Quantifying Variability in Growth Kinetics of Lactobacillus plantarum and Lactobacillus paracasei technological and probiotic strains: a preliminary study

Abstract

Lactobacillus plantarum and Lactobacillus paracasei are species generally used as starters in food fermentation and/or as probiotics. The growth cardinal values of these strains, characteristic parameters independent from the food matrix, can be exploited in predictive microbiology to set the appropriate food processing/manufacturing conditions. In particular, the decision making tool Sym'Previus (http://www.symprevius.org) is used to describe the behaviour of bacteria on a food matrix using these parameters. In the current study, the growth kinetics of four L. plantarum strains (from sourdough and table olives) and four L. paracasei strains (from table olives and probiotic human isolates) were investigated in order to identify the optimal temperature conditions for growth. Strains were grown in liquid medium and incubated at nine temperature levels (5.5, 11, 18, 20, 22, 27, 35, 39, 40°C). The growth was automatically monitored by a Bioscreen C using the turbidimetry method or determined manually after static incubation. Maximum growth rates (?max) for each temperature were obtained fitting data by the Rosso model. To estimate the cardinal growth values, the ?max values relevant to each temperature were fitted to the growth cardinal model. The following average of cardinal values were identified: for Lactobacillus plantarum strains Tmin 2.05±0.54°C, Topt 33.74±0.63°C and Tmax 39.79±0.50°C. In the case of L. paracasei strains: Topt 34.90±1.70 °C and Tmax 38.99±1.72°C. The Tmin values were lower than 0°C for three out of four strains highlighting a strain variability in growth abilities at low temperatures. The ?opt values ranged from 0.778 and 0.90 h-1 for L. plantarum strains and from 0.553 and 0.654 h-1 for L. paracasei strains. This preliminary study demonstrates a bacterial variability in growth abilities, mainly for L. paracasei strains, which should be taken into account in predictive microbiology to obtain a reliable prediction of growth in food conditions.


Tutti gli autori

  • F. Postollec; M. Di Biase; A. Bavaro; M.-L. Divanac'h; Y. Le Marc; P. Lavermicocca; F. Valerio

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2017

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