Skip to main content
Log in

Rapid Assessment of Quality Change and Insect Infestation in Stored Wheat Grain Using FT-NIR Spectroscopy and Chemometrics

  • Published:
Food Analytical Methods Aims and scope Submit manuscript

Abstract

Major qualitative changes during storage of wheat are attributed to infestation by weevils, beetles, and moths. Alteration in inherent macro and micro nutrients and corresponding reduction in grain mass is the ultimate indicator of the deteriorations. Sitophilus oryzae and Ryzopertha dominica are two commonly found insects in stored wheat, which cause the major losses by feeding upon the grain mass and contaminating the grain bulk with their metabolic waste. The current study focused on development of a rapid and non-destructive FT-NIR spectroscopic method for the determination of insect infestation by analyzing the quality changes in grain due to infestation. A total of 128 wheat samples of varying moisture content, insect count, and storage days were analyzed for quality parameters. FT-NIR library was generated and the spectral data were analyzed using partial least squares regression (PLS) with various preprocessing techniques. The best models for properties with lowest root mean square error of cross-validation values for moisture, protein, uric acid, 1000 kernel weight, and hardness were 0.485, 0.248, 2.58, 0.576, and 0.762, respectively. R 2 obtained for the abovesaid quality parameters were 0.901, 0.938, 0.895, 0.907, and 0.912 demonstrating good fit of the PLS models. The developed methods will be very much useful for storage godowns, bakery industries, graders, and exporters providing rapid, reliable, and precise quality estimation during reception of the raw material without involvement of hazardous chemicals.

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

Similar content being viewed by others

References

  • Agelet LE, Hurburgh CR Jr (2010) A tutorial on near infrared spectroscopy and its calibration. Crit Rev Anal Chem 40(4):246–260

    Article  CAS  Google Scholar 

  • AOAC (2005) Cereal foods. Official methods of analysis of AOAC International, pp27–28

  • Arazuri S, Arana JI, Arias N, Arregui LM, Gonzalez-Torralba J, Jaren C (2012) Rheological parameters determination using near infrared technology in whole wheat grain. J Food Eng 111(1):115–121

    Article  Google Scholar 

  • Azizian H, Kramer JK (2005) A rapid method for the quantification of fatty acids in fats and oils with emphasis on trans-fatty acids using Fourier transform near infrared spectroscopy (FT-NIR). Lipids 40(8):855–867

    Article  CAS  Google Scholar 

  • Baker JE, Dowell FE, Throne JE (1999) Detection of parasitized rice weevils in wheat kernels with near-infrared spectroscopy. Biol Control 16:88–90

    Article  Google Scholar 

  • Cai J, Chen Q, Wan X, Zhao J (2011) 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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Chitra J, Ghosh M, Mishra HN (2017) Rapid quantification of cholesterol in dairy powders using Fourier transform near infrared spectroscopy and chemometrics. Food Control 78:342–349

  • CODEX International Food Standard, CODEX STAN 1988–1995 (2017). Standards for wheat and durum wheat. Accessed from http://www.fao.org/fao-who-codexalimentarius/sh-proxy/en/?lnk=1&url=https%253A%252F%252Fworkspace.fao.org%252Fsites%252Fcodex%252FStandards%252FCODEX%2BSTAN%2B199-1995%252FCXS_199e.pdf on 1st March 2017

  • Dowell FE, Throne JE, Wang D, Baker JE (1999) Identifying stored-grain insects using near-infrared spectroscopy. Journal of Economic Entomology 92: 165–169.

  • Elizabeth BM, Dowell FE, Baker JE, Throne JE (2002) Detecting single wheat kernels containing live or dead insects using near infrared reflectance spectroscopy. ASAE Paper No. 023067 Chicago, IL: ASAE

  • Ferreira DS, Pallone JL, Poppi RJ (2013) Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition. Food Res Int 51(1):53–58

    Article  CAS  Google Scholar 

  • Foca G, Ferrari C, Sinelli N, Mariotti M, Lucisano M, Caramanico R, Ulrici A (2011) Minimisation of instrumental noise in the acquisition of FT-NIR spectra of bread wheat using experimental design and signal processing techniques. Anal Bioanal Chem 399(6):1965–1973

    Article  CAS  Google Scholar 

  • FSSAI (2012) Cereal and cereal products. Manual of methods of analysis of foods, pp8–9

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

    Article  CAS  Google Scholar 

  • Hartree EF (1972) Determination of protein: a modification of the Lowry method that gives a linear photometric response. Anal Biochem 48(2):422–427

  • Jarruwat P, Choomjaihan P (2014) Feasibility study on estimation of rice weevil quantity in rice stock using near-infrared spectroscopy technique. J Innovative Optical Health Sci 7(4):1450001–1450008

    Article  Google Scholar 

  • Jood S, Kapoor AC (1993) Protein and uric acid contents of cereal grains as affected by insect infestation. Food Chem 46:143–146

    Article  CAS  Google Scholar 

  • Karunakaran C, Jayas DS, White NDG (2003) X-ray image analysis to detect infestations caused by insects in grain. Cereal Chem 80(5):553–557

    Article  CAS  Google Scholar 

  • Kim SS, Phyu MR, Kim JM, Lee SH (2003) Authentication of rice using near infrared reflectance spectroscopy. Cereal Chem 80(3):346–349

    Article  CAS  Google Scholar 

  • Lorber A, Wangen LE, Kowalski BR (1987) A theoretical foundation for the PLS algorithm. J Chemom 1(1):19–31

    Article  CAS  Google Scholar 

  • Manely M (2014) Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chem Soc Rev 43:8200–8214

    Article  Google Scholar 

  • McClure WF, Maeda H, Dong J, Liu Y, Ozaki Y (1996) 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

    Article  CAS  Google Scholar 

  • Neethirajan S, Karunakaran C, Jayas DS, White NDG (2007) Detection techniques for stored-product insects in grain. Food Control 18:157–162

    Article  CAS  Google Scholar 

  • Paliwal J, Wang W, Symons SJ, Karunakaran C (2004) Insect species and infestation level determination in stored wheat using near-infrared spectroscopy. Canadian Biosystem Engineering 46:7.16–7.24

    Google Scholar 

  • Pandey R, Mishra HN (2015) Fourier transform near-infrared spectroscopy for rapid and simple determination of phytic acid content in green gram seeds (Vigna radiata). Food Chem 172(1):880–884

    Article  Google Scholar 

  • Pasquini C (2003) Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. J Braz Chem Soc 14(2):198–219

    Article  CAS  Google Scholar 

  • Plumier BM, Danao MGC, Singh V, Rausch KD (2013) Analysis and prediction of unreacted starch content in corn using FT-NIR spectroscopy. Trans ASABE 56(5):1877–1844

    CAS  Google Scholar 

  • Reich G (2005) Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications. Adv Drug Deliv Rev 57(8):1109–1143

    Article  CAS  Google Scholar 

  • Sileoni V, Berg F, Marconi O, Perretti G, Fantozzi P (2011) Internal and external validation strategies for the evaluation of long-term effects in NIR calibration models. J Agric Food Chem 59:1541–1154

    Article  CAS  Google Scholar 

  • Siuda R, Balcerowska G, Sadowski C (2006) Comparison of the usability of different spectral ranges within the near ultraviolet, visible and near infrared ranges (UV-VIS-NIR) region for the determination of the content of scab-damaged component in blended samples of ground wheat. Food Additives Contaminants 23(11):1201–1207

    Article  CAS  Google Scholar 

  • Susmel P, Piani B, Toso B, Stefanon B (2004) Prediction of purine derivatives, creatinine and total nitrogen concentrations in urine by FT-near-infrared reflectance spectroscopy (FT-NIR). In: Estimation of microbial protein supply in ruminants using urinary purine derivatives. Springer, Netherlands, pp 160–166

    Chapter  Google Scholar 

  • White NDG (1995) Insects, mites, and insecticides in stored grain ecosystems, stored-grain ecosystems. DS Jayas, NDG. White, WE Muir, Eds, pp. 123–168, Marcel Dekker Inc., New York

  • Williams P (2001) How do we do it: a brief summary of the methods we use in developing near infrared calibrations. Near infrared spectroscopy: the future waves, NIR Publications, Chichester, UK (1995), pp. 185–188

  • Williams PC, Sobering D (1995) How do we do it: a brief summary of the methods we use in developing near infrared calibrations. In: AMC D, Williams PC (eds) Near infrared spectroscopy: the future waves. NIR Publications, Chichester, pp 185–188

    Google Scholar 

  • Wold S, Sjostrorn M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2):109–130

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gayatri Mishra.

Ethics declarations

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

Gayatri Mishra declares that she has no conflict of interest. Shubhangi Srivastava declares that she has no conflict of interest. Brajesh Kumar Panda declares that he has no conflict of interest. Hari Niwas Mishra declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mishra, G., Srivastava, S., Panda, B.K. et al. Rapid Assessment of Quality Change and Insect Infestation in Stored Wheat Grain Using FT-NIR Spectroscopy and Chemometrics. Food Anal. Methods 11, 1189–1198 (2018). https://doi.org/10.1007/s12161-017-1094-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12161-017-1094-9

Keywords

Navigation