Online Monitoring of Biotransformations in High Viscous Multiphase

Anal. Chem. 2010, 82, 6008–6014

Online Monitoring of Biotransformations in High Viscous Multiphase Systems by Means of FT-IR and Chemometrics Jakob J. Mu¨ller,† Markus Neumann,† Paul Scholl,‡ Lutz Hilterhaus,† Marrit Eckstein,§ Oliver Thum,§ and Andreas Liese*,† Institute of Technical Biocatalysis, Hamburg University of Technology, Denickestrasse 15, 21073 Hamburg, Germany, Mettler-Toledo AutoChem Inc., 7075 Samuel Morse Drive, Columbia, Maryland 21046, and Evonik Goldschmidt GmbH, Goldschmidtstrasse 100, 45127 Essen, Germany In unstable emulsion systems, the determination of concentrations is a challenge. The use of standard methods like GC, HPLC, or titration is highly inaccurate and makes the acquisition of precise data for these systems complex. In addition, the handicap of high viscosity often comes into play. To overcome these fundamental limitations, the online FT-IR technique was identified in combination with chemometric modeling in order to improve accuracy. The reactor type used in this study is a bubble column reactor with up to four dispersed phases (solid catalyst, two liquid immiscible substrates, and a gaseous phase). The investigated reactions are solvent free enzymatic esterifications yielding myristyl myristate (10 mPa s) and high viscous polyglycerol-3-laurate (300-1500 mPa s), representative industrial products for cosmetic applications. For both reactions, chemometric models were successfully set up and reproducibly applied in the prediction of progress curves of a new set of experiments. This allows the automated determination of sensitive kinetic and thermodynamic data as well as reaction velocities in high viscous multiphase (bio)chemical systems. Online monitoring devices are gaining more and more importance in the field of (bio)process technology.1,2 Higher process reproducibility, efficiency, and a deeper process understanding are the consequences,3 all of which are important factors for (bio)catalytical processes.4 One example for such a technology is FT-IR spectroscopy, which has gained more importance in recent years due to increased availability of the apparatus and fiber-probe technology.5 ATR probes (attenuated total reflection) * To whom correspondence should be addressed. Telephone: +49-(0)40-428783018. Fax: +49-(0)40-42878-2127. E-mail: [email protected]. † Hamburg University of Technology. ‡ Mettler-Toledo Autochem Inc. § Evonik Goldschmidt GmbH. (1) Hashimoto, A.; Kameoka, T. Appl. Spectrosc. Rev. 2008, 43 (5), 416–451. (2) Junker, B. H.; Wang, H. Y. Biotechnol. Bioeng. 2006, 95 (2), 226–261. (3) Haake, C.; Landgrebe, D.; Scheper, T.; Rhiel, M. Chem. Ing. Tech. 2009, 81 (9), 1385–1396. (4) Liese, A.; Seelbach, K.; Wandrey, C. Industrial Biotransformations, 2nd ed.; Wiley-VCH: Weinheim, 2006. (5) Vigano, C.; Ruyssehaert, J. M.; Goormaghtigh, E. Talanta 2005, 65 (5), 1132–1142.

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allow the acquisition of precise information about chemical components with short optical path lengths (0.5-5 µm), and the fiber cables give the flexibility for adapting the devices to new systems, leading to reproducible results compared to metallic lightguides. For analysis, mainly the near-infrared (13 000-4000 cm-1) and mid-infrared regions (4000-400 cm-1) are monitored. This data needs to be analyzed appropriately by, i.e., chemometric methods or 2D analysis.6 For emulsion systems, FT-IR technology was already applied for the prediction of polymerization and copolymerisation reactions,7,8 where the peak height of specific vibrations was analyzed over the process time and used for prediction of the reaction progress. In the field of enzymatic hydrolysis, FT-IR technology has been used widely, mainly for reactions such as the splitting of fats, oils, or other esters.9,10 In unpublished results, we proved the applicability of FT-IR technology for the prediction of conversion for myristyl myristate synthesis in a stirred tank reactor.11 The solvent free enzymatic esterification of high viscous substances forming emulsions, we recently described,12 displays a reaction system of FT-IR technology in combination with chemometrics that has not been applied in detail in the literature before. The resulting fatty acid esters are important components in cosmetic products such as cre`mes or shampoos.13 The production of these substances is mainly accomplished chemically at elevated temperatures with complex multistep downstream processing.14,15 In the synthesis of myristyl myristate and similar cosmetic oils, a life cycle assessment proved that the enzyme catalyzed process is economically competitive to the chemical process with a better (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Noda, I. Appl. Spectrosc. 1990, 44 (4), 550–561. Jovanovic, R.; Dube´, M. A. Polym. React. Eng. 2003, 11 (3), 233–257. Hua, H.; Dube´, M. A. Polym. React. Eng. 2002, 10 (1-2), 21–40. Pacheco, R.; Karmali, A.; Serralheiro, M. L. M.; Haris, P. I. Anal. Biochem. 2005, 346 (1), 49–58. Ruckebusch, C.; Duponchel, L.; Huvenne, J. P. Anal. Chim. Acta 2001, 446 (1-2), 257–268. Neumann, M.; Goldberg, K.; Kara, S.; Mu ¨ ller, J. J.; Liese, A. Hamburg University of Technology, 2009, unpublished results. Hilterhaus, L.; Thum, O.; Liese, A. Org. Process Res. Dev. 2008, 12 (4), 618–625. Rieger, M. M.; Rhein, L. D. Surfactants in Cosmetics, 2nd ed., surfactant science series Vol. 68, Marcel Dekker: New York, 1997. Thum, O. Tenside Surf. Det. 2004, 41, 287–290. Zoller, U.; Sosis, P. Handbook of Detergents Part F: Production, surfactant science series Vol. 142; CRC Press, Taylor & Francis Group: Boca Raton, FL, 2009. 10.1021/ac100469t  2010 American Chemical Society Published on Web 06/16/2010

ecological performance.16,17 The currently applied conventional reactor types, such as fixed bed or stirred tank reactors, are replaced by new concepts: Bubble columns that principally work faster than the fixed bed reactors due to efficient water stripping within the esterification reaction. In addition, this reactor concept allows the conversion of high viscous substrates and emulsion systems and requires a low energy input for mixing.11 This range of different reactor types allows one to choose production strategies with a better flexibility for enzymatic reactions on an industrial scale. The implementation of online FT-IR in such reaction systems opens up several benefits. The reduced effort for offline sampling, the ability to analyze process curves online, and the application of automatic feed back control systems are only some of the advantages. A profound problem, which occurs when working with immiscible substrates that lead to unstable emulsion systems such as the esterification of polyglycerol-3 with fatty acids, is also discussed in this work. Here, standard analytical methods like gas chromatography or titration are often inaccurate and nonreproducible due to inaccurate offline sampling from a nonhomogeneous mixture, complicating initial reaction rate determination. For this specific purpose, FT-IR technology overcomes the abovementioned limitations of off-line sampling, as will be shown for reactions in a bubble column reactor containing up to four phases. In the case of polyglycerol-3-laurate synthesis, these are the enzyme carrier as a solid phase, the two immiscible substrates, and a mixing gas as the gaseous phase. EXPERIMENTAL SECTION Online FT-IR Spectroscopy and Chemometrics. Two FTMIR instruments were used for the experiments. Both were equipped with external ATR probes. The Mettler Toledo ReactIR 45 m was equipped with an AgX FibreConduit diamond ATR probe. Spectral data was collected from 2800 to 650 cm-1. 256 scans were taken per spectrum and analyzed directly by iC IR software from Mettler Toledo (version 4.0.636.0). The chemometric analysis was done with the iCQuant module (version 4.0.636.0). The spectra were centered, cut (2800-1900 cm-1 and 900-650 cm-1), and baseline corrected (first or second order correction) prior to data processing. The Bruker Vertex 70 was equipped with the IN350-T, a silver halide fiber diamond ATR probe.18 The spectral data was recorded from 3500 to 560 cm-1. 96 scans per spectrum were taken by a nitrogen cooled MCT (mercury cadmium telluride) detector and recorded by an OPUS 6.0 software package. The multivariate analysis was carried out with the Matlab software package and the N-PLS program from N-Way Toolbox.19 The spectra were preprocessed by centering (nprocess command, n-way toolbox for Matlab), baseline correcting (msbackadj command, bioinformatics toolbox for Matlab, stepsize 100, (16) Tufvesson, L. M.; Bo ¨rjesson, P. Int. J. Life Cycle Assess. 2008, 13, 328– 338. (17) Thum, O.; Oxenbøll, K. M. IFCCC Congress; Osaka, Japan, 2006, http:// www.novozymes.com/NR/rdonlyres/8540B350-F264-4676-9C14F772573A5390/0/A10155Thum.pdf. (18) Minnich, C. B.; Buskens, P.; Steffens, H. C.; Ba¨uerlein, P. S.; Butvina, L. N.; Ku ¨ pper, L.; Leitner, W.; Liauw, M. A.; Greiner, L. Org. Process Res. Dev. 2007, 11, 94–97. (19) Anderson, C. A.; Bro, R. Chemom. Intell. Lab. Syst. 2000, 52 (1), 1–4.

windowsize 100), and cutting (3500-1870 cm-1 and 600-560 cm-1). Both systems were flushed with 5 L/min dry nitrogen with a constant humidity and CO2 concentration in the measurement device. Liquid nitrogen was used for cooling the MCT detectors. Experimental Setup. All experiments were carried out in a glass bubble column reactor with a total volume of 120 mL. The column was thermostated and equipped with an inlet for the FTIR ATR probes (Figure 1). At the bottom of the reactor, a perforated PTFE plate (9 holes with a diameter of 0.4 mm) as gas distributor was used. The column was aerated with compressed air at a flow rate of 3 L/min. The biocatalyst used was Candida antarctica lipase B physically adsorbed on a hydrophobic methacrylate carrier (Novozym 435) from Novozymes.20 The offline conversion values were determined via the acid value (AV) with titration against 0.1 M KOH in ethanol. The conversion calculated in this work is based on the acid consumption:

Xacid )

nacid,0 - nacid AV )1nacid,0 AV0

(1)

where Xacid ) conversion, based on acid consumption, nacid,0 ) molar amount of acid at the beginning of the reaction, nacid ) molar amount of acid during the reaction, AV ) acid value during reaction, and AV0 ) acid value at the beginning of reaction. Solvent Free Enzymatic Esterification Yielding Myristyl Myristate. For this reaction, 50 g of liquid myristyl alcohol (t ) 60 °C) was filled into the reactor, equipped with an ATR probe. The corresponding amount of myristic acid for setting up an equimolar mixture was added to the system. When complete mixing and a constant temperature in the reactor of 60 °C were obtained, the reaction was initiated by adding the biocatalyst (1% (w/w) Novozym 435). Solvent Free Enzymatic Esterification Yielding Polyglycerol-3-laurate. Polyglycerol-3 (50 g) and the equimolar amount of lauric acid were added to the column. The reaction was carried out at 75 °C with an enzyme amount of 2.5-4% (w/w) Novozym 435. The experimental procedure was done as mentioned above. Error Analysis. The error of the calibration and the prediction were calculated by the root-mean-square error of calibration (RMSEC) and the root-mean-square error of prediction (RMSEP).

RMSEC or RMSEP )




n

∑ (cˆ - c )

2

i

i

i)1

n

(2)

where cˆi ) parameter measured or weighed in (in conversion or acid value), ci ) parameter predicted (in conversion or acid value), and n ) number of data points. The calculations from the offline data (conversion or acid value) were compared with the predicted values obtained by the chemometric models. RESULTS AND DISCUSSION Prediction of the Reaction Course in Myristyl Myristate Synthesis. The mixture of myristic acid and myristyl alcohol is a one phase liquid system at 60 °C. This corresponds to an overall (20) Bartling, K.; Thompson, J. U. S.; Pfromm, P. H.; Czermak, P.; Rezac, M. E. Biotechnol. Bioeng. 2001, 75 (6), 676–681.

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Figure 1. Reactor schematic with FT-IR device and the external ATR probe. In the box, the tip of the FT-IR probe and the four phase system are displayed.

Figure 2. (A) Conversion as function of time for the enzymatic myristyl myristate production. (B) Corresponding time-resolved FT-IR spectra. (myristyl myristate synthesis in bubble column reactor (100 g of total amount), equimolar amount of substrates, T ) 60 °C, 1% (w/w) Novozym 435).

three phase system in the bubble column reactor. The solid biocatalyst and the gas for stripping and mixing display the two additional phases. This system has a relatively low viscosity of approximately 10 mPa s, and the offline analytics are well established with a low error. The main issue here is the accuracy of FT-IR measurements in this solvent free three phase system. Four experiments were carried out under the same conditions. FT-IR spectra and the acid value as offline data were determined as a function of the reaction progress. A process curve is shown 6010

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in Figure 2 derived by titration and the corresponding spectra. The absorption at 1720 cm-1 corresponds to the carbonyl vibration of the acid functional group, representing the decreasing amount of myristic acid. In addition, the carbonyl vibration of the ester functional group absorbs at 1750 cm-1, representing the increasing amount of product. Additionally, a range of frequencies across the fingerprint region (1600-800 cm-1) change as a function of conversion, which is ideal for chemometric analysis.

Figure 3. From the spectral FT-MIR information and the offline concentration data, a model is created by the N-way Toolbox in Matlab or the ICQuant module of the IC IR software of Mettler Toledo. The model is then used for the concentration prediction of a new experiment.

For all four experiments, a chemometric model was created according to the general procedure shown in Figure 3. Besides the main spectral range (1900-1700 cm-1), part of the fingerprint region (1700-900 cm-1) was additionally taken into account for chemometric analysis due to the high information content yielding a robust model. The spectra were cut above 1900 cm-1 and below 900 cm-1 to avoid errors by CO2 and other noise signals and were baseline corrected in order to reach reproducible spectral information and centered for an appropriate usage for PLS regression. The four models, each based on one experiment, were tested for accuracy by cross validation and PRESS (predictive residual sum of squares) value determination. The most accurate model was used then as the primary model. The obtained data for this model is shown in Figure 4. The PRESS value was calculated according to eq 3. n

PRESS )

∑ (cˆ

i

- ci)2

(3)

i)1

where cˆi ) parameter measured or weighed in (in conversion or acid value) and ci ) parameter predicted (in conversion or acid value). Although the creation of the model was based on only 13 measurements, it nonetheless generated respectable results. The prediction works with a RMSEP of 1.51% conversion. By checking the PRESS value against the number of used factors, a minimum of three factors was found and used for the external validation experiments. Using this data, the generated primary model verified via cross validation was tested in the three additional experiments mentioned above, which were carried out under the same conditions. Hereby, the conversion measured by titration was compared with the predicted conversion by the primary model. The results are shown in Figure 5. The prediction of conversion fits the measured data in all experiments (only two predictions shown here). Any interference (step changes) on the signal and on the predictions caused by bubbles and/or solid enzyme immobilizate is not detectable.

Variation of the Molar Ratio. For an increase in accuracy over a wider range of initial substrate concentrations, experiments were carried out with varying starting molar ratios of nacid:nalcohol ) 50:50, 60:40, 40:60, and 45:55. The experiments with the molar ratios of 50:50, 60:40, and 40: 60 were used for the creation of one model. The same three experiments were predicted with this model (Figure 6A). There was no preprocessing of the spectra applied, except cutting and centering when necessary. A new experiment was carried out with a molar ratio of 45:55. This experiment was predicted with this model with a low RMSEP of 1.16% conversion (Figure 6B). This shows the strength and robustness of multivariate data analysis: A wider range of concentrations was used for calibration and with the resulting model the prediction of an experiment with differing starting conditions (in the range of calibration) was possible. Due to the different starting concentrations in the solvent free system, all spectra of the predicted experiment differ from the spectra used for the model. Consequently, the same acid value obtained in different experiments yields different spectra. Nevertheless, the model is sensitive enough for a precise prediction of all four experiments. Prediction of the Synthesis of Polyglycerol-3-laurate. The polyglycerol-3-laurate system is more challenging in view of applying precise analytics compared to the myristyl myristate system. Polyglycerol-3 and lauric acid form a two phase system due to different polarities with a viscosity of 300 mPa s at 60 °C. The viscosity is approximately 300 times higher as compared to water. During the course of the reaction, the viscosity is increased from 300 to 1500 mPa s.11 Because of this highly viscous and unstable emulsion system, the collection of a representative ratio of both substrates from the reactor is impossible and leads to incorrect titration results. This was demonstrated in static experiments with no catalyst. Substrates and product were added to the column in corresponding amounts to conversions between 0 and 99.5%. For these individual experiments, the acid value was taken 10 times. At the same time, FT-IR spectra were recorded. For the titration, a systematical and an absolute error of up to 100% was observed. Both errors decreased with conversion and were below 2% at conversions above 20%. The surface active properties of the products promote the change of the thermodynamically unstable two phase system into a stable emulsion system. This enables a precise sampling and reduces the titration error drastically (Figure 7A). It must be stated at this point that to the best of our knowledge there is no analytical device known that can display the starting region up to 20% conversion accurately. Conversely, FT-IR spectroscopy was used in order to achieve a more precise concentration determination, especially in the sensitive region at the beginning of the reaction. For this purpose, a multivariate model was created in advance by weighing in substrates and product in amounts corresponding to conversion points while at the same time FT-IR spectra were recorded. With this data, a chemometric model was created, which is not based on misleading titration information, since the acid content is calculated by the amounts added to the reactor. The error of prediction of up to 20% conversion is tremendously reduced, as shown in Figure 7B. Only at a conversion below 4% was an error of up to 10% observed. This observation can be attributed to the Analytical Chemistry, Vol. 82, No. 14, July 15, 2010

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Figure 4. Cross validation of the created model from an experiment yielding myristyl myristate with the predicted and measured values (A), PRESS value as function of number of factors with a minimum at 3 (B) (myristyl myristate synthesis in bubble column reactor (100 g of total amount), equimolar amount of substrates, T ) 60 °C, 1% (w/w) Novozym 435).

Figure 5. Prediction of the conversion by chemometric modeling. The model was created according to one experiment (A) and applied to three additional experiments. Due to simplification, the predictions of two experiments are shown (B) (myristyl myristate synthesis, synthesis in bubble column reactor (100 g of total amount), equimolar amount of substrates, T ) 60 °C, 1% (w/w) Novozym 435).

Figure 6. (A) Creation of a chemometric model based on three experiments with different substrate concentrations (molar ratios: 50:50 (0), 60:40 (O), and 40:60 (])). All three experiments were successfully predicted with this model. (B) A fourth experiment was carried out with a different molar ratio of 55:45 and was also successfully predicted with the previous created model (A) (myristyl myristate synthesis in bubble column reactor (100 g of total amount), varied amount of substrates, T ) 60 °C, 1% (w/w) Novozym 435).

different affinities of the two starting materials to the ATR crystal due to highly different viscosities, resulting in misleading spectral data. We can conclude here that the FT-IR can measure more accurately in the unstable two phase region. Only a low amount of formed surface active product (3 to 4% conversion which 6012

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corresponds to an amount of 3 to 4% (w/w)) is needed for a stable signal and a precise prediction of the concentration, whereby the offline determination is inaccurate up to 20% conversion. As an alternative to the procedure described above, where static experiments were used, the prediction of conversion was

Figure 7. Comparison of the accuracy of the acid value determination in a high viscous multiphase system by titration (A) and the prediction by a chemometric model based on FT-IR spectra (B) in a bubble column (added amounts of polyglycerol-3, lauric acid, and polyglycerol-3laurate that correspond to a specific conversion, T ) 75 °C).

Figure 8. Polyglycerol-3-laurate production was monitored five times. A chemometric model was created on the basis of one experiment (A) and applied for four individual experiments. The predicted conversion is shown in comparison to the concentration measured by titration. Representative two predictions are shown (B) (polyglycerol-3-laurate synthesis in a bubble column reactor (100 g of total amount), equimolar amount of substrates, T ) 75 °C, 4% (w/w) Novozym 435).

carried out in the same way as for the myristyl myristate system (Figure 8) in dynamic experiments. In this example, five conversion experiments were carried out and recorded via titration and FT-IR. All experiments were checked with regard to their accuracy via cross validation (not shown here). The most accurate one was then used as primary model (Figure 8A). The gaps in the prediction curves are because of measurement stops due to insufficient cooling of the detector with liquid nitrogen overnight. The external prediction of conversion based on the chemometric model was carried out in all four cases with a mean RMSEP of 5.37% conversion. However, below 20% conversion, the absolute error is +/-10% for the prediction via FT-IR whereas for offline sampling errors up to +/-30% were observed here. As pointed out before, several inaccurate data points are obtained under 20% conversion, which represents the region where the substrates are immiscible. Specifically here, the method of in situ measurements is of an advantage, enabling the analysis of the substrate mixture at constant emulsion properties directly in the reactor. Two approaches were used for the model calibration in this region (0-20% conversion) to avoid the use of misleading titration data: The offline data was fitted over the whole conversion curve leading to accurate data points below 20% conversion for model creation. This is possible since the titration data becomes more precise with higher conversions, due to the stabilizing effect of the product

(Figure 7). Additionally, the zero value was calculated theoretically by the amounts added to the reactor. These two approaches (fitting, calculation of the zero value) avoids the use of misleading titration data below 20% conversion for model calibration, while taking advantage of the increased accuracy of the in situ spectral information in unstable emulsion systems leading to precise predictions over the whole reaction course. Proof of Accuracy. One major issue in the production of fatty acid esters is the purity of the product. The odor or the color is highly important, since these esters are used, i.e., in products for personal care. With the enzymatic production process, a high quality grade of the cosmetic oils or emulgators is achiveable.13 Thus, the chemometric models used for these systems are also subject to a requirement of high accuracy. To prove that the models can accurately predict the actual concentrations, the respective root-mean-square errors of prediction (RMSEP) and root-mean-square errors of calibration (RMSEC) are compared in Table 1 for all conversion experiments. The RMSEC and RMSEP are in a range of 0.56-2.02% absolute conversion for the myristyl myristate experiments. It is obvious that the RMSE is more lower for the experiment they are based on but not significantly less accurate for the external experiments. The same is true for the polyglycerol-3-laurate experiments. As a consequence of the fast increase of conversion in the beginning, Analytical Chemistry, Vol. 82, No. 14, July 15, 2010

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Table 1. RMSEC and RMSEP for All Conversion Experimentsa experiments

RMSEC/RMSEP in % conversion or acid value [mgKOH/gsample] RMSEC: ± 1.51% conversion

myristyl myristate synthesis experiments under same conditions (1 internal prediction, 3 external predictions)

RMSEP: ± 2.02% conversion RMSEC: ± 0.56 [mgKOH/gsample]

myristyl myristate synthesis with different substrate concentrations (3 internal predictions, 1 external prediction)

RMSEP: ± 1.16 [mgKOH/gsample] RMSEC: ± 1.73% conversion

polyglycerol-3-laurate synthesis experiments under same conditions (1 internal prediction, 4 external predictions)

RMSEP: ± 5.37% conversion

a

It is the differentiation between the predictions of the experiment(s) the model was based on (RMSEC) and the experiments which were carried out additionally (RMSEP).

this region is more sensitive to prediction and measurement errors. However, for the overall determination of the error, two factors have to be considered. The first factor is that the determination of concentration data via the acid value (by titration) results in an absolute error of 2-30%. This error impacts not only the capability to create a good model but also it has an impact on the RMSEP determination, since these data points (the actual values) are directly compared to the predicted values. The second factor is the difficulty of acquiring spectra at a certain distinct time point, since every collected spectrum is an average of 96-256 scans which are taken over an approximately 1 min time interval. In summary, the prediction of concentration data can currently be carried out with good accuracy; however, it can be further optimized. The next steps will focus on the accuracy of long-term FT-IR monitored reactions. The focus here is a gain in reproducibility and comparability of the acquired data. CONCLUSION Online FT-IR information in combination with multivariate data analysis gives reliable predictions for the enzymatic esterification of fatty acid esters in highly viscous solvent free systems. The application of an ATR probe of FT-IR in a bubble column reactor is possible. Neither the solid particles nor the gas bubbles had any significant influence on the spectral data. This was demonstrated in experiments yielding myristyl myristate. Furthermore, online analysis of biotransformations in emulsion systems consist-

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ing of up to four phases is possible. However, in the case of four phases, irregularities are observed when the two liquid phases display different viscosities. Sufficient mixing in the whole reactor system is of high importance and is more precisely measured by online FT-IR. One critical point is the calibration of these systems, since the obtained chemometric models are only as good as the calibration. Thus, it is necessary to develop precise techniques and follow logical assumptions and correlations for analysis to obtain good models. These precautions reduced the absolute error for this system from ±30% (titration) to ±10% (FT-IR) conversion. This makes unstable two phase systems accessible for analytics which could not have been displayed appropriately before. Additional advantages of applying this technique besides the increased accuracy are as follows: no volume or catalyst loss by sampling and the possibility of automated live analysis in these complicated systems. A promising prospect is the possibility for transferring this technique to other chemically or biochemically catalyzed nonaqueous emulsion systems. ACKNOWLEDGMENT The authors thank Janosch Fagaschewski and Jakob Gebhard for their excellent experimental work. Received for review February 21, 2010. Accepted June 4, 2010. AC100469T