Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

Identification and Evaluation of Technology for Detection of Aflatoxin Contaminated Peanut

Chaitra C., K.V. Suresh. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Chaitra C., K.V. Suresh
10.5120/cae2016652100

Chaitra C. and K V Suresh. Article: Identification and Evaluation of Technology for Detection of Aflatoxin Contaminated Peanut. Communications on Applied Electronics 4(5):46-50, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Chaitra C. and K.V. Suresh},
	title = {Article: Identification and Evaluation of Technology for Detection of Aflatoxin Contaminated Peanut},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {5},
	pages = {46-50},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Aflatoxin belongs to a group of fungal toxins known as mycotoxins, and is widespread in agricultural products and food. Consumption of aflatoxin contaminated peanuts causes severe health problems, like immune system suppression, cancer, and may lead to death. Therefore, quality classification of peanut using an efficient non-destructive method is very essential for food grain industries. In this paper imaging techniques such as thermal imaging, fluorescence imaging and color imaging are identified and evaluated. The results show that, thermal and fluorescence imaging techniques are not suitable for detection of contaminated peanuts. Hence, an algorithm for color imaging technique is proposed as an effective alternative method to detect contaminated peanuts based on external appearance. The main objective of the proposed algorithm is to classify peanuts into good and bad, based on color feature. The captured images are first pre-processed, and database is prepared automatically. Statistical and histogram features are then extracted for classification using Feed Forward Neural Network (FFNN), and Linear Discriminant Analysis (LDA). Proposed algorithm is developed using MATLAB 7.12, and tested on several peanut samples.

References

  1. Eduardo Micotti da Gloria, “Aflatoxin Contamination Distribution among Grain and Nuts,” Aflatoxins-Detection, Measurement and control, Dr. Irineo Torres-Pacheco (Ed.), ISBN: 978-953-307-711-6, InTech, 2011.
  2. K. Kamei, A. Wantanabe, ”Aspergillus mycotoxins and their effect on the host,” Medical Mycology Supplement, Taylor and Francis, 43, S95-S99, 2005.
  3. Shami Elhaj Alssafi Bakhiet, Ahmed Altayed Altayeb Ahmed Musa, “Survey and determination of aflatoxin levels in stored peanut in sudan,” Jordan Journal of Biological Sciences, Vol. 4, ISSN 1995-6673, pp. 13-20, 2011.
  4. Peter J. Cotty, Ramon Jaime-Garcia, “Influences of climate on aflatoxin producing fungi and aflatoxin contamination,” International journal of food microbiology, 119, 109-115, 2007.
  5. Jocelyne Tan, Good Eating Tip of the Month, Univ. of Michigan Health System: Patient Food and Nutrition Services, February 2011.
  6. C. W. Hesseltine, O. Shotwell, ”New methods for rapid detection of aflatoxin,” Pure and Applied Chemistry, Vol. 35, pp. 259-266, 1973.
  7. Alejandro Espinosa-Calderon, Luis Miguel Contreras-Medina, Rafael Francisco Munoz- Huerta, Jesus Roberto Millan-Almaraz, Ramon Gerardo Guevara Gonzalez, Irineo Torres-Pacheco, “Methods for Detection and Quantification of Aflatoxins,” Aflatoxins - Detection, Measurement and Control , Irineo Torres-Pacheco (Ed.), ISBN: 978-953-307-711-6, InTech, 2011.
  8. T. C. Pearson, D.T. Wicklow, E. B. Maghirang, F. Xie, F. E. Dowell,”Detecting Aflatoxin in Single Corn Kernels by Transmittance and Reflectance Spectroscopy,” Transactions of the ASAE 2001, ISSN 0001-2351. Vol. 44(5):1247-1254, 2001.
  9. Hellebrand, Hans Jurgen et al.,”Plant evaluation by NIR imaging and thermal imaging”.
  10. J. G. Tallada, D. T. Wicklow, T. C. Pearson, P. R. Armstrong, ”Detection of fungus-infected corn kernels using near-infrared reflectance spectroscopy and color imaging,” Transactions of the ASABE, Vol. 54 (3), pp-1151-1158, 2011.
  11. Haibo Yao, Zuzana Hruska, Russell Kincaid, Ambrose Ononye, Robert L. Brown et al., “Correlation and classification of single kernel fluorescence Hyperspectral data with aflatoxin concentration in corn kernels inoculated with aspergillus flavus spores,” Food additives and contaminants, Vol. 27, No. 5, pp-701-709, 2010.
  12. Haibo Yao, Zuzana Hruska, Russell Kincaid, Ambrose Ononye, Robert L. Brown and Thomas E. Cleveland, ”Spectral angle mapper classification of fluorescence Hyperspectral image for aflatoxin contaminated corn,” Hyperspectral image and signal processing: Evaluation in Remote sensing, pp. 1-4, 2010.
  13. M. Rajalakshmi, P.Subashini, ”A study on non-destructive method for detecting toxin in pepper using neural networks,” International journal for artificial intelligence and application, Vol. 3-Vol. 4, 2012.
  14. L. Wes Burger, ”Development of Rapid non-destructive Hyperspectral imaging methodology to measure fungal growth and aflatoxin in corn,” food safety research information office, USDA. Project number: MIS-721140, 2010-2014.
  15. Hong Chen, Jing Wang, Qiaoxia Yuan, Peng Wan, “Quality Classification of peanuts based on image processing,” Journal of food, Agriculture and Environment, Vol. 9 (3&4), pp-205-209, 2011.
  16. Tom Pearson, Dan Brabee, Scott Haley, ”Color image based sorter for separating red and white wheat,” Sensing and Instrumentation for food quality and safety, pp. 280-288, 2008.
  17. Anil Kannur, Asha Kannur and Vijay S. Rajapurohit, “Classification And Grading Of Bulk Seeds Using Artificial Neural Networks,” International Journal of Machine Intelligence, Vol. 3, pp. 62-73, 2011.
  18. Chaoxin Zheng, Da-Wen Sun and Liyun Zheng, “Recent developments and applications of image features for food quality evaluation and inspection a review,” Trends in Food Science & Technology, Vol. 17, pp. 642-655, 2006.
  19. Atris Suyantohadi and Rudiati Evi Masithoh, “Development of Machine Vision Based on Image Processing Technique to Identify Toxin Contamination In Peanuts,” Australian Journal of Basic and Applied Sciences, vol. 6, pp. 135-141, 2012.
  20. Han Zhongzhi, Deng Limiao, and Yu Renshi, “Study on Origin Traceability of Peanut Pods Based on Image Recognition,” International Conference on System Science, Engineering Design and Manufacturing Informatization, IEEE, vol. 2, pp. 93-96, 2011.

Keywords

Aflatoxin, Peanut, Thermal imaging, Fluorescence imaging, Color imaging, Quality.