Output ................................................................................................... 55
APPENDIX E. Sensory Panel Questionnaire ............................................................................... 61
APPENDIX F. Sensory Panel JMP Output .................................................................................. 66
APPENDIX G. Disqualification of a second field from the study ............................................... 84
vi
LIST OF TABLES
Table 1 Betalains and anthocyanins reported by Cai et al., 2005, and entered into the Mass Hunter Qualitative Analysis Software to assist with pigment identification in orach extracts.... 18
Table 2 Betalains, anthocyanins, and potential isomers tentatively identified in orach pigment extract. ........................................................................................................................................... 19
Table 3 Color changes in yogurt containing orach extract following refrigerated storage under light (200 Lux) for up to 30 days or in the dark for up to 90 days................................................ 20
Table 4 Total color (△E*ab), chroma (△C*ab), and hue (△H*ab) differences in yogurt colored with orach extract and stored refrigerated in the dark for 90 days................................................ 20
Table 5 Consumer sensory acceptance of yogurts colored with orach extract or carmine (control) at Days 0, 45, and 90 (n=50). ........................................................................................................ 21
vii
LIST OF FIGURES
Figure 1 Orach pigment extract chromatogram (a) and powdered bilberry extract chromatogram (b) .................................................................................................................................................... 22
1
INTRODUCTION
Since the consumer relationship with food starts with an initial impression of the color and
appearance of a product (Delgado-Vargas et al., 2010), color perception is considered one of the
most important characteristics of food quality. Different studies have shown that food color is
correlated not only with appearance scores but also with flavor judgement, thus playing an
important role in the consumer assessment of overall food quality (Bridle and Timberlake, 1997).
Many foods are naturally pigmented, while others incorporate color additives to achieve
the desired effect. Color additives can be classified as naturally-derived or synthetic. Naturally-
derived pigments are those produced in nature and are typically obtained from plants, though
pigments from insects and other sources are also used. Synthetic pigments, by contrast, are
obtained by different commercial manufacturing techniques and processes (Delgado-Vargas et al.,
2010).
The synthetic pigments have the advantage of being stable, homogenous, of higher
efficacy, inexpensive, and mostly odorless and flavorless (Badui, 2006). However, because of
strict regulations, just nine certified synthetic colors are approved to be used in food applications
in the United States (FDA, 2017). In addition to these regulations, use of synthetic colors is
affected by the increasing consumer demand for “clean label” ingredients. According to a recent
survey, 45% of people in 63 countries said that they were more likely to buy products without
artificial colors (Anon., 2015). In the United States, 31% of people said that they were very or
extremely concerned about color additives (Simon et al, 2017).
Due to the regulations and concerns with synthetic colors, there is a need to identify viable
color additives derived from natural pigments. Plants are the main source of natural pigments due
to the extensive variety of plants and the wide range of colors that they produce. Many plant
2
varieties have not been investigated as potential sources of natural pigments. The main reason for
this is the instability of natural plant pigments caused by sensitivity to pH, temperature and light
(Wissgott and Bortlik, 1996). Consequently, the industry has been engaged in research to find
new natural pigment sources, and to overcome the stability problems associated with products
containing natural colors (Ghidouche et al., 2013).
Food products that use synthetic red color additives in their formulation are very common;
and reformulation with natural-sourced colors is affected by the limited stability of natural red
pigments (Fernandez-Lopez et al, 2013). Strawberry yogurt, for example, typically contains red
color additives as part of its formulation, to achieve the desired red color in the finished product.
Other colored yogurts also use red color additives to increase the visual appeal and compensate
for color degradation of the fruit. One of the most common, naturally-derived red colors used in
yogurt is carmine, also known as cochineal extract (O’Rell and Chandan, 2006), which shows
good color stability in this application (Müller-Maatsch and Gras, 2016). Although it has many
advantages compared to other natural red hues, carmine is extracted from Dactylopius coccus
insects, and some consumers have reported allergic responses or other health issues after
consuming it. Also, vegetarians do not consume products made from animal sources, and carmine
does not have a Kosher or Halal certification, limiting the number of people who can consume
products formulated with carmine. Identification of additional plant-based red color additives for
yogurt and other applications would be very useful to the food industry.
Atriplex hortensis L., also known as orach or mountain spinach, is a plant in the
Amaranthaceae family that produces large leaves, and some varieties are red or purple in color
(USDA, 2018). Because of its drought and salt tolerance, it can be grown in otherwise inhospitable
soils and could serve as a new source for natural red pigments (Carlsson and Hallqvist, 1981). The
3
leaves are also a source of good quality protein, which makes the leaf-residue remaining after
pigment extraction a very useful by-product (Carlsson and Hallqvist, 1981). Being from the
Amaranthaceae family, the red pigments in orach are expected to be betalains, rather than
anthocyanins. Cai, et al. (2005) reported that plants in the Amaranthaceae family produce only
betalains, further stating that the betalain and anthocyanin synthesis pathways are not known to
exist together in the same plant. However, Sai, et al. (2012) reported extraction and identification
of anthocyanins from Atriplex hortensis L.
The purpose of this study was to identify the types of red pigments present in purple orach
leaves, and to evaluate their stability and use in a red-pigmented yogurt.
4
MATERIALS AND METHODS
Orach Leaf Preparation
A purple/red variety of Atriplex hortensis L., known as ‘Triple Purple,’ was analyzed in
this study. Orach was grown in an irrigated field in Burley, Idaho by being planted in spring and
harvested in summer.
The leaves were separated from the plants in an early stage (40 days) of the life cycle and
transported on ice to the university for further testing and analysis. The fresh leaves were washed
with lightly chlorinated water and held refrigerated (4ºC) for not longer than two weeks prior to
pigment extraction.
Pigment Extraction
Cleaned whole leaves were added to water with agitation at 80ºC for 40 minutes. The
mixture was sieved and the aqueous extract was filtered using a vacuum filter with a Whatman
No.1 paper filter. The pigment extract was held refrigerated until it was spray dried. The extract
was spray dried at 180ºC using an Armfield model SD-Basic spray dryer (LabPlant UK: Filey,
UK) with a flow of 5.7 ml per minute.
HPLC Analysis of Color Extract
For HPLC analysis, a 30 mg/mL solution of spray-dried pigment extract in 2% HCl in
methanol was vortexed and sonicated for 30 seconds to solubilize the powder. A 2.5 ml aliquot of
the sonicated solution was brought to 10 ml volume using 10% phosphoric acid, and the solution
was filtered using a Whatman filter size 602 (2 µm) and transferred to HPLC vials.
An anthocyanin color standard was prepared in the same fashion using a 1 mg/mL solution
of powdered bilberry extract standard (US Pharmacopeial, LOT F0H286) in 2% HCl in methanol
solution.
5
HPLC was performed following a modified method published by Dionex (2016), using an
Agilent 1100 chromatograph (Agilent Technologies: Los Angeles, CA) and a 4.6x150 mm Luna
(2) 3.5 µm C18 Column (Phenomenex: Torrance, CA). Eluent A was 10% formic acid in water,
and eluent B was 10% formic acid, 22.5% methanol, and 22.5% acetonitrile in water. An injection
volume of 2 µL and flow rate of 0.475 mL/min were used along with the elution gradient proposed
by the method. Absorbance detection was performed at 520 nm using a UV-VIS detector (Dionex
Corporation, 2016).
Pigment Identification by LC-MS
The orach pigment extract was further evaluated using LC-MS. A modified method of
Sapers (1982) was used to purify the red pigments from orach and eliminate some water-soluble
components in the sample before LC-MS injection. Briefly, a Pasteur pipet was filled with ~0.5
cm of glass wool, ~0.5 cm of sand, and ~5 cm of Amberlite XAD-7 mesh, which had been
previously charged with distilled H2O, methanol, and 0.1 M HCl. The column was first washed
with ~5 mL of deionized water, then with methanol. Spray-dried orach extract (0.5 g) was
dissolved in 3 ml of deionized water, and 0.5 ml of this solution was added to the column. The
eluent was collected using 300 µL autosampler vials, and aliquots containing colored solution were
analyzed via LC-MS.
LC-MS was performed as per the HPLC method described previously but using a 1290
Infinity II LC System using a diode-array detector coupled to a 6530 Accurate-Mass Q-TOF mass
spectrometer (Agilent Technologies: Los Angeles, CA) and a 15 cm Luna omega C18 column
(Phenomenex: Torrance, CA). In the absence of anthocyanin mass spectra in the instrument’s
spectral library (METLIN Metabolite Personal Compound Database and Library (PCDL) B.08.00
(Agilent Technologies, Santa Clara, CA), tentative identifications were made by comparing m/z
6
ratios with several previously identified anthocyanins and betalains (see Table 1) (Cai et al., 2005).
Mass spectrometry data was analyzed for m/z consistent with known anthocyanins and betalains
using the Mass Hunter Qualitative Analysis Software Version B.07 (Agilent Technologies: Los
Angeles, CA). Compounds with matching mass spectra, having confidence scores of 75 or higher
(out of 100), are reported in the results section.
Yogurt Color Base Preparation
Swiss-style (stirred) yogurts are typically prepared by blending a yogurt white mass, with
a viscous syrup or gel flavor/color base, which may contain thickeners, sweeteners, colors, flavors
and preservatives. For this study, a simple 40° brix color base preparation (without flavor) was
prepared using a formula provided by a commercial company. The color base preparation
contained water, sugar, citric acid, sodium citrate, and pectin. Spray-dried orach pigment extract
was incorporated at a level of 0.94% (w/w). This percentage of extract was determined in
preliminary work to be the level required to closely match the color of a commercial lot of Swiss-
style strawberry yogurt (Yoplait), which incorporated a carmine-based color/flavor base. Yoplait
Original Strawberry yogurt was used as a commercial control. Two replicate batches of orach
color base preparation were prepared following the same formulation and manufacturing
procedure. The color base was prepared following a standard commercial hot-fill process, with
the preparation (including orach extract) being heated to 85ºC and held for five minutes before
pouring into jars, and cooling. Color bases were kept refrigerated (4ºC) until use.
Yogurt Preparation
Two batches of colored Swiss-style yogurt were prepared with 13% (w/w) color base and
87% of fresh plain yogurt obtained from a single batch of white mass from the university creamery.
The yogurt and color base were mixed with a planetary mixer using a flat paddle attachment at
7
low speed (60 rpm 5-qt. tilt-head Artisan Stand Mixer) for three minutes. The pH of the yogurt
was adjusted using sodium citrate until the pH was 4.0, to match the commercial standard. The
yogurt (110g portions) was filled into 100 ml clear, transparent, polyethylene terephthalate cups
with lids, which were labeled with the batch number, treatment and specific day to be pulled, and
was then placed into stability test conditions as indicated below.
Stability Test
The two batches of orach-colored yogurt and a single lot of the commercial carmine-
colored strawberry yogurt were placed into a 4ºC environmental chamber and held for 90-days to
evaluate color stability over time. Twenty cups from each treatment batch and the commercial
control were placed into a light impermeable cardboard box, and 10 cups from each treatment were
placed on open shelving in the same cooler, exposed to 200 Lux light intensity, which was
determined to be the average light exposure in local retail dairy cases. Samples from each batch
were pulled every 15 days through day 30 (for the light treatment) and through day 90 (for the
dark treatment). Each sample of yogurt was tested for pH and instrumental color. Samples pulled
at 0, 45 and 90 were also subjected to a visual sensory evaluation using an untrained consumer
panel, as indicated below.
pH
Yogurt pH was measured directly, using an ORION STAR A111 pH meter (Water
Analysis Instruments, Beverly, MA, USA). The pH was measured in triplicate.
Colorimeter
Yogurt color was determined with a Hunter ColorFlex Colorimeter model CFLX-45
(Hunter Associates Laboratory Inc. Reston, VA, USA). A 6.3x5 cm glass sample cylinder,
containing100 ml of yogurt, was placed on the colorimeter’s measuring port and evaluated three
8
times. The L* (light-dark, where 0=black, 100=white), a* (red-green, where negative values
indicate green, and positive indicate red), and b* (blue-yellow, where negative values indicate
blue, and positive indicate yellow) scores were recorded. Differences in △L*, △a*, △b*, △E*ab
(Total color difference), △C*ab (Chrome), and △H*ab (Hue), were calculated to evaluate the color
changes between the samples at different days compared with Day 0 using the following formulas
from Kheng, (2002):
∆𝐿𝐿∗ = 𝐿𝐿∗1 − 𝐿𝐿∗0
∆𝑎𝑎∗ = 𝑎𝑎∗1 − 𝑎𝑎∗0
∆𝑏𝑏∗ = 𝑏𝑏∗1 − 𝑏𝑏∗0
∆𝐸𝐸∗𝑎𝑎𝑎𝑎 = [(∆𝐿𝐿∗)2 + (∆𝑎𝑎∗)2 + (∆𝑏𝑏∗)2]12
∆𝐶𝐶∗𝑎𝑎𝑎𝑎 = 𝐶𝐶∗𝑎𝑎𝑎𝑎,1 − 𝐶𝐶∗𝑎𝑎𝑎𝑎,0 = �𝑎𝑎∗12 + 𝑏𝑏∗1
2�12 − �𝑎𝑎∗0
2 + 𝑏𝑏∗02�
12
∆𝐻𝐻∗𝑎𝑎𝑎𝑎 = [(∆𝐸𝐸𝑎𝑎𝑎𝑎∗)2 − (∆𝐿𝐿∗)2 − (∆𝐶𝐶∗𝑎𝑎𝑎𝑎)2]
12
Visual Consumer Sensory Testing
The appearance and color acceptance of the colored yogurts were evaluated by frequent
strawberry yogurt consumers (n=50). Overall appearance, color liking, and freshness perception
were rated by panelists using a 9-point hedonic scale. Color intensity was rated by panelists using
a 5-point just-about-right (JAR) scale, where 1= too intense, 3= JAR, 5= not intense enough. The
natural color perception was evaluated using a 5-point scale, where 1= very unnatural and 5= very
natural. The amount of browning in the samples was rated using a 0-100 graphic line scale, where
0 = not at all brown and 100 = extremely brown. (Questionnaire form shown in Appendix E).
Three separate panels, each on separate days, were done to evaluate the yogurt’s
appearance over time. The first panel was done at Day 0. The second panel was done at Day 45,
9
which represents the middle of the stability study. The third and last panel was done at day 90, or
the end of the stability test, as 75-90 days is a standard retail shelf-life for yogurt. Each panelist
evaluated a total of five samples, including light and dark treatments from two treatment batches
and the commercial control. The samples were presented in a randomized and sequential monadic
format in transparent plastic portion cups, each containing 10 g of yogurt.
Due to rapid color degradation, noticed at day 4 in samples stored in the light, the objective
measurements for these samples were changed to be taken on days 0, 5, 7, 15 and 30. In addition,
the consumer sensory data from the light-exposed treatment samples were not included in the
sensory panel analysis and are not reported.
Statistical Analysis
Differences between treatment means were assessed by ANOVA and comparison of means
were performed according to Tukey-Kramer using the Statistical System JMP Version 13.0
(SAS Institute, Cary, NC, 1989-2007). A significance value of p = 0.05 was used to distinguish
significant differences between treatments. Tukey’s HSD (Honestly Significant Difference) test
was also applied to all sensory data. R2 values were calculated for L*a*b* values to correlate the
differences with storage time.
10
RESULTS AND DISCUSSION
Pigment Identification
Using the bilberry extract anthocyanin standard, six different anthocyanin compounds were
tentatively identified in the orach extract, based on retention times (RT) (See Figure 1).
The anthocyanins tentatively identified are delphinidin-3-0-glucoside chloride (RT 13.176
min), delphinidin-3-arabinoside chloride (RT 17.314 min), petunidin-3-O-glucoside chloride (RT
32.183 min), petunidin-3-O-arabinoside chloride (RT 34.093 min), peonidin-3-arabinoside
chloride (RT 36.743min), and malvidin-3-O-glucoside chloride (RT 38.540 min).
Several significant orach pigment peaks did not match any of those in the bilberry standard,
and were determined to be either other anthocyanins, for which standards were not available, or
perhaps betalains, which are commonly found in the Amaranthaceae family. To further elucidate
the identity of these peaks, LC-MS analysis was carried out on the orach extract and the bilberry
standard.
Specific formulas and precursor ion m/z of the betalain compounds reported by Cai et al,
(2005) (see Table 1) were entered into the Mass Hunter Qualitative Analysis Software. In the
orach extract, three compounds corresponded to m/z values for the following betacyanins:
‘amaranthin or isoamaranthin’, ‘betanin or isobetanin or gomphrenin I or isogomphrenin I’, and
‘celosianin II’ (see Table 2).
Literature provided with the bilberry anthocyanin standard gave the mass spectral data for
fifteen anthocyanins. With the combination of mass spectra and HPLC retention times, two
anthocyanins were definitely confirmed present in orach: cyanidin chloride and delphinidin
chloride (see Table 2).
11
Four more compounds with similar masses were also detected (see Table 2). These compounds
were consistent with structural isomers of ‘cyanidin-3-O-arabinoside’ with m/z of 419; ‘cyanidin-
3-O-galactoside or cyanidin-3-O-glucoside or petunidin-3-O-arabinoside’ with m/z of 449;
‘delphinidin-3-O-arabinoside’ with a m/z of 435; and ‘delphinidin-3-O-galactoside or delphinidin
3-O-glucoside’with a m/z of 465. The precursor ion (m/z) for these tentatively identified
compounds matched those from anthocyanin compounds found in the bilberry standard, but had
differing retention times. These compounds may correspond to related molecules from the
anthocyanin complex with some other unidentified constituent, or more likely positional
substitutions of sugar residues. Further research needs to be done to confirm the specific identity
of these compounds.
According to the mass spectrophotometry analysis, orach pigments tentatively include a
mix of betalains and anthocyanins. As previously stated, the combination of these two classes of
pigments have not been found in any plant. Some authors have suggested that the genetic basis
for the mutually exclusive occurrence of these two pigments is the lack of transcriptional activation
of the necessary biosynthetic genes needed to produce anthocyanins in betalain-producing species
(Harris et al., 2012). The betalains tentatively identified in orach are specifically betacyanins,
found in the Amaranthaceae family (Cai et al., 2005), to which orach belongs.
12
Color stability in yogurt
Because anthocyanin color is affected by pH, yogurt sample pH was tested initially and
throughout the storage study. The initial pH range for the colored yogurts was 4.0 to 4.3, which
did not change over the course of the study. There were no statistical differences (p=0.96) in the
pH between any of the treatments (See appendix B for raw data).
There was a statistically significant difference (p<0.05) in both the L* and a* scores
between all the samples that were exposed to the light from Days 7 to 30 (see Table 3). The R2
correlations between storage time and color change were calculated to be 0.94 for L* and 0.90 for
a*. These differences in L* and a* values were expected, based on visual observations, as the light
exposure had a readily apparent bleaching and lightening effect on the samples (See Appendix C).
The difference between Day 5 and 7 was not significant since the time difference was very short
compared with the rest of the samples. The colorimeter CIELab values from Day 0 were not
included in the statistical analysis because the samples had been exposed to the light for three
hours prior to analysis, causing the color to degrade.
The b* scores between stored samples within the light treatment were also statistically
different (p < 0.05) between Day 7 and 15 (see Table 3). These differences in the yellow and blue
coordinates correlate with the visual changes happening in the yogurt as the red color disappeared,
and the residual coloration began to lighten and turn yellow (see Appendix B for raw data).
Poor light stability is very common in natural pigments, but the spray-dried orach extract
used in this study seems to be particularly susceptible to light degradation. This agrees with
Guidouche et al. (2013) who reported that light has a greater effect on natural color degradation
than heat.
13
In the dark treatment, there was a statistically significant difference (p< 0.05) in the CIE
L* (R2=0.94) and a* (R2=0.98) values over days stored between the yogurt with orach extract and
the control samples, when comparing Day 0 samples to those stored at least 30 days in the dark
(see Table 3). However, no practical significance is attributed to the differences in L* value,
because the difference between means (0.34 - 0.68) was very small. The a* scores showed a loss
of redness over time. There were not statistically significant differences between any of the
samples in the b* score.
There were not statistically significant differences in the total color difference (△Eab*)
(R2=0.63), difference in chroma (△C*ab) (R2=0.74), and difference in hue (△H*ab) (R2=0.80)
between any of the storage days with the dark treatment (see Table 4). The △E*ab showed that
there was not a measurable color difference between any of the samples during the storage time,
which was a desirable trait. The lack of difference in the △C*ab and △H*ab results was evidence
that the color remained stable during the storage time in dark conditions, as the color chroma and
spectrum did not change. The lack of significance in these three parameters, gives evidence of the
stability of the color under conditions in which the packaging protects it from the light.
Although some instrumental color differences were statistically significant, they did not
appear to affect consumer liking over time. The visual consumer sensory test results for yogurt
samples stored in the dark revealed no statistically significant difference between the two batches
of yogurt colored with orach and the control yogurt, for any of the attributes or color perceptions
measured for panels on Days 0 and 45 (see Table 5). The results showed that, for all samples, the
overall appearance (p=0.6873), color acceptance (p=0.7481), freshness perception (p=0.6171),
color intensity (p=0.2979), natural color perception (p = 0.6095), and browning (p=0.1923) were
not statistically different on the first panel (Day 0). For the second panel (Day 45) there were also
14
no significant differences, and p-values were: overall appearance (p=0.747), color acceptance
(p=0.7167), freshness perception (p=0.694), color intensity (p=0.6579), natural color perception
(p = 0.8496), and browning (p=0.2102). Finally, the third and last panel (Day 90), showed no
statistical differences in overall appearance (p=0.1535), color acceptance (p=0.8314), color
intensity (p=0.0726), and browning (p=0.1188); however, there was a significant difference in
freshness perception (p=0.0117) and natural color perception (p = 0.0399) (see Table 5).
According to the consumer perception, the control sample looked fresher than the orach colored
yogurt, and the orach colored yogurt was perceived as more natural in appearance than the
carmine-colored control yogurt.
15
CONCLUSION
A variety of betalains and anthocyanins in orach extract have been tentatively identified
using LC-MS. Additional standards are needed to allow more definitive identification of further
molecular species. The finding that both anthocyanins and betalains are present in orach leaves is
a novel finding in botanical research. Understanding that both classes of pigments are present can
help us to understand potential stability issues and limitations of orach pigments in food
applications.
Purple orach leaf pigments provide a heat-stable red color that can be used as a natural
color additive in yogurts when protected from light. The light instability may be addressed using
opaque packaging, which is commonly used in the industry.
16
REFERENCES
Anon. (2015). Global health and wellness report. The Nielsen Company. CZT/ACN Trademarks, L.L.C.
Badui, S. (2006). Química de los alimentos. Mexico, Mexico: Pearson Education. Bridle, P., and Timberlake, C. (1997). Anthocyanins as natural food colours—selected aspects.
Food Chemistry, 58(1-2), 103-109. Cai, Y., Sun, M., and Corke, H. (2005). Identification and Distribution of Simple and Acylated
Betacyanins in the Amaranthaceae. Journal of Agriculture Food Chem, 49(4). Carlsson, R., and Hallqvist, W. (1981). Atriplex hortensis L.—Revival of a Spinach Plant. Acta
Agriculturae Scandinavica, 31(3), 229-234. Delgado-Vargas, F., Jimenez, A., and Paredes-Lopez, O. (2010). Natural Pigments:
Carotenoids, Anthocyanins, and Betalains - Characteristics, Biosynthesis, Processing, and Stability. Critical Reviews in Food Science and Nutrition, 40(3), 173-289.
Dionex Corporation. (2016). LC-MS Analysis of Anthocyanins in Bilberry Extract. Thermo Scientific, Application Brief 134.
Dionex Corporation. (2016). Rapid and Sensitive Determination of Anthocyanins in Bilberries Using UHPLC. Thermo Scientific, Application Note 281.
FDA, Listing of color additives subject to certification (21 CFR 24 2017). Fernandez-Lopez, J., Angosto, J., Gimenez, P., and Leon, G. (2013). Thermal Stability of
Selected Natural Red Extracts Used as Food Colorants. Plant Foods for Human Nutrition, 68(1), 11–17.
Ghidouche, S., Rey, B., Michel, M., and Galaffu, N. (2013). A Rapid tool for the stability assessment of natural food colours. Food Chemistry, 139(1-4), 978–985.
Harris, N., Javellana, J., Davies, K., Lewis, D., Jameson, P., Deroles, S., Calcot, K., Gould, K. and Schwinn, K. (2012). Betalain production is possible in anthocyanin-producing plant species given the presence of DOPA-dioxygenase and L-DOPA. BMC Plant Biology, 12(34).
Kheng, L. W. (2002). Color Spaces and Color-Difference Equations. Retrieved from National University of Singapore: https://www.comp.nus.edu.sg/~leowwk/papers/colordiff.pdf
Müller-Maatsch, J., and Gras, C. (2016). The “Carmine Problem” and Potential Alternatives. In R. Carle, and R. Schweiggert , Handbook on Natural Pigments in Food and Beverages (p. 385=420). Elsevier Ltd.
O’Rell, K., and Chandan, R. (2006). Yogurt: Fruit Preparations and Flavoring Materials. En R. Chandan, Manufacturing Yogurt and Fermented Milks (págs. 151-160). Blackwell Publishing.
Sai, K., Karray, B., Jaffel, K., Rejeb, M., Leclerc, J., and Ouerghi, Z. (2012). Water Deficit-Induced Oxidative Stress in Leaves of Garden Orach (Atriplex hortensis). Research Journal of Biotechnology, 7, 46-52.
17
Sapers, G. (1982). Deodorization of a Colorant Prepared from Red Cabbage. Journal of Food Science, 47, 972-973.
Simon, J. E., Decker, E. A., Ferruzzi, M. G., Giusti, M. M., Mejia, C. D., Goldschmidt, M., and Talcott, S. T. (2017). Establishing Standards on Colors from Natural Sources. Journal of Food Science, 82(11), 2539–2553.
USDA. (2018). Natural Resources Conservation Service. Retrieved May 2018, from The PLANTS Database: http://plants.usda.gov
Wissgott, U., and Bortlik, K. (1996). Prospects for new natural food colorants. Trends in Food Science and Technology, 7(9), 298-302.
18
TABLES
Table 1 Betalains and anthocyanins reported by Cai et al., 2005, and entered into the Mass Hunter Qualitative Analysis Software to assist with pigment identification in orach extracts.
Formula Precursor ion (m/z) Compound Name Type
C30 H34 N2 O19 727 Amaranthin or Isoamaranthin Betacyanin
C39 H38 N2O O21 872 Celosianin I Betacyanin
C40 H42 N2 O22 903 Celosianin II Betacyanin
C36 H42 N2 O23 871 Iresinin I Betacyanin
C24 H26 N2 O13 551 Betanin or Isobetanin or Gomphrenin I or Isogomphrenin I Betacyanin
C33 H32 N2 O15 697 Gomphrenin II Betacyanin
C34 H34 N2 O16 727 Gomphrenin III Betacyanin
C18 H18 N2 O6 361 3-Methoxytyra- mine-betaxanthin Betaxanthin
C20 H19 N3 O6 347 Miraxanthin V Betaxanthin
C17 H18 N2 O6 398 (S)-Tryptophan-betaxanthin Betaxanthin
C21 H21 O12 465 Delphinidin-3-O-galactoside or Delphinidin 3-O-glucoside Anthocyanin
C21 H21 O11 449 Cyanidin-3-O-galactoside or Cyanidin-3-O-glucoside or Petunidin-3-O-arabinoside
Anthocyanin
C20 H19 O11 435 Delphinidin-3-O-arabinoside Anthocyanin
C22 H23 O12 479 Petunidin-3-O-galactoside or Petunidin-3-O-glucoside Anthocyanin
C20 H19 O10 419 Cyanidin-3-O-arabinoside Anthocyanin
C15 H11 O7 303 Delphinidin Anthocyanin
C22 H23 O11 463 Peonidin-3-O-galactoside or Peonidin-3-O-glucoside or Malvidin-3-O-arabinoside
Anthocyanin
C23 H25 O12 493 Malvidin-3-O-galactoside or Malvidin-3-O-glucoside Anthocyanin
C21 H21 O10 433 Peonidin-3-O-arabinoside Anthocyanin
C15 H11 O6 287 Cyanidin Chloride Anthocyanin
C16 H13 O7 317 Petunidin Chloride Anthocyanin
C16 H13 O6 301 Peonidin Chloride Anthocyanin
C17 H15 O7 331 Malvidin Chloride Anthocyanin
19
Table 2 Betalains, anthocyanins, and potential isomers tentatively identified in orach pigment extract.
Label Source RT Precursor ion (m/z) Compound Type
C30 H34 N2 O19 Orach Sample 4.30 726.18 Amaranthin or Isoamaranthin Betacyanins
C24 H26 N2 O13 Orach Sample 3.41 550.14 Betanin or Isobetanin or Gomphrenin I or Isogomphrenin I
Betacyanins
C40 H42 N2 O22 Orach Sample 18.96 902.22 Celosianin Betacyanins
C15 H11 O6 Bilberry Standard 22.57 287.06 Cyanidin chloride Anthocyanin
C15 H11 O6 Orach Sample 22.80 287.06 Cyanidin chloride Anthocyanin
C15 H11 O7 Bilberry Standard 19.57 303.05 Delphinidin chloride Anthocyanin
C15 H11 O7 Orach Sample 19.69 303.05 Delphinidin chloride Anthocyanin
C20 H19 O10 Bilberry Standard 23.93 419.10 Cyanidin-3-O-arabinoside Anthocyanin
C20 H19 O10 Orach Sample 18.79 419.10 Cyanidin-3-O-arabinoside Anthocyanin
C21 H21 O11 Bilberry Standard 20.43 449.11 Cyanidin-3-O-galactoside or Cyanidin-3-O-glucoside or Petunidin-3-O-arabinoside
Anthocyanin
C21 H21 O11 Orach Sample 16.27 449.11 Cyanidin-3-O-galactoside or Cyanidin-3-O-glucoside or Petunidin-3-O-arabinoside
Anthocyanin
C20 H19 O11 Bilberry Standard 21.57 435.09 Delphinidin-3-O-arabinoside Anthocyanin
C20 H19 O11 Orach Sample 6.64 435.09 Delphinidin-3-O-arabinoside Anthocyanin
C20 H19 O11 Orach Sample 13.40 435.09 Delphinidin-3-O-arabinoside Anthocyanin
C21 H21 O12 Bilberry Standard 17.32 465.10 Delphinidin-3-O-galactoside or Delphinidin 3-O-glucoside
Anthocyanin
C21 H21 O12 Orach Sample 10.78 465.10 Delphinidin-3-O-galactoside or Delphinidin 3-O-glucoside
Anthocyanin
C21 H21 O12 Orach Sample 10.76 465.10 Delphinidin-3-O-galactoside or Delphinidin 3-O-glucoside
Anthocyanin
20
Table 3 Color changes in yogurt containing orach extract following refrigerated storage under light (200 Lux) for up to 30 days or in the dark for up to 90 days.
Storage Time
(Days)
Light treatment Dark treatment
L* a* b* L* a* b*
5 82.33a 7.43a 4.91a - - -
7 82.92a 6.72a 5.29a - - -
15 84.14b 5.18b 6.34b 80.25a 5.18a 6.34a
30 85.85c 3.40c 7.49b 80.40ab 3.40a 7.49a
45 - - - 80.59bc 7.43ab 4.91a
60 - - - 80.67bcd 6.72ab 5.29a
75 - - - 80.874cd 5.18ab 6.34a
90 - - - 80.93d 3.40b 7.49a L*,0=black, 100=white; a*, negative values indicate green, positive values indicate red; b*, negative values indicate blue, positive values indicate yellow Table 4 Total color (△E*ab), chroma (△C*ab), and hue (△H*ab) differences in yogurt colored with orach extract and stored refrigerated in the dark for 90 days.
Storage Time
(Days) △E*ab △C*ab △H*ab
15 1.37a -0.20a 0.40a 30 1.31a -0.25a 0.52a 45 1.34a -0.49a 0.60a 60 1.39a -0.57a 0.63a 75 1.38a -0.56a 0.83a 90 1.76a -0.80a 1.36a
21
Table 5 Consumer sensory acceptance of yogurts colored with orach extract or carmine (control) at Days 0, 45, and 90 (n=50).
Day Batch Overall Appearance1 Color liking1 Freshness2 Color intensity3 Natural
perception4 Browning5
0
Orach 6.91 ± 0.15 a 7.04 ± 0.13 a 6.81 ± 0.13 a 2.95 ± 0.06 a Just about right 3.47 ± 0.11 a 3.73 ± 0.64 a
Control 7.02 ± 0.21 a 7.12 ± 0.19 a 6.69 ± 0.18 a 3.08 ± 0.09 a Just about right 3.37 ± 0.16 a 2.26 ± 0.91 a
HSD 1.04 0.59 0.91 0.47 0.82 4.42
45
Orach 6.9 ± 0.09 a 7.23 ± 0.08 a 6.55 ± 0.11 a 3.17 ± 0.05 a Just about right 3.40 ± 0.08 a 7.92 ± 1.08 a
Control 7.19 ± 0.13 a 7.17 ± 0.11 a 6.63 ± 0.15 a 2.13 ± 0.07 a Just about right 3.43 ± 0.12 a 5.56 ± 1.53 a
HSD 0.64 0.57 0.76 0.36 0.60 7.42
90
Orach 7.07 ± 0.10 a 7.22 ± 0.11 a 6.72 ± 0.12 b 3.05 ± 0.06 a Just about right 3.54 ± 0.105 a 6.58 ± 0.92 a
Control 7.34 ± 0.15 a 7.18 ± 0.15 a 7.24 ± 0.17 a 2.85 ± 0.08 a Just about right 3.16 ± 0.14 b 4.06 ± 1.31 a
HSD 0.73 0.75 0.81 0.42 0.72 6.35
19-point hedonic scale, where 9=like extremely and 1=dislike extremely 29-point hedonic scale, where 9= extremely fresh and 1=extremely unfresh 35-point just-about-right (JAR) scale, where 1=definitely too intense, 3=JAR, 5=definitely not intense enough 45-point hedonic scale, where 5=definitely natural looking color and 1=definitely unnatural looking color 50-100 graphic line scale, where 0 = not at all brown and 100 = extremely brown
22
FIGURES
Figure 1 Orach pigment extract chromatogram (a) and powdered bilberry extract chromatogram (b). Six anthocyanins were tentatively identified based on retention time of orach pigment extract and bilberry extract (standard).
23
APPENDIX A. Review of Literature
Natural pigments
Naturally derived food colors from plant sources are composed by four major pigments,
which are known as chlorophylls, carotenoids, anthocyanins and betacyanins. Chlorophylls are
responsible for the typical green pigments we see in plants. Carotenoids are those who provide a
color range between yellow and red, are commonly known as precursors of Vitamin A, and are
known for having a high antioxidant activity. Anthocyanins and Betalains are the pigments
responsible for giving the strong and bright red color of many plants and flowers (Rodriguez-
Amaya, D., 2018).
Anthocyanins are considered water-soluble pigments, allowing simple extractions. There
are six commonly known aglycone anthocyanidins: pelargonidin, cyaniding, delphinidin,
peonidin, petunidin, and malvidin (Rodriguez-Amaya, D., 2018). It has been estimated that more
than 400 anthocyanins have been found in nature (Horbowicz et al., 2008). They can vary in the
number and position of hydroxyl groups, degree of methylation of the hydroxyl groups, the identity
and number of sugar moieties and the positions at which they are attached, the extent of sugar
acylation, and the identity of the acylating agent. The intense red color provided by anthocyanins
is caused by a chromophore which is comprised of eight conjugated double bonds with a positive
charge on the heterocyclic oxygen. In aqueous mediums, anthocyanins have reversible color
change with pH. The ideal pH for anthocyanins is between 3 and 6. Several factors will affect the
color and stability of anthocyanins in food such as the chemical structure and concentration of the
anthocyanins, temperature, pH, light, oxygen, presence of enzymes, proteins, metallic ions,
presence of other flavonoids and phenolic compounds, sugars, and ascorbic acid. The major
24
sources of anthocyanins include purple grapes, cherry, plum, strawberry, blueberry, red cabbage
and red wine (Rodriguez-Amaya, D., 2018).
Betalains are water-soluble pigments as well; they have a nitrogen molecule attached that
allows them to be identified. Betalains are formed by betacyanins and betaxanthins. Their stability
is dependent on the concentration of the pigment, low aw, low pH, antioxidants, chelating agents,
low temperature, darkness and nitrogen atmosphere. According to different studies, betalains
optimal pH is between 4 and 6. Beetroot has been considered as the major source of betalains.
Betalains are not as abundant as other pigments, but have been found in Malabar Spinach, cactus
pear, red-purple pitaya and some Amaranthus species (Rodriguez-Amaya, D., 2018).
Heat stability
Research by the Technical University of Cartagena tested the heat stability and color
alteration in red pigments from elderberry, red cabbage, hibiscus, red beet, opuntia (prickly pear)
fruit and red cochineal extracts (Fernandez-Lopez et al., 2013). Elderberry, hibiscus and red
cabbage extracts are sources of anthocyanins. Opuntia fruits and red beets are a source of betalains
which give the red-violet color. Temperature is one of the major causes of color instability in
anthocyanin and betalains. Effective red or pink natural colors are hard to obtain because they are
either destroyed during processing or fade during storage.
Figure 1 (Fernandez-Lopez et al., 2013) shows the color degradation following different
temperature treatments (50°C, 70°C, 90°C) for each extract mentioned above. The cochineal
extract was the most thermo-resistant of all the pigments even at the highest temperature. Betalains
from red beets and opuntia fruit were highly unstable at the three incubation temperatures. The
anthocyanins from the hibiscus, red cabbage and elderberry had color loss, but were not as
sensitive as betalains. The elderberry extract was the most stable of the anthocyanins followed by
25
the red cabbage and the hibiscus. These patterns of instability were more notorious as the
temperature and time of incubation were increased (See Figure 1, 2, and 3). According to the
research made by the Technical University of Cartagena, there is a significant impact on the
stability of red pigments after using high temperatures (Fernandez-Lopez et al., 2013).
Figure 1 Color degradation over time at 50C
Figure 2 Color degradation over time at 70C
26
Figure 3 Color degradation over time at 90C
Light stability
Research made by the Nestle Research Center (Ghidouche et al., 2013) in Switzerland
tested the effect of light on the degradation and color changes in carmine and black carrot at
different pH conditions (3, 5 and 7), evaluate the color difference using CIELab* values, and
calculated the color difference (△Eab*) caused by color degradation after a pasteurization process
(△E(H)) and after being exposed to normal daylight conditions (△E(L)). The research showed
that the heat process affected the color in carmine more at pH 3. However, after the light treatment,
the sample at pH 7 had the highest degradation of color. The color difference after the light
treatment was three times higher compared to the heat treatment. In the black carrot samples, the
sample with the highest pH had the biggest difference in color after the heat treatment. The sample
with the lowest pH had the highest difference after the light treatment. From this first analysis,
we can conclude that the light impact on the color is correlated with the pH, but also that there is
a significant difference in the color between samples after the heat treatment and after the light
treatment (see table 1).
27
Table 1 Color degradation after a pasteurization process △E(H) and after being exposed to normal daylight △E(L).
Color △E(H) △E(L)
Carmine pH 3 14.2 33.3
Carmine pH 5 2.4 37.9
Carmine pH 5 6.9 40.9
Black Carrot pH 3 3.1 40.3
Black Carrot pH 5 2.6 33.9
Black Carrot pH 7 21.5 21.7
The second part of the study compared the same color sources samples at dark and light
conditions at a constant temperature of 25°C. Carmine and black carrot were evaluated again at
different pH (see Table 2). According to the results published, the main degradation of the color
in all the samples was due to the light. Carmine’s main color degradation was caused by the effect
of light exposure. Black carrot color was also affected by the light, but only the sample with the
lowest pH was significant.
Table 2 Ratio of color degradation under normal light conditions
In correlation with the results published, light accelerates the degradation reactions in
color, especially when products are exposed to daylight irradiation (Delgado Vargas et al., 2010).
According to Souhila Ghidouche (Guidouche et al.,2013), light exposure has a more detrimental
28
effect on the color degradation than temperature. As mentioned before, the pH and the color
changes are correlated. The pH plays an important role on physical and chemical changes in the
pigments and will make a difference if heat or light is added.
Orach
Atriplex hortensis is commonly known as orach, orache, or mountain spinach in the
literature. Orach is considered to be one of the oldest cultivated plants and is native to Europe and
Siberia (Stephens, 2015).
Agronomically, Atriplex hortensis L. is a drought resistant and salt tolerant plant. Orach
is an annual plant with leaves that can grow to at least 2 ft long and 1 ft wide, with triangular or
ovate-triangular shapes. Orach can be found in different colors, including green, yellow-green,
magenta and purple (Cornell University, 2006).
According to the Acta Agriculturae Scandinavica, Atriplex Hortensis L. has been used as
a substitute for spinach since 1977 (Carlsson and Hallqvist, 1981). Because of its common use as
a replacement for spinach, orach and spinach have been compared in several studies, indicating
that orach produces more fresh-weight, dry matter and true protein than spinach (Carlsson and
Hallqvist, 1981). According to our previous work and analysis, we know that orach powder, made
out of dried leaves, has a protein content of 35%, measured by Dumas analysis, on a fresh-weight
basis; and has a PDCAAS of 0.85. These results show that orach leaves can be used as a good
protein source or can be added to increase the protein content in different products.
The protein content in orach is a promising area of study, though the bitter flavor of orach
impedes the use of orach as a common food source because it is not acceptable to most palates
and. Bitterness is the most common sensory attribute that has been associated to saponins content,
but also the combination of phenolic compounds (bitter flavor) (Naczk and Shanhidi, 2004) and
29
trimethylamine (fish flavor), which causes the characteristic flavor in orach (Güçlü-Üstündag and
Mazza, 2007).
Colors use in industry Color perception is considered one of the most important characteristics from a food quality
perspective. Food quality is mostly rated by three general characteristics which are: color or
appearance, flavor, and texture. According to Simon et al., color perception accounts for 62 to
90% of the acceptance or rejection of a product. In most cases, the visual perception of color will
overrule the notion of taste and smell (Simon et al, 2017).
The food industry uses color additives to restore color lost after chemical changes during
processing and storing, to promote color uniformity, to intensify the color, to protect flavor and
some photosensitive vitamins, and to improve the overall appearance of a food. Both natural and
synthetic pigments are used for these purposes (Delgado-Vargas et al., 2010).
The U.S. Food and Drug Administration defines a color additive as “any material, (…)
that is a dye, pigment, or other substance made by a process of synthesis or similar artifice, or
extracted, isolated, or otherwise derived, …, from a vegetable, animal, mineral, or other source
and that, when added or applied to a food, drug, or cosmetic … is capable…of imparting a color
thereto” (21CFR70.3F).
Synthetic colors are regulated because of some concerns about their safety. Some behavioral
issues in children and potential links to cancer have been attributed to the use of certain color
additives (Badui, 2006). Even though the data are inconclusive, many consumers have concerns
with synthetic pigments.
One popular natural color is carmine, or cochineal extract, which has been used frequently in
beverages, dairy, meat and fruit preparations. In the United States and the European Union,
30
carmine is considered a natural colorant and is approved to be used as an alternative to artificial
colorants.
Several publications have shown that carmine can cause some allergic reactions. However, the
FDA has concluded that “Although the color additives have been shown to produce allergic
responses in certain sensitized individuals, there is no evidence of a significant hazard to the
general population when the color additives are used as specified by the color additive regulations”
(Federal Register, 2006). According to different studies, no genotoxic, carcinogenic or
significantly toxic cases have been reported after ingestion of cochineal by humans. (Müller-
Maatsch and Gras, 2016). Consequently, the industry has been engaged in research to find new
natural pigment sources, and to overcome the stability problems associated with products
containing natural colors (Ghidouche et al., 2013).
31
References Badui, S. (2006). Química de los alimentos. Mexico, Mexico: Pearson Education. Carlsson, R., and Hallqvist, W. (1981). Atriplex hortensis L.—Revival of a Spinach Plant. Acta
Agriculturae Scandinavica, 31(3), 229-234. Code of Federal Regulations. (n.d.). 21 CFR 70.3 F. Code of Federal Regulations. (n.d.). 21 CFR 74. Listing of color additives subject to
certification. Cornell University. (2006). Cornell University. Retrieved from Growing Guide:
http://www.gardening.cornell.edu/homegardening/scene80ea.html Delgado-Vargas, F., Jimenez, A., and Paredes-Lopez, O. (2010). Natural Pigments:
Carotenoids, Anthocyanins, and Betalains - Characteristics, Biosynthesis, Processing, and Stability. Critical Reviews in Food Science and Nutrition, 40(3), 173-289.
Federal Register. (2006). Listing of Color Additives Exempt From Certification; Food, Drug, and Cosmetic Labeling: Cochineal Extract and Carmine Declaration. Federal Register, 71(19), 4841-4843.
Fernandez-Lopez, J., Angosto, J., Gimenez, P., and Leon, G. (2013). Thermal Stability of Selected Natural Red Extracts Used as Food Colorants. Plant Foods for Human Nutrition, 68(1), 11–17.
Güçlü-Üstündağ, O., and Mazza, G. (2007). Saponins: Properties, Applications and Processing. Critical Reviews in Food Science and Nutrition, 47(3), 231-258.
Ghidouche, S., Rey, B., Michel, M., and Galaffu, N. (2013). A Rapid tool for the stability assessment of natural food colours. Food Chemistry, 139(1-4), 978–985.
Horbowicz, M., Kosson, R., Grzesiuk, A., and Dębski, H. (2008). Anthocyanins of fruits and vegetables - their occurrence, analysis and role in human nutrition. 68, 5-22.
Müller-Maatsch, J., and Gras, C. (2016). The “Carmine Problem” and Potential Alternatives. In R. Carle, and R. Schweiggert , Handbook on Natural Pigments in Food and Beverages (p. 385=420). Elsevier Ltd.
Naczk, M., and Shahidi, F. (2004). Extraction and analysis of phenolics in food. Journal of Chromatography(1054), 95–111.
Rodriguez-Amaya, D. (2018). Natural Food Pigments and Colorants. 1-35. Simon, J. E., Decker, E. A., Ferruzzi, M. G., Giusti, M. M., Mejia, C. D., Goldschmidt, M.,
and Talcott, S. T. (2017). Establishing Standards on Colors from Natural Sources. Journal of Food Science, 82(11), 2539–2553.
Stephens, J. (2015, September). EDIS. Retrieved from EDIS: http://edis.ifas.ufl.edu/pdffiles/MV/MV10300.pdf
32
APPENDIX B. Color Base Formulation
The color base used to color the yogurt was formulated using the fruit preparation provided
by a commercial yogurt company as reference. Two batches with their respective replicates were
prepared following the same formulation. The color preparation contained water, sugar, citric
acid, sodium citrate, orach extract powder, and pectin. The product was heated to a final
temperature of 85ºC and held for five minutes with constant stirring. The color preparations were
poured in jars, placed immediately into an ice bath until cool, and kept on the refrigerator at 4ºC.
pH and Brix were evaluated, and adjusted to 4.0 and 40º for solids, after 30 min in the ice bath and
after 24 hours in refrigeration. The complete formulation and manufacturing procedure are
proprietary, but additional questions regarding the color base may be addressed to Dr. Michael L.
Dunn, in the department of Nutrition, Dietetics and Food Science at Brigham Young University.
33
APPENDIX C. Color JMP Output
DARK TREATMENT L* scores
Summary of Fit
RSquare 0.945958
RSquare Adj 0.900923
Root Mean Square Error 0.081292
Mean of Response 80.61417
Observations (or Sum Wgts) 12
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 5 0.69404167 0.138808 21.0050 0.0010*
Error 6 0.03965000 0.006608
Corrected Total 11 0.73369167
34
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
15 80.245000 0.05748188 80.104347 80.385653 80.2450
30 80.395000 0.05748188 80.254347 80.535653 80.3950
45 80.590000 0.05748188 80.449347 80.730653 80.5900
60 80.665000 0.05748188 80.524347 80.805653 80.6650
75 80.865000 0.05748188 80.724347 81.005653 80.8650
90 80.925000 0.05748188 80.784347 81.065653 80.9250
LSMeans Differences Tukey HSD
Level -Level Difference Std Err Dif Lower CL Upper CL p-Value
90 15 0.6800000 0.0812917 0.356461 1.003539 0.0013*
75 15 0.6200000 0.0812917 0.296461 0.943539 0.0021*
90 30 0.5300000 0.0812917 0.206461 0.853539 0.0048*
75 30 0.4700000 0.0812917 0.146461 0.793539 0.0088*
60 15 0.4200000 0.0812917 0.096461 0.743539 0.0153*
45 15 0.3450000 0.0812917 0.021461 0.668539 0.0379*
90 45 0.3350000 0.0812917 0.011461 0.658539 0.0431*
75 45 0.2750000 0.0812917 -0.048539 0.598539 0.0956
60 30 0.2700000 0.0812917 -0.053539 0.593539 0.1023
90 60 0.2600000 0.0812917 -0.063539 0.583539 0.1173
75 60 0.2000000 0.0812917 -0.123539 0.523539 0.2675
45 30 0.1950000 0.0812917 -0.128539 0.518539 0.2861
30 15 0.1500000 0.0812917 -0.173539 0.473539 0.5045
60 45 0.0750000 0.0812917 -0.248539 0.398539 0.9267
90 75 0.0600000 0.0812917 -0.263539 0.383539 0.9691
35
A* scores Summary of Fit
RSquare 0.901993
RSquare Adj 0.82032
Root Mean Square Error 0.163783
Mean of Response 9.3975
Observations (or Sum Wgts) 12
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 5 1.4812750 0.296255 11.0440 0.0055*
Error 6 0.1609500 0.026825
Corrected Total 11 1.6422250
36
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
15 9.8650000 0.11581235 9.5816174 10.148383 9.86500
30 9.7500000 0.11581235 9.4666174 10.033383 9.75000
45 9.4450000 0.11581235 9.1616174 9.728383 9.44500
60 9.2950000 0.11581235 9.0116174 9.578383 9.29500
75 9.2300000 0.11581235 8.9466174 9.513383 9.23000
90 8.8000000 0.11581235 8.5166174 9.083383 8.80000
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
15 90 1.065000 0.1637834 0.413145 1.716855 0.0048*
30 90 0.950000 0.1637834 0.298145 1.601855 0.0087*
45 90 0.645000 0.1637834 -0.006855 1.296855 0.0523
15 75 0.635000 0.1637834 -0.016855 1.286855 0.0558
15 60 0.570000 0.1637834 -0.081855 1.221855 0.0858
30 75 0.520000 0.1637834 -0.131855 1.171855 0.1205
60 90 0.495000 0.1637834 -0.156855 1.146855 0.1429
30 60 0.455000 0.1637834 -0.196855 1.106855 0.1880
75 90 0.430000 0.1637834 -0.221855 1.081855 0.2229
15 45 0.420000 0.1637834 -0.231855 1.071855 0.2385
30 45 0.305000 0.1637834 -0.346855 0.956855 0.4965
45 75 0.215000 0.1637834 -0.436855 0.866855 0.7711
45 60 0.150000 0.1637834 -0.501855 0.801855 0.9287
15 30 0.115000 0.1637834 -0.536855 0.766855 0.9748
60 75 0.065000 0.1637834 -0.586855 0.716855 0.9980
37
B* scores
Summary of Fit
RSquare 0.740465
RSquare Adj 0.524185
Root Mean Square Error 0.30373
Mean of Response 3.49125
Observations (or Sum Wgts) 12
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 5 1.5791937 0.315839 3.4236 0.0830
Error 6 0.5535125 0.092252
C. Total 11 2.1327062
38
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
15 2.9875000 0.21476974 2.4619774 3.5130226 2.98750
30 3.2050000 0.21476974 2.6794774 3.7305226 3.20500
45 3.3750000 0.21476974 2.8494774 3.9005226 3.37500
60 3.5450000 0.21476974 3.0194774 4.0705226 3.54500
75 3.7250000 0.21476974 3.1994774 4.2505226 3.72500
90 4.1100000 0.21476974 3.5844774 4.6355226 4.11000
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
90 15 1.122500 0.3037303 -0.08634 2.331341 0.0678
90 30 0.905000 0.3037303 -0.30384 2.113841 0.1499
75 15 0.737500 0.3037303 -0.47134 1.946341 0.2771
90 45 0.735000 0.3037303 -0.47384 1.943841 0.2795
90 60 0.565000 0.3037303 -0.64384 1.773841 0.4974
60 15 0.557500 0.3037303 -0.65134 1.766341 0.5091
75 30 0.520000 0.3037303 -0.68884 1.728841 0.5693
45 15 0.387500 0.3037303 -0.82134 1.596341 0.7887
90 75 0.385000 0.3037303 -0.82384 1.593841 0.7926
75 45 0.350000 0.3037303 -0.85884 1.558841 0.8441
60 30 0.340000 0.3037303 -0.86884 1.548841 0.8578
30 15 0.217500 0.3037303 -0.99134 1.426341 0.9727
75 60 0.180000 0.3037303 -1.02884 1.388841 0.9877
45 30 0.170000 0.3037303 -1.03884 1.378841 0.9905
60 45 0.170000 0.3037303 -1.03884 1.378841 0.9905
39
Total color difference (△Eab*)
Summary of Fit
RSquare 0.766029
RSquare Adj 0.602249
Root Mean Square Error 0.469307
Mean of Response 1.830556
Observations (or Sum Wgts) 18
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 7 7.2110056 1.03014 4.6772 0.0144*
Error 10 2.2024889 0.22025
C. Total 17 9.4134944
40
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
15 1.9500000 0.27095442 1.3462759 2.5537241 1.95000
30 1.6966667 0.27095442 1.0929426 2.3003907 1.69667
45 1.9033333 0.27095442 1.2996093 2.5070574 1.90333
60 1.9200000 0.27095442 1.3162759 2.5237241 1.92000
75 1.3433333 0.27095442 0.7396093 1.9470574 1.34333
90 2.1700000 0.27095442 1.5662759 2.7737241 2.17000
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
90 75 0.8266667 0.3831874 -0.50427 2.157599 0.3334
15 75 0.6066667 0.3831874 -0.72427 1.937599 0.6255
60 75 0.5766667 0.3831874 -0.75427 1.907599 0.6693
45 75 0.5600000 0.3831874 -0.77093 1.890933 0.6934
90 30 0.4733333 0.3831874 -0.85760 1.804266 0.8111
30 75 0.3533333 0.3831874 -0.97760 1.684266 0.9318
90 45 0.2666667 0.3831874 -1.06427 1.597599 0.9783
15 30 0.2533333 0.3831874 -1.07760 1.584266 0.9826
90 60 0.2500000 0.3831874 -1.08093 1.580933 0.9835
60 30 0.2233333 0.3831874 -1.10760 1.554266 0.9900
90 15 0.2200000 0.3831874 -1.11093 1.550933 0.9907
45 30 0.2066667 0.3831874 -1.12427 1.537599 0.9930
15 45 0.0466667 0.3831874 -1.28427 1.377599 1.0000
15 60 0.0300000 0.3831874 -1.30093 1.360933 1.0000
60 45 0.0166667 0.3831874 -1.31427 1.347599 1.0000
41
Difference in chroma (△Cab*)
Summary of Fit
RSquare 0.742151
RSquare Adj 0.561656
Root Mean Square Error 0.17033
Mean of Response -0.51722
Observations (or Sum Wgts) 18
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 7 0.8350389 0.119291 4.1118 0.0220*
Error 10 0.2901222 0.029012
C. Total 17 1.1251611
42
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
15 -0.3166667 0.09833992 -0.5357817 -0.0975517 -0.31667
30 -0.3533333 0.09833992 -0.5724483 -0.1342183 -0.35333
45 -0.4766667 0.09833992 -0.6957817 -0.2575517 -0.47667
60 -0.5900000 0.09833992 -0.8091150 -0.3708850 -0.59000
75 -0.6866667 0.09833992 -0.9057817 -0.4675517 -0.68667
90 -0.6800000 0.09833992 -0.8991150 -0.4608850 -0.68000
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
15 75 0.3700000 0.1390737 -0.113047 0.8530474 0.1683
15 90 0.3633333 0.1390737 -0.119714 0.8463807 0.1802
30 75 0.3333333 0.1390737 -0.149714 0.8163807 0.2434
30 90 0.3266667 0.1390737 -0.156381 0.8097140 0.2597
15 60 0.2733333 0.1390737 -0.209714 0.7563807 0.4209
30 60 0.2366667 0.1390737 -0.246381 0.7197140 0.5594
45 75 0.2100000 0.1390737 -0.273047 0.6930474 0.6665
45 90 0.2033333 0.1390737 -0.279714 0.6863807 0.6931
15 45 0.1600000 0.1390737 -0.323047 0.6430474 0.8497
30 45 0.1233333 0.1390737 -0.359714 0.6063807 0.9414
45 60 0.1133333 0.1390737 -0.369714 0.5963807 0.9581
60 75 0.0966667 0.1390737 -0.386381 0.5797140 0.9784
60 90 0.0900000 0.1390737 -0.393047 0.5730474 0.9841
15 30 0.0366667 0.1390737 -0.446381 0.5197140 0.9998
90 75 0.0066667 0.1390737 -0.476381 0.4897140 1.0000
43
Difference in hue (△Hab*)
Summary of Fit
RSquare 0.803159
RSquare Adj 0.665371
Root Mean Square Error 0.63553
Mean of Response 1.320556
Observations (or Sum Wgts) 18
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 7 16.480106 2.35430 5.8289 0.0067*
Error 10 4.038989 0.40390
C. Total 17 20.519094
44
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
15 1.2866667 0.36692365 0.469110 2.1042235 1.28667
30 1.1466667 0.36692365 0.329110 1.9642235 1.14667
45 1.3933333 0.36692365 0.575776 2.2108902 1.39333
60 1.3933333 0.36692365 0.575776 2.2108902 1.39333
75 0.8133333 0.36692365 -0.004224 1.6308902 0.81333
90 1.8900000 0.36692365 1.072443 2.7075568 1.89000
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
90 75 1.076667 0.5189084 -0.72567 2.879002 0.3693
90 30 0.743333 0.5189084 -1.05900 2.545668 0.7093
90 15 0.603333 0.5189084 -1.19900 2.405668 0.8444
45 75 0.580000 0.5189084 -1.22234 2.382335 0.8635
60 75 0.580000 0.5189084 -1.22234 2.382335 0.8635
90 45 0.496667 0.5189084 -1.30567 2.299002 0.9215
90 60 0.496667 0.5189084 -1.30567 2.299002 0.9215
15 75 0.473333 0.5189084 -1.32900 2.275668 0.9346
30 75 0.333333 0.5189084 -1.46900 2.135668 0.9846
45 30 0.246667 0.5189084 -1.55567 2.049002 0.9961
60 30 0.246667 0.5189084 -1.55567 2.049002 0.9961
15 30 0.140000 0.5189084 -1.66234 1.942335 0.9997
45 15 0.106667 0.5189084 -1.69567 1.909002 0.9999
60 15 0.106667 0.5189084 -1.69567 1.909002 0.9999
60 45 0.000000 0.5189084 -1.80234 1.802335 1.0000
45
LIGHT TREATMENT L* scores
Summary of Fit
RSquare 0.991011
RSquare Adj 0.984269
Root Mean Square Error 0.181625
Mean of Response 83.80625
Observations (or Sum Wgts) 8
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 3 14.546838 4.84895 146.9934 0.0002*
Error 4 0.131950 0.03299
Corrected Total 7 14.678788
46
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
5 82.325000 0.12842800 81.968427 82.681573 82.3250
7 82.915000 0.12842800 82.558427 83.271573 82.9150
15 84.135000 0.12842800 83.778427 84.491573 84.1350
30 85.850000 0.12842800 85.493427 86.206573 85.8500
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
30 5 3.525000 0.1816246 2.78563 4.264367 0.0001*
30 7 2.935000 0.1816246 2.19563 3.674367 0.0003*
15 5 1.810000 0.1816246 1.07063 2.549367 0.0020*
30 15 1.715000 0.1816246 0.97563 2.454367 0.0024*
15 7 1.220000 0.1816246 0.48063 1.959367 0.0088*
7 5 0.590000 0.1816246 -0.14937 1.329367 0.0995
47
A* scores Summary of Fit
RSquare 0.989231
RSquare Adj 0.981155
Root Mean Square Error 0.228227
Mean of Response 5.67875
Observations (or Sum Wgts) 8
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 3 19.139338 6.37978 122.4820 0.0002*
Error 4 0.208350 0.05209
Corrected Total 7 19.347688
48
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
5 7.4250000 0.16138076 6.9769352 7.8730648 7.42500
7 6.7150000 0.16138076 6.2669352 7.1630648 6.71500
15 5.1750000 0.16138076 4.7269352 5.6230648 5.17500
30 3.4000000 0.16138076 2.9519352 3.8480648 3.40000
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
5 30 4.025000 0.2282269 3.09592 4.954078 0.0002*
7 30 3.315000 0.2282269 2.38592 4.244078 0.0005*
5 15 2.250000 0.2282269 1.32092 3.179078 0.0021*
15 30 1.775000 0.2282269 0.84592 2.704078 0.0051*
7 15 1.540000 0.2282269 0.61092 2.469078 0.0087*
5 7 0.710000 0.2282269 -0.21908 1.639078 0.1125
49
B* scores Summary of Fit
RSquare 0.950545
RSquare Adj 0.913454
Root Mean Square Error 0.323979
Mean of Response 6.00625
Observations (or Sum Wgts) 8
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 3 8.0697375 2.68991 25.6274 0.0045*
Error 4 0.4198500 0.10496
Corrected Total 7 8.4895875
50
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
5 4.9100000 0.22908787 4.2739501 5.5460499 4.91000
7 5.2850000 0.22908787 4.6489501 5.9210499 5.28500
15 6.3400000 0.22908787 5.7039501 6.9760499 6.34000
30 7.4900000 0.22908787 6.8539501 8.1260499 7.49000
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
30 5 2.580000 0.3239792 1.26113 3.898872 0.0047*
30 7 2.205000 0.3239792 0.88613 3.523872 0.0084*
15 5 1.430000 0.3239792 0.11113 2.748872 0.0384*
30 15 1.150000 0.3239792 -0.16887 2.468872 0.0766
15 7 1.055000 0.3239792 -0.26387 2.373872 0.0988
7 5 0.375000 0.3239792 -0.94387 1.693872 0.6796
51
YOGURT COLOR RESULTS (Raw Data)
BATCH PULL TREATMENT L* a* b* △L* △a* △b* △Eab*
1 0 DARK 81.27 10.46 2.41 0 0 0 0.00000
1 15 DARK 80.14 10.06 2.64 -1.13 -0.4 0.23 1.22057
1 30 DARK 80.34 9.89 2.96 -0.93 -0.57 0.55 1.22160
1 45 DARK 80.64 9.53 3.21 -0.63 -0.93 0.8 1.37906
1 60 DARK 80.68 9.37 3.37 -0.59 -1.09 0.96 1.56774
1 75 DARK 80.92 9.31 3.6 -0.35 -1.15 1.19 1.69148
1 90 DARK 80.92 8.86 3.96 -0.35 -1.6 1.55 2.25499
2 0 DARK 81.8 9.57 3.82 0 0 0 0.00000
2 15 DARK 80.35 9.67 3.335 -1.45 0.1 -0.485 1.53223
2 30 DARK 80.45 9.61 3.45 -1.35 0.04 -0.37 1.40036
2 45 DARK 80.54 9.36 3.54 -1.26 -0.21 -0.28 1.30771
2 60 DARK 80.65 9.22 3.72 -1.15 -0.35 -0.1 1.20623
2 75 DARK 80.81 9.15 3.85 -0.99 -0.42 0.03 1.07583
2 90 DARK 80.93 8.74 4.26 -0.87 -0.83 0.44 1.28039
1 0 LIGHT 81.27 10.46 2.41 0 0 0 0.00000
1 5 LIGHT 82.3 7.64 4.54 1.03 -2.82 2.13 3.68106
1 7 LIGHT 83.08 6.66 5.15 1.81 -3.8 2.74 5.02232
1 15 LIGHT 84.01 5.37 6.12 2.74 -5.09 3.71 6.86876
1 30 LIGHT 86 3.27 7.57 4.73 -7.19 5.16 10.03467
2 0 LIGHT 81.8 9.57 3.82 0 0 0 0.00000
2 5 LIGHT 82.35 7.21 5.28 0.55 -2.36 1.46 2.82908
2 7 LIGHT 82.75 6.77 5.42 0.95 -2.8 1.6 3.36192
2 15 LIGHT 84.26 4.98 6.56 2.46 -4.59 2.74 5.88450
2 30 LIGHT 85.7 3.53 7.41 3.9 -6.04 3.59 8.03615
52
BATCH PULL TREATMENT L* a* b* △L* △a* △b* △Eab*
C 0 DARK 80.74 15.31 1.76 0 0 0 0.00000
C 15 DARK 80.96 14.13 4.62 0.22 -1.18 2.86 3.10168
C 30 DARK 80.71 14.3 4.01 -0.03 -1.01 2.25 2.46648
C 45 DARK 80.89 14.24 4.58 0.15 -1.07 2.82 3.01990
C 60 DARK 80.79 14.09 4.48 0.05 -1.22 2.72 2.98149
C 75 DARK 81.06 14.45 0.9 0.32 -0.86 -0.86 1.25762
C 90 DARK 80.57 14.27 4.55 -0.17 -1.04 2.79 2.98238
C 0 LIGHT 80.74 15.31 1.76 0 0 0 0.00000
C 5 LIGHT 80.96 14.58 1 0.22 -0.73 -0.76 1.07652
C 7 LIGHT 81.15 14.54 0.78 0.41 -0.77 -0.98 1.31202
C 15 LIGHT 81.13 14.62 0.78 0.39 -0.69 -0.98 1.26040
C 30 LIGHT 81.16 14.54 0.46 0.42 -0.77 -1.3 1.56822
53
BATCH PULL TREATMENT △Cab* △Hab*
1 0 DARK 0.00000 0.00000
1 15 DARK -0.33341 0.31896
1 30 DARK -0.41059 0.67736
1 45 DARK -0.67795 1.02239
1 60 DARK -0.77644 1.22753
1 75 DARK -0.75226 1.47401
1 90 DARK -1.02934 1.97559
2 0 DARK 0.00000 0.00000
2 15 DARK -0.07530 0.48944
2 30 DARK -0.09372 0.36016
2 45 DARK -0.29718 0.18489
2 60 DARK -0.36206 0.03754
2 75 DARK -0.37725 0.18703
2 90 DARK -0.58132 0.73795
1 0 LIGHT 0.00000 0.00000
1 5 LIGHT -1.84691 3.01301
1 7 LIGHT -2.31513 4.07281
1 15 LIGHT -2.59210 5.74049
1 30 LIGHT -2.48797 8.49304
2 0 LIGHT 0.00000 0.00000
2 5 LIGHT -1.36765 2.41469
2 7 LIGHT -1.63191 2.78152
2 15 LIGHT -2.06810 4.92937
2 30 LIGHT -2.09637 6.70633
54
BATCH PULL TREATMENT △Cab* △Hab*
C 0 DARK 0.00000 0.00000
C 15 DARK -0.54472 3.04553
C 30 DARK -0.55923 2.40205
C 45 DARK -0.45242 2.98205
C 60 DARK -0.62575 2.91466
C 75 DARK -0.93283 0.78040
C 90 DARK -0.43300 2.94588
C 0 LIGHT 0.00000 0.00000
C 5 LIGHT -0.79658 0.68990
C 7 LIGHT -0.84992 0.91155
C 15 LIGHT -0.77004 0.91844
C 30 LIGHT -0.86356 1.23983
55
APPENDIX D. pH JMP Output
pH Dark
Summary of Fit
RSquare 0.131083
RSquare Adj -0.59302
Root Mean Square Error 0.143991
Mean of Response 4.191667
Observations (or Sum Wgts) 12
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 5 0.01876667 0.003753 0.1810 0.9597
Error 6 0.12440000 0.020733
C. Total 11 0.14316667
56
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
15 4.2650000 0.10181683 4.0158632 4.5141368 4.26500
30 4.2200000 0.10181683 3.9708632 4.4691368 4.22000
45 4.1750000 0.10181683 3.9258632 4.4241368 4.17500
60 4.1750000 0.10181683 3.9258632 4.4241368 4.17500
75 4.1700000 0.10181683 3.9208632 4.4191368 4.17000
90 4.1450000 0.10181683 3.8958632 4.3941368 4.14500
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
15 90 0.1200000 0.1439907 -0.453081 0.6930807 0.9500
15 75 0.0950000 0.1439907 -0.478081 0.6680807 0.9806
15 45 0.0900000 0.1439907 -0.483081 0.6630807 0.9846
15 60 0.0900000 0.1439907 -0.483081 0.6630807 0.9846
30 90 0.0750000 0.1439907 -0.498081 0.6480807 0.9931
30 75 0.0500000 0.1439907 -0.523081 0.6230807 0.9989
15 30 0.0450000 0.1439907 -0.528081 0.6180807 0.9994
30 45 0.0450000 0.1439907 -0.528081 0.6180807 0.9994
30 60 0.0450000 0.1439907 -0.528081 0.6180807 0.9994
45 90 0.0300000 0.1439907 -0.543081 0.6030807 0.9999
60 90 0.0300000 0.1439907 -0.543081 0.6030807 0.9999
75 90 0.0250000 0.1439907 -0.548081 0.5980807 1.0000
45 75 0.0050000 0.1439907 -0.568081 0.5780807 1.0000
60 75 0.0050000 0.1439907 -0.568081 0.5780807 1.0000
60 45 0.0000000 0.1439907 -0.573081 0.5730807 1.0000
57
pH Light
Summary of Fit
RSquare 0.292261
RSquare Adj -0.23854
Root Mean Square Error 0.131814
Mean of Response 4.285
Observations (or Sum Wgts) 8
Analysis of variance
Source DF Sum of Squares Mean Square F Ratio Prob > F
Model 3 0.02870000 0.009567 0.5506 0.6743
Error 4 0.06950000 0.017375
C. Total 7 0.09820000
58
Least Squares Means Table
Level Least Sq Mean Std Error Lower 95% Upper 95% Mean
5 4.3400000 0.09320676 4.0812165 4.5987835 4.34000
7 4.2950000 0.09320676 4.0362165 4.5537835 4.29500
15 4.3200000 0.09320676 4.0612165 4.5787835 4.32000
30 4.1850000 0.09320676 3.9262165 4.4437835 4.18500
LSMeans Differences Tukey HSD
Level - Level Difference Std Err Dif Lower CL Upper CL p-Value
5 30 0.1550000 0.1318143 -0.381597 0.6915967 0.6703
15 30 0.1350000 0.1318143 -0.401597 0.6715967 0.7466
7 30 0.1100000 0.1318143 -0.426597 0.6465967 0.8367
5 7 0.0450000 0.1318143 -0.491597 0.5815967 0.9844
15 7 0.0250000 0.1318143 -0.511597 0.5615967 0.9972
5 15 0.0200000 0.1318143 -0.516597 0.5565967 0.9985
59
YOGURT PH RESULTS (Raw Data)
BATCH PULL TREATMENT PH
1 0 DARK 4.3
1 15 DARK 4.36
1 30 DARK 4.33
1 45 DARK 4.28
1 60 DARK 4.27
1 75 DARK 4.27
1 90 DARK 4.25
2 0 DARK 4.2
2 15 DARK 4.17
2 30 DARK 4.11
2 45 DARK 4.07
2 60 DARK 4.08
2 75 DARK 4.07
2 90 DARK 4.04
1 0 LIGHT 4.3
1 5 LIGHT 4.44
1 7 LIGHT 4.37
1 15 LIGHT 4.41
1 30 LIGHT 4.29
2 0 LIGHT 4.2
2 5 LIGHT 4.24
2 7 LIGHT 4.22
2 15 LIGHT 4.23
2 30 LIGHT 4.08
60
BATCH PULL TREATMENT PH
C 0 DARK 4.073
C 15 DARK 4.12
C 30 DARK 4.03
C 45 DARK 3.98
C 60 DARK 4.14
C 75 DARK 4.12
C 90 DARK 4.08
C 0 LIGHT 4.073
C 5 LIGHT 4.25
C 7 LIGHT 4.23
C 15 LIGHT 4.23
C 30 LIGHT 4.13
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APPENDIX E. Questionnaire
STRAWBERRY YOGURT CONSUMER TEST –
APPEARANCE ONLY
Welcome to the Food Science Sensory Laboratory. A copy of the form titled “Consent to Be a Research Subject” is posted in each booth. Please read it carefully before continuing. By signing your name above, you acknowledge that you have read and understand the consent form, and desire of your own free will and volition to participate in this study. You may withdraw at any time without penalty. Please inform the receptionist if you wish to withdraw. In this session, you will evaluate FIVE samples of STRAWBERRY YOGURT one at a time.
DO NOT EAT THE SAMPLES. This is a visual only test.
Please note: Throughout this questionnaire, you must select 'CONTINUE' or 'NEXT QUESTION', as appropriate, in order to advance to the next screen. Question #1 Using the key board on or under the counter, please enter your full name. By entering your name, you acknowledge that you have read and understand the consent form. ___________________________________________________________________________ Please read all instructions and questions carefully; we are depending on your conscientious evaluation. Before you receive your samples, please answer these questions by checking the appropriate circles. Question #2 What is your age category?
� Younger than 20 years � 20 - 29 years � 30 - 39 years � 40 - 49 years � 50 - 60 years � Older than 60 years
Question #3 What is your gender?
� Female � Male
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Question #4 What is your attitude about STRAWBERRY YOGURT?
� Positive � Neutral � Negative
Question #5 How often do you eat STRAWBERRY YOGURT?
� More than once a week � Once a week � Once every two weeks � Once every month � Once every three months � Less than every three months
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STRAWBERRY YOGURT CONSUMER TEST You will first evaluate the OVERALL APPEARANCE of each sample. During the course of this test, you should NEVER taste the samples. This testing is for appearance information only. DO NOT EAT ANY OF THESE SAMPLES. If at any time during the test you need help press the button by the “HELP” LIGHT to the right of the screen. Please fill in the code numbers on the top of the columns in the same order in which they are presented to you. LOOK AT THE SAMPLE. Question #6 – Sample <<Sample 1>> EVERYTHING CONSIDERED how much do you like or dislike the OVERALL APPEARANCE of the sample?
� 9 Like Extremely � 8 Like Very Much � 7 Like Moderately � 6 Like Slightly � 5 Neither Like Nor Dislike � 4 Dislike Slightly � 3 Dislike Moderately � 2 Dislike Very Much � Dislike Extremely
Question #7 – Sample <<Sample 1>> How much do you like or dislike the COLOR of each sample?
� 9 Like Extremely � 8 Like Very Much � 7 Like Moderately � 6 Like Slightly � 5 Neither Like Nor Dislike � 4 Dislike Slightly � 3 Dislike Moderately � 2 Dislike Very Much � Dislike Extremely
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STRAWBERRY YOGURT CONSUMER TEST Question #8 – Sample <<Sample 1>> Based on the color and appearance of the sample, how FRESH do you feel the yogurt is?
� 9 Extremely fresh � 8 Very fresh � 7 Moderately fresh � 6 Slightly fresh � 5 Neither fresh nor unfresh � 4 Slightly unfresh � 3 Moderately unfresh � 2 Very unfresh � Extremely unfresh
Question #9 – Sample <<Sample 1>> How do you feel about the INTENSITY OF COLOR of each sample?
� 1 Definitely too intense � 2 Slightly too intense � 3 Just about right � 4 Slightly not intense enough � 5 Definitely not intense enough
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Question #10 – Sample <<Sample 1>> How NATURAL do you feel the COLOR of the sample is?
� 5 Definitely natural looking color � 4 lightly natural looking color � 3 Neither natural nor unnatural looking color � 2 Slightly unnatural looking color � 1 Definitely unnatural looking color
Question #11 – Sample <<Sample 1>> On the line below, please rate the amount of BROWNING (if any) in each sample?
If you would like to COMMENT on any of the samples you may do so below; however, it is not required. Please make your comments brief and concise. Refer to the sample number in your comment. _____________________________________________________________________________ _____________________________________________________________________________ _____________________________________________________________________________ You are finished. Please place the samples and tray in the pass-through compartment and PRESS THE BUTTON BY THE “FINISHED” LIGHT. Please give this questionnaire to the receptionist. THANK YOU!
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APPENDIX F. Sensory Panel JMP Output
Panel 1 – Day 0 Q#6: Overall Appearance Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.38782 0.38782 0.1626 0.6873 Error 154 367.20192 2.38443 C. Total 155 367.58974
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.38782051 0.1626 0.6873 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Orach 6.9134615 0.15141728 6.6143385 7.2125846 6.91346 Control 7.0192308 0.21413637 6.5962069 7.4422546 7.01923
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.1057692 0.2622624 -0.412327 0.6238655 0.6873
67
Panel 1 – Day 0 Q#7: Color Acceptance Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.20513 0.20513 0.1035 0.7481 Error 154 305.15385 1.98152 C. Total 155 305.35897
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.20512821 0.1035 0.7481 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 7.1153846 0.1952079 6.7297537 7.5010155 7.11538 Orach 7.0384615 0.13803283 6.7657793 7.3111437 7.03846
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.0769231 0.2390799 -0.395376 0.5492225 0.7481
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Panel 1 – Day 0 Q#8: Freshness Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.46154 0.46154 0.251 0.6171 Error 154 283.23077 1.83916 C. Total 155 283.69231
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.46153846 0.251 0.6171 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 6.6923077 0.18806509 6.3207874 7.063828 6.69231 Orach 6.8076923 0.1329821 6.5449878 7.0703969 6.80769
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 0.1153846 0.2303318 -0.339633 0.5704022 0.6171
69
Panel 1 – Day 0 Q#9: Color Intensity Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.541667 0.541667 1.0911 0.2979 Error 154 76.451923 0.496441 C. Total 155 76.99359
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.54166667 1.0911 0.2979 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 3.0769231 0.09770846 2.8839012 3.269945 3.07692 Orach 2.9519231 0.06909032 2.815436 3.0884102 2.95192
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.125 0.1196679 -0.111403 0.3614026 0.2979
70
Panel 1 – Day 0 Q#10: Natural Color Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.38782 0.38782 0.262 0.6095 Error 154 227.97115 1.48033 C. Total 155 228.35897
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.38782051 0.262 0.6095 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 3.3653846 0.16872441 3.0320716 3.6986977 3.36538 Orach 3.4711538 0.11930617 3.2354659 3.7068418 3.47115
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 0.1057692 0.2066444 -0.302454 0.5139927 0.6095
71
Panel 1 – Day 0 Q#11: Browning Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 74.5393 74.5393 1.7149 0.1923 Error 154 6693.6851 43.4655 C. Total 155 6768.2244
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 74.539263 1.7149 0.1923 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 2.2596154 0.91426187 0.453502 4.0657288 2.25962 Orach 3.7259615 0.64648077 2.4488465 5.0030766 3.72596
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 1.466346 1.119738 -0.745682 3.678374 0.1923
72
Panel 2 – Day 45 Q#6: Overall Appearance Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 4.84496 4.84496 3.2038 0.0747 Error 256 387.13953 1.51226 C. Total 257 391.9845
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 4.8449612 3.2038 0.0747 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 7.1860465 0.13260642 6.9249081 7.4471849 7.18605 Orach 6.8953488 0.0937669 6.7106961 7.0800015 6.89535
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.2906977 0.162409 -0.02913 0.6105255 0.0747
73
Panel 2 – Day 45 Q#7: Color Acceptance Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.15698 0.15698 0.132 0.7167 Error 256 304.5407 1.18961 C. Total 257 304.69767
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.15697674 0.132 0.7167 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 7.1744186 0.11761249 6.9428074 7.4060298 7.17442 Orach 7.2267442 0.08316459 7.0629703 7.390518 7.22674
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 0.0523256 0.1440453 -0.231339 0.3359902 0.7167
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Panel 2 – Day 45 Q#8: Freshness Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.32752 0.32752 0.1551 0.694 Error 256 540.62209 2.11181 C. Total 257 540.94961
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.32751938 0.1551 0.694 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 6.627907 0.15670314 6.3193156 6.9364984 6.62791 Orach 6.5523256 0.11080585 6.3341185 6.7705326 6.55233
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.0755814 0.1919214 -0.302364 0.4535271 0.694
75
Panel 2 – Day 45 Q#9: Color Intensity Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.09496 0.094961 0.1965 0.6579 Error 256 123.70349 0.483217 C. Total 257 123.79845
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.09496124 0.1965 0.6579 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 3.127907 0.07495865 2.9802929 3.2755211 3.12791 Orach 3.1686047 0.05300377 3.0642257 3.2729836 3.1686
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 0.0406977 0.0918052 -0.140092 0.2214873 0.6579
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Panel 2 – Day 45 Q#10: Natural Color Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.04845 0.04845 0.036 0.8496 Error 256 344.40116 1.34532 C. Total 257 344.44961
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.04844961 0.036 0.8496 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 3.4302326 0.12507286 3.1839298 3.6765353 3.43023 Orach 3.4011628 0.08843987 3.2270005 3.5753251 3.40116
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.0290698 0.1531823 -0.272588 0.3307278 0.8496
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Panel 2 – Day 45 Q#11: Browning Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 321.025 321.025 1.5777 0.2102 Error 256 52088.727 203.472 C. Total 257 52409.752
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 321.02519 1.5777 0.2102 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 5.5581395 1.5381641 2.5290732 8.587206 5.55814 Orach 7.9244186 1.0876462 5.7825453 10.066292 7.92442
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 2.366279 1.883859 -1.34355 6.076113 0.2102
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Panel 3 – Day 90 Q#6: Overall Appearance Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 2.92742 2.92742 2.0541 0.1535 Error 184 262.23387 1.42518 C. Total 185 265.16129
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 2.9274194 2.0541 0.1535 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 7.3387097 0.15161412 7.039584 7.6378353 7.33871 Orach 7.0725806 0.10720737 6.8610669 7.2840944 7.07258
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.266129 0.1856886 -0.100224 0.6324816 0.1535
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Panel 3 – Day 90 Q#7: Color Acceptance Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 0.0672 0.0672 0.0454 0.8314 Error 184 272.16935 1.47918 C. Total 185 272.23656
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 0.0672043 0.0454 0.8314 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 7.1774194 0.15445958 6.8726798 7.4821589 7.17742 Orach 7.2177419 0.10921942 7.0022585 7.4332254 7.21774
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 0.0403226 0.1891736 -0.332906 0.4135508 0.8314
80
Panel 3 – Day 90 Q#8: Freshness Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 11.35753 11.3575 6.4801 0.0117* Error 184 322.49194 1.7527 C. Total 185 333.84946
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 11.357527 6.4801 0.0117* Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 7.2419355 0.16813366 6.9102178 7.5736532 7.24194 Orach 6.7177419 0.11888845 6.4831821 6.9523018 6.71774
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Control Orach 0.5241935 0.2059208 0.117924 0.9304631 0.0117*
81
Panel 3 – Day 90 Q#9: Color Intensity Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 1.548387 1.54839 3.2596 0.0726 Error 184 87.403226 0.47502 C. Total 185 88.951613
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 1.5483871 3.2596 0.0726 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 2.8548387 0.08753041 2.6821464 3.027531 2.85484 Orach 3.0483871 0.06189335 2.9262752 3.170499 3.04839
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 0.1935484 0.1072024 -0.017956 0.4050524 0.0726
82
Panel 3 – Day 90 Q#10: Natural Color Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 5.93817 5.93817 4.2817 0.0399* Error 184 255.18548 1.38688 C. Total 185 261.12366
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 5.938172 4.2817 0.0399* Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 3.1612903 0.14956268 2.8662121 3.4563686 3.16129 Orach 3.5403226 0.10575678 3.3316707 3.7489744 3.54032
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 0.3790323 0.1831761 0.0176367 0.7404279 0.0399*
83
Panel 3 – Day 90 Q#11: Browning Perception Analysis of Variance
Source DF Sum of Squares
Mean Square F Ratio Prob > F
Model 1 262.517 262.517 2.4561 0.1188 Error 184 19666.603 106.884 C. Total 185 19929.12
Effect Test
Source Nparm DF Sum of Squares F Ratio Prob > F
Batch 1 1 262.5168 2.4561 0.1188 Least Squares Means Table
Level Least Sq Mean Std Error Lower
95% Upper 95% Mean
Control 4.0645161 1.3129855 1.4740739 6.6549584 4.06452 Orach 6.5846774 0.9284209 4.7529581 8.4163967 6.58468
LSMeans Differences Student's t
Level Level Difference Std Err Dif Lower CL Upper CL p-Value Orach Control 2.520161 1.608072 -0.652470 5.692792 0.1188
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APPENDIX G. Disqualification of a second field from the study
The original experimental design included two fields where orach was being grown as a
part of the treatment variables. One field was in Murtaugh, Idaho and the second one was located
in Burley, Idaho. The field in Burley, Idaho was a small plot that was planted and harvested as
planned in the same season. However, the plants grown in Murtaugh were volunteer plants
growing up in significant quantities in an alfalfa field from the previous year’s orach growth. What
appeared to be Triple purple leaves were harvested to be processed following the experimental
design proposed.
All the produce obtained from both fields was cleaned, and the color was extracted with
the same conditions explained in the methods section. Due to the much larger quantity of leaves
harvested from the volunteer field in Murtaugh, the volume of aqueous extract obtained (Extract
#1) was significantly higher than obtained from the much smaller Burley plot (Extract #2).
Extract 2 was quickly spray-dried first, because the volume of colored water was lower.
The powder was removed and it was stored in jars with aluminum foil. The same process was
followed for extract 1, but it took significantly longer to process due to the large volume. After a
few hours, the extract color began to brown. Because of the volunteer nature of the field, the color
browning, and the extended processing time, the pigment from the Murtaugh field was not
included for further study.
Due to some unknown conditions of the genetic purity of the plants that grew involuntary,
higher heat exposure in the spray-drier, and the almost immediate color degradation, all the
samples from this field were taken out of the statistical analysis.