Authors: Morten Bertz | Masahiro Yanagisawa | Takayuki Homma
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning Combined with Surface-Enhanced Raman Spectroscopy for Chemical Sensing and Recognition Surface-enhanced Raman spectroscopy (SERS) combines the unique advantages of Raman spectroscopy – namely detection of specific molecular fingerprints without requiring prior labelling – with enhanced sensitivity. SERS takes advantage of the amplification of the incident electric field by localized surface plasmons on noble metal nanoparticle surfaces, which allows the rapid, non-destructive detection of a wide variety of analytes ranging from small molecules to entire microorganisms. The data obtained from SERS experiments, however, can be difficult to interpret. Spectra are dominated by the chemical species in closest contact with the plasmonic field, and consequently spectra recorded at different points in time from the same sample can exhibit considerable differences. The rich chemical information contained in the spectral data therefore is often not utilized to its full potential or lost altogether. Here, we combine deep convolutional neural networks (CNNs) with SERS to classify series of spectra obtained from mixtures of different sample species (1). We find that CNNs outperform standard chemometric methods, such as principal component analysis, but also support-vector machine classifiers, shallow artificial neural networks, or even trained human experts while requiring minimal data preprocessing, such as background subtraction or smoothing. We discuss optimizing the classifier with regards to network topology, learning strategy, and data augmentation. In addition, we show that we can extract useful information from trained networks. In simple cases, activation maximization spectra, which represent the spectral features that are most relevant to discriminate between different sample classes, can provide a good approximation of the spectra in the original sample. We also train generative adversarial networks (GANs) (2) to generate / extract characteristic spectra from large, more heterogeneous datasets. These spectra can provide additional insight into the composition of the sample and might thus be relevant to guide further experiments. Deep learning and SERS are two powerful techniques that complement each other. The high sensitivity of SERS quickly generates large, but often highly complex spectral datasets, which are in turn well-suited to the analysis by CNNs. Most importantly, deep learning not only excels at classification but can be used as a more general tool to facilitate the interpretation of heterogeneous, seemingly intractable datasets. Given the tremendous attention deep learning has attracted over the past decade, we anticipate that it will play an increasingly prominent role in spectral analysis as well. Yang, J. et al., 2019. Deep learning for vibrational spectral analysis: Recent progress and a practical guide. Analytica chimica acta , 1081, pp.6–17. Odena, A., Olah, C. &amp; Shlens, J., 2016. Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv:1610.09585 [stat.ML] Figure 1 Artificial intelligence ; Surface-enhanced Raman spectroscopy ; Computer science ; Pattern recognition (psychology) ; Principal component analysis ; Raman spectroscopy ; Plasmon ; Artificial neural network ; Convolutional neural network ; Smoothing ; Classifier (UML) ; Machine learning ; Biological system ; Materials science ; Raman scattering ; Physics ; Optics ; Optoelectronics ; Computer vision Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection