Food Recommendations Research Articles

Advisory Committee on the Microbiological Safety of Food recommendations for standardisation of antimicrobial resistance terminology.

Advisory Committee on the Microbiological Safety of Food recommendations for standardisation of antimicrobial resistance terminology.

AI Impact on Hotel Guest Satisfaction via Tailor-Made Services: A Case Study of Serbia and Hungary

This study examines the level of implementation of artificial intelligence (AI) in the personalization of hotel services and its impact on guest satisfaction through an analysis of tourists’ attitudes and behaviors The focus of the research is on how personalized recommendations for food and beverages, activities, and room services, delivered by trustworthy AI systems, digital experience, and the perception of privacy and data security, influence overall guest satisfaction. The research was conducted in Serbia and Hungary, using structural models to assess and analyze direct and indirect effects. The results show that AI personalization significantly contributes to guest satisfaction, with mediating variables such as trust in AI systems and technological experience playing a key role. A comparative analysis highlights differences between Hungary, a member of the European Union, and Serbia, a country in transition, shedding light on specific regulatory frameworks and cultural preferences in these countries.

Packaging optimization model using cost-based behavioral perspective approach in food and beverage industry

The high competition in the food and beverage industry in Indonesia, where packaging has a very important function, with the main function to protect the product, as well as being a factor in attracting consumers' interest in making purchases. Packaging production is dominated by several companies, which when categorized are divided into middle-up manufacturing and middle-low manufacturing. The research is necessary to create an optimal packaging design model by considering the culture and habits of consumer. Capturing customer behavior and costs has a control function. Ethnographic studies are used to explore packaging elements that are important to consumer and Structural Equation Modeling (SEM) was used for data processing. The result provides a conceptual model as well as recommendations for food and beverage manufacturers in terms of the packaging used. Seven elements of packaging that deserve primary attention: design, image, typography, color, size, material and quality. Using habit and cost factors has the control variables, with maximum profit as the objective function. Recommendations for middle-up manufacturing are focused on four packaging elements: design, image, color and quality. It is recommended to maximize strategies for customer retention and improving customer loyalty. Middle-low manufacturing is focused on four packaging elements: color, size, material and quality. It is recommended to maximize strategies to encourage repeat business and boost productivity. The features identified in this research are very possible to be implemented by food and beverage manufacturer, along with recommended strategies. For example, at the middle and low manufacturing level, it is very important to pay attention to product availability in several sizes

Foodies Full Stack Web Application: Connecting You to Your College Canteens

The "Foodies Full Stack Web Application" is designed to revolutionize the way college students interact with their campus canteen services by providing real-time access to menus, nutritional information, and personalized food recommendations. This project aims to bridge the gap between canteen owners and students, offering seamless communication and better decision-making for daily meals. By leveraging the power of the Spoonacular API, the platform can not only display available menu items but also offer detailed nutritional data, which is crucial for students with dietary preferences or restrictions. Additionally, the application uses machine learning to suggest dishes based on individual preferences, ensuring a tailored dining experience for each user. This project has been developed using modern web development technologies, integrating both front-end and back-end functionalities to create a responsive, user-friendly platform. The application has the potential to enhance user satisfaction, improve the efficiency of canteen operations, and promote healthier eating habits among students. This paper outlines the design, implementation, and testing of the Foodies Web Application, while also discussing future improvements and scalability.

NUTRIFLEX

The NUTRIFLEX project is a comprehensive AI-powered application designed to provide personalized nutrition and health management solutions. The goal of the project is to assist individuals in optimizing their diet based on their unique biological and emotional needs, including gut health, mood, and overall well-being. By combining artificial intelligence (AI) and user input, NUTRIFLEX offers tailored meal plans, calorie predictions, mood-enhancing food recommendations, and a gamified approach to motivate users to maintain a healthy lifestyle. The problem addressed by NUTRIFLEX is the growing challenge people face in maintaining a balanced and healthy diet. Modern lifestyles, characterized by fast-paced routines and unhealthy food choices, contribute to poor physical and mental health. Many individuals struggle to find the right balance between their nutritional intake and their health goals, whether it's weight loss, muscle gain, or general wellness. Furthermore, gut health, which plays a critical role in overall health, is often neglected in conventional meal planning applications. Mood fluctuations also significantly impact people's food choices, leading to stress eating or energy dips. NUTRIFLEX solves these problems by offering a holistic, AI-driven solution that adapts to users' unique needs. By using machine learning models, NUTRIFLEX predicts the calorie content of meals and creates personalized meal plans based on user-specific data such as Basal Metabolic Rate (BMR) and Total Energy Expenditure (TEE). The application also provides mood-based meal suggestions, helping users manage their emotional state through food choices that boost energy, focus, and relaxation. One of the most innovative features of NUTRIFLEX is its gut health functionality. By allowing users to input microbiome data, the app generates personalized meal recommendations designed to improve gut health, which in turn enhances overall physical and mental well-being. This focus on gut health distinguishes NUTRIFLEX from traditional meal planners. In addition, NUTRIFLEX integrates gamification to keep users engaged and motivated. Users can set health goals, track their progress, and earn rewards, making the health journey enjoyable and sustainable.

KAWANKULINER: PERSONALISED FOOD RECOMMENDATION APP USING BMR AND TDEE FOR OPTIMAL DAILY NUTRITION

People often have difficulty in determining the type of food that is suitable for the composition of their body. This is due to the many types of food available, as well as the lack of information about the nutritional content of each type of food. By fulfilling the daily calorie and macro-nutrient needs required by the body, its metabolism can be maintained, preventing various health problems such as malnutrition. Based on the results of the Basic Health Research in 2018 malnutrition is one of the most serious problems in Indonesia. Several studies related to calculating calorie estimates using the BMR approach with the utilization of technology have been conducted.However, these studies were limited to only displaying the amount of daily calorie needs that were not accompanied by what foods were needed to meet these daily needs. The purpose of this research is to build an app to help individuals understand their daily calorie needs based on their Basal Metabolic Rate (BMR) and total daily expenditure energy (TDEE), using smart scale to provide body composition information and spoonacular API to provide food recommendations that match their energy needs and give them recipes on how to make their food meanwhile waterfall model is used as a software development method. The test results show that the app meets the functional and usability requirements well, with a high accuracy rate of 99.86% for calories, protein, fat, and carbohydrates. In terms of user response, 13 respondents had a very positive response, and considered the app useful for making it easier to get information about their calorie and macro-nutrient needs, as well as provide food recommendations based on their daily needs.

What Teenagers Eat and What Specific Food Recommendations Can Be Made to Improve Their Poor Diet Quality

What Teenagers Eat and What Specific Food Recommendations Can Be Made to Improve Their Poor Diet Quality

Food intake in an Australian Aboriginal rural community facing food and water security challenges: A cross-sectional survey.

Researchers were invited by Aboriginal leaders to collaborate on this study which aimed to assess food intake in the Walgett Aboriginal community to inform long-term community-led efforts to improve food and water security and nutrition. Aboriginal adults living in or near Walgett, a remote community in north-west NSW, Australia, completed an adapted Menzies Remote Short-item Dietary Assessment Tool, which was administered verbally and face-to-face in early 2022. Aboriginal people were involved in the survey design, training and collection, and analysis of data. Descriptive statistics were tabulated, overall and by gender, age, and location. Differences by sex, age group (18-44 years versus ≥45 years), and location (Walgett town or other) were determined using a chi-square test. A total of 242 participants completed the survey; 55% were female. Three-quarters of participants reported meeting the recommendations for discretionary foods (73%); however, more than half (56%) exceeded the recommended maximum serves of sugar-sweetened beverages. The proportion of participants meeting core food group guidelines was 72% for meat, 36% for fruit, 20% for bread and cereals, 6% for dairy, and 3% for vegetables. Overall, none of the participants met the recommended serves of all food groups outlined in the Australian Dietary Guidelines. Findings show that Walgett Aboriginal community members surveyed were consuming a healthier diet than national data reported for Aboriginal and Torres Strait Islander people in Australia. However, none of the participants were meeting all of the national dietary guidelines, placing them at increased risk of diet-related chronic disease. Local Aboriginal community-led efforts to improve food and water security should include specific strategies to improve nutrition.

Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method

Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images.

Soilless Production of Lettuce (Lactuca sativa) in the Atacama Desert Using Fog Water: Water Quality and Produce Mineral Composition

Soilless vegetable production in the Atacama Desert of Northern Chile is spreading since it is perceived as an alternative that requires much less water than open field soil production. However, strong competition between mining and urban use for human population consumption exists, forcing growers to use alternative water sources. Fog is commonly present in the coastal areas of Northern Chile; however, little information exists with regards to its chemical composition and the effect on nutrient quality of the produce. To address this knowledge gap, a set of experiments was carried out in Chañaral, a small town located in the Atacama Desert of Northern Chile. There, a 200 m2 greenhouse equipped with twenty deep flow pools was used in two consecutive growing cycles. Water for the mixing of the nutrient solution was collected from the fog using fog-catchers and later stored in 2000-L tanks. Fog water quality (electrical conductivity, pH and mineral content) was monitored directly from the storage tanks. Two types of lettuce, green butterhead and red oak leaf, were compared on their yield and accumulation of nutrients and heavy metals. The results indicate that fog water is of good quality for soilless production, with an electrical conductivity value of 0.65 ± 0.18 and low content of heavy metals. Plants’ heavy metal accumulation is below the recommendation of Food and Agriculture Organization and World Health Organization. Fog water presents as a viable water source for soilless production in Northern Chile.

Molecular and cellular level analysis of the mechanism of nutritional intervention in preventing epidemic virus infection

Molecular nutrition encompasses a wider range of investigations than nutritional genomics, which can be considered a scientific investigation of how different nutrients and dietary components influence the cellular and molecular mechanisms of the body and health. In effect, increasing antioxidant elements in daily diets can assist with combating the inflammatory response caused by cytokines in the human body. Antioxidants can impede the oxidation process and hence avoid the creation of free radicals in the cytoplasm that can damage the cells via chain reactions. Contamination sequences, batch effects, uneven sampling, unreported taxa, technological biases, and heteroscedasticity are all significant issues in molecular microbial biology. Artificial Intelligence (AI) methodologies to uncover hidden patterns, connections, and interactions within metabolomics data, allowing them to provide personalized food recommendations based on individual health profiles. A multi-scale convolutional neural network (MCNN) classifies cellular images into phenotypes in a single coherent step utilizing the image’s pixel intensity values. Hence, the proposed AI-MCNN has been an essential nutrient that assists the immune system in various ways, including acting as an antioxidant to protect healthy cells, promoting immune cell development and function, and creating antibodies. According to epidemiological research, people who are undernourished are more likely to get bacterial, viral, and other diseases. Nutritional epidemiology investigates dietary or nutritional parameters concerning illness prevalence in communities. Nutritional epidemiology results often add to the evidence that provides dietary recommendations for preventing disease and related disorders.

Analysis of Food Nutrients Impact for Improving Public Health Using Multi Features Deep Recurrent Neural Network for Health Care Management

The food nutrients in daily life is incredible source to make good food for good life cycle. In recent analysis, the new food styles and ingredients affects the public health especially children to cause various disease and impacts which leads dangerous to healthier life. By analyzing food nutrients and presence healthy ingredients in daily life is important. World health organization pays number of research to healthcare management and suggestions to improve the public health by recommending different protocols. The risk is identifying presence of nutrients scaling is tedious due to prevailing techniques machine learning techniques does not provide good results to make better good food recommendation. The problem is more data labels and features are need to analyze which increase the false rate to reduce accuracy. To address the issue, to propose a deep LSTM gated recurrent neural network (LSTM-GRNN) based on Support vector feature selection (SVFS) to identify the presence of food nutrients to recommend the good food to improve the public health. Initially the food product and nutrients scaling logs are collected and to make preprocessing based on C-score normalization form feature labels. Then nutrients scaling impact rate is analyzed for each food ingredients presence by extracting the margins from features list. The important feature is selected by using SVSR to reduce the feature margins based on support vector. Then the selected features are trained into LSTM unit with recurrent neural network to identify the presence of nutrients rate in each food and to categorize the class by presence. The higher scaling rate of class is considered as good food to recommend for healthy facts. The proposed system attains higher detection accuracy in precision, recall rate and f1 measure, also the lower false negative rate increase the performance of accuracy compared to the existing system.

P157 THE USE OF PARTICIPATORY ACTION RESEARCH TO PROMOTE INCREASED CONSUMPTION OF VEGETABLES AMONG AFRICAN WOMEN WITH/AT RISK OF HYPERTENSION IN GQEBERHA, SOUTH AFRICA

Background and objectives: Poor vegetable and fruit intake are reported in multiple studies of the South African population, especially among people with less income and lower educational status and it is a known risk factor for non-communicable diseases such as hypertension. “South Africa has one of the highest levels of hypertension globally, coupled with poor rates of diagnosis, treatment and control” Ware et al., (2019). It has been suggested that indigenous vegetables may aid in increasing vegetable intake, a recommendation of the South African Food Based Dietary Guidelines. As urbanisation and the nutrition transition increases in South Africa, the loss of indigenous knowledge and utilisation of indigenous vegetables is significant and is worse compared to countries such as Kenya and Tanzania. The aim of this study was to collaboratively promote the use of indigenous vegetables by African women in a peri-urban area with/at risk of hypertension with the goal of improving agency to increase vegetable intake. Methods: A qualitative participatory action research (PAR) methodology was utilised. African adult women with hypertension or at risk of hypertension were recruited from a local healthcare facility for a one year long project, with a total enrolment of 12-24 participants. Data collection included observations, focus groups, key informant interviews and the research journal of the researcher. Results: Participants worked collaboratively to co-construct relevant nutrition education materials, recipe development using local indigenous vegetables, clinic open day and a seminar for other community members. There was greater uptake of vegetable gardening at home and at the local community centre. Conclusions: using PAR methodologies to address inadequate vegetable intake and promote the use of indigenous vegetable crops may benefit personal agency which includes an individual's perceptions about how much control they have over themselves and their environment, in order to carry out a certain behaviour; self-confidence or efficacy; the ability to self-regulate and monitor behaviour and the ability to identify and use good coping skills.

Food Recommendation System for a Healthy Liver Using Machine Learning

Nowadays, the work routine, the problems we face, make us not pay proper attention to healthy eating. As a result, people get sick. We have created through mathematical knowledge (statistics, probability, linear algebra, geometry), combining the concepts of Machine Learning (Content Based Filtering, TD IDF Vectorizer Algorithm, Conditional Independence, Count Vectorization) with Python language, a Recommendation System for all people suffering from liver. Liver is one of the most important organs of our body, because it makes 500 essential functions in the organism. But we have to be aware about the types of food that we use. Also, we must be very careful with our lifestyle and diet on foods. The aim of this article is to recommend the most appropriate foods for a healthy liver. This article will help the p

Exploring dietary habits: a study of food consumption patterns among school children aged 10-16 in Mysuru

Background: Diet plays an important role in the growth and development of children. Poor diet in children in Mysuru is linked to future health risks. This study examines the eating habits of children aged 10-16 years to create better food recommendations for children in preventing future consequences. Methods: A cross-sectional study was conducted in the Mysuru district. Data from 126 students aged 10-16 years from 2 schools were collected using A semi-structured food frequency questionnaire (FFQ) on dietary intake developed based on a 24-hour recall method and included items on demographic data, frequency of meal consumption in different food categories, and food frequency data. Anthropometric measurements were done. Descriptive statistics were used to analyze the data. Results: Among the students, most consume rice and idli daily; dosa and khichdi frequently. Pasta, puffed rice, and legumes are less common. Milk and milk products vary, with coffee/tea and paneer often consumed once a month. Fruits like apples, bananas, and grapes are consumed twice a week, while sweet lime, mango, and orange are mostly monthly. Boiled eggs and chicken are often bi-weekly; fish, beef, red meat, and prawns/crab are mostly consumed once a month or less. Biscuits and gobi are frequently consumed weekly; cakes, French fries, samosas, pizza, and chocolates are mainly consumed monthly. Conclusions: This study observes the dietary habits of Mysuru students (aged 10-16), to understand nutritional status and frequency of food consumption aiding targeted interventions to promote healthier eating and reduce chronic disease risk.

Nutrition-Related Knowledge Graph Neural Network for Food Recommendation.

Food recommendation systems are becoming increasingly vital in modern society, given the fast-paced lifestyle and diverse dietary habits. Existing research and implemented solutions often rely on user preferences and past behaviors for recommendations, which poses significant issues. Firstly, this approach inadequately considers the nutritional content of foods, potentially leading to recommendations that are overly homogeneous and lacking in diversity. Secondly, it may result in repetitive suggestions of the same types of foods, thereby encouraging users to develop unhealthy dietary habits that could adversely affect their overall health. To address this issue, we introduce a novel nutrition-related knowledge graph (NRKG) method based on graph convolutional networks (GCNs). This method not only enhances users' ability to select appropriate foods but also encourages the development of healthy eating habits, thereby contributing to overall public health. The NRKG method comprises two key components: user nutrition-related food preferences and recipe nutrition components. The first component gathers nutritional information from recipes that users show interest in and synthesizes these data for user reference. The second component connects recipes with similar nutritional profiles, forming a complex heterogeneous graph structure. By learning from this graph, the NRKG method integrates user preferences with nutritional data, resulting in more accurate and personalized food recommendations. We evaluated the NRKG method against six baseline methods using real-world food datasets. In the 100% dataset, the five metrics exceeded the performance of the best baseline method by 2.8%, 5.9%, 1.5%, 9.7%, and 6.0%, respectively. The results indicate that our NRKG method significantly outperforms the baseline methods, including FeaStNet, DeepGCN, GraphSAGE, GAT, UniMP, and GATv2, demonstrating its superiority and effectiveness in promoting healthier and more diverse eating habits. Unlike these baseline methods, which primarily focus on hierarchical information propagation, our NRKG method offers a more comprehensive approach by integrating the nutritional information of recipes with user preferences.

A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships

A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships

Ontology-based Food Menu Recommender System for Pregnant Women Using SWRL Rules

Pregnancy is a crucial period in a woman's life because her body must prepare and support the growth and development of the fetus. During pregnancy nutritional needs will increase. Lack of nutritional intake during pregnancy can cause serious health problems, one of which is anemia. However, excess nutrition during pregnancy also has a negative impact on pregnant women. Therefore, a recommender system is required to provide food menu recommendations according to the daily nutritional needs of pregnant women. Currently, there has been a lot of research on ontology-based food recommender systems that can provide food recommendations to users, but there is no research that specifically provides food menu recommendations that suit the needs of pregnant women. Therefore, in this research, we propose an ontology-based food menu recommender system using SWRL (Semantic Web Rule Language) rules for pregnant women. In this food menu recommender system, ontology is used to represent food knowledge and its nutritional content, and SWRL rules are used to reason logical rules in the ontology to determine the appropriate food menu for pregnant women. This recommender system also considers diseases and allergies that pregnant women have so that it can provide food menu recommendations that are more suitable for users. From 15 data samples from pregnant women, the system provides 75 food menu recommendations for pregnant women. Based on the validation results that have been carried out, the precision value is 0.986, the recall is 1, and the F1-score is 0.992.

Web scraping of user-simulated online nutrition information for people with multiple sclerosis

BackgroundPeople diagnosed with multiple sclerosis (MS) often seek to modify their diet guided by online advice, however this advice may not align with national dietary guidelines. The aim of this study was to simulate an online search for dietary advice conducted by a person with MS and evaluate the content. It was hypothesised that a variety of eating patterns are promoted for MS online and these dietary approaches can be contradictory. MethodsAn online search was simulated using Google Trends-informed search terms and Google and Bing search engines. URLs were extracted using R. Nutrition data were extracted including recommendations for diets, foods, supplements, and health professional consultation. Statistical analyses were conducted using R. Results73 URLs from 49 websites were extracted, with only 14 results common to both search engines. Dietary recommendations included overall eating patterns (58 webpages, 79%), individual foods (55 webpages, 75%), and supplements (33 webpages, 45%). The most promoted eating pattern for MS was a balanced diet (33 recommendations, 48%), more likely by nonprofit organisations and health information websites (14 and 17 recommendations, 100% and 89%); lifestyle program websites were more likely to recommend restrictive diets (19 recommendations, 100%) (p<0.001). 52% pages advised consulting a health professional, most often a doctor or dietitian. ConclusionA balanced diet is the most recommended eating pattern for MS online, though advice promoting restrictive diets persists.

Utilizing Artificial Intelligence (AI) in Customer’s Purchase Intentions on Online Food Delivery Service

Generally, food delivery services like GrabFood act as couriers, transporting consumer needs from restaurants or stores directly to their doorsteps. The rise of Artificial Intelligence (AI) has further revolutionized this convenience, allowing people to order meals and other goods from the comfort of their homes. This research investigates how AI is utilized to influence customer purchase intentions on GrabFood. The study examines the impact of six independent variables: instant food delivery, estimated delivery time, customized food recommendations, interactivity, cashless payment methods, and consumer behavior. These variables are analyzed in relation to the dependent variable – the customer's intention to use GrabFood. To gather data, an online survey was conducted with 100 respondents. The collected data was then verified using SPSS software. The findings revealed that delivery speed is a key driver, with both instant delivery and estimated delivery time showing a significant positive correlation (β = 0.457) with purchase intention. However, other features like personalized recommendations (β = 0.174), cashless payment methods (β = 0.119), and user interaction (β = -0.188) did not significantly impact user decisions. These findings require further exploration to understand user preferences for these features