We demonstrate a recognition and feature visualization method that uses a deep
convolutional neural network for Raman spectrum analysis. The visualization is achieved by …

Preprocessing of Raman spectra is generally done in three separate steps:(1) cosmic ray
removal,(2) signal smoothing, and (3) baseline subtraction. We show that a convolutional …

Machine learning methods have found many applications in Raman spectroscopy,
especially for the identification of chemical species. However, almost all of these methods …

Machine learning has had a significant impact on the value of spectroscopic characterization
tools, particularly in biomedical applications, due to its ability to detect latent patterns within …

Raman spectroscopy has been widely used to provide the structural fingerprint for molecular
identification. Due to interference from coexisting components, noise, baseline, and …

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively
gives chemical structural information with the Raman spectrometer. Despite its technical …

Deep learning approaches, especially convolutional neural network (CNN) models, have
achieved excellent performances in vibrational spectral analysis. The critical drawback of …

Dedicated handheld spectrometers have been adopted by first responders and law
enforcement agencies for in situ identification of unknown substances. Real-time spectral …

Pesticide detection is of tremendous importance in agriculture, and Raman
spectroscopy/Surface-Enhanced Raman Scattering (SERS) has proven extremely effective …

With the development of Raman spectroscopy and the expansion of its application domains,
conventional methods for spectral data analysis have manifested many limitations. Exploring …