Scientific Publications - Work Done by Microbiology Reader Bioscreen C

Zurera-Cosano, G., García-Gimeno, R.M., Rodríguez-Pérez, R. and Hervás-Martínez, C., 
Performance of Response Surface model for prediction of Leuconostoc mesenteroides growth parameters under different experimental conditions, Food Control, 2004

ABSTRACT

The combined effects of different temperatures (10.5 to 24.5ºC), pH level (5.5 to 7.5), sodium chloride levels (0.25 to 6.25%) and sodium nitrite levels (0 to 200 ppm) on the predicted growth rate and lag-time of Leuconostoc mesenteroides under aerobic and anaerobic conditions was studied. The Response Surface (RS) model developed provided reliable estimates of the three kinetic parameters studied, with a bias factor between 0.86 and 1.18 and an accuracy factor between 1.13 and 1.31, in aerobic and anaerobic conditions, respectively. For both conditions, SEP values ranged between 15.62% and 27.63%. The developed models are a valuable tool, enabling its application for shelf-life estimation of a food product.

Keywords: predictive microbiology; Leuconostoc mesenteroides; Response Surface model

INTRODUCTION

The deterioration of food products owing to spoilage microorganisms is a highly important social and economic problem, which affects both the food industry and consumers. Specifically, in the case of cooked meat products that are vacuum-packed, alterations in the product are chiefly caused by lactic acid bacteria, such as Leuconostoc mesenteroides. (Huis in´t Veld, 1996; Zhang & Holley, 1999). These bacteria contribute to the alteration process of food products via the fermentation of sugars, thus forming lactic acid, and producing slime and CO2, which cause pH levels to drop and result in the appearance of strange smells and flavors. This affects the sensorial qualities of the food product, and its acceptability to the consumer (Huis in´t Veld, 1996), resulting in significant economic losses for the food industry. It is therefore important to know the growth capacity of this microorganism to multiply in the food product under the conditions experienced during processing, preservation, storage and distribution. We will therefore be able to control the deterioration of the food product and estimate its shelf-life, thus avoiding economic losses.

Predictive microbiology is an important tool in the food industry to predict the behavior of microorganisms. The main objective is to use mathematical models to describe the evolution of food-based microorganisms under the influence of intrinsic environmental factors (pH, aw) and extrinsic factors (temperature, gaseous atmosphere).

The development of predictive models requires a large amount of growth data. The time-consuming nature of traditional plate-count techniques has prompted a need for swifter and more convenient data-collection methods, which would represent a considerable saving in effort and resources (Cole, 1991). One proposed alternative is based on absorbance measurements (Dalgaard, Ross, Kamperman, Neumeyer & McMeekin, 1994; Begot, Desnier, Daudin, Labadie & Lebert, 1996): predictive models derived from automated optical density data are reliable, generally validate well against models based on traditional methods, and provide a favorable estimation of microbial response (Dalgaard, Mejlholm, & Huss, 1997; Neumeyer, Ross, Thomson, McMeekin, 1997b; Nerbrink, Borch, Blom & Nesbakken, 1999; Dalgaard & Koutsoumanis, 2001).

Growth predictive models are currently accepted as informative tools that assist in rapid and cost-effective assessment of microbial growth for product development, risk assessment and education purposes (Ross, 1999). Although, over the past few years, much effort has been directed towards developing models describing the 3 combined effects of environmental factors on microbial growth of pathogens in foods (Ross, Dalgaard, & Tienungoon, 2000; Devlieghere, Geeraerd, Versyck, Van De Waetere, Van Impe & Debevere, 2001; García-Gimeno, Hervás-Martinez, Barco-Alcala, Zurera-Cosano & Sanz-Tapia, 2003; Zurera-Cosano, Castillejo-Rodriguez, Garcia-Gimeno & Rincon-Leon, 2004), predictive microbiology has been used to forecast the growth of spoilage microorganisms in order to study the shelf life of a food product. Specific spoilage organisms are selected for certain food products and used as test organisms such as Brochothrix thermosphacta (Baranyi, Robinson, Kaloti & Mackey, 1995), Pseudomonas (Neumeyer, Ross, & McMeekin, 1997a), Lactobacillus sake (Devlieghere, Debevere, & Van Impe, 1998), Lactobacillus curvatus (Wijtzes, Rombouts, Kant-Muermans, van´t Riet & Zwietering, 2001), or Lactobacillus plantarum (García-Gimeno, Hervás-Martínez, & de Silóniz, 2002).

The relationships between the combination of factors and the growth curve parameters are most frequently described using Response Surface Methodology (Devlieghere, et al. (1998)). Given the lack of a mathematical model for L. mesenteroides in current scientific literature, the aim of the present study was to elaborate models for predicting the combined effects of temperature, pH, salt and nitrite concentrations in aerobic and anaerobic conditions on the growth rate, lag-time and maximum population density of L. mesenteroides growth and to evaluate the relative importance of these environmental factors in controlling the growth of this microorganism.

MATERIAL AND METHODS

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To obtain L. mesenteroides growth data, the Bioscreen C analyser (Labsystem, Helsinki, Finland) was used, with which optical density measurements were taken. 200 µl of sterile MRS broth from the different test conditions were transferred into each well of the Bioscreen C plates, along with 50 µl of L. mesenteroides inoculum with a concentration close to 106 ufc/ml. Optical density measurements were taken each hour until the microorganism had reached the stationary stage of growth. To simulate the anaerobic environment, the wells were covered with 200 µl of liquid paraffin. For each atmospheric condition, 150 growth curves were obtained for further development of the model, and another 60 curves for the test itself, giving a total of 420 growth curves.

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Last modified: May 25, 2005