Supervised classification of slightly bruised peaches with respect to the time after bruising by using hyperspectral imaging technology

By Xiong Li, Yande Liu, Xiaogang Jiang. 2020

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Abstract Peaches are sensitive to bruising from mechanical impact and compression. Peaches affected by bruises will tend towards fermentation, decay or mildew and infect other non-bruised ones after damage occurrence. A moderate amount of bruising is a barrier to purchase desire of consumers instead of price. For the early bruised peaches, softening and browning does not appear immediately (within 2 days), and it is difficult to distinguish the problem of fruit bruises. The hypersecretion imaging device was used to collect the data of normal peaches and the data of 12, 24, 36 and 48 hours after the encounter, and the spectral characteristics of these five types of peaches were analyzed to establish the PCA classification model of physiological indexes of peaches after the collision. Extract the spectrum and image features of the discolored area and the normal area. Secondly, divide the collection of image data sets into the modelling set and the prediction set at a ratio of 3:1. Input the spectrum and image feature to establish a time discrimination model for peach bruises. The results show that it is based on the PLS-DA algorithm When the input variable is a spectral feature, the classification accuracy rates at 12, 24, 36, and 48 hours after bruising are 96.67%, 96.67%, 93.33%, and 83.33%, respectively, and the correlation coefficient (Rc) of the modelling set reaches 0.928; Based on LS -SVM algorithm, when the input variable is a spectral feature, the kernel function RBF_Kernel classifies correctly to 80%, 96.67%, 100%, 100%. This study shows that the spectral feature of hypersecretion technology can identify early bruises of peaches, which are online testing provide a theoretical basis for predicting short-term bumps of peaches.

Subfields: Analytical Chemistry, Biophysics, Insect Science

View Paper: https://www.semanticscholar.org/paper/a2f03ba8abc3b74d66d32d977bf11362b4e9643f