Comparison of linear and non-linear calibration models for non-destructive firmness determining of mazafati date fruit by near infrared spectroscopy

Abstract

The selection of calibration method is one of the most important factors affecting the measurement accuracy with near infrared spectroscopy. In this research, the performance of two general calibration methods, namely, linear partial least squares regression and nonlinear back propagation artificial neural networks for firmness predicting of Mazafati date fruit was investigated. A total of 175 date samples harvested during fruit ripening were selected as the data set. Optical scanning was performed with a fiber optic near infrared spectrometer with a range of 900-1700 nm. The inputs of back propagation artificial neural networks were the first five principal components resulting from the principal component analysis, namely, principal component analysis artificial neural network modeling or the first six latent variables obtained from partial least squares regression, namely, latent variable artificial neural network modeling. Both the leave-one-out cross validation and test set validation showed the priority of latent variable artificial neural network model with respect to partial least squares and also principal component analysis artificial neural network models. The best latent variable artificial neural network model could predict the firmness of Mazafati date fruits with Rp2 of 0.90, RMSEP of 1.30 N, and SDRp of 3.28. The results also recommended the adoption of nonlinear latent variable artificial neural network modeling for applying light scattering properties of fruits in order to predict indirect property of firmness with near infrared spectroscopy. © 2014 Copyright Taylor & Francis Group, LLC.