Literature DB >> 33677359

Utilizing pulse dynamics for non-invasive Raman spectroscopy of blood analytes.

Maciej S Wróbel1, Jeong Hee Kim2, Piyush Raj2, Ishan Barman3, Janusz Smulko4.   

Abstract

Non-invasive measurement methods offer great benefits in the field of medical diagnostics with molecular-specific techniques such as Raman spectroscopy which is increasingly being used for quantitative measurements of tissue biochemistry in vivo. However, some important challenges still remain for label-free optical spectroscopy to be incorporated into the clinical laboratory for routine testing. In particular, non-analyte-specific variations in tissue properties introduce significant variability of the spectra, thereby preventing reliable calibration. For measurements of blood analytes such as glucose, we propose to decrease the interference from individual tissue characteristics by exploiting the known dynamics of the blood-tissue matrix. We reason that by leveraging the natural blood pulse rhythm, the signals from the blood analytes can be enhanced while those from the static components can be effectively suppressed. Here, time-resolved measurements with subsequent pulse frequency estimation and phase-sensitive detection are proposed to recover the Raman spectra correlated with the dynamic changes at blood-pulse frequency. Pilot in vivo study results are presented to establish the benefits as well as outline the challenges of the proposed method in terms of instrumentation and signal processing.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Blood constituent monitoring; Glucose; Modulation techniques; Optical diagnostics; Raman spectroscopy; Turbid media

Mesh:

Year:  2021        PMID: 33677359     DOI: 10.1016/j.bios.2021.113115

Source DB:  PubMed          Journal:  Biosens Bioelectron        ISSN: 0956-5663            Impact factor:   10.618


  4 in total

1.  Muscle Mass Measurement Using Machine Learning Algorithms with Electrical Impedance Myography.

Authors:  Kuo-Sheng Cheng; Ya-Ling Su; Li-Chieh Kuo; Tai-Hua Yang; Chia-Lin Lee; Wenxi Chen; Shing-Hong Liu
Journal:  Sensors (Basel)       Date:  2022-04-18       Impact factor: 3.847

2.  Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques.

Authors:  Yongjun Zhang; Guangheng Gao
Journal:  J Healthc Eng       Date:  2022-04-11       Impact factor: 3.822

3.  Virtual Spectral Selectivity in a Modulated Thermal Infrared Emitter with Lock-In Detection.

Authors:  David Santalices; Juan Meléndez; Susana Briz
Journal:  Sensors (Basel)       Date:  2022-07-21       Impact factor: 3.847

Review 4.  Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?

Authors:  Biagio Todaro; Filippo Begarani; Federica Sartori; Stefano Luin
Journal:  Front Chem       Date:  2022-09-26       Impact factor: 5.545

  4 in total

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