Appl Biochem Biotechnol DOI 10.1007/s12010-015-1666-3

Modelling Growth and Bacteriocin Production by Lactobacillus plantarum BC-25 in Response to Temperature and pH in Batch Fermentation Kang Zhou 1 & Yi-ting Zeng 1 & Xin-feng Han 1 & Shu-liang Liu 1

Received: 13 September 2014 / Accepted: 11 May 2015 # Springer Science+Business Media New York 2015

Abstract The use of bacteriocin-producing probiotics to improve food fermentation processes seems promising. However, lack of fundamental information about their functionality and specific characteristics may hinder their industrial use. Predictive microbiology may help to solve this problem by simulating the kinetics of bacteriocin-producing strains and optimising the cell growth and production of beneficial metabolites. In this study, a combined model was developed which could estimate, from a given initial condition of temperature and pH, the growth and bacteriocin production of Lactobacillus plantarum BC-25 in MRS broth. A logistic model was used to model the growth of cells, and the Luedeking-Piret model was used to simulate the biomass and bacteriocin production. The parameters generated from these primary models were used in a response surface model to describe the combined influence on cell growth, biomass and bacteriocin production. Both the temperature and pH influenced cell and bacteriocin production significantly. The optimal temperature and pH for cell growth is 35 °C and 6.8, and the optimal bacteriocin production condition is a range dependent on two growth-associated constants (YA/X and K), where temperature is from 27 to 34 °C, and pH is 6.35 to 6.65. The developed model is consistent with similar studies and could be a useful tool to control and increase the production of lactic acid bacteria in bioreactors. Keywords Modelling . Lactobacillus plantarum . Plantaricin . Fermentation . Temperature . pH

* Kang Zhou [email protected] 1

College of Food Science, Sichuan Agricultural University, No. 46 Xin Kang Lu, Yu Cheng District, Yaan 625014, China

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Introduction Lactic acid bacteria (LAB) are one of the most important microbial groups for industrial purposes, since their fermentative activity involves a notable preserving ability due to acid production; competition for nutrients and formation of antimicrobial metabolites such as lactic acid, ethanol and bacteriocin [1]. Bacteriocins are defined as proteins or protein complexes antagonistic to bacteria genetically closely related to the producer organism [1]. Currently, many studies are being completed on the production and use of bacteriocins as ‘natural food bio-preservatives to control spoilage and pathogenic bacteria’. Traditionally, optimisation of bacteriocin production has been performed by physiological and metabolic control. The effects of temperature and pH are very important for bacteriocin production and have been studied in several lactic acid bacteria such as Lactobacillus curvatus [2], Lactobacillus amylovorus [3], and Lactobacillus casei [4]. However, strains that display bacteriocin activity under laboratory conditions do not necessarily perform well when applied in a food under fermentation conditions [5]. In food microbiology, predictive microbiology generally focuses on the potential outgrowth of spoilage bacteria and food-borne pathogens in foods. It is only recently that some attention has been paid to the bio-kinetics of beneficial food-grade microorganisms, such as lactic acid bacteria. Predictive microbiology may help to clarify how specific conditions that prevail in the food environment during fermentation influence the performance of bacteriocin-producing starter cultures [6]. The logistic and Luedeking-Piret models were widely used in this novel area to study the production of lactic acid bacteria [7]. In this paper, a model-based examination of the impact of temperature and pH on cell growth and bacteriocin production by Lactobacillus plantarum BC-25 was developed. The influence of temperature and pH on cell growth rate and growth-associated bacteriocin production rate is studied by a response surface model.

Materials and Methods Microorganisms and Media Lactobacillus plantarum BC-25, isolated from the traditional Chinese fermented pickle, produces the bacteriocin plantaricin BC-25 and was used for growth and bacteriocin production throughout this study. Listeria monocytogenes was used as an indicator for the estimation of bacteriocin activity. The strains were propagated twice at 30 °C for 24 h before experimental use. Cultures were grown in de Man, Rogosa and Sharpe (MRS) broth at different temperatures and pH. Solid medium was prepared by adding 1.5 % agar to the broth. The overlays needed for the estimation of the bacteriocin titre were prepared with 0.7 % agar. Saline (8.5 g/l NaCl) was used for all serial dilutions for plate counts.

Fermentation Experiments and Assay All fermentations were performed in a 5.0-l laboratory fermenter containing 4.5 l of MRS broth. The vessel was sterilised at 121 °C for 20 min. For the preparation of the inoculum, 10ml MRS broth was inoculated with 0.05 ml of freshly prepared L. plantarum BC-25 culture and incubated for 24 h at 30 °C. After incubation, the final cell concentration was

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approximately 109 colony-forming units (CFU)/ml, which was determined by plate count. This preculture was diluted 1 in 1000 in MRS broth to reach a concentration of 106 CFU/ml, and 4.5 ml of this culture was used to inoculate the fermentor (1 %, v/v). Temperature and pH control was performed online. The pH was controlled to within pH 0.05 of the set point by automatic addition of 10 mol/l NaOH. Temperature stayed within 0.1 °C of the set point. Moderate agitation (50 rpm) was performed to ensure homogeneity of the broth. The experiments were designed by a central composite design (CCD), which was applied using Design-Expert Version 7.1.4 (StatEase, Inc., Minneapolis, MN, USA). The variables were temperature and pH (37 °C, pH 6.5; 35 °C, pH 7.3, 5.7; 30 °C, pH 7.6, 6.5, 5.4; 25 °C, pH 7.3, 5.7; 23 °C, pH 6.5). At appropriate time intervals during incubation, samples were withdrawn aseptically from the fermenter in order to determine cell growth, cell dry mass (CDM) and bacteriocin activity [8]. Cell growth was determined by decimal dilutions and plate counts. CDM was determined by membrane filtration (0.45-μm pore size membranes) of 100 ml of fermentation liquor and then washing the filter with demineralised water and drying it overnight at 105 °C. Plantaricin BC-25 activity was determined by a serial twofold dilution method. Each experiment was carried out in triplicate, and an average value at each sampling point was used to determine the growth, CDM and activity.

Modelling A primary model for growth is defined as a sigmoidal function that describes a bacterial growth curve exclusively as a function of time at constant environmental conditions, such as temperature, pH, water activity, etc. A logistic model (Eq. 1) was applied to describe the bacterial growth rate in this study [9]: xðt Þ ¼ C þ

A 1 þ e−Bðt−DÞ

ð1Þ

where x(t) is log10 (CFU/ml) of cell concentration at time, t, C is the lower asymptote in units of log10 (CFU/ml), A is equal to log10 (xmax/x0), x0 is the initial population density, xmax is the maximum population density, B is the maximum relative growth rate (μmax), and D is the time in hours at which the absolute growth rate is maximum. CDM and bacteriocin production by L. plantarum BC-25 were modelled with the Luedeking-Piret model, as follows [10]: CDM ¼

Xm   4⋅vm 1 þ exp 2 þ ⋅ðL−tÞ Xm

 4⋅v ⋅t  2 3 − m Y A=X ⋅X m K⋅X m 2 4 X 0⋅ e X −1 þ X m 5   −Y A=X ⋅X 0 þ ⋅ln AU ¼ Xm 4⋅vm ⋅t 4⋅vm Xm 1þ −1 ⋅e− X X0

ð2Þ

ð3Þ

where CDM is the biomass (g/l), Xm is the maximum biomass concentration (g/l), vm is the CDM maximum growth rate (/h), L is the lag time (h), t is the time (h), AU is the bacteriocin

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activity (AU/ml), X0 is the initial biomass (g/l), YA/X is the growth-associated constant for bacteriocin production (AU/g), and K is the non-growth-associated constant for bacteriocin production [(AU/g)/h]. This function permits one to classify the metabolites as ‘primaries’, if the rate of formation depends only on the growth rate of biomass (YA/X ≠0; K=0), ‘secondaries’, if this relies on the biomass present (YA/X =0; K≠0), and ‘mixed’, if this relies on both the rate and biomass present (YA/X ≠0; K≠0) [11]. A response surface model was used to describe the combined influence of temperature and pH on cell growth rate (μmax), growth-associated constant for bacteriocin production (YA/X) and non-growth-associated constant for bacteriocin production (K), as follows [12]:   Ln μmax Þ; Y A=X and K ¼ C 0 þ C 1 ⋅T þ C 2 ⋅T 2 þ C 3 ⋅pH þ C 4 ⋅pH2 þ C 5 ⋅T ⋅pH ð4Þ where T is degrees Celsius (°C) and Cn are constants.

Validation and Evaluation Parameter correlation coefficients (r2) and mean-square-error (MSE) were calculated to estimate the goodness of fit. Besides, the bias and accuracy factors introduced by Ross [13] were also used to check the fitness. The significance of the effect of environmental factors was analysed by one-way ANOVA. The coefficients of these models and the statistical analysis were performed using Microsoft Excel (Version 2003, Microsoft, Inc., USA) and Origin (Version 8, OriginLab Corporation, USA)

Results and Discussion Fermentation Kinetics and Modelling Predictive microbiology helps us not only to understand the food spoilage bacteria or pathogens and associated food poisoning but also to estimate the beneficial effects, such as bacteriocin production, exopolysaccharide formation and aroma development [14]. Modelling of probiotics can help to improve safety and quality and can also be used to facilitate the development and improvement of new fermentation processes, which are important for both food consumers and processors [15]. In our study, the influence of temperature and pH on cell growth, biomass and bacteriocin production of L. plantarum BC-25 was investigated (Figs. 1, 2, and 3). The kinetics of cell growth, biomass production and bacteriocin production were modelled by logistic and Luedeking-Piret models. Figure 1 describes the cell growth rate influenced by both the temperature and pH. The maximum cell growth rate was found at 35 °C and pH 5.7; the values of the rates are shown in Table 1. Figure 1 shows that the maximum cell concentration could be obtained after more than 45 h when the temperature is 23 °C but less than 30 h when the temperature is 30 °C. The growth rate decreased slightly when the temperature exceeded 35 °C, and the maximum concentration was obtained after more than 30 h. The influence of various pH values at a controlled fermentation temperature of 30 °C suggests that in a slightly acidic environment, cells would grow faster.

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11 10 9

Log (CFU/ml)

8 7 6 5 4 3 2

0

10

20

30

40

50

Time (h) Fig. 1 Observed and predicted growth of Lactobacillus plantarum BC-25 in different conditions. Curves are predictions by the logistic model and the scatter dots are observed data: 23 °C, pH 6.5 (black diamond); 25 °C, pH 5.7 (black triangle); 25 °C, pH 7.3 (white triangle); 30 °C, pH 5.3 (white square); 30 °C, pH 6.5 (multiplication sign); 30 °C, pH 7.7 (black square); 35 °C, pH 5.7 (white circle); 35 °C, pH 7.3 (black circle); 37 °C, pH 6.5 (white diamond)

Biomass (g/l)

4.5

3

1.5

0 0

10

20

30

40

50

Time (h) Fig. 2 Observed and predicted biomass production of Lactobacillus plantarum BC-25 in different conditions. Curves are predictions by the logistic model and the scatter dots are observed data: 23 °C, pH 6.5 (black diamond); 25 °C, pH 5.7 (black triangle); 25 °C, pH 7.3 (white triangle); 30 °C, pH 5.3 (white square); 30 °C, pH 6.5 (multiplication sign); 30 °C, pH 7.7 (black square); 35 °C, pH 5.7 (white circle); 35 °C, pH 7.3 (black circle); 37 °C, pH 6.5 (white diamond)

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1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0

10

20

30

40

50

Fig. 3 Observed and predicted bacteriocin production of Lactobacillus plantarum BC-25 in different conditions. Curves are predictions by the logistic model and the scatter dots are observed data: 23 °C, pH 6.5 (black diamond); 25 °C, pH 5.7 (black triangle);; 25 °C, pH 7.3 (white triangle); 30 °C, pH 5.3 (white square); 30 °C, pH 6.5 (multiplication sign); 30 °C, pH 7.7 (black square); 35 °C, pH 5.7 (white circle); 35 °C, pH 7.3 (black circle); 37 °C, pH 6.5 (white diamond)

The biomass production rate influenced by both the temperature and pH has almost the same trend as cell growth rate. The maximum biomass was found after 48 h at 23 °C and after only 25 h when the temperature was higher than 30 °C (Fig. 2). The optimum condition for biomass production is also a weak acid condition, and the biomass production rate is slow when the environment is alkali. The observed optimum temperature and pH for biomass

Table 1 Estimated values and evaluation of μmax, vm, Xm, YA/X, and K of L. plantarum BC-25 grown in MRS medium at the combined conditions Temperature (°C) pH Cell growth μmax (/h) r2

Biomass

Bacteriocin

vm (/h) Xm (g/l) r2

YA/X [(AU/g)/h] K [(AU/g)/h] r2

23

6.5 0.101

0.995 0.144

5.709

0.974 0.836

−2.014

0.954

25

5.7 0.157

0.991 0.130

3.301

0.958 0.731

−1.412

0.909

25

7.3 0.138

0.994 0.129

3.729

0.976 0.713

−1.558

0.955

30

5.3 0.179

0.991 0.193

4.319

0.924 0.461

−0.244

0.938

30

6.5 0.213

0.999 0.201

3.771

0.970 0.500

−0.195

0.989

30

7.7 0.210

0.995 0.198

3.780

0.947 0.508

−0.217

0.951

35

5.7 0.282

0.999 0.234

3.316

0.974 0.491

−0.072

0.951

35 37

7.3 0.261 6.5 0.217

0.999 0.270 0.999 0.182

4.130 3.350

0.966 0.535 0.880 0.228

−0.161 −0.040

0.980 0.918

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production rate are 35 °C and 7.3; the predicted maximum concentration is 5.709 g/l when the temperature and pH are 23 °C and 6.5 (Table 1). Bacteriocin activity increased rapidly while cells were growing exponentially. This finding suggests that the production rate of bacteriocin is related to the production rate of cell and biomass. The growth-associated constant for bacteriocin production is 0.836 AU/g when the temperature and pH are 23 °C and 6.5; however, the non-growth-associated constant for bacteriocin production is also high in this condition, −2.014 (Table 1). This indicates that these parameters are associated. The yields of bacteriocin are relatively high when the temperature is between 25 and 35 °C (Fig. 3). At the same temperature, the yields of bacteriocin are not significantly different (P=0.973) when the pH varies from 5.3 to 7.7, analysed by one-way ANOVA, whereas temperature affects the yields of bacteriocin significantly (P= 0.003) at the same pH. Furthermore, the maximum bacteriocin would be followed by a decrease in activity when the adsorption/degradation term becomes larger than the bacteriocin production term. So, the yield per unit biomass is not constant and increases with time, since a constant or decreasing YA/X would result in a downward curvature in Fig. 3 [11].

Combined Secondary Model Combined response surface models for the estimation of μmax, YA/X, and K were developed. The models were valid for a range of 20–40 °C and 5.0–8.0 for temperature and pH, respectively. All the parameters in the secondary model for the three constants, μmax, YA/X and K present in Table 2, the standard error and t value of each parameters were also shown in the table. The response surface model shows the influence of temperature and pH on μmax, YA/X, and K. Figure 4a shows the influence of combined conditions of temperature and pH on the maximum cell growth rate for the bacteriocin-producing L. plantarum BC-25 strain. The predicted maximum growth rate is 0.250(CFU/ml)/h at 35 °C and pH 6.8. The maximum cell growth rate varied slightly between 30 and 40 °C but decreased significantly when the temperature was lower than 25 °C. pH affects the cell growth rate slightly; at different temperatures, the optimum pH values are always between 6.2 and 7.0. The parameters of bacteriocin, YA/X and K, were investigated in Fig. 4b, c. When the temperature and pH are 27 °C and 6.65, the rate of bacteriocin production reaches a maximum, which is 0.851(AU/g)/h. The model also indicates that 34 °C and pH 6.35 could lead to

Table 2 Estimated values of parameters in the secondary model and statistical analysis ln(μmax)

YA/X

K

Value

SE

t value

Value

SE

t value

Value

SE

t value

C0

−9.516

9.381

−0.965

−14.989

7.288

C1

0.409

0.276

1.428

0.352

0.214

0.594

−23.002

8.508

−2.696

−0.722

1.221

0.250

C2

−0.006

1.895

0.119

−0.005

1.472

−0.222

4.892

−0.018

1.719

0.428

C3

0.299

0.004

−1.481

3.359

0.003

0.521

0.764

0.004

−5.080

C4

−0.028

0.138

−0.175

−0.223

0.107

0.161

−0.070

0.125

−0.538

C5

0.003

0.021

0.151

−0.014

0.016

0.243

0.004

0.019

0.192

SE standard error

Appl Biochem Biotechnol Fig. 4 Surface plots of ln(μmax), YA/X, and K of Lactobacillus plantarum BC-25 as a function of temperature and pH. The symbols represent the observed data

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optimal K, which is 0.052(AU/g)/h. The bacteriocin-producing parameter YA/X is remarkably influenced by both the temperature and pH, whereas the bacteriocin non-production constant K is highly affected by temperature but not sensitive to the change of pH. In this study, the optimum temperature and pH values for bacteriocin production and cell growth are not the same. Some authors have claimed that good cell growth frequently leads to good bacteriocin production [16, 17]. However, optimal cell growth does not always result in high bacteriocin yield. Many studies have shown that the highest bacteriocin yield is obtained at a lower temperature and pH value than the optimum conditions for cell growth [18–20], and this was in accordance with our results. Information and data on the prediction of probiotics in food ecosystems are scarce. In this study, logistic modelling was used to model the cell growth of L. plantarum BC-25 under selected temperature and pH. The combined model reveals that the growth rate of cells was significantly influenced by temperature, which is in agreement with previous studies on Lactobacillus spp. [14, 21]. Bacteriocin production by lactic acid bacteria is a growthassociated process [7]. The Luedeking-Piret model was successfully used to investigate the biomass and bacteriocin production, and the bacteriocin production and bacteriocin inactivation constant suggested that the optimal temperature range for bacteriocin production is from 27 to 34 °C and pH range from 6.65 to 6.35.

Validation and Evaluation Table 1 gives the values of the bio-kinetic parameters derived from the primary model. The correlation of the experimental values and the model was assessed by correlation coefficients, which were all higher than 0.9. Table 3 evaluates the secondary model for μmax, YA/X, and K, by correlation coefficient, MSE, bias and accuracy factor. The correlation coefficient and MSE are an internal evaluation. All the correlation coefficients are higher than 0.75, the MSEs are lower than 0.02, and the bias and accuracy factors were close to 1, which indicates that the model is satisfactory to describe the data. The correlation coefficient of the primary model developed indicated that the model could effectively simulate the growth of cells and the production of bacteriocin. Using correlation coefficient, MSE bias and accuracy factors for the internal evaluation of the response surface model, the result showed a goodness-of-fit characteristic of the developed secondary model. Predictive modelling of beneficial microbial behaviour represents a novel but also very promising and rich area of research [22, 23]. The model developed in this study for growth of cells and production of biomass and bacteriocin proved to be a powerful tool to optimise and simulate the growth conditions for L. plantarum BC-25. Predictive microbiology may yield precious information about the relationship between the cell and its metabolites, hence contributing to optimisation of the strain selection and food Table 3 Evaluation of combined temperature and pH by response surface model X

r2

MSE

Bias factor

Accuracy factor

P value

μmax

0.902

0.009

0.9981

1.0494

0.0001

YA/X

0.755

0.019

0.9757

1.2203

0.0091

K

0.986

0.008

1.1970

1.1843

0.0030

Appl Biochem Biotechnol

processing design. This can result in better process control, reduction of economic spending, food safety and quality enhancement.

Conclusion In this study, a kinetic model together with experimental data of L. plantarum BC-25 from the batch fermentation was presented. The dynamic growth and metabolism of this bacterium were studied by logistic and Luedeking-Piret models, and a response surface model was applied to analyse the correlation of the growth conditions and parameters. The optimal temperature and pH for cell growth are 35 °C and 6.8, and the preferred condition for bacteriocin production is a range dependent on two growth-associated constants, where temperature is from 27 to 34 °C, and pH is 6.35 to 6.65. The developed primary and secondary models are consistent with published studies and could be a promising tool to monitor and increase the production of L. plantarum in bioreactors. Furthermore, the potential advantage of this study is that this developed model could also be applied into other lactic acid bacteria or culture medium to predict growth and production, resulting in a better understanding of the metabolism of the bacteria. Acknowledgments This research was supported by the Scientific Research Program of Sichuan Education Department and the Scientific Program from Education Office of Sichuan province (13ZA0265). We are also very thankful to S. George for her helpful English correction of this manuscript.

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Modelling Growth and Bacteriocin Production by Lactobacillus plantarum BC-25 in Response to Temperature and pH in Batch Fermentation.

The use of bacteriocin-producing probiotics to improve food fermentation processes seems promising. However, lack of fundamental information about the...
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