Introduction
The need to screen consumer products for specic organic compounds is increasing across many industry sectors, both for product quality assurance and as a consequence of regulatory requirements. An example is the monitoring of allergenic compounds in fragranced products perEU Directive 76/768/EEC. Twenty six fragrance compounds which have the potential to cause contact allergy, as assigned by the Scientic Committee on Consumer Products (SCCP), are incorporated into this Directive. Of these 26, 24 are volatile and can be analyzed by GC(MS). The International Fragrance Association (IFRA) species a GC/MS method which complies with EU Directive 76/768/EEC, allowing the analysis of the 24
compounds of interest1. To complement this method and simplify in-house data interpretation, TargetView™software has been designed to automatically identify predetermined (target) compounds within GC(MS) proles. A user can either create their own library of target compounds or import established ones; this library may extend from an
individual entry up to several hundred if more comprehensive sample screening is required.
TargetView incorporates a sophisticated ‘dynamic background compensation’ (DBC) algorithm to minimise baseline noise from the total ion chromatogram (TIC). The software then adopts a chemometric approach to data analysis; initially, a spectral deconvolution
algorithm is applied to distinguish between peak spectra. This is then followed by ‘principle component analysis’ (PCA) to locate specic compounds of interest. To demonstrate the datamining prociency of TargetView, a fragrance sample was analysed for the presence of the 24 SCCP allergens. The sample was run under both slow and fast GC/MS chromatographic conditions, the latter signicantly compressing the
chromatography and, as a consequence, increasing compound co-elution. The results from each analysis were then compared to see if the increased complexity (i.e.co-elution) of data acquired under fast chromatographic conditions compromised compound identication by TargetView.
Method
Liquid samples were directly injected into the GC/MS system under the conditions listed below.
Analytical conditions GC
Column: 30 m, 0.25 mm i.d., 0.25 µm HP INNOWax Column ow: 1.5 mL/min constant
ow
Sample volume: 0.2 µl, split 100:1 Oven (slow): 80°C (1 min), 5°C/min,
250°C (2 min) – Run time 37.0 min
Oven (fast): 80°C (1 min), 35°C/min, 260°C (4 min) – Run time 10.4 min
Injection volume: 0.2 µL split 100:1 MS
Instrument: BenchTOF-dx™ (TOF-MS) Mass range: 15–350 amu Acquisition rate: 5 Hz (10 kHz
continuous), 2000 pulses/cycle
N.B. BenchTOF-dx data les are automatically converted post-run into le formats compatible with existing, commercially available software platforms.
TargetView
A library of the 24 allergenic compounds listed by the SCCP was created in TargetView using their NIST library entries. Table 1 lists these compounds with their International Nomenclature of Cosmetic Ingredients (INCI) name and CAS number.
The resulting GC/MS data les were processed by TargetView with the ‘Allergens’ library.
Application of TargetView
™
software in the fragrance industry:
Results
Target compound analysis
Figure 1 shows the TIC prole from the slow ramp GC/MS analysis. The run time is approximately 40 minutes and shows relatively good peak resolution; however, co-elution is evident. The TIC is also affected by a signicant amount of background ion interference, producing visible baseline anomalies.
When the data le is imported into TargetView, the software initially applies a dynamic background compensation
(DBC) algorithm to the TIC in order to suppress background (noise) ions (Figure 2). DBC signicantly improves both baseline quality and spectral purity. Mass ions within real TIC peaks, including common ions, are unaffected by this process. A second DBC-processed data le is created by TargetView, preserving the original.
A comparison of the slow oven ramp data pre- and post-DBC processing shows that the baseline construction has been signicantly improved and anomalies such as air/water offset, column bleed, humps/step-changes, etc.
are minimised. This is reected in the enhanced (cleaner) spectra for TIC peaks. DBC is essential for TargetView as it provides superior data for the subsequent stages of spectral deconvolution and PCA.
By increasing the oven ramp rate from 5°C/min to 35°C/min, the
chromatography was signicantly compressed in time. Figure 3 shows the (DBC) TIC prole for this analysis; the run time has been reduced from ~40 minutes to ~9 minutes. This not only increased the degree of co-elution but also the concentration overload. If two or more
Table 1. Library of target compounds (SCCP allergens)
INCI name CAS # INCI name CAS #
Amyl cinnamal 122-40-7 Hydroxyisohexyl- 3-cyclohexene carboxaldehyde 31906-04-4 Benzyl alcohol 100-51-6 Anise alcohol 105-13-5 Cinnamyl alcohol 104-54-1 Benzyl cinnamate 103-41-3 Citral 5392-40-5 Farnesol 4602-84-0 Hydroxycitronellal 107-75-5 Butylphenyl methylpropional 80-54-6 Eugenol 97-53-0 Linalool 78-70-6 Isoeugenol 97-54-1 Benzyl benzoate 120-51-4 Amylcinnamyl alcohol 101-85-9 Citronellol 106-22-9 Benzyl salicylate 118-58-1 Hexyl cinnamal 101-86-0 Cinnamal 104-55-2 Limonene 5989-27-5 Coumarin 91-64-5 Methyl 2-octanoate 111-12-6 Geraniol 106-24-1 Alpha-isomethyl ionone 127-51-5
peaks co-elute within the original GC/MS TIC (including matrix effects) then the resulting mass spectrum taken across the elution time window will be impure as there will be a varying contribution from each compound. This makes
identication very difcult and typically unsuccessful using conventional search strategies and commercial libraries. To alleviate the problem of co-elution, TargetView employs a novel
deconvolution algorithm post-DBC to clean and extract the spectral data in preparation for the PCA process. PCA then correlates deconvoluted spectra with the target library spectra to highlight matches. Reliability of match is enhanced by the “classical” spectra provided by the BenchTOF-dx. This enablesdirect comparison with the NIST database; a powerful attribute of this TOF-MS system.
After analysis, a post-run report is automatically generated/printed by TargetView. The text le is MS Excel®-compatible, enabling customisation if required.
Comparison of slow and fast chromatography reports
By compressing the chromatography with the fast oven ramp, the data mining efciency of the software is really challenged. The three principle processes within TargetView, i.e.DBC,
deconvolution and PCA, operate synergistically to ensure identication of the target compounds under these more demanding conditions.
Figure 4 shows the post-run report for target allergens detected under the slow oven ramp conditions, and gure 5, the results from the fast oven ramp. A user-selectable match co-efcient exclusion value of 0.7 (range 0-1) highlighted the compounds of greatest certainty.
Comparing the results in gure 4 with gure 5, TargetView identied the same seven compounds with no signicant difference in the match co-efcient values.
Retention time information can also be used to conrm compound identity. This is particularly useful where isomeric compounds are present in the sample. If the absolute retention time values are known for compounds, these values may be use as a penalty criterion for the match co-efcient values derived by the software. A window of identication is specied in seconds and all compounds with a dened RT value must fall within this range. If a compound falls outside this range, a penatly is applied to the match value, reducing its value in proportion to the discrepancy. Based on the minimum allowed match value, this could ultimately exclude the compound from the nal report. The delta RT column in the report highlights compounds which do not match their known values.
Compound identication
The TargetView user interface can be seen in gure 6. The upper display shows a specic time window of the fast-run DBC TIC. A red histoplot (HPlot) overlay can be congured within the upper TIC to show only target compounds found or all compounds (including unknowns). When selecting a target compound from the drop-down box, the presence of a bar in the lower window conrms its
detection; the match co-efcient value with respect to retention time is also given. The target compound selected in this instance is benzyl alcohol, and a match co-efcient of 0.797 has been calculated, indicating a condent hit. A comparison of the sample spectra and library entry is shown in gure 7. The deconvoluted sample spectrum detected at 5.06 minutes is shown (upper window) and below this the library spectrum for the target compound (benzyl alcohol), originally imported from the NIST database. The two spectra are very
Figure 4. TargetView post-run report with slow oven ramp conditions
similar, resulting in a condent match. Also shown in gure 7 is the capacity to cross-search the sample spectrum against the NIST database.
Figure 8 shows the cross-search results for benzyl alcohol against the “mainlib” database. The NIST match of 746 (rst hit) veries the TargetView identication. Figure 9 shows the location of benzyl alcohol within a smaller time window. In this gure, the HPlot overlay has been congured to show all components, i.e.
targets and unknowns. This acts as a useful indicator of peak purity; the presence of multiple red bars within a
single peak demonstrates co-elution. The height of each bar is a representation of peak area, based on the summation of certain mass ions (identied during the deconvolution process) within each scanset across the peak.
Benzyl alcohol has been shown to elute within a particularly complex point of the spectrum (Figure 9) and is present at a relatively low concentration in comparison to the co-eluting peaks. Given these factors, the match co-efcient is an indicator of the capability of TargetView. Identication of this compound would be extremely
difcult when using conventional search methods. As proof, an attempt was made to identify benzyl alcohol using the spectrum obtained from the original GC/MS TIC under fast oven ramp conditions, without the help of TargetView.
The retention time (RT) of the target compound was identied previously to be 5.06 min (gures 5 & 6). Figure 10 shows the appropriate area of the TIC, viewed using commercial GC/MS software. The cursor was positioned at 5.06 min and the spectrum taken. Static background subtraction of the spectrum
Figure 6. TargetView user interface identifying benzyl alcohol at 5.06 min (Fast GC conditions; HPlot overlay showing target compounds only)
Figure 8. NIST cross-search conrmation of benzyl alcohol Figure 9. TargetView user interface showing multiple compounds co-eluting with benzyl alcohol (HPlot overlayshowing all compounds; area of interest highlighted)
was employed in an attempt to minimize baseline noise and improve the spectrum quality for library searching.
Figure 11 shows the probability-based match (PBM) identication using the standard GC/MS software. This did not identify benzyl alcohol in a hit list of 20 compounds using either the NIST or WILEY commercial libraries. The compound was identied with a low match value as ethyl thiocyanate. By comparing the original TIC spectrum with the deconvolved TargetView spectrum (Figure 12) the efciency of the deconvolution algorithm can be
appreciated. Clearly the low mass ions which are not present within the
compound of interest (i.e.benzyl alcohol) have been removed from the spectrum producing a much cleaner prole and, as a result, condent identication.
Conclusions
TargetView identied several SCCP-dened allergens in the sample data with high match co-efcient values.
Compressing the chromatography to achieve quicker results increased both the degree of co-elution and
concentration load, but did not result in fewer compounds identied, nor did it signicantly affect the condence values. This proves TargetView to be an ideal tool for data analysis, especially where fast chromatography is required. This was conrmed by cross-identication using the NIST database. The efciency of TargetView is achieved by a synergy between the algorithms within the software (DBC, deconvolution and PCA). As an illustration of its enhanced capabilities, conventional library searching alone was not able identify the target allergen benzyl alcohol, which was submerged within the complexity of the TIC data.
Figure 10. Magnied GC/MS non-background-subtracted TIC (top) and spectrum at 5.06 min (bottom)
Figure 11. PBM misidentication
Figure 12. Comparison of TargetView (upper) and TIC spectra (lower) for the compound eluting at 5.06 min
References
1. International Fragrance Association (2006) GC/MSquantitation of potential fragrance allergens in fragrance compounds http://www.ifraorg.org/les/documentsp ublished/1/en-us/Analytical%20Guidelines/22296_Anal ytical%20Guidelines_2010_04_27_Analyt ical_Procedure_for_Fragrance_Allergens. pdf
Trademarks
TargetView™and BenchTOF-dx™ are trademarks of ALMSCO International, UK Excel® is a registered trademark of Microsoft Corp., USA
