Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy
This study aims to rapidly and non-destructively identify the geographical origin of black beans (Phaseolus vulgaris) using a portable near-infrared (NIR) spectrometer, addressing the challenge of distinguishing black beans due to significant regional variations in quality. A total of 400 black bean...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Current Research in Food Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665927125001625 |
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| _version_ | 1849710164685357056 |
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| author | Zhihang Cao Xiaohong Wu Bin Wu Zexi Zhang Jun Sun |
| author_facet | Zhihang Cao Xiaohong Wu Bin Wu Zexi Zhang Jun Sun |
| author_sort | Zhihang Cao |
| collection | DOAJ |
| description | This study aims to rapidly and non-destructively identify the geographical origin of black beans (Phaseolus vulgaris) using a portable near-infrared (NIR) spectrometer, addressing the challenge of distinguishing black beans due to significant regional variations in quality. A total of 400 black bean samples were collected from five regions in China. To improve classification accuracy, a novel model combining uncorrelated discriminant transform (UDT) with extreme gradient boosting (XGBoost) was proposed for feature extraction and classification. When evaluated with k-nearest neighbor (KNN), naive Bayes (NB), and support vector machine (SVM) classifiers, UDT achieved accuracies of 96.25 %, 93.75 %, and 96.25 %, respectively, outperforming Foley-Sammon transform (FST) and discriminant principal component analysis (DPCA). The UDT + XGBoost combination achieved the highest classification accuracy of 100 %. For robust validation, a 5-fold cross-validation strategy was applied to the UDT + XGBoost model, achieving an average accuracy of 96.00 %. This study provides a reliable method for black bean origin traceability and authenticity. |
| format | Article |
| id | doaj-art-8a77314c0436402b8d00963b980a810f |
| institution | DOAJ |
| issn | 2665-9271 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Food Science |
| spelling | doaj-art-8a77314c0436402b8d00963b980a810f2025-08-20T03:15:02ZengElsevierCurrent Research in Food Science2665-92712025-01-011110113110.1016/j.crfs.2025.101131Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopyZhihang Cao0Xiaohong Wu1Bin Wu2Zexi Zhang3Jun Sun4Mengxi Honors College, Jiangsu University, Zhenjiang, 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China; High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang, 212013, China; Corresponding author. School of Electrical and Information Engineering, Jiangsu University, Xuefu Road No. 301, Zhenjiang, 212013, China.Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, China; School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China; Corresponding author. Department of Information Engineering, Chuzhou Polytechnic, Fengle Street No. 2188, Chuzhou, 239000, China.Mengxi Honors College, Jiangsu University, Zhenjiang, 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, ChinaThis study aims to rapidly and non-destructively identify the geographical origin of black beans (Phaseolus vulgaris) using a portable near-infrared (NIR) spectrometer, addressing the challenge of distinguishing black beans due to significant regional variations in quality. A total of 400 black bean samples were collected from five regions in China. To improve classification accuracy, a novel model combining uncorrelated discriminant transform (UDT) with extreme gradient boosting (XGBoost) was proposed for feature extraction and classification. When evaluated with k-nearest neighbor (KNN), naive Bayes (NB), and support vector machine (SVM) classifiers, UDT achieved accuracies of 96.25 %, 93.75 %, and 96.25 %, respectively, outperforming Foley-Sammon transform (FST) and discriminant principal component analysis (DPCA). The UDT + XGBoost combination achieved the highest classification accuracy of 100 %. For robust validation, a 5-fold cross-validation strategy was applied to the UDT + XGBoost model, achieving an average accuracy of 96.00 %. This study provides a reliable method for black bean origin traceability and authenticity.http://www.sciencedirect.com/science/article/pii/S2665927125001625Black beanNear-infrared spectroscopyGeographical originExtreme gradient boostingUncorrelated discriminant transform |
| spellingShingle | Zhihang Cao Xiaohong Wu Bin Wu Zexi Zhang Jun Sun Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy Current Research in Food Science Black bean Near-infrared spectroscopy Geographical origin Extreme gradient boosting Uncorrelated discriminant transform |
| title | Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy |
| title_full | Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy |
| title_fullStr | Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy |
| title_full_unstemmed | Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy |
| title_short | Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy |
| title_sort | combining udt with xgboost to identify the geographical origin of black beans by near infrared spectroscopy |
| topic | Black bean Near-infrared spectroscopy Geographical origin Extreme gradient boosting Uncorrelated discriminant transform |
| url | http://www.sciencedirect.com/science/article/pii/S2665927125001625 |
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