One-step global parameter estimation of kinetic inactivation parameters for Bacillus sporothermodurans spores under static and dynamic thermal processes. (November 2016)

• Record Type: Journal Article
• Authors: Cattani, F.; Dolan, K.D.; Oliveira, S.D.; Mishra, D.K.; Ferreira, C.A.S.; Periago, P.M.; Aznar, A.; Fernandez, P.S.; Valdramidis, V.P.
• Is Part Of: Food research international. Volume 89(2016) Part 1
• Page Start: 614
• Page End: 619
• Publication Date: 2016-11
• Subjects: Thermal technologies -- High heat resistance -- Bacillus sporothermodurans -- Spore forming bacteria, one-step regression; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2016.08.027
• Languages: English
• ISSNs: 0963-9969

Abstract

Abstract: Bacillus sporothermodurans produces highly heat-resistant endospores, that can survive under ultra-high temperature. High heat-resistant sporeforming bacteria are one of the main causes for spoilage and safety of low-acid foods. They can be used as indicators or surrogates to establish the minimum requirements for heat processes, but it is necessary to understand their thermal inactivation kinetics. The aim of the present work was to study the inactivation kinetics under both static and dynamic conditions in a vegetable soup. Ordinary least squares one-step regression and sequential procedures were applied for estimating these parameters. Results showed that multiple dynamic heating profiles, when analyzed simultaneously, can be used to accurately estimate the kinetic parameters while significantly reducing estimation errors and data collection. Graphical abstract: Highlights: Dynamic heating profiles can result in accurate and precise parameter estimation. Experimental effort can be reduced significantly with dynamic optimization. Ordinary least squares and sequential procedures are efficient optimization tools.