Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique
Main Article Content
Keywords
classification, fungal infection, hyperspectral imaging, long-wave near-infrared,, pistachio
Abstract
Hyperspectral imaging is a non-destructive technique with great capability to detect food defects and diseases. In this research, infection of pistachio kernels by two different isolates of Aspergillus flavus, KK11 and R5, was determined using long-wave near-infrared hyperspectral imaging. Seven fungal growing stages on pistachio kernels, for both isolates, were investigated. The features from the hypercubes of healthy and infected pistachios were extracted, selected and then used for classification by linear discriminant analysis and quadratic discriminant analysis (QDA) methods which were performed by 10-fold cross validation technique in MATLAB 2010a. The QDA model gave the highest classification accuracy for all classes (healthy and infected kernels by different fungi and at different stages). For classification of infection by different fungi at the last fungal growing stage, QDA had the accuracy of 91.7%. As KK11 is an aflatoxin-producing and R5 is a non-aflatoxin-producing fungus isolate, the importance of the technique to detect aflatoxin contamination in pistachio is significant.
References
Chelladurai, V., Jayas, D.S. and White, N.D.G., 2010. Thermal imaging for detecting fungal infection in stored wheat. Journal of Stored Products Research 46: 174-179.
Dale, L.M., Thewis, A., Boudry, C., Rotar, I., Dardenne, P., Baeten, V. and Fernández Pierna, J.A., 2013. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review. Applied Spectroscopy Reviews 48: 142-159.
Davies, E.R., 2009. The application of machine vision to food and agriculture: a review. The Imaging Science Journal 57: 197-217.
Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A.A., Bonifazi, G. and Fanelli, C., 2010. Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. International Journal of Food Microbiology 144: 64-71.
Delwiche, S.R., 2003. Classification of scab and other mold damaged wheat kernels by near-infrared reflectance spectroscopy. Transactions of ASAE 46: 731-738.
Diener, U.L., Cole, R.J., Sanders, T.H., Payne G.A., Lee, L.S. and Klich, M.A., 1987. Epidemiology of aflatoxin formation by Aspergillus flavus. Annual Review of Phytopathology 25: 249-270.
Ehrlich, K.C., Montalbano, V.G. and Cotty, P.J., 2003. Sequence comparison of aflR from different Aspergillus species provides evidence for variability in regulation of aflatoxin production. Fungal Genetics and Biology 38: 63-74.
Gade, R. and Moeslund, T.B., 2014. Thermal cameras and applications: a survey. Machine Vision and Applications 25: 245-262.
Georgiadou, M., Dimou, A. and Yanniotis, S., 2012. Aflatoxin contamination in pistachio nuts: a farm to storage study. Food Control 26: 580-586.
Gomez-Sanchis, J., Gomez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Molto, E. and Blasco, J., 2008. Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering 89: 80-86.
Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G. and Frias, J.M., 2007. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control. Trends in Food Science and Technology 18: 590-598.
Kaliramesh, S., Chelladurai, V., Jayas, D.S., Alagusundaram, K., White, N.D.G. and Fields, P.G., 2013. Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging. Journal of Stored Products Research 52: 107-111.
Mahesh, S., Jayas, D.S., Paliwal, J. and White, N.D.G., 2015. Hyperspectral imaging to classify and monitor quality of agricultural materials. Journal of Stored Products Research 61: 17-26.
Manickavasagan, A., Jayas, D.S., White, N.D.G. and Paliwal, J., 2008. Wheat class identification using thermal imaging: a potential innovative technique. Transactions of the ASABE 51: 649-651.
Mathanker, S.K., Weckler, P.R. and Bowser T.J., 2013. X-ray applications in food and agriculture: a review. Transactions of the ASABE 56: 1227-1239.
McLachlan, G.J., Do, K.A. and Ambroise, C., 2004. Analyzing microarray gene expression data. 2004. Wiley, New York, NY, USA.
Narvankar, D.S., Singh, C.B., Jayas, D.S. and White, N.D.G., 2009. Assessment of soft X-ray imaging for detection of fungal infection in wheat. Biosystems Engineering103: 49-56.
Peng, Y., Zhang, J., Wang, W., Li, Y., Wu, J., Huang, H., Gao, X. and Jiang, W., 2011. Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles. Journal of Food Engineering 102: 163-169.
Qin, J., Burks, T.F., Ritenour, M.A. and Bonn, G.W., 2009. Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering 93: 183-191.
Qin, J., Burks, T.F., Zhao, X., Niphadkar, N. and Ritenour, M.A., 2012. Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering 108: 87-93.
Saremi, H., Okhovvat, M. and Saremi, H., 2007. Control managements of Aspergillus flavus a main aflatoxin producers and soil borne fungi on pistachio in Kerman. Iranian Food Science and Technology Research Journal 3: 27-31.
Singh, C.B., 2009. Detection of insect and fungal damage and incidence of sprouting in stored wheat using near-infrared hyperspectral and digital color imaging. Ph.D. dissertation. University of Manitoba, Winnipeg, Canada.
Singh, C.B., Jayas, D.S., Paliwal, J. and White, N.D.G., 2012. Fungal damage detection in wheat using short-wave near-infrared hyperspectral and digital colour imaging. International Journal of Food Properties 15: 11-24.
Wang, D., Dowell, F.E., Ram, M.S. and Schapaugh, W.T., 2004. Classification of fungal-damaged soybean seeds using near-infrared spectroscopy. International Journal of Food Properties 7: 75-82.
Article Sidebar
Article Details
