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Frontiers of Optoelectronics

Front. Optoelectron.    2018, Vol. 11 Issue (3) : 296-305
Discrete combination method based on equidistant wavelength screening and its application to near-infrared analysis of hemoglobin
Tao Pan1(), Bingren Yan1, Jiemei Chen2, Lijun Yao1()
1. Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
2. Department of Biological Engineering, Jinan University, Guangzhou 510632, China
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A wavelength selection method for discrete wavelength combinations was developed based on equidistant combination-partial least squares (EC-PLS) and applied to a near-infrared (NIR) spectroscopic analysis of hemoglobin (Hb) in human peripheral blood samples. An allowable model set was established through EC-PLS on the basis of the sequence of the predicted error values. Then, the wavelengths that appeared in the allowable models were sorted, combined, and utilized for modeling, and the optimal number of wavelengths in the combinations was determined. The ideal discrete combination models were obtained by traversing the number of allowable models. The obtained optimal EC-PLS and discrete wavelength models contained 71 and 42 wavelengths, respectively. A simple and high-performance discrete model with 35 wavelengths was also established. The validation samples excluded from modeling were used to validate the three models. The root-mean-square errors for the NIR-predicted and clinically measured Hb values were 3.29, 2.86, and 2.90 g·L−1, respectively; the correlation coefficients, relative RMSEP, and ratios of performance to deviation were 0.980, 0.983, and 0.981; 2.7%, 2.3%, and 2.4%; and 4.6, 5.3, and 5.2, respectively. The three models achieved high prediction accuracy. Among them, the optimal discrete combination model performed the best and was the most effective in enhancing prediction performance and removing redundant wavelengths. The proposed optimization method for discrete wavelength combinations is applicable to NIR spectroscopic analyses of complex samples and can improve prediction performance. The proposed wavelength models can be utilized to design dedicated spectrometers for Hb and can provide a valuable reference for non-invasive Hb detection.

Keywords near-infrared (NIR) spectroscopy      equidistant combination-partial least squares (EC-PLS)      allowable model set discrete combination models      hemoglobin     
Corresponding Authors: Tao Pan,Lijun Yao   
Just Accepted Date: 10 April 2018   Online First Date: 08 May 2018    Issue Date: 31 August 2018
 Cite this article:   
Tao Pan,Bingren Yan,Jiemei Chen, et al. Discrete combination method based on equidistant wavelength screening and its application to near-infrared analysis of hemoglobin[J]. Front. Optoelectron., 2018, 11(3): 296-305.
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Tao Pan
Bingren Yan
Jiemei Chen
Lijun Yao
Fig.1  NIR spectra of 300 human peripheral blood samples
wavelength/nm N F RMSEPM/(g·L−1) RP,M
400–2498 1050 6 4.39 0.952
400–1800 and 2100–2300 842 8 3.80 0.965
Tab.1  Parameters and prediction effects of PLS models with the entire scanning region and the unsaturated region
Fig.2  SG derivative spectra (d = 2, p = 2, 3, m = 31) of all samples at 400–1880 and 2100–2300 nm
Fig.3  RMSEPM of the local optimal models for each m distinguished by different orders of derivative
d p m F RMSEPM/(g·L−1) RP,M
0 6 41 10 3.24 0.973
1 2 17 11 3.18 0.976
2 2 31 11 3.14 0.977
3 3 49 12 3.26 0.971
Tab.2  Effects of the local optimal SG-PLS model in 400–1880 and 2100–2300 nm corresponding to each order of derivative
Fig.4  RMSEPM of the local optimal models with EC-PLS for (a) initial wavelength, (b) number of wavelength, and (c) number of wavelength gaps
method wavelengths models F RMSEPM/(g·L−1) RP,M
SG-PLS 400–1880 and 2100–2300 nm 11 3.14 0.977
EC-PLS I=1230 nm, N=71, G=6 7 2.67 0.983
DC-PLS N=42 8 2.55 0.985
N=35 8 2.62 0.984
Tab.3  Parameters and prediction effects of the SG-PLS model, optimal EC-PLS model, and two discrete combination models
Fig.5  First 200 values of RMSEPM with EC-PLS
Fig.6  RMSEPM and number of adopted wavelengths of the optimal discrete combination model corresponding to each allowable model set
Fig.7  Wavelength combinations of the optimal EC-PLS model and optimal discrete combination model labeled in the average spectrum of the samples
Fig.8  RMSEPM of each discrete combination model in the case of S=40
Fig.9  Relationship between the predicted and measured values of the validation samples for the optimal EC-PLS model
Fig.10  Relationship between the predicted and measured values for the selected discrete combination models with (a) N=42 and (b) N=35
method N RMSEP/(g·L−1) RP RRMSEP RPD
EC-PLS 71 3.29 0.980 2.7% 4.6
DC-PLS 42 2.86 0.983 2.3% 5.3
35 2.90 0.981 2.4% 5.2
Tab.4  Validation effects of the optimal EC-PLS model and two discrete combination models
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