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Classification of Titrimetric Analysis Based on Reaction Types

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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

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Machine Learning and SHAP Feature Analysis: Classification Model for Aroma Components in Green Plum Wine.

Xuhui Zhang¹, Mengsheng Deng¹, Yu Lei¹

¹School of Food and Liquor Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.

Foods (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

This study used machine learning to analyze fermented green plum wine flavors. Ethyl octanoate and other esters significantly contribute to the distinct floral and fruity aromas identified.

Keywords:

SHAP analysis green plum wine machine learning volatile substances

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Fruit Volatile Analysis Using an Electronic Nose

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Published on: March 30, 2012

Area of Science:

* Food Science and Technology
* Analytical Chemistry
* Machine Learning Applications

Background:

* Fermented fruit wines, like green plum wine, possess complex volatile flavor profiles.
* Understanding these profiles is crucial for quality control and product development.
* Traditional methods for flavor analysis can be time-consuming and lack comprehensive interpretation.

Purpose of the Study:

* To systematically investigate and differentiate volatile flavor profiles in fermented green plum wines.
* To evaluate the effectiveness of machine learning (ML) algorithms for flavor profiling and classification.
* To identify key volatile compounds contributing to the distinct aromas using SHapley Additive exPlanations (SHAP).

Main Methods:

* Integration of Gas Chromatography-Mass Spectrometry (GC-MS) for volatile compound identification.
* Sensory evaluation and Odor Activity Value (OAV) analysis to assess aroma impact.
* Application of machine learning algorithms (including fuzzy c-means clustering and decision tree models) and SHAP for data interpretation.

Main Results:

* Floral and fruity aromas were predominant, with esters like ethyl benzoate and ethyl octanoate being major contributors.
* Fuzzy c-means clustering successfully categorized wines into three distinct flavor groups.
* The decision tree model achieved high accuracy (95.13%) in flavor classification, with ethyl octanoate, benzyl ethanoate, and 2-phenylethyl ethanoate identified as key influential compounds by SHAP analysis.

Conclusions:

* Machine learning provides a powerful and efficient approach for classifying and interpreting complex flavor profiles in fermented beverages.
* The study demonstrates the successful application of ML and SHAP in identifying key aroma contributors in green plum wine.
* These findings have broad implications for flavor science and the quality assessment of fermented fruit products.