Raman spectroscopy and multivariate regression analysis in biomedical research, medical diagnosis, and clinical analysis

High operational costs of modern medical devices and the required specialized, skilled personnel with certifications to operate most medical instrumentation remains a challenge and an impediment to rapid medical diagnosis and clinical analysis. The simplicity, portability, and ease of operation makes Raman spectrometers appealing for rapid medical diagnosis and clinical analysis at an affordable cost. Besides, the combined use of Raman spectroscopy and multivariate analyses has further facilitated effective pattern recognition providing accurate classification, and/or differentiation of biological and clinical samples. This review article highlights recent advances in Raman spectroscopy in medical diagnosis and clinical analysis between January 2018 and December 2020. Recent innovations in the use of Raman spectroscopy for chemical analysis in human specimens are discussed. Applications of Raman spectroscopy in cancer immunotherapy, cancer imaging, and detecting disease biomarkers in clinical samples are further highlighted. The review article highlights recent innovations in the use of Raman spectroscopy for the detection of various pathogens in human specimens. Moreover, recent innovations of combined uses of Raman spectroscopy and multivariate regression analyses for pattern recognition, and/or classification of clinical samples are discussed. Furthermore, insights into the projection in the use of Raman spectroscopy in medical diagnosis and clinical sample analysis are discussed.

Automated summary

The high operational costs and specialized personnel required for modern medical devices present challenges to rapid medical diagnosis and clinical analysis. However, the simplicity and effectiveness of Raman spectrometers make them a cost-effective option for quick medical diagnosis. Additionally, the combination of Raman spectroscopy and multivariate analyses has improved pattern recognition and classification accuracy of biological and clinical samples.