Factors associated with food label use: focus on healthy aspects of orthorexia and orthorexia nervosa.

PURPOSE: This study aimed to investigate the potential relationships between the use of different section of food label, and healthy and pathological aspects of orthorexia among adults. METHODS: This cross-sectional study was conducted using an online survey (n = 1326). Inclusion criteria were being 19-64 years and graduated from at least primary school. Pregnant and lactating women were excluded. Data were collected using questionnaire including socio-demographic variables, lifestyle factors, body weight and height, frequency of reading different sections of food label ("always", "when buying a food for the first time", "when comparing similar packaged foods", "rarely", "never"), food label literacy, and Teruel Orthorexia Scale. Participants were categorized as nutrition facts panel-users, ingredients list-users or claim-users if they read at least one item from the relevant parts. RESULTS: The proportions of nutrition facts, ingredients list, and claims sections users were 72.3%, 76.3%, and 79.9%, respectively. Both healthy and pathological aspects of orthorexia were associated with reading food labels. The healthy orthorexia had the strongest association with using the ingredients list (OR 1.76, 95% CI 1.41-2.20), whereas the orthorexia nervosa showed the highest association with using nutrition facts panel (OR 1.48, 95% CI 1.20-1.81). While women, physically active participants and those with higher food label literacy were more likely to use all sections of food labels; older age, having children, and chronic disease increased the likelihood of using claims and ingredients list (p < 0.05). Besides, following a diet was associated with higher use of nutrition facts and ingredients list (p < 0.05). CONCLUSIONS: The study demonstrates that food label users have higher orthorexia tendencies compared to non-users. Of the food label sections, healthy orthorexia showed the strongest association with use of the list of ingredients, while pathological orthorexia showed the strongest association with use of the nutrition facts panel. LEVEL OF EVIDENCE: Level V, cross-sectional study.

NutriGreen image dataset: a collection of annotated nutrition, organic, and vegan food products.

Introduction: In this research, we introduce the NutriGreen dataset, which is a collection of images representing branded food products aimed for training segmentation models for detecting various labels on food packaging. Each image in the dataset comes with three distinct labels: one indicating its nutritional quality using the Nutri-Score, another denoting whether it is vegan or vegetarian origin with the V-label, and a third displaying the EU organic certification (BIO) logo. Methods: To create the dataset, we have used semi-automatic annotation pipeline that combines domain expert annotation and automatic annotation using a deep learning model. Results: The dataset comprises a total of 10,472 images. Among these, the Nutri-Score label is distributed across five sub-labels: Nutri-Score grade A with 1,250 images, grade B with 1,107 images, grade C with 867 images, grade D with 1,001 images, and grade E with 967 images. Additionally, there are 870 images featuring the V-Label, 2,328 images showcasing the BIO label, and 3,201 images without before-mentioned labels. Furthermore, we have fine-tuned the YOLOv5 segmentation model to demonstrate the practicality of using these annotated datasets, achieving an impressive accuracy of 94.0%. Discussion: These promising results indicate that this dataset has significant potential for training innovative systems capable of detecting food labels. Moreover, it can serve as a valuable benchmark dataset for emerging computer vision systems.

Investigation of the 1-week effect of traffic light nutrition labeling on diet selection among Japanese university students: a randomized controlled trial.

BACKGROUND: The method of displaying nutrition information labels on the front of food packaging (FOP: Front of Pack) has been implemented worldwide to prevent lifestyle-related diseases. This study aimed to investigate whether the use of the UK's Traffic Light Food (TLF) label, known as the FOP label, influences the dietary choices of Japanese youth and promotes healthy dietary choices. METHODS: Diet selection was performed for one week each during the baseline and intervention periods. During the intervention period, TLF labels were displayed on meal images of the intervention group. Participants chose what they would like to have for dinner of the day from 15 images. Each meal was scored based on the color of the nutrition label, and a comparison between groups was made to determine whether TLF labeling influenced meal selection for dinner. The psychological stress caused by the presence or absence of nutrition labels and nutritional components when choosing meals was also evaluated. RESULTS: A total of 69 participants were randomly assigned to two groups. Dietary choice scores indicated that the TLF-labeled group made significantly healthier dietary choices than the unlabeled group. Additionally, the TLF-labeled group showed a significant increase in the percentage of people conscious of nutritional components when choosing meals. Furthermore, a significant increase in the number of people conscious of protein, a nutritional ingredient not indicated on the TLF label, was observed. During the test period, no difference in psychological stress caused by the presence and absence of the TLF labels was observed. CONCLUSIONS: The use of TLF labels also encouraged healthy dietary choices among Japanese university students. The use of FOP nutrition labels should be considered in Japan to prevent lifestyle-related diseases through healthy dietary choices. TRIAL REGISTRATION: UMIN Clinical Trials Registry Number: UMIN000047268. Registered March 23, 2022.

Establishing an EU-wide front-of-pack nutrition label: Review of options and model-based evaluation.

This paper reviews the effectiveness of four types of front-of-pack nutrition labels (FoPLs) in influencing calorie purchases. The four FoPL types are poised for unified implementation across European countries. Further, this study extends its analysis to evaluate the impacts of the voluntary adoption of these FoPLs within 27 EU nations. Nutri-Score displays higher potential for yielding positive health and economic outcomes, compared with other FoPLs. Across EU countries, Nutri-Score is projected to avert nearly two million cases of non-communicable diseases, in total, between 2023 and 2050. Keyhole demonstrates effects of a similar magnitude but with no statistical significance. Nutri-Repere shows smaller impacts, while Nutri-Couleurs has non-significant effects. Nutri-Score is projected to significantly lower annual healthcare spending by 0.05%, whereas the other labels have negligible impacts. By reducing cases of disease, FoPLs have the potential to improve employment and work productivity. Nutri-Score surpasses the other labels with an estimated annual gain of 10.6 full-time equivalent workers per 100,000 individuals of working age across EU countries. In all, mandatory implementation of any of the four labels would lead to greater effects than those obtained with a voluntary implementation, providing evidence to inform legislation proposal for an EU-wide nutrition labelling system.

Factors Contributing to the Comprehensive Use of Food Labels in Jordan.

Food labels are low-cost, informational tools that can help curb the spread of diet-related non-communicable diseases. This study described consumers' knowledge, attitudes, and practices related to food labels in Jordan and explored the relationship between knowledge and attitude with comprehensive use of food labels. A cross-sectional, online survey assessed Jordanian adult consumers' ability to comprehend the nutritional contents of food labels (knowledge score), their attitudes towards food labels (attitude scale), and how frequently they used different parts of food labels (practice scale). Multivariate logistic regression models assessed predictors of comprehensive use of food labels. A total of 939 adults participated in the study. Total mean scores for the practice scale (14 questions), attitude scale (8 questions), and knowledge score (4 questions) were 49.50 (SD, 11.36; min, 5; max, 70), 29.70 (SD, 5.23; min, 5; max, 40), and 1.39 (SD, 1.33; min, 0; max, 4), respectively. Comprehensive users of food labels (26.4%) were more likely female, responsible for grocery shopping, and had higher mean knowledge and attitude scores. Jordanian consumers seem to have good practices and attitudes related to food label use but suboptimal knowledge regarding content. Future interventions should focus more on enhancing knowledge and awareness related to food labels.

Memory Abilities Are Selectively Related to Food Label and Numeracy Nutrition Skills.

OBJECTIVE: We investigated the relationship between nutrition literacy, diet quality, carotenoid status, and cognition. METHODS: Adults aged 37.5 ± 17.0 years (n = 52) completed the 42-item Nutrition Literacy Assessment Instrument (NLit). The Dietary History Questionnaire III was analyzed to determine the Healthy Eating Index. Skin carotenoids were assessed as a diet quality biomarker. Selective attention, relational memory, and pattern separation abilities were assessed using the flanker, spatial reconstruction, and mnemonic similarity tasks, respectively. Statistical adjustments included age, sex, education, and body mass index. RESULTS: No correlations were observed for NLit scores and NLit subscales with Healthy Eating Index and skin carotenoid status. However, the NLit's food label and numeracy subscale was related to greater pattern separation abilities (ρ = 0.33, r2 = 0.11, P = 0.03). CONCLUSIONS AND IMPLICATIONS: Comprehension of food labels and numeracy information was associated with memory abilities. Future work is needed to test whether targeting working memory and attentional processes during memory retrieval in larger samples may facilitate the acquisition of nutrition knowledge.

Accelerating the Classification of NOVA Food Processing Levels Using a Fine-Tuned Language Model: A Multi-Country Study.

The consumption and availability of ultra-processed foods (UPFs), which are associated with an increased risk of noncommunicable diseases, have increased in most countries. While many countries have or are planning to incorporate UPF recommendations in their national dietary guidelines, the classification of food processing levels relies on expertise-based manual categorization, which is labor-intensive and time-consuming. Our study utilized transformer-based language models to automate the classification of food processing levels according to the NOVA classification system in the Canada, Argentina, and US national food databases. We showed that fine-tuned language models using the ingredient list text found on food labels as inputs achieved a high overall accuracy (F1 score of 0.979) in predicting the food processing levels of Canadian food products, outperforming traditional machine learning models using structured nutrient data and bag-of-words. Most of the food categories reached a prediction accuracy of 0.98 using a fined-tuned language model, especially for predicting processed foods and ultra-processed foods. Our automation strategy was also effective and generalizable for classifying food products in the Argentina and US databases, providing a cost-effective approach for policymakers to monitor and regulate the UPFs in the global food supply.

Estado de las Políticas de regulación para el consumo de azúcar y para la comida chatarra en 3 países de América Latina

Introducción: El consumo de bebidas azucaradas y alimentos no saludables es un problema de Salud Pública y de las Políticas Públicas que buscan reducir el impacto de las Enfermedades No Trasmisibles (ENT) en América Latina. Objetivo: identificar las Políticas Públicas generadas en Colombia, Argentina y Chile para la regulación del consumo de azúcar y comida chatarra. Materiales y Métodos: Análisis exploratorio a través de una revisión de literatura y revisión de documentos normativos. Resultados: Colombia en el año 2021 sancionó la Ley 2120 denominada "Ley de Comida Chatarra", que promueve el acceso a información necesaria para fomentar entornos alimentarios saludables y prevenir las ENT, que incluye nuevas normas para el etiquetado de alimentos y describir sus características al consumidor y así limitar el consumo de alimentos procesados y ultraprocesados. Chile fue pionero en el desarrollo de los sellos de advertencia y desde el año 2014, aplica un impuesto adicional a las bebidas no alcohólicas. Argentina en 2021, sancionó la ley que obliga a la industria de alimentos a disponer de etiquetas en los envases que alerten al consumidor sobre los excesos de azúcares, grasas y sodio Conclusiones: Los tres países investigados cuentan con políticas de etiquetado de alimentos procesados, y en Chile existen impuestos a las bebidas azucaradas. Es importante estudiar el impacto de dichas políticas en la prevalencia y severidad de las ENT y de la caries.

Abstract: The consumption of sugar-sweetened beverages and unhealthy foods is a public health problem that has become a regulatory issue for the public policies aimed at reducing the impact of Non Communicable Diseases (NCDs) in Latin America. Objective: to identify the Public Policies in Colombia, Argentina and Chile for the regulation of the consumption of sugar and junk food. Materials and methods. Exploratory analysis through a literature and policy documents review. Results: In 2021, Colombia approved the Law 2120 called "Junk Food Law" promoting public access to information for a healthy food environment to prevent NCDs. It includes food labeling for overweight and obesity prevention and establishes a specific labeling regulation for processed and ultra-processed food and sweet drink products. Chile was pioneer in the development of warning stamps on food packages and since 2014 also taxes sweet non-alcoholic beverages. In 2021, Argentina approved a law to add labels on food packages to alert consumers about excess of sugars, fats and sodium. Conclusions: The three investigated countries have labeling policies for processed food, and Chile puts taxes on sugary drinks. It is important to study the impact of these policies on NCD prevalence, severity and on dental caries.

Cross-Classification Analysis of Food Products Based on Nutritional Quality and Degree of Processing.

This study aims to compare the classification of foods available in the Portuguese market using Nutri-Score and NOVA classifications and to analyse their ability to discriminate the fat, saturated fat, sugar, and salt content of foods. A sample of 2682 food products was collected. The nutritional quality of foods was established using the Nutri-Score, classifying them into five categories (from A to E). The NOVA classification was used to classify foods according to the degree of food processing into unprocessed/minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods (UPF). The nutritional content of food products was classified using a Multiple Traffic Light label system. It was observed that 73.7% of UPF were classified as Nutri-Score C, D, and E, 10.1% as Nutri-Score A, and 16.2% as Nutri-Score B. Nutri-Score was positively correlated with NOVA classification (ρ = 0.140, p < 0.001) and with the Multiple Traffic Lights system (ρTotal Fat = 0.572, ρSaturated Fat = 0.668, ρSugar = 0.215, ρSalt = 0.321, p < 0.001). NOVA classification negatively correlated with the Multiple Traffic Lights system for total fat (ρ = -0.064, p < 0.001). Our findings indicate the presence of many UPFs in all Nutri-Score categories. Since food processing and nutritional quality are complementary, both should be considered in labelling.

Food allergy risks and dining industry - an assessment and a path forward.

Food allergies have increased in prevalence over the last few decades and continue to grow. Consumption of even trace amounts of common foods can cause a rapid allergic reaction (generally within minutes) which can be mild to severe to even life-threatening. Eating at restaurants poses a risk of allergic reactions for those with food allergies due to inadequate, inconsistent labeling of allergens in foods. Here, we review food labeling rules and practices in the restaurant industry and compare and contrast it with food labeling for prepackaged foods. We review global and United States trends, and provide a brief historical overview. The paper describes the key legal and economic motivations behind restaurant food labeling. Next, we describe novel risk-driven policies and new biotechnologies that have the potential to change food labeling practices worldwide. Finally, we outline desirable federal regulations and voluntary information disclosures that would positively impact the public health aspects of restaurant food labeling and improve the quality of life for people with severe food allergies.

Assessment of food labeling knowledge and associated reading barriers among patients with diabetes.

Background: The most challenging part of diabetes management for a patient with diabetes is selecting a healthy diet. The purpose of this study is to evaluate participants' knowledge of food labels, to find out the relationship between the type of diabetes mellitus (DM) and knowledge score of food labels, and to explore the barriers that prevent patients from reading food labels. Methodology: This observational study was conducted on patients with type 1 or type 2 diabetes using a validated self-administered questionnaire. The study was conducted at diabetes clinics at King Khalid University Hospital and King Abdul-Aziz University Hospital, Riyadh, Saudi Arabia, from November 2019 to February 2020. Data were analyzed using SPSS. Results: A total of 310 participants were enrolled in this study, of which 50.3% had type 1 DM, and more than half of them were female (51.6%). Patients with type 1 DM had higher mean declarative and applied knowledge scores than those with type 2 DM, regardless of whether they were taking pre meals insulin or not. The highest proportion (39.9%) had difficulty in understanding the content of the nutrition labels, and some of them (37.2%) did not receive any educational session about it. Only 9.5% of the participants did not have any difficulties in reading food labels. Conclusion: Patients with both types of diabetes tended to have poor total knowledge about food labels and faced difficulties in reading them. Provided educational sessions by primary health care and specialized physician and DM educator about food labels are recommended to help them to choose food properly.

Processing and Nutritional Quality of Breakfast Cereals Sold in Italy: Results from the Food Labelling of Italian Products (FLIP) Study.

This study aimed to compare the level of processing (as assessed by the NOVA classification) and the nutritional quality (as assessed by nutrition values, Nutri-Score and NutrInform battery) of breakfast cereals currently on the Italian market. A total of 349 items were found, mostly belonging to the NOVA 4 group (66.5%) and to Nutri-Score C and A (40% and 30%, respectively). The NOVA 4 products showed the highest energy, total fat, saturates, and sugar content per 100 g and had the highest number of items with Nutri-Score C (49%) and D (22%). Conversely, NOVA 1 products had the highest content of fibre and protein, the lowest amounts of sugars and salt, and 82% of them were Nutri-Score A, while few Nutri-Score B and C were found. Differences were attenuated when products were compared for their NutrInform battery, with NOVA 4 items showing only slightly fuller batteries for saturated fats, sugar, and salt than NOVA 1 and NOVA 3 products. Overall, these results suggest that the NOVA classification partially overlaps with systems based on the nutritional quality of foods. The lower nutritional quality of NOVA 4 foods may at least partially explain the association found between the consumption of ultra-processed foods and the risk of chronic diseases.

Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach.

BACKGROUND: Micronutrient deficiencies represent a major global health issue, with over 2 billion individuals experiencing deficiencies in essential vitamins and minerals. Food labels provide consumers with information regarding the nutritional content of food items and have been identified as a potential tool for improving diets. However, due to governmental regulations and the physical limitations of the labels, food labels often lack comprehensive information about the vitamins and minerals present in foods. As a result, information about most of the micronutrients is absent from existing food labels. OBJECTIVE: This paper aims to examine the possibility of using machine learning algorithms to predict unreported micronutrients such as vitamin A (retinol), vitamin C, vitamin B1 (thiamin), vitamin B2 (riboflavin), vitamin B3 (niacin), vitamin B6, vitamin B12, vitamin E (alpha-tocopherol), vitamin K, and minerals such as magnesium, zinc, phosphorus, selenium, manganese, and copper from nutrition information provided on existing food labels. If unreported micronutrients can be predicted with acceptable accuracies from existing food labels using machine learning predictive models, such models can be integrated into mobile apps to provide consumers with additional micronutrient information about foods and help them make more informed diet decisions. METHODS: Data from the Food and Nutrient Database for Dietary Studies (FNDDS) data set, representing a total of 5624 foods, were used to train a diverse set of machine learning classification and regression algorithms to predict unreported vitamins and minerals from existing food label data. For each model, hyperparameters were adjusted, and the models were evaluated using repeated cross-validation to ensure that the reported results were not subject to overfitting. RESULTS: According to the results, while predicting the exact quantity of vitamins and minerals is shown to be challenging, with regression R2 varying in a wide range from 0.28 (for magnesium) to 0.92 (for manganese), the classification models can accurately predict the category ("low," "medium," or "high") level of all minerals and vitamins with accuracies exceeding 0.80. The highest classification accuracies for specific micronutrients are achieved for vitamin B12 (0.94) and phosphorus (0.94), while the lowest are for vitamin E (0.81) and selenium (0.83). CONCLUSIONS: This study demonstrates the feasibility of predicting unreported micronutrients from existing food labels using machine learning algorithms. The results show that the approach has the potential to significantly improve consumer knowledge about the micronutrient content of the foods they consume. Integrating these predictive models into mobile apps can enhance their accessibility and engagement with consumers. The implications of this research for public health are noteworthy, underscoring the potential of technology to augment consumers' understanding of the micronutrient content of their diets while also facilitating the tracking of food intake and providing personalized recommendations based on the micronutrient content and individual preferences.

Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods.

BACKGROUND: Food categorization and nutrient profiling are labor intensive, time consuming, and costly tasks, given the number of products and labels in large food composition databases and the dynamic food supply. OBJECTIVES: This study used a pretrained language model and supervised machine learning to automate food category classification and nutrition quality score prediction based on manually coded and validated data, and compared prediction results with models using bag-of-words and structured nutrition facts as inputs for predictions. METHODS: Food product information from University of Toronto Food Label Information and Price Database 2017 (n = 17,448) and University of Toronto Food Label Information and Price Database 2020 (n = 74,445) databases were used. Health Canada's Table of Reference Amounts (TRA) (24 categories and 172 subcategories) was used for food categorization and the Food Standards of Australia and New Zealand (FSANZ) nutrient profiling system was used for nutrition quality score evaluation. TRA categories and FSANZ scores were manually coded and validated by trained nutrition researchers. A modified pretrained sentence-Bidirectional Encoder Representations from Transformers model was used to encode unstructured text from food labels into lower-dimensional vector representations, followed by supervised machine learning algorithms (i.e., elastic net, k-Nearest Neighbors, and XGBoost) for multiclass classification and regression tasks. RESULTS: Pretrained language model representations utilized by the XGBoost multiclass classification algorithm reached overall accuracy scores of 0.98 and 0.96 in predicting food TRA major and subcategories, outperforming bag-of-words methods. For FSANZ score prediction, our proposed method reached a similar prediction accuracy (R2: 0.87 and MSE: 14.4) compared with bag-of-words methods (R2: 0.72-0.84; MSE: 30.3-17.6), whereas structured nutrition facts machine learning model performed the best (R2: 0.98; MSE: 2.5). The pretrained language model had a higher generalizable ability on the external test datasets than bag-of-words methods. CONCLUSIONS: Our automation achieved high accuracy in classifying food categories and predicting nutrition quality scores using text information found on food labels. This approach is effective and generalizable in a dynamic food environment, where large amounts of food label data can be obtained from websites.

Indian adolescents' perceptions of packaged food and food labels - A qualitative inquiry.

In the last two decades, India has witnessed a dramatic rise in the consumption of packaged foods, especially among adolescents. Food labelling is often recognised as an instrumental population-based approach which can enable consumers to make informed food choices by providing all the necessary details about the food product on the packaging. In the Indian context, where adolescent obesity and the ensuing non-communicable diseases are escalating, it is crucial to understand adolescents' views on the use of non-nutritious packaged foods and food labels. Therefore, the aim of this qualitative inquiry was to explore Indian adolescents' perceptions regarding the consumption of packaged food and use of food labels. Convenience sampling was employed to recruit 29 boys and 15 girls (aged 10-19 years) from rural Varanasi, India. Semi-structured, face-to-face interviews were conducted in the local language. Interview recordings were transcribed verbatim and translated to English. Narrative data were subjected to thematic analysis using NVivo software program. The adolescents reported frequent consumption of packaged food like potato chips, biscuits, chocolates, deep-fried Indian snacks both at school and home. Packaged foods were regarded as tasty, safe, and fresh. Brand name, date of expiry and cost were often read by the study respondents. Nevertheless, they reported difficulty in understanding other components of the label (e.g., nutrition information) and they were not aware of the negative impact of consuming unhealthy packaged foods. Nearly all the respondents insisted that food labels should be written in the local language for easy understanding. These emerging findings underscore the need to design school-based food literacy programs for vulnerable Indian adolescents to address obesity and diet-related complications in early adulthood.

Acceptance and effectiveness of the Healthier Choice Logo (HCL) among food industries in Malaysia

@#Introduction: The Healthier Choice Logo (HCL) was introduced in 2017 by the Ministry of Health Malaysia. This paper analysed acceptance of HCL, effectiveness of HCL in encouraging healthier product reformulation, and factors affecting reformulation among food industries. Methods: An online self-administered questionnaire consisting of four sections utilising multiple choice and 5-point Likert scale questions was distributed to food industries in Malaysia. Sample size calculation yielded 100 respondents. Results: Food industries had a higher acceptance of the processes and requirements involved in HCL implementation. HCL was highly effective in encouraging product reformulation among food industries in Malaysia. Meeting consumer demand, improving brand image, public health, more awareness around nutrition labelling, logo and national nutrition target, more technical knowledge and budget were found to motivate healthier product reformulation. However, product suitability, consumer acceptability, difficulties maintaining taste and shelf life, and limited budget were the challenges faced in product reformulation. There was no correlation between HCL acceptance and factors encouraging or inhibiting reformulation. Conclusion: These findings are expected to help relevant authorities or stakeholders make changes, if necessary, towards processes and requirements involved in HCL application to ensure wider HCL implementation. Future research should identify the relationship between HCL implementation and public health improvement among the Malaysian population.

Food Labeling Systems in Japan: Nutrition and Health Claims.

Japan is one of the countries with the highest life expectancy in the world, and maintaining good health is the key component to extend healthy life expectancy. According to World Health Organization, self-care is the ability to promote health, prevent disease, and maintain health. Food labels play an important role in healthy dietary habits for self-care. Food labels comprise nutrition claims and health claims. In Japan, the nutrition component exhibits the contents of energy, protein, fat, carbohydrates, and salt equivalent, which are mandatory, and saturated fat and dietary fiber, which are recommended. On the other hand, the health portion exhibits health maintenance and health promotion by nutrients/ingredients in foods. Under the Food Labeling Act, foods allowed to display health claims, are specified as "Foods with Health Claims" in Japan. The Consumer Affairs Agency reported that most consumers could not utilize food labels, even though the nutrition label serves as a parameter for a healthier food choice. In this regard, front-of-pack labels (FOPLs), are a beneficial tool which encourages people to choose healthier foods, and conduct self-care. However, FOPLs is still unfamiliar in Japan, so we have to investigate which nutrients and which type of FOPLs are the best for Japanese people. In addition to FOPL promotion, education is important to get consumers using food labels for extending their healthy life expectancy.

The Impact of Interpretive Packaged Food Labels on Consumer Purchase Intention: The Comparative Analysis of Efficacy and Inefficiency of Food Labels.

The objectives of this study are twofold. Firstly, the current study elucidates the impact and efficacy of food labels in developing consumers' attitudes and intentions towards the selection of nutritional food. Secondly, the inefficacy of labels in developing consumers' attitudes and intentions towards healthy packaged food selection is demonstrated. The supportive theories of the current model are those of reasoned action and protection motivation. The data of 797 respondents have been collected from four major grocery stores in Pakistan. The structural equation model has been employed for the analysis of data. The results indicate that the efficacy of food labels has a positive significant effect on attitudes towards familiar and unfamiliar foods. In contrast to this, inefficacy in labelling has shown a positive significant effect on familiar foods but is insignificant for unfamiliar foods. The user-friendly food labels significantly affect unfamiliar foods in terms promoting consumer attitudes. Reciprocally, the inefficacy of labels creates a hindrance to the reading of unfamiliar labels while purchasing food items. The study findings reveal the fact that food label information and its format influences consumer attitudes and intentions at the point of purchase.

Preparation and Testing of Food Freshness Indicators: an Application-Oriented Learning Module Integrating Basic Concepts of Microbiology and Chemistry Laboratory.

We describe a novel 4-day food microbiology laboratory learning module for a first-year, introductory undergraduate course. In the module, the students test the suitability of four different pH indicator dyes as freshness indicators for dairy products. The concepts of serial dilutions, microbial growth, microbial metabolism, pH as well as pKa, and basic microbial laboratory practices are a part of the designed activity. It is a relatively inexpensive module and can be executed with little infrastructural support. It can be delivered as a stand-alone structured inquiry. The associated variables and applications indicate that the activity can perhaps be developed into a more elaborate course-based undergraduate research experience, or CURE.