Application of multi-dimensional GC techniques to the analysis of cigarette smoke

Transcription

1 Application of multi-dimensional GC techniques to the analysis of cigarette smoke M. Brokl, J. Foçant, University of Liège, Belgium L. Bishop, J.Ticha, C. Wright, British American Tobacco Group Research & Development, Southampton UK 68th Tobacco Science Research Conference, Charlottesville VA, 28 Sept 1 Oct 2014

2 Academic Partners - University of Liège Professor Jean-François Foçant, associate professor, leading researcher in high resolution MS & multidimensional TOF analysis Dr Michal Brokl, post doctoral researcher, working on project since

3 Overview Challenges & current methodology Multidimensional GC Sample preparation & processing Example chromatograms Data processing Conclusions Next steps 3

4 Challenge Mass spectrometry scans of cigarette smoke are used to evaluate mechanisms of smoke formation and new materials & technologies - Needed to determine changes in smoke profile including toxicants but also aroma & processing components Need to use GC/MS for volatile/semi-volatile species & LC/MS for non-volatile species Require high throughput method to evaluate samples before targeted testing Require automation of data analysis 4

5 Traditional Methodology GC-MS (single quadrupole mass analyser) Lacks sensitivity Gives limited chromatographic resolution Non-volatile species not measured Low throughput Labour intensive data analysis Analyst dependent 5

6 Traditional Methodology - Example 3R4F Particulate phase, methanol extraction of CFP Abundance TIC: _02.D\data.ms Time-->

7 Traditional Methodology - Example 3R4F Particulate phase, headspace SPME of CFP Abundance 4e+07 TIC: D 3.5e+07 3e e+07 2e e+07 1e Time-->

8 Smoke Scan Method Improvement Improve sensitivity Time of Flight Mass Spectrometer Quad: detection limit μg/ml TOF: detection limit ng/ml Improve capacity & separation Multidimensional GC allows greater resolution ToF MS, high scan rate also gives improved resolution Use automated software to identify & compare components in the analyses 1D scan detect ~ 200 components in SPME PPS 2D scan detect >2500 components in SPME PPS Use more robust statistical analysis Improve reproducibility Evaluate product differences 8

9 GC GC Conventional column 9 From: Accessed on 04/09/2014

10 GC GC Conventional column Fast GC column 10 From: Accessed on 04/09/2014

11 GC GC Conventional column Fast GC column 11 From: Accessed on 04/09/2014

12 GC GC Conventional column Fast GC column 12

13 Instrumentation at Liège LECO Pegasus 4D GCxGC-TOFMS 13

14 Sample Preparation & Analysis Whole smoke Particulate Phase Gas Phase CFP trapped particulate Solvent extraction GC GC-TOFMS DHS SPME 14

15 Example of 2D chromatogram 3R4F, 2 cigarettes at HCI, Polyacrylate SPME 15

16 Example of 2D chromatogram 3R4F, 2 cigarettes at HCI, Polyacrylate SPME 16

17 1D GC-MS(Q) vs. 2D GC-TOF PPS scan data 1D GC-MS (single quadrupole) 2D GC GC-TOFMS Substances typically identified 200 > 2000 Detector sensitivity 1 g/ml 1ng/mL Time required for manual data processing of one chromatogram a day 10 days 17

18 1D GC-MS(Q) vs. 2D GC-TOF PPS scan data 1D GC-MS (single quadrupole) 2D GC GC-TOFMS Substances typically identified 200 > 2000 Detector sensitivity 1 g/ml 1ng/mL Time required for manual data processing of one chromatogram a day 10 days For example, estimated time required for manual data processing of 10 samples from 2D GC GC. 18

19 1D GC-MS(Q) vs. 2D GC-TOF PPS scan data 1D GC-MS (single quadrupole) 2D GC GC-TOFMS Substances typically identified 200 > 2000 Detector sensitivity 1 g/ml 1ng/mL Time required for manual data processing of one chromatogram a day 10 days Robust Statistical Analysis needed to process complex data 19

20 Comparison of samples A B Cumulative template PCA Fisher ratio (F)

21 2D GC-TOF analysis of particulate phase smoke from cigarette with: Cellulose Carbon acetate filter 21

22 Second Dimension Retention Time Mean [sec] Calculate Fisher ratios A Fisher ratio is the class-to-class variation of the detector signal divided by the sum of the within-class variations of the detector signal 1 1. Fisher, R. A. Statistical Methods for Research Workers, 14 ed.; A. Constable Ltd.: Edinburgh, First Dimension Retention Time Mean [min] 22

23 Second Dimension Retention Time Mean [sec] Calculate Fisher ratios A Fisher ratio is the class-to-class variation of the detector signal divided by the sum of the within-class variations of the detector signal 1 1. Fisher, R. A. Statistical Methods for Research Workers, 14 ed.; A. Constable Ltd.: Edinburgh, First Dimension Retention Time Mean [min] 23

24 Example of component identification Blob ID Area Name 1 t R mean [min] 2 t R mean [s] F Peak volume mean (A) x10 6 Peak volume mean (B) x10 6 Peak volume mean ratio 28 Menthol Menthyl acetate Unknown Unknown ,3-Dimethyl-4-phenylbutene methyl-1-(2-methylbutyl)benzene Naphthalene, 2,6-dimethyl Benzene, 1-methyl-4-(1-methylethenyl) Benzene, (1,2,2-trimethyl-3-butenyl) Phenol Pyrazine, 2-ethenyl-6-methyl Naphthalenol, 3-methyl Naphthalene, 1,4,5-trimethyl H-Indole, 3-methyl

25 Peak volume differences 25

26 Peak volume differences 26

27 Conclusions The methodology developed at Liège is: - Sensitive - Able to separate complex mixture of components in Particulate phase smoke - Capable of automated de-convolution - Capable of identifying differences between samples through sophisticated statistical analysis techniques - Operator independent 27

28 Next Steps Currently investigating other modified filters e.g., cavity filters filled with CR20 resin vs. empty cavity Extrapolate method for analysis of e-cigarettes Use of 2D GC coupled to high resolution-tof MS Next phase of study to analyse vapour phase of smoke Integration of 2D GC data into Cheminformatics programme 28

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