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Characterising corn grain using infrared imaging and spectroscopic techniques: a review

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Abstract

Corn is the largest cultivated grain crop in the world and the need to characterize corn grains based on various quality parameters is becoming essential as the demand for corn is increasing continuously in various end product applications. The conventional methods like visual inspection and analytical techniques involves sample destruction and are either subjective or time-consuming resulting in a growing requirement for the development of non-destructive techniques for a rapid and accurate determination of corn grain constituents and contaminants. This paper reviews the potential of infrared (IR) based imaging and spectroscopic techniques to determine quality parameters of corn grains, and their opportunities to incorporate in the supply chain of corn-based agro-industries. The variety and hardness of corn grains could be efficiently identified through the near infrared (NIR) hyperspectral imaging with accuracies ranging from 80 to 95% and 60 to 85%, respectively. IR imaging techniques determined the oil content of corn grains with standard error ranging between 0.7 and 1.3%. The detection of fungal infestations and mycotoxins in corn grains were widely studied using NIR, short-wave infrared (SWIR) and fluorescence hyperspectral imaging techniques with accuracies ranging from 75 to 98% and 70 to 97%, respectively. These techniques showed a better accuracy for infestations and variety classification (85–90%) and kernel hardness (60–65%) when used in the reflectance operational mode and proved effective for both single and bulk kernel analysis.

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References

  1. L.E. Agelet, D.D. Ellis, S. Duvick, A.S. Goggi, C.R. Hurburgh, C.A. Gardner, Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels. J. Cereal Sci. 55(2), 160–165 (2012)

    Article  CAS  Google Scholar 

  2. A. Ambrose, L.M. Kandpal, M.S. Kim, W.H. Lee, B.K. Cho, High speed measurement of corn seed viability using hyperspectral imaging. Infrared Phys. Technol. 75, 173–179 (2016)

    Article  CAS  Google Scholar 

  3. A. Ambrose, S. Lohumi, W.H. Lee, B.K. Cho, Comparative nondestructive measurement of corn seed viability using Fourier transform near-infrared (FT-NIR) and Raman spectroscopy. Sens. Actuators B Chem. 224, 500–506 (2016). https://doi.org/10.1016/j.snb.2015.10.082

    Article  CAS  Google Scholar 

  4. Bach, H., ed. By Giovanna Cecchi, Torsten Lamp, Rainer Reuter, Konradin Weber, Yield estimation of corn with multispectral data and the potential of using imaging spectrometers. In Remote Sensing of Vegetation and Water, and Standardization of Remote Sensing Methods (Vol. 3107, pp. 15–23). International Society for Optics and Photonics. (1997). https://doi.org/10.1117/12.274731

  5. T.M. Baye, T.C. Pearson, A.M. Settles, Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy. J. Cereal Sci. 43(2), 236–243 (2006)

    Article  CAS  Google Scholar 

  6. B.W. Boote, D.J. Freppon, G.N. De La Fuente, T. Lübberstedt, B.J. Nikolau, E.A. Smith, Haploid differentiation in maize kernels based on fluorescence imaging. Plant Breed. 135(4), 439–445 (2016). https://doi.org/10.1111/pbr.12382

    Article  CAS  Google Scholar 

  7. X. Cheng, A. Vella, M.J. Stasiewicz, Classification of aflatoxin contaminated single corn kernels by ultraviolet to near infrared spectroscopy. Food Control 98, 253–261 (2019). https://doi.org/10.1016/j.foodcont.2018.11.037

    Article  CAS  Google Scholar 

  8. R.P. Cogdill, C.R. Hurburgh, G.R. Rippke, S.J. Bajic, R.W. Jones, J.F. McClelland, T.C. Jensen, J. Liu, Single-kernel maize analysis by near-infrared hyperspectral imaging. Trans. ASAE 47(1), 311 (2004)

    Article  Google Scholar 

  9. L.A. Corp, E.M. Middleton, C.S. Daughtry, A.L. Russ, P.K. Campbell, K.F. Huemmrich, Y.B. Cheng, in 2010 IEEE International Geoscience and Remote Sensing Symposium. Forecasting corn yield with imaging spectroscopy. IEEE, 2010, pp. 1819–1822

  10. G.N. De La Fuente, J.M. Carstensen, M.A. Edberg, T. Lü bberstedt, Discrimination of haploid and diploid maize kernels via multispectral imaging. Plant Breed. 136(1), 50–60 (2017)

    Article  CAS  Google Scholar 

  11. A. Del Fiore, M. Reverberi, A. Ricelli, F. Pinzari, S. Serranti, A.A. Fabbri, G. Bonifazi, C. Fanelli, Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int. J. Food Microbiol. 144(1), 64–71 (2010)

    Article  PubMed  Google Scholar 

  12. D.Z.S. Della Riccia Giacomo, A multivariate regression model for detection of fumonisins content in maize from near infrared spectra. Food Chem. 141, 4289–4294 (2013)

    Article  PubMed  CAS  Google Scholar 

  13. F.E. Dowell, T.C. Pearson, E.B. Maghirang, F. Xie, D.T. Wicklow, Reflectance and transmittance spectroscopy applied to detecting fumonisin in single corn kernels infected with Fusarium verticillioides. Cereal Chem. 79(2), 222–226 (2002)

    Article  CAS  Google Scholar 

  14. T. Falade, Y. Sultanbawa, M.T. Fletcher, G. Fox, Near infrared spectrometry for rapid non-invasive modelling of Aspergillus-contaminated maturing kernels of maize (Zea mays L.). Agriculture 7(9), 77 (2017)

    Article  CAS  Google Scholar 

  15. A.S. Fassio, E.A. Restaino, D. Cozzolino, Determination of oil content in whole corn (Zea mays L.) seeds by means of near infrared reflectance spectroscopy. Comput. Electron. Agric. 110, 171–175 (2015)

    Article  Google Scholar 

  16. E.E. Finney, K.H. Norris, Determination of moisture in corn kernels by near-infrared transmittance measurements. Trans. ASAE 21(3), 581–0584 (1978)

    Article  Google Scholar 

  17. Global Statistics—Corn production in 2018/2019, by country. https://www.statista.com/statistics/254292/global-corn-production-by-country/. Accessed on 18 June 2019

  18. S. GopalaPillai, L. Tian, In-field variability detection and spatial yield modeling for corn using digital aerial imaging. Trans. ASAE 42(6), 1911 (1999)

    Article  Google Scholar 

  19. K. Hicks, R. Morean, D. Johnson, L. Doner, V. Singh, in Conference Proceedings: Corn Utilization and Technology Conference. Potential new uses for corn fiber (2002)

  20. C.G. Hopkins, L.H. Smith, E.M. East, ed. By J.W.Dudley., in Seventy Generations of Selection for Oil and Protein in Maize. The structure of the corn kernel and the composition of its different parts (1974), pp. 33–63. Crop Science Society of America. https://doi.org/10.2135/1974.seventygenerations

  21. Z. Hruska, H. Yao, R. Kincaid, D. Darlington, R.L. Brown, D. Bhatnagar, T.E. Cleveland, Fluorescence imaging spectroscopy (FIS) for comparing spectra from corn ears naturally and artificially infected with aflatoxin producing fungus. J. Food Sci. 78(8), T1313–T1320 (2013)

    Article  CAS  PubMed  Google Scholar 

  22. M. Huang, J. Tang, B. Yang, Q. Zhu, Classification of maize seeds of different years based on hyperspectral imaging and model updating. Comput. Electron. Agric. 122, 139–145 (2016)

    Article  Google Scholar 

  23. M. Huang, W. Zhao, Q. Wang, M. Zhang, Q. Zhu, Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel. Int. Agrophys. 29(1), (2015)

    Article  Google Scholar 

  24. L.M. Kandpal, S. Lee, M.S. Kim, H. Bae, B.K. Cho, Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels. Food Control 51, 171–176 (2015)

    Article  CAS  Google Scholar 

  25. D. Kimuli, K. Lawrence, S.C. Yoon, W. Wang, G. Heitschmidt, X. Zhao, A SWIR hyperspectral imaging method for classifying Aflatoxin B1 contaminated maize kernels. In 2017 ASABE Annual International Meeting (American Society of Agricultural and Biological Engineers, 2017), p. 1

  26. D. Kimuli, W. Wang, H. Jiang, X. Zhao, X. Chu, Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels. Infrared Phys. Technol. 89, 351–362 (2018)

    Article  CAS  Google Scholar 

  27. M. Manley, P. Williams, D. Nilsson, P. Geladi, Near infrared hyperspectral imaging for the evaluation of endosperm texture in whole yellow maize (Zea maize L.) kernels. J. Agric. Food Chem. 57(19), 8761–8769 (2009)

    Article  CAS  PubMed  Google Scholar 

  28. C.M. McGoverin, M. Manley, Classification of maize kernel hardness using near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 20(5), 529–535 (2012)

    Article  CAS  Google Scholar 

  29. Merriam-Webster Inc., Cereal processing. Corn: layers and structures of corn kernel. Art, from Encyclopædia Britannica. Online Academic Edition. Inc., 2012. http://www.britannica.com/EBchecked/topic/103350/cereal-processing. Accessed 18 June 2019

  30. G.P. Munkvold, S. Arias, I. Taschl, C. Gruber-Dorninger, ed. By Sergio O. Serna-Saldivar in Corn. Mycotoxins in corn: occurrence, impacts, and management (AACC International Press, 2019), pp. 235–287

  31. B.A. Orman, R.A. Schumann, Nondestructive single-kernel oil determination of maize by near-infrared transmission spectroscopy. J. Am. Oil Chem. Soc. 69(10), 1036–1038 (1992)

    Article  CAS  Google Scholar 

  32. T.C. Pearson, D.T. Wicklow, Detection of corn kernels infected by fungi. Trans. ASABE 49(4), 1235–1245 (2006)

    Article  Google Scholar 

  33. T.C. Pearson, D.T. Wicklow, E.B. Maghirang, F. Xie, F.E. Dowell, Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy. Trans. ASAE 44(5), 1247 (2001)

    Article  CAS  Google Scholar 

  34. Y. Pomeranz, C.R. Martin, D.D. Traylor, F.S. Lai, Corn hardness determination. Cereal Chem. 61(2), 147–150 (1984)

    Google Scholar 

  35. D. Ramchandran, Effects of corn quality and storage on dry grind ethanol production (Doctoral dissertation, University of Illinois at Urbana-Champaign, 2016)

  36. J. Siska, C.R. Hurburgh Jr., Corn density measurement by near-infrared transmittance. Trans. ASAE 38(6), 1821–1824 (1995)

    Article  Google Scholar 

  37. D.S. Smith, P.W. Maxwell, Use of quantitative PCR to evaluate several methods for extracting DNA from corn flour and cornstarch. Food Control 18(3), 236–242 (2007)

    Article  CAS  Google Scholar 

  38. G. Spielbauer, P. Armstrong, J.W. Baier, W.B. Allen, K. Richardson, B. Shen, A.M. Settles, High-throughput near-infrared reflectance spectroscopy for predicting quantitative and qualitative composition phenotypes of individual maize kernels. Cereal Chem. 86(5), 556–564 (2009)

    Article  CAS  Google Scholar 

  39. J.G. Tallada, N. Palacios-Rojas, P.R. Armstrong, Prediction of maize seed attributes using a rapid single kernel near infrared instrument. J. Cereal Sci. 50(3), 381–387 (2009)

    Article  CAS  Google Scholar 

  40. J.G. Tallada, D.T. Wicklow, T.C. Pearson, P.R. Armstrong, Detection of fungus-infected corn kernels using near-infrared reflectance spectroscopy and color imaging. Trans. ASABE 54(3), 1151–1158 (2011)

    Article  Google Scholar 

  41. H.L. Trenk, P.A. Hartman, Effects of moisture content and temperature on aflatoxin production in corn. Appl. Microbiol. 19(5), 781–784 (1970)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. C. Wakholi, L.M. Kandpal, H. Lee, H. Bae, E. Park, M.S. Kim, C. Mo, W.H. Lee, B.K. Cho, Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics. Sens. Actuators B Chem. 255, 498–507 (2018)

    Article  CAS  Google Scholar 

  43. W. Wang, G.W. Heitschmidt, W.R. Windham, P. Feldner, X. Ni, X. Chu, Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging. J. Food Sci. 80(1), M116–M122 (2015)

    Article  CAS  PubMed  Google Scholar 

  44. W. Wang, K.C. Lawrence, G.W. Heitschmidt, W.R. Windham, Y. Peng, X. Chu, N. Zhang, Classification of different level of Aflatoxin B1 on corn kernels surface using short-wave infrared reflectance hyperspectral imaging. In 2013 Kansas City, Missouri, July 21–July 24, 2013 (American Society of Agricultural and Biological Engineers, 2013), p. 1

  45. W. Wang, K.C. Lawrence, S.C. Yoon, X. Ni, G.W. Heitschmidt, Rapid screening for Aflatoxin B1 in Single Maize Kernels Using Vis/NIR Hyperspectral Imaging. In 2015 ASABE Annual International Meeting (American Society of Agricultural and Biological Engineers, 2015), p. 1

  46. W. Wang, X. Ni, K.C. Lawrence, S.C. Yoon, G.W. Heitschmidt, P. Feldner, Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging. J. Food Eng. 166, 182–192 (2015)

    Article  CAS  Google Scholar 

  47. Y. Wang, Y. Lv, H. Liu, Y. Wei, J. Zhang, D. An, J. Wu, Identification of maize haploid kernels based on hyperspectral imaging technology. Comput. Electron. Agric. 153, 188–195 (2018)

    Article  Google Scholar 

  48. B.A. Weinstock, J. Janni, L. Hagen, S. Wright, 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), 9–16 (2006)

    Article  CAS  PubMed  Google Scholar 

  49. P. Williams, P. Geladi, G. Fox, M. Manley, Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Anal. Chim. Acta 653(2), 121–130 (2009)

    Article  CAS  PubMed  Google Scholar 

  50. P. Williams, M. Manley, G. Fox, P. Geladi, Indirect detection of Fusarium verticillioides in maize (Zea mays L.) kernels by near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 18(1), 49–58 (2010)

    Article  CAS  Google Scholar 

  51. P.J. Williams, S. Kucheryavskiy, Classification of maize kernels using NIR hyperspectral imaging. Food Chem. 209, 131–138 (2016)

    Article  CAS  PubMed  Google Scholar 

  52. P.J. Williams, P. Geladi, T.J. Britz, M. Manley, Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. J. Cereal Sci. 55(3), 272–278 (2012)

    Article  Google Scholar 

  53. X. Yang, H. Hong, Z. You, F. Cheng, Spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification. Sensors 15(7), 15578–15594 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  54. H. Yao, Z. Hruska, R.L. Brown, T.E. Cleveland, ed. By Yud-Ren Chen, George E. Meyer, Shu-I Tu, Hyperspectral bright greenish-yellow fluorescence (BGYF) imaging of aflatoxin contaminated corn kernels. In Optics for Natural Resources, Agriculture, and Foods (Vol. 6381, p. 63810B). International Society for Optics and Photonics. (2006). https://doi.org/10.1117/12.686217

  55. H. Yao, Z. Hruska, R. Kincaid, R.L. Brown, D. Bhatnagar, T.E. Cleveland, Hyperspectral image classification and development of fluorescence index for single corn kernels infected with Aspergillus flavus. Trans. ASABE 56(5), 1977–1988 (2013)

    Google Scholar 

  56. H. Yao, Z. Hruska, R. Kincaid, R.L. Brown, D. Bhatnagar, T.E. Cleveland, ed. By Moon S. Kim, Shu-I Tu, Kuanglin Chao, Utilizing fluorescence hyperspectral imaging to differentiate corn inoculated with toxigenic and atoxigenic fungal strains. In Sensing for Agriculture and Food Quality and Safety IV (Vol. 8369, p. 83690B). International Society for Optics and Photonics. (2012). https://doi.org/10.1117/12.919580

  57. H. Yao, Z. Hruska, R. Kincaid, A. Ononye, R.L. Brown, T.E. Cleveland, in 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Spectral angle mapper classification of fluorescence hyperspectral image for aflatoxin contaminated corn (IEEE, 2010), pp. 1–4

  58. X. Zhao, W. Wang, X. Chu, H. Jiang, B. Jia, Y. Yang, D. Kimuli, in 2017 ASABE Annual International Meeting. Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging (American Society of Agricultural and Biological Engineers, 2017), p. 1

  59. X. Zhao, W. Wang, X. Chu, C. Li, D. Kimuli, Early detection of Aspergillus parasiticus infection in maize kernels using near-infrared hyperspectral imaging and multivariate data analysis. Appl. Sci. 7(1), 90 (2017)

    Article  Google Scholar 

  60. Y. Zhao, S. Zhu, C. Zhang, X. Feng, L. Feng, Y. He, Application of hyperspectral imaging and chemometrics for variety classification of maize seeds. RSC Adv. 8(3), 1337–1345 (2018)

    Article  CAS  Google Scholar 

  61. F. Zhu, H. Yao, Z. Hruska, R. Kincaid, R. Brown, D. Bhatnagar, T. Cleveland, ed. By Moon S. Kim, Kuanglin Chao, Bryan A. Chin,  Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis. In Sensing for Agriculture and Food Quality and Safety VII (Vol. 9488, p. 94880M). International Society for Optics and Photonics. (2015). https://doi.org/10.1117/12.2176578

  62. F. Zhu, H. Yao, Z. Hruska, R. Kincaid, R.L. Brown, D. Bhatnagar, T.E. Cleveland, in 2015 ASABE Annual International Meeting. Visible near-infrared (VNIR) reflectance hyperspectral imagery for identifying aflatoxin-contaminated corn kernels (American Society of Agricultural and Biological Engineers, 2015), p. 1

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Rathna Priya, T.S., Manickavasagan, A. Characterising corn grain using infrared imaging and spectroscopic techniques: a review. Food Measure 15, 3234–3249 (2021). https://doi.org/10.1007/s11694-021-00898-7

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