Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives

With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2021-03, Vol.93 (8), p.3653-3665
Main Authors: He, Hao, Yan, Sen, Lyu, Danya, Xu, Mengxi, Ye, Ruiqian, Zheng, Peng, Lu, Xinyu, Wang, Lei, Ren, Bin
Format: Article
Language:English
Subjects:
Biological properties
Biological samples
Chemistry
Datasets
Deep learning
Feature extraction
Imaging
Information processing
Instrumentation
Sampling methods
Spectral analysis
Spectrum analysis
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Summary:With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.0c04671