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In-silico investigation towards the non-invasive optical detection of blood lactate. Sci Rep 2021; 11:14274. [PMID: 34253775 PMCID: PMC8275594 DOI: 10.1038/s41598-021-92803-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/08/2021] [Indexed: 02/06/2023] Open
Abstract
This paper uses Monte Carlo simulations to investigate the interaction of short-wave infrared (SWIR) light with vascular tissue as a step toward the development of a non-invasive optical sensor for measuring blood lactate in humans. The primary focus of this work was to determine the optimal source-detector separation, penetration depth of light at SWIR wavelengths in tissue, and the optimal light power required for reliable detection of lactate. The investigation also focused on determining the non-linear variations in absorbance of lactate at a few select SWIR wavelengths. SWIR photons only penetrated 1.3 mm and did not travel beyond the hypodermal fat layer. The maximum output power was only 2.51% of the input power, demonstrating the need for a highly sensitive detection system. Simulations optimized a source-detector separation of 1 mm at 1684 nm for accurate measurement of lactate in blood.
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Mamouei M, Budidha K, Baishya N, Qassem M, Kyriacou PA. An empirical investigation of deviations from the Beer-Lambert law in optical estimation of lactate. Sci Rep 2021; 11:13734. [PMID: 34215765 PMCID: PMC8253732 DOI: 10.1038/s41598-021-92850-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 06/14/2021] [Indexed: 11/09/2022] Open
Abstract
The linear relationship between optical absorbance and the concentration of analytes-as postulated by the Beer-Lambert law-is one of the fundamental assumptions that much of the optical spectroscopy literature is explicitly or implicitly based upon. The common use of linear regression models such as principal component regression and partial least squares exemplifies how the linearity assumption is upheld in practical applications. However, the literature also establishes that deviations from the Beer-Lambert law can be expected when (a) the light source is far from monochromatic, (b) the concentrations of analytes are very high and (c) the medium is highly scattering. The lack of a quantitative understanding of when such nonlinearities can become predominant, along with the mainstream use of nonlinear machine learning models in different fields, have given rise to the use of methods such as random forests, support vector regression, and neural networks in spectroscopic applications. This raises the question that, given the small number of samples and the high number of variables in many spectroscopic datasets, are nonlinear effects significant enough to justify the additional model complexity? In the present study, we empirically investigate this question in relation to lactate, an important biomarker. Particularly, to analyze the effects of scattering matrices, three datasets were generated by varying the concentration of lactate in phosphate buffer solution, human serum, and sheep blood. Additionally, the fourth dataset pertained to invivo, transcutaneous spectra obtained from healthy volunteers in an exercise study. Linear and nonlinear models were fitted to each dataset and measures of model performance were compared to attest the assumption of linearity. To isolate the effects of high concentrations, the phosphate buffer solution dataset was augmented with six samples with very high concentrations of lactate between (100-600 mmol/L). Subsequently, three partly overlapping datasets were extracted with lactate concentrations varying between 0-11, 0-20 and 0-600 mmol/L. Similarly, the performance of linear and nonlinear models were compared in each dataset. This analysis did not provide any evidence of substantial nonlinearities due high concentrations. However, the results suggest that nonlinearities may be present in scattering media, justifying the use of complex, nonlinear models.
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Affiliation(s)
- M Mamouei
- Deep Medicine, Nuffield Department of Women's and Reproductive Health, Oxford Martin School, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK. .,Research Centre for Biomedical Engineering (RCBE), School of Mathematics, Computer Science and Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK.
| | - K Budidha
- Research Centre for Biomedical Engineering (RCBE), School of Mathematics, Computer Science and Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK
| | - N Baishya
- Research Centre for Biomedical Engineering (RCBE), School of Mathematics, Computer Science and Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK
| | - M Qassem
- Research Centre for Biomedical Engineering (RCBE), School of Mathematics, Computer Science and Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK
| | - P A Kyriacou
- Research Centre for Biomedical Engineering (RCBE), School of Mathematics, Computer Science and Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK
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Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection. SENSORS 2021; 21:s21051891. [PMID: 33800350 PMCID: PMC7962825 DOI: 10.3390/s21051891] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/01/2021] [Accepted: 03/04/2021] [Indexed: 12/12/2022]
Abstract
Near Infrared (800–2500 nm) spectroscopy has been extensively used in biomedical applications, as it offers rapid, in vivo, bed-side monitoring of important haemodynamic parameters, which is especially important in critical care settings. However, the choice of NIR spectrometer needs to be investigated for biomedical applications, as both the dual beam dispersive spectrophotomer and the FTNIR spectrometer have their own advantages and disadvantages. In this study, predictive analysis of lactate concentrations in whole blood were undertaken using multivariate techniques on spectra obtained from the two spectrometer types simultaneously and results were compared. Results showed significant improvement in predicting analyte concentration when analysis was performed on full range spectral data. This is in comparison to analysis of limited spectral regions or lactate signature peaks, which yielded poorer prediction models. Furthermore, for the same region, FTNIR showed 10% better predictive capability than the dual beam dispersive NIR spectrometer.
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