Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu

The advantages of pottery jars in the aging process of Baijiu are evident, but the impact of their material composition and pore structure on the flavor of Baijiu has not been widely studied. This study systematically analyzed the effects of six types of pottery jars on metal ions and flavor substances during the storage process of Maotai-flavored Baijiu. It was found that changes in the content of Fe and Zn metals, as well as pore parameters in the jars, significantly affected the content of AL, Mg, K, Na, and Ca ions in Baijiu. Based on three feature ranking methods and three machine learning models, a feature selection method related to flavor substances was established, identifying the key features (i.e., key metal ions) for each flavor group. The final key features of each flavor group can accurately predict the corresponding flavor substance content (Rp<sup>2</sup> > 0.87). The comprehensive analysis results indicate that the increase in the content of Fe, as well as the increases in P-max and P-min in the pottery jar, collectively promoted the formation of three flavor groups represented by ethyl valerate (G2), ethyl lactate (G7), and ethyl linoleate (G10), with an increase of 3% to 5%. In contrast, the increase in Zn inhibited the formation of the flavor group represented by 2,3-butanediol (G3), with a decrease of 14%. These results further clarify the impact of pottery jar formulations on the changes in flavor substances and provide a more effective method for analyzing the influence mechanism of jars on Baijiu.

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Publication Details

Journal
Foods
Published
2025-03-20
DOI
https://doi.org/10.3390/foods14061063
Citations
1
Primary Topic
Cultural Heritage Materials Analysis
Type
article
Field-Weighted Citation Impact
8.27

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article

Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu

Jun Tian, Dan Huang, Haili Yang, Xinjun Hu et al.
1 citations
Foods
Cultural Heritage Materials Analysis
8.27
article

Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu

Jun Tian, Dan Huang, Haili Yang, Xinjun Hu, Liangliang Xie, Man Chen
article en
1 citations

Abstract

The advantages of pottery jars in the aging process of Baijiu are evident, but the impact of their material composition and pore structure on the flavor of Baijiu has not been widely studied. This study systematically analyzed the effects of six types of pottery jars on metal ions and flavor substances during the storage process of Maotai-flavored Baijiu. It was found that changes in the content of Fe and Zn metals, as well as pore parameters in the jars, significantly affected the content of AL, Mg, K, Na, and Ca ions in Baijiu. Based on three feature ranking methods and three machine learning models, a feature selection method related to flavor substances was established, identifying the key features (i.e., key metal ions) for each flavor group. The final key features of each flavor group can accurately predict the corresponding flavor substance content (Rp<sup>2</sup> > 0.87). The comprehensive analysis results indicate that the increase in the content of Fe, as well as the increases in P-max and P-min in the pottery jar, collectively promoted the formation of three flavor groups represented by ethyl valerate (G2), ethyl lactate (G7), and ethyl linoleate (G10), with an increase of 3% to 5%. In contrast, the increase in Zn inhibited the formation of the flavor group represented by 2,3-butanediol (G3), with a decrease of 14%. These results further clarify the impact of pottery jar formulations on the changes in flavor substances and provide a more effective method for analyzing the influence mechanism of jars on Baijiu.

FoodsVol. 14(6)
Yibin University (CN), Sichuan University of Science and Engineering (CN)
Sichuan Province Science and Technology Support Program
Openalex Percentile: Top 5%
Cultural Heritage Materials Analysis
8.27
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