Prediction of Plasma Hemoglobin Concentration by Near-Infrared Spectroscopy

Lee, Youn Suk; Lee, Sang Seok; In, Junyong; Chung, Seung Hyun; Yon, Jun Heum

doi:10.3346/jkms.2008.23.4.674

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…Given that Hb levels can be measured by NIR spectroscopy, [11][12][13] we assume that Hb HbA1c can also be measured using the same method. With the simultaneous measurement of the two indicators, the predictive value of HbA1c can be obtained.…”

Section: Near-infrared (Nir) Spectroscopy Mainly Re°ects Absorption Omentioning

confidence: 99%

Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy

Pan

Chen

et al. 2014

J. Innov. Opt. Health Sci.

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A new strategy for quantitative analysis of a major clinical biochemical indicator called glycated hemoglobin (HbA1c) was proposed. The technique was based on the simultaneous near-infrared (NIR) spectral determination of hemoglobin (Hb) and absolute HbA1c content (Hb HbA1c) in human hemolysate samples. Wavelength selections were accomplished using the improved moving window partial least square (MWPLS) method for stability. Each model was established using an approach based on randomness, similarity, and stability to obtain objective, stable, and practical models. The optimal wavebands obtained using MWPLS were 958 to 1036 nm for Hb and 1492 to 1858 nm for Hb HbA1c, which were within the NIR overtone region. The validation root mean square error and validation correlation coe±cients of prediction (V -SEP, V -R P ) were 3.4 g L À1 and 0.967 for Hb, respectively, whereas the corresponding values for Hb HbA1c were 0.63 g L À1 and 0.913. The corresponding V -SEP and V -R P were 0.40% and 0.829 for the relative percentage of HbA1c. The experimental results con¯rm the feasibility for the quanti¯cation of HbA1c based on simultaneous NIR spectroscopic analyses of Hb and Hb HbA1c.

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Section: Near-infrared (Nir) Spectroscopy Mainly Re°ects Absorption Omentioning

confidence: 99%

Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy

Pan

Chen

et al. 2014

J. Innov. Opt. Health Sci.

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“…Near infrared (NIR) spectroscopy is a green and developing analytical technique, which has been widely used in life sciences, [1][2][3][4][5][6][7] agricultural products and food, [8][9][10][11] soil, [12][13][14] and other fields. 15,16 For NIR spectroscopic analysis of complex system, wavelength selection is necessary and difficult.…”

Section: Introductionmentioning

confidence: 99%

Optimal Partner Wavelength Combination Method Applied to NIR Spectroscopic Analysis of Human Serum Globulin

Yun

Zhou

et al. 2020

Preprint

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Human serum globulin (GLB), which contains various antibodies in healthy human serum, is of great significance for clinical trials and disease diagnosis. In this study, the GLB in human serum was rapidly analyzed by near infrared (NIR) spectroscopy without chemical reagents. Optimal partner wavelength combination (OPWC) method was employed for selecting discrete information wavelength. For the OPWC, the redundant wavelengths were removed by repeated projection iteration based on binary linear regression, and the result converged to stable number of wavelengths. By the way, the convergence of algorithm was proved theoretically. Moving window partial least squares (MW-PLS) and Monte Carlo uninformative variable elimination PLS (MC-UVE-PLS) methods, which are two well-performed wavelength selection methods, were also performed for comparison. The optimal models were obtained by the three methods, and the corresponding root-mean-square error of cross validation and correlation coefficient of prediction (SECV, RP,CV) were 0.813 g·L-1 and 0.978 with OPWC combined with PLS (OPWC-PLS), and 0.804 g L-1 and 0.979 with MW-PLS, and 1.153 g L-1 and 0.948 with MC-UVE-PLS, respectively. The OPWC-PLS and MW-PLS methods achieved almost the same good results. However, the OPWC only contained 28 wavelengths, so it had obvious lower model complexity. Thus it can be seen that the OPWC-PLS has great prediction performance for GLB and its algorithm is convergent and rapid. The results provide important technical support for the rapid detection of serum.

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“…People have investigated the potential of infrared and FTIR spectroscopy as a rapid, reagent‐free method for the simultaneous analysis of multiple indicators. In preliminary experiments, Lee et al demonstrated the feasibility of HGB quantitation in human blood by using FTIR spectroscopy . Brandstetter utilized infrared quantum cascade laser to monitor multiple clinically relevant parameters in human blood plasma .…”

Section: Introductionmentioning

confidence: 99%

“…In preliminary experiments, Lee et al demonstrated the feasibility of HGB quantitation in human blood by using FTIR spectroscopy. 22 Brandstetter utilized infrared quantum cascade laser to monitor multiple clinically relevant parameters in human blood plasma. 23 Liu developed infrared spectroscopic analytical methods for the simultaneous quantitation of cholesterol (total, HDL, and LDL) and triglycerides in serum samples.…”

Section: Introductionmentioning

confidence: 99%

Use of random forest in FTIR analysis of LDL cholesterol and tri‐glycerides for hyperlipidemia

Chen

Tang

et al. 2015

Biotechnology Progress

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A quantitative determination method for the diagnosis of hyperlipidemia was developed using Fourier transform infrared (FTIR) spectroscopy. Random forest (RF) was demonstrated as a potential multivariate algorithm for the FTIR analysis of low-density lipoprotein cholesterol (LDL-C) and tri-glycerides (TG) in human serum samples. The informative wavebands for LDL-C and TG were selected based on the Gini importance. The selected wavebands were mainly within the fingerprint region. The RF modeling results were better than those derived using PLS in validation process, because the chance for over-fitting was possibly eliminated in RF algorithm. ARF also demonstrated favorable results in the test process. The prospective model exhibited a higher than 90% true prediction in negative/positive properties for male and female samples. These clinical statistical results indicated the optimization of RF algorithm performed accurately in the FTIR determination of LDL-C and TG. RF is evaluated as a promising tool for diagnosing and controlling hyperlipidemia in populations. The parameter optimization methodology is useful in the improving model accuracy using FTIR spectroscopic technology.

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Prediction of Plasma Hemoglobin Concentration by Near-Infrared Spectroscopy

Cited by 13 publications

References 14 publications

Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy

Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy

Optimal Partner Wavelength Combination Method Applied to NIR Spectroscopic Analysis of Human Serum Globulin

Use of random forest in FTIR analysis of LDL cholesterol and tri‐glycerides for hyperlipidemia

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