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Agriculture 2017, 7(9), 77; doi:10.3390/agriculture7090077

Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

1
Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane QLD 4108, Australia
2
International Institute of Tropical Agriculture, Headquarters & West African Hub, Ibadan 200001, Nigeria
*
Author to whom correspondence should be addressed.
Received: 25 July 2017 / Revised: 30 August 2017 / Accepted: 13 September 2017 / Published: 19 September 2017
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Abstract

Aflatoxin-producing Aspergillus spp. produce carcinogenic metabolites that contaminate maize. Maize kernel absorbance patterns of near infrared (NIR) wavelengths (800–2600 nm) were used to non-invasively identify kernels of milk-, dough- and dent-stage maturities with four doses of Aspergillus sp. contamination. Near infrared spectrometry (NIRS) spectral data was pre-processed using first derivative Savitzky-Golay (1d-SG) transformation and multiplicative scatter correction on spectral data. Contaminated kernels had higher absorbance between 800–1134 nm, while uninoculated samples had higher absorbance above 1400 nm. Dose and maturity clusters seen in Principal Component Analysis (PCA) score plots were due to bond stretches of combination bands, CH and C=O functional groups within grain macromolecules. The regression model at 2198 nm separated uninoculated and inoculated kernels (p < 0.0001, R2 = 0.88, root mean square error = 0.15). Non-invasive identification of Aspergillus-contaminated maize kernels using NIR spectrometry was demonstrated in kernels of different maturities. View Full-Text
Keywords: NIR; maize; Aspergillus NIR; maize; Aspergillus
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MDPI and ACS Style

Falade, T.D.; Sultanbawa, Y.; Fletcher, M.T.; Fox, G. Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.). Agriculture 2017, 7, 77.

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