Identification and prediction of milk-derived bitter taste peptides based on peptidomics technology and machine learning method

Yu, Yang; Liu, Shengchi; Zhang, Xinchen; Yu, Wenhao; Pei, Xiaoyan; Liu, Li; Jin, Yan

doi:10.1016/j.foodchem.2023.137288

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2024

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Cited by 3 publications

References 33 publications

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Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning: A Feasibility Study

Mamat,

Wong,

Yew

et al. 2024

Internet of Things

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Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning: A Feasibility Study

Mamat,

Wong,

Yew

et al. 2024

Internet of Things

View full text Add to dashboard Cite

Contemporary bioinformatics and cheminformatics support for food peptidomics

Minkiewicz,

Iwaniak,

Darewicz

2024

Current Opinion in Food Science

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Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review

Liu,

Shi,

et al. 2024

IJMS

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Bitter peptides are small molecular peptides produced by the hydrolysis of proteins under acidic, alkaline, or enzymatic conditions. These peptides can enhance food flavor and offer various health benefits, with attributes such as antihypertensive, antidiabetic, antioxidant, antibacterial, and immune-regulating properties. They show significant potential in the development of functional foods and the prevention and treatment of diseases. This review introduces the diverse sources of bitter peptides and discusses the mechanisms of bitterness generation and their physiological functions in the taste system. Additionally, it emphasizes the application of bioinformatics in bitter peptide research, including the establishment and improvement of bitter peptide databases, the use of quantitative structure–activity relationship (QSAR) models to predict bitterness thresholds, and the latest advancements in classification prediction models built using machine learning and deep learning algorithms for bitter peptide identification. Future research directions include enhancing databases, diversifying models, and applying generative models to advance bitter peptide research towards deepening and discovering more practical applications.

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Identification and prediction of milk-derived bitter taste peptides based on peptidomics technology and machine learning method

Cited by 3 publications

References 33 publications

Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning: A Feasibility Study

Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning: A Feasibility Study

Contemporary bioinformatics and cheminformatics support for food peptidomics

Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review

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