Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MS
Flavor is one of the crucial factors that influences the quality and consumer acceptance of baijiu. In this study, we analyzed the volatile organic compound (VOC) profiles of five different quality grades of Nongxiangxing baijiu (NXB), fermented during the summer of 2024, using 2D gas chromatography...
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MDPI AG
2025-05-01
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| author | Dongliang Shao Wei Cheng Chao Jiang Tianquan Pan Na Li Mengmeng Li Ruilong Li Wei Lan Xianfeng Du |
| author_facet | Dongliang Shao Wei Cheng Chao Jiang Tianquan Pan Na Li Mengmeng Li Ruilong Li Wei Lan Xianfeng Du |
| author_sort | Dongliang Shao |
| collection | DOAJ |
| description | Flavor is one of the crucial factors that influences the quality and consumer acceptance of baijiu. In this study, we analyzed the volatile organic compound (VOC) profiles of five different quality grades of Nongxiangxing baijiu (NXB), fermented during the summer of 2024, using 2D gas chromatography time-of-flight mass spectrometry (GC×GC–TOF-MS). We employed machine-learning (ML)-based classification and prediction models to evaluate the flavor. For TW, the scores of the sensory evaluation for coordination and overall evaluation were the highest. TW contained the highest concentration of ethyl caproate; we detected 965 VOCs in total, including several pyrazine compounds with potential health benefits. Principal component analysis (PCA) combined with orthogonal partial least squares discriminant analysis (OPLS-DA) enabled us to distinguish different samples, with eight VOCs emerging as primary contributors to the aroma of the samples, possessing variable importance in projection (VIP) values > 1. Furthermore, we tested eight ML models; random forest (RF) demonstrated the best classification performance, effectively discriminating samples based on their VOC profiles. The key VOC contributors that showed quality-grade specificity included 1-butanol, 3-methyl-1-butanol, and ethyl 5-methylhexanoate. The results elucidate the flavor-based features of NXB and provide a valuable reference for discriminating and predicting baijiu flavors. |
| format | Article |
| id | doaj-art-84ef9d1e87e04bee812d8492376d182f |
| institution | DOAJ |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-84ef9d1e87e04bee812d8492376d182f2025-08-20T03:14:34ZengMDPI AGFoods2304-81582025-05-011410171410.3390/foods14101714Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MSDongliang Shao0Wei Cheng1Chao Jiang2Tianquan Pan3Na Li4Mengmeng Li5Ruilong Li6Wei Lan7Xianfeng Du8School of Food and Nutrition, Anhui Agricultural University, Hefei 230036, ChinaSchool of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, ChinaAnhui WenWang Brewery Co., Ltd., Fuyang 236400, ChinaSchool of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, ChinaSchool of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, ChinaSchool of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, ChinaSchool of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, ChinaSchool of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, ChinaSchool of Food and Nutrition, Anhui Agricultural University, Hefei 230036, ChinaFlavor is one of the crucial factors that influences the quality and consumer acceptance of baijiu. In this study, we analyzed the volatile organic compound (VOC) profiles of five different quality grades of Nongxiangxing baijiu (NXB), fermented during the summer of 2024, using 2D gas chromatography time-of-flight mass spectrometry (GC×GC–TOF-MS). We employed machine-learning (ML)-based classification and prediction models to evaluate the flavor. For TW, the scores of the sensory evaluation for coordination and overall evaluation were the highest. TW contained the highest concentration of ethyl caproate; we detected 965 VOCs in total, including several pyrazine compounds with potential health benefits. Principal component analysis (PCA) combined with orthogonal partial least squares discriminant analysis (OPLS-DA) enabled us to distinguish different samples, with eight VOCs emerging as primary contributors to the aroma of the samples, possessing variable importance in projection (VIP) values > 1. Furthermore, we tested eight ML models; random forest (RF) demonstrated the best classification performance, effectively discriminating samples based on their VOC profiles. The key VOC contributors that showed quality-grade specificity included 1-butanol, 3-methyl-1-butanol, and ethyl 5-methylhexanoate. The results elucidate the flavor-based features of NXB and provide a valuable reference for discriminating and predicting baijiu flavors.https://www.mdpi.com/2304-8158/14/10/1714Nongxiangxing baijiu (NXB)volatile organic compounds (VOCs)characteristic componentsmachine learningdiscrimination and prediction of flavor |
| spellingShingle | Dongliang Shao Wei Cheng Chao Jiang Tianquan Pan Na Li Mengmeng Li Ruilong Li Wei Lan Xianfeng Du Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MS Foods Nongxiangxing baijiu (NXB) volatile organic compounds (VOCs) characteristic components machine learning discrimination and prediction of flavor |
| title | Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MS |
| title_full | Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MS |
| title_fullStr | Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MS |
| title_full_unstemmed | Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MS |
| title_short | Machine-Learning-Assisted Aroma Profile Prediction in Five Different Quality Grades of Nongxiangxing Baijiu Fermented During Summer Using Sensory Evaluation Combined with GC×GC–TOF-MS |
| title_sort | machine learning assisted aroma profile prediction in five different quality grades of nongxiangxing baijiu fermented during summer using sensory evaluation combined with gc gc tof ms |
| topic | Nongxiangxing baijiu (NXB) volatile organic compounds (VOCs) characteristic components machine learning discrimination and prediction of flavor |
| url | https://www.mdpi.com/2304-8158/14/10/1714 |
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