Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne
Objective: To explore a rapid and accurate method for evaluating the quality of Prunus mandshurica (Maxim.) Koehne (P. mandshurica, Ku Xing Ren) during rancidity using machine vision and learning. Methods: Sensory evaluation and chemometrics were used to classify P. mandshurica quality grades after...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Journal of Traditional Chinese Medical Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095754825000092 |
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| author | Yashun Wang Huirong Chen Jianting Gong Yang Cui Huiqin Zou Yonghong Yan |
| author_facet | Yashun Wang Huirong Chen Jianting Gong Yang Cui Huiqin Zou Yonghong Yan |
| author_sort | Yashun Wang |
| collection | DOAJ |
| description | Objective: To explore a rapid and accurate method for evaluating the quality of Prunus mandshurica (Maxim.) Koehne (P. mandshurica, Ku Xing Ren) during rancidity using machine vision and learning. Methods: Sensory evaluation and chemometrics were used to classify P. mandshurica quality grades after rancidity. Chemical indicators of the P. mandshurica quality change were determined to verify the obtained grades and support the subsequent modeling. The International Commission on Illumination color space was used to extract the color features of the P. mandshurica. Discrimination and prediction models based on color features combined with multiple machine learning algorithms were established using 10-fold cross-validation and external test set validation. Results: The P. mandshurica rancidity samples were allocated to three quality grades. The Bayes net model based on powder color successfully identified the P. mandshurica at different grades with an accuracy of 88.89% and 100% using two validations, and the naive Bayes model based on section color achieved the same accuracy with an receiver operating characteristic area of 0.979. The instance-based k-nearest neighbors model based on powder color performed best in predicting the amygdalin content [R2 = 0.9801, mean absolute error (MAE) = 0.2071, root mean squared error (RMSE) = 0.4170], followed by the random committee model in predicting the acid value (R2 = 0.9580, MAE = 1.5121, RMSE = 1.9099) and the random forest model in predicting the peroxide value (R2 = 0.8857, MAE = 0.0027, RMSE = 0.0035). Conclusion: This study demonstrates that color digitization analysis is a potential method for rapidly evaluating the quality of P. mandshurica across the rancidity process, providing a new reference for the quality assessment of traditional Chinese medicines. |
| format | Article |
| id | doaj-art-5dc71494259241a092d45fcb5ee1dd1f |
| institution | OA Journals |
| issn | 2095-7548 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Traditional Chinese Medical Sciences |
| spelling | doaj-art-5dc71494259241a092d45fcb5ee1dd1f2025-08-20T02:29:34ZengElsevierJournal of Traditional Chinese Medical Sciences2095-75482025-04-0112228729610.1016/j.jtcms.2025.03.005Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) KoehneYashun Wang0Huirong Chen1Jianting Gong2Yang Cui3Huiqin Zou4Yonghong Yan5School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, ChinaClinical Study Department, Beijing Highthink Pharmaceutical Technology Service Co., Ltd., Beijing 100161, ChinaBeijing Institute of Traditional Chinese Medicine, Beijing Hospital of Traditional Chinese Medicine, Beijing 100010, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, ChinaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; Corresponding author.Objective: To explore a rapid and accurate method for evaluating the quality of Prunus mandshurica (Maxim.) Koehne (P. mandshurica, Ku Xing Ren) during rancidity using machine vision and learning. Methods: Sensory evaluation and chemometrics were used to classify P. mandshurica quality grades after rancidity. Chemical indicators of the P. mandshurica quality change were determined to verify the obtained grades and support the subsequent modeling. The International Commission on Illumination color space was used to extract the color features of the P. mandshurica. Discrimination and prediction models based on color features combined with multiple machine learning algorithms were established using 10-fold cross-validation and external test set validation. Results: The P. mandshurica rancidity samples were allocated to three quality grades. The Bayes net model based on powder color successfully identified the P. mandshurica at different grades with an accuracy of 88.89% and 100% using two validations, and the naive Bayes model based on section color achieved the same accuracy with an receiver operating characteristic area of 0.979. The instance-based k-nearest neighbors model based on powder color performed best in predicting the amygdalin content [R2 = 0.9801, mean absolute error (MAE) = 0.2071, root mean squared error (RMSE) = 0.4170], followed by the random committee model in predicting the acid value (R2 = 0.9580, MAE = 1.5121, RMSE = 1.9099) and the random forest model in predicting the peroxide value (R2 = 0.8857, MAE = 0.0027, RMSE = 0.0035). Conclusion: This study demonstrates that color digitization analysis is a potential method for rapidly evaluating the quality of P. mandshurica across the rancidity process, providing a new reference for the quality assessment of traditional Chinese medicines.http://www.sciencedirect.com/science/article/pii/S2095754825000092Quality evaluationPrunus mandshurica (Maxim.) KoehneRancidityMachine visionMachine learning |
| spellingShingle | Yashun Wang Huirong Chen Jianting Gong Yang Cui Huiqin Zou Yonghong Yan Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne Journal of Traditional Chinese Medical Sciences Quality evaluation Prunus mandshurica (Maxim.) Koehne Rancidity Machine vision Machine learning |
| title | Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne |
| title_full | Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne |
| title_fullStr | Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne |
| title_full_unstemmed | Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne |
| title_short | Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne |
| title_sort | machine vision and learning for evaluating different rancidity grades of prunus mandshurica maxim koehne |
| topic | Quality evaluation Prunus mandshurica (Maxim.) Koehne Rancidity Machine vision Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2095754825000092 |
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