Hyperspectral Technology for Determining the Soluble Solids Content of “Yuluxiang” Pear Based on GA-BP Neural Network
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
In order to improve the detection accuracy of the soluble solids content of “Yuluxiang” Pear, an optimized back propagation (BP) neural network-based SSC prediction method for “Yuluxiang” Pear was proposed in this study. Spectral information on the surface of “Yuluxiang” Pear was collected by the hyperspectral imager. After the removal of the abnormal samples, different types of pre-processing were performed on the spectral data to determine the optimal pre-processing method. Genetic algorithm (GA) was used to optimize the initial weights and thresholds of the BP neural network, and establish GA-BP, BP and PLSR prediction models for the SSC of “Yuluxiang” Pear. The results showed that the median filter (MF) pre-processing method was the best. For the same training sample, the established GA-BP model performed the best, with the coefficient of determination in the established model (Rc2) being 0.98, RMSEC value being 0.19, the Rp2 value being 0.86, and RMSEP value being 0.43, and residual predictive deviation (RPD) being 2.45. On the basis of these results, different numbers of samples were used to train the GA-BP network. When the number of training samples was 300, the Rc2 value of the established GA-BP model reached to 0.99, with the RMSEC value being 0.22, Rp2 value being 0.98, and RMSEP value being 0.20. Thus, the SSC of “Yuluxiang” Pear can be quickly and accurately detected by GA-BP neural network combined with hyperspectral imaging technology. When the training samples reached a certain number, the prediction accuracy of the model can be further improved. This research provides a theoretical basis for detecting “Yuluxiang”Pear’s SSC based on BP neural network.
