Biofluid analysis and classification using IR and 2D-IR spectroscopy

Vibrational spectroscopy has produced valuable information for biomedical research owing to its label-free and high-throughput capabilities. However, the complexity of and large number of variables of spectral datasets has seen the increasing application of multivariate analysis (MVA) and machine learning algorithms in recent years. In particular, the use of these techniques applied to the analysis of IR spectra of biological samples has been demonstrated as a powerful tool for the rapid sample analysis and diagnosis of disease. In this article, we review a variety of classification techniques employed for the analysis of infrared (IR) spectral datasets of biofluids, quoting prediction accuracies to demonstrate their effectiveness. With the advent of new technologies, two-dimensional infrared spectroscopy (2D-IR) has recently been applied to biomedical problems and shows potential future applications in biofluid analysis, however with complex multi-dimensional datasets there is a desire for advanced analytical techniques. As the application of 2D-IR to biofluids and physiological protein samples is in its infancy, large spectral datasets of biofluids suitable for classification are not readily available. It is imperative to establish in what way 2D-IR datasets respond to pre-processing and analytical methods. For the first time we draw on the classification techniques applied to IR datasets discussed in this review and relevant 2D-IR studies to discuss the future of machine learning algorithms in 2D-IR spectroscopy.

Automated summary

Vibrational spectroscopy plays a crucial role in biomedical research due to its label-free and high-throughput capabilities. Recently, there has been an increasing application of multivariate analysis (MVA) and machine learning algorithms in analyzing spectral datasets, particularly in the field of IR spectra of biological samples. The emergence of two-dimensional infrared spectroscopy (2D-IR) shows potential for future applications in biofluid analysis, but advanced analytical techniques are desired due to the complexity of multi-dimensional datasets.