Predictive modeling of survival/death of Listeria monocytogenes in liquid media: Bacterial responses to cinnamon essential oil, ZnO nanoparticles, and strain. (March 2017)

• Record Type: Journal Article
• Authors: Abdollahzadeh, Esmail; Ojagh, Seyed Mahdi; Hosseini, Hedayat; Irajian, Gholamreza; Ghaemi, Ezzat Allah
• Is Part Of: Food control. Volume 73:Part B(2017)
• Page Start: 954
• Page End: 965
• Publication Date: 2017-03
• Subjects: Listeria monocytogenes -- ZnO nanoparticles -- Strain variation -- Essential oil -- Neuro-fuzzy inference system; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2016.10.014
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: To predict Listeria monocytogenes population during storage (8 °C) as a function of time (1–16 days), cinnamon essential oil (EO), ZnO nanoparticles (NPs; 10–30 nm), and two different genotypes in liquid microbiological medium, an adaptive neuro fuzzy inference system (ANFIS) was developed. For this purpose, 32 modeling scenarios were investigated. The ANFIS scenarios were fed with 4 inputs of EO concentration (0, 0.8, 1.6, and 2.4%), ZnO NPs (0, 5, 10, and 15 mg/ml), strain (2 strains), and storage time (1–16 days). Our findings demonstrate that the final ANFIS architecture with triangular-shaped membership function (MF) provides the best prediction accuracy (RMSE = 0.214; R 2 = 0.974) over models with other MFs. Moreover, the effects of antibacterial activity of cinnamon EO were investigated in a food model system, vegetable broth. The bacterial counts decreased with increasing cinnamon oil and ZnO NPs concentrations; however, some strain variation was observed. These observations demonstrate the reliability of the ANFIS model for prediction of L. monocytogenes population and confirm its potential use as a supplemental tool in predictive microbiology. Highlights: Development of a neuro-fuzzy architecture for modeling L.M in vitro and in situ. ANFIS is proven to be a useful method in modeling L.M in complex food systems. ANFIS performance improved with triangular-shaped membership functions. Using cinnamon oil and ZnO NPs generated some strain-specific variation.