Deep learning accurately predicts food categories and nutrients based on ingredient statements

Food Compass Score-10: validation of a method for evaluating the healthfulness of foods and beverages using ingredient list information

BACKGROUND

The Food Compass, a novel food profiling system, provides a holistic, validated assessment of the healthfulness of foods, beverages, and meals using 54 attributes across 9 domains. However, information on several of these attributes is not commonly available.

OBJECTIVES

We aimed to develop and validate an approach, Food Compass Score-10 (FCS-10), to estimate FCSs using information commonly available on package labels.

METHODS

Missing attributes were calculated using weighted scores of each product's ingredients, derived from a dataset of ∼10,000 foods and beverages. The final FCS-10 was scaled from 1 (least healthful) to 10 (most healthful). As part of this validation study, diagnostic accuracy analysis was conducted to evaluate the performance of the FCS-10 compared with the original score. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated by comparing the FCS-10 recommendation categorizations with the FCS recommendation categorizations (≥7 for foods to encourage, 4-6 for foods to consume in moderation, ≤3 for foods to limit).

RESULTS

FCS-10 produced scores within 1 unit of the original score (when rescaled 1-10 for comparison) for 89% of products (n = 481/538); none deviated >2 units. The correlation between FCS-10 and the original score was high (r = 0.93). FCS-10 also performed well in identifying products to encourage, moderate, or limit, with overall sensitivity and specificity of 87% and 93%, respectively.

CONCLUSIONS

FCS-10 offers a practical approach for estimating the healthfulness of diverse packaged foods and beverages using readily available label data while maintaining the strengths of the original system.

NutriRAG: Unleashing the Power of Large Language Models for Food Identification and Classification through Retrieval Methods

Objective

This study explores the use of advanced Natural Language Processing (NLP) techniques to enhance food classification and dietary analysis using raw text input from a diet tracking app.

Materials and Methods

The study was conducted in three stages: data collection, framework development, and application. Data were collected via the myCircadianClock app, where participants logged their meals in free-text format. Only de-identified food-related entries were used. We developed the NutriRAG framework, an NLP framework utilizing a Retrieval-Augmented Generation (RAG) approach to retrieve examples and incorporating large language models such as GPT-4 and Llama-2-70b. NutriRAG was designed to identify and classify user-recorded food items into predefined categories and analyzed dietary patterns from free-text entries in a 12-week randomized clinical trial (RCT: NCT04259632). The RCT compared three groups of obese participants: those following time-restricted eating (TRE, 8-hour eating window), caloric restriction (CR, 15% reduction), and unrestricted eating (UR).

Results

NutriRAG significantly enhanced classification accuracy and effectively identified nutritional content and analyzed dietary patterns, as noted by the retrieval-augmented GPT-4 model achieving a Micro F1 score of 82.24. Both interventions showed dietary alterations: CR participants ate fewer snacks and sugary foods, while TRE participants reduced nighttime eating.

Conclusion

By using AI, NutriRAG marks a substantial advancement in food classification and dietary analysis of nutritional assessments. The findings highlight NLP's potential to personalize nutrition and manage diet-related health issues, suggesting further research to expand these models for wider use.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation

The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation.

Machine learning accurately predicts food exchange list and the exchangeable portion

Introduction

Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs-the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion.

Methods

Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost.

Results

The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents.

Discussion

Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

Emerging technologies for preservation and quality evaluation of postharvest edible mushrooms: A review

Edible mushrooms are the highly demanded foods of which production and consumption have been steadily increasing globally. Owing to the quality loss and short shelf-life in harvested mushrooms, it is necessary for the implementation of effective preservation and intelligent evaluation technologies to alleviate this issue. The aim of this review was to analyze the development and innovation thematic lines, topics, and trends by bibliometric analysis and review of the literature methods. The challenges faced in researching these topics were proposed and the mechanisms of quality loss in mushrooms during storage were updated. This review summarized the effects of chemical processing (antioxidants, ozone, and coatings), physical treatments (non-thermal plasma, packaging and latent thermal storage) and other emerging application on the quality of fresh mushrooms while discussing the efficiency in extending the shelf-life. It also discussed the emerging evaluation techniques based on the various chemometric methods and computer vision system in monitoring the freshness and predicting the shelf-life of mushrooms which have been developed. Preservation technology optimization and dynamic quality evaluation are vital for achieving mushroom quality control. This review can provide a comprehensive research reference for reducing mushroom quality loss and extending shelf-life, along with optimizing efficiency of storage and transportation operations.

Aptamer recognition-promoted specific intercalation of iridium complexes in G-quadruplex DNA for label-free and enzyme-free phosphorescence analysis of kanamycin

In consideration of relevance of antibiotic with food security, it is extremely desirable to propose sensitive and credible methods for antibiotic screening. Nevertheless, most of known approaches are developed based on fluorescence technique, which suffered from the interferences of background fluorescence and autoluminescence, and tedious labeling procedures, ascribing to the deficiency of high-performance and multifunctional dyes. Herein, we developed a novel iridium (III) complex (Ir-QAU)-based aptamer-promoted phosphorescence sensor for label-free, enzyme-free and highly sensitive detection of target antibiotic (kanamycin, Kan) based on target-switched hybridizing chain reaction (HCR). Ir-QAU was elaborately devised to present a signal-on response to G-quadruplex (G4) DNA against other DNAs due to its specific intercalation in G4 DNA and subsequent restriction of intra-molecular rotation. The recognition of H1 by Kan promoted the formation of Kan@H1 complexes, which hybridized with H2 and H3 via toehold-mediated hybridization reaction, subsequently switching HCR to produce large numbers of G4 DNA. Compared to Kan absence, abundant Ir-QAU was locked in G4 DNA to yield a significantly increased luminescence, which switches the luminescence analysis process of Kan with a limit of detection down to 0.38 pM. Furthermore, the Ir-QAU-based sensor was triumphantly applied to detect Kan in milk sample. We anticipate this work will disclose a new way to development of high-efficiency and practical luminescence sensor, and show a great potential for antibiotic-related food security.

Computational screening for new neuroprotective ingredients against Alzheimer's disease from bilberry by cheminformatics approaches

Bioactive ingredients from natural products have always been an important resource for the discovery of drugs for Alzheimer's disease (AD). Senile plaques, which are formed with amyloid-beta (Aβ) peptides and excess metal ions, are found in AD brains and have been suggested to play an important role in AD pathogenesis. Here, we attempted to design an effective and smart screening method based on cheminformatics approaches to find new ingredients against AD from Vaccinium myrtillus (bilberry) and verified the bioactivity of expected ingredients through experiments. This method integrated advanced artificial intelligence models and target prediction methods to realize the stepwise analysis and filtering of all ingredients. Finally, we obtained the expected new compound malvidin-3-O-galactoside (Ma-3-gal-Cl). The in vitro experiments showed that Ma-3-gal-Cl could reduce the OH· generation and intracellular ROS from the Aβ/Cu²⁺/AA mixture and maintain the mitochondrial membrane potential of SH-SY5Y cells. Molecular docking and Western blot results indicated that Ma-3-gal-Cl could reduce the amount of activated caspase-3 via binding with unactivated caspase-3 and reduce the expression of phosphorylated p38 via binding with mitogen-activated protein kinase kinases-6 (MKK6). Moreover, Ma-3-gal-Cl could inhibit the Aβ aggregation via binding with Aβ monomer and fibers. Thus, Ma-3-gal-Cl showed significant effects on protecting SH-SY5Y cells from Aβ/Cu²⁺/AA induced damage via antioxidation effect and inhibition effect to the Aβ aggregation.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.