ReviewJournal of agricultural and food chemistry2023
Data-Driven Elucidation of Flavor Chemistry.
Review in Journal of agricultural and food chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 53 citations in OpenAlex.
- PharmaGNN: a model for odor prediction based on graph neural networks.Journal of the science of food and agriculture · 2026Article
- Modulation of Maillard reaction pathways and aroma profiles in peanut oil using enzymatically tailored peanut meal hydrolysates.Food chemistry: X · 2026Article
- Molecular Atlas of Key Food Odorants Reveals Mixture-Level Organization and Enables Generative Aroma Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.Journal of food science · 2026Review
- High-pressure processing mitigates flavor loss in sauced duck legs during storage: Evidence from volatile profiling and myofibrillar protein-flavor compound interactions.Food chemistry: X · 2026Article
- GC-MS and GC-IMS Comprehensive Analysis of Volatile Compounds in the Peel and Pulp of Six Lemon Varieties and Their Interactions with Olfactory Receptors: Molecular Docking and Molecular Dynamics Simulations Studies.Foods (Basel, Switzerland) · 2026Article
- Machine learning unveils three layers of food complexity.NPJ science of food · 2026Review
- Flavoromics integrated with machine learning to elucidate key aroma compounds in aroma-directed chili peppers and predict sensory quality.Current research in food science · 2026Article
- Article
- Flavor and Well-Being: A Comprehensive Review of Food Choices, Nutrition, and Health Interactions.Food science & nutrition · 2025Review
- Interpretable multitask deep learning models for odor perception based on molecular structure.Current research in food science · 2025Article
- FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data.Journal of cheminformatics · 2024Article
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study.Foods (Basel, Switzerland) · 2024Article
- Discovery of potential anti-Heliyon · 2024Article
- Mlp4green: A Binary Classification Approach Specifically for Green Odor.International journal of molecular sciences · 2024Article
- Enhanced Deep-Learning Model for Carbon Footprints of Chemicals.ACS sustainable chemistry & engineering · 2024Article
- Biosynthesis and metabolic engineering of isoflavonoids in model plants and crops: a review.Frontiers in plant science · 2024Review
- Relevance of Phytochemical Taste for Anti-Cancer Activity: A Statistical Inquiry.International journal of molecular sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.
Indexed as
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.