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Classifying Cheeses by Volatile APCI (vAPCI) Compact Mass Spectrometry

Mass Spec: expression® CMS
Sampling: vAPCI

INTRODUCTION

Cheese is one of the world’s most popular food types, with a wide variety available for consumers. We commonly eat cheeses from cows, goats, and sheep. The scents and flavors of cheeses, so characteristic to each type of cheese, stem from a complex mixture of chemicals, including free fatty acids. While this mixture is affected by a wide variety of factors we can use the mass spectra to characterize the volatile profiles of different types of cheeses.

Figure 1: (A) Goat cheese, (B) Blue Stilton,  (C) Red Leicester, (D) Wensleydale.
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Figure 2: Schematic of the vAPCI source inlet system.

In this application note, we demonstrate the capability of the Advion expression® CMS to analyze volatile fatty acids of various types of cheeses using our volatile APCI (vAPCI) ion source. By heating the cheese samples, we released various volatile compounds, mainly fatty acids, and analyzed the headspace without any sample preparation or derivatization. We then performed statistical analysis to group the cheese samples by their different volatile profiles.

 

METHODS

Several cheeses of different types were warmed in vessels to 70°C for 2 hours, and the headspaces of the vessels were analyzed using the CMS with a vAPCI ion source, using solvent flow (10 mM4NH4OAc in 1:1 MeOH:H2O) to aid in ionization.

While the cheese samples contained many of the same fatty acids, ions invisible to the naked eye will provide the required information to separate the profiles for each cheese. To look for these differences we performed principle component analysis (PCA) on the mass spectra.

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Figure 3: A selection of fatty acids commonly found in different cheese.

RESULTS AND DISCUSSION

The mass spectra show that a wide variety of fatty acids evolve from each of the cheese samples when warmed (Figure 3). Each cheese sample contained many of the same fatty acids (Table 1).

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Figure 4: Mass spectra of representative samples of four types of cheese: (A) Goat Cheese, (B) Blue Stilton, (C) Red Leicester, and (D) Wensleydale.

Table 1: Fatty acids observed using vAPCI analysis of cheese samples.

PCA is a statistical tool that is used to look for patterns in data. The resulting plot (Figure 6) shows grouping based on how similar or different samples are from each other. By performing PCA on the data from several samples of each type of cheese, we found that the different cheeses indeed can each be grouped together based on their mass spectra allowing rapid identification using vAPCI analysis. For example, the various goat cheeses had statistically similar spectra and are thus grouped together on the PCA plot. This is generally true of each type of cheese.

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Figure 6: PCA of cheese volatile profiles.

The mass spectra of each type of cheese were characteristic; not only were the spectra for cheese samples similar within each type of cheese, but they were substantially different between the different types of cheeses analyzed.

CONCLUSIONS

We used the Advion expression® CMS with a vAPCI ion source to analyze the fatty acids in vapor given off by warmed cheese samples without any additional sample preparation or derivatization. Additionally, we used PCA to show that the spectra of each type of cheese is characteristic to that type of cheese, which allows us to classify the different cheese samples by their type. This would further allow us to identify cheeses by their type using a simple volatile mass spectrometry set up.

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Analysis of Volatile Compounds in the Fermentation of Beer

Mass Spec: expression® CMS
Sampling: vAPCI

INTRODUCTION

The chemical analysis of alcoholic beverages is an important step in quality control, being used to monitor flavour profiles across batches, study chemical changes in the product over time, and identify the source of any problems (e.g. off flavours).

The complex flavour of beer is primarily a result of the ingredients used, the brewing method, and conditions during fermentation, and the analysis of beer throughout this process can be invaluable in monitoring fermentation and establishing the point at which problems occur. Being one of the most widely consumed beverages worldwide, rapid and reliable analytical techniques are essential to keep up with demand and production.

Gas or liquid chromatography-mass spectrometry (GC/MS or LC/MS, respectively) are traditionally utilised for quality control in the spirit and beverage industry; however, these techniques can be relatively time-consuming and not necessarily ideal for rapid, high-throughput analysis.

METHOD

Figure 1: Advion expression® CMS with vAPCI heat transfer line.page2image34676944

Figure 2: Schematic of vAPCI/CMS.

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Aliquots of the homebrew (1 mL) were collected and analysed 12 hours, 4 days, and 14 days into the fermentation process, in addition to mosaic hop leaves (1 g). The homebrew also contained simcoe and citra hops, which were not analysed.

Each aliquot was sealed in a glass vial and heated to 70oC for 10 minutes. The headspace was drawn directly into the CMS by the Venturi Effect of the vAPCI source for analysis. Samples were analysed in positive ion mode over a range of 30-300 m/z, with a scan time of 400 ms.

RESULTS AND DISCUSSION

Figure 3: Mass spectra of homebrew headspace at (A) 12 hours, (B) 4 days, and (C) 14 days) into fermentation.

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There were distinct changes in the overall volatile profile, notably the gradual increase in the m/z 93 ion, likely the protonated ethanol dimer (Figure 3). The concentration of this ion plateaus at the 4 day timepoint, demonstrating fermentation primarily occurred in the first few days.

Figure 4: Mass spectrum of mosaic hops, added 4 days into fermentation.

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The headspace of mosaic hops used in in this homebrew were also analysed. The hops mass spectrum (Figure 4) was dominated by ions at m/z 81, 137 and 273, all of which are common ions associated with terpenes, a class of compounds responsible for many of the aromas and flavours of hops. Many of these compounds are of the same molecular weights and thus further analysis would be required to differentiate and identify these components. Components derived from hops are readily detected in the beer aliquots, particularly after the 4 day timepoint, when additional hops were added.

CONCLUSIONS

This study demonstrates the use of the Advion expression® CMS with vAPCI for the analysis of volatile compounds from the headspace of home-brew beer and hops. The Venturi-assisted interface of the instrument enabled rapid sampling of volatiles, allowing the changing volatile profile of the homebrew to be observed throughout the fermentation process. This simple method would be suitable for fast quality control during alcoholic beverage production.