Spectroscopic approach for dynamic bioanalyte tracking with minimal concentration information

• Published in: Scientific reports Vol. 4; no. 1; p. 7013
• Main Authors: Spegazzini, Nicolas, Barman, Ishan, Dingari, Narahara Chari, Pandey, Rishikesh, Soares, Jaqueline S., Ozaki, Yukihiro, Dasari, Ramachandra Rao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.11.2014; Nature Publishing Group
• Subjects: 140/133; 631/114/2397; 631/1647/527/1821; Blood; Blood Glucose - analysis; Blood Glucose - metabolism; Diabetes mellitus; Diffusion; Glucose; Glucose monitoring; Glucose tolerance; Glucose Tolerance Test; Humanities and Social Sciences; Humans; Kinetics; Least-Squares Analysis; multidisciplinary; Optics and Photonics - instrumentation; Optics and Photonics - methods; Raman spectroscopy; Science; Skin - blood supply; Spectroscopy; Spectrum analysis; Spectrum Analysis, Raman - instrumentation; Spectrum Analysis, Raman - methods; Vibration
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep07013

Vibrational spectroscopy has emerged as a promising tool for non-invasive, multiplexed measurement of blood constituents - an outstanding problem in biophotonics. Here, we propose a novel analytical framework that enables spectroscopy-based longitudinal tracking of chemical concentration without necessitating extensive a priori concentration information. The principal idea is to employ a concentration space transformation acquired from the spectral information, where these estimates are used together with the concentration profiles generated from the system kinetic model. Using blood glucose monitoring by Raman spectroscopy as an illustrative example, we demonstrate the efficacy of the proposed approach as compared to conventional calibration methods. Specifically, our approach exhibits a 35% reduction in error over partial least squares regression when applied to a dataset acquired from human subjects undergoing glucose tolerance tests. This method offers a new route at screening gestational diabetes and opens doors for continuous process monitoring without sample perturbation at intermediate time points.