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A novel hyperspectral-based approach for identification of maize kernels infected with diverse Aspergillus flavus fungi

Feifei Tao, Haibo Yao, Zuzana Hruska, Russell Kincaid, Kanniah Rajasekaran, Deepak Bhatnagar
Biosystems engineering 2020 v.200 no. pp. 415-430
Aspergillus flavus, Zea mays, aflatoxins, algorithms, corn, discriminant analysis, fungal contamination, hyperspectral imagery, image analysis, least squares, near-infrared spectroscopy, nontoxigenic strains, plant pathogenic fungi, seeds, toxigenic strains
Near infrared hyperspectral imaging over the spectral range of 900–2500 nm was investigated for its potential to identify maize kernels inoculated with aflatoxigenic fungus (AF13) from healthy kernels and kernels inoculated with non-aflatoxigenic fungus (AF36). A total of 900 kernels were used with 3 treatments, namely, each 300 kernels inoculated with AF13, AF36 and sterile distilled water as control, separately. One hundred kernels from each treatment of 300 kernels were incubated for 3, 5 and 8 days, to obtain diverse samples. Based on the full mean spectra extracted from the same kernel side(s), the best mean overall prediction accuracies achieved were 96.3% for the 3-class (control, non-aflatoxigenic and aflatoxigenic) classification and 97.8% for the 2-class (aflatoxigenic-negative and -positive) classification using the partial least-squares discriminant analysis (PLS-DA) method. The 3-class and 2-class models using the full mean spectra extracted from different kernel sides had the best mean overall prediction accuracies of 91.5% and 95.1%. Using the most important 30, 55 and 100 variables determined by the random frog (RF) algorithm, the simplified type I-RF-PLSDA models achieved the mean overall prediction accuracies of 87.7%, 93.8% and 95.2% for the 2-class discrimination using different kernel sides’ information. Among the most important 55 and 100 variables, a total of 25 and 67 variables were consistently selected in the 100 random runs and were therefore used further for establishing the type II-RF-PLSDA models. Using these 25 and 67 variables, the type II-RF-PLSDA models obtained the mean overall prediction accuracies of 82.3% and 94.9% separately.