A review of machine learning for near-infrared spectroscopy

The analysis of infrared spectroscopy of substances is a non-invasive measurement tech nique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy fr...

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Main Authors: Zhang, Wenwen, Kasun, Liyanaarachchi Chamara, Wang, Qi Jie, Zheng, Yuanjin, Lin, Zhiping
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167770
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author Zhang, Wenwen
Kasun, Liyanaarachchi Chamara
Wang, Qi Jie
Zheng, Yuanjin
Lin, Zhiping
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhang, Wenwen
Kasun, Liyanaarachchi Chamara
Wang, Qi Jie
Zheng, Yuanjin
Lin, Zhiping
author_sort Zhang, Wenwen
collection NTU
description The analysis of infrared spectroscopy of substances is a non-invasive measurement tech nique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
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spelling ntu-10356/1677702023-05-22T15:38:39Z A review of machine learning for near-infrared spectroscopy Zhang, Wenwen Kasun, Liyanaarachchi Chamara Wang, Qi Jie Zheng, Yuanjin Lin, Zhiping School of Electrical and Electronic Engineering School of Physical and Mathematical Sciences Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Machine Learning Near-Infrared Spectroscopy Non-Invasive Measurement Deep Architectures Light Absorption The analysis of infrared spectroscopy of substances is a non-invasive measurement tech nique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction. Agency for Science, Technology and Research (A*STAR) National Medical Research Council (NMRC) National Research Foundation (NRF) Published version This work was partially supported by the Agency for Science, Technology, and Research (A*STAR) (grant no A2090b0144), the National Research Foundation Singapore (grant nos. NRF CRP18-2017-02 and NRF-CRP19-2017-01), and the National Medical Research Council (NMRC) (grant no 021528-00001). 2023-05-22T06:24:32Z 2023-05-22T06:24:32Z 2022 Journal Article Zhang, W., Kasun, L. C., Wang, Q. J., Zheng, Y. & Lin, Z. (2022). A review of machine learning for near-infrared spectroscopy. Sensors, 22(24), 9764-. https://dx.doi.org/10.3390/s22249764 1424-8220 https://hdl.handle.net/10356/167770 10.3390/s22249764 24 22 9764 en A2090b0144 NRF-CRP18-2017-02 NRMC-021528-00001 NRF-CRP19- 2017-01 Sensors © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
spellingShingle Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Machine Learning
Near-Infrared Spectroscopy
Non-Invasive Measurement
Deep Architectures
Light Absorption
Zhang, Wenwen
Kasun, Liyanaarachchi Chamara
Wang, Qi Jie
Zheng, Yuanjin
Lin, Zhiping
A review of machine learning for near-infrared spectroscopy
title A review of machine learning for near-infrared spectroscopy
title_full A review of machine learning for near-infrared spectroscopy
title_fullStr A review of machine learning for near-infrared spectroscopy
title_full_unstemmed A review of machine learning for near-infrared spectroscopy
title_short A review of machine learning for near-infrared spectroscopy
title_sort review of machine learning for near infrared spectroscopy
topic Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Machine Learning
Near-Infrared Spectroscopy
Non-Invasive Measurement
Deep Architectures
Light Absorption
url https://hdl.handle.net/10356/167770
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