Spectroscopic technologies and data fusion: Applications for the dairy industry

被引：21

作者：

Hayes, Elena ^{[1
,2
]}

Greene, Derek ^{[3
]}

O'Donnell, Colm ^{[1
]}

O'Shea, Norah ^{[2
]}

Fenelon, Mark A. ^{[1
,2
]}

机构：

[1] Univ Coll Dublin, Univ Coll Dublin UCD Sch Biosyst & Food Engn, Dublin, Ireland

[2] Teagasc Food Res Ctr, Moorepk, Fermoy, Ireland

[3] Univ Coll Dublin, Univ Coll Dublin UCD Sch Comp Sci, Dublin, Ireland

来源：

FRONTIERS IN NUTRITION | 2023年 / 9卷

基金：

爱尔兰科学基金会;

关键词：

dairy processing; chemometrics; spectroscopy; milk; data fusion; dairy; FRONT-FACE FLUORESCENCE; NEAR-INFRARED SPECTROSCOPY; FINE MILK-COMPOSITION; MIDINFRARED SPECTROSCOPY; BOVINE-MILK; RAMAN-SPECTROSCOPY; NUTRITIONAL PARAMETERS; COAGULATION PROPERTIES; TITRATABLE ACIDITY; PREDICTION;

D O I：

10.3389/fnut.2022.1074688

中图分类号：

R15 [营养卫生、食品卫生]; TS201 [基础科学];

学科分类号：

100403 ;

摘要：

Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.

引用

页数：12

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