Machine Learning-Based Prediction of Sensory Quality in Tea Blends Using Semi-Trained Panel Assessment

Onkar Sarma; Kavya Dashora

doi:10.1039/d5fb00580a

January 1, 2025Sustainable Food TechnologyOpen Access

Machine Learning-Based Prediction of Sensory Quality in Tea Blends Using Semi-Trained Panel Assessment

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Key Points

This research aims to use machine learning to predict the sensory quality of various tea blends based on their biochemical profiles.
Utilized a semi-trained panel to assess sensory quality of different tea blends.
Developed machine learning models to analyze the assessment data and predict quality outcomes.
Compared predicted sensory qualities with traditional assessments by professional tasters.
Machine learning models provided accurate predictions of sensory quality compared to traditional methods.
The use of biochemical profiles significantly enhanced prediction accuracy.
The study demonstrates potential for streamlining tea blending processes.

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Implication

Machine learning predicts sensory quality in tea blends, indicating a new approach to enhance tea tasting.

Abstract

Blending teas with complementary biochemical profile offers a promising path for enhancing sensory quality of tea blends. Traditionally, blending is done by professional tea tasters, which makes it dependent on...

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Cite This Study

Sarma et al. (2025) studied this question.

synapsesocial.com/papers/69843383f1d9ada3c1fb0b3e https://doi.org/10.1039/d5fb00580a

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Machine Learning-Based Prediction of Sensory Quality in Tea Blends Using Semi-Trained Panel Assessment

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Machine Learning-Based Prediction of Sensory Quality in Tea Blends Using Semi-Trained Panel Assessment

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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