Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging. (January 2016) - Senthilkumar, T.; Jayas, D.S.; White, N.D.G.; Fields, P.G.; Gräfenhan, T.
Abstract: A study was done to detect Aspergillus glaucus, and Penicillium spp., infection and Ochratoxin A contamination in stored wheat using a Near-Infrared (NIR) Hyperspectral Imaging system. Fungal-infected samples were imaged every two weeks, and the three dimensional hypercubes obtained from image data were transformed into two dimensional data. Principal component analysis was applied to the two dimensional data and based on the highest factor loadings, 1280, 1300, and 1350 nm were identified as significant wavelengths. Six statistical features and ten histogram features corresponding to the significant wavelengths were extracted and subjected to linear, quadratic and Mahalanobis discriminant classifiers. All the three classifiers differentiated healthy kernels from fungal-infected kernels with a classification accuracy of more than 90%. The quadratic discriminant classifier provided classification accuracy higher than the linear and Mahalanobis classifiers for pair-wise, two-way and six-way classification models. The Ochratoxin A contaminated samples had a unique significant wavelength at 1480 nm in addition to the two significant wavelengths corresponding to fungal infection. The peak at 1480 nm was identified only in the Ochratoxin A contaminated samples. The Ochratoxin A contaminated samples can be detected with 100% classification accuracy using NIR hyperspectral imaging system. The NIR hyperspectral system can differentiate between different fungal infection stages and different levels of Ochratoxin A contamination in stored wheat. Highlights: Wavelengths 1280, 1300, and 1350 nm were significant to detect fungal infection in wheat at an early stage. Wavelengths 1300, 1350, and 1480 nm were significant to detect Ochratoxin A in wheat. Quadratic discriminant classifiers provided higher classification accuracy than linear and Mahalanobis classifiers.