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Raman spectroscopy is an inelastic scattering technique that measures the molecular vibrational states of a sample with little to no sample preparation. These vibrational states are molecule-specific, therefore different compounds can be identified through rapid analysis. Raman spectroscopy has been implemented in a variety of different research areas, for example, forensic analysis, pharmaceutical product design, material identification, disease diagnostics, etc. Although Raman spectroscopy has been demonstrated in various applications, it still has limitations with data processing due to its innate weak signals. Historically, chemometrics techniques have been widely used for Raman spectroscopy for preprocessing data such as feature extraction (or feature selection), and data modeling. These models are often generated by using analytical data from different sources, enhancing model discrimination and prediction abilities, but this is limited by how much data is provided. Our group has designed a portable A.I. Raman spectrometer using machine learning through training and deep learning. This spectrometer uses a miniature Raman spectrometer paired with a well plate reader for multiple and rapid sample measurement. As sample measurements are taken the system will implement machine learning software to preprocess and postprocess Raman spectral data. This will minimize the workload of complicated analysis on the condition that there exists sufficient training data. Implementing a well plate reader aids in data collection for the AI training by mimicking experiments for preprocess and adding Raman standards. Through machine learning as more data is provided the system will learn how to implement past data on new data sets, therefore minimizing the amount of time and analysis needed by human interaction.
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