NEURAL NETWORK BASED PREDICTION OF SOLUBLE SOLIDS CONCENTRATION IN ORIENTAL MELON USING VIS/NIR SPECTROSCOPY

Models for predicting the soluble solids concentration (SSC) of oriental melons were developed and evaluated by applying near infrared spectroscopy and an artificial neural network technique. For the evaluation, a total of 300 oriental melons, both ripe and unripe, were mixed together and sampled. To develop an SSC prediction model, the actual SSC values of specimens having the same spectra as those of the visible/near infrared wavelength bands were measured. The measured spectra were preprocessed using eight methods [Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Robust Normal Variate, Savitzky-Golay 1st and 2nd; Min-Max Normalization; Robust Normalization; Standardization], and the SSC prediction model was developed by applying three techniques (Partial Least Squared Regression [PLSR], Artificial Neural Network [ANN], and Convolutional Neural Network [CNN]). Among them, the PLSR technique also applied a Variable Importance in Projection (VIP) method for wavelength selection. Among the PLSR-based SSC prediction models, the SNV-preprocessed PLSR model showed the best SSC prediction performance (RMSEtest, 0.67; R-test(2), 0.81). Among the ANN-based models, the MSC-preprocessed PLS-ANN model showed the best SSC prediction performance (RMSEtest: 0.63, R-test(2): 0.83). Among the CNN-based models, the DeepSpectra model was applied, but showed the lowest prediction performance (RMSEtest: 0.79, R-test(2): 0.74). In conclusion, among the three SSC prediction algorithms tested in this study, the PLS-ANN-based prediction model showed the best SSC prediction performance, which was found to be higher than that of the PLSR-based SSC prediction model applied to the sugar sorters currently used in agricultural products at processing centers.