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Research on the Evaluation of Baijiu Flavor Quality Based on Intelligent Sensory Technology Combined with Machine Learning

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Published in:Chemosensors vol. 12, no. 7 (2024), p. 125
Main Author: Aliya
Other Authors: Liu, Shi, Zhang, Danni, Cao, Yufa, Sun, Jinyuan, Jiang, Shui, Liu, Yuan
Published:
MDPI AG
Subjects:
China
Accuracy
Sensory evaluation
Classification
Discriminant analysis
Pattern recognition systems
Electronic noses
Signal processing
Flavors
Metal oxides
Machine learning
Aroma
Prediction models
Learning algorithms
Electronic tongues
Quality assessment
Flavor compounds
Gases
Technology assessment
Sensory properties
Sensors
Sensory perception
Spirits
Alcohol
Statistical methods
Online Access:Citation/Abstract
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Abstract:Baijiu, one of the world’s six major distilled spirits, has an extremely rich flavor profile, which increases the complexity of its flavor quality evaluation. This study employed an electronic nose (E-nose) and electronic tongue (E-tongue) to detect 42 types of strong-aroma Baijiu. Linear discriminant analysis (LDA) was performed based on the different production origins, alcohol content, and grades. Twelve trained Baijiu evaluators participated in the quantitative descriptive analysis (QDA) of the Baijiu samples. By integrating characteristic values from the intelligent sensory detection data and combining them with the human sensory evaluation results, machine learning was used to establish a multi-submodel-based flavor quality prediction model and classification model for Baijiu. The results showed that different Baijiu samples could be well distinguished, with a prediction model R2 of 0.9994 and classification model accuracy of 100%. This study provides support for the establishment of a flavor quality evaluation system for Baijiu.
ISSN:2227-9040
DOI:10.3390/chemosensors12070125
Source:Materials Science Database