Abstract
Determination of freshness is an important issue for rice quality. Near infrared (NIR) spectroscopy, a rapid nondestructive inspection method based on specific absorptions within a given range of wavelengths corresponding to the constituents in the sample, has been widely applied for evaluation of internal quality of agricultural products. Since NIR spectra of a mixture may be approximated as the linear addition of individual spectra of the constituents in the mixture, such a mixture spectrum thus can be regarded as 'blind sources' as the proportion of constituents in the samples remains unknown. A multiuse statistical approach, independent component analysis (ICA), is capable of disassembling the mixture signals of Gaussian distribution into non-Gaussian independent constituents, and (with assumption of independent constituent spectral response) can give a complete explanation about the property of constituents in the mixture. By example, a total of 180 white rice samples were collected from 6 crop seasons (from 2006 to 2010) for the purpose of developing an ICA NIR based procedure for rice freshness. , Values of pH were determined by a conventional (bromothymol blue methyl red) method. The calibration model of white rice yielded R c = 0.939, SEC = 0.202, r p = 0.803 and SEP = 0.233 using original full wavelength range (400 to 2498 nm) spectra and 5 independent components (ICs). Freshness of the white rice can be distinguished either visually by 3-dimensional diagram composed from ICs 2, 3 and 4, or statistically by a calibration model. The results show that ICA with NIR can quickly identify and effectively quantify the pH value in white rice with high predictability, and has the potential to be a useful tool for evaluating rice freshness. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).