Monitoring and control of Gluconacetobacter xylinus fed-batch cultures using in situ mid-IR spectroscopy

Journal of Biotechnology 113 (2004) 231–245

Monitoring and control of Gluconacetobacter xylinus fed-batch cultures using in situ mid-IR spectroscopy Henri Kornmanna , Sergio Valentinottib , Philippe Dubocc , Ian Marisond , Urs von Stockard,∗ a

Serono Biotech Center, route de Fenil, Z.I. B, CH-1809 Fenil-sur-Corsier, Switzerland Firmenich S.A., Rue de la Berg`ere 7, Case postale 148, 1217 Meyrin 2, Switzerland c Nestl´ e Research Center, CH-1000 Lausanne 26, Switzerland Laboratory of Chemical and Biological Engineering, Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland b

d

Received 4 August 2003; received in revised form 5 March 2004; accepted 19 March 2004

Abstract A partial least-squares calibration model, relating mid-infrared spectral features with fructose, ethanol, acetate, gluconacetan, phosphate and ammonium concentrations has been designed to monitor and control cultivations of Gluconacetobacter xylinus and production of gluconacetan, a food grade exopolysaccharide (EPS). Only synthetic solutions containing a mixture of the major components of culture media have been used to calibrate the spectrometer. A factorial design has been applied to determine the composition and concentration in the calibration matrix. This approach guarantees a complete and intelligent scan of the calibration space using only 55 standards. This calibration model allowed standard errors of validation (SEV) for fructose, ethanol, acetate, gluconacetan, ammonium and phosphate concentrations of 1.16 g/l, 0.36 g/l, 0.22 g/l, 1.54 g/l, 0.24 g/l and 0.18 g/l, respectively. With G. xylinus, ethanol is directly oxidized to acetate, which is subsequently metabolized to form biomass. However, residual ethanol in the culture medium prevents bacterial growth. On-line spectroscopic data were implemented in a closed-loop control strategy for fed-batch fermentation. Acetate concentration was controlled at a constant value by feeding ethanol into the bioreactor. The designed fed-batch process allowed biomass production on ethanol. This was not possible in a batch process due to ethanol inhibition of bacterial growth. In this way, the productivity of gluconacetan was increased from 1.8 × 10−3 [C-mol/C-mol substrate/h] in the batch process to 2.9 × 10−3 [C-mol/C-mol substrate/h] in the fed-batch process described in this study. © 2004 Elsevier B.V. All rights reserved. Keywords: Gluconacetan production; Gluconacetobactor xylinus; On-line MIR spectroscopy; Exopolysaccharide production; Closed-loop control

∗

Corresponding author. Tel.: +41-21-693-3191; fax: +41-21-693-3680. E-mail address: [email protected] (U. von Stockar). 0168-1656/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jbiotec.2004.03.029

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1. Introduction Over the past decade, the interest for bioprocess monitoring using non-invasive infrared spectroscopic sensors has been increasing. In contrast to the classical, labor-intensive and time-consuming off-line analysis, these sensors provide a rapid multianalyte determination, are in situ sterilizable, show long-term stability and have low need for maintenance during operation. Among the IR spectral regions, both nearinfrared (NIR) and mid-infrared (MIR) have been used for bioprocess monitoring. NIR shows many advantages over other techniques, such as ease of in situ sampling using fiber optics and inexpensive instrumentation (Cooper et al., 1997). Various metabolites such as biomass (Arnold et al., 2002; Zhang et al., 2002), glucose (Arnold et al., 2000; Jung et al., 2002; Rhiel et al., 2002b; Sivakesava et al., 2001a,b), lactose (Macedo et al., 2002), glutamine (Rhiel et al., 2002b), glutamate (Arnold et al., 2000), exopolysaccharides (EPS) (Macedo et al., 2002), ammonia (Arnold et al., 2000; Rhiel et al., 2002b), lactate (Rhiel et al., 2002b; Sivakesava et al., 2001a,b) and acetate (Zhang et al., 2002) have been monitored in the bioreactor using NIR technique. In comparison to NIR, MIR yields more distinctive spectral features, especially between 1800 cm−1 and 800 cm−1 , commonly called the “fingerprint region”. Absorbance in this region corresponds to the fundamental vibration modes of molecules, and shows enhanced sensitivity and selectivity for bioprocess monitoring compared to NIR (Sivakesava et al., 2001a,b; Vaidyanathan et al., 1999). Recent studies have successfully used MIR spectroscopy to monitor various metabolites during fermentations (Crowley et al., 2000; Doak and Phillips, 1999; Fayolle et al., 2000; Kansiz et al., 2001; Kornmann et al., 2003c; Pollard et al., 2001; Rhiel et al., 2002c; Sivakesava et al., 2001a,b). Although the major advantage in using on-line monitoring is the utilization of real-time signals for process control and decision making, the use of MIR signals for this purpose has received little attention. In the present work, the cultivation of Gluconacetobacter xylinus I2281 on ethanol and the production of gluconacetan, a food-grade exopolysaccharide, on fructose have been used as case studies. Gluconacetan shows industrial interest as it can be used as texturing agent in food products. In this system, ethanol strongly reduces bacterial

Fig. 1. Metabolic states for biomass and gluconacetan production.

growth and is oxidized into acetate. After depletion of ethanol, acetate is further oxidized and allows biomass accumulation (Kornmann et al., 2003b). It has also been demonstrated that EPS production is non-growth associated (Kornmann et al., 2003a). Consequently, cultures were undertaken in which biomass was first produced by feeding the bioreactor with ethanol. It was observed that oxidation of ethanol into acetate (metabolic state 1 on Fig. 1) was faster than oxidation of acetate resulting in biomass production (metabolic state 2 on Fig. 1) (Kornmann et al., 2003b). Therefore, by maintaining a constant acetate concentration, the ethanol concentration should remain very low, resulting in biomass accumulation. Once biomass had accumulated to a desired level, the ethanol feed was replaced by a concentrated fructose solution in order to produce gluconacetan (metabolic state 3 on Fig. 1). The fructose concentration was maintained constant until the desired amount of EPS had been produced. This system is an ideal case study to validate the application of MIR signals in the close-loop control of a bioprocess. Firstly, it requires very precise and reliable signals to prevent any ethanol accumulation within the bioreactor since bacterial growth is completely inhibited by the presence of very low concentration (0.05 g/l) of ethanol (Kornmann et al., 2003b). Secondly, it illustrates fermentation in which other on-line sensors, such as FIA or chromatographic methods are ineffective. The latter sensors usually require a pre-filtration step to achieve cell-free sample preparations, however, this is not possible for culture sample containing EPS due to the very high viscosity. Difficulties in spectrometer calibration have inhibited widespread use of this method. Culture media are usually composed of many IR-active molecules showing broadly overlapping spectral features. The sum of the absorbance features of each IR-active component

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Fig. 2. Most culture media components are IR-active molecules showing broadly overlapping spectral features. This results in very complex spectra, which can only be analyzed using multivariate calibration algorithms. (A) Absorbance spectra collected of the fed-batch fermentation. (B) Absorbance spectra of acetate () and ethanol (
). (C) Absorbance spectra of gluconacetan (•) and fructose (). (D) Absorbance spectra of phosphate () and ammonium ().

within a culture medium induces very complex spectra (Fig. 2A) that can only be quantitatively analyzed by advanced chemometric methods, such as partial leastsquares regression (PLSR) (Beebe and Kowalski, 1987;

Geladi and Kowalski, 1986; Martens and Naes, 1988; Sj¨ostr¨om and Wold, 1983; Wold et al., 1984). A PLSR algorithm describes the relationship (socalled calibration model) between analyte concentra-

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tion and spectral features. Due to the complexity of culture media, a large number (close to 100) of standards used to calibrate the spectrometer are usually collected during fermentation (Arnold et al., 2000, 2002; Doak and Phillips, 1999; Fayolle et al., 2000; Macedo et al., 2002; Pollard et al., 2001; Sivakesava et al., 2001a,b; Zhang et al., 2002). However, these types of standards naturally possess a metabolism-induced concentration correlation between cellular substrates and metabolic waste products. Meaningful calibration models demand that analytes are not correlated with the concentrations of any other species within the chemical matrix (Standard practices for infrared, multivariate, quantitative analysis (e 1655–97), 1999). Since such a calibration model is valid only if the correlation is valid and fails when these conditions change even slightly (Kornmann et al., 2003c), this type of calibration model can only predict metabolite concentrations under strict culture conditions. In this paper, they are defined as correlated calibration model. This study presents a new approach to design calibration models based on experimental design and using only a few synthetic solutions to model the fermentation broth. The quality and the robustness of the calibration model are validated using a very sensitive system requiring very accurate on-line signals. The case study is the fed-batch fermentation of G. xylinus in order to produce biomass on ethanol and gluconacetan on fructose, in which MIR signals were used in a closed-loop feeding strategy.

2. Materials and methods 2.1. Microorganism, medium and inoculum preparation Bacterial strain G. xylinus I2281 was provided by the Nestl´e Research Center (Lausanne, Switzerland). Stock cultures were stored at −80 ◦ C in a solution containing 15% (w/v) skim milk powder and 15% (w/v) malt extract. Cells were reactivated in a 1 l shake flask containing 100 ml of defined medium at 30 ◦ C for 60 h. The defined medium contained (per liter): 35 g fructose; 5 g acetate, 0.5 g MgCl2 ·6H2 O; 5 g KH2 PO4 ; 1.7 g NH4 Cl; 1.060 g Na2 CO3 ; 0.5 g Na2 SO4 and 15 ml of trace element solution. The latter contained (per liter): 1.47 g CaCl2 ·2H2 O; 0.27 g FeCl3 ·6H2 O;

0.085 g MnSO4 ·H2 O; 0.024 g Na2 MoO4 ·2H2 O; 0.016 g CuSO4 ·5H2 O; 0.024 g CoCl2 ·6H2 O; 0.024 g NiCl2 ·2H2 O; 0.144 g ZnSO4 ·7H2 O; 4.1 g HCl 25% (w/v). The pH was adjusted to 4.0 using 2N KOH or 1N HCl. The medium was sterilized by filtration (0.2 ␮m, Minisart, Sartorius AG, G¨ottingen, Germany). 2.2. Bioreactor culture Cultivation experiments were undertaken using a 15 l bioreactor (Fermenteur 15 LP, LSL Biolafitte SA, Saint-Germain-en-Laye, France) with a working volume of 10 l. The inoculum (500 ml) was incubated in shake flasks for 24 h using the same defined medium as for the bioreactor cultures. Six liters of medium containing 6 g/l acetate, 31 g/l fructose and no carbonate were inoculated. Temperature was maintained at 30 ◦ C and pH at 4.0 by automatic addition of 2N KOH or 1N HCl. In order to avoid foaming, a level probe activated the addition of a 10 g/l anti-foam solution (Structol J673, Schill and Seilacher, Hamburg, Germany). The aeration rate was maintained at 10 l/h using a thermal mass flow controller (5850E, Brooks Instrument, Hatfield, USA) and the stirring rate at 1000 rpm. A polarographic pO2 probe (Infit 765-50, Mettler Toledo, Greifensee, Switzerland) was used to monitor dissolved oxygen. During fed-batch operation on ethanol, the bioreactor was fed with a solution containing 100 g/l ethanol, 0.5 g/l MgCl2 ·6H2 O, 5 g/l KH2 PO4 , 6 g/l NH4 Cl, 0.5 g/l Na2 SO4 adjusted to pH 4. During fed-batch operation on fructose, the bioreactor was fed with a solution containing 200 g/l fructose, 0.5 g/l MgCl2 ·6H2 O, 5 g/l KH2 PO4 , 2 g/l NH4 Cl, 0.5 g/l Na2 SO4 adjusted to pH 4. 2.3. In situ analysis of substrates and metabolites using MIR spectroscopy Single-beam spectra were collected with a ReactIR4000 spectrometer (Mettler-Toledo, Greifensee, Switzerland) equipped with a MCT detector and an ATR diamond probe (DiComp 14.25 long, 0.625 diameter) with an optical conduit. The ATR probe was introduced in the bioreactor through a standard DN 25 port and aligned in order to optimize signal intensity. Once installed, the geometrical settings were kept constant in order to minimize spectral changes due to

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alignment shifts (Rhiel et al., 2002c). The spectrometer housing, the optical conduit and the probe shaft were purged with dry air at a flow rate of 20 l/min from a purge gas generator (Model 75-52, Whatman International Ltd., Kent, England). The infrared radiation is guided by mirrors from the spectrometer to the in situ probe tip and back to the detector. Spectra of 512 co-added scans at 8 cm−1 resolution required a collection time of 3 min. Twelve scans were obtained per hour of bioreactor operation. The spectral range was set between 400 cm−1 and 4000 cm−1 . The probe tip and shaft were steam sterilized in situ. Synthetic standard solutions were prepared in a buffer containing 0.5 g/l Na2 SO4 , 0.5 g/l MgCl2 ·6H2 O and 3 g/l KH2 PO4 and maintained at pH 4 and 30 ◦ C in order to avoid spectral changes due to pH and temperature shifts (Fayolle et al., 1996). The composition and the concentration of the standards were determined according to Brereton (2000) in order to generate linearly independent concentrations (Appendix 1). Maximum concentrations in the calibration matrix were 32 g/l fructose, 7 g/l ethanol, 15 g/l acetate, 10 g/l gluconacetan, 15 g/l NH4 Cl and 8 g/l KH2 PO4 . A gluconacetan stock solution was prepared using in-house purified EPS. By contrast, all other chemicals were commercially available (Fluka, Buchs, Switzerland). Spectra collection was achieved by placing 2 ml standard mixture on the tip of the ATR probe, care being taken to avoid entrapment of air bubbles. In addition, one spectrum before inoculation of the bioreactor under process conditions (aeration and agitation) was used as an additional standard. The latter has been shown to remove probable off-sets due to the differences between the conditions of standards collection and the fermentation process (Kornmann et al., 2003c). Quantitative analysis was directly performed on the acquired single beam spectra using the QuantIR software package (ASI Applied Systems, Millerville, MD) supplied with the spectrometer system. The analyzed spectral range was fixed between 1500 cm−1 and 950 cm−1 . All data sets were mean centered. An optimum number of factors were determined from a PRESS (Predictive Residual Error Sum of Squares) plot when the PRESS value was at a minimum (Beebe and Kowalski, 1987; Geladi and Kowalski, 1986). The calibration model performance was determined for each analyte by the standard error of calibration (SEC) values using the 56 mixture standard solutions, the stan-

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dard error of validation (SEV) values using 18 samples collected during two different batch fermentations and the standard error of prediction (SEP) values using off-line samples collected during the closed-loop controlled fed-batch fermentation and analyzed off-line. 2.4. Off-line and reference analysis of substrates and metabolites Culture samples (approximately 15 m) were collected using a purpose-built auto-sampler (Cannizzaro, 2001) and kept at 2 ◦ C for up to 8 h before handling. For cell dry weight measurements, biomass was recovered from 10 ml of culture sample by centrifugation (15 min at 20,000 g, 2 ◦ C), resuspended in water and filtered using pre-weighed membranes (HT-200, Pall Corporation, Ann Arbor, USA). Filters were dried for 15 min in a microwave (power at 150 W) and reweighed (Schulze, 1995). A correlation was established between dry cell weight and optical density at 600 nm. This correlation was used to estimate biomass concentrations below 0.4 g/l. Fructose, acetate, ethanol and phosphate were determined by HPLC analysis (1100 series, Agilent Technologies, Palo Alto, USA). An ion exchange chromatography column (Supelcogel H 300 mm, Supelco, Bellefonte, USA) with a guard column (Superlguard C610H, Supelco, Bellefonte, USA) was used at 30 ◦ C. A 5 mM sulfuric acid solution in ultrapure water was applied at a constant eluent flow rate of 0.5 ml/min. Metabolites were measured using a refractive index detector. Ammonium concentration was determined using a commercially available enzyme assay (Urea/Ammonia, Roche Biopharm, Darmstadt, Germany) by monitoring the depletion of NADH at 340 nm. EPS was recovered after precipitation of one volume sample with two volumes of industrial ethanol followed by centrifugation (10 min at 4500 g and 4 ◦ C). The pellets were washed twice with a 70% ethanol solution, then freeze-dried and weighed (MacCormick et al., 1993). The oxygen uptake rate (OUR) and carbon dioxide evolution rate (CER) in the bioreactor off-gas were determined using paramagnetic (serie 1100, Servomex, Crowborough, UK) and infrared (serie 2500, Servomex, Crowborough, UK) analysers, respectively.

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The measured values of oxygen and carbon dioxide were corrected for water vapor according to Duboc and von Stockar (1998). 2.5. Adaptive control algorithm An automated sequence collected real-time spectra every 5 min. After deconvolution by the calibration model, real-time concentrations were sent to an adaptive control algorithm. The control strategy employed was based on the controller described by Valentinotti et al. (2003). Two different approaches were employed depending on the substrate that was fed into the reactor. For ethanol feed, the macroscopic biological processes taking place in the reactor were divided in two. On the one hand, ethanol transformation into acetate was taken as a constant process and a simple model used to describe it. This model was in turn employed to tune a two-degreeof-freedom controller (RST) (Longchamp, 1995). On the other hand, the exponential consumption of ethanol used for biomass growth was considered as a disturbance. An indirect estimation of this disturbance was incorporated as an extra degree of freedom into the controller (Valentinotti et al., 2003). On-line adaptation of this signal allowed maintaining a constant acetate concentration while feeding ethanol exponentially. Tuning of the controller required only the ethanol to acetate yield, while implementation of the controller necessitated only the on-line concentration measurement of acetate. For fructose feed, fructose transformation into gluconacetan was considered as a constant process, and a simple model, requiring the knowledge of the fructose to gluconacetan yield, was used to describe it. The model was used to tune a standard RST controller, which was implemented to maintain a constant fructose concentration in the reactor.

3. Results 3.1. Design and evaluation of the PLS calibration model MIR spectroscopy, especially between 1800 cm−1 and 800 cm−1 , corresponds to the fundamental vibration modes of molecules. Absorbance in this region is

therefore very important for most culture media components (Fig. 2B–D). PLS regression of the spectral data is necessary to calculate a calibration model, which relates spectral features of the culture medium (Fig. 2A) with component concentrations. In this study, a systematic approach using only synthetic mixtures to model the fermentation broth have been used as standards for PLS calibration. To reduce the number of standards, only the major components of the culture medium have been taken into account in the calibration (i.e. fructose, ethanol, acetate, gluconacetan, phosphate and ammonium). In order to guarantee non-correlation between standards, the composition and the concentration of the calibration matrix has been constructed according to Brereton (2000) (Appendix 1). This methodology is based on cyclical shifts from a first column representing five concentration levels of one metabolite randomly distributed. The concentrations of all the metabolites are uniformly distributed over the concentration space. Special attention is paid to obtain the maximum number of non-correlated concentrations as possible with the minimum number of experimental trials. The performance of a calibration model is usually estimated by standard error values, which are provided in Table 1. The standard error of calibration is based on the 56 mixture solutions used to calibrate the specTable 1 Standard errors of calibration (SEC), validation (SEV) and prediction (SEP) of the PLS calibration model

Fructose Ethanol Acetate Gluconacetan Ammonium Phosphate

SEC (g/l)

SEV (g/l)

SEP biomass production (g/l)

SEP gluconacetan production (g/l)

0.093 0.022 0.004 0.169 0.163 0.115

1.16 0.36 0.22 1.54 0.24 0.18

1.09 0.09 0.20 3.24 0.17 0.24

2.22 0.08 0.10 2.23 0.22 0.45

SEC calculation is based on the 56 mixture solutions used as standards in the calibration model. SEV is calculated using 18 off-line samples collected during two different batch fermentations and analyzed by reference methods. Fourteen and eight off-line samples have been collected during the biomass production step and gluconacetan production step, respectively. Metabolite concentration was determined by reference analytical methods and used for SEP calculation. Low SEP values for ethanol are influenced by the measurement range.

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trometer. The SEV validation values (Table 1) were calculated by comparing concentrations predicted by MIR spectroscopy with 18 reference sample concentrations collected during two batch fermentations (data not shown). 3.2. Fed-batch process control using MIR signals On-line MIR signals have been used to monitor and control fed-batch fermentation of G. xylinus. The experiments were started in batch mode on 6 g/l acetate and 31 g/l fructose (Fig. 3) and an initial 0.35 g/l biomass was produced. At 23.20 h, a 100 g/l ethanol solution was fed into the bioreactor (T1 on Fig. 3). As expected, ethanol was rapidly oxidized into acetate. The set-point for the control of acetate concentration was fixed at 3.0 g/l. Fig. 3A shows that this assignment was maintained by the controller until 32.5 h. Ethanol was fed exponentially without any large ethanol accumulation in the bioreactor. Consequently, biomass continued to accumulate to reach 1.82 g/l (Fig. 3A). At 32.5 h, a technical problem resulted in an increase in the gain of the controller. As a result, acetate began to accumulate in the bioreactor (Fig. 3A). Bacterial growth was not affected until 35.2 h (T2), which corresponds to a limitation in dissolved oxygen concentration due to high metabolic activity (Fig. 3C). At this time, all the ethanol fed to the bioreactor was oxidized to carbon dioxide. At 38.90 h (T3), the feed of ethanol was stopped. As a result, ethanol in the fermentation broth fell quickly and the dissolved oxygen rose sufficiently for re-initiation of bacterial growth (Fig. 3C). Thus, an additional 0.41 g/l biomass was produced on the remaining acetate with a final biomass concentration reaching 2.25 g/l (Fig. 3A). The remainder of the culture served to use this biomass in order to transform fructose into EPS (see below). The phosphate concentration remained in large excess and, from the ammonium signal, bacteria were never limited in nitrogen source (Fig. 3D). During biomass production, fructose has also been consumed and 7.5 g/l gluconacetan was produced (Fig. 3B). At 42.76 h (T4), a feed of fructose solution (200 g/l) was fed into the bioreactor. The set-point for the fructose concentration was fixed at 13 g/l. This value was maintained constant by the controller until 66.69 h (Fig. 3B). As expected, biomass concentration remained unchanged since acetate was depleted, and

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thus fructose was fed linearly into the bioreactor. The specific production rate of gluconacetan was 0.07 g/g*h. At 66.69 h (T5), the gluconacetan concentration reached 10.31 g/l, and the feed of fructose was stopped. The fructose that remained in the bioreactor was then consumed and gluconacetan continued to be produced to reach 11.30 g/l by the end of the experiment.

4. Discussion 4.1. Design and evaluation of the PLS calibration model As discussed in the introduction section, calibration of the spectrometer based on samples from fermentations usually provides correlated models. A good study of the effects of correlated models has been discussed recently by Rhiel et al. (2002a). In this study, lactate concentration could be precisely monitored by collecting only glucose spectra during batch cultures of animal cells. This is mainly due to the fact that in batch culture, glucose and lactate are metabolically correlated (i.e. the yield lactate/glucose is constant). In closed-loop control applications, the real-time MIR spectra has to be deconvoluted by using a completely uncorrelated calibration model. The correlation observed in batch cultures would not be valid anymore for fed-batch cultures since at least one substrate concentration is controlled at a constant value. Thus, in the animal cell culture example, maintenance of a glucose concentration would result in a correlated model predicting a constant lactate concentration in the bioreactor, which is clearly not the case and is an artefact of correlated models. Different calibration procedures, partly based on standards collected from the fermentation broth, have been proposed to eliminate concentration correlation in bioprocess within a set of samples (Kornmann et al., 2003c; Rhiel et al., 2002a; Riley et al., 1998). As shown in Table 1, the new approach described in this study allowed low SEC and SEV values. The low SEC values obtained for each metabolite (Table 1) illustrate the fact that the spectral features between them are sufficiently different to be described by a PLS model. The very good SEC values for ethanol and acetate can be explained with the specific spectral features generated by the C O stretching of alcohol

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Fig. 3. Real-time data collected during two consecutive fed-batch cultures of G. xylinus on ethanol and fructose. T1 (23.20 h): 100 g/l ethanol is fed into the bioreactor. T2 (35.30 h): pO2 falls to zero. T3 (38.90 h): feed of ethanol is stopped. T4 (42.76 h): 200 g/l fructose is fed into the bioreactor. T5 (66.69 h): feed of fructose is stopped.

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groups (1086 cm−1 and 1045 cm−1 ) and the symmetric stretching of the carboxyl group (1430–1390 cm−1 ), respectively (Fig. 2B). On the other hand, the SEC value for gluconacetan is due to the low absorbance of this metabolite combined with similarities with the fructose spectrum. These two spectra are characterized by an absorbance of between 1160 cm−1 and 1140 cm−1 due to the C O C vibrational mode and peaks at 1106 cm−1 and between 1090 cm−1 and 1030 cm−1 corresponding to the vibrational mode of the C O H bonds and the C O stretching of alcoholic groups, respectively. The SEV values in Table 1 illustrate that the design of the calibration model was good and provided a reliable calibration model. As a result, fructose, acetate, ethanol, gluconacetan, ammonium and phosphate can be monitored in real-time and simultaneously with an acceptable standard error. Furthermore, it shows that the major spectral variations in the fermentation broth are only related to six metabolites. During the first step of the process (i.e. production of biomass by feeding cells with ethanol in a closed-loop control strategy designed to maintain a constant acetate concentration), the MIR acetate concentration has to be very precise in order to prevent any ethanol accumulation into the bioreactor. The SEV value for acetate (0.221 g/l) is sufficiently low that it may be used for control of the process. The second step of the process (i.e. production of gluconacetan by feeding cells with fructose in a closed-loop control strategy, designed to maintain a constant fructose concentration) requires less precision from the MIR signal. Indeed, no fructose inhibition has been observed on gluconacetan production at concentrations tested (<40 g/l), consequently the SEV value for fructose (1.162 g/l) was considered acceptable for this process. According to ASTM guidelines (Standard practices for infrared, multivariate, quantitative analysis (e 1655–97), 1999), the number of standards used in such calibration models should be at least six times the number of absorbing components in order to obtain good predictions from the model. In the present study, this represents 60 standards, which would be extremely time consuming to collect. However, due to the robustness of the actual MIR spectrometer, it has been shown that the calibration model remains valid over long periods (at least 2.3 years) (Rhiel et al., 2002c). Furthermore, depending on the application,

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the number of standards can be reduced without significantly affecting the SEP value (Fairbrother et al., 1991; Navarrovilloslada et al., 1995). The use of synthetic standards has the added advantage of saving time in the calibration process. Indeed, standards collected from a fermentation broth are only valid for this specific fermentation. Spectra from synthetic standards may be stored in a data bank and re-used to calibrate metabolites in a totally new application since they are specific for metabolites and not for fermentation. 4.2. Biomass production during batch and fed-batch on ethanol To validate the reliability of this method, a feeding strategy which requires on-line acetate and fructose concentrations has been explored. In parallel, ethanol, gluconacetan, ammonium and phosphate concentrations were also monitored. The first step of the process was the production of biomass. The results using Gluconacetobacter strains have shown that a complex medium containing ethanol must be supplemented by a source of carbohydrate in order to observe a net bacterial growth (Sch¨uller et al., 2000). During batch fermentation on ethanol and fructose, biomass accumulated only during acetate oxidation, after exhaustion of ethanol by oxidation. By contrast, bacterial growth was initiated with no delay on fructose and acetate (Kornmann et al., 2003b). Feeding the cell with ethanol in a closed-loop control strategy, designed to maintain a constant acetate concentration allowed to grow G. xylinus on ethanol. Fig. 4 is a close-up of Fig. 3. It is a good illustration of the previously described negative effects of ethanol on bacterial growth (Kornmann et al., 2003b). A change in the slope of the acetate signal was due to the immediate oxidation of ethanol into acetate (Fig. 4A). However, when the feed exceeded oxidation capacity, ethanol started to accumulate. OUR and CER signals, which are usually identical for pure oxidative metabolism on acetate, began to diverge (Fig. 4B). This increase of the OUR signal is probably due to ethanol oxidation to acetate by alcohol dehydrogenase (Kornmann et al., 2003b) and is confirmed by the pO2 signal (Fig. 4). The decrease of CER is due to a lower carbon flux into the TCA cycle, which can be correlated with a reduction of bacterial growth. Trace of

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Fig. 4. Influence of ethanol on G. xylinus physiology. Ethanol induces a decoupling of CER and OUR signals. OUR increases due to the oxidation of ethanol in acetate. CER decreases due to a reduction of carbon flux into the TCA cycle. This can be related to a reduction of bacterial growth in the presence of ethanol. T1 (23.20 h): 100 g/l ethanol is fed into the bioreactor.

ethanol was not detected using classical off-line analysis, however, this may be due to the time for sample preparation (less than a minute) being sufficiently long that any ethanol was oxidized to acetate. The ethanol MIR signal correlated well with the oscillations observed on pO2 , CER and OUR signals, and seems to indicate that the ethanol concentration in the bioreactor was oscillating around a low value. These oscillations may be explained by a poor controller tuning. The confidence in real-time MIR concentration profiles can be evaluated in relation to cell physiology. Ammonium and phosphate signals remained almost stable, which would be expected for low biomass concentrations. The concentration profile of MIR ethanol seems to be correct since bacterial growth, which requires very low ethanol concentrations in the bioreactor, was observed. Complete depletion of the MIR acetate concentration corresponds to the end of the bacterial growth. Once this had occurred, the signal remained low without any drift. Furthermore, MIR concentrations could be related to 14 off-line samples that

were collected during the growth experiment and analyzed with reference methods. The SEP values presented in Table 1 show that most of the MIR signals were accurate. A low absorbance and similar spectral features with fructose can explain the high SEP value of gluconacetan (3.24 g/l). The in situ MIR probe provides an accurate prediction of acetate concentration, which allowed feeding the culture with ethanol without any large ethanol accumulation. Using this feeding strategy, it was possible to produce biomass on ethanol. By comparison, ethanol present during batch experiments needs to be fully converted to acetate before oxidation of acetate initiates and biomass begins to accumulate. This experiment also showed that the metabolic model, which assumes a fast oxidation of ethanol into acetate and the conversion of this last into biomass was correct. A closer look at the data (Fig. 4) confirmed the physiological characteristics observed previously (Kornmann et al., 2003b). As in other acetic acid bacteria (Luttik et al., 1997), the biomass yield of G. xylinus remained

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generally low. However, the final biomass concentration (2.25 g/l) was four-fold higher than in batch mode. In this process, biomass production was limited by the rapid depletion of the dissolved oxygen concentration (Fig. 3C). Biomass concentration could be increased by the use of an adapted bioreactor, such as the airlift bioreactor usually applied for acetic acid production (de Ory et al., 1999; Sokollek et al., 1998). For the production of other EPS such as xanthan gum, the success of any production scheme is linked to the overall fermenter design (Quinn, 1996). 4.3. Gluconacetan production The second step of the process was the production of gluconacetan on fructose. The objective was to maintain the real-time MIR fructose concentration constant, using a close-loop controller, in order to ensure sufficient substrate for the biotransformation. This allowed feeding a highly concentrated fructose solution to the culture. The performance of real-time MIR concentration profiles can be evaluated by an analysis of their general profiles. During the production of gluconacetan, ethanol and acetate measured concentrations remained close to zero. As expected, the phosphate concentration was not affected and ammonium concentration increased due to the 2 g/l ammonium salt used in the feed. Eight off-line samples were collected and analyzed with reference methods. The SEP values in Table 1 show that concentration values for most of the metabolites were predicted correctly by the MIR sensor. The large SEP value for fructose is probably due to a 2 g/l off-set between the HPLC and MIR fructose concentrations. However, the proposed process does not require a high accuracy for fructose determination. Other on-line sensors, such as FIA or gas/liquid chromatography might provide better SEP values. However, these techniques usually require a prefiltration step such as cross-flow filtration, for the production of cell-free samples. Such devices have been used to filter fermentation broth, but rapidly became clogged due to the high viscosity of gluconacetan. Therefore, in situ MIR spectroscopy represents an advantageous tool to access real-time metabolites concentrations during G. xylinus fermentations. Access to real-time fructose concentration resulted in

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an increase in productivity of gluconacetan from 1.8 × 10−3 [C-mol/C-mol substrates/h] in batch process (data not shown) to 2.9 × 10−3 [C-mol/C-mol substrates/h] in the fed-batch process described in this study.

5. Conclusions In conclusion, an in situ MIR sensor has been calibrated using synthetic component solutions. Concentrations of standards have been assigned to guarantee complete and intelligent scan of the calibration space using few 56 standards. The calibration model was good enough to provide fructose, acetate, ethanol, gluconacetan, ammonium and phosphate concentration profiles in real-time with an acceptable standard error of prediction. In contrast to calibration models based on standards collected directly in the fermentation broth, this strategy provides a non-correlated model. The time saved may be used to undertake a first fermentation to collect standards. The synthetic standards may be stored and directly used to calibrate metabolites in a totally new application. Therefore, the systematic calibration method explored in this study reduces the calibration time and extends the application of MIR spectroscopy in the biotech field. The major issue of on-line monitoring compared to classical off-line analysis is the utilization of realtime signals for process control. In order to validate the performance of MIR spectroscopy for this purpose, MIR acetate and fructose concentrations have been used for the close-loop control of gluconacetan production. Access to accurate in situ acetate concentration measurements enabled cell growth on ethanol; a situation which cannot be achieved by batch culture. Real-time monitoring of fructose concentrations in the bioreactor allowed the concentration to be maintained constant. In addition, using the same analytical tool, ethanol, gluconacetan, phosphate and ammonium concentration was monitored in the bioreactor. The feeding strategy utilized increased the overall productivity of gluconacetan compared to batch process. However, the rapid depletion of dissolved oxygen results in limited productivity. This study validates the utilization of in situ MIR spectroscopy to monitor and closely control highly viscous fermentations. In comparison to other on-line

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tools, MIR sensors offer many advantages, including rapid analysis (less than 3 min), ease of operation, ease of maintenance, multiplicity of analysis, thermal sterilazibilty and non-invasiveness. The accuracy of MIR concentrations is sufficiently high to allow application in many biotech processes (Bellon, 1993; Fayolle et al., 1996). Associated to an appropriate calibration model, MIR sensors are powerful tools that can be used to control bioprocesses without disturbing the fermentation due to extensive sampling. It also allows to the speeding of bioprocess development and strain charac-

terization. Furthermore, with the recent development of optic fibres suitable for use in the MIR region, together with non-invasiveness of the method, it should be possible to use MIR spectroscopy for monitoring metabolite concentrations in microbioreactors.

Fructose

Ethanol

Acetate

Gluconacetan

NH4 Cl

KH2 PO4

16.22 16.22

3.65 0.00

7.30 2.43

3.24 2.16

7.72 5.15

5.23 6.72

0.00 5.41 10.81 10.81

1.22 2.43 2.43 6.08

4.86 4.86 12.16 9.73

2.16 5.41 4.32 5.41

12.86 10.29 12.86 7.72

5.97 6.72 5.23 5.97

27.03 21.62 27.03 16.22 21.62 0.00 27.03 27.03

4.86 6.08 3.65 4.86 0.00 6.08 6.08 7.30

12.16 7.30 9.73 0.00 12.16 12.16 14.59 4.86

3.24 4.32 0.00 5.41 5.41 6.49 2.16 6.49

10.29 0.00 12.86 12.86 15.44 5.15 15.44 7.72

3.00 6.72 6.72 7.46 4.49 7.46 5.23 4.49

32.43 10.81 32.43 16.22

2.43 7.30 3.65 2.43

14.59 7.30 4.86 9.73

3.24 2.16 4.32 6.49

5.15 10.29 15.44 15.44

5.97 7.46 7.46 3.74

10.81 21.62 32.43 32.43 5.41 27.03 5.41 16.22

4.86 7.30 7.30 1.22 6.08 1.22 3.65 6.08

14.59 14.59 2.43 12.16 2.43 7.30 12.16 4.86

6.49 1.08 5.41 1.08 3.24 5.41 2.16 1.08

2.57 12.86 2.57 7.72 12.86 5.15 2.57 2.57

6.72 3.74 5.23 6.72 4.49 3.74 3.74 3.00

Appendix 1 Concentration (g/l) of the calibration matrix according to Brereton (2000)

H. Kornmann et al. / Journal of Biotechnology 113 (2004) 231–245

243

Fructose

Ethanol

Acetate

Gluconacetan

NH4 Cl

KH2 PO4

27.03 10.81 5.41 5.41 0.00 32.43 0.00 16.22 32.43 27.03 0.00 0.00 21.62 5.41 21.62 16.22 5.41 32.43 21.62 21.62 10.81 0.00 10.81 0.00 0.00 0.00 40.00 0.00 0.00 0.00

2.43 1.22 1.22 0.00 7.30 0.00 3.65 7.30 6.08 0.00 0.00 4.86 1.22 4.86 3.65 1.22 7.30 4.86 4.86 2.43 0.00 2.43 3.65 0.00 0.00 0.00 0.00 9.00 0.00 0.00

2.43 2.43 0.00 14.59 0.00 7.30 14.59 12.16 0.00 0.00 9.73 2.43 9.73 7.30 2.43 14.59 9.73 9.73 4.86 0.00 4.86 7.30 0.00 0.00 0.00 0.00 0.00 0.00 9.00 0.00

1.08 0.00 6.49 0.00 3.24 6.49 5.41 0.00 0.00 4.32 1.08 4.32 3.24 1.08 6.49 4.32 4.32 2.16 0.00 2.16 3.24 0.00 1.08 0.00 20.00 10.00 0.00 0.00 0.00 0.00

0.00 15.44 0.00 7.72 15.44 12.86 0.00 0.00 10.29 2.57 10.29 7.72 2.57 15.44 10.29 10.29 5.15 0.00 5.15 7.72 0.00 2.57 5.15 0.00 0.00 0.00 0.00 0.00 0.00 9.52

7.46 3.00 5.23 7.46 6.72 3.00 3.00 5.97 3.74 5.97 5.23 3.74 7.46 5.97 5.97 4.49 3.00 4.49 5.23 3.00 3.74 4.49 4.49 3.00 3.00 3.00 3.00 3.00 3.00 3.00

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