Improving Raman spectroscopy analysis using machine learning approaches

Abstract

Raman spectroscopy is a versatile and powerful method for determining the chemical composition of samples. However, there are challenges when analyzing macromolecules with similar properties, such as proteins and fatty acids, or when one component of a mixture is present in low concentrations. Traditional approaches that involve approximating each Raman band to determine its position, full width at half maximum, and intensity are not effective for complex tasks. Machine learning algorithms can enhance the analysis and provide more accurate results in such cases. Techniques for reducing the dimensionality of the data, such as principal component analysis and t-distributed stochastic neighbor embedding, can be used to identify patterns in the data and improve the signal-to-noise ratio without significantly reducing the Raman intensity. Complex tasks may require the training of regression or classification models. A one-step-simpler approach than neural networks can be found in ensemble models that use several separate models. These individual models can operate concurrently or sequentially within the ensemble. Thus, the following approaches will be discussed: voting, bagging (in random forest), boosting (gradient and adaptive variants), and stacking. The advantage of using ensemble models is that with a sufficient number of simple models in the ensemble, the classification accuracy and the coefficient of regression determination increase. Overfitting is also reduced. The disadvantages, especially in the case of ensembles with consistent use of individual models (bagging), include the increasing time spent on training.

Speaker

Ekaterina S. Prikhozhdenko
Laboratory of medical equipment in the field of in vitro diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow region, Russia
Russia

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