Spectral line

Spectroscopy

Quantum mechanics

Principal component regression

Algorithm

Residual

Statistics

Chromatography

Principal component analysis

Calibration

Linearity

Absorbance

Physics

Mathematics

Optics

Astronomy

Near-infrared spectroscopy

Leverage (statistics)

Mathematical analysis

Analytical Chemistry (journal)

Multiplicative function

Chemistry

Applied Spectroscopy

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.

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