AIMC Topic: Taste

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Quantitative non-volatile sensometabolome of Longjing tea and discrimination of taste quality by sensory analysis, large-scale quantitative metabolomics and machine learning.

Food chemistry
Study on quantitative non-volatile sensometabolome of Longjing tea remains lacked. Herein, the taste and molecular features of 42 Longjing tea samples were analyzed by sensory quantitative analysis and quantitative metabolomics. A comprehensive lands...

Decoding the quantitative structure-activity relationship and astringency formation mechanism of oxygenated aromatic compounds.

Food research international (Ottawa, Ont.)
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...

Synergizing meat science and interpretable AI: Quantifying crispness gradients for quality authentication of Tilapia fillet processing.

Food chemistry
Crispy tilapia has become a popular aquatic product due to its unique texture and high market demand. However, fillets at different stages of crispness vary significantly in nutritional value and taste, directly affecting product quality and consumer...

From prediction to design: Revealing the mechanisms of umami peptides using interpretable deep learning, quantum chemical simulations, and module substitution.

Food chemistry
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
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
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
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
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.)
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.)
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...