ReviewFoods (Basel, Switzerland)2025
Machine Learning in Transforming the Food Industry.
Review in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Extruded Pseudocereal Snacks Mathematical Modelling Approaches for Prediction and Optimisation: A Review.Foods (Basel, Switzerland) · 2026Review
- Leakage-Free Benchmarking of Electronic Noses for Beef Freshness: A Signal-Richness Criterion for Model Selection.Foods (Basel, Switzerland) · 2026Article
- Valorization of Food Industry By-Products for Sustainable Functional Food Production: Recent Advances and Future Perspectives.Foods (Basel, Switzerland) · 2026Review
- Targeted Prediction and Comprehensive Study of Stirred-Type Yogurt with MayangFoods (Basel, Switzerland) · 2026Article
- Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.Food chemistry: X · 2026Review
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
The continued evolution and advances in Artificial Intelligence (AI) technologies are offering innovative solutions and setting the futuristic trends in the food sector. The use of different Machine Learning (ML)-based models has demonstrated promising applications in the food processing industry. Processing operations such as drying, frying, cooking, heating, and baking are complex and challenged by multifaceted problems due to simultaneous heat, mass and momentum transport processes. The ML-based tools could potentially categorize each food material and efficiently predict its processing kinetics for optimization of the processing conditions. Furthermore, ML technologies have shown excellent applications in ensuring the traceability of food provenance and quality, enhancing the transparency and traceability from farm to fork, and providing consumers with more reliable product information. Overall, ML tools have untapped potential to identify and accelerate multiple development opportunities across the entire agri-food sector to improve productivity, profitability, and sustainability in the future.
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.