The use of a D-optimal design to model the effects of temperature, NaCl, type and acid concentration on Lactobacillus pentosus IGLAC01

Abstract

Aims: To study the effects of temperature, NaCl and acid (HCl, citric, acetic and lactic) concentrations on the specific growth rate (μ), lag phase (λ), and h0 of Lactobacillus pentosus IGLAC01. Methods and Results: Response surface (RS) methodology (D‐optimal design) was used with a dummy variable, to account for the different types of acids. The variable ranges were: 16–30°C, 0–70 g l−1 NaCl, and 0–5 g l−1 acid (or 0–2·5 g l−1 HCl). Time to detection from optical density data was used to deduce μ and λ. The RS models for log2μ and log2λ, according to acid types, were estimated and the effects of variables were quantified by their z‐generalized values. A relationship between ln h0 with temperature was also found. Conclusions: The μ of L. pentosus IGLAC01 can be doubled by increasing temperature by 10·3°C or by decreasing NaCl by 48 g l−1 (harmonic, averaged, z values, Z); citric was the least inhibitory acid (z = −96·2) and lactic the strongest (z = −5·7), according to their generalized z values, z. A twofold λ increase was achieved from a decrease of 3·1°C (HCl), or 4·27°C (citric) or 36 g l−1 NaCl increase (both acids) (expressed as z); the same effect was obtained from a decrease of 4·37°C, 54 g l−1 NaCl increase, or 10 g l−1 acetic or lactic acid additions (expressed as Z values). Significance and Impact of the Study: Valuable information on the effects of environmental variables on the biological parameters of L. pentosus IGLAC01, which could be used for the optimization of olive, cucumber or other vegetable fermentations, is obtained.

Improved cordycepin production in a liquid surface culture of Cordyceps militaris isolated from wild strain

“…This model validation was done in 500 ml flasks and the predicted amount was 289 mg (data not shown). D-optimal design analysis and one-factor design analysis have been used for the optimization of microbial growth, protein expression and bioactive compound production in microorganisms [21][22][23][24]. These analyses are useful to improve the production of bioactive compounds, secondary metabolites and enzymes in microorganisms.…”

A predictive growth model for Yarrowia lipolytica ATCC 9773 in wastewater

“…Considering that most of the secondary models are developed under real and abusive environmental conditions, a validation process must be carried out in order to verify the predictive accuracy of the models. Therefore, statistical indices such as accuracy (Af) and bias (Bf) have been suggested for validating secondary models (Baranyi et al, 1999;López et al, 2006).…”

Role of Predictive Microbiology in Food Preservation

“…These designs considerably reduce the total number of experiments to be performed with respect to a factorial design, and consequently save cost and time. They have already been satisfactorily applied to model different Lactobacillus and yeast species related with foods (Tsapatsaris and Kotzekidou, 2004;Arroyo et al, 2005;López et al, 2006). On the contrary, a full or fractional factorial design is most appropriate to estimate the growth/no-growth boundaries of microorganisms, as was proved recently for Monascus ruber, Saccharomyces cerevisiae and Staph.…”

“…A set of new experiments, not included originally in the experimental design, is carried out and the observed responses are compared with those predicted by the models. The accuracy (A) and bias (B) factors (Baranyi et al, 1999) have been satisfactorily used in the case of polynomial model validations (Arroyo et al, 2005;López et al, 2006). Accuracy (A) is the sum of absolute differences between predictions and observations and measures the overall model error.…”