Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis

Microbial Pathogenesis

Volumn 67-68, Issue 1, 2014, Pages 36-40

  Yolmeh, Mahmoud ^a   Habibi Najafi, Mohammad B ^b   Salehi, Fakhreddin ^a  

^a GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES   (Iran)

^b FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Annatto dye; Genetic algorithm; Mayonnaise; Salmonella enteritidis; Sensitivity analysis

Indexed keywords

ANNATTO DYE; FOOD DYE; UNCLASSIFIED DRUG;

ADAPTIVE NEURO FUZZY INFERENCE SYSTEM; ANTIBACTERIAL ACTIVITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; CORRELATION COEFFICIENT; DAIRY PRODUCT; FUZZY SYSTEM; GENETIC ALGORITHM; MAYONNAISE; NONHUMAN; PREDICTION; PRIORITY JOURNAL; SALMONELLA ENTERITIDIS; SENSITIVITY ANALYSIS; STORAGE TEMPERATURE; STORAGE TIME; TIME;

ANNATTO DYE; GENETIC ALGORITHM; MAYONNAISE; SALMONELLA ENTERITIDIS; SENSITIVITY ANALYSIS;

ALGORITHMS; ANTI-BACTERIAL AGENTS; BIXACEAE; CAROTENOIDS; FOOD MICROBIOLOGY; FOOD STORAGE; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PLANT EXTRACTS; SALMONELLA ENTERITIDIS;

EID: 84895773373     PISSN: 08824010     EISSN: 10961208     Source Type: Journal    
DOI: 10.1016/j.micpath.2014.02.003     Document Type: Article

References (23)

1. BahramParvar M., Salehi F., Razavi S.M.A. Predicting total acceptance of ice cream using artificial neural network. JFood Process Preserv 2013, 1-9.
2. Boonmar S., Bangtrakulnonth A., Pornrunangwong S., Terajima J., Watanabe H., Kaneko K., et al. Epidemiological analysis of Salmonella enteritidis isolates from humans and broiler chickens in Thailand by phages typing and pulsed-field gel electrophoresis. JClin Microbiol 1998, 36(4):971-974.
3. Castello M., Chandra N., Phatak A., Madhuri S. Estimation of bixin in seeds of Bixa orellana L. from different locations in Western Maharashtra. Indian J Plant Physiol 2004, 9(2):185-188.
4. Cheroutre-Vialette M., Lebert A. Application of recurrent neural network to predict bacterial growth in dynamic conditions. Int J Food Microbiol 2002, 73:107-118.
5. Da Silva J.P.L., Franco B.D.G. Application of oregano essential oil against salmonella enteritidis in mayonnaise salad. Int J Food Sci Nutr Eng 2012, 2(5):70-75.
6. Entchev E., Yang L. Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation. JPower Sources 2007, 170:122-129.
7. Ghoush M.A., Al-Mahasneh M., Samhouri M., Al-Holy M., Herald T. Formulation and fuzzy modeling of viscosity of orange-flavored beverages fortified with carboxymethylcellulose-whey protein isolate emulsions. Jordan J Biol Sci 2009, 2(3):109-118.
8. Gulbag A., Temurtas F. Astudy on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems. Sens Actuators B 2006, 115:252-262.
9. Haykin S. Neural networks: a comprehensive foundation 1994, 696. Macmillan Publishing Company, Englewood Cliffs, NJ.
10. Heo S.K., Lee J.Y., Jo S.H., Bae D.H., Kim Y.S., Chung M.S., et al. Inactivation of Salmonella enterica serovar typhimurium in commercial mayonnaise at different temperatures and pH. JKorean Soc Appl Biol Chem 2010, 53(3):351-355.
11. Koutsoumanis K., Lambropoulou K., Nychas G.-J.E. Apredictive model for the non-thermal inactivation of Salmonella enteritidis in a food model system supplemented with a natural antimicrobial. Int J Food Microbiol 1999, 49:63-74.
12. Lauro G.J. Aprimer on natural colors. Cereal Foods World 1991, 36:949-953.
13. Lock J.L., Board R.G. The fate of Salmonella enteritidis PT4 in deliberately infected commercial mayonnaise. Food Microbiol 1994, 11:499-504.
14. Lou W., Nakai S. Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH and water activity. Food Res Int 2000, 34:573-579.
15. Magiatis P., Melliou E., Skaltsounis A., Chinou I., Mitaku S. Chemical composition and antimicrobial activity of the essential oils of Pistacia lentiscus var. chia. Planta Med 1999, 65:749-752.
16. Mashrei M.A., Abdulrazzaq N., Abdalla T.Y., Rahman M.S. Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members. Eng Struct 2010, 32:1723-1734.
17. Morimoto T. Genetic algorithm. Handbook of food and bioprocess modeling techniques 2006, 405-434. CRC Press, New York. S.S. Sablani, M.S. Rahman, A.K. Datta, A.S. Mujumdar (Eds.).
18. Perales I., Garcia M.I. The influence of pH and temperature on the behaviour of Salmonella enteritidis phage type 4 in home-made mayonnaise. Lett Appl Microbiol 1990, 10:19-22.
19. Rajashekara G., Munir S., Alexeyev M.F., Halvorson D.A., Wells C.L., Nagaraja K.V. Pathogenic role of SEF14, SEF17 and SEF21 fimbriae in Salmonella enterica serovar enteritidis infection of chickens. Appl Environ Microbiol 2000, 66(4):1759-1763.
20. Rumelhart D.E., Durbin R., Golden R., Chauvin Y. Backpropagation: theory, architectures, and applications backpropagation: the basic theory 1994, 1-34. Lawrence Erlbaum Associates, New Jersey.
21. Salehi F., Razavi S.M.A. Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural network. Desalin Water Treat 2012, 41:95-104.
22. Salehi F., Razavi S.M.A. Applying the genetic algorithm-artificial neural network modeling for prediction of permeate flux, NaCl rejection and total hydraulic resistance of nanofiltration process. JSep Transport Phenom 2011, 22(1):29-50.
23. Smittle R.B. Microbiological saftey of mayonnaise, salad dressing and sauces produced in the United States. JFood Prot 2000, 63(8):1144-1153.