Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm
The chemical composition of base baijiu is complex and diverse. A classification model for base baijiu of different sensory grades was established based on the gas chromatography-mass spectrometric (GC-MS) data for their volatile composition. In order to improve the accuracy and generalization capac...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
China Food Publishing Company
2025-03-01
|
| Series: | Shipin Kexue |
| Subjects: | |
| Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-6-031.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850200585646637056 |
|---|---|
| author | PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin |
| author_facet | PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin |
| author_sort | PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin |
| collection | DOAJ |
| description | The chemical composition of base baijiu is complex and diverse. A classification model for base baijiu of different sensory grades was established based on the gas chromatography-mass spectrometric (GC-MS) data for their volatile composition. In order to improve the accuracy and generalization capacity of the classification model, a method combining genetic algorithm (GA) and bootstrap aggregating (Bagging) was proposed to optimize the support vector machine (SVM) classifier. Using Spearman’s correlation analysis, 36 key substances were selected, and 12 kernel principal components were extracted as input to the model by kernel principal component analysis, which together accounted for 96.06% of the total variance. The radial basis kernel function support vector machine with the best performance was selected, and the parallel computing Bagging ensemble algorithm with strong adaptability to data diversity was used to construct a Bagging-SVM classifier for base baijiu classification. Finally, GA was used to optimize the parameters (C, γ, and N) of the Bagging-SVM classifier to construct a GA-Bagging-SVM model. The results showed that the accuracy, precision, recall rate, and F1-Score of the GA-Bagging-SVM model were 96.77%, 96.90%, 96.77%, and 96.78%, respectively, which were 6.45%, 5.61%, 6.45%, and 6.42% higher than those of the SVM model, and 3.22%, 2.29%, 3.22%, and 3.15% higher than those of the Bagging-SVM model, respectively. This method can be used as an optimization method for the quality evaluation model for base baijiu. |
| format | Article |
| id | doaj-art-5487a67d4ab1451d8eb9df0867c5bab6 |
| institution | OA Journals |
| issn | 1002-6630 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | China Food Publishing Company |
| record_format | Article |
| series | Shipin Kexue |
| spelling | doaj-art-5487a67d4ab1451d8eb9df0867c5bab62025-08-20T02:12:19ZengChina Food Publishing CompanyShipin Kexue1002-66302025-03-0146627528410.7506/spkx1002-6630-20240801-002Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating AlgorithmPANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin0(1. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644000, China; 2. School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China; 3. Liquor Making Biological Technology and Application of Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644000, China; 4. Liquor Making Biotechnology and Intelligent Manufacturing of Key Laboratory of China National Light Industry, Sichuan University of Science & Engineering, Yibin 644000, China; 5. Engineering Practice Center, Sichuan University of Science & Engineering, Yibin 644000, China; 6. School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin 644000, China)The chemical composition of base baijiu is complex and diverse. A classification model for base baijiu of different sensory grades was established based on the gas chromatography-mass spectrometric (GC-MS) data for their volatile composition. In order to improve the accuracy and generalization capacity of the classification model, a method combining genetic algorithm (GA) and bootstrap aggregating (Bagging) was proposed to optimize the support vector machine (SVM) classifier. Using Spearman’s correlation analysis, 36 key substances were selected, and 12 kernel principal components were extracted as input to the model by kernel principal component analysis, which together accounted for 96.06% of the total variance. The radial basis kernel function support vector machine with the best performance was selected, and the parallel computing Bagging ensemble algorithm with strong adaptability to data diversity was used to construct a Bagging-SVM classifier for base baijiu classification. Finally, GA was used to optimize the parameters (C, γ, and N) of the Bagging-SVM classifier to construct a GA-Bagging-SVM model. The results showed that the accuracy, precision, recall rate, and F1-Score of the GA-Bagging-SVM model were 96.77%, 96.90%, 96.77%, and 96.78%, respectively, which were 6.45%, 5.61%, 6.45%, and 6.42% higher than those of the SVM model, and 3.22%, 2.29%, 3.22%, and 3.15% higher than those of the Bagging-SVM model, respectively. This method can be used as an optimization method for the quality evaluation model for base baijiu.https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-6-031.pdfbase baijiu; support vector machine; bootstrap aggregating; genetic algorithm; classification prediction |
| spellingShingle | PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm Shipin Kexue base baijiu; support vector machine; bootstrap aggregating; genetic algorithm; classification prediction |
| title | Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm |
| title_full | Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm |
| title_fullStr | Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm |
| title_full_unstemmed | Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm |
| title_short | Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm |
| title_sort | quality evaluation method for base baijiu based on support vector machine optimized by genetic and bootstrap aggregating algorithm |
| topic | base baijiu; support vector machine; bootstrap aggregating; genetic algorithm; classification prediction |
| url | https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-6-031.pdf |
| work_keys_str_mv | AT pangtingtingzhangguiyuliukecailixiaopingtuoxianguopengyingjiezengxianglin qualityevaluationmethodforbasebaijiubasedonsupportvectormachineoptimizedbygeneticandbootstrapaggregatingalgorithm |