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Near‐infrared spectroscopy and data analysis for predicting milk powder quality attributes
Author(s) -
Khan Asma,
Munir Muhammad Tajammal,
Yu Wei,
Young Brent R.
Publication year - 2021
Publication title -
international journal of dairy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.061
H-Index - 53
eISSN - 1471-0307
pISSN - 1364-727X
DOI - 10.1111/1471-0307.12734
Subject(s) - partial least squares regression , near infrared spectroscopy , spectroscopy , multivariate statistics , analytical chemistry (journal) , materials science , particle size , infrared spectroscopy , food science , chemistry , mathematics , chromatography , statistics , optics , organic chemistry , physics , quantum mechanics
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.