Application of multi-dimensional GC techniques to the analysis of - - PowerPoint PPT Presentation

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

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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 2011

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

Overview

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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

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• 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

Traditional Methodology

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Traditional Methodology - Example

• 3R4F Particulate phase, methanol extraction of CFP

1 . 1 5 . 2 . 2 5 . 3 . 3 5 . 2 4 6 8 1 1 2 1 4 1 6 1 8 2 2 2 2 4 2 6 2 8 3 3 2 3 4 T im e

• >

A b u n d a n c e T I C : 2 1 3 6 6 _ 2 . D \d a t a . m s

11.00 11.20 11.40 11.60 11.80 12.00 12.20 12.40 12.60 12.80 13.00 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000 65000 70000 75000 80000 85000 90000 95000 100000 105000 110000 Time--> Abundance TIC: 20130606_02.D\data.ms

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Traditional Methodology - Example

• 3R4F Particulate phase, headspace SPME of CFP

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 5000000 1e+07 1.5e+07 2e+07 2.5e+07 3e+07 3.5e+07 4e+07 Time--> Abundance TIC: 2012-04-13-13.D

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• 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

Smoke Scan Method Improvement

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GC× × × ×GC

From: http://www.leco.cz/cz/support_service/separation_science.htm. Accessed on 04/09/2014 Conventional Conventional Conventional Conventional column column column column

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GC× × × ×GC

Conventional Conventional Conventional Conventional column column column column From: http://www.leco.cz/cz/support_service/separation_science.htm. Accessed on 04/09/2014 Fast Fast Fast Fast GC GC GC GC column column column column

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GC× × × ×GC

Conventional Conventional Conventional Conventional column column column column From: http://www.leco.cz/cz/support_service/separation_science.htm. Accessed on 04/09/2014 Fast Fast Fast Fast GC GC GC GC column column column column

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GC× × × ×GC

Conventional Conventional Conventional Conventional column column column column Fast Fast Fast Fast GC GC GC GC column column column column

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Instrumentation at Liège

LECO Pegasus

   4D GCxGC-TOFMS

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Sample Preparation & Analysis

GC× × × ×GC-TOFMS DHS SPME

CFP trapped particulate Whole smoke Solvent extraction Gas Phase Particulate Phase

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

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

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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 1µg/mL 1ng/mL Time required for manual data processing of one chromatogram ∼ a day ∼ 10 days

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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 1µg/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…………….

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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 1µg/mL 1ng/mL Time required for manual data processing of one chromatogram ∼ a day ∼ 10 days

Robust Statistical Analysis needed to process complex data

A B PCA PCA Fisher ratio (F) Fisher ratio (F)

Comparison of samples

Cumulative template Cumulative template

Carbon filter Cellulose acetate filter

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2D GC-TOF analysis of particulate phase smoke from cigarette with:

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 signal1

• 1. Fisher, R. A. Statistical Methods for Research Workers, 14 ed.; A. Constable Ltd.: Edinburgh, 1970.

First Dimension Retention Time Mean [min] Second Dimension Retention Time Mean [sec] 22

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 signal1

• 1. Fisher, R. A. Statistical Methods for Research Workers, 14 ed.; A. Constable Ltd.: Edinburgh, 1970.

First Dimension Retention Time Mean [min] Second Dimension Retention Time Mean [sec] 23

Example of component identification

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Blob ID Area Name

1tR mean

[min]

2tR mean

[s] F Peak volume mean (A) x106 Peak volume mean (B) x106 Peak volume mean ratio 28 Menthol 21.33 1.25 6648 213.12 17.69 0.08 499 Menthyl acetate 26.28 1.25 3900 15.26 2.15 0.14 847 Unknown 25.53 1.03 2712 4.45 0.62 0.14 811 Unknown 25.71 1.07 2436 3.53 0.56 0.16 92 3,3-Dimethyl-4-phenylbutene 24.00 1.38 2316 54.91 8.02 0.15 461 4-methyl-1-(2-methylbutyl)benzene 24.47 1.28 2160 11.67 1.60 0.14 451 Naphthalene, 2,6-dimethyl- 33.19 2.20 2136 1.94 2.37 1.22 51 Benzene, 1-methyl-4-(1-methylethenyl)- 17.07 1.36 2088 89.72 7.95 0.09 548 Benzene, (1,2,2-trimethyl-3-butenyl)- 26.00 1.38 1992 14.81 2.69 0.18 4 Phenol 12.31 1.52 1668 776.82 627.62 0.81 325 Pyrazine, 2-ethenyl-6-methyl- 13.82 1.70 1608 40.68 5.19 0.13 388 1-Naphthalenol, 3-methyl- 32.33 2.29 1464 6.31 7.24 1.15 783 Naphthalene, 1,4,5-trimethyl- 36.44 2.02 1188 1.83 1.95 1.07 685 1H-Indole, 3-methyl- 31.43 2.61 1140 3.91 5.13 1.31

Peak volume differences

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Peak volume differences

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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

Conclusions

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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

Next Steps

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