Integrating Machine Learning into Analytical Chemistry: A Focus on Pattern Recognition and Data Analysis in Spectrometry

Abstract

Machine learning with its integration into analytical chemistry, and more specifically spectrometry, is imposing new trends in data analysis and pattern recognition. On the other hand, spectral measurement is an important technique to analyze the influence of matter and electromagnetic radiation that generates complex and multidimensional data, which is difficult to study effectively with traditional methods. Infrared, Raman, mass, and nuclear magnetic resonance spectroscopy achieve high-throughput analyses by accuracy and efficiency with the help of machine learning algorithms, which include support vector machines, random forests, neural networks, and clustering techniques. This review represents an application of machine learning at preliminary data extraction, modeling, and validation stages in IR, Raman, mass, and NMR spectroscopy. In infrared spectroscopy, machine learning models help to identify organic compounds, while subtle improvements in spectral differences enable diagnosis by Raman spectroscopy. In mass spectrometry, machine learning helps in identifying complex mixtures and trace quantification, and in nuclear magnetic resonance spectroscopy, it improves the deconvolution of overlapping signals. Quality of data, computational needs, and multidisciplinary expertise required are still some of the challenges, but the continuous process of research and collaboration keeps innovating. In cloud computing, high performance can compute problems and attain real-time, high-degree analysis. With a view to providing researchers and practitioners in analytical chemistry with a better understanding of the status, challenges, and future prospects in this cross-application of machine learning in spectroscopic analysis, the present study is conducted.