Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400–1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.

The implementation of near infrared spectroscopy in monitoring diesel fuel properties is highly dependent on the capability of its predictive model. This study investigates the feasibility of a single layer artificial neural networks among various predictive models in predicting the diesel fuel properties using near infrared spectrum. Results were compared and discussed with that reported in previous studies that used the same data in predicting the diesel fuel properties. Findings show that the proposed single layer outperforms popular models, and is comparable with a recent advanced models in predicting the diesel fuel properties using near Infrared spectrum without data reduction.