Rapid and non-destructive determination of soluble solid content of crown pear by visible/near-infrared spectroscopy with deep learning regression

Abstract

The development of rapid and non-destructive prediction technology for fruit quality after harvest could enhance market competitiveness and profitability. The soluble solids content (SSC) is an essential quality index of fruit. This study aims to predict the SSC of crown pear using visible/near-infrared (Vis/NIR) spectroscopy (397–1187 nm) with deep learning model MLP-CNN-TCN. Firstly, the spectral information of crown pears was collected, and Savitzky-Golay (SG) smoothing method and Standard normal variate (SNV) were used to preprocess the spectral data. Secondly, the Multi-layer perceptron (MLP) method was used to reduce the dimension of the preprocessed data, and the reduced data was input into a one-dimensional convolutional neural network (1D-CNN) to extract spectral features. Finally, a Temporal convolutional neural network (TCN) was used to establish a regression model to predict pear SSC. This method was also compared with feedforward neural network (FNN), MLP, 1D-CNN, partial least squares regression (PLSR), and support vector regression (SVR). The MLP-CNN-TCN model obtained better prediction performance, with a prediction determination coefficient (RP2) of 0.956 for SSC. This study demonstrated that the combination of Vis/NIR spectroscopy and MLP-CNN-TCN method could rapidly and non-destructively detect SSC of crown pear, and provide a new regression alternative for the prediction of fruit SSC.