ReviewNPJ science of food2026
Machine learning unveils three layers of food complexity.
Review in NPJ science of food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Elucidating the interaction between pyrazine flavor compounds and coffee proteins: Insights from multiscale structural and molecular dynamics simulations.Current research in food science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Food is a more complex system than commonly perceived, comprising tens of thousands of molecules whose compositions and interactions ultimately shape human perception. To conceptualize this multifaceted nature, we frame food complexity across three interconnected layers: the molecular composition that defines its chemical foundation, the component interactions that shape food properties, and the perceptual responses that arise from human sensory systems. This review discusses how machine learning is advancing our ability to decode each of these layers, together with multimodal and data-fusion frameworks. Understanding these three layers may enable more accurate prediction of food properties, guide food product innovation, and deepen our scientific understanding of food.
Identifiers
What Socratic 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 Socratic graph.