Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction
were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7-68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the
effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.
Norwegian Food Research Institute, P.O. Box 50, N-1432 Aas-NLH, Norway
Publication date: September 1, 1988
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