Deep learning

Physics

Pattern recognition (psychology)

Feature (linguistics)

Preprocessor

Operating system

Optics

Instrumentation (computer programming)

Machine learning

Philosophy

Chemometrics

Artificial intelligence

Chemistry

Focus (optics)

Data science

Computer science

Linguistics

Analytical Chemistry

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.

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