1988/09/01 by Tomas Isaksson, Tormod Næs
Biochemistry, Genetics and Molecular Biology · Chemistry · Engineering · #Advanced Chemical Sensor Technologies #Spectroscopy Techniques in Biomedical and Chemical Research #Spectroscopy and Chemometric Analyses
paper · doi:10.1366/0003702884429869
openalex publication_date 1988/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.