Raman spectroscopy is a vital technique being able to detect and identify molecular information with advantages of being fast and non-invasive. This technique also enables numbers of potential applications, including forensic drugs detector, explosive detection, and biomedical analysis. In this work, we investigated the identification performance of a custom-made low-resolution Raman system equipped with machine learning capability to classify various types of materials. Here, a relatively broadband laser diode with center wavelength of 808 nm was used for Raman excitation. An off-axis parabolic mirror with through hole was used in place of a beamspiltter for sample excitation, as well as collection, and collimation of scattered light from long working distance of 50 mm. The signal was filtered and delivered to a cooled spectrometer via an optical fiber for spectra measurements. Raman spectra of test samples were on the range of 100-2000 cm−1 with 7.65 cm−1 data steps. For spectral analysis, a convolutional neural network (CNN) was implemented as classification algorithm with feature extraction from multiple layers together with error-back propagation, which displayed the performance in term of accuracy. It was found that with only three sets of convolution layers up to 96.7% testing performance can be achieved even with low spectral resolution input.

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Raman spectroscopy is a potent and widespread optical analytical technique thanks to its non-invasive and high-specification for the detection of targeted molecules. However, for the case of trace detection, it is common that a weak Raman signal is easily swamped by noise and thus unable to be resolved. Here, we demonstrated a facile fabrication of a three-dimensional surface enhanced Raman spectroscopy (SERS) substrate, based on low-vacuum sputtering of gold nanofilm on hierarchically rough fumed silica monolayers deposited by layer-by-layer self-assembly technique. Due to the much lower surface energy of the silica-air heterostructure compared to metallic materials, deposited gold layers dewetted the surface spontaneously, forming nano-sized spherical gold particles without the requirement of an extra annealing process. Plasmonic effects were studied through optical absorption measurements, while the surface morphology and topography were examined using SEM and AFM for various sputtering durations. Furthermore, the enhancement of Raman spectrum was investigated for 10−4 M of methylene blue (MB), using 532 nm and 0.57 mW excitation laser. An initial Raman enhancement factor of 17 was observed at 1645 cm−1 peak, even with yet to be optimized fabrication procedures.