Visualized pattern recognition optimization for apple mechanical damage by laser relaxation spectroscopy

Autor: Junbo Lian, Ling Ma, Xincan Wu, Ting Zhu, Quan Liu, Yuqi Sun, Zhenghao Mei, Jingyuan Ning, Haifen Ye, Guohua Hui, Xiongwei Lou
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: International Journal of Food Properties, Vol 26, Iss 1, Pp 1566-1578 (2023)
Druh dokumentu: article
ISSN: 10942912
1532-2386
1094-2912
DOI: 10.1080/10942912.2023.2221404
Popis: ABSTRACTThis 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.
Databáze: Directory of Open Access Journals
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