Evolution of vibrational biospectroscopy: multimodal techniques and miniaturisation supported by machine learning

Abstract

The field of vibrational biospectroscopy has undergone continuous evolution, advancing from its earliest pioneers to the current innovators. Emerging frontier technologies have enabled vibrational biospectroscopy to reach new heights, expanding its applications in biomedical and clinical settings. Key advancements include the incorporation of multimodal spectroscopy, improvements in spatial resolution and the miniaturization of spectrometers coupled with machine learning. Multimodal spectroscopy is a growing subfield within vibrational biospectroscopy, offering different perspectives of the same sample to better understand the origins of vibrational modes. Meanwhile, the miniaturization of spectrometers has opened the door for field studies and personalized diagnostics, made possible by the integration of machine learning. The combination of miniaturized spectrometers and machine learning has paved the way for novel disease detection approaches. This review will discuss the historical progression of vibrational biospectroscopy and its potential for future applications, with a particular focus on the use of machine learning, multimodal spectroscopy, and miniaturized spectrometers in biomedicine. The primary goal of this review is to provide insight into the prospects of vibrational biospectroscopy, identify gaps in the current literature for future applications, and assess the potential impact of this field in the biomedical domain.