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An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis

An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in... Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast nature, precision, control, accuracy, repeatability, and reproducibility has been extensively employed in most industries for food quality measurements. Its coupling to different modeling techniques has been identified as a way of improving the accuracy and robustness of non-destructive measurement of foodstuffs. This review provides an overview of the application of non-linear algorithms in food quality and safety specific to NIR spectroscopy. The review also provides in-depth knowledge about the principle of NIR spectroscopy along with different non-linear models such as artificial neural network (ANN), AdaBoost, local algorithm (LA), support vector machine (SVM), and extreme learning machine (ELM). Moreover, non-linear algorithms coupled with NIR spectroscopy for ensuring food quality and their future perspective has been discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Food Engineering Reviews Springer Journals

An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis

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References (137)

Publisher
Springer Journals
Copyright
Copyright © Springer Science+Business Media, LLC, part of Springer Nature 2020
ISSN
1866-7910
eISSN
1866-7929
DOI
10.1007/s12393-020-09210-7
Publisher site
See Article on Publisher Site

Abstract

Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast nature, precision, control, accuracy, repeatability, and reproducibility has been extensively employed in most industries for food quality measurements. Its coupling to different modeling techniques has been identified as a way of improving the accuracy and robustness of non-destructive measurement of foodstuffs. This review provides an overview of the application of non-linear algorithms in food quality and safety specific to NIR spectroscopy. The review also provides in-depth knowledge about the principle of NIR spectroscopy along with different non-linear models such as artificial neural network (ANN), AdaBoost, local algorithm (LA), support vector machine (SVM), and extreme learning machine (ELM). Moreover, non-linear algorithms coupled with NIR spectroscopy for ensuring food quality and their future perspective has been discussed.

Journal

Food Engineering ReviewsSpringer Journals

Published: Jun 10, 2020

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