Skip to main content

Advertisement

Log in

Vis-NIR spectroscopy with moving-window PLS method applied to rapid analysis of whole blood viscosity

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

A rapid analytical method of human whole blood viscosity with low, medium, and high shear rates [WBV(L), WBV(M), and WBV(H), respectively] was developed using visible and near-infrared (Vis-NIR) spectroscopy combined with a moving-window partial least squares (MW-PLS) method. Two groups of peripheral blood samples were collected for modeling and validation. Separate analytical models were established for male and female groups to avoid interference in different gender groups and improve the homogeneity and prediction accuracy. Modeling was performed for multiple divisions of calibration and prediction sets to avoid over-fitting and achieve parameter stability. The joint analysis models for three indicators were selected through comprehensive evaluation of MW-PLS. The selected joint analysis models were 812–1278 nm for males and 670–1146 nm for females. The root-mean-square errors (SEP) and the correlation coefficients of prediction (RP) for all validation samples were 0.54 mPa•s and 0.91 for WBV(L), 0.25 mPa•s and 0.92 for WBV(M), and 0.22 mPa•s and 0.90 for WBV(H). Results indicated high prediction accuracy, with prediction values similar to the clinically measured values. Overall, the findings confirmed the feasibility of whole blood viscosity quantification based on Vis-NIR spectroscopy with MW-PLS. The proposed rapid and simple technique is a promising tool for surveillance, control, and treatment of cardio-cerebrovascular diseases in large populations.

The caption/legend of the online abstract figure: The selected wavebands and the prediction effects for the three indicators of whole blood viscosity

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

MW-PLS:

Moving-window partial least squares

PLS:

Partial least squares

RP :

Correlation coefficients for prediction

RPD:

Ratio of standard error of performance to standard deviation

SEP:

Root-mean-square errors for prediction

Vis-NIR:

Visible and near-infrared

WBV:

Whole blood viscosity

WBV(H):

Whole blood viscosity at high shear rate

WBV(L):

Whole blood viscosity at low shear rate

WBV(M):

Whole blood viscosity at medium shear rate

References

  1. Devereux RB, Case DB, Alderman MH, Pickering TG, Chien S, Laragh JH. Possible role of increased blood viscosity in the hemodynamics of systemic hypertension. Am J Cardiol. 2000;85:1265–8.

    Article  CAS  Google Scholar 

  2. Sloop GD, Garber DW. The effects of low-density lipoprotein and high-density lipoprotein on blood viscosity correlate with their association with risk of atherosclerosis in humans. Clin Sci. 1997;92:473–9.

    Article  CAS  Google Scholar 

  3. Rosenson RS, Shott S, Tangney CC. Hypertriglyceridemia is associated with an elevated blood viscosity. Rosenson: triglycerides and blood viscosity. Atherosclerosis. 2002;161:433–9.

    Article  CAS  Google Scholar 

  4. Lowe GD, Lee AJ, Rumley A, Price JF, Fowkes FGR. Blood viscosity and risk of cardiovascular events: the Edinburgh Artery Study. Br J Haematol. 1997;96:168–73.

    Article  CAS  Google Scholar 

  5. Jeong SK, Cho YI, Duey M, Rosenson RS. Cardiovascular risks of anemia correction with erythrocyte stimulating agents: should blood viscosity be monitored for risk assessment? Cardiovasc Drugs Ther. 2010;24:151–60.

    Article  CAS  Google Scholar 

  6. Backer TLD, Buyzere MD, Segers P, Carlier S, Sutter JD, Wiele CV, et al. The role of whole blood viscosity in premature coronary artery disease in women. Atherosclerosis. 2002;165:367–73.

    Article  Google Scholar 

  7. Cozzolino D, Moron A. Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions. Soil Tillage Res. 2006;85:78–85.

    Article  Google Scholar 

  8. Rossel RAV, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma. 2006;131:59–75.

    Article  Google Scholar 

  9. Chen HZ, Pan T, Chen JM, Lu QP. Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods. Chemom Intell Lab. 2011;107:139–46.

    Article  CAS  Google Scholar 

  10. Pan T, Li MM, Chen JM. Selection method of quasi-continuous wavelength combination with applications to the near-infrared spectroscopic analysis of soil organic matter. Appl Spectrosc. 2014;68:263–71.

    Article  CAS  Google Scholar 

  11. Pan T, Han Y, Chen JM, Yao LJ, Xie J. Optimal partner wavelength combination method with application to near-infrared spectroscopic analysis. Chemom Intell Lab. 2016;156:217–23.

    Article  CAS  Google Scholar 

  12. Chen JY, Iyo C, Terada F, Kawano S. Effect of multiplicative scatter correction on wavelength selection for near infrared calibration to determine fat content in raw milk. J Near Infrared Spectrosc. 2002;10:301–7.

    Article  CAS  Google Scholar 

  13. Chen JY, Zhang H, Matsunaga R. Rapid determination of the main organic acid composition of raw Japanese apricot fruit juices using near-infrared spectroscopy. J Agric Food Chem. 2006;54:9652–7.

    Article  CAS  Google Scholar 

  14. Liu ZY, Liu B, Pan T, Yang JD. Determination of amino acid nitrogen in tuber mustard using near-infrared spectroscopy with waveband selection stability. Spectrochim Acta A. 2013;102:269–74.

    Article  CAS  Google Scholar 

  15. Lyu N, Chen JM, Pan T, Yao LJ, Han Y, Yu J. Near-infrared spectroscopy combined with equidistant combination partial least squares applied to multi-index analysis of corn. Infrared Phys Technol. 2016;76:648–54.

    Article  Google Scholar 

  16. Sousa AC, Lucio MMLM, Bezerra Neto OF, Marcone GPS, Pereira AFC, Dantas EO, et al. A method for determination of COD in a domestic wastewater treatment plant by using near-infrared reflectance spectrometry of seston. Anal Chim Acta. 2007;588:231–6.

    Article  CAS  Google Scholar 

  17. Pan T, Chen ZH, Chen JM, Liu ZY. Near-infrared spectroscopy with waveband selection stability for the determination of COD in sugar refinery wastewater. Anal Methods UK. 2012;4:1046–52.

    Article  CAS  Google Scholar 

  18. Istvan VN, Karoly JK, Janos MJ, Éva G, Gyula D. Application of near infrared spectroscopy to the determination of hemoglobin. Clin Chim Acta. 1997;264:117–25.

    Article  Google Scholar 

  19. Hazen KH, Arnold MA, Small GW. Measurement of glucose and other analytes in undiluted human serum with near-infrared transmission spectroscopy. Anal Chim Acta. 1998;371:255–67.

    Article  CAS  Google Scholar 

  20. Jiang JH, Berry RJ, Siesler HW, Ozaki Y. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data. Anal Chem. 2002;74:3555–65.

    Article  CAS  Google Scholar 

  21. Kasemsumran S, Du YP, Murayama K, Huehne M, Ozaki Y. Simultaneous determination of human serum albumin, γ-globulin, and glucose in a phosphate buffer solution by near-infrared spectroscopy with moving window partial least-squares regression. Analyst. 2003;128:1471–7.

    Article  CAS  Google Scholar 

  22. Kasemsumran S, Du YP, Murayama K, Huehne M, Ozaki Y. Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares. Anal Chim Acta. 2004;512:223–30.

    Article  CAS  Google Scholar 

  23. Lee Y, Lee S, In JY, Chung SH, Yon JH. Prediction of plasma hemoglobin concentration by near-infrared spectroscopy. J Korean Med Sci. 2008;23:674–7.

    Article  CAS  Google Scholar 

  24. Xie J, Pan T, Chen JM, Chen HZ, Ren XH. Joint optimization of Savitzky-Golay smoothing models and partial least squares factors for near-infrared spectroscopic analysis of serum glucose. Chin J Anal Chem. 2010;38:342–6.

    Article  CAS  Google Scholar 

  25. Pan T, Liu JM, Chen JM. Rapid determination of preliminary thalassaemia screening indicators based on near-infrared spectroscopy with wavelength selection stability. Anal Methods UK. 2013;5:4355–62.

    Article  CAS  Google Scholar 

  26. Pan T, Li MM, Chen JM, Xue HY. Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy. J Innov Opt Health Sci. 2014;7:1–9.

    Article  Google Scholar 

  27. Han Y, Chen JM, Pan T, Liu GS. Determination of glycated hemoglobin using near-infrared spectroscopy combined with equidistant combination partial least squares. Chemom Intell Lab. 2015;145:84–92.

    Article  CAS  Google Scholar 

  28. Yao LJ, Lyu N, Chen JM, Pan T, Yu J. Joint analyses model for total cholesterol and triglyceride in human serum with near-infrared spectroscopy. Spectrochim Acta A. 2016;159:53–9.

    Article  CAS  Google Scholar 

  29. Williams P, Norris K (2001) Near-infrared technology in the agricultural and food industries. Am Assoc Cereal Chemists USA

  30. Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikström C, Wold S. Multi- and megavariate data analysis, Part 1: basic principles and applications. Umea: Umetrics Academy; 2006.

    Google Scholar 

  31. Long XL, Liu GS, Pan T, Chen JM. Waveband selection of reagent-free determination for thalassemia screening indicators using Fourier transform infrared spectroscopy with attenuated total reflection. J Biomed Opt. 2014;19:1–11.

    Article  Google Scholar 

  32. Jung JM, Lee DH, Kim KT, Choi MS, Cho YG. Reference intervals for whole blood viscosity using the analytical performance-evaluated scanning capillary tube viscometer. Clin Biochem. 2014;47:489–93.

    Article  CAS  Google Scholar 

  33. Irace C, Scavelli F, Carallo C, Serra R, Gnasso A. Plasma and blood viscosity in metabolic syndrome. Nutr Metab. 2009;19:476–80.

    CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by the Science and Technology Project of Guangdong Province of China (no. 2014A020212445, no. 2014A020213016).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tao Pan.

Ethics declarations

As the blood samples were collected and used in this study, the informed consent of all individual participants was obtained.

Ethical approval

Experiments were performed in compliance with the relevant laws and institutional guidelines and approved by local medical institution.

Conflict of Interest

Jiemei Chen, Zhiwei Yin, Yi Tang, Tao Pan declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Yin, Z., Tang, Y. et al. Vis-NIR spectroscopy with moving-window PLS method applied to rapid analysis of whole blood viscosity. Anal Bioanal Chem 409, 2737–2745 (2017). https://doi.org/10.1007/s00216-017-0218-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-017-0218-9

Keywords

Navigation