Development of ML Model to Assess Taste in Plants

International Journal of Engineering Innovations and Management Strategies 1 (3):1-13 (2024)
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Abstract

Taste is an important aspect for the assessment of medicinal plants as such assessment helps to determine the therapeutic property and application of medicinal plants. Traditionally, plant taste has been assessed based on human sensory perception. This project aims at developing a machine learning (ML) model that will quantify and predict plant taste in terms of their chemical composition. Given the dataset of chemical compounds, the model will relate a specific compound to the known taste types: sweet, bitter, pungent, sour, salty. The methodology in this encompasses data preprocessing, feature extraction, and supervised learning techniques over the preparation of the inference model. Major steps include training over labeled data and validating the accuracy over cross-validation. In addition, techniques of web scraping and API integration are used in order to expand the dataset with the properties of medicinal plants from a chemical standpoint. The last model will be a tool for researchers and practitioners within the field of pharmacology and herbal medicine so as to have a more rapid and more objective evaluation of plant taste, which could thus be useful in identifying new applications of medicinal interest.

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