Agilent 7200 GC/Q-TOF Applications. The Experience so far · Page 1 Agilent 7200 GC/Q-TOF...

Agilent 7200 GC/Q-TOF Applications.

The Experience so far

Sofia Aronova GC/Q-TOF Application Chemist

Agilent Technologies, Santa Clara

• 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 350-400 for SQ

• Full scan spectra with high sensitivity • Higher sensitivity than MSD in full scan mode

• S/N 2500:1 for 1 pg OFN, 10 fg IDL

• Fast 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

Value of Q-TOF

• 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-TOF 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

7200 Q-TOF Covers All Applications

• Environmental

• Fluorotelomer alcohols

• PAH, PCB, OCP, PBDE, and polycyclic musks in SPMD river extracts

• Food testing and flavors

• Olive oil characterization

• Sulfur-containing compounds in beverages

• Food 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

List of Some Applications Performed with GC/Q-TOF

Environmental:

Fluorotelomer 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

• 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 F3C(CF2)N-1(CH2)MOH

• N:M FTOH is the shorthand notation

Fluorotelomer Alcohols: Background

PCI, methane

-H20, -HF

F

F

F F

F

F

F

F

F

OH2+

-F2

F

F

F F

F

F

F

OH2+

CH2+

F

F

F F

F

F

F F-H2O -HF

Exact Mass: 265.0269

Exact Mass: 38.0168Exact Mass: 227.0102

Exact Mass: 227.0301

C+

F

F

F F

F

F

F F

Exact Mass: 37.9968

Dm = 38.0166 Da.

Dm = 0.020 Da.

Acronym Observed Base Peak m/z Molecular ion -F2 m/z Dppm Molecular ion -H20, -HF m/z Dppm

4:2 FTOH 227.0104 227.0301 86.77 227.0102 -0.88

Fluorotelomer Alcohols: Neutral Loss Mechanism Inferred

-H20, -HF

Dm = 38.0166 Da.

Acronym Precursor m/z CE Transition m/z Dppm Loss

10:2 FTOH 565 10V 526.9918 -1.3 -H2O, -HF565.0078

TIC

EIC for m/z 526.9918

10:2 FTOH, MS/MS Confirms Neutral Loss

• EIC of m/z=580.9784

overlaid on m/z=600.9845

• Excellent qualifier ratio in the

presence of nearly 103 excess

of matrix

Quant ion

m/z = 580.9784

EIC

Qualifier ion

m/z = 600.9845

11:1 FTOH Spiked in Biosolid Extract, Quantitation

Environmental:

Anthony Gravell, Environment Agency, England and Wales

Pollutants in SPMD Extracts in River

• IRM to improve mass accuracy

• Dynamic range

Compounds studied

• Polyaromatic Hydrocarbons (PAHs)

• Polychlorinated Biphenyls (PCBs)

• Organochlorine Pesticides (OCPs)

• Polybrominated Diphenyl Ethers (PBDEs)

• Polycyclic Musk Fragrances (PCMs)

Samples - river water and marine sediment

• Extracts from passive samplers (SPMDs)

• Solvent extracts from marine sediments

The choice of compounds was based on the current requirements of the Water

Framework Directive (WFD) which form a significant part of the Environment

Agency‟s monitoring program.

Samples and Target Compounds

100 ppb Molecular

formula

Exact mass Mass error (ppm) Mass error (ppm)

fluorene C13H10 165.0699 5.45 0.61

hexachlorobutadiene C4Cl6 224.8408 4.45 0.89

hexachlorobenzene C6Cl6 283.8096 3.88 0.71

dieldrin C12H8Cl6O 262.8564 2.28 -1.52

BZ # 52 (2,2',5,5' -tetrachlorobiphenyl) C12H6Cl4 291.9189 2.74 -0.34

BDE-47 C12H6Br4O 485.7106 0.21 -2.47

DPMI (Cashmeran) C14H22O 191.143 3.66 -0.52

HHCB (Galaxolide) C18H26O 243.1743 2.06 -0.82

Average 3.09 -0.43

uncorrected corrected

0.5 ppb Molecular

formula

Exact mass Mass error (ppm) Mass error (ppm)

fluorene C13H10 165.0699 -12.12 -3.63

hexachlorobutadiene C4Cl6 224.8408 -3.11 -1.33

hexachlorobenzene C6Cl6 283.8096 -9.16 -3.88

dieldrin C12H8Cl6O 262.8564 -6.47 -6.09

BZ # 52 (2,2',5,5' -tetrachlorobiphenyl) C12H6Cl4 291.9189 -8.56 -4.11

BDE-47_1 C12H6Br4O 485.7106 4.94 4.32

DPMI (Cashmeran) C14H22O 191.143 -1.05 3.66

HHCB (Galaxolide) C18H26O 243.1743 0.00 0.00

Average -4.44 -1.38

uncorrected corrected

No IRM used IRM applied

Mass Accuracy: IRM Helps if Error > 2 ppm

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

1 10 100 1,000 10,000 100,000 1,000,000

Ma

ss

Err

or,

PP

M

Number of Ions Detected

Mass Accuracy: Mass Error at Low Concentration

Compound Matrix Avg (n = 2), ppb Exact mass Measured mass ppm diff

acenaphthylene PAH_extract from SRM 1941b 19.2 ± 1.1 152.062052 152.062147 0.62

hexachlorobenzene PAH_extract from SRM 1941b 2.98 ± 0.04 283.809618 283.809178 -1.55

BZ # 52 PAH_extract from SRM 1941b 0.81 ± 0.32 291.918862 291.918215 -2.22

BDE- 47 PAH_extract from SRM 1941b 1.10 ± 0.03 485.710574 485.708862 -3.53

Cashmeran Waste water SPMD drain 573 ± 11 191.143042 191.14355 2.66

Good mass accuracy is important for target compound

confirmation

Mass Accuracy in Matrix

TIC

Accurate Mass is Necessary to Eliminate Matrix

Interferences SPMD river, Dual Gain

Hexachlorobenzene

SPMD river, Dual Gain

Hexachlorobenzene

TIC

EIC 283.8096 0.5 amu


Interferences

0.6 pg on-column

TIC

EIC 283.8096 0.5 amu

EIC 283.8096 2 ppm Hexachlorobenzene

1 ppb – 5000 ppb

R2 > 0.997


Interferences SPMD river, Dual Gain

Hexachlorobenzene

Food Testing and Flavors:

Olive Oil Characterization

UC Davis Olive Center

&

Stephan Baumann, Agilent Technologies

• MPP for statistical processing of GC/Q-TOF data

• MS library searching using GC/Q-TOF spectra

• CI data provide accurate mass information for molecular ions

• Olive oil samples had been subjected to sensory test and classified as passed or failed

• GC/Q-TOF data then were acquired in both EI and PCI modes

• Chromatographic deconvolution was performed with MassHunter Qual, and the data

were exported as CEF 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

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

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

Olive Oil Characterization: Data Filtering

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.

Olive Oil Characterization: Visualization of Data Clustering

Principal Component Analysis (PCA) of MPP helps to visualize

clustering of the data

failed

passed

Olive Oil Characterization: Fold Change Analysis

The Volcano Plot (on the right) shows fold-change for each entity on the x-axis and

significance on the y-axis.

Compounds accumulated

in the samples that failed

the sensory test.

Olive Oil Characterization: Library Search

Commercial unit mass EI spectral libraries can be searched using accurate mass

EI GC/Q-TOF data to identify compounds

Compound spectrum

NIST library spectrum

Compound spectrum

(accurate mass)

EI

Olive Oil Characterization: Combining EI and PCI Data

Tentative NIST ID Formula EI , M*+ PCI, (M+H)+

Calculated Measured Mass error, ppm Calculated Measured Mass error, ppm

Hexadecanoic acid C16H32O2 256.2397 256.2385 4.68 257.2475 257.247 1.94 Ethyl-octadecanoate C20H40O2 312.3023 312.3008 4.80 313.3101 313.3091 3.19

Squalene C30H50 410.3907 410.3904 0.73 411.3985 411.3987 0.49

α-Cubebene C15H24 204.1873 204.1883 4.90 205.1951 205.1945 2.92

Unknown C14H26O2 226.1927 N/A N/A 227.2006 227.1987 8.36

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

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‟

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

• Further MS/MS experiments allowed to generate „clean‟ spectra in the

presence of matrix interference and could possibly be used for structure

elucidation

Olive Oil Characterization: MS/MS Example

C12H17

5.11 ppm C9H11

-3.58 ppm

C8H9

-2.63 ppm

C10H13

0.93 ppm

α-Cubebene, full scan

C15H24

α-Cubebene: MS/MS

Precursor: 204

CE: 10 eV

(replib) α-Cubebene

40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 2300

50

100

41

55

69 77

8191

105

119

133147

161

175 189

204

Accurate masses of ion fragments are consistent with molecular formula

Food Safety:

Pesticides in Food Matrices

Hans Mol, RIKILT, Netherlands

• MS/MS to improve selectivity

• MS/MS for target confirmation

• Involves both the quantitative analysis of frequently

occurring contaminants and qualitative analysis of non-target

compounds and unknowns

• Can profit from Q-TOF 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-TOF will ensure best

possible selectivity for target quantitation and facilitate target

confirmation

Comprehensive Food Safety Analysis

EIC (Leek extract)

drins 10 pg

262.8564±0.5 Da

drins 10 pg

262.8564±20 ppm

ald

rin

isodrin

die

ldrin

endrin

Low Concentrations Analytes in Heavy Matrix (TOF mode)

CE 20 eV

CE 35 eV

product ion spectrum of endrin m/z 263

MS/MS generates multiple ion fragments that can be used for confirmation

C7H2Cl3[37Cl]

6.86 ppm

C7H2Cl2[37Cl]

1.09 ppm C5HCl3[37Cl]

6.1 ppm

C7H2Cl4[37Cl]

1.85 ppm

(mainlib) Endrin

20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 4000

50

100

14

27

39

53

67

73

81

86

101

113

121139

147

159

173 183 193209

217

245

253

263

273

281

317

327

345

380

O

Cl

ClCl

Cl Cl

Cl

MS/MS for Target Confirmation

endrin

dieldrin

endosulfan-

alpha,

chlordane

drins 10 pg, EIC full scan

262.8564±20 ppm

drins 10 pg, MS/MS

EIC of product ion

263 > 192.9150

en

dri

n

MS/MS to Obtain Ultimate Selectivity

Natural Products:

Structure Elucidation of the

Compound from Kava Extract

Viorica Lopez-Avila, Agilent Labs

• MS/MS for structure elucidation of unknown

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

• 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

MS/MS for Structure Elucidation

(M – H)+

269.0802

Candidate

structures

EI Full Scan

The Problem – Confirm Most Likely Structure Kava Extract - Compound “B”, C16H14O4

(Rings + Double Bonds = 10)

(M – H)+

269.0802

Candidate

structures

EI Full Scan

m/z

(experimental)

Formula Error

(ppm)

Score

269.0802 C16H13O4 2.2 80.7

193.0494 C10H9O4 0.6 96.7

167.0334 C8H7O4 3.0 N/A

166.0259 C8H6O4 0.6 N/A

138.0310 C7H6O3 1.1 98.1

110.0359 C6H6O2 3.0 N/A

95.0127 C5H3O2 0.9 99.5

– CH2=CH–C6H5

– CO

– CH3

– CO

– H

– C6H5

– CH=CH–C6H5

MS/MS experimental

measurements



(M – H)+

269.0802

Candidate

structures

EI Full Scan

m/z

(experimental)

Formula Error

(ppm)

Score

269.0802 C16H13O4 2.2 80.7

193.0494 C10H9O4 0.6 96.7

167.0334 C8H7O4 3.0 N/A

166.0259 C8H6O4 0.6 N/A

138.0310 C7H6O3 1.1 98.1

110.0359 C6H6O2 3.0 N/A

95.0127 C5H3O2 0.9 99.5

– CH2=CH–C6H5

– CO

– CH3

– CO

– H

– C6H5

– CH=CH–C6H5

MS/MS experimental

measurements

X

X



(M – H)+

269.0802

Candidate

structures

EI Full Scan

m/z

(experimental)

Formula Error

(ppm)

Score

269.0802 C16H13O4 2.2 80.7

193.0494 C10H9O4 0.6 96.7

167.0334 C8H7O4 3.0 N/A

166.0259 C8H6O4 0.6 N/A

138.0310 C7H6O3 1.1 98.1

110.0359 C6H6O2 3.0 N/A

95.0127 C5H3O2 0.9 99.5

– CH2=CH–C6H5

– CO

– CH3

– CO

– H

– C6H5

– CH=CH–C6H5

MS/MS experimental

measurements



c ?

Mass at 138 is consistent with:

loss of COCH=CH-C6H5 (131.04969) from 269.08020

or

loss of C2H4CH=CH-C6H5 (131.086075) from 269.08020.

However, measured value of 269.0802 - 138.0310 = 131.04920 is

consistent only with COCH=CH- C6H5.

The Problem – Confirm Most Likely Structure The Problem – Confirm Most Likely Structure

(M – H)+

269.0802

Candidate

structures

EI Full Scan

m/z

(experimental)

Formula Error

(ppm)

Score

269.0802 C16H13O4 2.2 80.7

193.0494 C10H9O4 0.6 96.7

167.0334 C8H7O4 3.0 N/A

166.0259 C8H6O4 0.6 N/A

138.0310 C7H6O3 1.1 98.1

110.0359 C6H6O2 3.0 N/A

95.0127 C5H3O2 0.9 99.5

– CH2=CH–C6H5

– CO

– CH3

– CO

– H

– C6H5

– CH=CH–C6H5

MS/MS experimental

measurements

For the 5 candidate structures, only one fit the losses

identified by CID experiments on multiple precursor ions

The Problem – Confirm Most Likely Structure

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

• Changes in sterol metabolism were evaluated

following the treatment with known inhibitors

(Fluconazole 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)

Metabolic Profiling of Yeast Sterols

Metabolites Fold Change in treated sample

Squalene 84.3 (up)

Lanosterol 87.6 (down)

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)

Erg1

Terbinafine

Terbinafine inhibits the ERG1 gene product, which is a squalene epoxidase. As we expected squalene abundance in terbinafine treated samples increased significantly.

Ergosterol

biosynthesis

pathway

Terbinafine Treatment: Proof of Concept

Fluconazole inhibits the ERG11 gene product, a 14α-demethylase that

prevents the conversion of lanosterol to its subsequent intermediate.

Fluconazole

Erg11

Metabolites Fold change in treated sample

Squalene 3.0(up)

Lanosterol 14.1(up)

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 >37.5 (down)

Ergosterol 2.8 (down)

Ergosterol

biosynthesis

pathway

Fluconazole Treatment: Further Proof of Concept

Downsteam enzymes are not very selective and accumulation of several

14α-methyl sterols is observed

Component Compound Formula Derivatized

MI

393 @ 28.577 Lanosterol C30H50O 498.4251

467 @ 28.39 14-α-desmethyl 3-keto-4-methylzymosterol C29H46O 482.3938

379 @ 28.177 14-α-desmethyl 4-α-methyl zymosterol C29H48O 484.4095

469 @ 28.0 14-α-desmethyl episterol C29H48O 484.4095

Compounds other than lanosterol were tentatively identified based on full

scan accurate mass information.

Terbinafine Treatment: Unexpected Observation

Metabolite Fold Change in Treated Sample

Squalene 2.5 (Down)

Lanosterol 1.7 (Up)

4,4-dimethyl-5α-cholesta-8,24-dien-3β-ol 1.4 (Up)

4α-carboxy-4β-methyl-5α-cholesta-8,24-dien-3β-ol 360.8 (Up)

Zymosterol 2.9 (Down)

Ergosterol 1.3 (Down)

Totarol

Erg26

Totarol Treatment: Revealing a Biochemical Target

Product ion spectrum confirms

proposed structure of

accumulated intermediate

4α-carboxy-4β-methyl-5α-cholesta-8,24-dien-3β-ol

MI

C31H52OSi 0.87 ppm

C35H62O3Si2 -3.24 ppm

Totarol Treatment: MS/MS for Structure Confirmation

The structure is not in

ChemSpider or NIST

library - .mol file for the

precursor has to be

generated

Precursor formula,

based on accurate mass

information

Structure of

corresponding

fragment

Totarol Treatment: Using Molecular Structure Correlator to

Predict the Structure of MS/MS Fragments

• The GC/Q-TOF 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-TOF 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-TOF

operation best suitable for your application

Summary

Agilent 7200 GC/Q-TOF Applications. The Experience so far · Page 1 Agilent 7200 GC/Q-TOF...

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