Quality classification of kiwifruit under different storage conditions based on deep learning and hyperspectral imaging technology

作者：Yuchen Zhao, Zhilong Kang, Lei Chen, Yanju Guo, Qingshuang Mu, Shenyi Wang, Bingjie Zhao, Changzhou Feng · 发表于：Journal of Food Measurement & Characterization · 年份：2022 · DOI：10.1007/s11694-022-01554-4 · 被引用次数：28 · 研究领域：Spectroscopy and Chemometric Analyses、Advanced Chemical Sensor Technologies、Water Quality Monitoring and Analysis

Kiwifruits contain various vitamins, amino acids, and minerals and are loved by consumers because of their sweet and soft taste. The storage time of kiwifruits after picking is short, and it needs to be stored in cold storage to prolong their maturation and softening time. In order to explore the effects of the storage environment on kiwifruit quality, hyperspectral imaging (HSI) technology was used to study the quality changes of kiwifruit under different storage conditions in the near-infrared (NIR) region. We proposed a deep learning-based extended morphology-nonlocal capsule network (EMP-NLCapsNet) algorithm to classify fruits stored at different temperatures [low temperature (4 °C, 75% relative humidity) and room temperature (18 ± 2 °C)] for different times (0, 2, 4, and 6 days). Extended morphological profile (EMP) and principal component analysis (PCA) are used in the EMP-NLCapsNet algorithm as the spatial and spectral feature extraction algorithms for kiwifruit hyperspectral images, respectively, and the extracted feature data blocks are fed into a non-local capsule network (NLCapsNet) to achieve classification. In addition, to further investigate the effect of storage time in the low-temperature environment on fruits, EMP-NLCapsNet was used to establish the association between hyperspectral deep features and fruits and classify the storage time for fruits. The classification map can visualize the difference between fresh and low-temperature stored fruits. The results...