LC-MS ESI Parameter Optimization with Bayesian Optimization for … · 2020-06-01 · LC-MS ESI...
Transcript of LC-MS ESI Parameter Optimization with Bayesian Optimization for … · 2020-06-01 · LC-MS ESI...
Based on the response surfaces of several compounds, we
changed default parameters in LCMS-8060NX. We
demonstrated that new default parameters improved
intensity about 2.6 times on average with various fields of
compounds (Fig. 5).
4. ConclusionWe’ve developed efficient ESI parameter optimization
method. In the process of this study, we found an ESI
parameter set which enable high signal intensity with various
fields of compounds.
Based on this study, We set this new ESI parameter set as
default parameters in LCMS-8060NX. By LCMS-8060NX,
users can obtain good measurement result without making
complicated analytic condition search, and decrease their
workload.
References
[1] Snoek et al., Practical Bayesian Optimization of Machine Learning
Algorithms, Proc. NIPS, Vol. 2, 2012
[2] Tagawa et al., LC-MS Interface Parameter Optimization for High
Sensitivity Measurement, Shimadzu Review, Vol. 75, 2018
Prominence is a trademark of Shimadzu Corporation.
LC-MS ESI Parameter Optimization with Bayesian Optimization for High Sensitivity MeasurementYusuke Tagawa1, Yuki Ishikawa1, Levi Mikael1, Wataru Fukui1, Jun Watanabe1, Hisanori Morita1
1 Shimadzu Corporation, Kyoto, Japan
WP 279
1. IntroductionElectrospray Ionization (ESI) is one of the methods to
ionize compounds in mass spectrometry (MS) (Fig.1). To
realize high sensitivity, ESI parameters are optimized by
measuring actual samples a few dozen times. It is important
to reduce the number of measurements during optimization
to save the time and the sample. In this study, we have
analyzed sensitivity behavior for ESI parameters, and
developed efficient optimization method.
2. Methods〇 Optimization Procedure
We have developed two step optimization procedure (Fig.
2). At the 1st step, three temperature parameters are
combined to one parameter set, and the temperature
parameter sets are optimized. At the 2nd step, other four
parameters are optimized by Bayesian optimization (BO)[1].
To apply BO to ESI parameter optimization, we have
developed noise-robust BO method in previous study[2].
This two step optimization procedure makes the latency time
for temperature stabilization short, and enables to find the
high sensitivity condition at a small number of measurement.
Figure 1 ESI parameters of LCMS-8060NX
Interface(I/F) voltage
Nebulizer gas flow
Heating gas flow
Drying gas flow
ESI heater(ESIH)
temperature
Block Heater(BH)
temperature
Desolvation Line(DL)
temperature
Hydrophillic compounds
1.0 1.5 2.0 2.5 3.0 3.5 4.0 min
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
(x107)
1.5 2.0 2.5 3.0 3.5 4.0 4.5 min
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
(x105)
Proline
Uric acid
Negativem/z 167.10>123.95
S:×2.4
S:×2.8
AEA
EPA
17.0 17.5 18.0 18.5 19.0 19.5 min
0.0
0.5
1.0
1.5
2.0(x106)
19.0 19.5 20.0 20.5 min
0.0
0.5
1.0
1.5
2.0
2.5
(x104)
Lipids
Positivem/z 348.20>62.10
S:×2.6
Negativem/z 301.20>257.20
S:×2.7
Pesticides
3.0 3.5 4.0 min
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
(x105)
3.5 4.0 4.5 min
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0(x104)
Positivem/z 255.00>208.90
Negativem/z 199.25>127.10
Fluroxypyr
Cloprop
S:×3.2
S:×2.2
Parameters
〇 Experimental Data Collection
To understand the true response surface for ESI
parameters, we collected LC-MS data with 2,088
combinatorial conditions. Because full factorial design of 7
parameters is difficult to conduct, we conducted two patterns
of combinatorial experiment (Table 1). Total 4 compounds
(Angiotensin II, Atrazine, Dicamba and Flurbiprofen) are
measured. In this experiment, Shimadzu HPLC
(Prominence™) and LC-MS (LCMS-8060, of which ESI
structure is very similar to LCMS-8060NX) are used.
3. Results〇 Evaluation of Optimization Performance
We evaluated two optimization performances, our method
and one factor at a time(OFAT) method. To evenly compare
these two optimization performances, we used same
experimental data collected as mentioned before as a
ground truth. As a result, our method could search for the
high sensitivity condition about half number of the
measurement compared to OFAT method (Fig. 3). In 4
compounds, our method was 49% efficient on average.
〇 Investigation of Appropriate Default Parameter
We built response surface models for ESI parameters from
the collected experimental data (Fig. 4).
Condition 1 Condition 2
I/F vol. [kV]5 levels:
0.2,1.5,3.0, 4.0, 5.0
Fixed to
optimal of
condition 1
gas
flow
[L/min]
Neb.4 levels:
0.5,1.5,2.5, 3.0
Heat.5 levels:
3,5,7,10,15<20
Dry.5 levels:
3,5,7,10,15
tempe
rature
[℃]
ESIH 5 sets: (IF, DL, BH)
(100,100,100),
(170,150,200),
(240,200,300),
(300,250,400),
(400,300,500)
5 levels:100,170,240,300,400
DL5 levels:100,150,200,250,300
BH5 levels:100,200,300,400,500
Table 1 Data collection conditionsFigure 3 Evaluation of Optimization Performance (Flurbiprofen)
1. ESIH, DL, HB
temperature set
Optimization
default
parametersOptimized
temperatures2. Heating Gas
3. Nebulizer Gas
4. Drying Gas
5. I/F Voltage
Bayesian Opt.
Opthigh
Inte
nsity
Optimized
parameters
SingleBays
e_Atrazine_
No1_iter19
Opt
x2
x3
x4
x5
Figure 2 Optimization Procedure
Low
Inteinsity
x1(temperature set)
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30
Rel
ati
ve In
ten
sity
number of measurement
Proposed methodOFAT
Three temperatures
Voltage
Three gas flows
Temperature set Voltage and three gas flows
Same Intensity at 14 steps
Figure 4 Examples of constructed response surfaces (Angiotensin)
(ESIH,DL,BH) = (100,100,100) (240,200,300) (400,300,500)
I/F Vol.(1~5)N
eb. G
as(1
~3)
Dry. Gas(3~17)
Hea
t. G
as(3
~17
)
high
Low
Inteinsity
Figure 5 Analyses of various samples with new default ESI parameters
NewConventional
Positivem/z 116.10>70.15