%0 Journal Article
	%A K. S. Chia and  H. Abdul Rahim and  R. Abdul Rahim
	%D 2013
	%J International Journal of Electrical and Computer Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 77, 2013
	%T Principal Component Regression in Noninvasive Pineapple Soluble Solids Content Assessment Based On Shortwave Near Infrared Spectrum
	%U https://publications.waset.org/pdf/13682
	%V 77
	%X The Principal component regression (PCR) is a
combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the
use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and
implemented in the non-destructive assessment of the soluble solid
content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean
squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation
(RMSECV) of 0.8323 Brix° with principal components
(PCs) of 14.
	%P 533 - 536