Optimizing process of producing tomato paste using fuzzy logic

Автор: Mojtaba Nouri, Leila Shaghaghi, Mohammad Moghadasi Incheh Keykanloo, Ali Abhari Segonbad, Mahsa Tabrizi, Mohammad Ali Shariati

Журнал: Биология в сельском хозяйстве @biology-in-agriculture

Рубрика: Актуальные исследования иностранных авторов

Статья в выпуске: 1 т.2, 2014 года.

Бесплатный доступ

The aim of this study is to investigate consistency heating (primary Brix) of tomato and mutual influence of different temperatures and brixes on the final consistency of produced paste using fuzzy logic. Tomato paste is the most important products of tomato and improving its properties is of high importance. The results of applying different amounts of these parameters showed that heating tomato in 79 centigrade degrees causes pectin to be preserved better and finally its consistency will be improved and the quality of tomato will be preserved. Increasing temperatures makes the apparent quality to be preserved. Also, the tests of evaluating consistency and color of the product in 70 confirmed the optimization of consistency and parameters L, a and b. Fuzzy logic created the argument-based system for this research so that its application helps us solve the problems of production line and unpredicted conditions through applying other conditions.

Еще

Primary brix, temperature, tomato paste, fuzzy, consistency

Короткий адрес: https://sciup.org/14770274

IDR: 14770274

Список литературы Optimizing process of producing tomato paste using fuzzy logic

  • Barreiro J.A., Milano M., Sandoval A.J. Kinetics of colour change of double concentrated tomato paste during thermal treatment, 1997, Journal of Food Engineering, 3-4, 359-371.
  • Eerikäinen T., Linko, P., Linko, S., Siimes, T., Zhu, Y.H. Fuzzy logic and neural network applications in food science and technology, 1993, Trends in Food Science & Technology, 8, 237-242.
  • Flahi M.(2005).Tomato paste Processing Industries.Marzedanesh Publication,Iran.P 30-85.
  • Fuzzy Logic Toolbox, for use with Matlab©7010.0 User’s Guide, 2010, The Math works Software, 30-59.
  • Jahns, G., Nielsen, H. and Paul, W. (2001).Measuring image analysis attributes and modeling fuzzy consumer aspects for tomato quality grading, Computers and Electronics in Agriculture, 31, 17-29.
  • Harris, J. (1998).Raw milk grading using fuzzy logic, International Journal of Dairy Technology. 51: 52-56.
  • Kavdir, I. and Guyer, D.E. Apple Grading Using Fuzzy Logic. 2003, Turkish J. of Agric, 27, 375-382.
  • Sing sii, H., Ruxton, T. and Wang, J. (2001).A fuzzy-logic-based approach to qualitative safety modeling for marine systems. Reliability Engineering and System Safety, 73:19-34.
  • Zimmermann H. J. (1996).Fuzzy Set Theory and It’s Applications, Kluwer Academic Publishers. Boston, Dordrecht, London, P 435.
Еще
Статья научная