| Özet: | 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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