Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion
Abstract Chenpi, or dried tangerine peel, is a traditional Chinese ingredient valued in medicine and edible for its digestive and respiratory benefits. The geographical origin of Chenpi is important, as it can impact its quality, active compounds and market value. This study develops a strategy to d...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Science of Food |
| Online Access: | https://doi.org/10.1038/s41538-025-00376-0 |
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| author | Xin Kang Li Li Jun Tang Ze Ying Li Dian Qiu Zhuo Ling Yang Xiao Yi Zhang Xiang-Zhi Zhang Jing Jing Guo Bao Qiong Li |
| author_facet | Xin Kang Li Li Jun Tang Ze Ying Li Dian Qiu Zhuo Ling Yang Xiao Yi Zhang Xiang-Zhi Zhang Jing Jing Guo Bao Qiong Li |
| author_sort | Xin Kang Li |
| collection | DOAJ |
| description | Abstract Chenpi, or dried tangerine peel, is a traditional Chinese ingredient valued in medicine and edible for its digestive and respiratory benefits. The geographical origin of Chenpi is important, as it can impact its quality, active compounds and market value. This study develops a strategy to distinguish Chenpi samples on its origin. Thirty-nine samples from eight regions in Xinhui district (Guangdong, China) are analyzed by gas chromatography (GC) and mid-infrared (MIR) technique. Four machine learning methods are employed to establish discrimination models based on GC and MIR data, with two mid-level data fusion strategies to combine the data. The results show that data fusion significantly improves Chenpi origin discrimination. The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. Machine learning in combination with modified mid-level data fusion strategy provides effective classification of Chenpi samples from different geographical origins. |
| format | Article |
| id | doaj-art-f8e2b7cd3182429ebb0f119b6b50fe01 |
| institution | Kabale University |
| issn | 2396-8370 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Science of Food |
| spelling | doaj-art-f8e2b7cd3182429ebb0f119b6b50fe012025-02-09T12:56:10ZengNature Portfolionpj Science of Food2396-83702025-02-019111010.1038/s41538-025-00376-0Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusionXin Kang Li0Li Jun Tang1Ze Ying Li2Dian Qiu3Zhuo Ling Yang4Xiao Yi Zhang5Xiang-Zhi Zhang6Jing Jing Guo7Bao Qiong Li8School of Pharmacy and Food Engineering, Wuyi UniversitySchool of Pharmacy and Food Engineering, Wuyi UniversitySchool of Pharmacy and Food Engineering, Wuyi UniversitySchool of Pharmacy and Food Engineering, Wuyi UniversitySchool of Pharmacy and Food Engineering, Wuyi UniversitySchool of Pharmacy and Food Engineering, Wuyi UniversitySchool of Pharmacy and Food Engineering, Wuyi UniversityFaculty of Applied Sciences, Macao Polytechnic UniversitySchool of Pharmacy and Food Engineering, Wuyi UniversityAbstract Chenpi, or dried tangerine peel, is a traditional Chinese ingredient valued in medicine and edible for its digestive and respiratory benefits. The geographical origin of Chenpi is important, as it can impact its quality, active compounds and market value. This study develops a strategy to distinguish Chenpi samples on its origin. Thirty-nine samples from eight regions in Xinhui district (Guangdong, China) are analyzed by gas chromatography (GC) and mid-infrared (MIR) technique. Four machine learning methods are employed to establish discrimination models based on GC and MIR data, with two mid-level data fusion strategies to combine the data. The results show that data fusion significantly improves Chenpi origin discrimination. The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. Machine learning in combination with modified mid-level data fusion strategy provides effective classification of Chenpi samples from different geographical origins.https://doi.org/10.1038/s41538-025-00376-0 |
| spellingShingle | Xin Kang Li Li Jun Tang Ze Ying Li Dian Qiu Zhuo Ling Yang Xiao Yi Zhang Xiang-Zhi Zhang Jing Jing Guo Bao Qiong Li Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion npj Science of Food |
| title | Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion |
| title_full | Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion |
| title_fullStr | Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion |
| title_full_unstemmed | Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion |
| title_short | Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion |
| title_sort | geographical origin discrimination of chenpi using machine learning and enhanced mid level data fusion |
| url | https://doi.org/10.1038/s41538-025-00376-0 |
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