Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transformed-near infrared spectroscopic technique:

A comparative study of three chemometric algorithms combined with spectroscopy with the aim of determining the best for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter was conducted. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent (R2 cal = 0.97), (R2 val = 0.97), low (root mean square error of cross-valid...