Agilent 7200 GC/Q-TOF Applications. The Experience so far

Transcription

1 Agilent 7200 GC/Q-TO Applications. The Experience so far Sofia Aronova GC/Q-TO Application Chemist Agilent Technologies, Santa Clara Page 1

2 Agilent 7200 GC/Q-TO Applications. The Experience so far Sofia Aronova GC/Q-TO Application Chemist Agilent Technologies, Santa Clara Page 2

3 Value of Q-TO High resolution full scan spectra Higher selectivity without MS/MS > 12K versus < 1K for SQ Accurate mass measurements Better qualitative decisions (molecular formula information) < 5 ppm (< 2 ppm after mass correction) versus for SQ ull scan spectra with high sensitivity Higher sensitivity than MSD in full scan mode S/N 2500:1 for 1 pg ON, 10 fg IDL ast acquisition of full spectra range 50 Hz max versus typically < 5 Hz max for SQ MS/MS with Product Ion spectra More selective than TQ (due to higher resolution) With accurate mass information for each product ion Powerful structural elucidation tools Page 3

4 7200 Q-TO Covers All Applications Non-targets (No standard available, but MS spectrum in a database) Ability to search commercially available MS libraries Non-target confirmation using accurate mass full spectrum EI and CI data Non-target confirmation using MS/MS product ion scan accurate mass data Targets (Standard available) Mass accuracy and high resolution data provides more confidence in target identification MS/MS accurate mass product ion spectrum is available for target confirmation Large dynamic range is available for best quantitation results Q-TO MS/MS provides ultimate selectivity for analytes in most complex matrices True unknowns (No MS spectrum in a database) Identification of empirical formula using CI mode of operation Structure elucidation using accurate mass full spectrum EI MS/MS studies of EI fragment ions will clarify MS spectral information Page 4

5 List of Some Applications Performed with GC/Q-TO Environmental luorotelomer alcohols PAH, PCB, OCP, PBDE, and polycyclic musks in SPMD river extracts ood testing and flavors Olive oil characterization Sulfur-containing compounds in beverages ood safety Pesticides in food matrices Natural products Structure elucidation of the compound from kava extract Metabolomics Yeast sterol profiling Petrochemicals Analysis of biomarkers in crude oil Industrial Doping Anabolic sterols Page 5

6 Environmental: luorotelomer Alcohols in Biosolids Shoji Nakajama, National Institute for Environmental Studies Anthony Macherone & Tom Doherty, Agilent Technologies Accurate mass to elucidate ambiguous neutral loss Qualifier ratio in matrix Page 6

7 luorotelomer Alcohols: Background Source unknown, but probably intermediate degradation products of fluorinated polymers Oxidize to form fluorinated carboxylic acid, some of which are toxic Methods needed for studying their transport and fate in the environment Have the form 3 C(C 2 ) N-1 (CH 2 ) M OH N:M TOH is the shorthand notation Page 7

8 luorotelomer Alcohols: Neutral Loss Mechanism Inferred PCI, methane CH 2 + Exact Mass: OH 2 + -H2O -H Exact Mass: Exact Mass: C + Dm = Da. -H20, -H -2 Exact Mass: Dm = Da. OH 2 + Exact Mass: Acronym Observed Base Peak m/z Molecular ion - 2 m/z Dppm Molecular ion -H 2 0, -H m/z Dppm 4:2 TOH Page 8

9 10:2 TOH, MS/MS Confirms Neutral Loss TIC EIC for m/z Acronym Precursor m/z CE Transition m/z Dppm Loss 10:2 TOH V H 2 O, -H -H 2 0, -H Dm = Da. Page 9

10 11:1 TOH Spiked in Biosolid Extract, Quantitation EIC Quant ion m/z = Qualifier ion m/z = EIC of m/z= overlaid on m/z= Excellent qualifier ratio in the presence of nearly 10 3 excess of matrix Page 10

11 Environmental: Pollutants in SPMD Extracts in River Anthony Gravell, Environment Agency, England and Wales IRM to improve mass accuracy Dynamic range Page 11

12 Samples and Target Compounds The choice of compounds was based on the current requirements of the Water ramework Directive (WD) which form a significant part of the Environment Agency s monitoring program. Compounds studied Polyaromatic Hydrocarbons (PAHs) Polychlorinated Biphenyls (PCBs) Organochlorine Pesticides (OCPs) Polybrominated Diphenyl Ethers (PBDEs) Polycyclic Musk ragrances (PCMs) Samples - river water and marine sediment Extracts from passive samplers (SPMDs) Solvent extracts from marine sediments Page 12

13 Mass Accuracy: IRM Helps if Error > 2 ppm No IRM used IRM applied 100 ppb Molecular Exact mass Mass error (ppm) Mass error (ppm) formula fluorene C13H hexachlorobutadiene C4Cl hexachlorobenzene C6Cl dieldrin C12H8Cl6O BZ # 52 (2,2',5,5' -tetrachlorobiphenyl) C12H6Cl BDE-47 C12H6Br4O DPMI (Cashmeran) C14H22O HHCB (Galaxolide) C18H26O Average uncorrected corrected 0.5 ppb Molecular Exact mass Mass error (ppm) Mass error (ppm) formula fluorene C13H hexachlorobutadiene C4Cl hexachlorobenzene C6Cl dieldrin C12H8Cl6O BZ # 52 (2,2',5,5' -tetrachlorobiphenyl) C12H6Cl BDE-47_1 C12H6Br4O DPMI (Cashmeran) C14H22O HHCB (Galaxolide) C18H26O Average uncorrected corrected Page 13

14 Mass Error, PPM Mass Accuracy: Mass Error at Low Concentration ,000 10, ,000 1,000,000 Number of Ions Detected Page 14

15 Mass Accuracy in Matrix Compound Matrix Avg (n = 2), ppb Exact mass Measured mass ppm diff acenaphthylene PAH_extract from SRM 1941b 19.2 ± hexachlorobenzene PAH_extract from SRM 1941b 2.98 ± BZ # 52 PAH_extract from SRM 1941b 0.81 ± BDE- 47 PAH_extract from SRM 1941b 1.10 ± Cashmeran Waste water SPMD drain 573 ± Good mass accuracy is important for target compound confirmation Page 15

16 Accurate Mass is Necessary to Eliminate Matrix Interferences SPMD river, Dual Gain Hexachlorobenzene TIC Page 16

17 Accurate Mass is Necessary to Eliminate Matrix Interferences SPMD river, Dual Gain Hexachlorobenzene TIC EIC amu Page 17

18 Accurate Mass is Necessary to Eliminate Matrix Interferences SPMD river, Dual Gain Hexachlorobenzene TIC EIC amu EIC ppm 0.6 pg on-column Hexachlorobenzene 1 ppb 5000 ppb R 2 > Page 18

19 ood Testing and lavors: Olive Oil Characterization UC Davis Olive Center & Stephan Baumann, Agilent Technologies MPP for statistical processing of GC/Q-TO data MS library searching using GC/Q-TO spectra CI data provide accurate mass information for molecular ions Page 19

20 Olive Oil Characterization: Workflow Goals: - to create a model that could predict whether olive oil sample would pass or fail sensory test - to recognize statistically significant olive oil components that are present at distinct levels depending on whether they passed or failed sensory test Olive oil samples had been subjected to sensory test and classified as passed or failed GC/Q-TO data then were acquired in both EI and PCI modes Chromatographic deconvolution was performed with MassHunter Qual, and the data were exported as CE files to perform statistical analysis using Mass Profiler Professional (MPP). MPP was used for statistical evaluation of the data including construction of class prediction model The model was able to correctly predict whether the sample would pass or fail the sensory test Page 20

21 Mass Profiler Professional: Where it Could be Helpful? Are you overlaying chromatograms to find the difference among samples in the Chromatograph? Are you trying to find the trace compounds that represent the uniqueness of the certain samples? Have you ever thought about introducing statistics to the data analysis to achieve better interpretation? Have you ever had to predict unknown samples using the prediction model? If you answered Yes to any one of the questions above, MPP can improve your workflow Page 21

22 Olive Oil Characterization: Data iltering 442 unique compounds were distinguished by chromatographic deconvolution, most of which occur only once or twice and are filtered out by MPP. The table shows how many of these 442 compounds were actually found in each sample. Page 22

23 Olive Oil Characterization: Visualization of Data Clustering failed passed Principal Component Analysis (PCA) of MPP helps to visualize clustering of the data Page 23

24 Olive Oil Characterization: old Change Analysis Compounds accumulated in the samples that failed the sensory test. The Volcano Plot (on the right) shows fold-change for each entity on the x-axis and significance on the y-axis. Page 24

25 Olive Oil Characterization: Library Search Compound spectrum (accurate mass) Compound spectrum NIST library spectrum EI Commercial unit mass EI spectral libraries can be searched using accurate mass EI GC/Q-TO data to identify compounds Page 25

26 Olive Oil Characterization: Combining EI and PCI Data Tentative NIST ID ormula EI, M *+ PCI, (M+H) + Calculated Measured Mass error, ppm Calculated Measured Mass error, ppm Hexadecanoic acid C16H32O Ethyl-octadecanoate C20H40O Squalene C30H α-cubebene C15H Unknown C14H26O N/A N/A PCI spectral data provided accurate mass information for molecular ions of the accumulated compounds in olive oils that fail the sensory test, including the case where the EI spectrum showed no prominent molecular ion Page 26

27 Olive Oil Characterization: MPP Results The model correctly predicted the pass or fail status of all samples, including those not used to construct the model. The samples that were not used for building the prediction model are listed with the Training parameter set as None Page 27

28 Olive Oil Characterization: Compound Identification EI spectra were used to search NIST library to obtain tentative identification of the compounds PCI data were used to obtain molecular formula for the compounds urther MS/MS experiments allowed to generate clean spectra in the presence of matrix interference and could possibly be used for structure elucidation Page 28

29 Olive Oil Characterization: MS/MS Example α-cubebene, full scan C 15 H (replib) α-cubebene α-cubebene: MS/MS Precursor: 204 CE: 10 ev C 8 H ppm C 9 H ppm C 10 H ppm C 12 H ppm Accurate masses of ion fragments are consistent with molecular formula Page 29

30 ood Safety: Pesticides in ood Matrices Hans Mol, RIKILT, Netherlands MS/MS to improve selectivity MS/MS for target confirmation Page 30

31 Comprehensive ood Safety Analysis Involves both the quantitative analysis of frequently occurring contaminants and qualitative analysis of non-target compounds and unknowns Can profit from Q-TO high sensitivity in full scan mode as well as accurate mass information for confirmation of targets and analysis of unknowns MS/MS capability of 7200 GC/Q-TO will ensure best possible selectivity for target quantitation and facilitate target confirmation Page 31

32 aldrin isodrin dieldrin endrin Low Concentrations Analytes in Heavy Matrix (TO mode) EIC (Leek extract) drins 10 pg ±0.5 Da drins 10 pg ±20 ppm Page 32

33 MS/MS for Target Confirmation product ion spectrum of endrin m/z 263 C 7 H 2 Cl 4 [ 37 Cl] CE 20 ev C 7 H 2 Cl 2 [ 37 Cl] C 7 H 2 Cl 3 [ 37 Cl] 1.85 ppm 1.09 ppm C 5 HCl 3 [ 37 Cl] 6.86 ppm 6.1 ppm CE 35 ev 263 Cl Cl Cl Cl 281 O 245 Cl Cl MS/MS generates multiple ion fragments that can be used for confirmation Page 33

34 endrin MS/MS to Obtain Ultimate Selectivity drins 10 pg, EIC full scan ±20 ppm drins 10 pg, MS/MS EIC of product ion 263 > dieldrin endosulfanalpha, chlordane endrin Page 34

35 Natural Products: Structure Elucidation of the Compound from Kava Extract Viorica Lopez-Avila, Agilent Labs MS/MS for structure elucidation of unknown Page 35

36 MS/MS for Structure Elucidation Start with full scan EI spectrum Use accurate mass to estimate molecular formula Perform CID experiments on molecular ion and selected fragment ions to confirm losses Use information from sequence of losses to estimate reconstructed molecular structure Precursor-product ion relationship is documented and ion molecular formula confirmed by accurate mass Requires multiple analyses and much more sensitive than NMR Will not replace NMR, but will complement nicely Page 36

37 The Problem Confirm Most Likely Structure Kava Extract - Compound B, C 16 H 14 O 4 (Rings + Double Bonds = 10) EI ull Scan (M H) Candidate structures Page 37

38 The Problem Confirm Most Likely Structure Kava Extract - Compound B, C 16 H 14 O 4 (Rings + Double Bonds = 10) EI ull Scan (M H) Candidate structures H MS/MS experimental measurements m/z (experimental) ormula Error (ppm) Score C 16 H 13 O C 6 H 5 CH=CH C 6 H 5 CH 2 =CH C 6 H 5 CO CO CH C 10 H 9 O C 8 H 7 O N/A C 8 H 6 O N/A C 7 H 6 O C 6 H 6 O N/A C 5 H 3 O Page 38

39 The Problem Confirm Most Likely Structure Kava Extract - Compound B, C 16 H 14 O 4 (Rings + Double Bonds = 10) EI ull Scan (M H) Candidate structures X H C 6 H 5 CH=CH C 6 H 5 MS/MS experimental measurements m/z (experimental) ormula Error (ppm) Score C 16 H 13 O C 10 H 9 O C 8 H 7 O N/A X CH 2 =CH C 6 H 5 CO CO CH C 8 H 6 O N/A C 7 H 6 O C 6 H 6 O N/A C 5 H 3 O Page 39

40 The Problem Confirm Most Likely Structure Kava Extract - Compound B, C 16 H 14 O 4 (Rings + Double Bonds = 10) EI ull Scan (M H) Candidate structures H MS/MS experimental measurements m/z (experimental) ormula Error (ppm) Score C 16 H 13 O C 6 H 5 CH=CH C 6 H 5 CH 2 =CH C 6 H 5 CO CO CH C 10 H 9 O C 8 H 7 O N/A C 8 H 6 O N/A C 7 H 6 O C 6 H 6 O N/A C 5 H 3 O Page 40

41 The Problem Confirm Most Likely Structure? c Mass at 138 is consistent with: loss of COCH=CH-C 6 H 5 ( ) from or loss of C 2 H 4 CH=CH-C 6 H 5 ( ) from However, measured value of = is consistent only with COCH=CH- C 6 H 5. Page 41

42 The Problem Confirm Most Likely Structure EI ull Scan (M H) Candidate structures H MS/MS experimental measurements m/z (experimental) ormula Error (ppm) Score C 16 H 13 O C 6 H C 10 H 9 O CH=CH C 6 H C 8 H 7 O N/A CH 2 =CH C 6 H 5 CO C 8 H 6 O N/A C 7 H 6 O CO C 6 H 6 O N/A CH C 5 H 3 O or the 5 candidate structures, only one fit the losses identified by CID experiments on multiple precursor ions Page 42

43 Metabolomics: Yeast Sterol Profiling Manhong Wu, Stanford & Stephan Baumann, Agilent Technologies Accurate mass full scan EI spectral data for analysis of unknowns MS/MS for molecular structure confirmation Mass Profiler Professional (MPP) for statistical evaluation of metabolomics data Page 43

44 Metabolic Profiling of Yeast Sterols Changes in sterol metabolism were evaluated following the treatment with known inhibitors (luconazole and Terbinafine) as well as totarol (antimicrobial and anti-cancer drug) Mass Profiler Professional (MPP) was used for statistical evaluation of the data after deconvolution (data filtering, statistical significance, finding unique compounds in trace levels and visualization) Page 44

45 Terbinafine Treatment: Proof of Concept Terbinafine inhibits the ERG1 gene product, which is a squalene epoxidase. As we expected squalene abundance in terbinafine treated samples increased significantly. Metabolites old Change in treated sample Squalene Lanosterol 84.3 (up) 87.6 (down) Erg1 Terbinafine 4,4-dimethyl-5a-cholesta-8,14,24-trien-3b-ol >27.7 (down) 4,4-dimethyl-5a-cholesta-8,24-dien-3b-ol >32.7 (down) Zymosterol 27.6 (down) Ergosterol 5.3 (down) Ergosterol biosynthesis pathway Page 45

46 luconazole Treatment: urther Proof of Concept luconazole inhibits the ERG11 gene product, a 14α-demethylase that prevents the conversion of lanosterol to its subsequent intermediate. Metabolites old change in treated sample Squalene Lanosterol 4,4-dimethyl-5a-cholesta-8,14,24-trien-3b-ol 3.0(up) 14.1(up) Erg11 >27.7 (down) luconazole 4,4-dimethyl-5a-cholesta-8,24-dien-3b-ol >32.7 (down) Zymosterol Ergosterol >37.5 (down) 2.8 (down) Ergosterol biosynthesis pathway Page 46

47 Terbinafine Treatment: Unexpected Observation Downsteam enzymes are not very selective and accumulation of several 14α-methyl sterols is observed Component Compound ormula Derivatized MI Lanosterol C 30 H 50 O α-desmethyl 3-keto-4-methylzymosterol C 29 H 46 O α-desmethyl 4-α-methyl zymosterol C 29 H 48 O α-desmethyl episterol C 29 H 48 O Compounds other than lanosterol were tentatively identified based on full scan accurate mass information. Page 47

48 Totarol Treatment: Revealing a Biochemical Target Metabolite old Change in Treated Sample Squalene 2.5 (Down) Lanosterol 1.7 (Up) 4,4-dimethyl-5α-cholesta-8,24-dien-3β-ol 4α-carboxy-4β-methyl-5α-cholesta-8,24-dien-3β-ol Zymosterol 1.4 (Up) (Up) 2.9 (Down) Erg26 Totarol Ergosterol 1.3 (Down) Page 48

49 Totarol Treatment: MS/MS for Structure Confirmation Product ion spectrum confirms proposed structure of accumulated intermediate 4α-carboxy-4β-methyl-5α-cholesta-8,24-dien-3β-ol C31H52OSi 0.87 ppm MI C35H62O3Si ppm Page 49

50 Totarol Treatment: Using Molecular Structure Correlator to Predict the Structure of MS/MS ragments Precursor formula, based on accurate mass information The structure is not in ChemSpider or NIST library -.mol file for the precursor has to be generated Structure of corresponding fragment Page 50

51 Summary The GC/Q-TO covers a broad scope of applications from targeted analysis to analysis of true unknowns You can take full advantage of all the benefits offered by GC/Q-TO for any of your applications: from high resolution, mass accuracy, full spectrum sensitivity to fast acquisition rate, and accurate mass product ion spectrum Because of the complexity of this instrument it is necessary to understand the optimal modes of the GC/Q-TO operation best suitable for your application Page 51