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Rapid and Non-Destructive Geographical Origin Identification of Chuanxiong Slices Using Near-Infrared Spectroscopy and Convolutional Neural Networks

Authors :
Yuxing Huang
Yang Pan
Chong Liu
Lan Zhou
Lijuan Tang
Huayi Wei
Ke Fan
Aichen Wang
Yong Tang
Source :
Agriculture, Vol 14, Iss 8, p 1281 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Ligusticum Chuanxiong, a perennial herb of considerable medicinal value commonly known as Chuanxiong, holds pivotal importance in sliced form for ensuring quality and regulating markets through geographical origin identification. This study introduces an integrated approach utilizing Near-Infrared Spectroscopy (NIRS) and Convolutional Neural Networks (CNNs) to establish an efficient method for rapidly determining the geographical origin of Chuanxiong slices. A dataset comprising 300 samples from 6 distinct origins was analyzed using a 1D-CNN model. In this study, we initially established a traditional classification model. By utilizing the Spectrum Outlier feature in TQ-Analyst 9 software to exclude outliers, we have enhanced the performance of the model. After evaluating various spectral preprocessing techniques, we selected Savitzky–Golay filtering combined with Multiplicative Scatter Correction (S-G + MSC) to process the raw spectral data. This approach significantly improved the predictive accuracy of the model. After 2000 iterations of training, the CNN model achieved a prediction accuracy of 92.22%, marking a 12.09% improvement over traditional methods. The application of the Class Activation Mapping algorithm not only visualized the feature extraction process but also enhanced the traditional model’s classification accuracy by an additional 7.41% when integrated with features extracted from the CNN model. This research provides a powerful tool for the quality control of Chuanxiong slices and presents a novel perspective on the quality inspection of other agricultural products.

Details

Language :
English
ISSN :
20770472
Volume :
14
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Agriculture
Publication Type :
Academic Journal
Accession number :
edsdoj.0f2448a68343c8ac3685745055ffd6
Document Type :
article
Full Text :
https://doi.org/10.3390/agriculture14081281