Optimal partner wavelength combination method applied to NIR spectroscopic analysis of human serum globulin

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 waveleng...

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Vydané v:BMC chemistry Ročník 14; číslo 1; s. 37 - 7
Hlavní autori: Han, Yun, Zhong, Yun, Zhou, Huihui, Kuang, Xuesong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 24.05.2020
Springer Nature B.V
BMC
Predmet:
Algorithms
Analytical Chemistry
Antibodies
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Computer simulation
Convergence
Correlation coefficients
Forecasting
Human serum globulin
Infrared analysis
Infrared spectroscopy
Iterative methods
Medical diagnosis
Methods
Monte Carlo simulation
Near infrared radiation
Near-infrared spectroscopy
Optimal partner wavelength combination
Performance evaluation
Reagents
Regression analysis
Research Article
Spectrum analysis
Technical services
Viral infections
Wavelengths
Human serum globulin
Optimal partner wavelength combination
Near-infrared spectroscopy
ISSN:2661-801X, 2661-801X
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Popis
Shrnutí: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, R P,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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ISSN:2661-801X
2661-801X
DOI:10.1186/s13065-020-00689-z