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

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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.

Keywords: Multiplicative scatter correction; NIR; Near-infrared reflectance; PCR; Principal component regression

Document Type: Research Article


Affiliations: Norwegian Food Research Institute, P.O. Box 50, N-1432 Aas-NLH, Norway

Publication date: September 1, 1988

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