In vivo Raman spectroscopy for non-invasive transcutaneous glucose monitoring on animal models and human subjects

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 329; p. 125584
• Main Authors: Liu, Jing, Chu, Jiahui, Xu, Jie, Zhang, Zhanqin, Wang, Shuang
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15.03.2025
• Subjects: Adult; Animal model; Animals; Blood Glucose - analysis; Blood Glucose Self-Monitoring - methods; Female; Human subjects; Humans; Male; Mice; Neural network; Neural Networks, Computer; Non-invasive glucose monitoring; Raman spectroscopy; Spectrum Analysis, Raman - methods; Human subjects; Animal model; Neural network; Raman spectroscopy; Non-invasive glucose monitoring
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2024.125584

[Display omitted] •In vivo Raman measurements were conducted on both mouse model and human subject for characterizing the glucose fingerprint.•Glucose fingerprint variations exhibited a strong correlation with the actual changes in blood glucose concentration.•Intraspectrum intensity ratio (I1125/I1445) can help mitigate measurement artifacts.•A PSO-BP-ANN model was developed to provide a reliable quantified prediction. Non-invasive glucose monitoring represents a significant advancement in diabetes management and treatment as non-painful alternatives than finger-sticks tests. After developing an integrated Raman spectral system with a 785 nm laser, this study systematically explores the application of in vivo Raman spectroscopy for quantitative, noninvasive glucose monitoring. In addition to observing characteristic glucose spectral information from a mouse model, a strong spectral correlation was also recognized with the blood glucose concentration. The glucose fingerprint information detected from the nailfolds of 30 human volunteers exhibited concentration dependent changes, especially when the intraspectrum intensity ratio was calculated between 1125 cm−1 and 1445 cm−1 to monitor normalized differences in the glucose Raman band. Furthermore, by accounting for all intersubject variations observed in the acquired spectral features, a particle swarm optimization-backpropagation artificial neural network (PSO-BP-ANN) model was proposed for linking measured Raman information with actual glucose concentrations quantitatively. Following model training and testing, the prediction accuracy of the PSO-BP-ANN model was evaluated using 12 spectra acquired from an additional three volunteers. Statistical evaluations indicated that the proposed methodology may have a good application potential for in vivo transcutaneous spectral glucose monitoring.