Characterizing Essential Oils with GC-MS

Extracts from plant materials that capture the plant's flavor and scent are known as essential oils and they have many uses.

Mass Spectrometry (MS) and Gas Chromatography (GC) and are excellent tools for the analysis of essential oils because the semi-volatile and volatile analytes that make up essential oils are readily separated, identified and quantified.

This can be helpful to obtain detailed chemical information on essential oils for a variety of quality control objectives, including process optimization, authentication and characterization.

When using the Pegasus® BT for GC-MS, it is possible to use chromatography to achieve the separation of individual chemicals and also to deconvolute the full m/z range data in instances of chromatographic coelution.

Many chromatographic coelutions can be unraveled in less time, meaning that more information is obtained by adding mathematical separation to chromatographic coelutions. Tentative identifications are made using GC-MS from both chromatographic retention order information and spectral information.

Spectral verification can be done by matching the acquired full m/z range TOFMS data with NIST library databases. For added confidence, it is possible to link the retention times of observed peaks to the retention index by using a known alkane standard allowing for retention index matching with the NIST library databases.

This article analyzes and characterizes a mint essential oil showing the benefits of full m/z range data, deconvolution and retention index determinations.

TIC Chromatogram for mint essential oil. Representative analytes of interest are shown along with a summary of the sample

Figure 1. TIC Chromatogram for mint essential oil. Representative analytes of interest are shown along with a summary of the sample's aroma characteristics. Image Credit: LECO Corporation


A mint essential oil was analyzed with GC-TOFMS after being diluted to 1% in acetone (shown in Table 1). The same methods for Retention Index (RI) determinations were also used to collect data for an alkane standard (C6 through C24).

Table 1. GC-TOFMS (Pegasus BT) Conditions. Source: LECO Corporation

Gas Chromatograph Agilent 7890 with LECO L-PAL 3 Autosampler
Injection 1 μL, split 100:1
Inlet 250 °C
Carrier Gas He @ 1.4 mL/min
Column Rxi-5ms, 30 m x 0.25 mm i.d. x 0.25 μm coating (Restek)
Temperature Program 40 °C ramp 10 °C/min to 280 °C
Transfer Line 300 °C
Mass Spectrometer LECO Pegasus BT
Ion Source Temperature 250 °C
Mass Range 33-500 m/z
Acquisition Rate 10 spectra/s


Results and Discussion

Figure 1 shows the representative GC-MS chromatogram for a mint essential oil.  Information on the detected peaks within the sample was provided using LECO's automated data processing software. Table 2 shows the area % quantification, aroma properties and identifications for the 30 most intense analytes in the sample.

Table 2. Identification Information for Top 30 Analytes. Source: LECO Corporation

  Name R.T. (s) Formula Sim RI Lib RI CAS Odor Type Area %
1 diacetone 228.7 C6H12O2 936 839.8 838 123-42-2   1.102
2 sabinene 346.2 C10H16 947 976.5 974 3387-41-5 woody 0.326
3 β-pinene 350.3 C10H16 939 981 979 127-91-3 herbal 0.656
4 3-octanol 362.7 C8H18O 944 994.6 994 589-98-0 earthy 0.835
5 α-terpinene 385.7 C10H16 902 1019.7 1017 99-86-5 woody 0.38
6 p-cymene 392.9 C10H14 925 1027.6 1025 99-87-6 terpenic 0.494
7 limonene 397.0 C10H16 938 1032.1 1030 138-86-3 citrus 1.978
8 eucalyptol 400.1 C10H18O 924 1035.4 1032 470-82-6 herbal 5.836
9 γ-terpinene 424.6 C10H16 909 1062.1 1060 99-85-4 terpenic 0.81
10 (Z)-sabinene
432.6 C10H18O 895 1070.7 1070 15537-55-0 balsam 2.248
11 linalool 459.9 C10H18O 883 1100.4 1099 78-70-6 floral 0.538
12 cis-menthone 513.4 C10H18O 946 1160.4 1164 491-07-6 mentholic 16.828
13 menthofuran 521.7 C10H14O 893 1169.6 1165 494-90-6 musty 3.12
14 (±)-menthol 522.4 C10H20O 781 1170.4 1169 1490-04-6 mentholic 2.842
15 l-menthone 523.1 C10H18O 857 1171.3   14073-97-3 minty 3.258
16 levomenthol 530.6 C10H20O 923 1179.7 1175 2216-51-5 mentholic 29.868
17 (-)-terpinen-4-ol 534.8 C10H18O 883 1184.3 1185 20126-76-5   3.113
18 neoisomenthol 539.9 C10H20O 939 1190 1188 491-02-1 mentholic 2.075
19 (1S,2R,5R)-(+)-
543.9 C10H20O 886 1194.5   23283-97-8 musty 0.503
20 α-terpineol 545.4 C10H18O 911 1196.2 1189 98-55-5 terpenic 0.848
21 pulegone 588.7 C10H16O 916 1247.4 1237 89-82-7 minty 2.196
22 p-menth-1-en-3-one 601.4 C10H16O 902 1262.4 1253 89-81-6 herbal 0.944
23 neomenthyl acetate 616.4 C12H22O2 909 1280.3 1274 2230-87-7   0.661
24 menthyl acetate 632.1 C12H22O2 937 1298.8 1295 89-48-5 mentholic 9.468
25 isomenthyl acetate 645.8 C12H22O2 935 1315.7 1305 20777-45-1   0.599
26 (-)-β-bourbonene 712.4 C15H24 922 1398 1384 5208-59-3 herbal 0.716
27 caryophyllene 740.6 C15H24 953 1434.9 1419 87-44-5 spicy 4.379
28 germacrene D 787.0 C15H24 922 1496 1481 23986-74-5 woody 2.316
29 β-cyclogermacrane 798.7 C15H24 899 1512 1495 24703-35-3 green 0.632
30 β-himachalene 801.9 C15H24 928 1516.6 1500 1461-03-6   0.432


Determinations regarding analyte identification were made by searching the observed mass spectral information against the NIST 2017 MS library database with similarity (Sim) scores (shown in Table 2).

Retention Index values were calculated for all peaks detected to add confidence to the identifications. The determinations were made by acquiring data for an alkane standard. Table 2 also shows that the Library RI information in the NIST database was used to verify the observed RI value.

Retention Index can help add confidence to identifications for analytes with very similar spectral information.

Figure 2. Retention Index can help add confidence to identifications for analytes with very similar spectral information. Image Credit: LECO Corporation

Figure 2 shows the way Retention Index was used to sort out some ambiguous peak identifications. After preliminary library searching, peaks #23-25 in Table 2 all matched to the same library spectrum, isomenthyl acetate.

The top spectra in Figure 2 show that the observed spectra for each of these three peaks are nearly identical, which is most often an indication of analytes or isomers with very similar chemical structures.

For this example, the retention index provided additional information related to the expected elution order that was used to clarify these isomers, with tentative identifications updated to isomenthyl acetate, menthyl acetate and neomenthyl acetate.

Deconvolution provides information on analytes that chromatographically coelute.

Figure 3. Deconvolution provides information on analytes that chromatographically coelute. Image Credit: LECO Corporation

The software’s data processing tools additionally offered the benefit of deconvolution, which is helpful in instances of chromatographic coelution. Within the data, there were some observations of instances of coelution. For example, Figure 3 shows peaks #13-15 in Table 2.

While it may appear that there is a single peak in the TIC view, plotting XICs specific to each analyte revealed that three separate analytes are coeluting.

Identifications (supported by Retention Index) of menthone, menthol and menthofuran were made through deconvolution, which provided clean spectral information for each coeluting analyte.

The upper right hand corner of Figure 3 shows the raw spectral information at the TIC apex, which is the combination of the coeluting analytes and is what would be available without deconvolution.

This spectrum matches 6-methyl-cyclodec-5- enol, which is a different analyte, that has a similarity score of 727; this indicates that the three coeluting analyte would be obscured without deconvolution.

Menthone, menthol and menthofuran have minty, mentholic and musty odor characteristics and are likely to be important contributors to the overall aroma profile. Therefore, without deconvolution, these analytes would have been difficult to detect.

The odor types listed in Table 2 were used to determine the associated aroma properties per analyte with analyte identifications. These aroma properties and the associated peak areas per analyte were then used to compile the overall sample characterization.

Directly connecting an analyte peak area to sensory detection requires the sensory threshold for that particular analyte as well as the response factor for that analyte on the instrument.

Without having these values, a chemical profile for the aroma characterizations can be provided by the peak areas.

ChromaTOF brand software was used to determine the peak areas by integrating the deconvoluted TIC peaks (the sum of all spectral peaks in the deconvoluted peak true spectrum integrated over the concentration profile for the chromatographic peak), and the Area % per analyte is reported in Table 2.

The pie chart in Figure 1 shows the top 30 analytes included in Table 2, which compiled peak area % by aroma type. As expected, mentholic or minty is the major aroma descriptor for this essential oil.


This work demonstrated the characterization of a mint essential oil through the application of GC-MS. For this sample, the individual analytes and the overall characterization based on aroma types were reported.

Deconvolution was crucial for distinguishing chromatographically coeluting analytes and retention index information was helpful for clarifying ambiguous analyte identifications.

Characterization information on the essential oil was provided by this detailed chemical information. GC-MS is a powerful tool for this type of analysis and can provide more information about a sample in less time.

Characterizing Essential Oils with GC-MS

Image Credit: LECO Corporation

This information has been sourced, reviewed and adapted from materials provided by LECO Corporation.

For more information on this source, please visit LECO Corporation.


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