A comparative study of vision-language models for food ingredient recognition and nutrient estimation.

Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances

BackgroundNew methods for assessing diet in research are being developed to address the limitations of traditional dietary assessment methods. Mobile device–assisted ecological momentary diet assessment (mEMDA) is a new dietary assessment method that has not yet been optimized and has the potential to minimize recall biases and participant burden while maximizing ecological validity. There have been limited efforts to characterize the use of mEMDA in behavioral research settings.ObjectiveThe aims of this study were to summarize mEMDA protocols used in research to date, to characterize key aspects of these assessment approaches, and to discuss the advantages and disadvantages of mEMDA compared with the traditional dietary assessment methods as well as implications for future mEMDA research.MethodsStudies that used mobile devices and described mEMDA protocols to assess dietary intake were included. Data were extracted according to Preferred Reporting of Systematic Reviews and Meta-Analyses and Cochrane guidelines and then synthesized narratively.ResultsThe review included 20 studies with unique mEMDA protocols. Of these, 50% (10/20) used participant-initiated reports of intake at eating events (event-contingent mEMDA), and 50% (10/20) used researcher-initiated prompts requesting that participants report recent dietary intake (signal-contingent mEMDA). A majority of the study protocols (60%, 12/20) enabled participants to use mobile phones to report dietary data. Event-contingent mEMDA protocols most commonly assessed diet in real time, used dietary records for data collection (60%, 6/10), and provided estimates of energy and nutrient intake (60%, 6/10). All signal-contingent mEMDA protocols used a near real-time recall approach with unannounced (ie, random) abbreviated diet surveys. Most signal-contingent protocols (70%, 7/10) assessed the frequency with which (targeted) foods or food groups were consumed. Relatively few (30%, 6/20) studies compared mEMDA with the traditional dietary assessment methods.ConclusionsThis review demonstrates that mEMDA has the potential to reduce participant burden and recall bias, thus advancing the field beyond current dietary assessment methods while maximizing ecological validity.

BackgroundDietary assessment is a fundamental component of nutrition research and plays a pivotal role in managing chronic diseases. Traditional dietary assessment methods, particularly in the context of Thai cuisine, often require extensive training and may lead to estimation errors. ObjectivesTo address these challenges, Institute of Nutrition, Mahidol University (INMU) iFood, an innovative artificial intelligence–based Thai food dietary assessment system, allows for estimating the nutritive values of dishes from food images. MethodsINMU iFood leverages state-of-the-art technology and integrates a validated automated Thai food analysis system. Users can use 3 distinct input methods: food image recognition, manual input, and a convenient barcode scanner. This versatility simplifies the tracking of dietary intake while maximizing data quality at the individual level. The core improvement in INMU iFood can be attributed to 2 key factors, namely, the replacement of Yolov4-tiny with Yolov7 and the expansion of noncarbohydrate source foods in the training image data set. ResultsThis combination significantly enhances the system’s ability to identify food items, especially in scenarios with closely packed food images, thus improving accuracy. Validation results showcase the superior performance of the INMU iFood integrated V7-based system over its predecessor, V4-based, with notable improvements in protein and fat estimation. Furthermore, INMU iFood addresses limitations by offering users the option to import additional food products via a barcode scanner, thus providing access to a vast database of nutritional information through Open Food Facts. This integration ensures users can track their dietary intake effectively, with expanded access to over 3000 food items added to or updated in the Open Food Facts database covering a wide variety of dietary choices. ConclusionsINMU iFood is a promising tool for researchers, health care professionals, and individuals seeking to monitor their dietary intake within the context of Thai cuisine and for ultimately promoting better health outcomes and facilitating nutrition-related research.

Reliable dietary data for adolescents in low- and middle-income countries (LMICs) are limited due to high costs and estimation errors in traditional dietary assessment methods. Although technology-assisted dietary assessment tools are becoming popular, few have been validated in LMICs. This study validated the PlantVillage Food Recognition Assistance and Nudging Insights (FRANI), an artificial intelligence-assisted mobile application for dietary assessment, against weighed food records (WFR) and multipass 24-h recalls (24HR) among adolescent girls aged 14‒18 y (n = 60) in urban/semi-urban communities in Sri Lanka. Dietary intake was assessed over 2 non-consecutive days using 3 methods: FRANI, WFR, and 24HR. The equivalence of nutrient intake was evaluated using mixed-effect models accounting for repeated measures by comparing intake ratios (FRANI/WFR and 24HR/WFR) with 10%, 15%, and 20% equivalence bounds. The concordance correlation coefficient was utilized to assess the agreement between methods. FRANI demonstrated equivalence with WFR at the 10% bound for energy and vitamin A; 15% for protein, fiber, iron, and zinc; and 20% for fat, niacin, and folate intakes. Comparisons between 24HR and WFR found that no nutrients fell within the 10% bound. Energy, protein, fat, iron, niacin, and vitamin A intakes were equivalent at 15% bound, whereas fiber, calcium, folate, and vitamin C intakes were equivalent at 20% bound. Concordance correlation coefficient ranged from 0.49 to 0.89 for FRANI compared to WFR, and 0.44 to 0.84 for 24HR compared with WFR. Omission errors were 2% for FRANI and 12% for 24HR, and intrusion errors were 7% and 9%, respectively. PlantVillage FRANI application accurately estimated nutrient intakes of adolescent girls in Sri Lanka compared to the WFR. Its performance was at least comparable to the traditional 24HR method, supporting its potential as a scalable alternative for dietary assessment in similar LMIC populations.

Coated fabrics for variety of military uses : ( Textile World, Vol. 93, No. 9)

<title>Abstract</title> <bold>Background:</bold> Nutrition plays a critical role in immune function and recovery from disease. Poor dietary intake and inadequate nutritional adjustment can impair healing in patients with chronic wounds, particularly in rural settings where malnutrition, parasitic infections, and unbalanced diets are common. <bold>Objective:</bold> To assess the nutritional status, dietary habits, and food composition of chronic wound patients (CWPs) at the time of recruitment in a wound management unit in Côte d’Ivoire. <bold>Methods:</bold> A prospective cross-sectional study was conducted from 2019 to 2020, including 68 CWPs from Taabo General Hospital. Nutritional assessment included anthropometry, haemoglobin and serum albumin measurements, a 24-hour dietary recall, food frequency questionnaire, dietary diversity score, and food composition analysis. Logistic regression was used to examine associations between nutritional status, haemoglobin, age, sex, wound type, and wound size (significance: p &lt; 0.05). <bold>Results:</bold> Diets were dominated by starchy foods (67%), protein sources (47%), vegetables (42%), and fruits (5%), with a low mean dietary diversity score (2.39 ± 3.32). Children (&lt;15 years) had lower BMI and haemoglobin levels than adults (10.8 vs 11.5 g/dL, p = 0.03). Nutritional status was significantly associated with wound size (p &lt; 0.0003), and haemoglobin level was associated with wound aetiology. Poor nutritional status was associated with larger wounds in 73% of patients (p = 0.01). <bold>Conclusion:</bold> CWPs exhibited malnutrition due to low intake of protein, fruits, and vegetables, leading to micronutrient deficiencies and larger wound size at recruitment. Children were particularly vulnerable. Integrating dietary counselling, nutritional support, and assessment into wound care services may improve healing outcomes and reduce infection risk.

Modulation recognition of communication signals is a key task in the fields of spectrum management and cognitive radio. Traditional methods rely on artificial feature design and have limited generalization ability in complex channel environment. In this study, a hybrid deep learning model combining convolutional neural network and long-term and short-term memory network is constructed to automatically extract the spatial local features of signals and capture their temporal dependencies through end-to-end learning. The model uses multi-modal inputs including time-frequency map, in-phase quadrature series and manual statistical features to process different modal data through parallel branch structure and fuse multi-dimensional information to enhance feature discrimination. Experimental results show that the accuracy, recall and AUC of the proposed model are superior to those of support vector machine and ResNet18 models in the recognition tasks of analog modulation, digital modulation and spread spectrum modulation on a dataset containing 120000 signal samples, which verifies the effectiveness of the joint spatial-temporal feature modeling. This study provides a robust solution for intelligent modulation recognition in complex electromagnetic environment.

Food composition database reliability in calculations of diet offers

In spite of the strides made globally in reducing hunger, the problems of micronutrient deficiencies and coexisting obesity and related cardiovascular and degenerative diseases constitute a formidable challenge for the future. Attempts to reverse this trend with single-nutrient intervention strategies have met with limited success, resulting in renewed calls for food-based approaches. The deployment of agricultural biodiversity is an approach that entails greater use of local biodiversity to ensure dietary diversity. To outline a new strategy proposed by the International Plant Genetic Resources Institute (IPGRI) that employs agricultural biodiversity as the primary resource for food security and health. The authors carried out a meta-analysis to review and assemble existing information on the nutritional and healthful properties of traditional foods based on a diverse set of case studies and food composition and nutritional analysis studies. The methods highlight particular examples of foods where analysis of nutrient and non-nutrient composition reveals important traits to address the growing problems of malnutrition associated with the rise of chronic diseases. Finally, the authors analyze social, economic, and cultural changes that undermine the healthful components of traditional diets. Based on this multidisciplinary and comparative approach, the authors suggest a holistic food-based approach that combines research to assess and document nutritional and healthful properties of traditional foods, investigating options in which nutritionally valuable traditional foods can contribute to better livelihoods, and ways that awareness and promotional campaigns can identify healthful components of traditional diets that fit the needs of urban and market-oriented consumers. There is an urgent need for agricultural research centers, national agricultural research systems, universities, and community-based organizations to work together under a shared policy framework with the aim of developing a strong evidence base linking biodiversity, nutrition, and health. Although these initiatives are still ongoing, the gains realized in small-scale and local pilot efforts have encouraged IPGRI to work with local partners toward the implementation of scale-up efforts in various regions.

Handheld NIR spectroscopy for real-time on-site food quality and safety monitoring.

The proteome is the totality of proteins present in a biological sample. In contrast to the static genome, the proteome is highly dynamic, influenced by the genome and many external factors, such as the state of development, tissue type, metabolic state, and various interactions. Thus, the proteome reflects very closely the biological (and chemical) processes occurring in a system. For proteome analysis, gel based and shotgun methods are most widely applied. Because of the potential to generate a systematic view of protein composition and biological as well as chemical interactions, the application of proteome analysis in food science is steadily growing. This tutorial review introduces several fields in food science, where proteomics has been successfully applied: analysis of food composition, safety assessment of genetically modified food, the search for marker proteins for food authentication, identification of food allergens, systematic analysis of the physiological activity of food, analysis of the effects of processing on food proteins and the improvement of food quality.

A food composition table was prepared for estimating the dietary composition of retort-packaged baby food to use in surveys on the dietary intake of infants. The food composition table was based on estimates by several registered dietitians of the ingredients and weight of food in retort-packaged baby food according to product photographs and the nutritional facts label on the packages. The data were prepared from 124 baby food products with high market share according to a point of sale database. The estimated weights of ingredients per 100g of baby food were 16-26g of rice or noodles, 6-9g of meat, fish, or hen's egg, and 19-32g of vegetables. These weights were less than those empirically recognized by the dietitians. This suggests that dietitians tend to over-estimate the weights of protein foods and vegetables which are the main ingredients in baby food. The estimated food composition of retort-pack-aged baby food was statistically classified into six types to provide a standardized tool for surveying the energy and 42 nutrients in infant food.

Aim: The variability in the methods of preparing indigenous foods have led to uncertainty in the nutrient composition, sensory properties and quality of many foods consumed in Nigeria. This study is aimed at standardizing the different methods of preparing melon and groundnut soups and assessing their nutrient compositions as consumed in Cross River State (CRS), Nigeria.&#x0D; Methodology: Melon (Citrullus vulgaris) and Groundnut (Arachis hypogea) seeds were purchased in Calabar, CRS, Nigeria in the month of February. Twelve Local Governments Areas (LGA) were randomly selected from the Eighteen LGAs in CRS. Focus group discussions (FGD) were conducted in each of the communities to determine common methods of cooking melon and groundnut soup and variations in recipes. Recipes collected during the FGD were standardized and prepared as described. Nutrient compositions were determined using standard laboratory methods. Data generated were analyzed using descriptive statistics. The least significant difference (LSD) was accepted at P&lt;0.05.&#x0D; Results: All the soup samples were liked and accepted. For melon soup, method 1 and 2 had better rating and similar values for all the sensory parameters, although, method 1 had better general acceptability score. For groundnut soup, method 1 and 3 had better ratings and similar values for aroma, taste, colour, texture and general acceptability. The range for the proximate compositions were as follows; for melon soup, moisture 68.8- 70.9%; protein 10.1-11 %, fat 9.5-11.2%, ash 2.3-2.7%, dietary fibre 5.5-6%. For the groundnut soup, moisture ranged from 68.6-71.1%, protein 10.1-10.9%, fat 9.4-12.1%, ash 2.2-2.5%, dietary fibre 6-6.8%. &#x0D; Conclusion: The standardized methods of preparing these soups will be useful in maintaining quality and quantity for easy reproducibility.

Calcium and magnesium dietary intakes and plasma and milk concentrations of Nepalese lactating women

BackgroundBoth fresh and processed foods are available in the modern food environment where taste can signal presence of nutrients. However, whether these taste–nutrient relationships are maintained across different degrees of food processing is not well understood, and less is known about the relative contribution of different taste qualities to population energy intakes. ObjectivesTo investigate the association between perceived intensity of 6 taste modalities and a food's nutrient content in the context of food processing and to further examine the relative contribution of different taste clusters to total energy intakes, stratified by weight status. MethodsDiet and lifestyle data from the Singapore Multi-Ethnic Cohort Phase 2 (N = 7011; aged 21–75 y) were collected through interviewer-administrated questionnaires. Taste and nutrient profiles for each of the 269 Singaporean foods were derived using a published taste database and food composition table. Each food was then categorized into the NOVA food-processing classification (unprocessed, processed, ultra-processed) to compare the strength of taste–nutrient relationships. Multivariable-adjusted models were used to examine associations between relative consumption of foods from different taste clusters and processing categories, energy intake, and BMI (in kg/m2) within a population cohort. ResultsSweet taste and mono- and disaccharide content of foods were significantly associated across all processing categories, although this association was weaker among ultra-processed foods (UPFs) (r = 0.42) than among unprocessed foods (r = 0.72). In contrast, associations between fat sensation and fat content (r = 0.74), as well as salt taste and sodium content (r = 0.84), were stronger for UPFs. Individuals who had higher energy intakes or were overweight (BMI >23) derived significantly greater percentage of energy from processed foods rather than UPFs, and this energy was higher from “savory–fatty” and lower from “neutral” tasting foods than those with lower energy intakes and normal weight (all P < 0.001). Eighty percent of individuals’ dietary energy was from both “savory–fatty” and “neutral” foods, independent of differences in total energy intake and weight status. ConclusionsTaste–nutrient relationships are maintained across different degrees of food processing. Greater consumption of foods that have a high “savory–fatty” taste was associated with increased energy intakes and overweight in the Asian population.