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

Abstract

Fungal infection and mycotoxin contamination in agricultural products are a serious food safety issue. The detection of fungal infection and mycotoxin contamination in food products should be in a rapid way. A Near-Infrared (NIR) hyperspectral imaging system was used to detect fungal infection in 2013 crop year canola, wheat, and barley at different periods after inoculation and different concentration levels of ochratoxin A in wheat and barley. Artificially fungal infected (Fungi: Aspergillus glaucus, Penicillium spp.) kernels of canola, wheat and barley, were subjected to single kernel imaging after 2, 4, 6, 8, and 10 weeks post inoculation in the NIR region from 1000 to 1600 nm at 61 evenly distributed wavelengths at 10 nm intervals.The acquired image data were in the three-dimensional hypercube forms, and these were transformed into two-dimensional data. The two-dimensional data were subjected to principal component analysis to identify significant wavelengths based on the highest principal component factor loadings. Wavelengths 1100, 1130, 1250, and 1300 nm were identified as significant for detection of fungal infection in canola kernels, wavelengths 1280, 1300, and 1350 nm were identified as significant for detection of fungal infection in wheat kernels, and wavelengths 1260, 1310, and 1360 nm were identified as significant for detection of fungal infection in barley kernels. The linear, quadratic and Mahalanobis statistical discriminant classifiers differentiated healthy canola kernels with > 95% and fungal infected canola kernels with > 90% classification accuracy. All the three classifiers discriminated healthy wheat and barley kernels with > 90% and fungal infected wheat and barley kernels with > 80% classification accuracy.ii The wavelengths 1300, 1350, and 1480 nm were identified as significant for detection of ochratoxin A contaminated wheat kernels, and wavelengths 1310, 1360, 1480 nm were identified as significant for detection of ochratoxin A contaminated barley kernels. All the three statistical classifiers differentiated healthy wheat and barley kernels and ochratoxin A contaminated wheat and barley kernels with a classification accuracy of 100%. The classifiers were able to discriminate between different durations of fungal infections in canola, wheat, and barley kernels with classification accuracy of more than 80% at initial periods (2 weeks) of fungal infection and 100% at the later periods of fungal infection. Different concentration levels of ochratoxin A contamination in wheat and barley kernels were discriminated with a classification accuracy of > 98% at ochratoxin A concentration level of ≤ 72 ppb in wheat kernels and ≤ 140 ppb in barley kernels and with 100% classification accuracy at higher concentration levels.iii

Determination of damaged wheat kernels with hyperspectral imaging analysis

“…Near-infrared hyperspectral imaging was used to detect fungal infection and Ochratoxin A contamination in stored wheat and PCA was applied to obtain significant wavelengths. All the three classifiers of linear, quadratic and Mahalanobis discriminant classifiers differentiated healthy kernels from fungal-infected kernels with a classification accuracy higher 90% [7] . Besides the application on crop quality detection, spectroscopy technology had been widely used on qualitative analysis of variety [8,9] , adulteration [10][11][12][13][14] , crops of different years [15] , etc.…”

Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review

“…Though a slight misclassification may occur when the AFB 1 concentration is lower than 10 μg/kg, aflatoxinfree samples could readily be separated from contaminated ones. Senthilkumar et al (2016a) was the first to apply HSI to the detection of OTA in stored wheat. After carrying out PCA, based on the highest factor loadings of PC 1 , 1,280, 1,300, and 1,350 nm were identified as the three significant wavelengths that corresponded to fungal (Aspergilus glaucus and Penicillium spp.)…”

Multispectral Imaging for Plant Food Quality Analysis and Visualization

“…Specific groups of feature wavelengths associated with Aspergillus glaucus , Penicillium spp., and ochratoxin A were identified as significant wavelengths according to the loading plot of PCA. These selected wavelengths were used in statistical discriminant analyses (such as linear, quadratic, and Mahalanobis discriminant classifiers) to differentiate fungal‐infected kernels with over 90% classification accuracy (Senthilkumar and others , , ). In the study of Sun and others (), fungal‐infected peaches were effectively distinguished based on multispectral imaging and band ratio (BR) method.…”