Predicting Bitterness Inducing Peptides Through Traditional Supervised Machine Learning
Year: 2026
Type: article
Abstract: Bitter peptides can affect both the taste and the healthiness of a given food product. Identification of bitter peptides can allow researchers to develop healthy and palatable foods and medicines. The program Orange by Demsar et al. (2013) was used to analyze a dataset of peptides and develop machine learning models to predict if a given peptide causes bitterness. The gradient boosting tree and random forest models performed the most optimally out of all models. This can be used as a basis for improving on bitter peptide detection through machine learning in the future.
DOI: 10.13021/mars/15253
Author: Muralikrishnan, Kavin
Language: English
Subfield: Nutrition and Dietetics
Field: Nursing
Domain: Health Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7128713039
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