August 25, 2014

GC Analysis - Part IV. Retention Indices

Alexis St-Gelais - Popularization

I mentioned in the first post of this series on GC analyzes that PhytoChemia resorted simultaneously to two capillary columns to analyze essential oils, one polar (Solgel-Wax) and one non-polar (DB-5). The rationale behind this technique will reveal some of the art of interpreting essential oils profiles.

We have seen that the detection by FID gives an interesting estimate of the amount of a molecule in a mixture, but provides no information on its structure. How then can we identify it? Instead of using information from the signal itself, we rather refer to the separation behavior of the molecule during GC analysis.

As long as we use the same analytical method, a given compound will always be eluted at the same speed, and therefore have a relatively constant retention time. However, columns degrade over time, and different laboratories may use different methods. The best way to compare data between laboratories is to use a relative reference scale called retention index. To find the index, we first inject a series of reference molecules (called alkanes) using our current analytical method and capillary columns. Alkanes are unbranched carbon chains of different lengths, comprising between 8 to 36 carbons in the case of PhytoChemia. They will be eluted sequentially from the shortest to the longest chain length, following the increase of their boiling points. To the retention time of the 8-carbons chain (octane), a retention index of 800 is assigned. For the chain comprising 10 carbons (decane), a 1000 retention index is assigned, and so on up to 3600. 

Once this is done, we calculate the retention index for each of the molecules of an essential oil by comparing their retention times with those of the alkanes. For example, a compound eluting halfway between alkanes with 10 and 11 carbons will have an index of 1050. Regardless of the method used, these indices are relatively robust: β-pinene will always have the same retention index, around 965, provided that the column used is similar. Therefore, with a proper database and a literature survey, one can identify the molecules using their retention index on DB-5 column, which is the most widely used in the field of essential oils. We can find a large number of reference data in the flagship book of Robert P. Adams (1) or through the NIST website (2), again for DB-5 columns. 

There are however two problems if only one column is used. First, the retention indices are not 100% robust. Differences between methods, laboratories and analyzed matrixes can induce a change of several retention indices units (we have so far observed the β-pinene between 962 and 966, for example). Consequently, two molecules with close retention indices can be confused using a single DB-5 column, and the FID does not provide any more information to clarify the situation. Moreover, it so happens that more than one compound is eluted at any given time. A typical case in DB-5 is that of τ-muurolol + τ-cadinol, which are almost always superimposed on each other with a retention index of about 1635. It is thus impossible to know what proportion of the peak aera should be related to each compound, or even to check if one of them is missing.

This is where a second column proves to be useful. Since its selectivity is different, the Solgel-Wax column separates the compounds of the mixture in a distinct way. Retention indices of the molecules on the second column are thus different. Instead of relying on a single retention index from DB-5, PhytoChemia rather uses pairs of retention indices. Thus, a peak at 1635 on DB-5 will be assigned to τ-cadinol if visible at 2122 on Solgel-Wax, and to τ-muurolol if rather found at 2138. The use of pairs of indices greatly increases confidence in our identifications, and also frequently allows to solve the problems of co-elution in order to individually quantify the two compounds (Figure 1). 


Figure 1. Example of the usefulness of analysing with two columns in case of coelution. On the DB-5 column (above), the two compounds are severely superposed, which prevents their individual quantification. On Solgel-Wax column (below), both compounds are distinct. Using pairs of retention indices, it is possible to find which compound is which and to quantify them individually.
Interpreting a spectra can be a relatively complex task that requires experience (embodied in part in the database we built), vigilance and time. That is why it is important to deal with professionals. 

Retention indices should always be shown on your analysis reports, even if the analyst used a MS detector to identify the molecules (3). Indeed, several molecules can produce a quite similar mass spectrum. The use of retention index and literature often prevents misidentifications. A convincing identification in gas chromatography should always be based on the correlation of two elements: either the retention indices on two columns, or a mass spectrum coupled with a consistent retention index. 

References
(1) Adams, RP, 2007 Identification of Essential Oil Components by Gas Chromatography / Mass Spectrometry, 4th ed., Allured Publishing Corporation, 804 p. 

(2) National Institute of Standards and Technology, 2011. NIST Chemistry WebBook [Online]. "Standard Reference Data Program". URL: http://webbook.nist.gov/chemistry/ 

(3) Marriott, PJ, Shellie, R., Cornwell, C. Gas chromatographic technologies for the analysis of essential oils. J. Chromatogr. A, 2001, 936, 1-22.

August 18, 2014

GC Analysis - Part III. Mass Spectrometry Detection (MS)

Alexis St-Gelais - Popularization

My last post gave some details about a detection method commonly used in GC, the FID. Another detector that is widely used for the analysis of volatile compounds, including at PhytoChemia, is the mass spectrometer (MS).

Unlike the FID, the MS is a complex detector. Typically, in GC-MS (although other modes are available), when a compound to be analyzed enters the detector, it is first subjected to a strong electric current which splits it into electrically charged fragments. A stream of gas then carries the fragments between a series of magnets that generate a magnetic field. That magnetic field can attract the fragment to the walls of the detector, where it is neutralized, or rather lead it the detector which produces an electrical signal. The more fragments reaching the detector, the more intense the signal is. Depending on the frequency of the magnetic field, fragments that are more or less heavy will be allowed to cross the magnets, and their atomic mass will be known. As the magnetic field rapidly oscillates, the detector performs a full scan for fragments of all selected atomic masses several times per second.

In GC-MS, the plotted chromatogram (Figure 1) is a sum of all the ions detected at a specific time. At any point in time, the spectrum can be inspected to extract the mass distribution of the detected ions.

Figure 1. Data obtained during a GC-MS analysis. Above, the chromatogram, which is a sum of the number of detected ions of any mass at any given time. Below, the mass spectrum extracted at 43.74 minutes, which provides the mass distribution of the ions sampled at this moment. This spectrum can be used to identify the peak of the chromatogram, corresponding to longifolene.
One must keep in mind that when we say that a compound reaches the detector, it is actually trillions of molecules that are broken simultaneously. Fragmentation does not occur purely randomly: some chemical bonds between atoms are easier to break than others. On several billions simultaneous fragmentations, fragments resulting from the cleavage of these weaker links will therefore be produced more often. Conversely, the more energy-demanding fragmentations are disadvantaged, and occur less often. In short, the mass spectrum above is a statistical distribution: the fragments whose intensity is the strongest are those most often observed when one breaks a large population of molecules from the analyzed compound. The result is a "fingerprint" which is relatively characteristic for a given chemical compound.

This fingerprint, called a mass spectrum, can be compared to databases. The software compares the spectrum observed with thousands of reference spectra, in order to identify the molecular structures that are the most consistent with the observed signal. In short, MS is a powerful tool for identifying compounds: it is its primary use. As such, when facing an essential oil in which we struggle to identify all the molecules by our usual methods (a future post will explain this technique), the use of MS often solves the problem.

But one has to pay attention to some pitfalls. Indeed, two different chemical compounds can produce very similar mass spectra. A database of mass spectra should never be used uncritically. It may also sometimes happen that a molecule is not part of the database. These are never fully complete, and it is even possible to stumble upon molecules that have never been previously identified (which implies that much more lab research will be required if the structure is to be found). Thus, the interpretation of a serious MS analysis involves looking at literature and concurrently using retention indices (see next post) to avoid confusion.

In short, detection by MS can generally improve the proportion of identified compounds in an essential oil or a mixture of other volatile products, and provides lots of information on the structure of molecules. It is a valuable ally of the chemist, but should be used with caution.

August 11, 2014

GC Analysis - Part II. The Flame Ionization Detector (FID)

Alexis St-Gelais - Popularization

The first part of this series dealt with the principles of separation of volatile molecules upon analysis by GC. After their separation, the analyst has yet to be able to detect molecules as they leave the capillary column, either to identify or quantify them. At PhytoChemia, we mainly use two types of detectors to do so: the flame ionization detector (FID) and the mass spectrometry detector (MS). This post focuses on the first of these detectors.

The FID is one of the simplest detectors used in organic chemistry. It relies on a flame generated by the combustion of a flow of hydrogen mixed with ultrapure air. The carrier gas coming out of the capillary column passes through the flame. When an organic molecule carried by the gas reaches the flame, it is oxidized and generates electrically charged ions. Electrodes near the flame measure the electrical current generated in real time, which is plotted on a graph called a chromatogram (Figure 1). This animation from Chromedia allows to visualize the process.

Figure 1. Example chromatogram obtained by gas chromatography with a flame ionization detector (FID).
As a detector, the FID is relatively universal, which is a very useful property. Indeed, almost all organic molecules that are suitable for GC analysis will oxidize and produce an electric current. Only very small molecules (e.g. formaldehyde) or those that are not prone to oxidation by ionization will not give good results in FID. In short, in the case of essential oils, for example, all the compounds in the mixture produce an electrical signal.

The FID is also very sensitive. The quantity of essential oil that we inject for analysis at PhytoChemia is thus very small. Taking into account all the parameters of injection, just over 0.2 nanoliter of essential oil is introduced in each capillary column. And we are able to detect compounds representing less than 0.05% of that sample! 

The FID has another useful advantage for the analysis of essential oils. The more abundant a molecule is, the more fragments it generates. The recorded electrical signal is thus relatively proportional to the concentration of the compound in the starting sample. It is therefore considered, by convention, that the total recorded electric current (all peaks in the chromatogram) represents the total number of molecules of the essential oil. The proportion of the total signal that each peak represents is the estimated concentration of the corresponding molecule in the sample (1,2). That is why our reports always refer to percentages. This convention avoids having to establish a calibration curve for every analysis and for each compound.

We must be aware that this is an approximation, not an absolute quantification. The response factor* of each analyzed molecule varies anyhow (although to a lesser extent for structurally related compounds, such as non-oxygenated monoterpenes (3)). I will come back to this aspect later on. That being said, for most essential oils norms (1) and for analysis certificates in the volatile products sector (2), as well as in the majority of scientific publications in related fields, the approximation "concentration ≈ % of total integration" is the way to go.

In short, the FID is an effective detection method, which is both sensitive and polyvalent. It is particularly indicated for the analysis of volatile natural compounds.

*The response factor is the constant by which the signal recorded by a detector must be divided to quantitate a molecule in mass units. For example, if 2.0 µg of a molecule passing through the detector producdes a signal of 50000 units (absorbance of light, electric current, etc.), the response factor of 25 000 units/µg. 

References
(1) ISO 11024-2:1998(F). « Directives générales concernant les profils chromatographiques – Partie 2: Utilisation des profils chromatographiques des échantillons d’huiles essentielles », via AFNOR.
"Using the information provided by the data analysis system used for chromatogram B, according to the area normalization quantification method (internal normalization method, as of ISO 7609), ensure that the concentrations (considered as being equivalent to the percentage of total signal corresponding to the peaks of interest) or the concentration ratios fall between the minimal and maximal values indicated under the "Chromatographic profile" heading of the norm corresponding to the studied essential oil." [Translated by author]
(2) IOFI Working Group on Methods of Analysis, 2011. Guidelines for the quantitative gas chromatography of volatile flavouring substances, from the Working Group on Methods of Analysis of the International Organization of the Flavor Industry (IOFI). Flavour Fragr. J. 26, 297-299.
(3) Raffa, K. F., Steffeck, R. J., 1988. Computation of response factors for quantitative analysis of monoterpenes by gas-liquid chromatography. J. Chem. Ecol. 14 (5), 1385-1390.

August 4, 2014

Extracting Essential Oils in the Lab

Alexis St-Gelais  Popularization

Many of the samples we analyze are sent to us directly as essential oils. Sometimes, however, a customer wishes to compare plants before their extraction. He may also need a measure of oil yield so that he can plan ahead larger extractions. In these cases, we use two main extraction techniques, each with its advantages and disadvantages: hydrodiffusion extraction and Clevenger apparatus extraction. Both techniques apply to oils that are less dense than water (specific glassware is otherwise required).

Figure 1. Hydrodiffusion extraction apparatus.
Hydrodiffusion (figure 1) consists of extracting the essential oil with steam that circulates through the plant material. At laboratory scale, we bring a few liters of water to a boil, and steam rises in a column containing the more or less finely ground plant. The vapor phase is then directed to a condenser, and the liquid is collected in a graduated burette. Thanks to a bended connection at the base of the burette, the hydrosol flows to the left in the beaker, while the essential oil remains in the burette. 

After typically 2 h of extraction (calculated from the first condensed drop), we can measure the volume of oil recovered and calculate the yield from the mass of plant introduced. This technique has the advantage of allowing us to recover the hydrosol. It also allows us to perform extractions on larger quantities of plants. For example, in the picture above, we are extracting 1.25 kg of cow parsnip stems for a research project. This technique is quite similar to that typically used in industry, at a much smaller scale. However, the extraction may be less exhaustive with hydrodiffusion than with a Clevenger apparatus. 

Figure 2. Clevenger extraction apparatus.
The Clevenger apparatus was named from its inventor, Joseph Franklin Clevenger, who published in 1928. A few models exist. The most common one (Figure 2) is a piece of specific glassware, as can be seen above the round bottom flask. The flask, of variable size, contains water which is boiled as well as the plant to be extracted. The steam rises in the assembly to a condenser (out of picture), and the condensate falls into the small burette on the right. Oil floats on the water, which for its part is gradually returned to the heated flask through the diagonal conduit. After 2 hours of extraction, the oil volume collected in the burette can be directly measured. 

Sometimes, the Clevenger apparatus gives higher yields than hydrodiffusion. Several factors can explain this phenomenon. In particular, the packing of the plant material in the hydrodiffusion column sometimes creates pockets that are poorly exposed to steam, and therefore not properly extracted. In addition, the Clevenger apparatus uses cohobation: the hydrosol is not recovered here, it is rather returned to the extractor. Therefore, compounds in solution in water are distilled again, and as water becomes saturated, these compounds begin to accumulate in the essential oil phase. The Clevenger apparatus is therefore considered to be the most representative of the "absolute" content in volatile compounds. 

On the other hand, this type of extraction is rather scarcely applied at large scale, and the yield is generally overestimated compared with what can be expected in an extraction plant. In addition, although we can change the size of the round bottom flask, we are much more limited for the amount of plant material that we can extract (here again for rods of cow parsnip, we are extracting 250 g). In short, the choice of method to use will depend on the objectives of the project and the estimated extraction capacities of the customer. 

Whether for one or the other of these methods, keep in mind that you can count on Laboratoire PhytoChemia should you need to estimate the essential oil yield of plant material without performing the extraction yourself.