Identification of critical batch operating parameters in fed-batch recombinant E. coli fermentations using decision tree analysis

To develop a useful fermentation process model, it is first necessary to identify which batch operating parameters are critical in determining the process outcome. To identify critical processing inputs in large databases, we have explored the use of Decision Tree Analysis with the decision metrics of Gain (i.e., Shannon Entropy changes), Gain Ratio, and a multiple hypergeometric distribution. The usefulness of this approach lies in its ability to treat "categorical" variables, which are typical of archived fermentation databases, as well as "continuous" variables. In this work, we demonstrate the use of Decision Tree Analysis for the problem of optimizing recombinant green fluorescent protein production in E. coli. A database of 85 fermentations was generated to examine the effect of 15 process input parameters on final biomass yield, maximum recombinant protein concentration, and productivity. The use of Decision Tree Analysis led to a considerable reduction in the fermentation database through the identification of the significant as well as insignificant inputs. However, different decision metrics selected different inputs and different numbers of inputs to classify the data for each output.     

Prediction of problematic wine fermentations using artificial neural networks

Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.     

Multiblock PLS analysis of an industrial pharmaceutical process

The performance of an industrial pharmaceutical process (production of an active pharmaceutical ingredient by fermentation, API) was modeled by multiblock partial least squares (MBPLS). The most important process stages are inoculum production and API production fermentation. Thirty batches (runs) were produced according to an experimental planning. Rather than merging all these data into a single block of independent variables (as in ordinary PLS), four data blocks were used separately (manipulated and quality variables for each process stage). With the multiblock approach it was possible to calculate weights and scores for each independent block. It was found that the inoculum quality variables were highly correlated with API production for nominal fermentations. For the nonnominal fermentations, the manipulations of the fermentation stage explained the amount of API obtained (especially the pH and biomass concentration). Based on the above process analysis it was possible to select a smaller set of variables with which a new model was built. The amount of variance predicted of the final API concentration (cross-validation) for this model was 82.4%. The advantage of the multiblock model over the standard PLS model is that the contributions of the two main process stages to the API volumetric productivity were determined.     

An integrated approach to optimization of Escherichia coli fermentations using historical data

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.     

State analysis of fermentation using a mass spectrometer with membrane probe

A new method of continuous process analysis in fermentation using a mass spectrometer (MS) membrane probe is described. A number of samples from industrial fermentations were analyzed for the occurrence of volatiles detectable with a silicone membrane probe connected to a quadrupole MS. In all fermentations, characteristic spectra were observed which were found to change systematically during the batch process. Factor analysis was used to treat the data. The factor scores were compared with the actual product concentrations (antibiotics, toxins, etc.), which were measured using other analytical methods and were found to correlate with them. On-line analysis was also carried out on a fermentation with an MS and an Apple II microcomputer. Direct monitoring of products, which are not directly measurable with the membrane MS probe requires a new calibration each time conditions such as medium composition are changed.     

Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data

We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.     

Temperature-Dependent Kinetic Model for Nitrogen-Limited Wine Fermentations

A physical and mathematical model for wine fermentation kinetics was adapted to include the influence of temperature, perhaps the most critical factor influencing fermentation kinetics. The model was based on flask-scale white wine fermentations at different temperatures (11 to 35°C) and different initial concentrations of sugar (265 to 300 g/liter) and nitrogen (70 to 350 mg N/liter). The results show that fermentation temperature and inadequate levels of nitrogen will cause stuck or sluggish fermentations. Model parameters representing cell growth rate, sugar utilization rate, and the inactivation rate of cells in the presence of ethanol are highly temperature dependent. All other variables (yield coefficient of cell mass to utilized nitrogen, yield coefficient of ethanol to utilized sugar, Monod constant for nitrogen-limited growth, and Michaelis-Menten-type constant for sugar transport) were determined to vary insignificantly with temperature. The resulting mathematical model accurately predicts the observed wine fermentation kinetics with respect to different temperatures and different initial conditions, including data from fermentations not used for model development. This is the first wine fermentation model that accurately predicts a transition from sluggish to normal to stuck fermentations as temperature increases from 11 to 35°C. Furthermore, this comprehensive model provides insight into combined effects of time, temperature, and ethanol concentration on yeast (Saccharomyces cerevisiae) activity and physiology.     

Temperature-dependent kinetic model for nitrogen-limited wine fermentations

A physical and mathematical model for wine fermentation kinetics was adapted to include the influence of temperature, perhaps the most critical factor influencing fermentation kinetics. The model was based on flask-scale white wine fermentations at different temperatures (11 to 35 degrees C) and different initial concentrations of sugar (265 to 300 g/liter) and nitrogen (70 to 350 mg N/liter). The results show that fermentation temperature and inadequate levels of nitrogen will cause stuck or sluggish fermentations. Model parameters representing cell growth rate, sugar utilization rate, and the inactivation rate of cells in the presence of ethanol are highly temperature dependent. All other variables (yield coefficient of cell mass to utilized nitrogen, yield coefficient of ethanol to utilized sugar, Monod constant for nitrogen-limited growth, and Michaelis-Menten-type constant for sugar transport) were determined to vary insignificantly with temperature. The resulting mathematical model accurately predicts the observed wine fermentation kinetics with respect to different temperatures and different initial conditions, including data from fermentations not used for model development. This is the first wine fermentation model that accurately predicts a transition from sluggish to normal to stuck fermentations as temperature increases from 11 to 35 degrees C. Furthermore, this comprehensive model provides insight into combined effects of time, temperature, and ethanol concentration on yeast (Saccharomyces cerevisiae) activity and physiology.     

Alcoholic fermentation under oenological conditions

The possibility of predicting sluggish fermentations under oenological conditions was investigated by studying 117 musts of different French grape varieties using an automatic device for fermentation monitoring. The objective was to detect sluggish or stuck fermentations at the halfway point of fermentation.Seventy nine percent of fermentations were correctly predicted by combining data analysis and neural networks.     

Application of pattern recognition to monitoring fermentations ofBacillus amyloliquefaciens

Summary Pattern recognition techniques were applied to analytical data to distinguish abnormal from normal microbial fermentations usingBacillus amyloliquefaciens as a model system. Patterns of fermentation end products during growth ofB. amyloliquefaciens were obtained from HPLC analysis of broth samples. Data were also obtained from fermentations using other bacterial species, strains, and environmental conditions, and were compared with the model data set. The bacterial species cultured includedB. subtilus, B. licheniformis, andEscherichia coli. Environmental variables included acration and temperature. The chromatographic patterns were compared by using hierarchical cluster and principal component analysis to obtain a quantitative measure of their similarity and to establish the normal variability within a model data set. Statistical analysis of the data indicated that individual fermentations can be assigned to distinct clusters on the basis of their divergence from the model system. Altered environments and other species can be identified as outliers from the model set. These results show that pattern recognition analysis has direct applicability to monitoring fermentation processes.     

Effects of preculture variability on clavulanic acid fermentation.

The production profile of clavulanic acid by Streptomyces clavuligerus was shown to be strongly dependent on inoculum activity. Two sets of fermentations (A and B) were investigated at industrial pilot-plant scale using complex media. Type A fermentations were inoculated using late exponential growth phase mycelia. Type B fermentations were inoculated using mycelia harvested at stationary phase. Productivities throughout type A fermentations were consistently higher than type B, reaching a maximum at about 70 h and then decaying to the same final productivities at 140 h of type B runs. Several scheduling alternatives, based on combinations of the two inocula types and different fermentation lengths, were compared in terms of the overall process economics (fermentation and downstream). An increase of ca. 22% on the overall process profit is predicted using late exponential growth phase inocula and a fermentation duration of only 96 h. A new operating strategy was thus proposed for inoculum production based on the control of preculture activity using off-gas analysis. This method ensures higher productivity and better batch-to-batch reproducibility of clavulanic acid fermentations than traditional methods based on constant age inocula.     

Simultaneous measurements of oxygen diffusion coefficients and solubilities in fermentation media with polarographic oxygen electrodes

A membrane-covered oxygen electrode was used to measure oxygen diffusion coefficients and solubilities in aqueous glucose solutions and various fermentation media following a newly developed methodology. The fermentation media studied were tryptic soy broth and those for fermentations of Penicillium chrysogenum, Saccharomyces cerevisiae, and Micrococcus glutamicus. The experimental results of oxygen diffusion coefficients and solubilities in glucose solutions were in good accord with the literature data. As for the fermentation media, both oxygen diffusion coefficients and solubilities were found to decrease with an increased fractional composition of these media, and log-additive behaviors of the oxygen diffusion coefficients and solubilities in fermentation media were observed.     

Control and prediction of the course of brewery fermentations by gravimetric analysis

A simple, fast and cheap test suitable for predicting the course of brewery fermentations based on mass analysis is described and its efficiency is evaluated. Compared to commonly used yeast vitality tests, this analysis takes into account wort composition and other factors that influence fermentation performance. It can be used to predict the shape of the fermentation curve in brewery fermentations and in research and development projects concerning yeast vitality, fermentation conditions and wort composition. It can also be a useful tool for homebrewers to control their fermentations.     

Clustering Analysis of a Colombian Toxicological Database

The database analysis allows the return of experience needed to support decision-making processes in risk management. A Colombian toxicological database (TED) developed and maintained by the Center of Safety Information and Chemical Products (CISPROQUIM by its Spanish abbreviation in) is analyzed here using a demographic clustering technique. Data-quality processes were performed on the raw data (more than 170 variables) and as a result the database was reduced to 20 meaningful variables. The variables characterized by values with categories were selected for clustering analysis: gender, age, type of emergency, emergency location, means of poisoning, product use, and physical state of the toxic substance. Clustering analysis showed that there are three profiles that are prevalent in the TED database: Young Adult Suicidal Woman, Unsupervised Child, and Man at Work. These profiles could not be identified using traditional statistical analyses performed on the data collected by CISPROQUIM or defined a priori from the categorical variables. The identification of vulnerable populations and the cause of the toxicological events are critical in order to develop national prevention programs and policies. The analysis described provided a methodology for a critical analysis of toxicological databases that can be applied to other databases such as security databases.     

Influence of temperature and backslopping time on the microbiota of a type I propagated laboratory wheat sourdough fermentation

Sourdough fermentation is a cereal fermentation that is characterized by the formation of stable yeast/lactic acid bacteria (LAB) associations. It is a unique process among food fermentations in that the LAB that mostly dominate these fermentations are heterofermentative. In the present study, four wheat sourdough fermentations were carried out under different conditions of temperature and backslopping time to determine their effect on the composition of the microbiota of the final sourdoughs. A substantial effect of temperature was observed. A fermentation with 10 backsloppings (once every 24 h) at 23°C resulted in a microbiota composed of Leuconostoc citreum as the dominant species, whereas fermentations at 30 and 37°C with backslopping every 24 h resulted in ecosystems dominated by Lactobacillus fermentum. Longer backslopping times (every 48 h at 30°C) resulted in a combination of Lactobacillus fermentum and Lactobacillus plantarum. Residual maltose remained present in all fermentations, except those with longer backslopping times, and ornithine was found in almost all fermentations, indicating enhanced sourdough-typical LAB activity. The sourdough-typical species Lactobacillus sanfranciscensis was not found. Finally, a nonflour origin for this species was hypothesized.     

Biochemical aspects of stuck and sluggish fermentation in grape must

Recently a number of studies have focused on the factors responsible for the occurrence of stuck and sluggish fermentations. Results from these studies indicate that together with nutritional deficiencies and inhibitory substances, technological practices could lead to such situations. This review explains, from a biochemical point of view, the influence of nutritional deficiencies, inhibitory substances and technological practices on yeast cell development and physiology and the fermentation process. Received 07 February 1997/ Accepted in revised form 01 July 1997     

Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques

Three multivariate statistical techniques (Multiway Principal Component Analysis, Multiway Partial Least Squares, and Stepwise Linear Discriminant Analysis) and one artificial intelligence method (Artificial Neural Networks) were evaluated to detect and predict early abnormal behaviors of wine fermentations. The techniques were tested with data of thirty-two variables at different stages of fermentation from industrial wine fermentations of Cabernet Sauvignon. All the techniques studied considered a pre-treatment to obtain a homogeneous space and reduce the overfitting. The results were encouraging; it was possible to classify at 72h 100% of the fermentation correctly with three variables using Multiway Partial Least Squares and Artificial Neural Networks. Additional and complementary results were obtained with Stepwise Linear Discriminant Analysis, which found that ethanol, sugars and density measurements are able to discriminate abnormal behavior.     

Thermodynamic prediction of hydrogen production from mixed-acid fermentations

The MixAlco? process biologically converts biomass to carboxylate salts that may be chemically converted to a wide variety of chemicals and fuels. The process utilizes lignocellulosic biomass as feedstock (e.g., municipal solid waste, sewage sludge, and agricultural residues), creating an economic basis for sustainable biofuels. This study provides a thermodynamic analysis of hydrogen yield from mixed-acid fermentations from two feedstocks: paper and bagasse. During batch fermentations, hydrogen production, acid production, and sugar digestion were analyzed to determine the energy selectivity of each system. To predict hydrogen production during continuous operation, this energy selectivity was then applied to countercurrent fermentations of the same systems. The analysis successfully predicted hydrogen production from the paper fermentation to within 11% and the bagasse fermentation to within 21% of the actual production. The analysis was able to faithfully represent hydrogen production and represents a step forward in understanding and predicting hydrogen production from mixed-acid fermentations.     

Influence of red wine fermentation oenological additives on inoculated strain implantation

Pure selected cultures of Saccharomyces cerevisiae starters are regularly used in the wine industry. A survey of S. cerevisiae populations during red wine fermentations was performed in order to evaluate the influence of oenological additives on the implantation of the inoculated strain. Pilot scale fermentations (500 L) were conducted with active dry yeast (ADY) and other commercial oenological additives, namely two commercial fermentation activators and two commercial tannins. Six microsatellite markers were used to type S. cerevisiae strains. The methodology proved to be very discriminating as a great diversity of wild strains (48 genotypes) was detected. Statistical analysis confirmed a high detection of the inoculated commercial strain, and for half the samples an effective implantation of ADY (over 80 %) was achieved. At late fermentation time, ADY strain implantation in fermentations conducted with commercial additives was lower than in the control. These results question the efficacy of ADY addition in the presence of other additives, indicating that further studies are needed to improve knowledge on oenological additives’ use.     

Species diversity, community dynamics, and metabolite kinetics of the microbiota associated with traditional ecuadorian spontaneous cocoa bean fermentations

Traditional fermentations of the local Ecuadorian cocoa type Nacional, with its fine flavor, are carried out in boxes and on platforms for a short time. A multiphasic approach, encompassing culture-dependent and -independent microbiological analyses of fermenting cocoa pulp-bean samples, metabolite target analyses of both cocoa pulp and beans, and sensory analysis of chocolates produced from the respective fermented dry beans, was applied for the investigation of the influence of these fermentation practices on the yeast and bacterial species diversity and community dynamics during cocoa bean fermentation. A wide microbial species diversity was found during the first 3 days of all fermentations carried out. The prevailing ethanol-producing yeast species were Pichia kudriavzevii and Pichia manshurica, followed by Saccharomyces cerevisiae. Leuconostoc pseudomesenteroides (glucose and fructose fermenting), Fructobacillus tropaeoli-like (fructose fermenting), and Lactobacillus fermentum (citrate converting, mannitol producing) represented the main lactic acid bacterial species in the fermentations studied, resulting in intensive heterolactate metabolism of the pulp substrates. Tatumella saanichensis and Tatumella punctata were among the members of the family Enterobacteriaceae present during the initial phase of the cocoa bean fermentations and could be responsible for the production of gluconic acid in some cases. Also, a potential new yeast species was isolated, namely, Candida sorbosivorans-like. Acetic acid bacteria, whose main representative was Acetobacter pasteurianus, generally appeared later during fermentation and oxidized ethanol to acetic acid. However, acetic acid bacteria were not always present during the main course of the platform fermentations. All of the data taken together indicated that short box and platform fermentation methods caused incomplete fermentation, which had a serious impact on the quality of the fermented dry cocoa beans.