Advertisement

Analytical and Bioanalytical Chemistry

, Volume 409, Issue 10, pp 2697–2703 | Cite as

Use of chemometrics to optimize a glucose assay on a paper microfluidic platform

  • Ani Avoundjian
  • Mehdi Jalali-Heravi
  • Frank A. GomezEmail author
Research Paper

Abstract

We describe the use of a chemometrics-based computational platform to optimize a glucose assay on a microfluidic paper-based analytical device (μPAD). Glucose is colorimetrically detected in the presence of glucose oxidase (GOx), horseradish peroxidase (HRP), and potassium iodide (KI). Using a Y-shaped paper microfluidic chip, the concentration of glucose, volume of reagents, and the length and width of the microfluidic channel were examined. The responses of the microfluidic chips were analyzed at the halfway point of the channel length. Variables affecting the response were screened by using a 24 factorial design, and among them, volume and concentration of the glucose were optimized by applying a rotatable central composite design (CCD). The optimum and experimental responses are 151.58 and 149.80, respectively, with an absolute error of 1.2%.

Keywords

Paper microfluidics Chemometrics Glucose oxidase Point-of-care diagnostic device 

Abbreviations

ANOVA

Analysis of variance

GOx

Glucose oxidase

HRP

Horseradish peroxidase

LOC

Lab-on-a chip

MD

Microfluidic device

MEMS

Microelectromechanical systems

POC

Point-of-care

μPAD

Microfluidic paper-based analytical device

Notes

Acknowledgments

The authors gratefully acknowledge financial support for this research by grants from the National Science Foundation (HRD-0934146, EEC-0812348, IIA-1448166, and HRD-1547723), the W. M. Keck Foundation, and the La Kretz Foundation.

Compliance with ethical standards

Conflict of interest

The authors have no relevant affiliations or financial involvement with an organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

References

  1. 1.
    Manz Z, Graber N, Widmer HM. Miniaturized total chemical analysis systems: a novel concept for chemical sensing. Sensor Actuat B Chem. 1990;1:244–8.CrossRefGoogle Scholar
  2. 2.
    Whitesides GM. The origins and future of microfluidics. Nature. 2006;442:368–73.CrossRefGoogle Scholar
  3. 3.
    Martinez AW, Phillips ST, Butte MJ, Whitesides GM. Patterned paper as a platform for inexpensive, low-volume, portable bioassays. Angew Chem Int Ed. 2007;46:1318–20.CrossRefGoogle Scholar
  4. 4.
    Martinez AW, Phillips ST, Carrilho E, Thomas III SW, Sindi H, Whitesides GM. Simple telemedicine for developing regions: camera phones and paper-based microfluidic devices for real-time, off-site diagnosis. Anal Chem. 2008;80:3699–707.CrossRefGoogle Scholar
  5. 5.
    Bruzewicz DA, Reches M, Whitesides GM. Low-cost printing of poly(dimethylsiloxane) barriers to define microchannels in paper. Anal Chem. 2008;80:3387–92.CrossRefGoogle Scholar
  6. 6.
    Martinez AW, Phillips ST, Wiley BJ, Gupta M, Whitesides GM. FLASH: A rapid method for prototyping paper-based microfluidic devices. Lab Chip. 2008;8:2146–50.CrossRefGoogle Scholar
  7. 7.
    Martinez AW, Phillips ST, Whitesides GM, Carrilho E. Diagnostics for the developing world: microfluidic paper-based analytical devices. Anal Chem. 2010;82:3–10.CrossRefGoogle Scholar
  8. 8.
    Schilling KM, Lepore AL, Kurian JA, Martinez AW. Fully enclosed microfluidic paper-based analytical devices. Anal Chem. 2012;84:1579–85.CrossRefGoogle Scholar
  9. 9.
    Cheng CM, Mazzeo AD, Gong JL, Martinez AW, Phillips ST, Jain N, et al. Millimeter-scale contact printing of aqueous solutions using a stamp made out of paper and tape. Lab Chip. 2010;10:3201–5.CrossRefGoogle Scholar
  10. 10.
    Gomez FA. Paper microfluidics in bioanalysis. Bioanalysis. 2014;6:2911–4.CrossRefGoogle Scholar
  11. 11.
    Nie J, Liang Y, Zhang Y, Shangwang L, Li D, Zhang S. One-step patterning of hollow microstructures in paper by laser cutting to create microfluidic analytical devices. Analyst. 2013;138:671–6.CrossRefGoogle Scholar
  12. 12.
    Renault C, Koehne J, Ricco AJ, Crooks RM. Three-dimensional wax patterning of paper fluidic devices. Langmuir. 2014;30:7030–6.CrossRefGoogle Scholar
  13. 13.
    Jang I, Song S. Facile and precise flow control for a paper-based microfluidic device through varying paper permeability. Lab Chip. 2015;15:3405–12.CrossRefGoogle Scholar
  14. 14.
    Wang H, Li J, Wei J–F, Xu J–R, Wang Y–H, Zheng G–X. Paper-based three-dimensional microfluidic device for monitoring of heavy metals with a camera cell phone. Anal Bioanal Chem. 2014;406:2799–807.CrossRefGoogle Scholar
  15. 15.
    Chitnis G, Ding ZW, Chang CL, Savranacde CA, Ziaiz B. Laser-treated hydrophobic paper: an inexpensive microfluidic platform. Lab Chip. 2011;11:1161–5.CrossRefGoogle Scholar
  16. 16.
    Gomez FA. The future of microfluidic point-of-care diagnostic devices. Bioanalysis. 2013;5:1–3.CrossRefGoogle Scholar
  17. 17.
    Weaver AA, Reiser H, Barstis T, Benvenuti M, Ghosh D, Hunckler M, et al. Paper analytical devices for fast field screening of beta lactam antibiotics and antituberculosis pharmaceuticals. Anal Chem. 2013;85:6453–60.CrossRefGoogle Scholar
  18. 18.
    Wang P, Ge L, Ge S, Yu J, Yan M, Huang J. A paper-based photoelectrochemical immunoassay for low-cost and multiplexed point-of-care testing. Chem Comm. 2013;4:3294–6.CrossRefGoogle Scholar
  19. 19.
    Rodriguez NM, Wong WS, Liu L, Dewar R, Klapperich CM. A fully integrated paperfluidic molecular diagnostic chip for the extraction, amplification, and detection of nucleic acids from clinical samples. Lab Chip. 2016;16:753–63.CrossRefGoogle Scholar
  20. 20.
    Kim WS, Shin JH, Park HK, Choi S. A low-cost, monometallic, surface-enhanced raman scattering-functionalized paper platform for spot-on bioassays. Sensor Actuat B Chem. 2016;222:1112–8.CrossRefGoogle Scholar
  21. 21.
    Arrastia M, Avoundjian A, Ehrlich PS, Eropkin M, Levine L, Gomez FA. Development of a microfluidic-based assay on a novel nitrocellulose platform. Electrophoresis. 2015;36:884–8.CrossRefGoogle Scholar
  22. 22.
    Ferrer IM, Valadez H, Estala L, Gomez FA. Paper microfluidic-based enzyme catalyzed double microreactor. Electrophoresis. 2014;35:2417–9.CrossRefGoogle Scholar
  23. 23.
    Zhang Y, Zuo P, Ye BC. A low-cost and simple paper-based microfluidic device for simultaneous multiplex determination of different types of chemical contaminants in food. Biosens Bioelectron. 2015;68:14–9.CrossRefGoogle Scholar
  24. 24.
    Ueland M, Blanes L, Taudte RV, Stuart BH, Cole N, Willis P, et al. Capillary-driven microfluidic paper-based analytical devices for lab on a chip screening of explosive residues in soil. J Chromatogr A. 2016;1436:28–33.CrossRefGoogle Scholar
  25. 25.
    Jin SQ, Guo SM, Zuo P, Ye BC. A cost-effective Z-folding controlled liquid handling microfluidic paper analysis device for pathogen detection via ATP quantification. Biosens Bioelectron. 2015;63:379–83.CrossRefGoogle Scholar
  26. 26.
    Gonzalez A, Estala L, Gaines M, Gomez, FG. Mixed thread/paper-based microfluidic chips as a platform for glucose assays. Electrophoresis, 37;1685–1690.Google Scholar
  27. 27.
    Scampicchio M, Mannino S, Zima J, Wang J. Chemometrics on microchips: towards the classification of wines. Electroanalysis. 2005;17:1215–21.CrossRefGoogle Scholar
  28. 28.
    Alvarado J, Hanrahan G, Nguyen HTH, Gomez FA. Implementation of a genetically tuned neural platform in optimizing fluorescence from receptor-ligand binding interactions on microchips. Electrophoresis. 2012;33:2711–7.CrossRefGoogle Scholar
  29. 29.
    Tohid U, Pacheco-Vega A. IMECE 2013, November 15-21, 2013, San Diego, California, USA, P 1-14Google Scholar
  30. 30.
    Jalali-Heravi M, Arrastia M, Gomez FA. How can chemometrics improve microfluidic research? Anal Chem. 2015;87:3544–55.CrossRefGoogle Scholar
  31. 31.
    Birnbaumer GM, Lieberzeit PA, Richtler L, Schirhagl R, Milnera M, Dickert FL, et al. Detection of viruses with molecularly imprinted polymers integrated on a microfluidic biochip using contact-less dielectric microsensors. Lab Chip. 2009;9:3549–56.CrossRefGoogle Scholar
  32. 32.
    Dochow S, Beleite C, Henkel T, Mayer G, Albert J, Clement J, et al. Quartz microfluidic chip for tumour cell identification by Raman spectroscopy in combination with optical traps. Anal Bioanal Chem. 2013;405:2743–6.CrossRefGoogle Scholar
  33. 33.
    Wlater A, Marz A, Schumacher W, Rosch P, Popp J. Towards a fast, high specific and reliable discrimination of bacteria on strain level by means of SERS in a microfluidic device. Lab Chip. 2011;11:1013–21.CrossRefGoogle Scholar
  34. 34.
    Mariey L, Signolle JP, Amiel C, Travert J. Discrimination, classification, idenfication of microorganisms using FTIR spectroscopy and chemometrics. Vibrat Spectrosc. 2001;26:151–9.CrossRefGoogle Scholar
  35. 35.
    Morgan E. Chemometrics: Experimental Design. London: Wiley; 1991.Google Scholar
  36. 36.
    DC. Montgomery, Design and Analysis of Experiments, 5th ed., Wiley, New York, 2001.Google Scholar
  37. 37.
    Leardi R. Experimental design in chemistry: a tutorial. Anal Chim Acta. 2009;652:161–72.CrossRefGoogle Scholar
  38. 38.
    Box GEP, Hunter WG, Hunter JS. An introduction to design, data analysis, and model building. New York: John Wiley; 1978.Google Scholar
  39. 39.
    Massart DL, Vandeginste BGM, Buydens LMC, De Jongs S, Lewi PJ, Smeyers-Verbeke J. Handbook of chemometrics and qualimetrics: Part A. Amsterdam: Elsevier; 1997.Google Scholar
  40. 40.
    Box GEP, Wilson KB. On the experimental attainment of optimum conditions. J Royal Statistical Society Series B. 1951;13:1–45.Google Scholar
  41. 41.
    Myers RH. Response surface methodology, published by author Blacksburg VA, 1976.Google Scholar
  42. 42.
    Zhi W, Song J, Quyang FJ. Application of response surface methodology to the modeling of alpha-amylase purification by aqueous two-phase systems. Biotechnol. 2005;118:157–65.Google Scholar
  43. 43.
    Kristo E, Biliaderis CG, Tzanetakis N. Modelling of the acidification process and rheological properties of milk fermented with a yogurt starter culture using response surface methodology. Food Chem. 2003;83:437–46.CrossRefGoogle Scholar
  44. 44.
    Jalali-Heravi M, Parastar H, Ebrahimi-Najafabadi H. Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography-mass spectrometry analysis. J Chromatogr A. 2009;1216:6088–97.CrossRefGoogle Scholar
  45. 45.
    Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta. 2008;76:965–77.CrossRefGoogle Scholar
  46. 46.
    Dejaegher B, Vander Heyden YJ. Experimental designs and their recent advances in set-up, data interpretation, and analytical applications. Pharm Biomed Anal. 2011;56:141–58.CrossRefGoogle Scholar
  47. 47.
    Dingstad G, Eglandsdal B, Naes T. Modeling methods for crossed mixture experiments-a case study from sausage production. Chemom Intell Lab Syst. 2003;66:175–90.CrossRefGoogle Scholar
  48. 48.
    Dingstad G, Westad F, Naes T. Three case studies illustrating the properties of ordinary and partial least squares regression in different mixture models. Chemom Intell Lab Syst. 2004;71:33–45.CrossRefGoogle Scholar
  49. 49.
    Awad ZK, Aravinthan T, Zhuge Y, Gonzalez F. A review of optimization techniques used in the design of fibre composite structures for civil engineering applications. Mater Des. 2012;24:375–9.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Ani Avoundjian
    • 1
  • Mehdi Jalali-Heravi
    • 1
  • Frank A. Gomez
    • 1
    Email author
  1. 1.Department of Chemistry and BiochemistryCalifornia State UniversityLos AngelesUSA

Personalised recommendations