Emerging Challenges in Agriculture and Food Science

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Emerging Challenges in

Agriculture and Food Science

Vol. 5

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Emerging Challenges in

Agriculture and Food Science

Vol. 5

India . United Kingdom

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Editor(s) Dr. Marcello Iriti

Professor of Plant Biology and Pathology, Department of Agricultural and Environmental Sciences, Milan State University, Italy.

Email: [email protected];

FIRST EDITION 2022

ISBN 978-93-5547-526-8 (Print) ISBN 978-93-5547-534-3 (eBook) DOI: 10.9734/bpi/ecafs/v5

_________________________________________________________________________________

© Copyright (2022): Authors. The licensee is the publisher (B P International).

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This book covers key areas of Agriculture and Food Science. The contributions by the authors include consumer behavior, food labeling, nutrition, biosorption, heavy metals, antioxidant activities, geographical Indication, ion mobility spectrometry, agricultural ecological map, virgin olive oil, topography, ecological zone, curd cheese enrichment, dough rheology, bread texture, in vitro starch digestibility, glycemic index, nutritional gains, sensory and consumer evaluation, dietary fiber, bakery cereal products, nutritional value, intoxicating substances, biomarkers, bioinformatics, epitope, biological stressors, heat shock protein. This book contains various materials suitable for students, researchers and academicians in the field of Agriculture and Food Science.

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_____________________________________________________________________________________________________

a Oklahoma State University, Design, Housing and Merchandising, Stillwater, OK-74078, USA.

Chapter 1

Print ISBN: 978-93-5547-526-8, eBook ISBN: 978-93-5547-534-3

Measuring Consumer Responses to Food Labels: A Descriptive Study

Greg Clare

^a*

DOI:10.9734/bpi/ecafs/v5/2005B

ABSTRACT

Consumer responses to beef steak label information placement variations were measured in this study, which controlled for chromaticity. To measure information flow to consumers across label variations, eye tracking and scan path entropy have been used. In the placement variations, safe handling messages had the lowest entropy. Except when the monochrome information panel was combined with colour label elements, monochrome information had lower entropy than coloured label information. The study emphasises the possibility of producing a variety of effects on consumer attention by strategically placing monochrome and colour label elements in food label systems. Scan path entropy should be considered when evaluating label designs that are meant to highlight specific information for a variety of purposes, such as marketing, health communications, and safety messages. The use of monochrome or colour label components appears to influence observed entropy in label systems where information competes for consumer attention, and observed entropy can be influenced by the placement and combinations of various label elements that can be measured and adapted to achieve message attention goals.

Keywords: Consumer behavior; eye tracking; food labeling; nutrition.

1. INTRODUCTION

When controlling for chromaticity, this eye tracking study investigated the effects of different positioning of food label information on consumer attention to the food label. There is a scarcity of empirical studies that assess the effects of changes to complex label systems on consumer attention to specific label components. The perceived benefits of shopping in brick and mortar stores may be influenced by retailer and manufacturer attempts to improve grocery store food labelling. The necessity of having differentiated in-store shopping experiences is highlighted by the increased adoption of online grocery shopping with home delivery or shop online and pick up in store. A critical component of favorable store shopping experiences is maximizing the customer’s perceived utility by increasing convenience during food labels evaluation and selection of product alternatives to meet consumer goals. Brick and mortar stores offer customers the advantage of inspecting multiple products which are organized by department classification when creating their shopping basket. The process for selecting products among large in-store assortments however is less than optimal due to hyper-segmented assortments and consumer information overload when evaluating grocery product alternatives. Improvements to food product labels may also facilitate the evaluation and selection of product alternatives. Brick and mortar stores have associated consumer costs for travel and access to stores, wayfinding, product handling and checkout processes. Each of these associated consumer opportunity costs reduce the utility of the in-store shopping experience. Improving the brick and mortar grocery shopping experience for consumers by improving the accessibility of important label information may reduce A) Time requirements to evaluate products B) Reduce shopping time and C) Reduce store labor costs related to customer service. Consumer evaluation has been used extensively over the past decades to evaluate acceptability and quality of food products. New methods have been developed to overcome some biases of traditional techniques [1,2].

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Emerging Challenges in Agriculture and Food Science Vol. 5 Measuring Consumer Responses to Food Labels: A Descriptive Study

Consumers react to food label information in different ways due to their specific shopping trip goals.

Competition for a consumer’s visual attention between competing product label messages is presumed to be governed by a consumer’s idiosyncratic wants and needs when shopping. Patterns in the ways that groups of consumers evaluate food label information may offer insights into ways of organizing key label information to increase the flow of relevant information.

2. LITERATURE REVIEW 2.1 Labeling

Food labels help consumers prioritize relevant information when evaluating and selecting food alternatives and may influence attention towards products [3]. Retailers frequently emphasize brand, product, and marketing claims (e.g fresh, natural) in front of package food label messages. Label information has been demonstrated to influence purchase behaviors based on the type of information provided [4]. Research using eye tracking suggests that simplified food label designs may influence increased attention to the food label messages [5]. Detailed food product information is generally found on the side and back panels of packages. Customers seeking additional product information such as the country of origin, safe handling, preparation and use instructions, product warnings, ingredients and nutritional information are often required to remove food products from shelves and turn them to access the additional information.

Increasing convenience for grocery shoppers through effective food label design requires a better understanding of how consumers prioritize competing label information in front of package label communications. The goal of saving customers time through effective organization of key label information may offer perceived customer advantages to shopping in the brick and mortar channel combining both the benefits of immediate delivery of products and shopping time savings.

The role of colored label stimuli including imagery, background and text has been found to influence consumer attention favorably compared to monochrome messages [6]. However, the ability of the label message to transfer information to consumers effectively controlling for chromaticity within a complex label system including varied product information has not been studied. The ways in which monochrome and color images transfer information to consumers are likely contingent on placement, context, content and design of the label message. A label system must further compete for the consumer’s attention based on the consumer’s goals and interpretation of competing food product label messages which influence decision making. Front of package label messages which are both clear and easy to interpret to maximize the consumer’s convenience and facilitate purchase decisions are presumed to support retailer best practices in packaging design.

2.2 Eye Tracking Overview

Eye tracking technology measures 2D or 3D stimuli using screen, head mounted, or eyeglass instruments. Fixations measure pupillary dilations at or above the 100-millisecond range and include the diameter of the pupil’s dilation during label observations. Saccades measure the movements of the eye including the paths and order in which visual stimuli are scanned.

2D computer screen-based eye tracking measures pupillary fixations and saccadic eye movements describing gazing patterns of predefined visual stimuli presented on a computer screen. Fixations and saccades may predict a consumer’s cognitive processing behavior [7]. The design of label information has been demonstrated to influence consumer choices measured with eye tracking (Chrea, et al.

2010; Tonkin, Ouzts & Duchowski 2011; 3).

Eye tracking research has suggested that combinations of bottom-up or top-down cognitive processing may be involved when shoppers prioritize and evaluate label information [8,9]. Bottom-up processing involves cues such as the lines, shapes and colors of a stimuli subconsciously or consciously to influence consumers. By contrast, top-down processing requires conscious cognitive

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effort by drawing on the consumer’s memory, emotions, and higher order cognitive evaluation of specific product attributes which influence overall product evaluations and purchase decision making.

Food label information may stimulate consumer’s bottom-up and top-down processing behaviors.

For purposes of this study, pupillary fixations in the range of 100 milliseconds or greater are assumed as markers of either bottom-up or top-down cognitive processing as consumers attend to various label information [10]. Extended time spent fixating or gazing at visual stimuli such as labels may also provide some evidence of top-down processing [7]. Physiological behaviors in eye tracking are quantified through the use of fixation and saccadic data independently or in combination with qualitative or quantitative analysis techniques. Heat maps are qualitative visual representations of measured stimuli showing areas which produce greater attention as measured through pupillary fixations with red gradients indicating greater attention to label areas of interest. Gaze path animation videos describe the timeline of participants’ fixations and their order and direction for label observations within predetermined time limits. AOI hits are quantitative measures of fixation behavior within specified areas of interest compiled from the raw eye tracking data file.

2.3 Labels

Labels support food packaging by providing shoppers with product information, while not necessarily contributing to the protection or storage of products. Labels provide the means for transmitting encoded product information to consumers. Labeling plays a role in presenting consumers with important product information in addition to inspection of a physical product’s appearance for some consumer-packaged food products. Labeling standards for food products are regulated to varying degrees by various governmental and non-governmental global regulatory authorities. Labeling regulations help to ensure that standards for product communication such as price, weights and measures, and other food product attributes are transmitted consistently to consumers. The interpretation of label information is presumed to occur within a consumer’s rationale for considering products and is contingent on the desire to satisfy utilitarian or hedonic needs [11]. Maximizing benefits of the consumer’s time spent evaluating products versus their effort required during purchase decisions is frequently influenced by labeling information [3].

Decoding label messages provides consumers one means of evaluating food product alternatives while grocery shopping. A consumer’s consideration set for food products may be further influenced by experience or credence from purchasing the food in the past or by in-store promotional and visual merchandising factors. Myriad factors may contribute to a consumer’s approach or avoidance behavior for products under consideration in stores.

Many grocery products use packaging and labeling to support the sale of food products which influence the consumer’s purchase decisions. The opportunity to draw attention to products through the use of imagery in label designs is well documented [7,12]. Imagery provides meaning and context for consumers when evaluating visual information and has been shown to influence consumer bias strength based on the form and location of information presented [13]. Bottom-up design label factors such as bold contrasting colors have also have been demonstrated to influence consumer choices through perceived healthiness of food products [14,15]. Chromaticity of the label may influence the effectiveness of the information transfer to consumers as observed in scan path entropy observed among label areas of interest.

2.4 Scan Path Entropy

Scan path entropy provides a means of decoding how consumers evaluate label information through the use of information theory [16]. The process through which consumer decodes the label information may include bottom-up or top- down processing behaviors. Scan path entropy is a hybrid approach of analyzing visual behavior combining participants’ pupillary fixations and gaze path observations within an eye tracked visual communication. Scan path entropy statistics combine the logarithm of probabilities in a timeline of discreet choices of ordered observations within specified areas of interest in a visual stimulus [17]. Scan path entropy may offer benefits to researchers when

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used in combination with traditional eye tracking measurements (e.g. fixation duration, fixation count, time to first fixation, area of interest hit count) to better quantify visual behavior by combining fixation and saccadic observational metrics into entropy statistics. Entropy statistics describe the degree of randomness of participants’ gaze observations within and across the message content. Lower entropy scores within a component area of interest indicate more stable flow of information among groups of consumers who decode the label information in similar ways as a group [18].

2.5 Beef Labeling

The U.S. beef industry is valued at $105 Billion [19]. The average consumer in the U.S. spent $258 in 2015 on meats, poultry, fish and eggs [20]. Research has demonstrated that beef consumers are influenced by perceived convenience, price, safety and variety of meat products [21]. Recent consumer beef purchasing trends highlight nutrition, health concerns, sustainability and local production as emerging factors influencing purchase decisions [22]. In spite of many factors that influence consumer choices of beef products, typical meat department labeling of beef products has remained relatively parsimonious to highlight the appearance of the beef products using translucent cellophane overwraps and small labels. The most common grocery prepackaged beef labeling is a small monochrome printed label highlighting key information which is applied to a cellophane overwrap of meat presented on a Styrofoam tray. The introduction of color printed labels to traditional meat department monochrome labels may increase consumer attention to label messages. Research has suggested that increasing benefits to consumers through increased product information cues and convenience are key variables to combat migration to shop online/pick-up in store strategies [23].

Another benefit of improving our understanding of consumer and label interactions may include ways of differentiating label designs among competitors. Designing more effective front of package food labels offers one such approach to combat rising consumer expectations in grocery stores compared to time saving online shopping alternatives. Research has also found that label design factors may directly influence perceived salience of marketing communications [24].

3. METHODS 3.1 Participants

This study measured consumer label viewing behavior of variations of a front of package beef steak label info using eye tracking. Two hundred and eighty-two participants (180 females, 102 males) with ages ranging from 18 to 60+ years (M=38.5 years, SD=7.46) were recruited to participate in the study.

All of the participants reported regular color vision and normal to corrected to normal vision using contact lenses or glasses. A flier and email recruiting campaign was conducted at a large Midwestern University and within grocery stores in the nearby community with the permission of store management. Study selection criteria required that participants had purchased items in grocery stores on at least a monthly basis. Participants reported the following grocery purchasing behaviors: 43%

made all of the purchasing decisions, 39% made most of the purchasing decisions, and 18% made some of the purchasing decisions for their household. The experimental protocol was reviewed by the university internal review board and participants completed informed consent documents prior to beginning the eye tracking experiment. Participants received $15.00 compensation for participating in the study.

3.2 Eye Tracking Device

A Tobii X 2-30 eye tracking system was used to conduct this study and data was analyzed using Tobii Studio Pro Software v 3.3.1. The eye tracking system is permanently mounted to a 17” computer monitor. The device measured participants’ visual fixations and gaze observations of a preprogrammed eye tracking script consisting of 24 beef steak label images presented on the computer monitor. A calibration process was followed by participants’ exposure to a counterbalanced eye tracking script to reduce learning effects during the repeated beef label exposures.

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3.3 Experimental Stimuli

A novel colored label designed for packaged beef steaks like those sold in open-sell grocery store cold meat cases was digitally created in partnership with a regional grocery chain (Fig. 1) Participants reviewed instructions explaining that they would review variations of the same meat label and were asked to look at parts of the label that most influenced their attention as they might do while shopping.

Each label variation exposure lasted eight seconds prior to advancing to the next image and label variation. The regional grocery retailer’s brand iconography and value proposition for locally produced meats were included in the label stimuli in addition to an icon based safe handling messages and a simplified stop light nutrition message. The monochrome label information panel served as the control condition for comparing the effects of color between the label designs. Each of the five included label sections were posited to influence attention to similar or varying degrees during each label exposure variation. The image variants were created with Adobe Photoshop CC 2015 and Microsoft Publisher 2013. The six image variants in the current study (i.e. three monochromes and three colored) were displayed at 600 X 450 pixels with equal mean illuminance centered against a black background at a total resolution of 1920 X 1080 pixels. The label size displayed on the computer screen at 3 1/2” X 4 3/4”.

3.4 Data Collection

The experiment was conducted in a university laboratory simulating a grocery store environment and was illuminated with fluorescent bulbs at an average illuminance of 400 LUX at level of the eye tracking computer screen. Prior to beginning the eye tracking experiment, participants completed a paper survey measuring food label use and demographic information.

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Fig. 1. Monochrome and color label variations of information box placement

Each of the participants sat at a table containing the eye tracking computer at a distance of 24 inches from the computer screen in a stationary desk chair. A nine-point calibration process integrated into the Tobii Studio Professional v 3.3.1 software was completed for each participant and evaluated by the research team to ensure the equipment was functioning properly and visual behavior was captured within software specifications. Prior to review of the experimental images, the following instruction screen was read by each participant, “On the next several slides, you will see images of meat labels. Focus with your eyes on areas of the labels that most

attract your attention as if you were shopping in a grocery store. The slides will change automatically.”

Participants were exposed to each of the six label variations for 8000 milliseconds with the goal of measuring attention effects based on chromaticity. The participants eye movements were recorded at 60 Hz.

3.5 Data Analysis

The characteristics of participants are presented in Table 1. Within the Tobii Studio Professional v 3.3.1 software, five areas of interest (AOIs) were specified for analysis in this study. AOI hit count data was aggregated from the eye tracking raw data file. The aggregated AOI hit counts provided baseline comparisons for entropy statistics of how specific label communications influence consumer attention to the messages presented including the region of production, nutrition, information panel, brand, and icon based safe handling information (Fig. 1).

Entropy is a method for quantifying the amount of information presented when discreet probabilities govern how the information is cognitively processed [25]. The assumptions of entropy are that consumers are more likely to focus varied attention on areas of interest when assessing information in a system [26]. Scan path entropy is a method applied to fixations and gaze path behavior (saccades) to compare the relative attention paid to specified AOIs controlling for the order of the AOI observations. Five AOIs were specified within the meat label in each of the six image placement variations including map, brand, info box, nutrition, and safe handling messages. The process for aggregating the gaze behavior was completed using the following steps: 1) Specification of the AOIs 2) Assigning a character to each AOI (e.g. I = info block, N=nutrition, etc.) 3) Determining the fixation order for each observed AOI from individual participant gaze tracing videos 4) Removing duplicate characters 5) Counting unique scan paths and transforming characters to integers representing each AOI (1-5). 6) Applying the entropy formula to the resulting data [17]. The AOI content tested included a map of Oklahoma including the state flag and text, “Proudly Produced in”, a local retailer brand logo, a product information panel, a stop-light nutrition summary panel, and icon-based safe handling instruction summary. Participant gaze paths and pupillary fixations on detailed portions of each AOI region may be specified to quantify attention to messages within each area of interest. For example, determining the entropy of the state flag image compared to either the local product or larger map image information transfer to participants. The specific order in which label sections were prioritized

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by participants among the six label variants and five areas of interest provided the sample of label information responses used to measure scan path entropy in this study. Future studies will explore the AOI hit counts and entropy statistics within specific AOIs to better understand granular effects on attention within variations to AOI designs. For the current study, each AOI in the label placement variations provides an entropy statistic which may be compared within and between images. Since the tested AOIs differed in pixel size on the labels, each AOI was weighted using Poisson Weighting to account for size variations within the label stimuli. The scan path entropy formula is listed below.

Table 1. Characteristics of the sample

Gender # % Education # %

Male 102 36% Grade School or Less 8 3%

Female 179 64% Some High School 1 0%

High School Graduate 18 6%

Age Some College 106 38%

18-29 154 55% College Graduate 77 27%

30-39 17 6% Graduate Work 69 24%

40-59 83 30%

60 and over 27 9% Marital Status

Now Married 104 37%

Household Income Widowed 4 1%

Below $20,000 102 36% Divorces 23 8%

$20,000 to $29,999 21 7% Separated 3 1%

$30,000 to $39,999 28 10% Never Married 146 52%

$40,000 to $49,999 25 9%

$50,000 to $59,999 16 6% Buy & Cook Meat Products

$60,000 to $74,999 25 9% Yes 260 92%

$75,000 to $99,999 26 9% No 22 8%

$100,000 or more 35 12%

Grocery Purchase Decision Making

All 120 43%

Most 110 39%

Some 51 18%

None 1 0%

Table 2. Area of interest fixation hit statistic comparison of monochrome and color image areas of interest

Image 1 Image 2 Image 3

Mono Color Mono Color Mono Color

Map 462 462 362 299 637 511

Brand 803 706 525 543 779 757

Info 1778 1372 1262 968 1414 1436

Nutrition 1307 1511 1448 1702 926 1161

Safe Handling 608 666 868 792 493 466

Total label AOI hit count statistics for the beef steak label placement variants are presented in Table 2. For the monochrome label variants, the information box produced the greatest number of AOI hits M=1485, SD 265). In the color label variants, the nutrition information summary produced the greatest number of AOI hit counts M=1458, SD 274. The map area of interest produced the lowest AOI hit counts across both the monochrome and color conditions at M=487, SD=139 and M=424, SD =111

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respectively. The lower AOI hit counts may be explained by the relative size of the map AOI compared to other label AOIs, placement at the top or center of the label stimuli supporting [27] where top and centrally placed label information is prioritized during visual processing. Another reason for lower AOI hit counts for the map image may have been influenced by the familiarity of participants with the state where the study was conducted.

Scan path entropy compensates for label stimuli size variations by equally weighting the number of pixels presented within the visual field (Table 3). In addition, the scan path entropy method quantifies label fixations and gaze path order effects and reduces signal noise from repeated observations within an area interest. The rank order of AOI observations provides a hierarchical order of ways that participants evaluate the label information individually which are then aggregated to produce group statistics for how the label was observed. Entropy statistics from the current study suggest that for the control condition information panel which is monochrome (e.g. bordered black and white text) in both the monochrome and color treatments produces less stable information transfer indicated by higher entropy statistics regardless of label position (top, center, bottom) when observed with another colored label information present (Table 4). This finding supports prior research suggesting positive effects on attention of colored label stimuli [6]. Variations in observed entropy between monochrome and color variations highlights the need for further research which expands AOI placement variations to multiple label positions while controlling for the observed entropy of the information panel across label conditions (top, center, bottom).

The monochrome labels produced lower entropy and greater information transfer than the same information presented in color which is likely related to participant attention effects produced by the contrasting colors compared to black and white gradients (Table 5). The map H(x)=2.20, brand H(x)=2.18 and safe handling icons H(x)=1.31 produced lower entropy statistics consistent with the AOI hit count statistics in the monochrome condition. However, when weighted for AOI pixels, entropy statistics suggest that researchers relying solely on AOI fixation hit count statistics could misinterpret participant visual behavior when reviewing the label based solely on fixation frequency. For example, the information panel produced over twice the number of AOI hit counts compared to the map, brand, and safe handling areas of interest, however the observed entropy within the AOIs was consistent for the Map H(x)=2.20, Brand H(x)=2.18 and Info Panel H(x)=2.17. In other words, the entropy statistics suggest that the information panel transferred similar information within the label stimuli in the monochrome condition as the map and brand information despite the higher number of fixations evident in the AOI hit count data which aggregates all AOI observations within the images. The simplified safe handling icon label information consistently transferred greater information to participants in both the color and monochrome treatments and varied label information positions based on the observed lower values of the entropy statistics across all images.

Table 3. Area of interest pixel comparison and weighted percentage of the visual field

AOI Pixels H() % of field

Map 24913 10.95%

Brand 36036 15.84%

Info 59696 26.23%

Nutrition 67662 29.73%

Safe Handling 39225 17.24%

Table 4. Entropy statistics comparison of monochrome and color image areas of interest

Image 1 Image 2 Image 3

Mono Color Mono Color Mono Color

Map 3.04 1.81 1.25 1.90 2.21 2.30

Brand 1.92 2.81 1.85 2.26 1.88 2.44

Info 3.90 3.50 1.29 4.29 1.72 2.39

Nutrition 3.32 2.60 3.50 2.35 2.00 2.80

Safe Handling 1.76 1.45 2.01 2.35 1.21 1.85

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Table 5. Mean entropy and standard deviation comparisons of monochrome and color label variants

Mono Color

M (SD) M (SD)

Map 2.20 (0.34) 2.41(0.58)

Brand 2.18 (0.54) 2.42(0.49)

Info 2.17(1.11) 3.53(1.01)

Nutrition 2.70 (0.75) 2.83(0.49)

Safe Handling 1.31(0.12) 1.98(0.32)

The nutrition panel message produced marginally lower entropy in the monochrome conditions similar to the map, brand, info, and safe handling messages. It is interesting to note that the monochrome info panel when combined with color map, brand, nutrition panel and safe handling messages produced greater observed entropy suggesting a reduced information flow in the presence of the competing colored label components (Table 5). Colored label elements consistently increased the observed entropy for each area of interest suggesting a reduced information flow compared to monochrome label elements. Additional study of the phenomenon of mixing monochrome and colored label elements with the goal of increasing observed entropy of targeted label messages is needed.

This finding highlights the potential of the scan path entropy method as a supporting means for evaluating information transfer when compared to fixation count data by reducing signal noise during repeated observations. The application of label design approaches based on scan path entropy could allow retailers to integrate label elements in varied label positions with the goal of increasing entropy effects for portions of the label message while reducing entropy for other critical communications within the label message. Likewise, the scan path entropy approach may offer insights into combining targeted monochrome and color information combinations on food labels strategically to increase the visual flow of information presented to consumers. For example, using colored text to highlight the price in the information box may reduce total entropy within the information box area of interest holding other monochrome label components constant. However, additional research is needed to test this supposition and other practical applications of the scan path entropy method.

4. DISCUSSION AND LIMITATIONS

This exploratory study measured consumer attention to five label areas of interest with varied placement on a hypothetical meat label. Observed entropy mean statistics within the five specified label regions remained consistent regardless of top/ bottom and left/right label placement. Entropy statistics observed were higher among color label regions. Safe handling information consistently demonstrated the lowest observed entropy when controlling for chromaticity. The scan path entropy method offers researchers an extension of traditional eye tracking metrics (e.g. time to first fixation, total fixation duration, fixation count, or AOI hit count) to better understand consumer fixations and gaze behavior by providing estimates of the information transferred from message components when the placement of the messages is varied on labels. The relationship between bi-polar ranges of observed entropy statistics within competing label information may correlate to the degree of top- down or bottom-up processing being used among groups of participants to review different parts of label messages. Testing the relationship of top-down or bottom-up processing used when evaluating label messages using eye tracking would benefit from further studies using fMRI, EEG or other methods to assess evidence of cognitive processing to better understand the relationship between scan path entropy statistics and cognitive processing behavior when participants encounter variations in labels or other visual systems. Attempts to strategically place label information based on mean observed entropy in areas of interest to stimulate or reduce channel noise contingent on consumer shopping goals may be possible through application of scan path entropy methods to food label design. Developing a better understanding of consumer goals in food purchases based on how they interpret label messages by incorporating questionnaires also offers a logical extension of this research agenda for the future to further support validity of the method. Measuring the effects of contrasting color combinations integrated into label designs requires future research using the scan path entropy approach. The current study has several limitations which warrant caution when

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interpreting the results without additional research. The sample is not representative of grocery shoppers in the United States and therefore may not reflect information flow of persons with varied geo-demographic characteristics interpretation of the label information. All participants resided in Oklahoma at the time of the study and were familiar with the local regional retailer tested in the label design variations and results would likely differ for consumers unfamiliar with the retail trade area tested, or when other stimuli were presented for varied geographical regions. Since the participants all demonstrated normal or normal-corrected vision and color perception the observed entropy may vary among persons who are color-blind when examining monochrome and color labels.

Practitioners should consider scan path entropy to evaluate label designs intended to highlight specific information for various purposes including marketing, health communications, and safety messages. Label systems in which information competes for consumer attention appears to be influenced by the use of monochrome or color label components and observed entropy can be influenced by placement and combinations of varied label elements which can be measured and adapted to achieve message attention goals.

COMPETING INTERESTS

Author has declared that no competing interests exist.

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Biography of author(s)

Greg Clare

Oklahoma State University, Design, Housing and Merchandising, Stillwater, OK 74078, USA.

He is an Associate Professor and Associate Department Head in the Design, Housing, and Merchandising department at Oklahoma State University. He teaches courses in profitable merchandising analysis, acquisitions and allocations, entrepreneurship and product development, professional development, advertising and promotions, consumer behavior at both the undergraduate and graduate levels. Gregory also serves as the Merchandising Program Leader for the Great Plains Interactive Distance Education Alliance and as the Treasurer for the American Collegiate Retailing Association. He graduated from Michigan State University in 2012 with a PhD in Retailing and holds a MBA in Human Resource Management from University of Phoenix. Prior to pursuing a career in academia he worked for over 20 years in the retailing industry in various leadership capacities. He researches the effects of electric lighting on human health in the workplace using spectrum actigraphy. His other consumer behavior research interests include: eye tracking, packaging design, and targeted health communications. He is interested in laboratory and field eye tracking research to better understand how consumers select healthy food items influenced by packaging information.

_________________________________________________________________________________

© Copyright (2022): Author(s). The licensee is the publisher (B P International).

DISCLAIMER

This chapter is an extended version of the article published by the same author(s) in the following journal.

Journal of Integrative Food Sciences & Nutrition, 2(1): 007, 2018.

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_____________________________________________________________________________________________________

a Department of Food Science and Technology, Iran National Standards Organization, Tehran, Iran.

bDepartment of Food Science & Technology, Science and Research Branch, Islamic Azad University, Tehran, Iran.

c Department of Food Science and Technology, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Science and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Chapter 2

Print ISBN: 978-93-5547-526-8, eBook ISBN: 978-93-5547-534-3

Evaluation of the Biosorption Capacity of

Saccharomyces cerevisiae for Heavy Metals in Milk

R. Masoud

^a*

, F. Mirmohammad-Makki

^b

and A. Zoghi

^c

DOI:10.9734/bpi/ecafs/v5/3704E

ABSTRACT

The application of biosorbents like bacteria, yeast, and algae is a biotechnological method for eliminating heavy metals from the environment. These microorganisms can also be used for the decontamination of heavy metals in food and water. Our research team has investigated the heavy metals biosorption in milk by using Saccharomyces cerevisiae. In this regard, initial heavy metals (Pb, Cd and Hg) and S. cerevisiae were added to milk and the bioremoval process was monitored during four days. The objective of this study is to evaluate the effect of some variables including exposure time, temperature, S. cerevisiae concentration and initial metal concentration in the heavy metals (Pb, Cd and Hg) bioremoval process in milk. The analysis of ANOVA showed that among the above the variables, S. cerevisiae concentration, initial metal concentration, and exposure time were statistically significantly associated with heavy metals removal (p values ≤0.05). The highest biosorption (70%) was observed after 4 days with 30×10⁸ CFU S. cerevisiae in milk. These findings provided further evidence for S. cerevisiae as a powerful biosorbent for heavy metals removal from milk and a potentially safe and green tool for providing safe and healthy food supply.

Keywords: Saccharomyces cerevisiae; biosorption; heavy metals; Pb; Cd; Hg; milk.

1. INTRODUCTION

World industrialization is growing very fast and adversely affect the quality of the water, food, feed, and weather [1]. Various industries such as chemical, food, textile, and metallurgy release high amounts of waste including toxic substances to the environment. Pesticides and chemical fertilizers in agriculture and vehicles for transportation discharge large quantities of pollutants containing heavy metals into the atmosphere [2].

Food safety is considerably threatened by heavy metal pollution [3]. Heavy metals naturally exist in the environment [1]. Accumulation of heavy metals in human organs and tissues has caused some diseases such as kidney, cardiovascular system, and nervous system disorders [1,2].

Various methods have been applied to re move these pollutants from the environment such as ion exchange, chemical precipitation, membrane technologies and activated carbon, electro chemical treatment and Polymers but most of them are very expensive and not applicable in food industry [4,5].

Dairy products especially milk play important roles in our food chain, specifically in children’s food;

therefore their contamination by toxins and heavy metals would be a concerning issue and negatively affect human’s health [3]. All dairy products are exposed to be contaminated by heavy metals due to the contaminated water and animal’s feed with toxic metals present in cement, effluents and industrial waste. Milk as the most common dairy product may be contaminated by various pollutants such as

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Emerging Challenges in Agriculture and Food Science Vol. 5 Evaluation of the Biosorption Capacity of Saccharomyces cerevisiae for Heavy Metals in Milk

heavy metals, so it is one of the most serious problems for the public health in the world [4]. Heavy metals can easily enter to our body from milk and cause so many health problems [5]. The level of toxic metals is an important issue in quality and safety of milk [4]. Codex standard for contaminants (heavy metals and toxins) in milk has allowed the maximum permissible limits as represents in Table 1 [6]:

Table 1. Maximum permissible limits of some heavy metals in Milk

Heavy metal Level (mg/kg)

Lead (Pb) 0.02

Cadmium (Cd) 0.02

Mercury (Hg) 0.001

Applying the bioremediation methods for decreasing the amount of heavy metals from food has attracted increasing attention. Among all the methods, the use of living organisms to absorb pollutants and remove heavy metals from the environment is quite interesting. Plants; fungi; and microorganisms such as yeasts, bacteria, algae, and cyanobacteria are usually used for the bioremediation of heavy metals [7-9].

The main advantages of using microorganisms to remove toxic elements is that they are being fast, cheap, efficient and also their safety for human [3]. One of these microorganisms is the yeast (Saccharomyces cerevisiae), which is widely used in the food industry [10,11].

The unique yeast “Saccharomyces cerevisiae” is commonly used in the bakery and brewery industries. It is an economically available biosorbent [10]. The advantages of using S. cerevisiae for the biosorption of heavy metals are easy cultivation in a large scale, easy growth by nonfermentation methods, use of cheap media, easy manipulation at the molecular level, and also high biomass production [3].

Heavy metals may enter in different stages of the food chain, and among all, Pb, Cd, As, and Hg have harmful effects on human health [12].

Fig. 1 briefly explains the entrance ways of the toxic elements (heavy metals) to human's food chain and also the bioremediation process (removing the heavy metals) using beneficial microorganisms as the biosorbents [13].

The permissible limit of these contaminants in most foods is very low, usually less than 0.5 mg/kg [14].

Bioremoval is an absorption process in which heavy metals are attached to the cell surface [15]. In general, heavy metals bioremoval occurs through different mechanisms. The functional groups (hydroxyl and carboxyl) in the cell wall of S. cerevisiae play an essential role in the bioremoval process. They are responsible for metal ions fixation during the process. Also, the intracellular metal accumulation happens in the cell membrane, and metal ions can bind to other cellular molecules [16].

Milk is the main part of human’s diet all over the world [17]. Milk contains many essential nutrients such as vitamins, amino acids, soluble lipid, calcium, sodium, potassium [18]. Different sources of environmental contaminations like industries, polluted soil and water with toxic heavy metals which enter into the food chain. In regions with chemical, petrochemical and fertilizer industries, higher levels of heavy metals have been observed [19,20]. It is so significant that milk should be free from contaminants such as chemicals, mycotoxins, heavy metals and other types of environmental pollutants. So it is imperative the heavy metals amounts in milk to be control and eliminate [13,21]. To control the milk quality, it is important to use an accurate analytical method for analysis [22,23].

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Fig. 1. The toxic elements in the food chain and the bioremediation process

2. MILK CONTAMINATIONS IN THE WORLD

Many articles reported milk contamination to heavy metals around the world. Table 2 shows some of them. Maximum Permissible Limits (MPL) of heavy metal contents in milk (considered by International Dairy Federation) is 2.6 µg/kg for Cadmium, 10 µg/kg for Cuprum, 20 µg/kg for Lead and 328 µg/kg for Zinc [24]. In some documents heavy metals are more than MPL like Lead in Iraq, Brazil, China, Spain, Iran and Italy, Cadmium in Poland and Spain.

Table 2. Milk contamination reports by heavy metals

Area Heavy metal / Concentration Reference

Iraq Lead (32 µg/L) [25]

Spain Lead (27.35 µg/L)

Cadmium (4.73 µg/L)

[26]

Italy Lead (33 µg/L) [27]

Iran Lead (22.9 µg/L)

Cadmium (0.3 µg/L) Mercury (3.1 µg/L)

[28]

China Lead (35.01 µg/L)

Cadmium (4.53 µg/L)

[29]

Turkey Lead (12.07 ng/ml)

Cadmium (1.82 ng/ml)

[30]

Palestine Lead (0.2 μg/g)

Cadmium (0.5 μg/g)

[31]

Poland Lead (0.04 mg/L)

Cadmium (3.23 mg/L)

[32]

Nigeria Lead (0.13 mg/L) [33]

3. HEAVY METALS BIOSORPTION IN FOODSTUFFS

Many scientific studies on heavy metals biodecontamination in dairy products have been done in recent decades. In Table 3 some of these researches in biosorption in dairy products are mentioned.

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Table 3. Bioremediation by probiotics dairy products

Microorganism Heavy Metal Removal % (W/W) Food / drink Ref.

S. cerevisiae Lead 70% milk [6]

S. cerevisiae Cadmium 70% milk [15]

S. cerevisiae Cadmium 85% milk [16]

S. cerevisiae Mercury 80% milk [13]

L. acidophilus Lead

Cadmium

75% milk [34]

L. acidophilus Mercury 70% milk [35]

Kluyveromyces marxianus

Nickel 81% Kefir [36]

L. lactis Lead

Cadmium

69%

80%

Kefir [36]

Our study aims to evaluate the capacity of S. cerevisiae for heavy metals absorption in milk.

Therefore, the effects of three main factors; initial metal concentration, biomass concentration and contact time on the biosorption capacity of S. cerevisiae were studied. These factors were chosen through the previous studies of heavy metal bioremoval and also based on the results of our research team.

3.1 Sample Preparation

Each sample was prepared for milk (50 mL) with levels of S. cerevisiae (10 × 10⁸ to 50 × 10⁸ CFU/mL) and different initial Cd concentrations (40, 50, 60, 70, 80 µg/L) and stored in the fridge for 4 days.

Then the effect of 3 variables; initial metal concentration (40–80 µg/L), biomass concentration (10–50

× 10⁸ CFU/mL) and contact time (1–4 days) on the biosorption capacity of S. cerevisiae were studied.

3.2 Central Composite Design (CCD)

The 3 variables; initial Cd concentration, S. cerevisiae biomass and contact time, having significant effects on Cd removal. CCD was used to find the optimal conditions of Cd biosorption with the experimental factors levels as shown in this study (Table 4).

Table 4. Levels of the main variables for the central composite design

Main variable Range and level

− α (−1.6) −1 0 +1 +α (+1.6) S. cerevisiae biomass dosage (× 10⁸

CFU)

10 20 30 40 50

Initial Cd concentration )μg/L) 40 50 60 70 80

Contact time (day) 0 1 2 3 4

3.3 ICP-MS Analysis

The inductively coupled plasma mass spectrometer (ICP- MS, England) applied in this study, with a standard torch, a cross flow nebulizer and a quartz spray chamber. It was tuned before each experiment started. All the samples were put in microwave 1200W (Milestone Micro oven) to be digested with segmented rotor MPR-600 [37].

3.4 Removal Evaluation

The milk samples containing S. cerevisiae and Cd were digested in the microwave and then centrifuged (at 2000× g) for 15 min. The supernatant was injected into the ICP-MS for metal residual determination. This was measured by using the ICP-MS. All the trials were repeated in triplicate. The metal removal efficiency (%) was calculated by the following Equation [38]:

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%Removal = 100 (Co − Cf)/Co

Co (µg/L): is the initial Cd concentration in solution;

Cf (µg/L): is the final Cd concentration in solution

The important variables for metal absorption by S. cerevisiae are as the following:

- S. cerevisiae Biomass Dosage

This removal is due to binding of heavy metals to the cell wall. In the food industry, S. cerevisiae is one of the potential cost-effective biosorbents for the decontamination of heavy metals from foodstuff.

The biosorption occurs through a surface binding process that relates to the functional groups like hydroxyl, carboxyl, and amide groups of S. cerevisiae [39].

The biosorption increased with S. cerevisiae concentration in milk samples as the active sites on the cell wall of S. cerevisiae became more available for attaching to the metal ions. By raising the biosorbent dosage, the number of available sites for biosorption increases, but these sites may have stayed un saturated within the biosorption process [3,22].

- Contact time

The contact time is one of the main factors that greatly influences the bioremoval [40]. The metal uptake enhances by increasing in the exposure time which is due to the attachment of more metal ions to S. cerevisiae receptor sites [13,15,40]. There are several articles that confirm increasing the heavy metals bioremoval (Pb, Cd, Hg) by increasing the exposure time [15,24].

- Metal ion concentration

Clearly, by increasing the number of metal ions, their adsorption to S. cerevisiae receptors would increase as a result of the higher driving force for metal ions to interact with S. cerevisiae membrane binding sites [13,15,41].

It is reason able that by increasing the contact time, more metal ions would connect to S. cerevisiae receptors, phosphate, amine and carboxyl groups in the cell wall and the biosorption process would be more sufficiently by time [42].

- pH

pH is considered to be the other most important factor in biosorption processes. It influences the competition of metallic ions and the activity of the biomass functional groups [43]. The biosorption of metal cations increase with an increase in the pH [44]. The optimum pH for the absorption of different elements is different, for example, the optimum pH for Cu removal by S. cerevisiae is pH 5 [45,46].

The results showed that the maximum bioremediation of heavy metals by this yeast occurs in pH 4-5 [47]. The solution pH affects the amount of ionized groups in the yeast cell wall. At low pH, an increase in the protonation in yeast cell wall ligands causes a decrease in the adsorption of metals [48].

- Temperature

Temperature seems to be one of the most important parameters in the bioremediation of heavy metals [3]. In the range of 15–40°C, the highest biosorption capacity of Pb, Ni, and Cr ions by S.

cerevisiae was observed at 25°C [45,49]. The scientific articles report that the temperature range for heavy metals removal by S. cerevisiae is 20-50 °C on the growth rate of S. cerevisiae [50-52].

Some other treatments like ethanol, caustic, acidic and heat may increase the bioremoval of heavy metals by microorganisms. In a study the ability of bakery’s yeast " S. cerevisiae " in cadmium and lead bioremoval with 3 different treatments (caustic, heat and ethanol) was evaluated. Ethanol-treated yeasts removed the highest content of metals and it would be explained by increasing the available binding sites of yeast and enhancing the metals accessibility [53,54].

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4. CONCLUSION

Using microorganisms as environmentally friendly biosorbents for toxic metals bioremediation from food and water resources is a novel method. According to the recent studies summarized in this review, it is revealed that using different microorganisms (such as probiotics) in different dairy products could result in the decrease of toxins and metals availability by creating bond between contamination and these microorganisms. S. cerevisiae is a desirable and eco-friendly biosorbent for toxic metal bioremediation from food and water resources.

In this chapter, three important variables; metals, biomass concentration and the contact time for metal bioremoval by S. cerevisiae were evaluated. The results showed the highest level of biosorption (70%) observed in the S. cerevisiae concentration of 30 × 10⁸ CFU and 80 µg/L of metals on the 4th day. This study shows the ability of this valuable yeast for metal remediation in very low concentrations (ppb) from milk. It also opens the window for the evaluation of the capacity of heavy metal binding by S. cerevisiae in milk. There is a need for more studies in this field to reduce the toxic effects of heavy metals in food and drink. According to reviewed articles, using the starters in fermented dairy products can already be helpful in decontamination. On the basis of these achievement, it is suggested that use more successful starters and probiotics in bioremediation can reduce availability of toxins and heavy metals in human body. This technique would be useful in the case of emergencies in the food and beverage industry.

COMPETING INTERESTS

Authors have declared that no competing interests exist.

REFERENCES

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Available:https:// doi.org/ 10.1016/ B978-0-12-821199- 1.00025-0

2. Ahmad W, Alharthy RD, Zubair M, et al. Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Sci Rep. 2021;11:17006.

Available:https:// doi.org/ 10.1038/s41598-021-94616-4

3. Massoud R, Hadiani MR, Khosravi Darani K, et al. Bioremediation of heavy metals in food industry: Application of Saccharomyces cerevisiae. Electron J Biotechnol. 2019;37-44.

Available:https://doi.org/10.1016/j.ejbt.2018.11.003

4. Zhao G, Huang X, Tang Z, et al. Polymer – based composites for heavy metal ions removal from aqueous solution: a review. Polym Chem. 2018;9:3562–3582.

5. Massoud R, Khosravi - Darani K, Sharifan A. Lead bioremoval from milk by Saccharomyces cerevisiae. Biocat Agr Biotech. 2019;22:101437.

Available:https:// doi.org/ 10.1016/j.bcab. 2019.101437

6. Codex stan 193-2009. Codex general standard for contaminants and toxins in food and feed.

7. Mirmahdi R, Zoghi A, Mohammadi F, et al. Biodecontamination of milk and dairy products by probiotics: Boon for bane. Italian J Food Sci. 2021;33:78–91.

Available:https:// doi.org/ 10.3390 / dairy1020011

8. Cherni, Y, Botta, C, Kasmi, M, et al. Mixed culture of Lactococcus lactis and Kluyveromyces marxianus isolated from kefir grains for pollutants load removal. Water Environ Res.

2020;92:2041–2048.

Lactococcus lactis https:// doi.org/ 10.1002/ wer.1363

9. Halder SP. Bioremediation of heavy metals through fresh water microalgae: A review. Scholars Acad J Biosci. 2014;2(11):825–30.

10. Wu Y, Wen Y, Zhou J, et al. The characteristics of waste Saccharomyces cerevisiae biosorption of arsenic (III). Environ Sci Pollut Res. 2012;19:3371–9.

Available:https:// doi.org/ 10. 1007/ s11356-012-0861-9

11. Soares EV, Soares HM. Bioremediation of industrial effluents containing heavy metals using brewing cells of Saccharomyces cerevisiae as a green technology: A review. Environ Sci Pollut Res. 2012;19:1066–83.

Available:https:// doi.org/ 10.1007/ s11356-011-0671-5

Emerging Challenges in Agriculture and Food Science

Emerging Challenges in

Agriculture and Food Science

Vol. 5

Emerging Challenges in

Agriculture and Food Science

Vol. 5

India . United Kingdom

Editor(s) Dr. Marcello Iriti

FIRST EDITION 2022

ISBN 978-93-5547-526-8 (Print) ISBN 978-93-5547-534-3 (eBook) DOI: 10.9734/bpi/ecafs/v5

Contents

Chapter 1

Measuring Consumer Responses to Food Labels: A Descriptive Study

Greg Clare

ABSTRACT

1. INTRODUCTION

2. LITERATURE REVIEW 2.1 Labeling

2.2 Eye Tracking Overview

2.3 Labels

2.4 Scan Path Entropy

2.5 Beef Labeling

3. METHODS 3.1 Participants

3.2 Eye Tracking Device

3.3 Experimental Stimuli

3.4 Data Collection

3.5 Data Analysis

4. DISCUSSION AND LIMITATIONS

COMPETING INTERESTS

REFERENCES

Chapter 2

Evaluation of the Biosorption Capacity of

Saccharomyces cerevisiae for Heavy Metals in Milk

R. Masoud

, F. Mirmohammad-Makki

and A. Zoghi

ABSTRACT

1. INTRODUCTION

2. MILK CONTAMINATIONS IN THE WORLD

3. HEAVY METALS BIOSORPTION IN FOODSTUFFS

3.1 Sample Preparation

3.2 Central Composite Design (CCD)

3.3 ICP-MS Analysis

3.4 Removal Evaluation

4. CONCLUSION

COMPETING INTERESTS

REFERENCES