Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry
Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. After preprocessing the NIR data through 5-point 2-fold convolutional smoothing, spectral f...
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China Food Publishing Company
2024-11-01
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| Series: | Shipin Kexue |
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| Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2024-45-21-032.pdf |
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| author | ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai |
| author_facet | ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai |
| author_sort | ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai |
| collection | DOAJ |
| description | Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. After preprocessing the NIR data through 5-point 2-fold convolutional smoothing, spectral feature wavelengths were selected using the competitive adaptive reweighted sampling (CARS) algorithm; combining Spearman’s rank correlation coefficient, maximum information coefficient (MIC) and random forest (RF) variable importance, the key flavor components (KC) identified by GC-MS affecting the grading of raw Baijiu were determined. Then, extreme gradient boosting tree (XGBoost) was applied to establish three grade identification models for raw Baijui based on NIR, GC-MS and their fused data. The results showed that the prediction accuracy of the model based on the spectral feature variables selected by CARS was 89.66%, the prediction accuracy of the model based on KC after feature selection was 94.83%, and the classification accuracy of the model based on the fused data of CARS + KC reached as high as 98.28%. This study shows that the fusion of effective feature information from GC-MS and NIR data can enable more accurate and stable grade identification of raw Nongxiangxin Baijiu than either analytical technique alone, which provides a new idea and theoretical basis for the grade identification and quality control of raw Baijiu. |
| format | Article |
| id | doaj-art-6bb1fcdd4dcd4ceda9a8782a1b4bf721 |
| institution | OA Journals |
| issn | 1002-6630 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | China Food Publishing Company |
| record_format | Article |
| series | Shipin Kexue |
| spelling | doaj-art-6bb1fcdd4dcd4ceda9a8782a1b4bf7212025-08-20T02:27:44ZengChina Food Publishing CompanyShipin Kexue1002-66302024-11-01452128829610.7506/spkx1002-6630-20240415-119Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass SpectrometryZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai0(1. School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China; 2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644000, China; 3. Engineering Practice Center, Sichuan University of Science & Engineering, Yibin 644000, China)Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. After preprocessing the NIR data through 5-point 2-fold convolutional smoothing, spectral feature wavelengths were selected using the competitive adaptive reweighted sampling (CARS) algorithm; combining Spearman’s rank correlation coefficient, maximum information coefficient (MIC) and random forest (RF) variable importance, the key flavor components (KC) identified by GC-MS affecting the grading of raw Baijiu were determined. Then, extreme gradient boosting tree (XGBoost) was applied to establish three grade identification models for raw Baijui based on NIR, GC-MS and their fused data. The results showed that the prediction accuracy of the model based on the spectral feature variables selected by CARS was 89.66%, the prediction accuracy of the model based on KC after feature selection was 94.83%, and the classification accuracy of the model based on the fused data of CARS + KC reached as high as 98.28%. This study shows that the fusion of effective feature information from GC-MS and NIR data can enable more accurate and stable grade identification of raw Nongxiangxin Baijiu than either analytical technique alone, which provides a new idea and theoretical basis for the grade identification and quality control of raw Baijiu.https://www.spkx.net.cn/fileup/1002-6630/PDF/2024-45-21-032.pdfraw nongxiangxin baijiu; near infrared spectroscopy; gas chromatography-mass spectrometry; data fusion; extreme gradient boosting tree |
| spellingShingle | ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry Shipin Kexue raw nongxiangxin baijiu; near infrared spectroscopy; gas chromatography-mass spectrometry; data fusion; extreme gradient boosting tree |
| title | Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry |
| title_full | Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry |
| title_fullStr | Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry |
| title_full_unstemmed | Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry |
| title_short | Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry |
| title_sort | grade identification of raw nongxiangxing baijiu based on fused data of near infrared spectroscopy and gas chromatography mass spectrometry |
| topic | raw nongxiangxin baijiu; near infrared spectroscopy; gas chromatography-mass spectrometry; data fusion; extreme gradient boosting tree |
| url | https://www.spkx.net.cn/fileup/1002-6630/PDF/2024-45-21-032.pdf |
| work_keys_str_mv | AT zhangweizhangguiyutuoxianguofunilixiaopingpangtingtingliukecai gradeidentificationofrawnongxiangxingbaijiubasedonfuseddataofnearinfraredspectroscopyandgaschromatographymassspectrometry |