Research on the Evaluation of Baijiu Flavor Quality Based on Intelligent Sensory Technology Combined with Machine Learning
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| Publicado en: | Chemosensors vol. 12, no. 7 (2024), p. 125 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/chemosensors12070125 |2 doi | |
| 035 | |a 3084716290 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231440 |2 nlm | ||
| 100 | 1 | |a Aliya |u Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China; <email>aly122150910101@sjtu.edu.cn</email> (A.); <email>jiangshui@sjtu.edu.cn</email> (S.J.) | |
| 245 | 1 | |a Research on the Evaluation of Baijiu Flavor Quality Based on Intelligent Sensory Technology Combined with Machine Learning | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Baijiu, one of the world’s six major distilled spirits, has an extremely rich flavor profile, which increases the complexity of its flavor quality evaluation. This study employed an electronic nose (E-nose) and electronic tongue (E-tongue) to detect 42 types of strong-aroma Baijiu. Linear discriminant analysis (LDA) was performed based on the different production origins, alcohol content, and grades. Twelve trained Baijiu evaluators participated in the quantitative descriptive analysis (QDA) of the Baijiu samples. By integrating characteristic values from the intelligent sensory detection data and combining them with the human sensory evaluation results, machine learning was used to establish a multi-submodel-based flavor quality prediction model and classification model for Baijiu. The results showed that different Baijiu samples could be well distinguished, with a prediction model R2 of 0.9994 and classification model accuracy of 100%. This study provides support for the establishment of a flavor quality evaluation system for Baijiu. | |
| 651 | 4 | |a China | |
| 653 | |a Accuracy | ||
| 653 | |a Sensory evaluation | ||
| 653 | |a Classification | ||
| 653 | |a Discriminant analysis | ||
| 653 | |a Pattern recognition systems | ||
| 653 | |a Electronic noses | ||
| 653 | |a Signal processing | ||
| 653 | |a Flavors | ||
| 653 | |a Metal oxides | ||
| 653 | |a Machine learning | ||
| 653 | |a Aroma | ||
| 653 | |a Prediction models | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Electronic tongues | ||
| 653 | |a Quality assessment | ||
| 653 | |a Flavor compounds | ||
| 653 | |a Gases | ||
| 653 | |a Technology assessment | ||
| 653 | |a Sensory properties | ||
| 653 | |a Sensors | ||
| 653 | |a Sensory perception | ||
| 653 | |a Spirits | ||
| 653 | |a Alcohol | ||
| 653 | |a Statistical methods | ||
| 700 | 1 | |a Liu, Shi |u Suqian Product Quality Supervision and Testing Institute, Suqian 223800, China; <email>sqcyf@163.com</email> | |
| 700 | 1 | |a Zhang, Danni |u Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai 200240, China; <email>dannizhang2019@sjtu.edu.cn</email> | |
| 700 | 1 | |a Cao, Yufa |u Suqian Product Quality Supervision and Testing Institute, Suqian 223800, China; <email>sqcyf@163.com</email> | |
| 700 | 1 | |a Sun, Jinyuan |u China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 102401, China | |
| 700 | 1 | |a Jiang, Shui |u Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China; <email>aly122150910101@sjtu.edu.cn</email> (A.); <email>jiangshui@sjtu.edu.cn</email> (S.J.) | |
| 700 | 1 | |a Liu, Yuan |u Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China; <email>aly122150910101@sjtu.edu.cn</email> (A.); <email>jiangshui@sjtu.edu.cn</email> (S.J.); School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China | |
| 773 | 0 | |t Chemosensors |g vol. 12, no. 7 (2024), p. 125 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3084716290/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3084716290/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3084716290/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |