Evaluation of Pigments from a Purple Variety of Atriplex Hortensis L. for Use in Food Applications Eva Graciela Vila Roa Brigham Young University

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2018-06-01 Evaluation of Pigments from a Purple Variety of Atriplex hortensis L. for Use in Food Applications Eva Graciela Vila Roa Brigham Young University

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This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. 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

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. 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

iv 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

v 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

vi LIST OF FIGURES

Figure 1 Orach pigment extract chromatogram (a) and powdered bilberry extract chromatogram (b) ...... 22

vii 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

1 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

2 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.

3 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.

4 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

5 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

6 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

7 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):

= ∗ ∗ ∗ 1 0 ∆𝐿𝐿 = 𝐿𝐿 − 𝐿𝐿 ∗ ∗ ∗ 1 0 ∆𝑎𝑎 = 𝑎𝑎 − 𝑎𝑎 ∗ ∗ ∗ ∆𝑏𝑏 𝑏𝑏 1 − 𝑏𝑏 0 = [( ) + ( ) + ( ) ] 1 ∗ ∗ 2 ∗ 2 ∗ 2 2 ∆𝐸𝐸 𝑎𝑎𝑎𝑎 ∆𝐿𝐿 ∆𝑎𝑎 ∆𝑏𝑏 = = + + , , 1 1 ∗ ∗ ∗ ∗ 2 ∗ 2 2 ∗ 2 ∗ 2 2 ∆𝐶𝐶 𝑎𝑎𝑎𝑎 𝐶𝐶 𝑎𝑎𝑎𝑎 1 − 𝐶𝐶 𝑎𝑎𝑎𝑎 0 �𝑎𝑎 1 𝑏𝑏 1 � − �𝑎𝑎 0 𝑏𝑏 0 � = [( ) ( ) ( ) ] 1 ∗ ∗ 2 ∗ 2 ∗ 2 𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎 2 Visual Consumer∆𝐻𝐻 Sensory∆𝐸𝐸 Testing− ∆𝐿𝐿 − ∆𝐶𝐶

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,

8 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.

9 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).

10 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.

11 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.

12 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

13 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.

14 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.

15 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.

16 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.

17 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.

Precursor Formula Compound Name Type ion (m/z) 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 Anthocyanin Petunidin-3-O-arabinoside

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 Anthocyanin Malvidin-3-O-arabinoside

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

18 Table 2 Betalains, anthocyanins, and potential isomers tentatively identified in orach pigment extract.

Precursor Label Source RT Compound Type ion (m/z) 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 Betacyanins Gomphrenin I or Isogomphrenin I

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 Anthocyanin Cyanidin-3-O-glucoside or Petunidin-3-O-arabinoside

C21 H21 O11 Orach Sample 16.27 449.11 Cyanidin-3-O-galactoside or Anthocyanin Cyanidin-3-O-glucoside or Petunidin-3-O-arabinoside

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 Anthocyanin or Delphinidin 3-O-glucoside

C21 H21 O12 Orach Sample 10.78 465.10 Delphinidin-3-O-galactoside Anthocyanin or Delphinidin 3-O-glucoside

C21 H21 O12 Orach Sample 10.76 465.10 Delphinidin-3-O-galactoside Anthocyanin or Delphinidin 3-O-glucoside

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.

Storage Light treatment Dark treatment Time (Days) 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 E*ab C*ab H*ab (Days) 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

Table 5 Consumer sensory acceptance of yogurts colored with orach extract or carmine (control) at Days 0, 45, and 90 (n=50).

Overall Natural Day Batch Color liking1 Freshness2 Color intensity3 Browning5 Appearance1 perception4

2.95 ± 0.06 a Orach 6.91 ± 0.15 a 7.04 ± 0.13 a 6.81 ± 0.13 a 3.47 ± 0.11 a 3.73 ± 0.64 a Just about right 0 3.08 ± 0.09 a Control 7.02 ± 0.21 a 7.12 ± 0.19 a 6.69 ± 0.18 a 3.37 ± 0.16 a 2.26 ± 0.91 a Just about right

HSD 1.04 0.59 0.91 0.47 0.82 4.42

3.17 ± 0.05 a Orach 6.9 ± 0.09 a 7.23 ± 0.08 a 6.55 ± 0.11 a 3.40 ± 0.08 a 7.92 ± 1.08 a Just about right 45 2.13 ± 0.07 a Control 7.19 ± 0.13 a 7.17 ± 0.11 a 6.63 ± 0.15 a 3.43 ± 0.12 a 5.56 ± 1.53 a Just about right

HSD 0.64 0.57 0.76 0.36 0.60 7.42

3.05 ± 0.06 a Orach 7.07 ± 0.10 a 7.22 ± 0.11 a 6.72 ± 0.12 b 3.54 ± 0.105 a 6.58 ± 0.92 a Just about right 90 2.85 ± 0.08 a Control 7.34 ± 0.15 a 7.18 ± 0.15 a 7.24 ± 0.17 a 3.16 ± 0.14 b 4.06 ± 1.31 a Just about right

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

21 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).

22 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

23 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

24 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

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).

26 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

27 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

28 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,

29 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).

30 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

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.

32 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

33 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

34 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

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

36 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

37 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

38 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

39 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

40 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

41 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

42 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

43 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

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

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

46 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

47 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

48 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

49 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

50 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

51 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

52 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

53 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

54 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

55 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

56 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

57 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

58 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

59 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

60 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

61 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

62 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 <> 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 <> 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

63 STRAWBERRY YOGURT CONSUMER TEST

Question #8 – Sample <> 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 <> 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

64 Question #10 – Sample <> 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 <> 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!

65 APPENDIX F. Sensory Panel JMP Output

Panel 1 – Day 0 Q#6: Overall Appearance

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.38782 0.38782 0.1626 0.6873 Error 154 367.20192 2.38443 C. Total 155 367.58974

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.38782051 0.1626 0.6873

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

66 Panel 1 – Day 0 Q#7: Color Acceptance

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.20513 0.20513 0.1035 0.7481 Error 154 305.15385 1.98152 C. Total 155 305.35897

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.20512821 0.1035 0.7481

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

Panel 1 – Day 0 Q#8: Freshness Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.46154 0.46154 0.251 0.6171 Error 154 283.23077 1.83916 C. Total 155 283.69231

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.46153846 0.251 0.6171

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

68 Panel 1 – Day 0 Q#9: Color Intensity

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.541667 0.541667 1.0911 0.2979 Error 154 76.451923 0.496441 C. Total 155 76.99359

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.54166667 1.0911 0.2979

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

69 Panel 1 – Day 0 Q#10: Natural Color Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.38782 0.38782 0.262 0.6095 Error 154 227.97115 1.48033 C. Total 155 228.35897

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.38782051 0.262 0.6095

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

70 Panel 1 – Day 0 Q#11: Browning Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 74.5393 74.5393 1.7149 0.1923 Error 154 6693.6851 43.4655 C. Total 155 6768.2244

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 74.539263 1.7149 0.1923

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

Panel 2 – Day 45 Q#6: Overall Appearance

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 4.84496 4.84496 3.2038 0.0747 Error 256 387.13953 1.51226 C. Total 257 391.9845

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 4.8449612 3.2038 0.0747

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

Panel 2 – Day 45 Q#7: Color Acceptance

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.15698 0.15698 0.132 0.7167 Error 256 304.5407 1.18961 C. Total 257 304.69767

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.15697674 0.132 0.7167

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

73 Panel 2 – Day 45 Q#8: Freshness Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.32752 0.32752 0.1551 0.694 Error 256 540.62209 2.11181 C. Total 257 540.94961

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.32751938 0.1551 0.694

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

74 Panel 2 – Day 45 Q#9: Color Intensity

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.09496 0.094961 0.1965 0.6579 Error 256 123.70349 0.483217 C. Total 257 123.79845

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.09496124 0.1965 0.6579

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

Panel 2 – Day 45 Q#10: Natural Color Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.04845 0.04845 0.036 0.8496 Error 256 344.40116 1.34532 C. Total 257 344.44961

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.04844961 0.036 0.8496

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

76 Panel 2 – Day 45 Q#11: Browning Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 321.025 321.025 1.5777 0.2102 Error 256 52088.727 203.472 C. Total 257 52409.752

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 321.02519 1.5777 0.2102

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

77 Panel 3 – Day 90 Q#6: Overall Appearance

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 2.92742 2.92742 2.0541 0.1535 Error 184 262.23387 1.42518 C. Total 185 265.16129

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 2.9274194 2.0541 0.1535

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

78 Panel 3 – Day 90 Q#7: Color Acceptance

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 0.0672 0.0672 0.0454 0.8314 Error 184 272.16935 1.47918 C. Total 185 272.23656

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 0.0672043 0.0454 0.8314

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

Panel 3 – Day 90 Q#8: Freshness Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 11.35753 11.3575 6.4801 0.0117* Error 184 322.49194 1.7527 C. Total 185 333.84946

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 11.357527 6.4801 0.0117*

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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*

Panel 3 – Day 90 Q#9: Color Intensity

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 1.548387 1.54839 3.2596 0.0726 Error 184 87.403226 0.47502 C. Total 185 88.951613

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 1.5483871 3.2596 0.0726

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

Panel 3 – Day 90 Q#10: Natural Color Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 5.93817 5.93817 4.2817 0.0399* Error 184 255.18548 1.38688 C. Total 185 261.12366

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 5.938172 4.2817 0.0399*

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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*

82 Panel 3 – Day 90 Q#11: Browning Perception

Analysis of Variance Sum of Mean Source DF F Ratio Prob > F Squares Square Model 1 262.517 262.517 2.4561 0.1188 Error 184 19666.603 106.884 C. Total 185 19929.12

Effect Test Sum of Source Nparm DF F Ratio Prob > F Squares Batch 1 1 262.5168 2.4561 0.1188

Least Squares Means Table Least Sq Lower Upper Level Std Error Mean Mean 95% 95% 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

83 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.