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Yan XQ, Wu HL, Wang B, Wang T, Chen Y, Chen AQ, Huang K, Chang YY, Yang J, Yu RQ. Front-face excitation-emission matrix fluorescence spectroscopy combined with interpretable deep learning for the rapid identification of the storage year of Ningxia wolfberry. Spectrochim Acta A Mol Biomol Spectrosc 2023; 295:122617. [PMID: 36963220 DOI: 10.1016/j.saa.2023.122617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 03/01/2023] [Accepted: 03/08/2023] [Indexed: 06/18/2023]
Abstract
Ningxia wolfberry stored for many years may be disguised as fresh wolfberry by unscrupulous traders and sold for huge profits. In this work, the front-face excitation-emission matrix (FF-EEM) fluorescence spectroscopy coupled with interpretable deep learning was proposed to identify the storage year of Ningxia wolfberry in a lossless, fast and accurate way. Alternating trilinear decomposition (ATLD) algorithm was used to decompose the three-way data array obtained by Ningxia wolfberry samples, extracting the chemically meaningful information. Meanwhile, a convolutional neural network (CNN) model for the identification of the storage year of Ningxia wolfberry, called EEMnet, was proposed. The model successfully classified wolfberry samples from different storage years by extracting the subtle feature differences of the spectra, and the correct classification rate of the training set, test set and prediction set was more than 98%. In addition, a series of interpretability analyses were implemented to break the "black box" of the deep learning model. These results indicated that the method based on FF-EEM fluorescence spectroscopy combined with EEMnet could quickly and accurately identify the year of Ningxia wolfberry in a green way, providing a new idea for the identification of the storage years of Chinese medicinal materials.
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Affiliation(s)
- Xiao-Qin Yan
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Hai-Long Wu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
| | - Bin Wang
- Hunan Key Lab of Biomedical Materials and Devices, College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou 412008, PR China
| | - Tong Wang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
| | - Yao Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China; Hunan Key Lab of Biomedical Materials and Devices, College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou 412008, PR China
| | - An-Qi Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Kun Huang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Yue-Yue Chang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
| | - Jian Yang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, State Key Laboratory Breeding Base of Dao-di Herbs, Beijng 100700, PR China
| | - Ru-Qin Yu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China
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