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The world leader in serving science
Innovazioni tecnologiche per l’analisi GC-MS in bassa ed alta risoluzione con il nuovo GC-Orbitrap Dott.ssa Cristina Neri, Ph.D Sales Specialist GC-GC&MS 1°Seminario di Spettrometria di Massa. Università degli studi di Milano 23/06/2017
2
• Bassa Risoluzione vs Alta Risoluzione
• Il GC-Orbitrap
• Untargeted Metabolomics using Orbitrap GC-MS • Conclusioni
• Q&A
Agenda
3 The world leader in serving science
Bassa Risoluzione vs Alta Risoluzione
4
Resolution- what is it?
• Ability of a mass spectrometer to distinguish between ions of nearly equal m/z ratios (isobars).
• m - measured mass • Δm - peak width measured at 50% peak intensity (Full Width Half Maximum)
- or the mass difference between two adjacent peaks of equal intensity, in this case pw @ 10% valley definition is used.
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Resolution- what is it?
• Ability of a mass spectrometer to distinguish between ions of nearly equal m/z ratios (isobars).
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Resolution- what is it? • At minimum the resolution of the mass analyzer should be sufficient to separate two
ions differing by one mass unit anywhere in the mass range scanned (unit mass resolution).
• typical values of resolution for low resolution mass analyzers (e.g. quadrupoles and ion traps) are below 5000.
• High resolution instruments have a resolution exceeding 15000.
7
• Selectivity:
• High mass resolution can greatly reduce matrix interference.
• It can also reduce interference between ions of the same compound (e.g. fine isotopic structure).
Why resolution is important?
±0.00157 amu ±0.5 amu
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Resolving Power: Selectivity
False negative
False negative
Positive Detection
High Selectivity ∴ high sensitivity and confidence in identification
Pyrimethanil in leek at 10 μg/Kg < 5 ppm ID criteria
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Mass Accuracy: what is it?
• Mass accuracy is the accuracy of which the mass is measured by the mass spectrometer. • Typical way of reporting mass error in ppm (relative mass error): • Absolute mass error can be used (mDa). • Main advantage: the possibility to determine the elemental composition of individual molecular or fragment ions, a powerful tool for the structural elucidation or confirmation.
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1 1,5 2 5 10 50
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Mass Accuracy (ppm)
Chemical element No
C 50 H 100 O 10 N 10 Cl 10
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2 5 7
16
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Compound ID confirmation
11 The world leader in serving science
Il GC-Orbitrap
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Introduzione del GC-Orbitrap
“An Illustrated History of Gas Chromatography: Thermo Scientific Q Exactive GC”
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Redefining Routine GC-MS
RP 60,000 (FWHM @ m/z 200)
EI/CI; Full-scan; Timed-SIM
Thermo Scientific™ Exactive™ GC system
New Addition to the Orbitrap GC-MS Family
Thermo Scientific Q Exactive GC system
Unprecedented Depth in Analysis
RP 120,000 (FWHM @ m/z 200)
EI/CI; Full-scan, Timed-SIM
MS/MS capability
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Exactive GC system: The Technology Inside
Thermo Scientific™ ExtractaBrite™ Ion Source technology
Routine grade robustness
Patented RF lens
Removable without breaking vacuum
Orbitrap mass analyzer
Incredible HRAM performance
Highly regarded Q Exactive GC system platform
Thermo Scientific™ TRACE™ 1310 GC System
Unique modular injector and detector design
Rapid heat cycling
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Bringing GC and Orbitrap Technology Together
C-TRAP
HCD Cell
AQT Quadrupole
Orbitrap Mass Analyzer
Bent Flatapole
ExtractaBrite™ Ion Source
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What the Orbitrap Looks like
Inner-electrode Split outer-electrode
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• Ions are stored and cooled in the RF-only C-trap
• Ions are ejected along lines converging to the pole of curvature (which coincides with the Orbitrap entrance).
• As ions enter the Orbitrap, they are picked up and squeezed by its electric field
Pulsing Ions into the Orbitrap: Curved Linear Trap (C-trap)
Push Trap Pull Lenses Orbitrap
Gate Deflector
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zmk/
=ω
Retaining the Ions in the Orbitrap
• Frequency of axial oscillations are independent of initial conditions of ions entering trap • Therefore these oscillations used for mass determination • Image current measured by outer split electrodes (no electron multiplier to replace!) • Ion frequencies determine by complex superposition of measured ring oscillations
through Fourier transform
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GC-MS Until Now
Challenge
Compromised Options
Multi-instrument approach • Can be inefficient, convoluted
and complicated
• GC single and triple quadrupole MS for target quantitation
• GC- Time-of-flight (Tof) MS with limited performance
Limited GC-MS analysis • No comprehensive
quantitative & qualitative analysis
• For quantitation: targeted approaches required
• HR/AM Qualitative approaches compromised
20
Breakthrough in GC-MS Performance
Sensitivity
ppt
Resolution
Up to 120,000 at m/z 200
• Highest available
• Maximum selectivity
• Fast enough for GC
Mass accurancy
< 1 ppm
• Every scan
• All concentrations
• In complex matrix
• Across the mass range
• Everyday!!
• In full scan
• High selectivity
• High spectra fidelity
Dinamic Range
>6 Orders
• Excellent coverage in sample profiling
• “Triple quad grade” quantitation in full scan
&
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Exactive GC System Main Workflows
Targeted Quantitation
expected
1x Full-scan
Scope
Non-targeted Screening
unexpected
Targeted Screening
suspected
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Non-targeted Screening Overview
• Sensitive and selective peak detection
• High resolution spectral deconvolution
• Clean spectrum
detect and refine
1 generate candidates
2 filter and identify
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• Search spectra against spectral libraries
• HRAM or unit mass
• Candidates list generated
• High resolution filtering of candidates
• Putative identifications made
Process semi/fully automated as preferred
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Identify the compound – searching NIST 14
26 Hits from NIST are sorted based on: 1. Spectral matching.
2. High Resolution Filtering (HRF) score. % of the ions in the
spectrum that can be explained by the element in the proposed compound.
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Identify the compound – searching NIST 14
Combined SI and HRF values give an overall score (%) to quickly and confidently identify the compound. Eliminates other hits that would be valid if only SI used.
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Fragments can be explained with < 1ppm mass accuracy
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Q Exactive GC: Compound Discovery and Identification
Search
Library
Identify
AM filtering
Known Unknown
Unknown Unknown …
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Q Exactive GC: Compound Discovery and Identification
Remove entire ion source or change to CI source in under 2 minutes without venting …
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EI & PCI spectra for peak at 15.17 mins.
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08OCT15_01 #2053-2062 RT: 15.65-15.66 AV: 10 NL: 1.19E7T: FTMS + p CI Full ms2 325.14@hcd10.00 [50.00-350.00]
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223.07545C 15 H11 O 2 = 223.07536
0.42975 ppm
251.10672C 17 H15 O 2 = 251.10666
0.26844 ppm195.08049C 14 H11 O = 195.08044
0.24907 ppm
105.03358C 7 H5 O = 105.03349
0.83676 ppm
145.02854C 9 H5 O 2 = 145.02841
0.92080 ppm
279.10166C 18 H15 O 3 = 279.10157
0.30906 ppm117.0701277.03894 161.90226
MS/MS m/z 325.14 to support proposed formula
>13 fragments can be explained based on C20H20O4
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No. of proposed formulae for m/z 324.13541
1 ppm 3 ppm 5 ppm 20 ppm 10 ppm
No.
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25 Top hit = C20H20O4
Elements used: C (1-30), H (1-60, N (1-5), O (1-5), P (1-2), S (1-2)
31 The world leader in serving science
Untargeted Metabolomics using Orbitrap GC-MS
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• Metabolomics aims to characterize and quantify the complete small molecule complement (the metabolome) of a biological system.
• The metabolome is very diverse mixture of small molecules (amino acids, sugars and phosphosugars, biogenic amines and lipids).
• Untargeted metabolomics - challenging due to the requirement to identify and quantify hundreds of different compounds with limited a priori knowledge.
• Ideally - use MS analytical platforms able of sensitive detection of molecules, in addition to providing accurate mass information for structural analysis of unknowns.
Introduction
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Introduction
• The goal of metabolomics is a comprehensive and systematic understanding of all metabolites in biological samples.
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• Targeted • Define specific metabolites • Detect them with appropriate parameters • Identity is unambiguous • Quantitation often absolute • Interpretation relatively easy • Validation
• Untargeted
• More challenging, less quantitative • Identity ambiguous due to breadth of separation • Greater coverage • Detect unexpected or new compounds • Interpretation and statistical analysis challenging • Discovery
Challenges in metabolomics
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Metabolic Profiling of bacterial ‘quorum sensing’
Developing a metabolomic toolbox for pathogen-pathogen interactions
1. Candia albicans: cells & media 2. Staphylococcus aureus: cells & media 3. C. albicans & S. aureus co-culture: cells & media
Q Exactive GC MS • Full MS • Relative quantitation and
identification • Compound ID using
NIST and pure standards
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Candida albicans and Staphylococcus aureus dual-species biofilms
Staphylococcus aureus
Candida albicans
• Leading pathogens in bloodstream and systemic infections
• Major cause of morbidity and
mortality
• Form biofilms
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Staphylococcus aureus
Candida albicans
Staphylococcus aureus + Candida albicans
Carlson, E., (1983), Infection and Immunity, 42 (1) 285-92.
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C. albicans S. aureus
S. aureus added to C. albicans biofilm
SEM images: 4000x magnification
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Aims
• Characterise the dual-species biofilm • Develop metabolomics methods for the analysis of
polymicrobial biofilms • Apply methods to determine the interaction between
Candida albicans and Staphylococcus aureus
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RT: 8.82 - 9.87 SM: 3B
8.9 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8Time (min)
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9.06
8.978.92 9.659.12 9.55 9.859.609.48 9.808.87 9.769.18 9.33 9.71
9.04 9.24
9.419.06
9.55 9.659.48 9.809.138.92 8.98 9.619.349.18 9.709.24
9.07
9.41
8.98 9.659.55 9.799.489.138.928.84 9.629.359.19 9.719.24
9.069.41
9.12 9.659.559.47 9.858.92 9.798.978.86 9.619.18 9.35 9.71
NL:2.84E7TIC MS MO3
NL:3.57E7TIC MS CAM3
NL:3.48E7TIC MS SAM2
NL:3.57E7TIC MS SCM3
Media blank
Candida media
S. aureus media
Co-culture
Media samples Chromatogram from GC-Orbitrap
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Principal Component Analysis
Media samples
Cells samples
SCC SAC
CAC MO SCM
SAM
CAM
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Amino Acid Uptake: media samples Fresh Medium CAM SAM SCM
L-Methionine, 2TMS 0 -6.8 -6.0 -6.1 L-Proline, 2TMS 0 -6.3 -1.0 -3.9 L-Histidine, 3TMS 0 -2.1 -0.2 -1.1 L-Threonine, 3TMS 0 -2.0 -3.5 -4.5 L-Glutamic acid, 3TMS 0 -1.9 -0.9 -3.6 L-Serine, 3TMS 0 -1.8 -2.8 -3.3 L-Leucine, 2TMS 0 -1.7 -2.8 -3.3 L-Isoleucine, 2TMS 0 -1.5 -3.6 -2.9 L-Phenylalanine, 2TMS 0 -1.0 -2.1 -2.2 L-Alanine, 2TMS 0 -1.0 -0.4 -0.7 L-Aspartic Acid, 3TMS 0 -1.0 -0.8 -2.6 L-Ornithine (and L-Argininine), 3TMS 0 -0.8 -0.7 -0.8 L-Valine, 2TMS 0 -0.7 -2.0 -1.8 L-Tryptophan, 3TMS 0 -0.3 -2.0 -1.3 L-Homoserine, 3TMS 0 -0.2 3.0 2.7 L-Tyrosine, 3TMS 0 0.0 -5.1 -1.7 Glycine_3TMS 0 0.0 0.1 0.0 L-Hydroxyproline, 3TMS 0 0.1 0.2 0.3 L-Cysteine, 3TMS 0 0.3 -1.6 0.1
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Central carbon metabolism: media samples
Fresh Medium CAM SAM SCM
D-Glucose, 6TMS + Oxime 0 -6.3 -8.2 -7.8
Succinic acid, 2TMS 0 0.2 0.4 0.3
Lactic Acid, 2TMS 0 0.1 -0.06 -0.76
Fucose + 4TMS + Oxime 0 0.04 -0.03 -0.06
Myo-Inositol + 6TMS 0 0.04 -0.1 -0.06
D-Lactose + 8TMS + Oxime 0 -0.6 -1.7 1.3
D-Arabinose + 4TMS + Oxime 0 -0.19 -0.3 -0.04
D-Ribose + 4TMS + Oxime 0 0.1 -0.1 -0.18
Sucrose + 8TMS 0 2.0 -0.2 0.01
Maltose + 8TMS + Oxime 0 0.5 -1.6 -1.7
D_Threose + 4TMS + Oxime 0 1.1 1.7 ND
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Intracellular sugar phosphates: cells samples
Normalised CAC SAC SCC
Myo-Inositol-1-phosphate + 7TMS 0 -4.5 0.1
D-Glucose 6-phosphate + 7TMS + Oxime 0 -3.0 0.1
Sedoheptulose 7-phosphate + 7TMS + Oxime 0 -0.1 1.0
D-ribose 5-phosphate + 5TMS + Oxime 0 1.3 -0.1
D-Erythrose 4-phosphate +5TMS 0 ND ND
D-Fructose 1,6-bisphosphate + 6TMS + Oxime 0 ND ND
D-Fructose 1-phosphate + 7TMS + Oxime 0 ND ND
D-Fructose 6-phosphate + 6TMS + Oxime 0 ND 0.7
Ribulose-5-phosphate + 5TMS + Oxime 0 ND ND
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Compound Discoverer 2.0 metabolomics workflow
Experiment definition (sample grouping)
Data processing (peak alignment & extraction)
Data export further statistical analysis using 3rd party software
Data review (based on %CV, p-values)
import .raw data (full scan EI or CI)
Statistical analysis (trend plot, PCA)
Compound ID (NIST or user database)
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Compound Discoverer results browser
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Untargeted Metabolomics
Dr. Karl Burgess
Glasgow Polyomics
University of Glasgow, UK
Application: Automated, untargeted metabolomics of decaying muscle tissue from various species Aim: Discovery of bio markers for time of death Results: PCA for time of death in Rats allowed group separation suggesting a prediction model for time of death can be obtained. Automatic identification of significant metabolites and potential biomarkers was possible with Q Exactive GC and intelligent deconvolution and identification software
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GC-MS chromatograms of a rat muscle tissue sample RT: 14.68 - 22.86
15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0 20.5 21.0 21.5 22.0 22.5Time (min)
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15.32 16.63
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16.59 16.8918.53 20.5115.66 17.6615.00
21.0418.0915.81 17.0416.17 22.5420.4119.88 20.97 22.0119.60 21.9221.2618.64 19.17
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21.0416.1714.99 20.5119.62 19.88 20.41 22.4021.9917.12 18.64 21.9621.5318.90 19.4218.19
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17.7620.49
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19.58 20.4015.17 19.8716.16 22.4021.0320.9218.63 21.9920.12 21.8921.2517.22 18.95 19.4118.3916.63 17.04
18.5317.7816.58
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18.0916.1814.82 19.6215.00 20.5119.88 21.04 22.4120.4015.88 18.64 18.9117.12 21.26 22.0021.5419.52 20.6718.19
NL:1.00E9TIC MS t0r1r
NL:1.00E9TIC MS t1r3r
NL:1.00E9TIC MS t2r5r
NL:1.00E9TIC MS t3r7r
T0
T1
T2
T3
Immediately post-mortem
Day 1 post-mortem
Day 2 post-mortem
Day 3 post-mortem
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• Automated peak picking using TraceFinder resulted in 1193 distinct peak clusters detected, 156 peaks as TMS derivatives.
• Example of deconvoluted peak cluster putatively identified as glutamate:
Peak Deconvolution
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Quantitation matrix of detected peak clusters
Base Peak Mass.
RT max
intensity T0 T1 T2 T3
ttest: T0
ttest: T1
ttest: T2
ttest: T3
244.09515 12.70 14200000 1.0 8.0 4.2 3.0 1.000 0.017 0.012 0.007
174.10451 20.93 3514752 1.0 4.1 2.4 2.9 1.000 0.048 0.131 0.019
115.08180 16.82 641130 1.0 2.3 2.8 2.8 1.000 0.032 0.083 0.043
246.14626 15.31 870807 1.0 2.4 2.0 2.4 1.000 0.004 0.014 0.010
267.07825 12.93 1375261 1.0 3.3 2.6 2.4 1.000 0.032 0.156 0.132
157.05594 18.64 1901910 1.0 3.9 3.0 2.4 1.000 0.023 0.044 0.066
204.10023 21.64 1306996 1.0 4.7 2.1 2.3 1.000 0.049 0.396 0.163
304.24032 23.89 1282461 1.0 4.2 2.6 2.2 1.000 0.049 0.248 0.218
409.16741 23.76 63600000 1.0 2.2 2.7 1.6 1.000 0.037 0.266 0.445
297.13302 23.76 1791507 1.0 2.3 2.5 1.5 1.000 0.031 0.252 0.515
220.07711 6.12 30600000 1.0 0.5 0.6 0.8 1.000 0.044 0.089 0.403
238.08772 13.80 662692 1.0 0.6 0.6 0.6 1.000 0.003 0.005 0.004
189.07643 8.96 1444272 1.0 0.4 0.4 0.6 1.000 0.046 0.067 0.141
179.04016 9.16 1710471 1.0 0.4 0.4 0.6 1.000 0.028 0.027 0.060
188.07996 8.76 1890000000 1.0 0.4 0.4 0.4 1.000 0.024 0.036 0.039
133.01916 8.76 1820872 1.0 0.3 0.4 0.4 1.000 0.024 0.035 0.035
230.07952 16.63 7380595 1.0 0.3 1.2 0.3 1.000 0.037 0.502 0.045
240.11144 14.63 1063520 1.0 0.2 0.3 0.3 1.000 0.022 0.033 0.026
214.04808 15.85 883184 1.0 0.3 0.3 0.2 1.000 0.047 0.063 0.041
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• Multivariate analysis that collapses high-dimensional data to principal components responsible for the majority of variance in the dataset.
Partial Least Squares-Discriminant Analysis
PC1
PC
2
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• Example of changing intensities of detected chemicals across biological groups (T0 T3).
Variables trend
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Putative compound ID RT (min)
NIST Forward Match
Fold increase
compared to T0
Base Peak Fragment elemental
composition
Ppm accuracy
(base peak)
ppm accuracy (Mol. ion)
L-Threonine, 3TMS 10.71 795 2.8 C9H24ONSi2 0.27 0.13
L-Aspartate, 3TMS 11.78 707 7.0 C9H22NO2Si2 0.18 0.34
L-Methionine, 2TMS 12.40 749 15.0 C7H18NSSi 0.24 0.04
L-Glutamine-3TMS 15.32 815 2.0 C7H14NOSi 0.53 0.21
Putrescine, 4TMS 16.18 870 2.0 C7H20NSi2 0.05 N/A
Untargeted metabolomics
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Conclusioni
• GC/MS market gap exists for simultaneous targeted and untargeted analysis with sensitivity and selectivity comparable to that of a triple quadrupole
• An Orbitrap based GC system provides the technology to fill this gap based on its sensitivity and ability to operate routinely at very high resolutions
• The very high resolution and accurate mass data the Orbitrap based GC system produces can increase confidence in unknown identification.
Q Exactive GC is an easy-to-use, dedicated GC-MS that provides the highest confidence in compound discovery, identification and quantitation for a comprehensive understanding of your samples. This is achieved through the superior resolving power, mass accuracy and sensitivity that only Orbitrap technology can deliver.
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Grazie per l’attenzione!!!
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