Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy
This study aims to enhance the efficiency and accuracy of nondestructive testing (NDT) for identifying mechanical damage in apples. Current methods have limitations in detection efficiency and require significant human resources. We conducted a study using laser relaxation spectroscopy and developed...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-09-01
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| Series: | International Journal of Food Properties |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10942912.2023.2221404 |
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| _version_ | 1846095418321010688 |
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| author | Junbo Lian Ling Ma Xincan Wu Ting Zhu Quan Liu Yuqi Sun Zhenghao Mei Jingyuan Ning Haifen Ye Guohua Hui Xiongwei Lou |
| author_facet | Junbo Lian Ling Ma Xincan Wu Ting Zhu Quan Liu Yuqi Sun Zhenghao Mei Jingyuan Ning Haifen Ye Guohua Hui Xiongwei Lou |
| author_sort | Junbo Lian |
| collection | DOAJ |
| description | This study aims to enhance the efficiency and accuracy of nondestructive testing (NDT) for identifying mechanical damage in apples. Current methods have limitations in detection efficiency and require significant human resources. We conducted a study using laser relaxation spectroscopy and developed a single-wavelength system to collect spectral data from damaged and undamaged Red Fuji apples. The data was pretreated using the Min-Max standardization algorithm and analyzed using pattern recognition models including the depth extreme learning machine (DELM), SSA-DELM (optimized by the sparrow search algorithm), and backpropagation (BP) neural networks. We introduced neural network visualization to improve accuracy during BP neural network analysis. The BP neural networks achieved the highest accuracy of 94.74% among the models tested. To further enhance accuracy, we proposed an optimized multicount measurement classification recognition (MMCR) model, which improved accuracy to 97.5% with ultrahigh detection speed. The proposed method offers advantages such as ease of operation, affordability, and fast detection, providing a novel approach to rapid fruit quality assessment. |
| format | Article |
| id | doaj-art-c7f5eecdb15045618cc21b1933cbc372 |
| institution | Kabale University |
| issn | 1094-2912 1532-2386 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Food Properties |
| spelling | doaj-art-c7f5eecdb15045618cc21b1933cbc3722025-01-02T10:41:34ZengTaylor & Francis GroupInternational Journal of Food Properties1094-29121532-23862023-09-012611566157810.1080/10942912.2023.2221404Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopyJunbo Lian0Ling Ma1Xincan Wu2Ting Zhu3Quan Liu4Yuqi Sun5Zhenghao Mei6Jingyuan Ning7Haifen Ye8Guohua Hui9Xiongwei Lou10School of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaSchool of Mathematics and Computer Sciences, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, ChinaThis study aims to enhance the efficiency and accuracy of nondestructive testing (NDT) for identifying mechanical damage in apples. Current methods have limitations in detection efficiency and require significant human resources. We conducted a study using laser relaxation spectroscopy and developed a single-wavelength system to collect spectral data from damaged and undamaged Red Fuji apples. The data was pretreated using the Min-Max standardization algorithm and analyzed using pattern recognition models including the depth extreme learning machine (DELM), SSA-DELM (optimized by the sparrow search algorithm), and backpropagation (BP) neural networks. We introduced neural network visualization to improve accuracy during BP neural network analysis. The BP neural networks achieved the highest accuracy of 94.74% among the models tested. To further enhance accuracy, we proposed an optimized multicount measurement classification recognition (MMCR) model, which improved accuracy to 97.5% with ultrahigh detection speed. The proposed method offers advantages such as ease of operation, affordability, and fast detection, providing a novel approach to rapid fruit quality assessment.https://www.tandfonline.com/doi/10.1080/10942912.2023.2221404AppleRelaxation spectroscopyNondestructive testingVisualizationBPMMCR |
| spellingShingle | Junbo Lian Ling Ma Xincan Wu Ting Zhu Quan Liu Yuqi Sun Zhenghao Mei Jingyuan Ning Haifen Ye Guohua Hui Xiongwei Lou Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy International Journal of Food Properties Apple Relaxation spectroscopy Nondestructive testing Visualization BP MMCR |
| title | Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy |
| title_full | Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy |
| title_fullStr | Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy |
| title_full_unstemmed | Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy |
| title_short | Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy |
| title_sort | visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy |
| topic | Apple Relaxation spectroscopy Nondestructive testing Visualization BP MMCR |
| url | https://www.tandfonline.com/doi/10.1080/10942912.2023.2221404 |
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