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 AJAC  Vol.3 No.1 , January 2012
Near-Infrared Spectroscopic Analysis of Hemoglobin with Stability Based on Human Hemolysates Samples
Abstract: Near-infrared (NIR) spectroscopy combined with the partial least-squares (PLS) regression was successfully applied for the rapid quantitative analysis of hemoglobin (HGB) based on human hemolysates samples. Based on the varied divisions for the calibration and prediction sets, an effective modeling approach using stable model parameters was proposed. Among 255 samples, 80 were randomly selected as the validation set. The remaining 175 samples were divided into the calibration set (110 samples) and the prediction set (65 samples) for a total of 30 times with certain similarities based on partial least squares cross-validation predictive basis (PLSPB). The optimal PLS factor was 8, the modeling effects M-SEPAve, M-RP,Ave, M-SEPStd and M-RP,Std were 3.84g/L, 0.967, 0.16g/L and 0.006, respectively, the validation effects V-SEP, V-RP and V-RSEP were 3.59g/L, 0.980 and 2.7%, respectively. It indicated that the method has high prediction precision and well stability. The results show that NIR spectroscopy of hemolysates is accurate to HGB’s determination, and it is hopeful to be applied to clinic.
Cite this paper: T. Pan, W. Huang, Z. Liu and L. Yao, "Near-Infrared Spectroscopic Analysis of Hemoglobin with Stability Based on Human Hemolysates Samples," American Journal of Analytical Chemistry, Vol. 3 No. 1, 2012, pp. 19-23. doi: 10.4236/ajac.2012.31004.
References

[1]   V. N. Istvan, J. K. Karoly, J. M. Janos, G. Eva and D. Gyula, “Application of near Infrared Spectroscopy to the Determination of Haemoglobin,” Clinica Chimica Acta, Vol. 264, No. 1, 1997, pp. 117-125. doi:10.1016/S0009-8981(97)00085-5

[2]   G. W. Yoon, S. W. Kim, Y. J. Kim, J. W. Kim and W. K. Kim, “Optimizations of Preprocessing and Wavelength Selection in Predicting Human Total Hemoglobin Concentrations Based on VIS/NIR Spec-troscopy,” Proceeding of SPIE, Vol. 3257, No. 126, 1998, pp. 126-133. doi:10.1117/12.306078

[3]   D. A. Burns and E. W. Ciurczak, “Handbook of Near- infrared Analysis,” 2nd Edition, Marcel Dekker Inc., New York, 2001.

[4]   H. W. Wang, “Partial Least Squares Regression Method and Application,” National Defense Industry Press, Beijing, 1999, pp. 97-99.

[5]   M. J. Mcshane, “Assessment of Partial Least-squares Calibration and Wavelength Selection for Complex Near- Infrared Spectra,” Applied Spectroscopy, Vol. 52, No. 6, 1998, pp. 878-884. doi:10.1366/0003702981944427

[6]   H. Z. Chen, T. Pan, J. M. Chen and Q. P. Lu, “Waveband Selection for NIR Spectroscopy Analysis of Soil Organic Matter Based on SG Smoothing and MWPLS Methods,” Chemometrics and Intelligent Laboratory Systems, Vol. 107, No. 1, 2011, pp. 139-146. doi:10.1016/2011.02.008

[7]   J. Xie, T. Pan, J. M. Chen, H. Z. Chen and X. H. Ren, “Joint Optimization of Savitzky-Golay Smoothing Models and Partial Least Squares Factors for Near-Infrared Spectroscopic Analysis of Serum Glucose,” Chi-nese Journal of Analytical Chemistry, Vol. 38, No. 3, 2010, pp. 342-346. doi:10.3724/1096.2010.00342

[8]   P. Cao, T. Pan and X. D. Chen, “Choice of Wave Band in Design of Minitype Near-Infrared Corn Protein Content Analyzer,” Optics and Precision Engineering, Vol. 15, No. 12, 2007, pp. 1952-1958.

[9]   F. R. Huang, T. Pan, G. L. Zhang, X. Z. Pan and D. F. Liu, “Rapid Measurement of Zinc Contents in Soils by Near-Infrared Diffuse Reflectance Spectroscopy,” Optics and Precision Engineering, Vol. 18, No. 3, 2010, pp. 586-592.

[10]   H. Z. Chen, T. Pan and J. M. Chen, “Combina-tion Optimization of Multiple Scatter Correction and Savitzky- Golay Smoothing Modes Applied to the Near Infrared Spec-troscopy Analysis of Soil Organic Matter,” Computers & Ap-plied Chemistry, Vol. 28, No. 5, 2011, pp. 518-522.

 
 
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