Skip to main content
Log in

Development of a non-destructive method for wheat physico-chemical analysis by chemometric comparison of discrete light based near infrared and Fourier transform near infrared spectroscopy

  • Original Paper
  • Published:
Journal of Food Measurement and Characterization Aims and scope Submit manuscript

Abstract

Rapid and non-destructive spectroscopic methods were developed using discrete light based near infrared (NIR) and Fourier transform near infrared (FTNIR) spectroscopy and compared for efficient determination of physico-chemical characteristics of wheat grain. The FTNIR spectra were analyzed using partial least squares regression with various preprocessing techniques. The best model for moisture, protein, ash, fat, thousand kernel weight and hardness with lowest RMSECV values 0.60, 0.17, 0.03, 0.02, 0.7, 1.2 and maximum correlation coefficient (R2) 0.97, 0.95,0.87,0.90,0.95 and 0.82 respectively were obtained. The discrete light based NIR spectral data were analyzed using multiple linear regression. The best model for moisture, protein, ash, fat, thousand kernel weight and hardness with lowest RMSECV values 0.94, 0.34, 0.04, 0.05, 1.09, 1.35 and maximum correlation coefficient (R2) 0.96, 0.90, 0.87, 0.75, 0.97 and 0.88 respectively were obtained. Comparing both the methods, FTNIR with lower relative error percentage was found to be useful for routine analysis in wheat processing industries.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Economic Survey, Department of Agricultural and Co-operation, India: Directorate of Economics and Statistics (2016). http://indiabudget.nic.in. Accessed 10 July 2016

  2. Y. Pomeranz, Modern Cereal Science and Technology (VCH Publishers Inc., New York, 1987), pp. 258–333

    Google Scholar 

  3. P.R. Shewry, Wheat. J. Exp. Bot., 60, 1537–1553 (2009)

    Article  CAS  Google Scholar 

  4. USDA Nutrient database for Standard Reference, Release 27, Wheat Flour, Whole Grain (2014). http://ndb.nal.usda.gov/ndb/foods/show/6489. Accessed 4 June 2014

  5. H. Shi, P. Yu, Comparison of grating-based near-infrared (NIR) and Fourier transform mid-infrared (ATR-FT/MIR) spectroscopy based on spectral preprocessing and wavelength selection for the determination of crude protein and moisture content in wheat. Food Control (2017). https://doi.org/10.1016/j.foodcont.2017.06.015

    Article  Google Scholar 

  6. G.A. de Oliveira, F. de Castilhos, C.M.C. Renard, S. Bureau, Comparison of NIR and MIR spectroscopic methods for determination of individual sugars, organic acids and carotenoids in passion fruit. Food Res. Int. 60, 154–162 (2014)

    Article  Google Scholar 

  7. B. Suart, Infrared Spectroscopy: Fundamental and Applications (Wiley, Chichester, 2004)

    Book  Google Scholar 

  8. T. Woodcock, G. Downey, C.P. O’Donnell, Review: better quality food and beverages: the role of near infrared spectroscopy. J. Near Infrared Spectrosc. 16(1), 1–29 (2008)

    Article  CAS  Google Scholar 

  9. D. Cozzolino, Recent trends on the use of infrared spectroscopy to trace and authenticate natural and agricultural food products. Appl. Spectrosc. Rev. 47, 518–530 (2012)

    Article  CAS  Google Scholar 

  10. A. Subramanian, L. Rodrigez-Saona, Fourier transform infrared (FTIR) spectroscopy, in Infrared Spectroscopy for Food Quality Analysis and Control, ed. by D.W. Sun (Academic Press, Amsterdam, 2009), pp. 146–174

    Google Scholar 

  11. L.E. Agelet, C.R. Hurburgh Jr., A tutorial on near infrared spectroscopy and its calibration. Crit. Rev. Anal. Chem. 40(4), 246–260 (2010)

    Article  CAS  Google Scholar 

  12. A.G. Olszak, J. Schmit, M.G. Heaton, Interferometry: Technology and Applications (Bruker, Billerica, 2012). Retrieved 1 April 2012

    Google Scholar 

  13. H.W. Siesler, Y. Ozaki, S. Kawata, H.M. Heise, Near-Infrared Spectroscopy: Principles, Instruments, Applications (Wiley, Weinheim, 2008)

    Google Scholar 

  14. L.M.L. Laurens, E.J. Wolfrum, Feasibility of spectroscopic characterization of algal lipids: chemometric correlation of NIR and FTIR spectra with exogenous lipids in algal biomass. Bio-Energy Res. 4, 22–35 (2011)

    Google Scholar 

  15. P.R. Armstrong, B.E. Maghirang, F. Xie, F.E. Dowell, Comparison of dispersive and Fourier-transform NIR instruments for measuring grain and flour attributes. Am. Soc. Agric. Biol. Eng. 22, 453–457 (2006)

    Google Scholar 

  16. J. Hell, M. Prückler, L. Danner, U. Henniges, S. Apprich, T. Rosenau, S. Böhmdorfer, A comparison between near-infrared (NIR) and mid-infrared (ATR-FTIR) spectroscopy for the multivariate determination of compositional properties in wheat bran samples. Food Control 60, 365–369 (2016)

    Article  CAS  Google Scholar 

  17. R. Jambunathan, S.M. Kherdekar, W.J. Stenhouse, Sorghum grain hardness and its relationship to mold susceptibility and mold resistance. J. Agric. Food Chem. 40, 1403–1408 (1992)

    Article  CAS  Google Scholar 

  18. AOAC, Officials Methods of Analysis, 18th edn. (Association of Officials Analytical Chemists, Washington, DC, 2005)

    Google Scholar 

  19. P. Geladi, B.R. Kowalski, Partial least square regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986)

    Article  CAS  Google Scholar 

  20. W. Srikham, N. Athapol, Milling quality assessment of Khao Dok Mali 105 milled rice by near-infrared reflectance spectroscopy technique. J. Food Sci. Technol. 52(11), 7500–7506 (2015)

    Article  CAS  Google Scholar 

  21. G. Mishra, D.C. Joshi, D. Mohapatra, V.B. Babu, Varietal influence on the microwave popping characteristics of sorghum. J. Cereal Sci. 65, 19–24 (2015)

    Article  Google Scholar 

  22. G. Mishra, S. Srivastava, B.K. Panda, H.N. Mishra, Rapid assessment of quality change and insect infestation in stored wheat grain using FT-NIR spectroscopy and chemometrics. Food Anal. Methods (2017). https://doi.org/10.1007/s12161-017-1094-9

    Article  Google Scholar 

  23. W. Vermerris, Protocol for the screening of the UniformMu maize population with near infrared reflectance spectroscopy (2006). https://cellwall.genomics.purdue.edu/techniques/8.html. Accessed 24 July 2017

  24. B.M. Plumier, M.G.C. Danao, V. Singh, K.D. Rausch, Analysis and prediction of unreacted starch content in corn using FT-NIR spectroscopy. Trans. ASABE 56(5), 1877–1844 (2013)

    CAS  Google Scholar 

  25. M. Meenu, U. Kamboj, A. Sharma, P. Guha, S. Mishra, Green method for determination of phenolic compounds in mung bean (Vigna radiata L.) based on near-infrared spectroscopy and chemometrics. Int. J. Food Sci. Technol. 51(12), 2520–2527 (2016)

    Article  CAS  Google Scholar 

  26. J. Cai, Q. Chen, X. Wan, J. Zhao, Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy. Food Chem. 126(3), 1354–1360 (2011)

    Article  CAS  Google Scholar 

  27. C. Shiroma, L. Rodriguez-Saona, Application of NIR and MIR spectroscopy in quality control of potato chips. J. Food Compos. Anal. 22, 596–605 (2009)

    Article  CAS  Google Scholar 

  28. J. Chitra, M. Ghosh, H.N. Mishra, Rapid quantification of cholesterol in dairy powders using Fourier transform near infrared spectroscopy and chemometrics. Food Control 78, 342–349 (2016)

    Article  Google Scholar 

  29. S. Tripathi, H.N. Mishra, A rapid FT-NIR method for estimation of aflatoxin B1 in red chilli powder. Food Control 20(9), 840–846 (2009)

    Article  CAS  Google Scholar 

  30. H. Chen, W. Ai, Q. Feng, Z. Jia, Q. Song, FT-NIR spectroscopy and Whittaker smoother applied to joint analysis of duel-components for corn. Spectrochim. Acta A 118, 752–759 (2014)

    Article  CAS  Google Scholar 

  31. V. Sileoni, O. Marconi, G. Perretti, P. Fantozzi, Evaluation of different validation strategies and long term effects in NIR calibration models. Food Chem. 141(3), 2639–2648 (2013)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors of the manuscript acknowledge Ministry of Human Resource Development (MHRD), Government of India for providing the research fund and Indian Institute of Technology Kharagpur for providing necessary lab facilities to conduct the experiments. The authors are grateful to Ms. Chitra Jayakumar, Research scholar of Agricultural and Food Engineering department for her guidance and support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gayatri Mishra.

Ethics declarations

Conflict of interest

The authors don’t have any conflict of interest. All the co authors are agreed for this submission.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandey, P., Mishra, G. & Mishra, H.N. Development of a non-destructive method for wheat physico-chemical analysis by chemometric comparison of discrete light based near infrared and Fourier transform near infrared spectroscopy. Food Measure 12, 2535–2544 (2018). https://doi.org/10.1007/s11694-018-9870-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11694-018-9870-9

Keywords