AIMC Topic: Taste
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Decoding the quantitative structure-activity relationship and astringency formation mechanism of oxygenated aromatic compounds.
Food research international (Ottawa, Ont.)
Apr 17, 2025
Astringency is a common sensory experience in the mouth, characterized by dryness, roughness, and puckering. Due to the inefficiency and expense of conventional astringency evaluation methods, the quantitative structure-activity relationship (QSAR) m...
From prediction to design: Revealing the mechanisms of umami peptides using interpretable deep learning, quantum chemical simulations, and module substitution.
Food chemistry
Apr 9, 2025
This study screened and designed umami peptides using deep learning model and module substitution strategies. The predictive model, which integrates pre-training, enhanced feature, and contrastive learning module, achieved an accuracy of 0.94, outper...
Machine learning-based exploration of Umami peptides in Pixian douban: Insights from virtual screening, molecular docking, and post-translational modifications.
Food chemistry
Mar 6, 2025
Pixian Doubanjiang (PXDB)'s distinctive umami profile is primarily attributed to its unique peptides; however, their structural characteristics, sensory mechanisms, and biosynthetic pathways during aging remain poorly understood. This study employed ...
Endogenous storage proteins influence Rice flavor: Insights from protein-flavor correlations and predictive modeling.
Food chemistry
Mar 5, 2025
This study investigated the correlation between endogenous storage proteins and aromatic compounds in rice, and their collective influence on rice eating quality. Six rice samples, varying in four endogenous storage proteins through gene editing gene...
Enhancing beef tallow flavor through enzymatic hydrolysis: Unveiling key aroma precursors and volatile compounds using machine learning.
Food chemistry
Feb 26, 2025
Lipids are critical precursors of aroma compounds in beef tallow. This study investigated how enzymatic hydrolysis treatment affected the aroma precursors and flavor of beef tallow during the manufacturing process. Using gas chromatography-mass spect...
Identification and taste presentation characteristics of umami peptides from soybean paste based on peptidomics and virtual screening.
Food chemistry
Feb 25, 2025
This research concentrated on soybean paste fermented with Tetragenococcus halophilus, employing peptidomics and machine learning methodologies to screen for novel umami peptides. Taste characteristics of umami peptides were evaluated through sensory...
A machine learning approach fusing multisource spectral data for prediction of floral origins and taste components of Apis cerana honey.
Food research international (Ottawa, Ont.)
Feb 25, 2025
This study explores the use of near-infrared (NIR), mid-infrared (MIR), and Raman spectral fusion for the rapid prediction of floral origins and main taste components in Apis cerana (A. cerana) honey. Feature-level fusion with the partial least squar...
Sensory-biased autoencoder enables prediction of texture perception from food rheology.
Food research international (Ottawa, Ont.)
Feb 15, 2025
Understanding how the physical properties of food affect sensory perception remains a critical challenge for food design. Here, we present an innovative machine learning strategy to decode the complex relationships between non-Newtonian rheological a...
Fruit wines classification enabled by combing machine learning with comprehensive volatiles profiles of GC-TOF/MS and GC-IMS.
Food research international (Ottawa, Ont.)
Feb 5, 2025
Fruit wines, produced through the fermentation of various fruits, are well-documented for their distinct flavor profiles. Intelligent sensory analysis, GC-TOF/MS and GC-IMS were used for the analysis of the volatile profile of eight types of fruit wi...
Machine learning-based classification and prediction of typical Chinese green tea taste profiles.
Food research international (Ottawa, Ont.)
Jan 27, 2025
The taste of Chinese green tea is highly diverse. In this study, a combination of unsupervised and supervised learning methods was utilized to develop a model for classifying and predicting typical Chinese green tea taste. Three clustering methods we...
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