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Food Labeling

Food Labeling is the process of providing information about the contents, nutritional value, and other attributes of food products through labels, packaging, and other forms of presentation.
This includes mandatory and voluntary labeling requirements, as well as the use of claims, symbols, and other marketing techniques to convey information to consumers.
Proper food labeling helps people make informed choices about the foods they purchase and consume, and supports public health and safety goals.
Key aspects of Food Labeling include nutrition facts, ingredient lists, allergen warnings, and other information designed to enhance transparency and consumer awareness.

Most cited protocols related to «Food Labeling»

1
Comprehensive Dietary Assessment in Adventists
The AHS-2 FFQ is a quantitative and comprehensive instrument originally designed to include foods commonly consumed by US Adventists. The questionnaire was later revised to reduce the respondents’ fatigue and to accommodate foods specific to black Adventists of US and Caribbean origin(7 ) All versions of the FFQ consist of two major sections. Across all versions, the first section is a food list that includes 130–141 items of fruits, vegetables, legumes, grains, oils, dairy, fish, eggs and beverages, and the second consists of sixty-three to seventy-nine items of commercially prepared products, such as dietary supplements, dry cereals and vegetarian protein products that require respondents to examine food labels. Frequency categories range from never or rarely to ≥6 servings/d and vary with food type to allow respondents to define their daily intake with greater specificity. Portion sizes (e.g. cup, tablespoon, slice, patty) include a given standard,
12 or less and
112 or more of the standard serving. Pictures of common foods or beverages typically served together are included with the questionnaire to assist participants in estimating portion sizes. The questionnaire was sent to each participant, completed at home and then mailed back to AHS-2. Respondents were asked to report on their intake over the previous 1 year. Upon receipt of the questionnaire, study personnel reviewed the FFQ for completeness and, when necessary, followed up by telephone to clarify any ambiguous or incomplete information.
The 24HDR was administered unannounced and information was obtained by telephone. Each participant was provided a two-dimensional food portion visual (The Nutrition Consulting Enterprises, Framingham, MA, USA) to assist with portion size estimates. Trained research dietitians used standard probes and a multiple-pass approach method to collect detailed information on all foods, beverages and supplements consumed by each participant during the previous 24 h. All recall interviews were digitally recorded for subsequent quality check. Later, an experienced research dietitian evaluated randomly selected recall interviews (~5%) and compared them with the recording, as a quality control measure.
Recall and FFQ data were entered using the Nutrition Data System for Research version 4·06 or 5·0 (NDS-R, Nutrition Coordinating Center, Minneapolis, MN, USA); the analytic data used in the present study were based on the NDS-R 2008 database. Information on foods not found in the NDS-R database was obtained from the US Department of Agriculture, from individual manufacturers and from the Caribbean Food and Nutrition Institute. Considerable attention was given to creating recipes for home-cooked vegetarian dishes (n>500), homemade and commercial soya and nut milks (n>180) and for commercial meat analogues (n 309) that were frequently consumed by our study population. For the latter we contacted manufacturers or worked with a senior food technologist with experience in this industry in order to create recipes.
Jaceldo-Siegl K., Fan J., Sabaté J., Knutsen S.F., Haddad E., Beeson W.L., Herring R.P., Butler T.L., Bennett H, & Fraser G.E. (2011). Race-specific validation of food intake obtained from a comprehensive FFQ: the Adventist Health Study-2. Public health nutrition, 14(11), 1988-1997.
Publication 2011
Attention Beverages Caribbean People Cereals Conditioning, Psychology Dietary Supplements Dietitian Eggs Fabaceae Fatigue Fishes Food Food Labeling Fruit Hyperostosis, Diffuse Idiopathic Skeletal Meat Mental Recall Milk Oils Proteins Soybeans Vegetables Vegetarians
2
Photographing Food Labels in Chilean Supermarkets
Photographs of food labels were taken with the use of 2 digital cameras (Canon EOS Rebel T3) because they produce photos at a higher resolution than mobile devices. One battery was included with each camera purchased. However, 1 extra battery was purchased for use with each camera. All of the equipment used in this study is shown in Table 2.
Data collection was scheduled to take place during the summer holiday in Chile, specifically the month of February, because supermarkets have fewer customers in those months. Fieldwork was prearranged to take place in each of the 11 supermarkets for, on average, 2 complete weekdays (0800–1700). When possible, fieldwork was avoided on Mondays and Fridays, because they constitute the 2 busiest weekdays at the supermarket (for new-item placement and greatest attendance of customers, respectively). All of the fieldwork for this study was conducted between 2 February and 1 April 2015.
All of the fieldworkers were instructed to wear a lab coat, identification badge, and closed-toe shoes in the supermarket during data collection. Fieldworkers were instructed to give a standard response if approached by customers (i.e., “What we are doing is for a study about nutritional labeling”).
Fieldworkers were paired into 2 teams of 2, and each team was assigned different food categories for the duration of the study (team A: beverages, sweets, ready-to-eat foods, fish and seafood, canned and preserved fruits and vegetables, sauces and spreads, and snacks; team B: bread and bakery products, breakfast cereals, meats, and dairy). Each team was instructed to take photos with the digital camera of all sides of the food label and was given a checklist to verify that photos visibly included the following aspects of the food label: bar code, package shape, volume or weight of the package, nutrition information table, and ingredients. Fieldworkers were instructed to take photos of as many processed food products as possible within a given subcategory. It was sometimes the case that fieldworkers did not have enough time to collect photos of all of the products within a given subcategory in a supermarket over the course of the 2 data collection days. In those cases, the coordinator assigned a total time for each subcategory and each team was instructed to take as many photos of different products as possible in accordance with the schedule provided and prioritize taking photos of store-brand products. The photos of store-brand products were prioritized because store-brand products are unique to each chain and a store from each chain was only visited 1 or 2 times for a limited number of days. Therefore, if photos of a store-brand product were not taken, there might not exist another opportunity to capture photos of this product compared with a multinational brand product, which would likely be found at supermarkets linked to >1 chain. Photos of the same product were taken from the same supermarket chain for each of the 2 neighborhood income categories (i.e., for analyzing differences by SES) but not in supermarkets within the same SES level. To avoid taking photos of the same product (i.e., duplicating photos), each day the fieldworkers would place all of the products photographed in a shopping cart and then take a photo of the cart contents, which could be reviewed to confirm whether or not a product had been already photographed. The results presented later in this article regard the ∼10,000 food items that were photographed during the study.
Kanter R., Reyes M, & Corvalán C. (2017). Photographic Methods for Measuring Packaged Food and Beverage Products in Supermarkets. Current Developments in Nutrition, 1(10), e001016.
Publication 2017
Beverages Bread CART protein, human Cereals Fingers Fishes Food Food Labeling Fruit Meat Seafood Snacks Vegetables
3
Comprehensive Canadian Food Label Database
The Food Label Information Program (FLIP) is a database of Canadian food and beverage package labels by brand name that is updated every three years at the University of Toronto (U of T). The purpose of the FLIP is to provide detailed assessments of the nutrition information found on the labels of food products in the Canadian marketplace, and to monitor changes over time. To date, two phases of the FLIP have been completed. The first phase, with data acquired in 2010/2011 (FLIP 2010), is described elsewhere [28 (link)]. The second phase, FLIP 2013, is described in this paper. The FLIP 2013 contains nutrition information for 15,342 unique products. Data collection took a similar approach as the FLIP 2010 with regards to acquiring food information from the top selling grocery retailers, although it was fully digitalized to enhance the ease and efficiency of collection and analysis. Food composition database software (University of Toronto and Dietitians of Canada, Toronto, ON, Canada) (web and mobile) was developed for FLIP 2013 in collaboration with the Dietitians of Canada, resulting in a shorter and more efficient food collection and data processing approach.
Bernstein J.T., Schermel A., Mills C.M, & L’Abbé M.R. (2016). Total and Free Sugar Content of Canadian Prepackaged Foods and Beverages. Nutrients, 8(9), 582.
Publication 2016
Beverages Dietitian Food Food Labeling Nutrition Assessment
4
Comprehensive Food Nutrition Analysis
A total of 15 342 pre-packaged foods and beverages from the University of Toronto’s Food Label Information Program (FLIP) 2013 database were examined. Data were collected in 2013 across the four largest grocery chains in Canada, which represented 75·4 % of the grocery retail market share(29). The product information collected included the Nutrition Facts table and ingredient list, among other data. Details of FLIP 2013 are provided elsewhere(29). Foods were classified into the twenty-two food categories as per Schedule M of the Food and Drug Regulations (version in force between March 2012 and December 2016)(38). A total of 115 products were excluded from the analyses: fifty-five products owing to manufacturer errors in the nutrient declarations in the Nutrition Facts table (i.e. >20 % difference between the energy content declared and energy content calculated using Atwater factors for macronutrients) and sixty products that did not align with any Schedule M category (i.e. fifty-five meal replacements, four instant or dry yeast products and one natural health product). Thus, 15 227 unique products were available for analyses. To generate the classifications of the foods for each NP model, the 15 227 foods in FLIP 2013 were first classified independently by two authors (M. A. and K. M. D. for Nutri-Score; M.-E. L. and C. M. for HCST; and T. P. and M.-E. L. for other models) into the food categories specific to the NP models using information from the ingredient lists and/or pre-classifications from the Schedule M food categories or sugar-focused categories from Bernstein et al.(29). For all models, nutrient data for products in their ‘as consumed’ form were used. Subsequently, the nutrient criteria for each model were applied to the foods.
Poon T., Labonté M.È., Mulligan C., Ahmed M., Dickinson K.M, & L’Abbé M.R. (2018). Comparison of nutrient profiling models for assessing the nutritional quality of foods: a validation study. The British Journal of Nutrition, 120(5), 567-582.
Publication 2018
Beverages Carbohydrates Food Food Labeling Macronutrient Nutrients Saccharomyces cerevisiae Surgical Replantation
5
Accuracy of Automated Bite Detection Device
This study was approved by Clemson University’s Institutional Review Board for the protection of human subjects. All subjects signed an approved consent form prior to participating in data collection. Two experiments were performed in a laboratory setting, in order to evaluate the accuracy of the device to detect bites. A third experiment was performed in unrestricted settings, in order to examine the correlation of device detected bites to kilocalories per meal.
In the laboratory experiments, each subject sat at a table and slipped the prototype device over his or her dominant hand, onto the wrist. A video camera was placed on a tripod a few meters from where the subject sat, and aimed and zoomed in order to record the eating of the meal. The video was only used to establish ground truth for evaluating the automated detection of bites from the sensor data. A custom piece of software was written that enabled simultaneous playback of the sensor data with the video. A sync time was established by manually observing the video along with the sensor data, and manually aligning them, based upon a review of the initial motions of the subject. Figure 5 shows a picture of the environment in which subjects ate, with the video synchronized to the sensor data.
In the first experiment, a total of 51 subjects (14 male, 37 female, ages 18–38) were monitored eating 139 meals (21 subjects ate once and 30 subjects ate four times, with two meals excluded due to missing data). In each meal, the subject was given three servings of toasted Kellogg’s Eggo cinnamon toast waffles (276 g, 870 Cal) to eat. Each mini-waffle was cut in half, creating fixed-sized pieces for a total of 72 possible bites. The food was placed on a plate and a fork was provided. This meal was chosen because waffles are a common breakfast food, easy to cut into uniform size bites, and easy to prepare in the laboratory. Two-hundred and fifty milliliters of water were provided in a cup, but the intake of liquid was not considered for this test, because our goal was to determine an accuracy for our method under relatively ideal conditions. The subject was given the following instructions: “I would like you to eat as you usually would. However, please eat only one piece of waffle at a time. You can take as much time as you like to complete the meal, and I would like you to stop when you are full. It is not necessary to eat all of the food on the plate. Please do not engage me in conversation while eating the waffles. But, if you would like more waffles or more water, you may ask me to bring them to you. Additionally, please drink only with your non-dominant hand which is the one that you do not have the sensor on. Similarly, if you use the napkin, please do so with your non-dominant hand, the same one you are using to take a drink of water.”
In the second experiment, many of the conditions were relaxed. Participants brought their own foods and liquids and ate however they wanted. The experimental supervisor engaged subjects in casual conversation in order to make the eating experience as natural as possible. The experimenter sat at a desk next to the table. The sensor package was wired to a computer on that desk. The experimenter operated software on that computer to record the raw sensor data while the subject ate. A total of 47 subjects were monitored eating 49 meals (two people participated twice). The subjects ranged in age from 18 to 31; 23 of them were male and 24 of them were female. BMIs were neither measured nor restricted because the goal of this experiment was to first determine the accuracy of the method across other variables, namely unrestricted foods.
In the third experiment, all meals were eaten outside the laboratory in unrestricted settings. Tested environments included homes, offices, restaurants, and social settings (e.g. a party). Four different subjects (3 male, 1 female, ages 24–42) wore the device for a total of 54 meals. Subjects kept written food diaries noting the foods eaten and estimated or measured the amount of each food eaten. Kilocalories were estimated by laboratory personnel from the diaries using food packaging labels, website information (for restaurants), and calorie look-up tables. The goal of this experiment was to determine if there was any correlation between bite count as measured by our device and kilocalories. Obviously many confounding factors must be considered; this experiment was intended only to determine whether further study of the utility of this device for measuring kilocalories is warranted.
Dong Y., Hoover A., Scisco J, & Muth E. (2012). A New Method for Measuring Meal Intake in Humans via Automated Wrist Motion Tracking. Applied psychophysiology and biofeedback, 37(3), 205-215.
Publication 2012
Bites Cinnamomum verum Dental Occlusion Eating Ethics Committees, Research Females Food Food Labeling Laboratory Personnel Males Medical Devices Wrist

Most recents protocols related to «Food Labeling»

1
Greenhouse Gas Emissions of Foods and Meals
GHGE values for individual foods and ready meals expressed as gCO2 equivalents (gCO2e) were obtained from a range of open-access sources, including academic studies, retailers and producers published between 2008 and 2016(20 ,21 ), added to the NDNS nutrient databank(21 ,22 ). GHGE values were based on the emissions of six greenhouse gases which were converted into an equivalent amount of carbon dioxide (CO2 equivalent or CO2e), based on the relative global warming impact of each gas, and the final carbon footprint was expressed as the weight of carbon dioxide(20 ). The climate metric used to aggregate the GHGE measurements into CO2e were those reported by Department for Environment Food and Rural Affairs, UK(23 ). GHGE values from studies using complete cradle-to-grave life cycle analysis (LCA)(20 ), obtained following the international PAS 2050 standard(24 ), were selected where possible. We identified CO2e for 153 food and drink items in the NDNS nutrient databank, and where a GHGE value for a specific item was not available, reasonable substitute data were discussed and imputed by a team of three nutrition scientists, based on the food type, food group and compositional similarity of the products.
To estimate the GHGE for home-cooked meals, we estimated GHGE of the raw ingredients, establishing the weight of each ingredient and the weight of the whole cooked meal using Nutritics, which is nutrition management software for recipe and menu management, food labels, diet and activity analysis, and meal planning (Nutritics Ltd). Based on BBC Good Food(25 ) and Sainsbury’s recipes(26 ), we established cooking methods and times. For home-cooked meals requiring more than one cooking method, GHGE data for each cooking method were added together. In addition, we recorded the longest cooking time suggested for the frozen versions of ready meals. If there was more than one suggested cooking method (e.g. oven and microwave), data for both methods were recorded separately.
To estimate the full GHGE until serving the meal, we combined the GHGE from the recipes’ ingredients or ready meals (value up to the supermarket shelf), which include emissions due to land use change, farm-related emissions, animal feed, processing, transport, retail and packaging) with GHGE produced by the different cooking methods. For the latter, GHGE of cooking appliances were based on manufacturer information(27 ) and adjusted to the conversion factors provided by the UK government in 2021(28 ) and cooking time (Equation 1):
where a is the cooking time, b is the GHGE of cooking appliances based on manufacturer information and adjusted to the conversion factors given by the UK government 2021, and c is the weight of the recipe or ready meal product.
Aceves-Martins M., Denton P, & de Roos B. (2023). Ready meals, especially those that are animal-based and cooked in an oven, have lower nutritional quality and higher greenhouse gas emissions and are more expensive than equivalent home-cooked meals. Public Health Nutrition, 26(3), 531-539.
Publication 2023
Carbon dioxide Carbon Footprint Climate Diet Food Food Labeling Freezing Greenhouse Gases Microwaves Nutrients
2
Development and Validation of Fatty Acids Perception Questionnaire
A questionnaire was developed to inquire about consumers’ knowledge and safety perceptions regarding FAs. The questions/scales were adopted from previously validated surveys in published literature [8 , 9 , 13 , 16 (link), 17 (link)]. It consisted of 37 questions, categorized into 6 sub-sections. The original questionnaire was in English. For the ease of native Arabic speakers, the questionnaire was also translated to Arabic. Care was taken to include neutral words and avoid any leading sentences or answer choices.
The content of the questionnaire was validated by a panel of experts in the field of food safety who were fluent in both the languages. The experts were asked to note the time it took to complete the questionnaire and to rate the questions on a Likert scale of 1 to 10 for clarity, language, simplicity, ambiguity, the vocabulary used, and whether the question met the objectives of the study. Minor recommendations were made by the experts and any question which scored less than 70% in any of the studied aspects was either amended or removed. The total questionnaire response time was around 15–20 min. Besides this, a pilot study consisting of 30 participants was performed to ensure the reliability of the questionnaire. Data from the pilot test was not included in the results. A Cronbach’s Alpha of 0.8 was recorded and the questions were considered reliable.
The first section of the questionnaire included the socio-demographic information of the participants (6 questions). The second section was about the respondents’ information source concerning FAs and the level of trust they had on food labels (2 questions), while the third section pertained to the knowledge of participants regarding FAs (8 questions). The fourth section tackled the attitudes of consumers towards FAs (12 questions). The fifth section assessed the consumer behavior with FAs (2 questions), while the last section was about the respondents’ needs in terms of FAs labelling (6 questions). The full questionnaire is provided as a supporting file (S1 File).
Osaili T.M., Obaid R.S., Alkayyali S.A., Ayman H., Bunni S.M., Alkhaled S.B., Hasan F., Mohamad M.N, & Cheikh Ismail L. (2023). Consumers’ knowledge and attitudes about food additives in the UAE. PLOS ONE, 18(3), e0282495.
Publication 2023
Food Labeling Safety
3
Nutrition, Physical Activity, and Body Image Knowledge, Attitude, and Practice Questionnaire for Saudi Adolescent Girls

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Publication 2023
Adolescent Adolescents, Female Age Groups Beverages Body Weight Carbohydrates Cheese Chocolate Diabetes Mellitus, Non-Insulin-Dependent DNA Chips Food Food Labeling Milk, Cow's Obesity Program Development Programmed Learning Satisfaction SELL protein, human
4
Evaluating Nutrition, Physical Activity, and Behavioral Insights

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Publication 2023
Body Weight Food Food Labeling Target Population
5
Pregnant Women's Nutrition Knowledge, Attitudes, and Practices
The study obtained ethical approval number LL-KT-2021158 from the University of Chinese Academy of Science Shenzhen Hospital Research and Ethics Review Committee. From December 2020 to February 2021, a cross-sectional survey was conducted on pregnant females' knowledge, attitudes, and behaviors regarding food nutrition at the University of Chinese Academy of Science Shenzhen Hospital. Criteria for inclusion in the study: Shenzhen's permanent population, aged 18–40, and no other underlying chronic diseases before pregnancy. Since respondents with other chronic diseases will have different knowledge requirements for nutrition, they were not considered in this study due to their specificity. Therefore interviewees, diagnosed with diabetes or hypertension, or other basic chronic diseases before pregnancy, or endocrine-related diseases such as hyperthyroidism before pregnancy were excluded. We randomly select 2 obstetricians from the hospital. At the time of those two obstetricians' outpatient visits, the investigator surveyed each pregnant female who came to the clinic by convenience sampling. The investigators are nurses and dietitians who have undergone uniform training. They distribute questionnaires to pregnant females who are undergoing obstetric check-ups in the hospital and supervise the completion of survey subjects. Informed consent preceded all interviews.
This questionnaire measures the KAP developed by the Institute of Nutrition and Food Safety of China Center for Disease Control and Prevention (Boheng Liang, 2016 ). This questionnaire was selected because it was suitable for the population of this study and had good reliability and validity. The Cronbach's α of the whole questionnaire was 0.90, and the retest reliability of knowledge, attitudes, and practices were all above 0.8. In addition, the content validity was also good, with a mean correlation coefficient of 0.65 between each item score and the total score. The content and main indicators of the questionnaire are as follows:

Basic information: Age, local Household Registration, Height, Gestational weeks, Times of Pregnant, Times of Birth, Times of Abortion, Weight before pregnancy, Current weight, Education Degree, Career, and Monthly income of Family.

Knowledge survey of nutrition labeling and basic nutrition: knowledge of basic nutrients (7 questions in total) and knowledge about nutrition labeling (4 questions in total). There are 11 questions in total.

Survey of attitudes and practice on nutrition labels: pregnant females' attitudes towards food nutrition labels, dietary behaviors, and whether to use nutrition labels when shopping, etc. There are 9 questions in total.

Scoring strategy: Single item worth 1 score, multiple choices all right worth 2 scores, part is right worth 1 score, and multiple choices answered wrongly worth 0 points.
Wang W.C., Zou S.M., Ding Z, & Fang J.Y. (2023). Nutritional knowledge, attitude and practices among pregnant females in 2020 Shenzhen China: A cross-sectional study. Preventive Medicine Reports, 32, 102155.
Publication 2023
Birth Chinese Diabetes Mellitus Diet Dietitian Disease, Chronic Endocrine System Diseases Food Food Labeling High Blood Pressures Households Hyperthyroidism Induced Abortions Nurses Nutrients Nutritional Requirements Obstetrician Outpatients Pregnancy Pregnant Women

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More about "Food Labeling"

Food Labeling is the process of providing critical information about food products, including their contents, nutritional value, and other attributes.
This encompasses mandatory and voluntary labeling requirements, as well as the use of claims, symbols, and marketing techniques to convey data to consumers.
Proper food labeling empowers people to make informed choices about the foods they purchase and consume, supporting public health and safety goals.
Key aspects of Food Labeling include nutrition facts, ingredient lists, allergen warnings, and other information designed to enhance transparency and consumer awareness.
Researchers can leverage powerful tools like PubCompare.ai to optimize their food labeling research, utilizing AI-driven comparison and reproducibility features to locate relevant protocols from literature, pre-prints, and patents, and identify the best protocols and products for their needs.
Food labeling research may also involve the use of analytical software and tools like SPSS version 25, SAS software version 9.4, Leco TruMac, Diamidino-2-phenylindole dihydrochloride (DAPI), Food Processor SQL software, Stata version 14, MACSQuant Analyzer 10, SPSS Statistics, SPSS Statistics for Windows, Version 21.0, and Stata/SE.
By combining the insights from these resources, researchers can simplify their food labeling research and make more informed decisions.