Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison |
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| Authors: | Kim-seng Chia Herlina Abdul Rahim and Ruzairi Abdul Rahim |
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| Institution: | Department of Control and Instrumentation, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia |
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| Abstract: | 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. |
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| Keywords: | Artificial neural network (ANN) Principal component regression (PCR) Visible and shortwave near infrared (VIS-SWNIR) Spectroscopy Apple Soluble solids content (SSC) |
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