Near‐infrared spectroscopy and data analysis for predicting milk powder quality attributes
Asma Khan, Muhammad Tajammal Munir, Wei Yu, Brent R. Young
Abstract
Near‐infrared (NIR) spectroscopy is a rapid analytical method for food products. In this study, NIR spectroscopy, data pretreatment techniques and multivariate data analysis were used to predict fine particle size fraction, dispersibility and bulk density of various milk powder samples, which are believed to have a significant impact on milk powder quality. Predictive models using partial least‐squares (PLS) regression were developed using NIR spectra and milk powder physical and functional properties, and it was concluded that the PLS models predicted milk powder quality with an accuracy of 88‐90 per cent.
Topics & Concepts
Partial least squares regressionNear-infrared spectroscopySpectroscopyMultivariate statisticsAnalytical Chemistry (journal)Materials scienceParticle sizeInfrared spectroscopyFood scienceChemistryMathematicsChromatographyStatisticsOpticsOrganic chemistryPhysicsQuantum mechanicsPhysical chemistrySpectroscopy and Chemometric AnalysesSpectroscopy Techniques in Biomedical and Chemical ResearchMeat and Animal Product Quality