ArticleCurrent research in food science2025
Predicting the quality of cookies with oleogel as a shortening substitute using hyperspectral imaging and artificial intelligence.
Article in Current research in food science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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Who cites it
1 citing paper in PubMed.
- Oral lubrication and flavor release in myofibrillar protein-based emulsion gel fat substitutes: Arginine-regulated interfacial architecture.Current research in food science · 2026Article
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4 authors.
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Abstract
A total of 36 oleogels, made from various waxes and vegetable oils, were utilized as shortening substitutes in cookies, and machine learning incorporating three boosting models (AdaBoost, GBM, and XGBoost) was applied to predict their effects on the quality attributes of the cookies. Carnauba wax oleogels exhibited the highest melting temperature and contributed to stronger cookie texture, while beeswax oleogels produced cookies with greater spreadability and lower hardness. Similar hyperspectral patterns were observed across different waxes and oils, with distinct clustering based on the level of wax through principal component analysis. By preprocessing the hyperspectral data of oleogels, the performances of three machine learning boosting models were improved to predict cookie spreadability and hardness, achieving an R
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