The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

TLDR

The study applied multiplicative scatter correction to NIR reflectance spectra of five food products, then performed principal component regression on both corrected and uncorrected data to calibrate and predict protein, fat, water, and carbohydrate contents. MSC reduced prediction errors by 7–68% compared to uncorrected spectra, and nonlinear regression further improved performance.

Abstract

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.