Raman Spectroscopy

Raman spectroscopy, a vibrational spectroscopic technique, is increasingly used to analyze the chemical composition of diverse samples non-destructively. Current research heavily utilizes machine learning, particularly convolutional neural networks (CNNs) and other deep learning architectures, to overcome challenges in data analysis, such as spectral noise and the need for efficient unmixing of complex mixtures. This approach enhances the accuracy and speed of identifying and quantifying components in various applications, ranging from food quality control and environmental monitoring to medical diagnostics and industrial process monitoring. The integration of machine learning with Raman spectroscopy is significantly improving the technique's analytical capabilities and expanding its applicability across numerous scientific fields.