Advertisement

Near Infrared Hyperspectral Imaging for White Maize Classification According to Grading Regulations

  • Kate Sendin
  • Marena Manley
  • Vincent Baeten
  • Juan Antonio Fernández Pierna
  • Paul J. WilliamsEmail author
Article
  • 37 Downloads

Abstract

Near infrared hyperspectral imaging with multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The study was based on grading regulations stipulated in South African legislation and aimed to provide an alternative to the tedious and subjective manual methods currently used. The types of undesirable materials regarded were divided into 13 classes and imaged using a hyperspectral imaging system (1118–2425 nm). Two approaches to data analysis, pixel-wise and object-wise, were investigated using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) modelling. Two-way classification models distinguished sound white maize from each type of undesirable material and were validated with independent image datasets. The pixel-wise PLS-DA demonstrated a high occurrence of errors (63–99% classification accuracy). The object-wise PLS-DA models yielded superior results, achieving 100% classification accuracy in 8 of the 13 models, with the remaining 5 incurring only one error each (98% classification accuracy). The overall classification accuracy achieved over the total 804 kernels/objects was 99.4%. Important spectral features were highlighted around 1219 and 1476 nm (associated with starch), 1941 nm (associated with moisture) and 2117 nm (associated with protein). An object-wise approach demonstrated good performance for distinguishing between the sound maize class and common grading defects and provided a classification for single, whole maize kernels, as would be conducted during the current manual grading methods. For industry implementation, this system may be simplified to a multispectral system for reduced cost and higher throughput.

Keywords

Near infrared hyperspectral imaging Chemometrics Multivariate image analysis Classification Maize Grading 

Notes

Acknowledgments

The authors thank Nicaise Kayoka from CRA-W for his support in the acquisition and pre-processing of the NIR hyperspectral images.

Funding

This work is based on the research supported in part by the National Research Foundation of South Africa (grant numbers 94031 and 95343) and The Maize Trust of South Africa.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

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

Informed Consent

Not applicable.

References

  1. Amigo, J. M., Martí, I. & Gowen, A. (2013). Hyperspectral imaging and chemometrics: a perfect combination for the analysis of food structure, composition and quality. In: Data handling in science and technology. Pp. 343–370. Amsterdam: Elsevier ScienceGoogle Scholar
  2. Baeten V, Fernandez Pierna JA, Vermeulen P, Dardenne P (2010) NIR hyperspectral imaging methods for quality and safety control of food and feed products: contributions to four European projects. NIR News 21(6):10–13Google Scholar
  3. Barker M, Rayens W (2003) Partial least squares for discrimination. J Chemometrics: J Chemometrics Soc 17(3):166–173Google Scholar
  4. Barnes RJ, Dhanoa MS, Lister SJ (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43(5):772–777Google Scholar
  5. Brereton, R. G. (2003). Signal Processing. In: Chemometrics: data analysis for the laboratory and chemical plant. Pp. 119–182. Chichester: John Wiley & SonsGoogle Scholar
  6. Burger J, Geladi P (2006) Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. Analyst 131(10):1152–1160Google Scholar
  7. Caporaso N, Whitworth MB, Fisk ID (2018a) Near-infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl Spectrosc Rev 53:667–687.  https://doi.org/10.1080/05704928.2018.1425214 Google Scholar
  8. Caporaso N, Whitworth MB, Fisk ID (2018b) Protein content prediction in single wheat kernels using hyperspectral imaging. Food Chem 240:32–42Google Scholar
  9. Cogdill RP, Hurburgh CR, Rippke GR, Bajic SJ, Jones RW, McClelland JF, Jensen TC, Liu J (2004) Single-kernel maize analysis by near-infrared hyperspectral imaging. Trans ASAE 47(1):311–320Google Scholar
  10. Dale LM, Thewis A, Boudry C, Rotar I, Dardenne P, Baeten V, Fernandez Pierna JA (2013) Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review. Appl Spectrosc Rev 48(2):142–159Google Scholar
  11. Del Fiore A, Reverberi M, Ricelli A, Pinzari F, Serranti S, Fabbri AA, Bonifazi G, Fanelli C (2010) Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int J Food Microbiol 144(1):64–71Google Scholar
  12. Delwiche SR, Hareland GA (2004) Detection of scab-damaged hard red spring wheat kernels by near-infrared reflectance. Cereal Chem 81(5):643–649Google Scholar
  13. Demirbaş A (2002) Fuel characteristics of olive husk and walnut, hazelnut, sunflower, and almond shells. Energy Sources 24(3):215–221Google Scholar
  14. Department of Agriculture (2009). Regulations relating to the grading, packing and marking of maize intended for sale in the Republic of South Africa. In: Agricultural Product Standards Act (Act No. 119 of 1990)Google Scholar
  15. Esbensen K, Geladi P (1989) Strategy of multivariate image analysis (MIA). Chemom Intell Lab Syst 7(1):67–86Google Scholar
  16. Fernández-Ibañez V, Soldado A, Martínez-Fernández A, De la Roza-Delgado B (2009) Application of near infrared spectroscopy for rapid detection of aflatoxin B1 in maize and barley as analytical quality assessment. Food Chem 113(2):629–634Google Scholar
  17. Fox G, Manley M (2009) Hardness methods for testing maize kernels. J Agric Food Chem 57(13):5647–5657Google Scholar
  18. Gowen AA, O'Donnell CP, Cullen PJ, Downey G, Frias JM (2007) Hyperspectral imaging—an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 18(12):590–598Google Scholar
  19. Johnson LA (2000) The major cereal of the Americas. In: Kulp K, Ponte JG (eds) In: Handbook of cereal science and technology, revised and expanded. CRC Press, New York, pp 31–80Google Scholar
  20. Kucheryavskiy S (2013) A new approach for discrimination of objects on hyperspectral images. Chemom Intell Lab Syst 120:126–135Google Scholar
  21. Mahesh S, Jayas DS, Paliwal J, White NDG (2015) Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk samples of Canadian wheat. Food Bioprocess Technol 8(1):31–40Google Scholar
  22. Manley M, Williams P, Nilsson D, Geladi P (2009) Near infrared hyperspectral imaging for the evaluation of endosperm texture in whole yellow maize (Zea maize L.) kernels. J Agric Food Chem 57(19):8761–8769Google Scholar
  23. Manley M, Du Toit G, Geladi P (2011) Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging. Anal Chim Acta 686(1):64–75Google Scholar
  24. McGoverin C, Manley M (2012) Classification of maize kernel hardness using near infrared hyperspectral imaging. J Near Infrared Spectrosc 20:529Google Scholar
  25. McGoverin CM, Engelbrecht P, Geladi P, Manley M (2011) Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics. Anal Bioanal Chem 401:2283–2289Google Scholar
  26. Osborne, B. G., & Fearn, T. (1986). Theory of near infrared spectroscopy. In: Near infrared spectroscopy in food analysis. Pp. 29–33. Harlow: Longman Scientific & TechnicalGoogle Scholar
  27. Pérez-Vich B, Velasco L, Fernández-Martínez J (1998) Determination of seed oil content and fatty acid composition in sunflower through the analysis of intact seeds, husked seeds, meal and oil by near-infrared reflectance spectroscopy. J Am Oil Chem Soc 75(5):547–555Google Scholar
  28. Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639Google Scholar
  29. Sendin K, Manley M, Williams PJ (2018a) Classification of white maize defects with multispectral imaging. Food Chem 243:311–318Google Scholar
  30. Sendin K, Williams PJ, Manley M (2018b) Near infrared hyperspectral imaging in quality and safety evaluation of cereals. Crit Rev Food Sci Nutr 58(4):575–590Google Scholar
  31. Serna-Saldivar, S. O. (2010). Physical properties, grading, and speciality grains. In: Cereal grains: properties, processing, and nutritional attributes. Pp. 43–80. Boca Raton: CRC PressGoogle Scholar
  32. Singh CB, Jayas DS, Paliwal J, White NDG (2010) Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Comput Electron Agric 73(2):118–125Google Scholar
  33. Vermeulen P, Ebene M, Orlando B, Fernández Pierna J, Baeten V (2017a) Online detection and quantification of particles of ergot bodies in cereal flour using near-infrared hyperspectral imaging. Food Addit Contam: Part A 34(8):1312–1319Google Scholar
  34. Vermeulen P, Flémal P, Pigeon O, Dardenne P, Fernández Pierna J, Baeten V (2017b) Assessment of pesticide coating on cereal seeds by near infrared hyperspectral imaging. J Spect Imaging 6:1–7Google Scholar
  35. Wang L, Liu D, Pu H, Sun DW, Gao W, Xiong Z (2014) Use of hyperspectral imaging to discriminate the variety and quality of Rice. Food Anal Methods 8(2):515–552Google Scholar
  36. Wang L, Sun DW, Pu H, Zhu Z (2015) Application of hyperspectral imaging to discriminate the variety of maize seeds. Food Anal Methods 9(1):1–10Google Scholar
  37. Weinstock BA, Janni J, Hagen L, Wright S (2006) Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis. Appl Spectrosc 60(1):9–16Google Scholar
  38. Williams PJ, Kucheryavskiy S (2016) Classification of maize kernels using NIR hyperspectral imaging. Food Chem 209:131–138Google Scholar
  39. Williams P, Geladi P, Fox G, Manley M (2009) Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal Chim Acta 653(2):121–130Google Scholar
  40. Williams PJ, Geladi P, Britz TJ, Manley M (2012) Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. J Cereal Sci 55(3):272–278Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Food ScienceStellenbosch University Private Bag X1StellenboschSouth Africa
  2. 2.Food and Feed Quality Unit, Valorisation of Agricultural Products DepartmentWalloon Agricultural Research Centre (CRA-W)GemblouxBelgium

Personalised recommendations