Nondestructive determination of edible quality and watercore degree of apples by portable Vis/NIR transmittance system combined with CARS-CNN

Visible Near-infrared (Vis/NIR) transmittance spectroscopy has been employed to evaluate various quality indicators of fruits, owing to the advantages of rapid non-destructive detection, real-time monitoring, and applicability to opaque samples. The portable Vis/NIR transmittance system was autonomously developed and coupled with chemometrics methods for the detection of quality indicators including soluble solids content (SSC), watercore degree, firmness and pH of apples. Preprocessing method of Savitzky-Golay (SG) smoothing combined with standard normal variate SNV) transformation was employed to mitigate spectra noise. The feature variables were selected using variable selection methods and coupled with partial least square (PLS) to establish linear regression prediction models. Furthermore, a deep learning model was developed by combining competitive adaptive reweighted sampling (CARS) with convolutional neural network (CNN). The Rp of SSC, firmness, pH and the watercore degree of CARS-CNN model were 0.951, 0.824, 0.828 and 0.943, respectively, and the prediction performance was improved by 3