Factors related to differential occurrence rates of aflatoxins in maize grains: investigations with near infrared spectrometry and metabolite analysis

Abstract

Aflatoxins are toxic secondary metabolites produced by aflatoxigenic moulds belonging to the Aspergillus section Flavi group. Aflatoxins contaminate food and feed with serious and fatal consequences. Therefore, toxin detection is critical for aflatoxin management. Current detection methods of aflatoxins involve sub-sampling to retrieve a representative sub-set for invasive analysis. However, as aflatoxins are heterogeneously distributed, sub-sampling contributes to errors. Also, invasive analysis is time consuming. Near infrared spectrometry (NIRS) is a non-invasive detection method that was explored for modeling contamination. Using a cross validation modeling, with ultra-high liquid chromatography (UPLC) as reference method, NIRS was explored for maize grain contamination by aflatoxigenic Aspergillus strains. Maize variety (33v62) and A. parasiticus (F75) strain were used. Maize grains at different stages of maturity had fungal colonisation in the following order: milk>dough>dent stages. NIRS pretreated data separated samples into clusters in principal component (PC) scores plot using the first two principal components. Data pretreatments involved Savitzy Golay first derivative and multiplicative scatter correction. The functional groups CH (1st overtone, 1st overtone combinations and 2nd overtone), OH (1st overtone, combinations), NH (1st overtone, combinations), C=O (2nd overtone), NH+OH (combinations), CH+CH (combinations) and CH+CC (combinations) were identified in plot loadings. These are mostly associated with sugar, amino acid and oils. The wavelengths that accounted for most of the variation were at 1220 nm, 1410 nm, 1690 nm and 1900 nm in PC1; and 1145 nm, 1400 nm, 1430 nm, 1890 nm and the combination band region in PC2. A single wavelength linear regression model was significant at 2198 nm with p 0.05) with aflatoxin levels. NIRS modeling challenges for aflatoxins may be attributable to variation in aflatoxin levels due to competition by co-occurring fungi in the grain. Controlled single fungus contamination produced better NIRS models than models with field contaminated samples. The influence of sugars, amino acids, sugar alcohols and other metabolites may also mask aflatoxin concentrations used for modelling A. flavus and aflatoxin contaminated maize grains. Further understanding the grain chemistry changes that occur with aflatoxin accumulation, may be advantageous to explore NIRS calibrations in future studies.