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

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Опубликовано в::	Chemosensors vol. 12, no. 7 (2024), p. 125
Главный автор:	Aliya
Другие авторы:	Liu, Shi, Zhang, Danni, Cao, Yufa, Sun, Jinyuan, Jiang, Shui, Liu, Yuan
Опубликовано:	MDPI AG
Предметы:	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
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Описание
Краткий обзор:	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
Источник:	Materials Science Database