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.

Convolutional neural networks directly learned chemical information from the periodic table to predict the enthalpy of formation and compound stability.

Raman imaging is a promising technique that allows the spatial distribution of different components in the sample to be obtained using the molecular fingerprint information on individual species. However, the imaging speed is the bottleneck for the current Raman imaging methods to monitor the dynamic process of living cells. In this paper, we developed an artificial intelligence assisted fast Raman imaging method over the already fast line scan Raman imaging method. The reduced imaging time is realized by widening the slit and laser beam, and scanning the sample with a large scan step. The imaging quality is improved by a data-driven approach to train a deep convolutional neural network, which statistically learns to transform low-resolution images acquired at a high speed into high-resolution ones that previously were only possible with a low imaging speed. Accompanied with the improvement of the image resolution, the deteriorated spectral resolution as a consequence of a wide slit is also restored, thereby the fidelity of the spectral information is retained. The imaging time can be reduced to within 1 min, which is about five times faster than the state-of-the-art line scan Raman imaging techniques without sacrificing spectral and spatial resolution. We then demonstrated the reliability of the current method using fixed cells. We finally used the method to monitor the dynamic evolution process of living cells. Such an imaging speed opens a door to the label-free observation of cellular events with conventional Raman microscopy.

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