Near-infrared spectroscopy and data analysis for predicting milk powder quality attributes

被引：19

作者：

Khan, Asma ^{[1
]}

Munir, Muhammad Tajammal ^{[2
]}

Yu, Wei ^{[1
]}

Young, Brent R. ^{[1
]}

机构：

[1] Univ Auckland, Fac Engn, Dept Chem & Mat Engn, Symond St, Auckland 1010, New Zealand

[2] Amer Univ Middle East, Coll Engn & Technol, Kuwait 1010, Kuwait

来源：

INTERNATIONAL JOURNAL OF DAIRY TECHNOLOGY | 2021年 / 74卷 / 01期

关键词：

Milk powder; Particle size; Quality; PARTICLE-SIZE DISTRIBUTION; DAIRY POWDERS; CLASSIFICATION; TECHNOLOGY; REGRESSION;

D O I：

10.1111/1471-0307.12734

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

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.

引用

页码：235 / 245

页数：11

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