پردازش تصاویر فراطیفی به منظور تشخیص آلودگی مغز پسته به دو جدایه KK11 و R5 قارچ Aspergillus flavus

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیئت علمی/گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایران.

2 عضو هیات علمی/گروه ماشین های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران، کرج، ایران

3 عضو هیات علمی/گروه ماشین های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران، کرج، ایران.

4 عضو هیات علمی/گروه گیاه پزشکی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران.

5 عضو هیات علمی/گروه زیست سامانه ها، دانشکده مهندسی، دانشگاه منی توبا، وینیپگ، کانادا

6 گروه علوم و صنایع غذایی، دانشکده کشاورزی، دانشگاه ایالتی کانزاس، منهتن، آمریکا.

7 استادیار، بخش تحقیقات غلات، مؤسسه تحقیقات اصلاح و تهیه نهال و بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، استان البرز، ایران

چکیده

فناوری­ تصویربرداری فراطیفی به عنوان روشی نوین و موثر در تشخیص آلودگی محصولات کشاورزی به کار می‌رود. این روش برای تشخیص پسته سالم و آلوده به قارچ­ Aspergillus flavus با و بدون در نظر گرفتن مراحل آلودگی، مورد استفاده قرار گرفت. جدایه‌های R5 وKK11  به ترتیب با و بدون قابلیت تولید سم آفلاتوکسین، به طور جداگانه، برای آلوده‌سازی پسته استفاده شد. از بین طول موج‌های 960 تا 1700 نانومتر، سه طول موج موثر 1090، 1280، و 1700 نانومتر با استفاده از روش تحلیل مولفه‌های اصلی انتخاب شد. پس از استخراج ویژگی­، از روش­های اعتبارسنجی K-بخشی، ماشین بردار پشتیبان، و شبکه عصبی مصنوعی برای طبقه­بندی استفاده شد. نتایج نشان داد که دقت روش اعتبارسنجی K-بخشی در طبقه‌بندی نمونه‌های پسته سالم و آلوده بدون در نظر گرفتن مراحل آلودگی و نوع جدایه (71/99 درصد) بالاتر بود. حداکثر دقت طبقه‌بندی الگوریتم‌های توسعه یافته در تشخیص نوع جدایه و مراحل آلودگی 69 تا 91 درصد به دست آمد.

کلیدواژه‌ها


عنوان مقاله [English]

Processing the Hyperspectral Images for Detecting Infection of Pistachio Kernel by R5 and KK11 Isolates of Aspergillus flavus Fungus

نویسندگان [English]

  • Kamran Kheiralipour 1
  • Hojjat Ahmadi 2
  • Ali Rajabipour 3
  • Shahin Rafiee 3
  • Mohammad Javan Nikkhah 4
  • Jayas Digvir 5
  • Kaliramesh Siliveru 6
  • Ali Malihipour 7
1 Faculty member/ Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Ilam University, Ilam, Iran.
2 Faculty member/Department of Agricultural, Faculty of agriculture and Natural Resources, University of Tehran, Karaj, Iran.
3 Faculty member/Department of Agricultural, Faculty of agriculture and Natural Resources, University of Tehran, Karaj, Iran.
4 Faculty member/Department of Plant protection, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
5 Faculty member/Department of Biosystems, Faculty of Engineering, University of Manitoba, Winnipeg, Canada.
6 Faculty member/ Department of Food Science and Industry, Faculty of Agriculture , Kansas State University, Manhattan, USA>
7 Assistant Professor, Cereal Research Department, Seed and Plant Improvement Institute (SPII), AREEO, Karaj, Alborz Province, Iran
چکیده [English]

Hyperspectral imaging technique as a new and efficient method is applied for detecting infection in agricultural products. It was used for classification of healthy and infected pistachio kernels by Aspergillus flavus fungus with and without considering infection stages. Two different fungus isolates, R5 and KK11 with and without capable of producing aflatoxin, respectively, were individually used to infect the pistachio kernel samples. From 960 to 1700 nm, three effective wavelengths of 1090, 1280, and 1700 nm were selected by principle component analysis method. After feature extraction, K-fold cross validation, support vector machine, and artificial neural network methods were used for classification. The results showed that the classification accuracy of the K-fold cross validation method was higher for classifying the healthy and infected pistachios without considering the infection stages and isolate type (99.71%). The maximum accuracy of the developed algorithms in classification of isolate type and infection stage was obtained as 69-91%.

کلیدواژه‌ها [English]

  • Pistachio
  • Fungal infection
  • Infection stage
  • Hyperspectral imaging technique
  • Image processing
Alizadeh, M., Pirsa, S., & Faraji, N. (2017). Determination of Lemon Juice Adulteration by Analysis of Gas Chromatography Profile of Volatile Organic Compounds Extracted with Nano-Sized Polyester-Polyaniline Fiber. Food Analytical Methods, 10, 2092-2101.
Anonymous, (2011). Pistachio properties. At: http://www.tebyan.net.
Bahar, B., & Altuğ, T. (2009). Carry-over of Aflatoxins to fig molasses from contaminated dried figs. International Journal of Food Properties, 12, 341-346.
Burges, C.J.C. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), 121-167.
Campbell, C. (2002). Kernel methods: a survey of current techniques. Neurocomputing, 48(1), 63-84.
Chelladurai, V., Jayas, D.S., & White, N.D.G. (2010). Thermal imaging for detecting fungal infection in stored wheat. Journal of Stored Products Research, 46, 174-179.
Choudhary, R., Mahesh, S., Paliwal, J., & Jayas, D.S. (2009). Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering, 102, 115-127.
Chu, X., Wang, W., Ni, X., Li, C., Li, Y. (2020). Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging. Infrared Physics & Technology, 105, 103242.
Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods, Cambridge University Press.
ElMasry, G., Sun, D.W., & Allen, P. (2012). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 110, 127-140.
ElMasry, G., Wang, N., ElSayed, A., & Ngadi, M. (2007). Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering, 81, 98-107.
Farokhzad, S., Modaress Motlagh, A., Ahmadimoghadam, P., Jalali Honarmand, S., & Khaieralipour, K. (2017). Fungal infection in potato tuber using thermal imaging. Iranian Journal of Biosystems Engineering, 48(3), 243-253.
Farokhzad, S., Modaress Motlagh, A., Ahmadi moghadam, P., Jalali Honarmand, Khaieralipour, K. (2020). Application of infrared thermal imaging technique and discriminant analysis methods for non-destructive identification of fungal infection of potato tubers. Journal of Food Measurement and Characterization, 14, 88-94.
Gomez-Sanchis, J., Gomez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Molto, E., & 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., Taghizadeh, M., & O’Donnell, C.P. (2009). Identification of mushrooms subjected to freeze damage using hyperspectral imaging. Journal of Food Engineering, 93, 7-12.
Grossman, R., Seni, G., Elder, J., Agarwal, N., & Liu, H. (2010). Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions". Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool. 2: 1-126.
Heidarian, R. (2004). Comparison of Aspergillus flavus isolates based on vegetative compatibility and polymerase chain reaction groups of pistachio area in Kerman Province. M.Sc. Thesis, University of Tehran, Karaj, Iran. (In Persian). 
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441.
Jolliffe, I.T. (2002). Principal Component Analysis. Springer Series in Statistics. New York: Springer-Verlag. 
Kheiralipour, K. (2012). Implementation and construction of a system for detecting fungal infection in pistachio kernel based on thermal imaging (TI) and image processing technology. Ph.D. Dissertation, University of Tehran, Karaj, Iran. (In Persian).
Kheiralipour, K. Ahmadi, H. Rajabipour, A., & Rafiee, S. (2018). Thermal Imaging, Principles, Methods and Applications, 1st ed. Ilam University Publication, Ilam. (In Persian).
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., & Jayas, D.S. (2013). Development of a New Threshold Based Classification Model for Analyzing Thermal Imaging Data to Detect Fungal Infection of Pistachio Kernel. Agricultural Research, 2(2): 127-131.
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., & Javan-Nikkhah, M. (2015). Classifying Healthy and Fungal Infected-Pistachio Kernel by Thermal Imaging Technology. International Journal of Food Properties, 18: 93-99.
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D.S. & Siliveru K. (2016). Detection of fungal infection in pistachio kernel by long-wave near infrared hyperspectral imaging technique. Quality Assurance and Safety of Crops & Foods, 8, 129-135.
Kheiralipour, K., & Abbas Pormah, A. (2017). Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of Food Process Engineering. 40(6), e12558.
Kumar, A., Bharti, V., Kumar, V., Kumar, U., & Meena, P.D. (2016). Hyperspectral imaging: A potential tool for monitoring crop infestation, crop yield and macronutrient analysis, with special emphasis to Oilseed Brassica. Journal of Oilseed Brassica, 7(2), 113-12.
Lin, K.M., & Lin, C.J. (2003). A Study on Reduced Support Vector Machines. IEEE Transactions on Neural Networks, 14(6), 1449-1459.
Li, J., Huang, W., Tian, X., Wang, C., Fan, S., & Zhao, C. (2016). Fast detection and visualization of early decay in citrus using vis-NIR hyperspectral imaging. Computers and Electronics in Agriculture. 127, 582-592.
Lu, B., Dao, P.D., Liu, J., He, Y., & Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12, 2659.
Lu, R. (2003). Detection of bruise on apples using near-infrared hyperspectral imaging. Transactions of the ASAE, 46, 523-530.
Mahesh, S., Manickavasagan, A., Jayas, D.S., Paliwal, J., & White, N.D.G. (2008). Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering, 101, 50-57.
Manolakis, D., Marden, D., & Shaw, G.A. (2003). Hyperspectral image processing for automatic target detection applications. Incoln Laboratory Journal, 14, 79-116.
McCulloch, W.S., & Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5(4), 115-133.
McLachlan, G.J., Do, K.A., & Ambroise, C. (2004). Analyzing microarray gene expression data. Wiley. At: http://en.wikipedia.org/wiki/Cross-validation (statistics).
Nakariyakul, S., & Casasent, D.P. (2011). Classification of internally damaged almond nuts using hyperspectral imagery. Journal of Food Engineering, 103, 62-67.
Narvankar, D.S., Singh, C.B., Jayas, D.S., & White, N.D.G. (2009). Assessment of soft X-ray imaging for detection of fungal infection in wheat. Biosystems Engineering, 103, 49-56.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(11): 559-572. 
Peng, Y., Zhang, J., Wang, W., Li, Y., Wu, J., Huang, H., Gao, X., & Jiang, W. (2011). Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles. Journal of Food Engineering, 102, 163-169.
Pirsa, S., & Mohammad Nejad, F. (2017). Simultaneous analysis of some volatile compounds in food samples by array gas sensors based on polypyrrole nano-composites. Sensor Review, 37(2), 155-164.
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., & White, N.D.G. (2007). Fungal detection in wheat using near-infrared hyperspectral imaging. Transactions of the ASAE, 50, 2171-2176.
Siripatrawan, U., & Makino, Y. (2015). Monitoring fungal growth on brown rice grains using rapid and nondestructive hyperspectral imaging. International Journal of Food Microbiology, 199, 93-100.
Sweeney, M.J., Pamies, P., & Dobson, A.D.W. (2000). The use of reverse transcription-polymerase chain reaction (RTPCR) for monitoring aflatoxin production in Aspergillus parasiticus 439. Int J Food Microbiol, 56, 97-103.
Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer, New York.
Vejarano, R., Siche, R., & Tesfaye, w. (2017). Evaluation of biological contaminants in foods by hyperspectral imaging: A review. International Journal of Food Properties. 20(2), 1264-1297.
Zijuan, Z. (2011). World production and trade of pistachios: the role of the U.S. and factors affecting the export demand of U.S. pistachios. M.Sc. Thesis. University of Kentucky, Lexington, KY, United States.