ReviewFood chemistry: X2026
Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.
Review in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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0 citing papers in PubMed.
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Authors and funding
7 authors.
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Abstract
Artificial intelligence (AI) is being applied by the bakery industry to substitute existing practices with data-informed strategies to enhance the accuracy of quality predictions, process optimization, and shelf-life prediction. This review discusses how AI, specifically machine learning (ML) algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), random forests (RF) and deep neural networks (DNNs), can be applied in bread-making and product innovation. With AI models, there is the possibility of analyzing the interactions between formulation, processing and quality features in a complex manner to predict the loaf volume, crumb structure, staling and risk of spoilage. The optimization of formulations with the help of AI is also used to produce cost-effective, nutritionally fortified products without losing the sensory quality. Interest of AI in combination with improved methods like gas chromatography-olfactometry and texture profiling,
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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.