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Nondestructive detection of reducing sugar of potato flours by near infrared spectroscopy and kernel partial least square algorithm

  • Xudong SunEmail author
  • Ke Zhu
  • Junbin Liu
Original Paper

Abstract

The feasibility of near infrared (NIR) spectroscopy combination with kernel partial least square (PLS) regression algorithms for quantitative determination of reducing sugar content in potato flours was investigated. The PLS, kernel-PLS, wide-kernel-PLS and least square support vector machine (LS-SVM) algorithms were performed comparatively to develop multivariate calibration models with the pretreatment spectral variables. Through comparing the performance of multivariate calibration models with new samples, the optimal models of reducing sugar content was obtained using wide-kernel-PLS algorithm with correlation coefficient (r) of 0.950 and root mean square error of prediction of 2.44 mg/g. Moreover, the predictive values for new potato flour samples obtained with wide-kernel-PLS model did not show significant difference with the reference values. These results suggested NIR spectroscopy coupled with wide-kernel-PLS algorithm was suitable for quantitative analysis of complicated chemical component of reducing sugar in potato flour.

Keywords

Near infrared spectroscopy Partial least square Kernel function Reducing sugar Potato 

Notes

Acknowledgements

The authors gratefully acknowledge the financial support provided by Jiangxi Outstanding Youth Talent Program (20171BCB23060), Jiangxi Provincial Education Department Project (GJJ160478), China Scholarship (201808360317), Jiangxi Association for Science and Technology (JAST) and Doctor Start-up Program (368).

References

  1. 1.
    B.M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W.K.I. Saeys, J. Lammertyna nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 46(2), 99–118 (2007)CrossRefGoogle Scholar
  2. 2.
    L.S. Magwaza, U.L. Opara, H. Nieuwoudt, P.J.R. Cronje, W. Saeys, B. Nicolaï, NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food Bioprocess Technol. 5(2), 425–444 (2012)CrossRefGoogle Scholar
  3. 3.
    S. López, I. Arazuri, J. García, C. Mangado, Jarén, A review of the application of near-infrared spectroscopy for analysis of potatoes. J. Agric. Food Chem. 61(23), 5413–5242 (2013)CrossRefGoogle Scholar
  4. 4.
    R. Hartmann, H. Büning-Pfaue, NIR determination of potato constituents. Potato Res. 41(4), 327–334 (1998)CrossRefGoogle Scholar
  5. 5.
    P.P. Subedi, K.B. Walsh, Assessment of potato dry matter concentration using short-wave near-infrared spectroscopy. Potato Res. 52(1), 67–77 (2009)CrossRefGoogle Scholar
  6. 6.
    N.U. Haase, Rapid estimation of potato tuber quality by near-infrared spectroscopy. Starch 58(6), 268–273 (2006)CrossRefGoogle Scholar
  7. 7.
    N.U. Haase, Estimation of dry matter and starch concentration in potatoes by determination of under-water weight and near infrared spectroscopy. Potato Res. 46(3–4), 117–127 (2003)CrossRefGoogle Scholar
  8. 8.
    N.U. Haase, Prediction of potato processing quality by near infrared reflectance spectroscopy of ground raw tubers. J. Near Infrared Spectrosc. 19(1), 37–45 (2011)CrossRefGoogle Scholar
  9. 9.
    S. López, C. Arazuri, J. Jarén, P. Mangado, J.I.R.D. Arnala, P. Galarreta, R. Riga, López, Crude protein content determination of potatoes by NIRS technology. Procedia Technol 8, 488–492 (2013)CrossRefGoogle Scholar
  10. 10.
    K. Brunt, W.C. Drost, Design, construction, and testing of an automated NIR in-line analysis system for potatoes. Part I: off-line NIR feasibility study for the characterization of potato composition. Potato Res. 53(1), 25–39 (2010)CrossRefGoogle Scholar
  11. 11.
    J. Jeong, H. Ok, O. Hur, C. Kim, Prediction of sprouting capacity using near-infrared spectroscopy in potato tubers. Am. J. Potato Res. 85(5), 309–314 (2008)CrossRefGoogle Scholar
  12. 12.
    K. Danzer, M. Otto, L.A. Currie, Guidelines for calibration in analytical chemistry Part 2. Multispecies calibration. Pure Appl. Chem. 76, 1215–1225 (2004)CrossRefGoogle Scholar
  13. 13.
    M. Blanco, I. Villarroya, NIR spectroscopy: a rapid-response analytical tool. Trends Anal. Chem. 21(4), 240–250 (2002)CrossRefGoogle Scholar
  14. 14.
    R.M. Balabin, R.Z. Safieva, E. Lomakina, Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction. Chemometr. Intell. Lab. Syst. 88(2), 183–188 (2007)CrossRefGoogle Scholar
  15. 15.
    B.M. Nicolaï, K.I. Theron, J. Lammertyn, Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemometr. Intell. Lab. Syst. 85(2), 243–252 (2007)CrossRefGoogle Scholar
  16. 16.
    F. Chauchard, R. Cogdill, S. Roussel, J.M. Roger, V. Bellon-Maurel, Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometr. Intell. Lab. Syst. 71(2), 141–150 (2004)CrossRefGoogle Scholar
  17. 17.
    G. Burgos, E. Salas, W. Amoros, M. Auqui, L. Muñoa, M. Kimura, M. Bonierbale, Total and individual carotenoid profiles in Solanum phureja of cultivated potatoes: I. Concentrations and relationships as determined by spectrophotometry and HPLC. J. Food Compos. Anal. 22(6), 503–508 (2009)CrossRefGoogle Scholar
  18. 18.
    M. Bonierbale, W. Grüneberg, W. Amoros, G. Burgos, E. Salas, E. Porras, T.Z. Felde, Total and individual carotenoid profiles in Solanum phureja cultivated potatoes: II. Development and application of near-infrared reflectance spectroscopy (NIRS) calibrations for germplasm characterization. J. Food Compos. Anal. 22(6), 509–516 (2009)CrossRefGoogle Scholar
  19. 19.
    H. Lindsay, A colorimetric estimation of reducing sugars in potatoes with 3,5-dinitrosalicylic acid. Potato Res. 16(3), 176–179 (1973)CrossRefGoogle Scholar
  20. 20.
    J. Trygg, S. Wold, PLS regression on wavelet compressed NIR spectra. Chemometr. Intell. Lab. Syst. 42(1–2), 209–220 (1998)CrossRefGoogle Scholar
  21. 21.
    S.D. Jong, C.J.F.T. Braak, Comments on the PLS kernel algorithm. J. Chemometr. 8(2), 169–174 (1994)CrossRefGoogle Scholar
  22. 22.
    F. Lindgren, P. Geladi, S. Wold, The kernel algorithm for PLS. J. Chemometr. 7(1), 45–59 (1993)CrossRefGoogle Scholar
  23. 23.
    S. Rännar, F. Lindgren, P. Geladi, S. Wold, A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: theory and algorithm. J. Chemometr. 8(2), 111–125 (1994)CrossRefGoogle Scholar
  24. 24.
    L. Leon, J.D. Kelly, G. Downey, Detection of apple juice adulteration using near-infrared transflectance spectroscopy. Appl. Spectrosc. 59(5), 593–599 (2005)CrossRefGoogle Scholar
  25. 25.
    L.H. Espinoza, D. Lucas, D. Littlejohn, S. Kyauk, Total organic carbon content in aqueous samples determined by near-IR spectroscopy. Appl. Spectrosc. 53(1), 103–107 (1999)CrossRefGoogle Scholar
  26. 26.
    W.F. McClure, H. Maeda, J. Dong, Y. Liu, Y. Ozaki, Two dimensional correlation of Fourier transform near-infrared and Fourier transform Raman spectra I: mixtures of sugar and protein. Appl. Spectrosc. 50(4), 467–475 (1996)CrossRefGoogle Scholar
  27. 27.
    S.E. Kay, W.R. Windham, F.E.I.I. Barton, Prediction of total dietary fiber by near-infrared reflectance spectroscopy in high-fat- and high-sugar-containing cereal. J. Agric. Food Chem. 46(3), 854–861 (1998)CrossRefGoogle Scholar
  28. 28.
    Q. Chen, J. Ding, J. Cai, J. Zhao, Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools. Food Chem. 135(2), 590–595 (2012)CrossRefGoogle Scholar
  29. 29.
    H. He, D. Sun, D. Wu, Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis. Food Res. Int. 62, 476–483 (2014)CrossRefGoogle Scholar
  30. 30.
    J. Shi, X. Zou, X. Huang, J. Zhao, Y. Li, L. Hao, J. Zhang, Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine. Food Chem. 138(1), 192–199 (2013)CrossRefGoogle Scholar
  31. 31.
    E. Teye, X. Huang, W. Lei, E. Dai, Feasibility study on the use of Fourier transform near-infrared spectroscopy together with chemometrics to discriminate and quantify adulteration in cocoa beans. Food Res. Int. 55, 288–293 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechatronics & Vehicle EngineeringEast China Jiaotong UniversityNanchangPeople’s Republic of China

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