Detection of fungal infection and Ochratoxin A contamination in stored barley using near-infrared hyperspectral imaging

Abstract

Aspergillus glaucus and Penicillium spp. infections and Ochratoxin A contamination were detected in stored barley using a Near-Infrared (NIR) hyperspectral imaging system. Fungal infected samples and Ochratoxin A contaminated samples were subjected to single kernel imaging every two weeks, and acquired three dimensional image data were transformed into two dimensional data. The two dimensional data corresponding to each fungal infected sample and Ochratoxin A contaminated sample were subjected to principal component analysis (PCA) for data reduction, and to identify significant wavelengths. The significant wavelengths 1260, 1310, and 1360 nm corresponding to A. glaucus, Penicillium spp., and non-Ochratoxin A producing Penicillium verrucosum infected kernels and wavelengths 1310, 1360, and 1480 nm corresponding to Ochratoxin A contaminated kernels were obtained based on the highest principal components (PC) factor loadings. Statistical and histogram features from significant wavelengths were extracted and used as input for linear, quadratic, and Mahalanobis statistical classifiers. Pair-wise, two-class, and six-class classification models were developed to differentiate between sterile and infected kernels. The three classifiers differentiated sterile kernels with classification accuracy of more than 94%, fungal infected kernels with more than 80% at initial periods of fungal infection and attained 100% classification accuracy after four weeks of fungal infection. Ochratoxin A contaminated kernels can be differentiated from sterile kernels with a classification accuracy of 100%. Different periods of fungal infection and different levels of Ochratoxin A contamination were discriminated with a classification accuracy of more than 82%.

Plant phenotyping relevance

NIRハイパースペクトル画像と分類器を用いて、オオムギ穀粒の真菌感染状態を直接推定する方法を開発・評価しており、植物の病害状態の取得が中心的です。

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