Abstract

Second derivative of interactance spectra (731–926 nm) of intact peaches and Brix values of extracted juice were used to develop a least squares support vector machine (LS-SVM) regression (based on an RBF kernel) and a PLS regression model. An iterative approach was taken with the LS-SVM regression, involving a grid search with application of a gradient-based optimisation method using a validation set for the tuning of hyperparameters, followed by pruning of the LS-SVM model with the optimised hyperparameters. The grid search approach led to five-fold faster and better determination of hyperparameters. Less than 45% of the initial 1430 calibration samples were kept in the models. In prediction of an independent test set with 120 samples, the pruned LS-SVM models performed better than the PLS model (RMSEP decreased by 9% to 14%).

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