ReviewACS omega2023
When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry.
Review in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Multi-applications and Aquaculture of Seaweeds: Environmental Improvement, Health Benefit, and Sustainable Valorization with Integrated Artificial Intelligence.Applied biochemistry and biotechnology · 2026Review
- Pyrazine-based fingerprinting for geographical origin discrimination of coffee: integrating chemometrics and machine learning.Food chemistry: X · 2026Article
- Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.Journal of agricultural and food chemistry · 2026Article
- Machine learning unveils three layers of food complexity.NPJ science of food · 2026Review
- Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.Current research in food science · 2026Review
- Predicting Temporal Liking of Food Pairings from Temporal Dominance of Sensations Data via Reservoir Computing on Crackers and Spreads.Foods (Basel, Switzerland) · 2025Article
- A Review of Processing, Marketing, and Demographic Influence on Consumer Perception of Pulse Milk- Insights From Traditional and AI-Facilitated Research.Journal of food science · 2025Review
- Application of Bioinformatics and Machine Learning Tools in Food Safety.Current nutrition reports · 2025Review
- Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis.Foods (Basel, Switzerland) · 2025Article
- Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer's Disease Therapy.Foods (Basel, Switzerland) · 2025Article
- Can artificial intelligence uncover the bioactive peptides' benefits for human health and knowledge? A narrative review.Frontiers in nutrition · 2025Review
- Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Frontiers in pharmacology · 2025Article
- FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data.Journal of cheminformatics · 2024Article
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
- Probabilistic Modelling of the Food Matrix Effects on Curcuminoid's In Vitro Oral Bioaccessibility.Foods (Basel, Switzerland) · 2024Article
- Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey.Foods (Basel, Switzerland) · 2024Article
- The Future of Food.Foods (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence (AI) is becoming popular in every field, AI methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with AI concepts and learning methods have emerged in the past few years. This review illustrates several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduce some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases are given as examples, demonstrating their applications in food pairing, food-drug interactions, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and AI will play an essential role in food science and food chemistry.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.