Potential of Raman spectroscopy for the analysis of plasma/serum in the liquid state: recent advances

Parachalil, Drishya Rajan; McIntyre, Jennifer; Byrne, Hugh J.

doi:10.1007/s00216-019-02349-1

Cited by 46 publications

(23 citation statements)

References 103 publications

(158 reference statements)

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“…Figure 5 gives an overview of basic Raman spectroscopy. The reason why Raman spectroscopy is preferred is that it has high sensitivity to detect tiny changes with a molecular size of 1 µm [81,82]. The general advantages of Raman spectroscopy are higher depth penetration compared to mid-infrared spectroscopy, being less sensitive to temperature changes compared to OCT, wide application and high specificity [77].…”

Section: Raman Spectroscopymentioning

confidence: 99%

Commercial and Scientific Solutions for Blood Glucose Monitoring—A Review

Xue

Thalmayer

Zeising

et al. 2022

Sensors

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Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

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Section: Raman Spectroscopymentioning

confidence: 99%

Commercial and Scientific Solutions for Blood Glucose Monitoring—A Review

Xue

Thalmayer

Zeising

et al. 2022

Sensors

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“…[9][10][11][12][13][14] Raman spectroscopy is a well-documented spectrographic method of examining the molecular composition of both solids and solutions. 3,9,13,15,16 When examining even dilute and complex aqueous solutions (such as urine), 3,[9][10][11][12][13][15][16][17][18][19][20] Raman spectroscopy may take on the order of seconds to generate spectral data, with minimal spectral interference from water. Analysis of aqueous solution spectra involves data transformation in the form of baseline correction, truncation, normalization, and statistical processing.…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine

2022

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Hematuria refers to the presence of blood in urine. Even in small amounts, it may be indicative of disease, ranging from urinary tract infection to cancer. Here, Raman spectroscopy was used to detect and quantify macro- and microhematuria in human urine samples. Anticoagulated whole blood was mixed with freshly collected urine to achieve concentrations of 0, 0.25, 0.5, 1, 2, 6, 10, and 20% blood/urine (v/v). Raman spectra were obtained at 785 nm and data analyzed using chemometric methods and statistical tests with the Rametrix toolboxes for Matlab. Goldindec and iterative smoothing splines with root error adjustment (ISREA) baselining algorithms were used in processing and normalization of Raman spectra. Rametrix was used to apply principal component analysis (PCA), develop discriminate analysis of principal component (DAPC) models, and to validate these models using external leave-one-out cross-validation (LOOCV). Discriminate analysis of principal component models were capable of detecting various levels of microhematuria in unknown urine samples, with prediction accuracies of 91% (using Goldindec spectral baselining) and 94% (using ISREA baselining). Partial least squares regression (PLSR) was then used to estimate/quantify the amount of blood (v/v) in a urine sample, based on its Raman spectrum. Comparing actual and predicted (from Raman spectral computations) hematuria levels, a coefficient of determination (R2) of 0.91 was obtained over all hematuria levels (0–20% v/v), and an R2 of 0.92 was obtained for microhematuria (0–1% v/v) specifically. Overall, the results of this preliminary study suggest that Raman spectroscopy and chemometric analyses can be used to detect and quantify macro- and microhematuria in unprocessed, clinically relevant urine specimens.

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“…It can thus potentially guide the identification of new biomarkers of cellular events, or recognition of phenomena not visible using other, for example fluorescence based techniques [9] . These techniques have been extensively explored to differentiate between healthy and diseased samples for diagnostic applications [10] , [11] , [12] , [13] , and recent studies using Raman and IR absorption spectroscopy have demonstrated great potential for their use with liquid biopsies samples [14] , [15] , [16] , [17] .…”

Section: Introductionmentioning

confidence: 99%