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New GC & GC/MS Technologies to Improve the Productivity of Your Laboratory
Agilent Science and Technology Symposium
May 2014
May 2014
1
Presentation Overview GC and GC/MS Productivity Tools
Challenges for the Current Analytical Laboratory
Capillary Flow Technology
Retention Time Locking
Deconvolution and Reporting Software
Self-Cleaning Ion Source
May 2014
Agilent Science and Technology Symposium 2014 2
Challenges faced by Today’s Customers Expecting More from Less
• Increasing cost of ownership
• Trace analysis in complex matrices Business
Challenges
• Less time for method development
• Limited technical experience Resource Allocation
• Demands for higher throughput • Quicker return on capital investment
Optimizing Efficiency
May 2014
Agilent Science and Technology Symposium 2014 3
Laboratory Challenges Analytical Requirements
Challenges Samples of varying nature
Identify closely related analytes:
isomers, analogs, etc.
Variable concentrations: column overload
to trace concentrations
Complex matrices
Cost of Operation
May 2014
Agilent Science and Technology Symposium 2014 4
Laboratory Challenges Analytical Requirements
Throughput Analyze complex mixtures
Efficient workflow
Speed of analysis
Quality data/results
Efficient data
processing
Simple sample
prep
May 2014
Agilent Science and Technology Symposium 2014 5
Technologies to Improve the Productivity of Your Laboratory
Increasing Throughput and Efficiency
May 2014
6
GC and MS Technologies Improving Sample Throughput
Thro
ughp
ut
CFT Backflush
Retention Time Locking
Self-Cleaning Ion Source
May 2014
Agilent Science and Technology Symposium 2014 7
Capillary Flow Technology Select Modes of Operation
Detector Splitting
Backflush
Solvent Venting
May 2014
Agilent Science and Technology Symposium 2014 8
CFT Backflush Eliminates less volatile matrix components from the GC column by reversing the column flow at a pressure junction point:
Inlet
Flow
MS
Flow
Inlet
Flow
MS
Flow
May 2014
Agilent Science and Technology Symposium 2014 9
C.-K. Meng, Agilent Application Brief 5989-6018EN
• Elimination of long “baked out” at a high temperature to remove less volatile, late eluting matrix components
• Reduced analysis time • Increased column life time • Prevention of the MS source contamination • Less frequent MS source maintenance
Increased sample
throughput
CFT Backflush - Benefits
May 2014
Agilent Science and Technology Symposium 2014 10
CFT Backflush - Benefits
Using a 1.0 minute backflush prevents heavy matrix compounds from carrying over to next run
TIC: Blnk_After_Mshrms_BF_5min.D\data.ms
TIC: Blnk_After_Mshrms_BF_1min.D\data.ms (*)
2 3 4 5 6 7 8 9
2 3 4 5 6 7 8 9
Blank following mushroom sample, 0.5 min backflush (too short)
Blank following mushroom sample, 1.0 min backflush (correct length)
Ghost Peaks
Eliminate Ghost Peaks
May 2014
Agilent Science and Technology Symposium 2014 11
Column Backflush - Benefits
M. Mezcua, M.A. Martinez-Uroz, P.L. Wylie, A.R. Fernandez-Alba, J. AOAC Int. 92 (2009) 1790-1806.
Improved Chromatography
May 2014
Agilent Science and Technology Symposium 2014 12
Overlays of GC-MS/MS chromatograms for selected analytes (at 50 ng/g ) obtained within a 2.5-day sequence of 125 dietary supplement sample injections
Deltamethrinm/z 253>174
GinsengRootPowder
Saw Palmetto Berry Powder
ScutellariaPowderedExtract
2x10
0
0.5
1
1.5
2
2.5
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3.5
4
4.5
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3.5
3x10
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3.5
4
Dichlorvosm/z 185>93
3x10
0
1
2
3
4
5
Malathionm/z 173>99
Ethionm/z 231>129
3x10
0
0.5
1
1.5
2
2.5
3x10
0
0.5
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3x10
0.5
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1.5
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Phosalonem/z 367>182
3x10
0
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1.5
2
2.5
3
3.5
3x10
0.5
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1.5
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3x10
0.5
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0
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2
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3.5
2x10
0
0.5
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1.5
2
2.5
3
3.5
4
Deltamethrinm/z 253>174
GinsengRootPowder
Saw Palmetto Berry Powder
ScutellariaPowderedExtract
2x10
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
3x10
0
0.5
1
1.5
2
2.5
3
3.5
3x10
0
0.5
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1.5
2
2.5
3
3.5
4
Dichlorvosm/z 185>93
3x10
0
1
2
3
4
5
Malathionm/z 173>99
Ethionm/z 231>129
3x10
0
0.5
1
1.5
2
2.5
3x10
0
0.5
1
1.5
2
2.5
3
3x10
0.5
1
1.5
2
2.5
2x10
0
1
2
3
4
5
6
7
Phosalonem/z 367>182
3x10
0
0.5
1
1.5
2
2.5
3
3.5
3x10
0.5
1
1.5
2
2.5
3
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3x10
0.5
1
1.5
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2.5
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2x10
0
0.5
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2
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3.5
2x10
0
0.5
1
1.5
2
2.5
3
3.5
4
Column Backflushing - Benefits
K. Mastovska and P.L. Wylie, J. Chromatogr. A 1265 (2012) 155-164
Accurate DB Match
May 2014
Agilent Science and Technology Symposium 2014 13
Retention Time Locking How Does It Work? For each method: • A set of five runs of retention time vs inlet pressure for a
single locking compound is collected (only once per method) • The calibration is stored with the method • When locking a new instrument, the locking compound is run and its RT is
measured • RTL software calculates new inlet pressure that makes the RT of the
locking compound and all analytes precisely match that of the original method
• Updating RTs of individual cal compounds and timed events unnecessary • If a table of hundreds of RTs is collected under RTL conditions, anyone
anywhere can lock to the same conditions and get the same RTs. This is basis of Agilent screening databases for Tox compounds, pesticides, etc.
May 2014
Agilent Science and Technology Symposium 2014 14
Retention Time Locking RTL Example
Initial run 4.72 psi
Trim 1 meter 4.72 psi
Relock 4.42 psi
4.296 min.
4.064 min.
4.297 min.
Original locked method
Column trimmed, locking compound run at original pressure (RT is too short)
RTL calculates new inlet pressure to restore RTs
RTL keeps the retention times of all analytes typically within 0.030 min absolute. Locking is a simple procedure using 1 compound. After pressure adjustment, all compounds fall within their RT recognition window.
May 2014
Agilent Science and Technology Symposium 2014 15
DB-5MS: Comparison of Different Speeds on MSD
1 x
2 x
3 x
4 x
30 m, 10C/min and 1.7 mL/min: 120V oven and Standard Turbo
15 m, 20C/min and 1.2 mL/min: 120V oven and Standard Turbo
15 m, 30C/min and 2.7 mL/min: 240V oven and Perf Turbo
15 m, 40C/min and 5.9 mL/min: 240V oven and Perf Turbo
4 8 12 16 20 24 28
2 4 6 8 10 12 14
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7
4X method RTs are precisely the same as 1X divided by 4
May 2014
Agilent Science and Technology Symposium 2014 16
• MRM Spectrum Unchanged
• Troubleshooting
• Repeatability - Run-to-run - Operator-to-operator - Instrument-to-instrument
Retention Time Locking (RTL) Improve Confidence with Retention Time Locking
Initial run 4.72 psi
Trim 1 meter 4.72 psi
Relock 4.42 psi
0
50
100
0
50
100
0
50
100
4.296 min.
4.064 min.
4.297 min.
Consistent Retention
Times
May 2014
Agilent Science and Technology Symposium 2014 17
GC/MS System Maintenance Potential disruption to workflow and productivity
System venting and disassembly of MS source
Manual cleaning requires expertise
Reassembly and system re-equilibration
System could be offline for full day of operation
May 2014
Agilent Science and Technology Symposium 2014 18
Self Cleaning Ios Source Add Hydrogen to the Source
Hydrogen In
Use CI transfer line on EI MSD. Hydrogen flows concentrically around end of column into ion volume, the same path used for CI reagent addition
May 2014
Agilent Science and Technology Symposium 2014 19
Self Cleaning Ion Source Add Hydrogen to the Source (top view)
Use CI transfer line on EI MSD. Hydrogen flows concentrically around end of column into ion volume, the same path used for CI reagent addition
Hydrogen in via CI transfer line
Repeller
Drawout (or extractor) lens
May 2014
Agilent Science and Technology Symposium 2014 20
Post Run Cleaning • Reduce frequency of cleaning ion source to less than once every 6 months • No changes to the user’s analytical method
In Situ Source Cleaning • High throughput users can reduce the need to vent their system to once every three months by
using In Situ Source Cleaning in combination with performing Post Run Clean once every two weeks.
• User would need to review analytical method for possible changes
Online vs Offline
May 2014
Agilent Science and Technology Symposium 2014 21
Self-Cleaning Ion Source Using H2 to Clean to MS Source
50 100 150 200 250 300 350 400 450 5000
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
65000
70000
75000
m/z-->
91.0
55.0
133.1
178.1
215.1
252.2289.1
326.2360.2402.3440.3476.3
Very Dirty Source: Background Spectrum
50,000
50 100 150 200 250 300 350 400 450 5000
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
65000
70000
75000
m/z-->
78.145.1 206.9 281.1134.9 355.1 445.5241.6168.1 320.6 388.9 477.9
After Cleaning with H2
50,000
50 100 150 200 250 300 350 400 450 5000
20
40
60
80
100
120
140
160
180
200
220
m/z-->
78.1
45.1 206.9
281.1134.9 355.1
445.5241.6168.1 320.6 388.9 477.9
Expanded scale
50
May 2014
Agilent Science and Technology Symposium 2014 22
Cleaning Restores Analyte (OFN) Detection Comparable to manual cleaning?
4.104.124.144.164.184.204.224.244.264.284.304.324.344.364.38140000
142000
144000
146000
148000
150000
152000
154000
156000
158000
160000
162000
164000
166000
168000
170000
172000
174000
176000
178000
Time-->
( ) p p
4.104.124.144.164.184.204.224.244.264.284.304.324.344.364.380
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
Time-->
S:N > 50 pk-pk > 200 rms
S:N ≈ 1 pk-pk > 2 rms
Very dirty source before cleaning
After cleaning with H2
May 2014
Agilent Science and Technology Symposium 2014 23
Self-Cleaning Ion Source Stable ISTD RF for PAHs
May 2014
Agilent Science and Technology Symposium 2014 24
Additional calibration sets, standards in solvent, after system left idle for 12 days, with system modifications
Self-Cleaning Ion Source Stable calibrations for PAHs
May 2014
Agilent Science and Technology Symposium 2014 25
Self-Cleaning Ion Source On-Board GC/MS System Maintenance
On-Line bleed prolongs time between cleaning
Eliminates MS venting and source disassembly
Eliminates reassembly & system re-equilibration
System offline for 2 hours versus full day
Improves Workflow and Data Quality
May 2014
Agilent Science and Technology Symposium 2014 26
Effi
cien
cy Deconvolution Reporting Software
Peal Explorer
MRM Databases
Accurate Mass Databases
GC and MS Technologies Efficient Data Analysis
May 2014
Agilent Science and Technology Symposium 2014 27
Improving Efficiency Deconvolution Reporting Software
Deconvolution of mass spectra removes/reduces interferences from chromatographically overlapped peaks: • Improve identification and confirmation of analytes in high matrix samples • Reduces data review time, especially for large screening methods • Reduces false positives and false negatives in dirty samples • Identification based on matching entire spectrum cleaned of interferences
against library. Much more reliable than target/qualifier ratio method.
May 2014
Agilent Science and Technology Symposium 2014 28
Deconvolution Reporting Software Deconvolution Pulls Out Individual Components and Related Spectra
Deconvoluted components and spectra Component 1
Component 2
Component 3
TIC
Component 1 Component 2 Component 3
Deconvolution
Components and Mixed Spectra
May 2014
Agilent Science and Technology Symposium 2014 29
Deconvolution Challenge Standard Deconvolution Reporting Software (DRS) Familiarization
3 4 5 6 7 8 9
What is this peak? Use conventional approach of average spectrum and PBM search to identify it
5.2 5.3 5.4
May 2014
Agilent Science and Technology Symposium 2014 30
Probability Based Matching Identifies THC Conventional Approach
• First hit is THC with 96 PBM match. • Second hit is hydrocodone with only
35 match
40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360
41 59 70 77 91 96 107
115
121 128
141 147 155 165 174
185 193 199
214 228
231
243
258
271
284
299
314
Average Spectrum
55 67 81 91 107 115 128 147
174 193 201 217
231
243 258
271
285
299
314
40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360
THC, 96 Match
165
5.2 5.3 5.4
May 2014
Agilent Science and Technology Symposium 2014 31
Peak Is THC, Right? How confident are you?
55 67 81 91 107 115 128 147 165
174 193 201 217
231
243 258
271
285
299
314
40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360
Hit 1:THC, 96 Match PBM
42 44 51 55
59 68 70 77 82
84 91
96
103
115
121
128
141 155 162 171
185
188 199
214 228
242
256 270 284
299
40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360
Hit 2: Hydrocodone, 35 Match PBM
40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360
41 59 70 77 91 96 107
115
121 128
141 147 155 165 174
185
193 199 214 228
231
243
258
271
284
299
314
Average Spectrum
May 2014
Agilent Science and Technology Symposium 2014 32
Overlap of THC and Hydrocodone As viewed in AMDIS
THC 5.281 min Hydrocodone 5.283 min
THC 314 THC 271
Hydrocodone 185 Hydrocodone 96
• Deconvolution finds 2 compounds closely merged together. • Peaks are separated by only 0.002 min. (Less than 1 scan)
May 2014
Agilent Science and Technology Symposium 2014 33
DRS Report for Challenge Standard Automated Deconvolution and Reporting
MSD Deconvolution Report Sample Name: THC/HC Data File: D:\MassHunter\GCMS\1\data\HCOD_THC.D Date/Time: 17:18 Wednesday, Oct 2 2013
Adjacent Peak Subtraction = 1 Resolution = High Sensitivity = Medium Shape Requirements = Medium
The NIST library was searched for the components that were found in the AMDIS target library.
Retention Time (R.T.) Cas # Compound Name Chemstation AMDIS Match R.T.
DiffReverse Match
Hit Number
3.2702 51799327 N-Propylamphetamine 99 -1.3 91 1
4.6881 3158858 10,11-Dihydrodibenz(b,f)(1,4)oxazepin-11-one 97 -0.7 90 1
5.2479 521379 Cannabidiol 68 5.6 64 3
5.281 1972083 Delta-9-tetrahydrocannabinol (THC) 95 0.5 85 1
5.283 125291 Hydrocodone 95 0.2 88 1
5.3145 466999 Hydromorphone 78 -0.1 67 2
5.3637 521357 Cannabinol 67 0.8 55 4
Amount (ng) AMDIS NIST
Automated report generation took about ~ 60 sec
May 2014
Agilent Science and Technology Symposium 2014 34
Agilent Peak Explorer Another View on Data
May 2014
Agilent Science and Technology Symposium 2014 35
October 13, 2014 Confidentiality Label
36
Agilent Peak Explorer Another View on Data
Rapidly Review Multiple Data Sets
October 13, 2014 Confidentiality Label
37
Agilent Peak Explorer Another View on Data
Efficient Data Review
MRM Databases for GC/MS/MS G9250AA – Pesticides & Environmental Pollutants
Average and exact Molecular Weight
Each compound classified into two categories
Database has RTs (and RIs) to be used with three GC methods (CF-40min, CP-40min, and CF-20min)
May 2014
Agilent Science and Technology Symposium 2014 38
Importance of MRM Transitions Malathion Identification
Confident Identification
May 2014
Agilent Science and Technology Symposium 2014 39
Why are MRM Transitions Important? Endosulfan Sulfate (?) in Tea Extract
3.3 ppb, Equiv : 0.016 mg/Kg in Tea Tea EU MRL : Sum Endo a+b+sulfate = 30 mg/kg
Std 5 ppb Reagent Blank Tea Extract A
• Single Qualifier Transition • Ion Ratio outside 80-120%
confidence band • Unconfirmed
Agilent Restricted
40
• Rerun of data • Ion Ratio within 80-120%
confidence band • CONFIRMED!!
• Demonstrates the value of multiple optimized transitions in the MRM Database.
• Not just to avoid matrix interferences but also for additional confirmation!
Tea Extract A
Accurate Confirmation
May 2014
Agilent Science and Technology Symposium 2014 40
Mass Profiler Professional - Totarol Treatment When you must identify that unknown
The structure is pulled out from ChemSpider or .mol file
Structure of corresponding
fragment
Precursor formula, based on accurate mass information
Compatibility score
: Using Molecular Structure Correlator to Predict the Structure of Fragments
May 2014
Agilent Science and Technology Symposium 2014 41
Mass Profiler Professional with 7200 Data Facilitating Data Review - Filtering, PCA, ANOVA, Volcano Plot
Mass Profiler Professional (MPP) was used for statistical evaluation of the data including construction of class prediction model to correctly predict
whether the sample would pass or fail the sensory test
Accurate Identification of
Unknowns
May 2014
Agilent Science and Technology Symposium 2014 42
Technologies for GC and GC/MS Improving Laboratory Efficiency
Efficient Workflow
Reduced Operating Expense
Improved Sample
Throughput
Robust Analytical
Performance
Efficient Data
Analysis
Confident Target and Non-target
ID
Consistent Accurate Results
May 2014
Agilent Science and Technology Symposium 2014 43
Questions… Thank you for your attention
May 2014
Agilent Science and Technology Symposium 2014 44