Sensitivity analysis of membership functions for fuzzy sorting of tomato as affected by storage temperature and duration

Document Type : Research Paper

Authors

University of Shiraz

Abstract

In the present study, classification of tomatoes by fuzzy logic and change in classification due to storage conditions were studied. In order to conduct the study, qualitative properties such as color, size and hardness of tomatoes were measured. Tomato samples were sorted by a deigned fuzzy logic system based on fuzzy rules, and compared with the experts. Sensitivity analysis of outputs of membership functions were tested by chi-square statistic. In cool room storage, sensitivity of membership functions started from six-day after storage with change from grade 1 to grade 2. For samples that kept at ambient temperature sensitivity started on third day after storage with the same change in sorted groups as previous tests. The results emphasized that any processing operation or marketing after initial sorting of tomatoes should spend time in a way that does not change the results of the initial classification.

Keywords

Main Subjects


Anonymous. (1997). United States Standards for Grades of Fresh Tomatoes, Reprinted January 1997. USA.
Anonymous. (2016). FAOSTAT. Retrieved Oct. 12, 2016, from http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor.
Beak, I. S., Cho, B. K. & Kim, Y. S. (2012). Development of a compact quality sorting machine for cherry tomatoes based on real-time color image processing. International Conference of Agricultural Engineering, 8-12 Sep. Valencia, Spain.
Davis, J. N. & Hobson, G. E. (1981). The constituents of tomato fruit the influence of environment, nutrition and genotype, CRC Critical Review in Food Science Nutrition, 15, 205-280.
Iraji M. S.  & Tosinia, A. (2011). Classification of tomatoes by machine vision with fuzzy the Mamdani inference, adaptive neuro fuzzy inference system based (Anfis - Sugeno). Australian Journal of Basic and Applied Sciences. 5, 846-853.
Kavdir I. & Guyer, D. (2003). Apple Grading Using Fuzzy Logic. Department of Agriculture Machinery, 27, 375-382.
Lino, A.C.L. Sanches, J. & Fabbro, I. M. D. (2008). Image processing techniques for lemons and tomatoes classification. Journal of Bragantia, 67, 785- 789.  
Lana, M. M., Tijskens L. M. M. & Van Kooten, O. (2005). Effects of storage temperature and fruit ripening on firmness of fresh cut tomatoes. Postharvest Biology and Technology, 35, 87-95.
Lana, M.M., Tijskens L. M. M. & Van Kooten, O. (2006). Effects of storage temperature and storage of ripening on RGB color aspects of fresh cut tomato pericarp using video image analysis. Journal of Food Engineering, 77, 871-879.
Miro, S., Hartmann, D. & Schanz, T. (2013). Global sensitivity analysis for subsoil parameter estimation in mechanized tunnelling. Computers and Geotechnics, 56, 80-88.
Nassiri, S.M., Khajavi, S. & Ramezaniyan, A. (2014).  Image processing application to determine the color of tomato lycopene content in different temperature conditions. The first national conference on new technologies and post-harvest agricultural products, Agriculture and Natural Resources Research Center of Khorasan Razavi, 18-19 February. Mashhad. (In Farsi).
Nassiri, S.M., Tahavvor, A. & Jafari, A. (2016). Classification of mature tomato based on color, size and hardness using Fuzzy logic. 14th International Conference on Agricultural and Biosystems Engineering. Aarhus University, Aarhus, Denmark.
Omid, M. (2011). Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier. Expert Systems with Applications, 38, 4339- 4347.
Plackett, R. L. 1983. Karl Pearson and the Chi-Squared Test, International Statistical Review, 51, 59–72.
Polder, G., Heijdena, G. W. A. M. & Young, I. T. (2003). Tomato sorting using independent component analysis on spectral images. Real-Time Imaging, 9, 253-259.
Sabery-Kamarposhty, R. & Pourreza, H. R. (2007). Classification and evaluation of image features of tomato by several image techniques. Third International Conference on Information and Knowledge Technology. 23 May, Ferdowsi University of Mashhad. Mashhad, Iran.
Schouten, R. E., Huijben, T. P. M., Tijskens, L. M. M. & Van Kooten, O. (2007). Modeling quality attributes of truss tomatoes: Linking color and firmness maturity. Postharvest Biology and Technology, 45, 298-306.
Tahavvor, A. (2014). Classification of mature tomato based on color, size and hardness using fuzzy logic. M.Sc. thesis on Mechanics of Agricultural Machinery, Shiraz University. Iran. (In Farsi).
Teshnehlab, M., Safarpour, N. & Afuni, D. (2010). Fuzzy systems and fuzzy control (1st ed). K. N. Toosi University of Technology Press. Pp 528.
Van Dijk, C., Boeriv, C., Peter, F., Stolle-smits, T. & Tijskens, L. M. M. (2006). The firmness of stored tomatoes (cv.Tradiro): kinetic and near infrared models to describe firmness and moisture loss. Journal of Food Engineering, 77, 575-584.