ReviewNature food2022
Computer-aided food engineering.
Review in Nature food, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Artificial intelligence for food innovation.Nature food · 2026Review
- Modeling and Simulation of Mass Transfer in Food Processing: Recent Advances in Governing Equations, Workflow, and Applications.Foods (Basel, Switzerland) · 2026Review
- A digital twin approach for optimizing okara-based Greek-style yogurt using hybrid Gompertz-LSTM modeling and data assimilation.Current research in food science · 2026Article
- A multiphase and multiscale mechanistic model for hot air drying ofCurrent research in food science · 2026Article
- AI for food: accelerating and democratizing discovery and innovation.NPJ science of food · 2025Review
- High-speed computed tomography to visualise the 3D microstructural dynamics of oil uptake in deep-fried foods.Nature communications · 2025Article
- Digital Twin Methodology in Food Processing: Basic Concepts and Applications.Current nutrition reports · 2024Review
- Unveiling the relationship between food unit operations and food industry 4.0: A short review.Heliyon · 2024Review
- Personalized, digitally designed 3D printed food towards the reshaping of food manufacturing and consumption.NPJ science of food · 2024Review
- Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems.Scientific reports · 2024Article
- Predicting and improving complex beer flavor through machine learning.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided food engineering (CAFE) can reduce resource use in product, process and equipment development, improve time-to-market performance, and drive high-level innovation in food safety and quality. Yet, CAFE is challenged by the complexity and variability of food composition and structure, by the transformations food undergoes during processing and the limited availability of comprehensive mechanistic frameworks describing those transformations. Here we introduce frameworks to model food processes and predict physiochemical properties that will accelerate CAFE. We review how investments in open access, such as code sharing, and capacity-building through specialized courses could facilitate the use of CAFE in the transformation already underway in digital food systems.
Identifiers
37118206What 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.