Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits
The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas te...
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MDPI AG
2024-12-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/1/5 |
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| author | Peng Chen Xutong Shao Guangyu Wen Yaowu Song Rao Fu Xiaoyan Xiao Tulin Lu Peina Zhou Qiaosheng Guo Hongzhuan Shi Chenghao Fei |
| author_facet | Peng Chen Xutong Shao Guangyu Wen Yaowu Song Rao Fu Xiaoyan Xiao Tulin Lu Peina Zhou Qiaosheng Guo Hongzhuan Shi Chenghao Fei |
| author_sort | Peng Chen |
| collection | DOAJ |
| description | The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law’s texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm’s effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, <i>p</i> < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity. |
| format | Article |
| id | doaj-art-17b9038f4ae24a9cb9e670fc0ac922d8 |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-17b9038f4ae24a9cb9e670fc0ac922d82025-01-10T13:17:29ZengMDPI AGFoods2304-81582024-12-01141510.3390/foods14010005Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its CounterfeitsPeng Chen0Xutong Shao1Guangyu Wen2Yaowu Song3Rao Fu4Xiaoyan Xiao5Tulin Lu6Peina Zhou7Qiaosheng Guo8Hongzhuan Shi9Chenghao Fei10Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, ChinaInstitute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, ChinaInstitute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, ChinaInstitute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, ChinaSuzhou Liliangji Health Industry Co., Ltd., Suzhou 215000, ChinaCollege of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, ChinaInstitute of Plant Resources and Chemistry, Nanjing Research Institute for Comprehensive Utilization of Wild Plants, Nanjing 210042, ChinaInstitute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, ChinaInstitute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, ChinaInstitute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, ChinaThe authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law’s texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm’s effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, <i>p</i> < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity.https://www.mdpi.com/2304-8158/14/1/5Ziziphi Spinosae Semennondestructive and fast judgmentfire–ice ion dimensionality reduction algorithmmultivariate statisticstraceability |
| spellingShingle | Peng Chen Xutong Shao Guangyu Wen Yaowu Song Rao Fu Xiaoyan Xiao Tulin Lu Peina Zhou Qiaosheng Guo Hongzhuan Shi Chenghao Fei Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits Foods Ziziphi Spinosae Semen nondestructive and fast judgment fire–ice ion dimensionality reduction algorithm multivariate statistics traceability |
| title | Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits |
| title_full | Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits |
| title_fullStr | Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits |
| title_full_unstemmed | Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits |
| title_short | Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits |
| title_sort | computer vision based fire ice ion algorithm for rapid and nondestructive authentication of ziziphi spinosae semen and its counterfeits |
| topic | Ziziphi Spinosae Semen nondestructive and fast judgment fire–ice ion dimensionality reduction algorithm multivariate statistics traceability |
| url | https://www.mdpi.com/2304-8158/14/1/5 |
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