Demonstrations of new tools for spectral data analysis ... · •FOSS XDS •transmittance,...
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Demonstrations of new tools for spectral data analysis & NIRS of Waters (treated with Magnetic Fields) Bernhard Pollner, Zoltan Kovacs Aquaphotomics: Understanding Water in the Biological World at the 5th Kobe University Brussels European Center Symposium 14th October 2014, Brussels, Belgium
Transcript of Demonstrations of new tools for spectral data analysis ... · •FOSS XDS •transmittance,...
Demonstrations of new tools for spectral data analysis & NIRS of Waters
(treated with Magnetic Fields) Bernhard Pollner, Zoltan Kovacs
Aquaphotomics: Understanding Water in the Biological Worldat the 5th Kobe University Brussels European Center Symposium
14th October 2014, Brussels, Belgium
Outline & Resume
R-Package „Aquaphotomics“
•Experiment-design, Data-Import, Data-Analysis
• Prelim. Results:• distinguish between different water species
• show effect of different magnetic treatments of water
Online „Aquagram-Maker“
•upload data to make Aquagram
•easy and fast
R-Package „Aquaphotomics“
a very brief introduction
•high level programming language•open source - freeware !• cross-platform•powerful - extendable•possible: something really new•widely used in academia
Why R-Project ?
Experiment Design
• randomize samples
• facilitates time resolved experimentsData Import
• e.g. straight from binary (.da) file
• controlled source and structure for class- and numerical variables
Data Analysis
• easy splitting and grouping of data
•parallelization of different data pre-treatments
• tools to compare those parallel results
R-Package „Aquaphotomics“
• standardized data structure
•encapsulated and „transportable“ color-coding
•nice to look at: view of NIR-data is minimized (but of course still possible)
• (regress on absTime)
Data Import
• splitting / slicing / grouping of data
• recyclable, separate data-analysis procedure file
• strict separation of calculations and plotting
• customizable parallel processing (if applicable; on UNIX)
• various methods implemented• and much more already on the „list“
Data Analysis
Data Pre-Treatment
• smoothing & derivatives, SNV, MSC, EMSC
•special Aquaphotomics data pretreatments (parallelized)
Evaluation / Analysis:
• subtractions in raw-spectra
•PCA, PLSR
•SIMCA
• (SVM, bootstrap, ...)
•Aquagram !
Data Analysis - Implemented Methods
• fully scriptable analysis routines• consisting of (user´s) custom
code, and
• easy-to-use analysis modules
• same analysis of an exact repetition of a previous experiment can so be done in a few seconds - just by plugging new data into the old (copied) analysis routine
R-Package „Aquaphotomics“
Think - Copy&Paste - Enjoy
Analysis ResultA
Analysis
ResultA
Dataset
Dataset
Preliminary Results
Spectrometer:
• FOSS XDS
• transmittance, pathlength = 1mm
•400nm - 2500nm; Δ=0.5nm
•T = 28.6ºC
•up to 5 different water species
•MilliQ as standard
•Abbreviations showing origin of water• StU: St. Ulrich
• Ob: Oberndorf
• MQ: MilliQ
•Analysis in 1st overtone of OH/water (1300-1600nm)
Different Water Species
1300 1350 1400 1450 1500 1550 1600
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Fusion−AllWaters no Treatment, ProxCuv@1300−to−1600
Aquagram Spectra no treatment (not N corr.)Wavelength
avg
Abso
rban
ce
MQStUObMQs
N= 2
16, 2
54, 1
90, 4
0
3(4) Different Water Species - no Treatment
•raw spectra
no treatment
PC 1
no treatment
−0.005 0.000 0.005
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PC 3
no treatment
−0.0
02−0
.001
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00.
001
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no treatment
−0.06 −0.02 0.02
−0.0
015
−0.0
005
0.00
05
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−0.002 −0.001 0.000 0.001 0.002
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PC 4
C_ConSNr ProxCuv@1300−to−1600
3(4) Different Water Species - no Treatment
PCA:
•biggest effect: consecutive scans (98.7%)
no treatment
PC 1
no treatment
−0.005 0.000 0.005
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−0.0015 −0.0005 0.0005
−0.0
6−0
.02
0.02
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no treatment
PC 4
C_waterNames ProxCuv@1300−to−1600
3(4) Different Water Species - no Treatment
PCA:
• single water species PC3: 0.127%PC4: 0.030%
1300 1350 1400 1450 1500 1550 1600
−0.1
0−0
.05
0.00
0.05
ProxCuv@1300−to−1600
no treatment PCA , bw=25 ,2 PCs for 99% var.wavelengths
Coe
ffici
ent
PC 1 (98.7022%)PC 2 (1.1061%)PC 3 (0.1269%)PC 4 (0.0303%)
1412
1362
1418
1490
13141324
1416
1509 1546 1593
1457
1362
1379
14641474
Loadings
MQ
@5
StU@
5
Ob@
5
MQ
s@5
no Treatment ProxCuv@1300−to−1600
no treatment
Inte
rcla
ss D
istan
ce
0.0
0.5
1.0
1.5
2.0
2.5
MQ StU Ob MQs
SIMCA - Interclass Distances
observe:
•MQs and MQ - same water in different containers
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12
34
5
no Treatment ProxCuv@1300−to−1600
(classic) no treatment class distance (Ob, MQs) = 2.72Ob@5
MQ
s
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SIMCA - Interclass Distances
Ob / MQs:
•nice separation
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12
34
5
no Treatment ProxCuv@1300−to−1600
(classic) no treatment class distance (StU, MQs) = 1.72StU@5
MQ
s
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12
34
5
no Treatment ProxCuv@1300−to−1600
(classic) no treatment class distance (MQ, MQs) = 0.61MQ@5
MQ
s
●
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MQStUObMQs
SIMCA - Interclass Distances
MQ / MQs:
• same water - different container
SIMCA - ClassificationApparent error rate 0.0371
PredictedActual MQ StU Ob MQsMQ 208 7 0 1StU 1 253 0 0Ob 0 5 185 0
MQs 12 0 0 28
50% new data - Apparent error rate 0.0657Predicted
Actual MQ StU Ob MQsMQ 107 3 0 0StU 1 127 0 0Ob 0 3 89 0
MQs 15 1 0 4
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01
23
45
6
StU@1300−to−1600 − Validation
no treatment color by C_ExpName, N=254measured
pred
icte
d
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DZ1hSeqDZ12hDZ48h1DZ48h2
RMSECV: 0.6442R2CV: 0.856
PLSR (number of consec. scan)
St. Ulrich Water
0 1 2 3 4
0.6
0.8
1.0
1.2
1.4
1.6
StU@1300−to−1600
no treatmentNr of components
erro
r val
ue
RMSECRMSECVRMSECV_adj
1300 1350 1400 1450 1500 1550 1600
−60
−40
−20
020
40
StU@1300−to−1600
no treatment mvr regressed on Y_ConSNr , RMSECV= 0.6442 , R2CV= 0.856 , bw=25wavelengths
Coe
ffici
ent
4 comps13081316 1336
14201434
1470
14841544
155415661586
1364
1386
14521460
1478 149615091517
1530
PLSR (number of consec. scan)
Classic
Fusion−AllWaters no Treatment, ProxCuv@1300−to−1600
no treatment grouping by C_waterNames (not N corr.)
−0.29
−0.15
0
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1342
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Mode: classic
N= 216, 254, 190, 40
Fusion−AllWaters no Treatment, ProxCuv@1300−to−1600
no treatment grouping by C_waterNames (not N corr.)
−0.1
−0.05
−0.01
0.04
0.08
C01
C12
C11
C10
C09
C08
C07
C06
C05
C04
C03
C02
MQStUObMQs
Mode: aucs.dce−diffMinus: MQCalib: 26.6−30.6 deg. C. T Exp.: 28.6 deg. C
N= 216, 254, 190, 40
aucs.dce-diff
Aquagram
Fusion−AllWaters no Treatment, ProxCuv@1300−to−1600
no treatment grouping by C_waterNames (not N corr.)
−0.1
−0.05
−0.01
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0.08
C01
C12
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C10
C09
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C07
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MQStUObMQs
Mode: aucs.dce−diffMinus: MQCalib: 26.6−30.6 deg. C. T Exp.: 28.6 deg. C
N= 216, 254, 190, 40
aucs.dce-diff
Aquagram
New Aquagram Algorythm
•completely different mathematics,
• but showing high similarity with results of „classic“ code
• independent, stable scale
•unit: ºC
now possible:
• use fix-scale aquagrams in time-resolved experiments
Spectrometer:
• FOSS XDS
• transmittance, pathlength = 1mm
•400nm - 2500nm; Δ=0.5nm
•T = 28.6ºC
•2 different kinds of magnetic treatment
•MilliQ as standard
•Analysis in 1st overtone of OH/water (1300-1600nm)•Preliminary Results: as this was an industry-financed experiment, I can
not give out hardcopies of even the preliminary results before we published them — I humbly ask for your understanding. Thank you.
Effect of Magnetic Fields on Water
Online Aquagram Makerin development ...
Online Aquagram Maker
•upload own data
• compare with available data
• easy to use
• in development -- coming soon...
• http://aquaphotomics.com
Acknowledgements•Prof. Roumiana Tsenkova & Zoltan Kovacs
•Zoltan Kovacs
•George Bázár & Alexandar Slavchev
•everybody at the Biomeasurement Technology Laboratory
Thank You !
