A network-based fuzzy inference system for sonodisruption process of re-assembled casein micelles

Journal of Food Engineering

Volumn 98, Issue 2, 2010, Pages 224-229

  Madadlou, Ashkan ^a   Emam Djomeh, Zahra ^a   Mousavi, Mohammad E ^a   Javanmard, Majid ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b IRANIAN RESEARCH ORGANIZATION FOR SCIENCE AND TECHNOLOGY IROST   (Iran)

Author keywords

Casein micelles; Fuzzy inference system; Neural networks

Indexed keywords

ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM; CASEIN MICELLES; FUZZY INFERENCE SYSTEM; FUZZY INFERENCE SYSTEMS; GRID PARTITION; MATHEMATICAL MODELING; NETWORK-BASED; RULE BASE; SIMPLE STRUCTURES; SUBTRACTIVE CLUSTERING;

CASEIN; FUZZY INFERENCE; FUZZY RULES; FUZZY SYSTEMS; MICELLES;

FUZZY NEURAL NETWORKS;

EID: 75749157770     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2009.12.031     Document Type: Article

References (26)

1. Cakmakci M. Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge. Bioprocess and Biosystem Engineering 30 (2007) 349-357
2. Chen J.Y., Qin Z., and Jia J. A weighted mean subtractive clustering algorithm. Information Technology Journal 7 2 (2008) 356-360
3. Chiu, S. (1994). Fuzzy Logic Toolbox: User's Guide. The MathWorks Inc.
4. Eftekhari M., and Katebi S.D. Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter. Applied Mathematical Modeling 32 (2008) 2634-2651
5. El-Sebakhy, E.A., Raharja, I., Adem, S., Khaeruzzaman, Y. (2007). Neuro-fuzzy systems modeling tools for bacterial growth. IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, 374-380.
6. Gänzle M.G., Kilimann K.V., Hartmann C., Vogel R., and Delgado A. Data mining and fuzzy modelling of high pressure inactivation pathway of Lactococcus lactis. Innovative Food Science and Emerging Technologies 8 (2007) 461-468
7. Garibaldi J.M., and Ifeachor E.C. Application of simulated annealing fuzzy model tuning to umbilical cord acid-base interpretation. IEEE Transactions on Fuzzy Systems 7 1 (1999) 72-84
8. Han M., Sun Y., and Fan Y. An improved fuzzy neural network based on T-S model. Expert Systems with Applications 34 (2008) 2905-2920
9. Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23 (1993) 665-685
10. Jantzen J. Neurofuzzy Modeling, Technical Report No 98-H-847 (1998), Technical University of Denmark, Lyngby, Denmark
11. Kilimann K.V., Hartmann C., Delgado A., Vogel R.F., and Gänzle M.G. A fuzzy logic-based model for the multistage high-pressure inactivation of Lactococcus lactis ssp. cremoris MG 1363. International Journal of Food Microbiology 98 (2005) 89-105
12. Konstantaras A., Varley M.R., Vallianatos F., Collins G., and Holifield P. A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors. Natural Hazards and Earth System Sciences 4 (2004) 641-646
13. Kupongsak S., and Tan J. Application of fuzzy set and neural network techniques in determining food process control set points. Fuzzy Sets and Systems 157 (2006) 1169-1178
14. Łe{ogonek}ski J., and Czogała E. A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. Fuzzy Sets and Systems 108 (1999) 289-297
15. Lin C.-T., and Lee G. Neural-network-based fuzzy logic control and decision system. IEEE Transactions on Computers 40 12 (1991) 1320-1336
16. Madadlou A., Emam-Djomeh Z., Mousavi M.E., Javanmard M., and Sheehan D. Response surface optimization of artificial neural network for predicting the size of re-assembled casein micelles. Computers and Electronics in Agriculture 68 (2009) 216-221
17. Madadlou A., Mousavi M.E., Emam-Djomeh Z., Sheehan D., and Ehsani M. Alkaline pH does not disrupt re-assembled casein micelles. Food Chemistry 116 (2009) 929-932
18. Madadlou, A., Mousavi, M.E., Emam-Djomeh, Z., Javanmard, M., unpublished data. Dual-frequency sonication for disruption of re-assembled casein micelles. International Journal of Dairy Technology, unpublished data.
19. Madadlou A., Mousavi M.E., Emam-Djomeh Z., Ehsani M., and Sheehan D. Sonodisruption of re-assembled casein micelles at different pH values. Ultrasonics Sonochemistry 16 (2009) 644-648
20. Madadlou A., Emam-Djoemh Z., Mousavi M.E., Ehsani M., and Sheehan D. Comparison of pH-dependent sonodisruption of re-assembled casein micelles by 35 and 130 kHz ultrasounds. Journal of Food Engineering 95 (2009) 505-509
21. Madadlou A., Emam-Djomeh Z., Mousavi M.E., Mohammadifar M.A., and Ehsani M. Acid-induced gelation behavior of sonicated casein solutions. Ultrasonics Sonochemistry 17 (2010) 153-158
22. Mohebbi M., Barouei J., Akbarzadeh-T M.R., Rowhanimanesh A.R., Habibi-Najafi M.B., and Yavarmanesh M. Modeling and optimization of viscosity in enzyme-modified cheese by fuzzy and genetic algorithm. Computers and Electronics in Agriculture 62 (2008) 260-265
23. Perrot N., Ioannou I., Allais I., Curt C., Hossenlopp J., and Trystram G. Fuzzy concepts applied to food product quality control: a review. Fuzzy Sets and Systems 157 (2006) 1145-1154
24. Samhouri M., Abu-Ghoush M., Yaseen E., and Herald T. Fuzzy clustering-based modeling of surface interactions and emulsions of selected whey protein concentrate combined to ι-carrageenan and gum arabic solutions. Journal of Food Engineering 91 (2009) 10-17
25. Wang Y., and Rong G. A self-organizing neural-network-based fuzzy system. Fuzzy Sets and Systems 103 (1999) 1-11
26. Wilamowski B.M. Neuro-fuzzy systems and their applications. IECON Proceedings (Industrial Electronic Conference) 35 (1998) 49