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 substan...
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MDPI AG
2025-03-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/6/1063 |
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| author | Haili Yang Xinjun Hu Jianpin Tian Liangliang Xie Manjiao Chen Dan Huang |
| author_facet | Haili Yang Xinjun Hu Jianpin Tian Liangliang Xie Manjiao Chen Dan Huang |
| author_sort | Haili Yang |
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| description | 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. |
| format | Article |
| id | doaj-art-fbce91c8a88d4424b38b553d4b084d33 |
| institution | DOAJ |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Foods |
| spelling | doaj-art-fbce91c8a88d4424b38b553d4b084d332025-08-20T02:42:30ZengMDPI AGFoods2304-81582025-03-01146106310.3390/foods14061063Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored BaijiuHaili Yang0Xinjun Hu1Jianpin Tian2Liangliang Xie3Manjiao Chen4Dan Huang5School of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaThe Liquor Making Biological Technology and Application of Key Laboratory of Sichuan Province, Yibin 644000, ChinaThe 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.https://www.mdpi.com/2304-8158/14/6/1063pottery jarmetal ionspore parametersflavor substancesfeature rankingmachine learning |
| spellingShingle | Haili Yang Xinjun Hu Jianpin Tian Liangliang Xie Manjiao Chen Dan Huang Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu Foods pottery jar metal ions pore parameters flavor substances feature ranking machine learning |
| title | Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu |
| title_full | Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu |
| title_fullStr | Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu |
| title_full_unstemmed | Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu |
| title_short | Exploring the Influence of Pottery Jar Formula Variables on Flavor Substances Through Feature Ranking and Machine Learning: Case Study of Maotai-Flavored Baijiu |
| title_sort | exploring the influence of pottery jar formula variables on flavor substances through feature ranking and machine learning case study of maotai flavored baijiu |
| topic | pottery jar metal ions pore parameters flavor substances feature ranking machine learning |
| url | https://www.mdpi.com/2304-8158/14/6/1063 |
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