Performance of response surface model for prediction of Leuconostoc mesenteroides growth parameters under different experimental conditions

Chilled Pacu (Piaractus mesopotamicus) fillets: Modeling Pseudomonas spp. and psychrotrophic bacteria growth and monitoring spoilage indicators by 1H NMR and GC-MS during storage

This study aimed to assess the growth of Pseudomonas spp. and psychrotrophic bacteria in chilled Pacu (Piaractus mesopotamicus), a native South American fish, stored under chilling conditions (0 to 10 °C) through the use of predictive models under isothermal and non-isothermal conditions. Growth kinetic parameters, maximum growth rate (μmax, 1/h), lag time (tLag, h), and (Nmax, Log10 CFU/g) were estimated using the Baranyi and Roberts microbial growth model. Both kinetic parameters, growth rate and lag time, were significantly influenced by temperature (P < 0.05). The square root secondary model was used to describe the bacteria growth as a function of temperature. Secondary models, √μ = 0.016 (T + 10.13) and √μ =0.017 (T + 9.91) presented a linear correlation with R2 values >0.97 and were further validated under non-isothermal conditions. The model's performance was considered acceptable to predict the growth of Pseudomonas spp. and psychrotrophic bacteria in refrigerated Pacu fillets with bias and accuracy factors between 1.24 and 1.49 (fail-safe) and 1.45-1.49, respectively. Fish biomarkers and spoilage indicators were assessed during storage at 0, 4, and 10 °C. Volatile organic compounds, VOCs (1-hexanol, nonanal, octenol, and indicators 2-ethyl-1-hexanol) showed different behavior with storage time (P > 0.05). 1H NMR analysis confirmed increased enzymatic and microbial activity in Pacu fillets stored at 10 °C compared to 0 °C. The developed and validated models obtained in this study can be used as a tool for decision-making on the shelf-life and quality of refrigerated Pacu fillets stored under dynamic conditions from 0 to 10 °C.

Selection of lactic acid bacteria for biopreservation of salmon products applying processing-dependent growth kinetic parameters and antimicrobial mechanisms

Biopreservation using lactic acid bacteria (LAB) is a promising technology to prevent the growth of pathogenic microorganisms in fresh and mildly processed food. The main aim of this study was to select LAB, originally isolated from ready-to-eat (RTE) seafood, for biopreservation of fresh salmon and processed salmon products. Ten LAB strains (five Carnobacterium and five Leuconostoc) were selected based on previously demonstrated bioprotective properties to investigate their antimicrobial mechanisms and temperature-dependent growth kinetics in a sterile salmon juice model system. Furthermore, five strains (three Carnobacterium and two Leuconostoc) were selected to test process-dependent growth kinetic parameters relevant to the secondary processing of salmon. Two strains (Carnobacterium maltaromaticum 35 and C. divergens 468) showed bacteriocin-like activity against Listeria innocua, while inhibitory effect of cell-free supernatants (CFS) was not observed against Escherichia coli. All selected strains were able to grow in sterile salmon juice at tested temperatures (4, 8, 12 and 16 °C), with specific growth rates (μ) ranging from 0.01 to 0.04/h at 4 °C and reaching a maximum population density of 8.4-9 log CFU/ml. All five strains tested for process-dependent growth kinetic parameters were able to grow in the range of 0.5-5% NaCl and 0.13-0.26% purified condensed smoke (VTABB and JJT01), with inter- and intraspecies variation in growth kinetics. According to the temperature-dependent growth kinetics and antimicrobial assay results, two strains, Leuconostoc mesenteroides 68 (Le.m.68) and C. divergens 468 (C d.468), were selected for in situ test to validate their ability to grow in vacuum-packed fresh salmon at 4 °C. Both strains were able to grow at maximum growth rates of 0.29 ± 0.04/d for Le. m.68 and 0.39 ± 0.06/d for C.d.468, and their final concentrations were 7.91 ± 0.31 and 8.02 ± 0.25 log CFU/g, respectively. This study shows that LAB, originally isolated from RTE seafood, have promising potential as bioprotective strains in fresh and processed salmon products.

Effect of salt and temperature on the growth of Escherichia coli PSII

Presence of pathogenic strains of Escherichia coli in foodstuffs may pose a health risk for a consumer. Therefore, knowledge on the effect of environmental factors on the growth ability of E. coli is of great importance. In this work, the effect of incubation temperature (6–46 °C) and the combined effect of temperature and water activity (0.991–0.930) on the growth dynamic of E. coli PSII were analysed. Based on the growth curves obtained, growth parameters were calculated by using the Baranyi D-model. Growth parameters were further analysed in secondary phase of predictive modelling. Using the CM model that describes the effect of combined factors, cardinal values (Tmin = 4.8 ± 0.4 °C, Topt = 41.1 ± 0.8 °C, Tmax = 48.3 ± 0.9 °C, awmin = 0.932 ± 0.001, and awopt = 0.997 ± 0.003) for the isolate were calculated. Under optimal conditions, the specific growth rate is µopt = 2.84 ± 0.08 h−1. The results obtained may contribute to the assessment of the risk associated with the possible E. coli presence in raw materials and to the search for preventive measures with defined degree of accuracy and reliability.

Growth of Staphylococcus aureus 2064 described by predictive microbiology: From primary to secondary models

The growth of Staphylococcus aureus 2064 isolate in model nutrient broth was studied as affected by temperature and water activity using principles and models of predictive microbiology. Specific rates resulting from growth curves fitted by the Baranyi model were modelled by the secondary Ratkowsky model for suboptimal temperature range (RTKsub) as well as the Ratkowsky extended model (RTKext) and cardinal model (CM) in the whole temperature range. With the biological background of the RTKext model, cardinal values of temperature T min = 6.06 °C and T max = 47.9 °C and water activity aw min = 0.859 were calculated and validated with cardinal values estimated by CM (T min = 7.72 °C, T max = 46.73 °C, aw min = 0.808). CM also provided other cardinal values, T opt = 40.63 °C, aw opt = 0.994, as well as optimal specific growth rate of 1.97 h–1 (at T opt and aw opt). To evaluate the goodness of fit of all models, mathematical and graphical validation was performed and the statistical indices proved appropriateness of all the secondary models used.

A mathematical modeling approach to the quantification of lactic acid bacteria in vacuum-packaged samples of cooked meat: Combining the TaqMan-based quantitative PCR method with the plate-count method

The TaqMan-based quantitative Polymerase Chain Reaction (qPCR) method and the Plate Count (PC) method are both used in combination with primary and secondary mathematical modeling, to describe the growth curves of Leuconostoc mesenteroides and Weissella viridescens in vacuum-packaged meat products during storage under different isothermal conditions. Vacuum-Packaged Morcilla (VPM), a typical cooked blood sausage, is used as a representative meat product, with the aim of improving shelf-life prediction methods for those sorts of meat products. The standard curves constructed by qPCR showed good linearity between the cycle threshold (C_T) and log₁₀ CFU/g, demonstrating the high precision and the reproducible results of the qPCR method. The curves were used for the quantification of L. mesenteroides and W. viridescens in artificially inoculated VPM samples under isothermal storage (5, 8, 13 and 18 °C). Primally, both the qPCR and the PC methods were compared, and a linear regression analysis demonstrated a statistically significant linear correlation between the methods. Secondly, the Baranyi and Roberts model was fitted to the growth curve data to estimate the kinetic parameters of L. mesenteroides and W. viridescens under isothermal conditions, and secondary models were used to establish the dependence of the maximum specific growth rate on the temperature. The results proved that primary and secondary models were adequate for describing the growth curves of both methods in relation to both bacteria. In conclusion, the results of all the experiments proved that the qPCR method in combination with the PC method can be used to construct microbial growth kinetics and that primary and secondary mathematical modeling can be successfully applied to describe the growth of L. mesenteroides and W. viridescens in vacuum-packaged morcilla and, by extension, other cooked meat products with similar characteristics.

The Effect of Salt and Temperature on the Growth of Fresco Culture

The effect of environmental factors, including temperature and water activity, has a considerable impact on the growth dynamics of each microbial species, and it is complicated in the case of mixed cultures. Therefore, the aim of this study was to describe and analyze the growth dynamics of Fresco culture (consisting of 3 different bacterial species) using predictive microbiology tools. The growth parameters from primary fitting were modelled against temperature using two different secondary models. The intensity of Fresco culture growth in milk was significantly affected by incubation temperature described by Gibson’s model, from which the optimal temperature for growth of 38.6 °C in milk was calculated. This cardinal temperature was verified with the Topt = 38.3 °C calculated by the CTMI model (cardinal temperature model with inflection), providing other cardinal temperatures, i.e., minimal Tmin = 4.0 °C and maximal Tmax = 49.6 °C for Fresco culture growth. The specific growth rate of the culture under optimal temperature was 1.56 h−1. The addition of 1% w/v salt stimulated the culture growth dynamics under temperatures down to 33 °C but not the rate of milk acidification. The prediction data were validated and can be used in dairy practice during manufacture of fermented dairy products.

Modeling for Predicting the Time to Detection of Staphylococcal Enterotoxin A in Cooked Chicken Product

Staphylococcal enterotoxins (SEs) produced by Staphylococcus aureus (S. aureus) are the cause of Saphylococcal food poisoning (SFP) outbreaks. Thus, estimation of the time to detection (TTD) of SEs, that is, the time required to reach the SEs detection limit, is essential for food preservation and quantitative risk assessment. This study was conducted to explore an appropriate method to predict the TTD of SEs in cooked chicken product under variable environmental conditions. An S. aureus strain that produces staphylococcal enterotoxin A (SEA) was inoculated into cooked chicken meat. Initial inoculating concentrations (approximately 102, 103, 104 CFU/g) of S. aureus and incubation temperatures (15 ± 1, 22 ± 1, 29 ± 1, and 36 ± 1°C) were chosen as environmental variables. The counting of S. aureus colonies and the detection of SEA were performed every 3 or 6 h during the incubation. The TTD of SEA was considered a response of S. aureus to environmental variables. Linear polynomial regression was used to model the effects of environmental variables on the TTD of SEA. Result showed that the correlation coefficient (R2) of the regressed equation is higher than 0.98, which means the obtained equation was reliable. Moreover, the minimum concentration of S. aureus for producing a detectable amount of SEA under various environmental conditions was approximately 6.32 log CFU/g, which was considered the threshold for S. aureus to produce SEA. Hence, the TTD of SEA could be obtained by calculating the time required to reach the threshold by using an established S. aureus growth predictive model. Both established methods were validated through internal and external validation. The results of graphical comparison, RMSE, SEP, Af , and Bf showed that the accuracy of both methods were acceptable, and linear polynomial regression method showed more accurately.

Modeling the Combined Effects of Temperature, pH, and Sodium Chloride and Sodium Lactate Concentrations on the Growth Rate of Lactobacillus plantarum ATCC 8014

Nowadays, microorganisms with probiotic or antimicrobial properties are receiving major attention as alternative resources for food preservation. Lactic acid bacteria are able to synthetize compounds with antimicrobial activity against pathogenic and spoilage flora. Among them, Lactobacillus plantarum ATCC 8014 has exhibited this capacity, and further studies reveal that the microorganism is able to produce bacteriocins. An assessment of the growth of L. plantarum ATCC 8014 at different conditions becomes crucial to predict its development in foods. A response surface model of the growth rate of L. plantarum was built in this study as a function of temperature (4, 7, 10, 13, and 16°C), pH (5.5, 6.0, 6.5, 7.0, and 7.5), and sodium chloride (0, 1.5, 3.0, 4.5, and 6.0%) and sodium lactate (0, 1, 2, 3, and 4%) concentrations. All the factors were statistically significant at a confidence level of 90%  (p<0.10). When temperature and pH increased, there was a corresponding increase in the growth rate, while a negative relationship was observed between NaCl and Na-lactate concentrations and the growth parameter. A mathematical validation was carried out with additional conditions, demonstrating an excellent performance of the model. The developed model could be useful for designing foods with L. plantarum ATCC 8014 added as a probiotic.

Acidic Electrolyzed Water as a Novel Transmitting Medium for High Hydrostatic Pressure Reduction of Bacterial Loads on Shelled Fresh Shrimp

Acidic electrolyzed water (AEW), a novel non-thermal sterilization technology, is widely used in the food industry. In this study, we firstly investigated the effect of AEW as a new pressure transmitting medium for high hydrostatic pressure (AEW-HHP) processing on microorganisms inactivation on shelled fresh shrimp. The optimal conditions of AEW-HHP for Vibrio parahaemolyticus inactivation on sterile shelled fresh shrimp were obtained using response surface methodology: NaCl concentration to electrolysis 1.5 g/L, treatment pressure 400 MPa, treatment time 10 min. Under the optimal conditions mentioned above, AEW dramatically enhanced the efficiency of HHP for inactivating V. parahaemolyticus and Listeria monocytogenes on artificially contaminated shelled fresh shrimp, and the log reductions were up to 6.08 and 5.71 log10 CFU/g respectively, while the common HHP could only inactivate the two pathogens up to 4.74 and 4.31 log10 CFU/g respectively. Meanwhile, scanning electron microscopy (SEM) showed the same phenomenon. For the naturally contaminated shelled fresh shrimp, AEW-HHP could also significantly reduce the micro flora when examined using plate count and PCR-DGGE. There were also no significant changes, histologically, in the muscle tissues of shrimps undergoing the AEW-HHP treatment. In summary, using AEW as a new transmitting medium for HHP processing is an innovative non thermal technology for improving the food safety of shrimp and other aquatic products.

Modeling and predicting the effect of temperature on the growth of Proteus mirabilis in chicken

A predictive model to study the effect of temperature on the growth of Proteus mirabilis was developed. The growth data were collected under several isothermal conditions (8, 12, 16, 20, 25, 30, 35, 40, and 45°C) and were fitted into three primary models, namely the logistic model, the modified Gompertz model, and the Baranyi model. The statistical characteristics to evaluate the models such as R(2), mean square error, and Sawa's Bayesian information criteria (BIC) were used. Results showed that the Baranyi model performed best, followed by the logistic model and the modified Gompertz model. R(2) values for the secondary model derived from logistic, modified Gompertz, and Baranyi models were 0.965, 0.974, and 0.971, respectively. Bias factor and accuracy factor indicated that both the modified Gompertz and Baranyi models fitted the growth data better. Therefore, the Baranyi model was proposed to be the best predictive model for the growth of P. mirabilis.

Characterization of mutual relations between Geotrichum candidum and Lactobacillus rhamnosus GG in milk: a quantitative approach

The growth interactions between Geotrichum candidum and Lactobacillus rhamnosus GG were studied in milk. The effect of temperature on the growth rate of the fungus was modelled using the cardinal temperature model with inflection. The secondary modelling was applied also on the other data set containing the growth rates of G. candidum in co-culture with a commercial starter culture. The low temperature in combination with L. rhamnosus GG in co-culture showed the most negative effect on the growth rate of G. candidum. On the other hand, neither L. rhamnosus GG nor the starter culture had significant effect on the optimum and maximum temperature parameters calculated for growth of G. candidum. Their values ranged from 28.9 °C to 31.3°C  and 35.3°C  to 37.3°C, respectively. The quantitative data presented in the study showed a non-specific effect of lactic acid bacteria on the growth rate of G. candidum observed mainly around the optimal temperature.

Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems

Background

An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

Methods

The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (B_f), accuracy factor (A_f), and absolute fraction of variance (R²). Graphical plots were also used for model comparison.

Conclusions

The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

Response surface modelling of Cr6+ adsorption from aqueous solution by neem bark powder: Box-Behnken experimental approach

The main aim of this study is to investigate the combined effect of different operating parameters like adsorbent dose, initial Cr(6+) concentration and pH on the removal of Cr(6+) from aqueous solution using neem bark powder (NBP). A series of batch experiments were performed to find out the adsorption isotherms and kinetic behaviour of Cr(6+) in the aqueous solution. The adsorption process was examined with three independent variables viz. NBP dosage, initial Cr(6+) concentrations and pH. Seventeen batch experiments designed by Box-Behnken using response surface methodology were carried out, and the adsorption efficiency was modelled using polynomial equation as the function of the independent variables. Based on the uptake capacity and economic use of adsorbent, the independent variables were optimized by two procedures. The desirability of first and second optimization procedures were found to be 1.00 and 0.84, respectively, which shows that the estimated function may well represent the experimental model. The kinetic study indicated that the rate of adsorption confirms to the pseudo-second-order rate equation. Thermodynamics study indicated that the adsorption process was spontaneous and endothermic in nature. The surface texture changes in NBP were obtained from FT-IR analysis. The optimized result obtained from RAMP plots revealed that the NBP was supposed to be an effective and economically feasible adsorbent for the removal of Cr(6+) from an aqueous system.

Optimization of auto-induction medium for G-CSF production by Escherichia coli using artificial neural networks coupled with genetic algorithm

Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.

The use of a radial basis neural network and genetic algorithm for improving the efficiency of laccase-mediated dye decolourization

A radial basis function neural network (RBF) and genetic algorithm (GA) were applied to improve the efficiency of the oxidative decolourization of the recalcitrant dye Reactive Black 5 (RB 5) by a technical laccase (Trametes spp.) and the natural mediator acetosyringone (ACS). The decolourization of RB 5 in aqueous solution was studied with a 3(4) factorial design including different levels of laccase (2, 100, 200 U L(-1)), acetosyringone (5, 50, 100 μM), pH value (3, 4.5, 6) and incubation time (10, 20, 30 min). The generated RBF network was mathematically evaluated by several statistical indices and revealed better results than a classical quadratic response surface (RS) model. The experimental data showed that within 10 min of incubation time a complete decolourization (>90%) was achieved by using the highest amount of laccase (200 U L(-1)) and acetosyringone (100 μM) at pH 6. By applying the RBF-GA methodology, the efficiency of the laccase-mediated decolourization was improved by minimising the required amount of laccase and acetosyringone by 25% and 21.7% respectively. Complete decolourization (>90%) was obtained within 10 min at the GA-optimised process conditions of laccase (150 U L(-1)) and acetosyringone (78.3 μM) at pH 5.67. These results illustrate that the RBF-GA methodology could be a powerful technique during scale-up studies.

Determination of optimal growth parameters for the bioincising fungus Physisporinus vitreus by means of response surface methodology

AIM

To evaluate the influence of water activity (a(w)), temperature and pH on the radial growth and lag phase of Physisporinus vitreus (E-642), a basidiomycete was used in the biotechnological process of bioincising.

METHODS AND RESULTS

Radial growth was monitored for 20 days on malt extract agar medium. Five levels of a(w) (0.998, 0.982, 0.955, 0.928, 0.892) were combined with three incubation temperatures (10, 15, 20 degrees C) and three pH values (4, 5, 6). Data analyses showed a highly significant effect of a(w) and temperature (P < 0.0001) and a significant effect of pH (P < 0.05). The radial growth rate and lag phase of P. vitreus were very sensitive to a(w) reduction. Although P. vitreus was able to grow at all the selected temperatures and pH values, the lag phase increased with decreasing a(w) and growth became inhibited at a(w) = 0.955. Optimal conditions for growth of P. vitreus were a(w) = 0.998, 20 degrees C and pH 5. The response surface model provided reliable estimates of these growth parameters and confirmed a greater dependence on a(w) than on temperature or pH under in vitro conditions.

CONCLUSIONS

Low levels of a(w) can prevent growth of P. vitreus, so wood moisture content should be adjusted accordingly.

SIGNIFICANCE AND IMPACT OF THE STUDY

Implementation of these results should contribute towards the optimization and efficiency of bioincising.

Response surface model for prediction of growth parameters from spores of Clostridium sporogenes under different experimental conditions

Clostridium sporogenes is considered to be a non-toxingenic equivalent of proteolytic Clostridium botulinum, and it also causes food spoilage. The effects of temperature (16.6-33.4 degrees C), pH value (5.2-6.8) and concentration of sodium chloride (0.6-7.4%) on the growth parameters of C. sporogenes spores were investigated. The growth curves generated within different conditions were fitted using Baranyi function. Two growth parameters (growth rate, GR; lag-time, LT) of the growth curves under combined effects of temperature, pH and sodium chloride were modeled using a quadratic polynomial equation of response surface (RS) model. Mathematical evaluation demonstrated that the standard error of prediction (%SEP) obtained by RS model was 1.033% for GR and was 0.166% for LT for model establishing. The %SEP for model validation were 43.717% and 5.895% for GR and LT, respectively. The root-mean-squares error (RMSE) was in acceptable range which was less than 0.1 for GR and was less than 8.0 for LT. Both the bias factor (B(f)) and accuracy factor (A(f)) approached 1.0, which were within acceptable range. Therefore, RS model provides a useful and accurate method for predicting the growth parameters of C. sporogenes spores, and could be applied to ensure food safety with respect to proteolytic C. botulinum control.

Evolutionary product unit based neural networks for regression

This paper presents a new method for regression based on the evolution of a type of feed-forward neural networks whose basis function units are products of the inputs raised to real number power. These nodes are usually called product units. The main advantage of product units is their capacity for implementing higher order functions. Nevertheless, the training of product unit based networks poses several problems, since local learning algorithms are not suitable for these networks due to the existence of many local minima on the error surface. Moreover, it is unclear how to establish the structure of the network since, hitherto, all learning methods described in the literature deal only with parameter adjustment. In this paper, we propose a model of evolution of product unit based networks to overcome these difficulties. The proposed model evolves both the weights and the structure of these networks by means of an evolutionary programming algorithm. The performance of the model is evaluated in five widely used benchmark functions and a hard real-world problem of microbial growth modeling. Our evolutionary model is compared to a multistart technique combined with a Levenberg-Marquardt algorithm and shows better overall performance in the benchmark functions as well as the real-world problem.

Modelling the growth of Leuconostoc mesenteroides by Artificial Neural Networks

The combined effect of temperature (10.5 to 24.5 degrees C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the predicted specific growth rate (Gr), lag-time (Lag) and maximum population density (yEnd) of Leuconostoc mesenteroides under aerobic and anaerobic conditions, was studied using an Artificial Neural Network-based model (ANN) in comparison with Response Surface Methodology (RS). For both aerobic and anaerobic conditions, two types of ANN model were elaborated, unidimensional for each of the growth parameters, and multidimensional in which the three parameters Gr, Lag, and yEnd are combined. Although in general no significant statistical differences were observed between both types of model, we opted for the unidimensional model, because it obtained the lowest mean value for the standard error of prediction for generalisation. The ANN models developed provided reliable estimates for the three kinetic parameters studied; the SEP values in aerobic conditions ranged from between 2.82% for Gr, 6.05% for Lag and 10% for yEnd, a higher degree accuracy than those of the RS model (Gr: 9.54%; Lag: 8.89%; yEnd: 10.27%). Similar results were observed for anaerobic conditions. During external validation, a higher degree of accuracy (Af) and bias (Bf) were observed for the ANN model compared with the RS model. ANN predictive growth models are a valuable tool, enabling swift determination of L. mesenteroides growth parameters.