Selected Ion Flow Tube–Mass Spectrometry for Online Monitoring of

Article pubs.acs.org/JAFC

Selected Ion Flow Tube−Mass Spectrometry for Online Monitoring of Submerged Fermentations: A Case Study of Sourdough Fermentation Simon Van Kerrebroeck,† Joeri Vercammen,‡ Roel Wuyts,‡ and Luc De Vuyst*,† †

Research Group of Industrial Microbiology and Food Biotechnology, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium ‡ Interscience bvba, Avenue Jean-Etienne Lenoir 2, B-1348 Louvain-la-Neuve, Belgium S Supporting Information *

ABSTRACT: Selected ion flow tube−mass spectrometry (SIFT-MS) has recently gained interest as an alternative method to traditional GC-MS for the detection of targeted volatile sample compounds, due to its ease of use, its speed and sensitivity, and its potential for real-time quantification. The feasibility of this technique was demonstrated using the case of the production of ethanol during sourdough fermentation. The potential of SIFT-MS as an online monitoring device for food fermentations was further demonstrated by the detection of acetoin in certain sourdough fermentations. This allowed discrimination between sourdough fermentation processes and illustrated the importance of real-time monitoring of food fermentations. KEYWORDS: SIFT-MS, GC-MS, fermentation, online monitoring, sourdough, Lactobacillus, volatile organic compounds

■

INTRODUCTION Food fermentation processes have been performed since ancient times, in the first place as a way to preserve food products and in the second place to increase their nutritional value and organoleptic properties.1 In contrast to the action of desirable microorganisms (bacteria, yeasts, and to a lesser extent, filamentous fungi), the outgrowth or action of undesirable microorganisms can lead to spoilage of the endproducts. Therefore, considerable research efforts have been made to the control and monitoring of food fermentation processes. One such way is the application of (functional) starter cultures, i.e., the inoculation of a large number of cultivated dedicated microbial cells.2 An example of a fermented food that has benefited from this development is sourdough. Sourdough is a fermented cereal-based product initially made through spontaneous fermentation of a cereal flour−water mixture or continuous reinoculation of fresh flour by the addition of a part of a previous fermentation batch.3 A more industrial approach to the production of sourdough involves the addition of selected starter cultures, which can be lactic acid bacteria (LAB) and/or yeasts.2,3 However, even modern sourdough production is subjected to considerable batch-to-batch variations. One way to minimize end-product variations is to improve the control of the fermentation processes, preferably through online monitoring of microbial growth, substrate consumption, and/or metabolite production.4 However, online monitoring through chromatography and/or mass spectrometry of food fermentation processes, whether spontaneous or starter culture-initiated, is challenging, in particular with respect to the analysis of volatile organic compounds in real-time. To the authors’ knowledge, only online gas chromatography for the quantitative analysis of offgases has been implemented.5 Semiquantitative analysis of volatile organic compounds has been made possible by means © 2014 American Chemical Society

of membrane inlet mass spectrometry and direct-injection mass spectrometric techniques.6,7 Selected ion flow tube−mass spectrometry (SIFT-MS) is a relatively new mass spectrometric technique that finds its origin in the study of gas-phase ion−molecule reactions.8,9 It applies soft chemical ionization of volatile compounds with selected reagent ions, i.e., H3O+, NO+, and O2+, which are generated in situ. As a result of the reaction of these reagent ions with the volatile organic compounds produced from the fermentation process, product ions of the volatile organic compounds are formed. Because of the soft ionization employed, little fragmentation of these product ions occurs. A quantitative measurement of the volatile compounds at ultratrace levels can be achieved without external calibration providing the fundamental kinetic parameters for the reagent ion-volatile organic compound reactions are known. The signal amplitudes of the selected product ions are used to calculate the concentrations of the targeted volatile compounds present in the sample. The use of three selected reagent ions and the ability to directly analyze the headspace above samples increase the identification power of the technique, rendering a priori separation and tedious or discriminating sample preparation obsolete, hence allowing real-time measurements. Since it was first described by Španěl and Smith in 1996, the SIFT-MS technique has advanced tremendously. 10 Its selectivity, sensitivity, and ease and speed of measurement have boosted its applications. So far, SIFT-MS has gained attention in the areas of environmental monitoring,11,12 (petro)chemical applications,13,14 and clinical analysis.15−17 Received: Revised: Accepted: Published: 829

October 24, 2014 December 15, 2014 December 29, 2014 December 29, 2014 DOI: 10.1021/jf505111m J. Agric. Food Chem. 2015, 63, 829−835

Article

Journal of Agricultural and Food Chemistry

was transferred to the SIFT-MS by means of the MS vacuum. This way, the evolution of volatile compound signals were followed as a function of time for multiple fermentation vessels and the SIFT-MS, controlled by LabSyft software (Syft Technologies Ltd.), could be operated under the analytical conditions required. Additionally, the autosampler device was equipped with a permeation oven, through which internal standards were added to the gas blend. Two internal standards were applied by means of permeation tubes, i.e., toluene (Metronics Inc. Poulsbo, WA, USA) with a diffusion rate of 75 ng min−1 at 50 °C and fluorobenzene (KIN-TEK Laboratories, La Marque, TX, USA) with a diffusion rate of 8.511 ng min−1 at 60 °C. This adaptation allowed the continuous monitoring of the performance of the SIFT-MS, correcting for incidental instrumental variations. The standards were applied separately. The permeation oven was set at 50 or 60 °C, depending on the permeation tubes used. A constant flow of permeation gas (nitrogen gas) at 200 mL min−1 was applied, which was mixed with the gas blend in the mixing chamber. To evaluate this setup, the breakthrough time, which was defined as the time needed to get 10% of the equilibrium response, and the mixing time, which was defined as the time needed to obtain a response in the SIFT-MS corresponding to 95% of the final equilibrium response, were determined by the addition of ethanol as a tracer compound, in a concentration of 34 mM, to a 2-L Biostat B-DCU fermentor (Sartorius AG, Melsungen, Germany) filled with 1.5 L of ultrapure water. Three aeration methods were compared, namely, headspace aeration and aeration through a ring sparger or a microsparger (Figure 2).

Because of the complexity of targeted chemicals and their concomitant matrices, applications of SIFT-MS in the field of food and flavor analysis present promising results.18−21 Indeed, the use of SIFT-MS for the identification and quantification of volatile organic compounds, suitable as biomarkers and often emitted by microbial cultures or related to (fermented) foods, has been described.22−24 However, the potential of SIFT-MS for the analysis of volatile organic compounds, indicating spoilage or contributing to the aroma of (fermented) foods, has not yet been realized fully.25−34 Moreover, no reports exist on the use of SIFT-MS as an online measurement technique for the monitoring of (food) fermentation processes. The aim of the present study was to develop and validate a SIFT-MS setup that is useful for the online monitoring of volatile metabolites during sourdough fermentations in parallel with quantitative online gas analysis.

■

MATERIALS AND METHODS

Design of an Online Monitoring SIFT-MS Setup. A unique SIFT-MS setup for the online monitoring of the headspace of fermentation vessels was designed (Figure 1). Sourdough fermenta-

Figure 2. Influence of the aeration device and sample gas flow rate (in mL min−1) on the online monitoring of ethanol through SIFT-MS, when ethanol is injected into the fermentor at t = 0. Ring sparger, 40 mL min−1, upper gray line; ring sparger, 10 mL min−1, upper black line; microsparger, 10 mL min−1, lower black line; headspace aeration, 10 mL min−1, lower gray line.

Figure 1. Scheme of the SIFT-MS setup to measure volatile sample compounds from multiple fermentation vessels in parallel with offgases through SIFT-MS/GC-MS and Compact-GC, respectively. tions involving heterofermentative LAB and generating typical volatile compounds, such as ethanol and acetic acid, were performed as a case study. To sweep volatiles toward the SIFT instrument (Syft Voice 200; Syft Technologies Ltd., Christchurch, New Zealand), nitrogen gas was added to the fermentation vessels (Biostat B-DCU fermentors; Sartorius AG, Melsungen, Germany) at 0.35 bar. In addition, this created a small overpressure (0.2 bar) area at the inlet of the autosampler (CGM2000; Umwelttechnik MCZ, Bad Nauheim, Germany), needed for its proper operation. Condensation pots (100 mL glass bottles) were connected directly with the headspace of the fermentors, bypassing the condensors and filters present at their standard gas outlets and operating as a physical water trap for water condensation at room temperature. Heated (105 °C) 1/4′′ polytetrafluoroethylene (PTFE) transfer lines connected these glass bottles with the entrance of the autosampler to avoid condensation of water and volatile compounds. The autosampler was equipped with four digital mass flow controllers and a mixing chamber. Three mass flow controllers were connected to the fermentation vessels and one to the dilution gas. The mass flow controllers were operated by the autosampler software, allowing the selection of gas inflow from a fermentation vessel and an appropriate amount of dilution gas (highgrade nitrogen gas), hence enabling the autosampler to act both as a selection and a dilution device. Dilution and sample gases were mixed in the mixing chamber and an amount of 25 mL min−1 of the gas blend

Applications. Strains and Media. Lactobacillus fermentum IMDO 130101, a strain previously isolated from a spontaneous laboratory rye sourdough, was used for the fermentations in wheat sourdough simulation medium (W-SSM).35,36 Lactobacillus sanf ranciscensis IMDO 150101, a strain isolated from a spontaneous laboratory teff sourdough (unpublished results), was used for the starter culture-initiated sourdough fermentations based on teff flour. The strains were stored at −80 °C in modified de Man-Rogosa-Sharpe-5 (mMRS-5) medium, 37 supplemented with 25% (vol vol −1 ) glycerol as cryoprotectant. Solid media of W-SSM and mMRS-5 were prepared by adding 1.5% (wt vol−1) agar (Oxoid Ltd., Basingstoke, Hampshire, UK) to the broth. Fermentations in Wheat Sourdough Simulation Medium. WSSM was composed of (per liter) wheat peptone, 12.0 g; granulated yeast extract, 12.0 g; MgSO4·7H2O, 0.2 g; MnSO4·H2O, 0.05 g; KH2PO4, 4.0 g; K2HPO4, 4.0 g; Tween 80, 1.0 mL; and vitamin solution, 1.0 mL. The latter had the following composition (per liter): cobalamine, 0.2 g; folic acid, 0.2 g; nicotinamide, 0.2 g; pantothenic acid, 0.2 g; pyridoxal-phosphate, 0.2 g; and thiamine, 0.2 g.36 All chemicals were obtained from VWR International (Darmstadt, Germany). Three W-SSM fermentations were carried out in parallel in 2-L Biostat B-DCU fermentors (Sartorius AG, Melsungen, 830

DOI: 10.1021/jf505111m J. Agric. Food Chem. 2015, 63, 829−835

Article

Journal of Agricultural and Food Chemistry

temperature, 110 °C. A four-position selection valve allowed automated sequential sampling of the three fermentation vessels and injection of calibration gas mixtures (Saphir calibration gases; Air Liquide, Paris, France). Each fermentor was sampled twice every hour. The carbon dioxide concentration was summarized over the entire fermentation period and expressed in millimolars. Volatile sample compounds were monitored through SIFT-MS. All fermentors were sampled consecutively during 20 min so that each fermentor was sampled once per hour. During the sampling time, a full scan (m/z−1 10 to 250 for H3O+, 15−250 for NO+, and 15−250 for O2+) and a selected ion mode (SIM) scan (settle time, 80 s; measurement time, 100 s) were run.42 A list of the ions measured is presented in Supporting Information, Table S1. To verify the results obtained by SIFT-MS, an online dynamic headspace-GC-MS analysis (Agilent 6890 gas chromatograph coupled to an Agilent 5973N mass spectrometer; Agilent Technologies, Santa Clara, CA, USA), was performed. Every hour, a gas sample of 1 mL was withdrawn from a Pall flow cell (CTC Analytics AG, Zwingen, Switzerland), which was connected to the gas outflow of one fermentation vessel by means of a 1/4′′ PTFE transfer line, and injected into a capillary column (DB-WAXetr, Agilent Technologies) at a rate of 500 μL s−1. The carbon recovery was calculated by dividing the total amount of carbon present in the metabolites by the total amount of carbon added as carbohydrates.

Germany). To estimate the variability of the methodology developed, each of these three parallel fermentations were carried out in triplicate. All medium components, except for carbohydrates and the vitamin solution, were dissolved in 1.25 L of distilled water, and the pH of the medium was adjusted to 5.5 by adding HCl and/or NaOH, prior to sterilization (121 °C, 2.1 bar, 20 min) of the fermentors. The carbohydrate mixture (250 mL), consisting of maltose, sucrose, glucose, and fructose in a final concentration of 10 g L−1, 2 g L−1, 0.5 g L−1, and 0.5 g L−1, respectively, was sterilized (121 °C, 2.1 bar, 20 min) separately, and the vitamin solution was filter-sterilized (0.2-μm Minisart PES filters; Sartorius AG, Goettingen, Germany); both were added aseptically to the sterile fermentors, prior to inoculation. The fermentation temperature was kept at 30 °C. The pH of the medium was kept constant at 5.5 through automatic addition of a 1.5 M NaOH solution to the fermentation broth. The stirring speed was fixed at 300 rpm. Temperature, pH, and agitation were controlled online (MicroMFCS for Windows NT software, Sartorius AG, Melsungen, Germany). Sterile nitrogen gas was continuously blown through the fermentation medium at a rate of 200 mL min−1. The inoculum was prepared through three subcultures of 12 h in W-SSM. The first two subcultures were carried out in 10 mL of medium; the third subculture in 100 mL. The transfer volume was always 1.0% (vol vol−1). Starter Culture-Initiated Teff Sourdough Fermentations. Starter culture-initiated sourdough fermentations (8 kg) were carried out in duplicate in 15-L Biostat C fermentors (Sartorius AG, Melsungen, Germany) filled with 6 L of sterile water and 2 kg of teff flour.38 An inoculum of 1.0% (vol vol−1) of the starter culture, subcultured twice in mMRS-5 overnight, was used. This dough was incubated at 30 °C for 72 h, while the mixture was kept homogeneous through stirring (300 rpm). The headspace of the fermentors was flushed with sterile air at a flow rate of 1 L min−1. Off-Line Sample Analyses. During fermentation, samples were regularly withdrawn from the fermentors for the determination of bacterial counts, residual carbohydrates, and metabolites produced, as described previously.36 Briefly, bacterial counts were determined by plating 10-fold dilutions of W-SSM fermentation samples or samples of fresh mixtures of 10 g of teff sourdough sample with 90 mL of sterile saline (0.85%, wt vol−1, NaCl) on W-SSM agar (W-SSM fermentations) or mMRS-5 agar (starter culture-initiated teff sourdough fermentations), followed by incubation at 30 °C for 48 h. For metabolite target analysis, a given amount of W-SSM (50 mL) or sourdough sample (approximately 100 g) was centrifuged (8,041g for 20 min at 4 °C) to remove solids, and the supernatant was stored at −20 °C until further analysis. Concentrations of residual carbohydrates (maltose, sucrose, fructose, and glucose) and mannitol produced were determined through high-performance anion exchange chromatography with pulsed amperometric detection, applying a standard addition protocol.39 Concentrations of lactic acid were determined using high-performance liquid chromatography with refractive index detection (HPLC-RI), through external standards.39 Concentrations of acetic acid, ethanol, and acetoin were determined through HPLC-RI and gas chromatography (GC) with flame ionization detection, using external standards.40 Concentrations of volatile compounds were determined through static headspace-GCMS (SH-GC-MS).41 Peak identification was achieved by comparison with pure standard compounds and library confirmation (NIST 08, National Institute of Standards and Technology, Gaithersburg, MD, USA). Online Analysis of Gases and Volatile Compounds. The headspace of the fermentation vessels was analyzed for carbon dioxide and targeted volatile compounds during the sourdough fermentation processes for either 48 or 72 h. Carbon dioxide was quantified through online gas chromatography by means of a Compact-GC (Interscience, Louvain-la-Neuve, Belgium).5 Briefly, a headspace sample was pumped through an analytical cell, containing a PoraBOND Q column (Varian, Palo Alto, CA, USA) coupled to a thermal conductivity detector. Helium was used as carrier gas. The following conditions were applied: valve temperature, 60 °C; injection volume, 20 μL; carrier gas module mode, constant pressure (70 kPa); split flow, 5 mL min−1; reference flow, 1 mL min−1; column temperature, 60 °C; and detector

■

RESULTS AND DISCUSSION Optimization of the Online Monitoring SIFT-MS Setup. The Syft Voice200 instrument is a very sensitive device since its linear range is a magnitude lower than the anticipated concentrations of volatile compounds emitted by a microbial culture.42,43 Consequently, it was necessary to dilute the headspace samples with dilution gas (nitrogen gas) prior to introduction into the instrument in a dilution chamber (part of the autosampler) to obtain more appropriate concentrations (Figure 1). The mass flow controllers of the autosampler required an input pressure of 0.2 bar, hence making it necessary to put the fermentation vessels under overpressure. It was found that the required pressure of the inflowing gas had to be 0.35 bar to compensate for the pressure drop between the gas inlet of the fermentors and the mass flow controllers of the autosampler. However, this altered the measurement of the carbon dioxide concentration with the Compact-GC. Indeed, when the gas outflow was restricted, the concentration of carbon dioxide quickly increased to values out of the linear range of the apparatus. Therefore, a sufficiently high gas flow rate of 200 mL min−1 was applied. A valve, regulating the outflow of gas and a union tee were installed, making continuous gas flow and decompression to atmospheric pressure possible. This way, the disturbance of the system, for instance by pressure swings influencing biological and physicochemical parameters of the fermentation processes, was minimized, and simultaneous measurements by Compact-GC and SIFT-MS were feasible. Addition of the condensation pots substantially increased the system’s mixing time to around 60 min, as measured with a sample gas flow rate of 10 mL min−1. This made it the limiting step of the measurements and hence determined the overall reaction time of the setup (Figure 2). To compensate for this, the sample gas flow rate was increased to 40 mL min−1. This in turn lowered the breakthrough time from around 9−11 to 6 min. With respect to the time frame of sourdough fermentation (48 h), both the mixing time and breakthrough time were considered acceptable. Unfortunately, an increase of the sample gas flow rate required a proportional increase of the dilution gas 831

DOI: 10.1021/jf505111m J. Agric. Food Chem. 2015, 63, 829−835

Article

Journal of Agricultural and Food Chemistry flow rate, to maintain a linear system response. To limit dilution gas usage, however, this load was allowed to be below 10 ppmv. Also, it is noteworthy that the aeration method did not determine the breakthrough time due to the installment of a condensation pot. A method for the determination of the concentrations of metabolites of heterofermentative LAB metabolism was developed. Additionally, common organic contaminants were included in the method to control the risk of misinterpretation due to pollution by laboratory air. The internal standards were added and measured to monitor the system performance of the SIFT-MS setup. Toluene exhibited interference with one or more volatile sample compounds, while the measured signal of fluorobenzene did not alter during the course of a W-SSM fermentation. Therefore, fluorobenzene was preferred as the internal standard and was used during subsequent experiments. Product ions that, according to the LabSyft software library, could cause interference were removed from the method in several optimization rounds. An initial screening, using online GC-MS and SH-GC-MS, showed that ethanol was the main volatile present. It was preferably detected by SIFT-MS. As a result, ethanol impeded the determination of other volatile sample compounds. No additional compounds were found upon dilution of the sample gas. The response of these compounds could have disappeared in the elevated noise due to their low expected level. Application of SIFT-MS during W-SSM Fermentation. The unique technical setup for the online monitoring of carbon dioxide and microbial volatiles and the method developed for the quantitative determination of these microbial volatiles were applied to follow up a fermentation with Lb. fermentum IMDO 130101 in W-SSM. Online SIFT-MS measurements of volatile compounds present in the headspace of the fermentors were compared with HPLC-RI measurements of the concentrations of these volatiles in the fermentation broth. A typical fermentation pattern, represented in Figure 3, shows the

converted into mannitol, allowing concomitant production of acetate instead of ethanol.36 However, the limited concentration of fructose (5 mM) present in W-SSM limited acetate formation to 5 mM. Only 80−85% of the carbon supplied under the form of carbohydrates could be accounted for in the metabolites measured. Following the heterofermentative metabolism of Lb. fermentum, 65 mM of carbon dioxide was produced, as measured by online gas chromatography. This was stoichiometrically correct, indicating a successful coupling of the Compact-GC with the fermentors as part of the whole configuration for online measurements of fermentation gases and microbial volatiles. Although lactic acid (60 mM) was produced equimolar to the sum of the ethanol and acetic acid concentrations, its concentration in the headspace of the fermentors was too low for detection by SIFT-MS. This might be due to several factors. First, the Henry coefficient, describing the division of volatile organic compounds between the gaseous and liquid phases, is much higher for ethanol than that for lactic acid.44,45 Second, as for all organic acids, the pH of the medium, which was automatically adjusted to 5.5, influenced the equilibrium.46 The latter effect also influenced the concentration of acetic acid in the headspace of the fermentors. Third, charged compounds could have been adsorbed on parts of the setup, removing them from the headspace.47 Therefore, neither lactic acid nor acetic acid could be found in the headspace of the fermentors by means of the current online SIFT-MS setup. In addition, ethanol evaporation, due to the high gas flow rates through the system, was not taken into account.48 Finally, a comparison of the current setup with a closed model system showed a pressure loss of around 10%. Nevertheless, a comparison between the concentrations of ethanol in the medium, as measured by off-line SH-GC-MS, and the concentrations of ethanol in the dynamic headspace, as measured by online SIFT-MS, showed their parallel evolution (Figure 4). Additionally, the online GC-MS measurements

Figure 3. Bacterial growth of and carbohydrate consumption and metabolite production by Lactobacillus fermentum IMDO 130101 during fermentation in wheat sourdough simulation medium at a constant pH of 5.5. Growth is represented as counts of colony forming units per mL (cfu mL−1, black circle); maltose (○), glucose (□), fructose (Δ), sucrose (◇), lactic acid (gray circle), ethanol (■), acetic acid (◆), mannitol (▲), carbon dioxide (−).

Figure 4. Comparison between the concentrations of ethanol in the fermentation broth as measured off-line by HPLC-RI (mM, Δ) and SH-GC-MS (AU, ⧫) and in the dynamic headspace of the fermentation vessels as measured by online SIFT-MS (ppbv, ), during fermentation with Lactobacillus fermentum IMDO 130101 in a wheat sourdough simulation medium.

simultaneous production of lactic acid, ethanol, acetic acid, and carbon dioxide, along with the consumption of the carbohydrates. As described previously, maltose and glucose were the preferred energy sources, while sucrose was consumed once these carbohydrates were exhausted.36 Fructose was used mainly as an alternative external electron acceptor and

showed the same parallel evolution as the online SIFT-MS measurements. This indicates that online measurement of ethanol concentrations in the dynamic headspace of a fermentor by SIFT-MS can be used as a tool to monitor the production of ethanol in the fermentation broth. As ethanol is a key metabolite of the heterofermentative metabolism of LAB, it 832

DOI: 10.1021/jf505111m J. Agric. Food Chem. 2015, 63, 829−835

Article

Journal of Agricultural and Food Chemistry

microbiota to grow out, which was confirmed by differences in the volatile sample compounds found through SIFT-MS, i.e., a lowered production of ethanol and acetoin. In conclusion, the feasibility of SIFT-MS to monitor fermentations through gas sampling in the headspace of fermentation vessels was illustrated, using the case of the production of ethanol during sourdough fermentation. The setup and methodology presented could be used not only to monitor a fermentation process online but also to (semi)quantify volatiles in real-time without the need for additional samplings. The potential of SIFT-MS as an online monitoring device for food fermentations was further demonstrated by the detection of acetoin in certain sourdough fermentations. This allowed discrimination between sourdough fermentation processes and illustrated the importance of real-time monitoring of food fermentations. However, the performance of the SIFT-MS technique in complex matrices needs further examination, as the current device is restricted to the monitoring of sufficiently volatile compounds.

can be used as biomarker for the online monitoring of food fermentations involving heterofermentative LAB. To correct for biological variation, the ratio between the ethanol concentrations in the headspace, as measured by online SIFT-MS, and the ethanol concentrations in the fermentation broths, as measured by HPLC-RI, was calculated and averaged, based on three series of fermentations carried out in triplicate, and was (1.1 ± 0.1) 10−4 (mol per liter in the gas phase) (mol per liter in the liquid phase)−1. Hence, analysis of the dynamic headspace by online SIFT-MS according to the setup of the present study could be used as a tool to monitor fermentations with a variability of only 13%. However, this variability may still be lowered, as the present study included changes in methodology or setup, for example, by the introduction of a SIM method. The advantage of a SIM method over a full mass scan is that only those volatile organic compounds of interest are monitored, and in the SIM mode of operation, ion products from these volatile organic compounds are averaged over longer periods, thus improving the accuracy of the measurements.42,49 Application of Online SIFT-MS during Starter CultureInitiated Teff Sourdough Fermentation. Heterofermentative metabolism of Lb. sanf ranciscensis IMDO 150101 resulted in the production of lactic acid, ethanol, acetic acid, and mannitol out of maltose, glucose, fructose, and sucrose during teff sourdough fermentation (data not shown). Besides ethanol and acetic acid, the application of online SIFT-MS during a Lb. sanf ranciscensis-initiated teff sourdough fermentation showed the production of another volatile sample compound, namely, acetoin (Figure 5), known to be produced by cocultures of Lb.

■

ASSOCIATED CONTENT

S Supporting Information *

Ions measured during SIM scans. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*Phone: +32 2 6293245. Fax: +32 2 6292720. E-mail: ldvuyst@ vub.ac.be. Funding

We acknowledge the financial support from Research Council of the Vrije Universiteit Brussel (SRP, IRP, and IOF projects), the Hercules Foundation, and Flanders’ FOOD. S.V.K. is the recipient of a Ph.D. fellowship from the Vrije Universiteit Brussel. Notes

The authors declare the following competing financial interest(s): Joeri Vercammen and Roel Wuyts belong to Interscience bvba, the official distributor of the SIFT instrument in the Benelux. They intensively contributed to the engineering of the unique SIFT-MS set-up (interface, transfer lines, etc.).

■

Figure 5. Profile of targeted sample compounds measured by online SIFT-MS, namely, ethanol (− − −), acetoin (···), and acetic acid (−).

ACKNOWLEDGMENTS We thank the technical support of Interscience bvba, in particular by Michel Verhaegen and Nico van der Groef. Also, Gunter Eerdekens (Interscience bvba) and Tom Balzarini (Vrije Universiteit Brussel) are gratefully acknowledged for their technical advice.

sanf ranciscensis and Saccharomyces cerevisiae under (acid) stress conditions.50 The low concentrations present in this sourdough (5 mM) corresponded with a large response (255 ppmv) in the SIFT-MS, which was in contrast with the low response for acetic acid (12 ppmv), despite the high concentrations of the latter in this sourdough (70 mM). Several factors could be accountable for the discrepancy. However, all this seems to limit the use of SIFT-MS to the monitoring of noncharged, volatile compounds, unless precautions are taken to trap these compounds. Nevertheless, the detection of acetoin and ethanol demonstrated the feasibility of SIFT-MS as a real-time monitoring unit for sourdough fermentation processes. Interestingly, extensive microbiological analysis of these fermentation processes showed that the Lb. sanf ranciscensis IMDO 150101 starter culture added did not dominate all of the fermentations performed (data not shown), allowing other

■

REFERENCES

(1) Caplice, E.; Fitzgerald, G. F. Food fermentations: role of microorganisms in food production and preservation. Int. J. Food Microbiol. 1999, 50, 131−149. (2) Leroy, F.; De Vuyst, L. Lactic acid bacteria as functional starter cultures for the food fermentation industry. Trends Food Sci. Technol. 2004, 15, 67−78. (3) De Vuyst, L.; Van Kerrebroeck, S.; Harth, H.; Huys, G.; Daniel, H.-M.; Weckx, S. Microbial ecology of sourdough fermentations: diverse or uniform? Food Microbiol. 2014, 37, 11−29. (4) Maiti, S. K.; Srivastava, R. K.; Bhushan, M.; Wangikar, P. P. Real time phase detection based online monitoring of batch fermentation processes. Process Biochem. 2009, 44, 799−811.

833

DOI: 10.1021/jf505111m J. Agric. Food Chem. 2015, 63, 829−835

Article

Journal of Agricultural and Food Chemistry (5) Falony, G.; Verschaeren, A.; De Bruycker, F.; De Preter, V.; Verbeke, K.; Leroy, F.; De Vuyst, L. In vitro kinetics of prebiotic inulin-type fructan fermentation by butyrate-producing colon bacteria: implementation of online gas chromatography for quantitative analysis of carbon dioxide and hydrogen gas production. Appl. Environ. Microbiol. 2009, 75, 5884−5892. (6) Bastidas-Oyanedel, J.-R.; Mohd-Zaki, Z.; Pratt, S.; Steyer, J.-P.; Batstone, D. J. Development of membrane inlet mass spectrometry for examination of fermentation processes. Talanta 2010, 83, 482−492. (7) Biasioli, F.; Yeretzian, C.; Märk, T. D.; Dewulf, J.; Van Langenhove, H. Direct-injection mass spectrometry adds the time dimension to (B)VOC analysis. Trends Anal. Chem. 2011, 30, 1003− 1017. (8) Adams, N. G.; Smith, D. The selected ion flow tube (SIFT); a technique for studying ion-neutral reactions. Int. J. Mass Spectrom Ion Phys. 1976, 21, 349−359. (9) Španěl, P.; Smith, D. Advances in on-line absolute trace gas analysis by SIFT-MS. Curr. Anal. Chem. 2013, 9, 525−539. (10) Španěl, P.; Smith, D. Selected ion flow tube: a technique for quantitative trace gas analysis of air and breath. Med. Biol. Eng. Comput. 1996, 34, 409−419. (11) Smith, D.; Španěl, P. Direct, rapid quantitative analyses of BVOCs using SIFT-MS and PTR-MS obviating sample collection. Trends Anal. Chem. 2011, 30, 945−959. (12) Heynderickx, P. M.; Van Huffel, K.; Dewulf, J.; Van Langenhove, H. Application of similarity coefficients to SIFT-MS data for livestock emission characterization. Biosyst. Eng. 2013, 114, 44−54. (13) Langford, V. S.; Gray, J. D. C.; McEwan, M. J. Selected ion flow tube studies of several siloxanes. Rapid Commun. Mass Spectrom. 2013, 27, 700−706. (14) Heynderickx, P. M.; De Clercq, S.; Saveyn, P.; Dewulf, J.; Van Langenhove, H. Determination of the sorption and desorption kinetics of perfume raw materials in the liquid phase with vesicular dispersion: Application of SIFT-MS. Chem. Eng. J. 2013, 217, 281−288. (15) Havlíček, V.; Lemr, K.; Schug, K. A. Current trends in microbial diagnostics based on mass spectrometry. Anal. Chem. 2013, 85, 790− 797. (16) Storer, M. K.; Hibbard-Melles, K.; Davis, B.; Scotter, J. Detection of volatile compounds produced by microbial growth in urine by selected ion flow tube mass spectrometry (SIFT-MS). J. Microbiol. Methods 2011, 87, 111−113. (17) Dummer, J.; Storer, M.; Swanney, M.; McEwan, M.; ScottThomas, A.; Bhandari, S.; Chambers, S.; Dweik, R.; Epton, M. Analysis of biogenic volatile organic compounds in human health and disease. Trends Anal. Chem. 2011, 30, 960−967. (18) West, R.; Seetharaman, K.; Duizer, L. M. Whole grain macaroni: Flavour interactions with sodium-reduced cheese sauce. Food Res. Int. 2013, 53, 149−155. (19) Langford, V. S.; Reed, C. J.; Milligan, D. B.; McEwan, M. J.; Barringer, S. A.; Harper, W. J. Headspace analysis of Italian and New Zealand parmesan cheeses. J. Food Sci. 2012, 77, C719−C726. (20) De Clippeleer, J.; Van Opstaele, F.; Vercammen, J.; Francis, G. J.; De Cooman, L.; Aerts, G. Real-time profiling of volatile malt aldehydes using selected ion flow tube mass spectrometry. LCGC North Am. 2010, 28, 386. (21) Huang, X. S.; Duan, H. Y.; Barringer, S. A. Effects of buffer and temperature on formation of furan, acetic acid and formic acid from carbohydrate model systems. Food Sci. Technol. 2011, 44, 1761−1765. (22) Thorn, R. M. S.; Reynolds, D. M.; Greenman, J. Multivariate analysis of bacterial volatile compound profiles for discrimination between selected species and strains in vitro. J. Microbiol. Methods 2011, 84, 258−264. (23) Shestivska, V.; Španěl, P.; Dryahina, K.; Sovova, K.; Smith, D.; Musilek, M.; Nemec, A. Variability in the concentrations of volatile metabolites emitted by genotypically different strains of Pseudomonas aeruginosa. J. Appl. Microbiol. 2012, 113, 701−713. (24) Scotter, J. M.; Langford, V. S.; Wilson, P. F.; McEwan, M. J.; Chambers, S. T. Real-time detection of common microbial volatile

organic compounds from medically important fungi by selected ion flow tube-mass spectrometry (SIFT-MS). J. Microbiol. Methods 2005, 63, 127−134. (25) Langford, V.; Gray, J.; Foulkes, B.; Bray, P.; McEwan, M. J. Application of selected ion flow tube-mass spectrometry to the characterization of monofloral New Zealand honeys. J. Agric. Food Chem. 2012, 60, 6806−6815. (26) Olivares, A.; Dryahina, K.; Navarro, J. L.; Smith, D.; Španěl, P.; Flores, M. SPME-GC-MS versus selected ion flow tube mass spectrometry (SIFT-MS) analyses for the study of volatile compound generation and oxidation status during dry fermented sausage processing. J. Agric. Food Chem. 2011, 59, 1931−1938. (27) Noseda, B.; Islam, M. T.; Eriksson, M.; Heyndrickx, M.; De Reu, K.; Van Langenhove, H.; Devlieghere, F. Microbiological spoilage of vacuum and modified atmosphere packaged Vietnamese Pangasius hypophthalmus fillets. Food Microbiol. 2012, 30, 408−419. (28) Amadei, G.; Ross, B. M. Quantification of character-impacting compounds in Ocimum basilicum and ‘Pesto alla Genovese’ with selected ion flow tube mass spectrometry. Rapid Commun. Mass Spectrom. 2012, 26, 219−225. (29) Sharp, M. D.; Kocaoglu-Vurma, N. A.; Langford, V.; RodriguezSaona, L. E.; Harper, W. J. Rapid discrimination and characterization of vanilla bean extracts by attenuated total reflection infrared spectroscopy and selected ion flow tube mass spectrometry. J. Food Sci. 2012, 77, C284−C292. (30) Bowman, T.; Barringer, S. Analysis of factors affecting volatile compound formation in roasted pumpkin seeds with selected ion flow tube-mass spectrometry (SIFT-MS) and sensory analysis. J. Food Sci. 2012, 77, C51−C60. (31) Huang, Y.; Barringer, S. A. Alkylpyrazines and other volatiles in cocoa liquors at pH 5 to 8, by selected ion flow tube-mass spectrometry (SIFT-MS). J. Food Sci. 2010, 75, C121−C127. (32) Davis, B. M.; Senthilmohan, S. T.; McEwan, M. J. Direct determination of antioxidants in whole olive oil using the SIFT-MSTOSC assay. J. Am. Oil Chem. Soc. 2011, 88, 785−792. (33) Sumonsiri, N.; Barringer, S. A. Application of SIFT-MS in monitoring volatile compounds in fruits and vegetables. Curr. Anal. Chem. 2013, 9, 631−641. (34) Huang, Y.; Barringer, S. A. Monitoring of cocoa volatiles produced during roasting by selected ion flow tube-mass spectrometry (SIFT-MS). J. Food Sci. 2011, 76, C279−C286. (35) Van der Meulen, R.; Scheirlinck, I.; Van Schoor, A.; Huys, G.; Vancanneyt, M.; Vandamme, P.; De Vuyst, L. Population dynamics and metabolite target analysis of lactic acid bacteria during laboratory fermentations of wheat and spelt sourdoughs. Appl. Environ. Microbiol. 2007, 73, 4741−4750. (36) Vrancken, G.; Rimaux, T.; De Vuyst, L.; Leroy, F. Kinetic analysis of growth and sugar consumption by Lactobacillus fermentum IMDO 130101 reveals adaptation to the acidic sourdough ecosystem. Int. J. Food Microbiol. 2008, 128, 58−66. (37) Meroth, C. B.; Walter, J.; Hertel, C.; Brandt, M. J.; Hammes, W. P. Monitoring the bacterial population dynamics in sourdough fermentation processes by using PCR-denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 2003, 69, 475−482. (38) Ravyts, F.; De Vuyst, L. Prevalence and impact of single-strain starter cultures of lactic acid bacteria on metabolite formation in sourdough. Food Microbiol. 2011, 28, 1129−1139. (39) Vrancken, G.; Rimaux, T.; Weckx, S.; Leroy, F.; De Vuyst, L. Influence of temperature and backslopping time on the microbiota of a type I propagated laboratory wheat sourdough fermentation. Appl. Environ. Microbiol. 2011, 77, 2716−2726. (40) Lefeber, T.; Gobert, W.; Vrancken, G.; Camu, N.; De Vuyst, L. Dynamics and species diversity of communities of lactic acid bacteria and acetic acid bacteria during spontaneous cocoa bean fermentation in vessels. Food Microbiol. 2011, 28, 457−464. (41) Ravyts, F.; Vrancken, G.; D’Hondt, K.; Vasilopoulos, C.; De Vuyst, L.; Leroy, F. Kinetics of growth and 3-methyl-1-butanol production by meat-borne, coagulase-negative staphylococci in view of sausage fermentation. Int. J. Food Microbiol. 2009, 134, 89−95. 834

DOI: 10.1021/jf505111m J. Agric. Food Chem. 2015, 63, 829−835

Article

Journal of Agricultural and Food Chemistry (42) Syft Technologies Ltd. Development of SIFT-MS Methods. In SIFT-MS Primer: An Introduction to the Chemistry and Methodology of SIFT-MS; Syft Technologies Ltd.: Christchurch, New Zealand, 2009; pp 59−72. (43) Prince, B. J.; Milligan, D. B.; McEwan, M. J. Application of selected ion flow tube mass spectrometry to real-time atmospheric monitoring. Rapid Commun. Mass Spectrom. 2010, 24, 1763−1769. (44) Saxena, P.; Hildemann, L. M. Water-soluble organics in atmospheric particles: A critical review of the literature and application of thermodynamics to identify candidate compounds. J. Atmos. Chem. 1996, 24, 57−109. (45) Yaws, C. L.; Yang, H.-C. Henry’s Law Constant for Compound in Water. In Thermodynamic and Physical Property Data; Yaws, C. L., Ed.; Gulf Publishing Company: Houston, TX, 1992; pp 181−206. (46) Smith, F. L.; Harvey, A. H. Avoid common pitfalls when using Henry’s law. Chem. Eng. Prog. 2007, 103, 33−39. (47) Yokelson, R. J.; Christian, T. J.; Bertschi, I. T.; Hao, W. M. Evaluation of adsorption effects on measurements of ammonia, acetic acid, and methanol. J. Geophys. Res. 2003, 108, 4649. (48) Moens, F.; Lefeber, T.; De Vuyst, L. Oxidation of metabolites highlights the microbial interactions and role of Acetobacter pasteurianus during cocoa bean fermentation. Appl. Environ. Microbiol. 2014, 80, 1848−1857. (49) Masucci, J. A.; Caldwell, G. W. Techniques for Gas Chromatography/Mass Spectrometry. In Modern Practice of Gas Chromatography, 4th ed.; Grob, R. L., Barry, E. F., Eds.; WileyInterscience: New York, 2004; pp 354−355. (50) Guerzoni, M. E.; Vernocchi, P.; Ndagijimana, M.; Gianotti, A.; Lanciotti, R. Generation of aroma compounds in sourdough: Effects of stress exposure and lactobacilli-yeasts interactions. Food Microbiol. 2007, 24, 139−148.

835

DOI: 10.1021/jf505111m J. Agric. Food Chem. 2015, 63, 829−835