The Unscrambler®
A Handy Tool for Doing Chemometrics
Prof. Waltraud Kessler
Prof. Dr. Rudolf KesslerHochschule Reutlingen, School of Applied Chemistry
Steinbeistransferzentrum Prozesskontrolle und Datenanalyse
Camo Process AS
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Topics
• The Unscrambler® by Camo
• Many possibilities for Analysing Data• Examples
• NIR-Spectra • Fluorescence Exitation Emission Spectra
• Life Demonstration• 3-way Data Handling
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The Unscrambler® Main Features
Exploratory AnalysisDescriptive statisticsPrincipal Component Analysis (PCA)
Multivariate Regression AnalysisPartial Least Squares regression (PLS)Principal Component Regression (PCR)Multiple Linear Regression (MLR)Prediction
ClassificationSoft Independent Modeling of Class Analogies (SIMCA)PLS-Discriminant Analysis
Experimental DesignFractional and full factorial designs, Placket-Burmann,
Box Behnken, Central Composite, Classical mixture designs, D-optimal designs
ANOVA, Response Surface ANOVA, PLS-R
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The Unscrambler® Also Features…
• Raw data checks• Data preprocessing• Over 100 pre-defined plots• Automatic outlier detection• Automatic variable selection• … and more
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Example: Fiber Board Production In-situ Measurements of Fibres in Blowpipe
Blowpipe:~ 180°C~ 5 bar~ velocity of fibres ~ 20 m/s
NIR FOSS Process Spectrometerwith fibre bundle and diffuse reflectance probe 400 - 2200 nm
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Fiber Board Production NIR-Spectra of Fibres in Blowpipe
Spectra contain the following information:
• kind of wood • fineness• degradation of lignin
Information is hidden within complete wavelength range Information overlaps – separation by PCA
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-0.05
0
0.05
-0.10 -0.05 0 0.05 0.10 RESULT8, X-expl: 72%,24%
oRfoRfoRfoRf oRfoRf
oRfoRf
oRfoRfoRfoRfoRfoRfoRfoRfoRf
oRfoRf
oRfoRf
oRf
oRf
oRf
oRfoRf oRfoRfoRfoRf
oRg
oRgoRg
oRgoRgoRgoRgoRgoRg
oRgoRg
oRgoRgoRgoRgoRg
oRgoRgoRg
oRg
oRgoRgoRgoRg
oRg
oRgoRgoRgoRgoRgoRg
oRg
oRg
oRg
oRgoRg
mRf
mRf
mRf
mRf
mRfmRf
mRf
mRfmRfmRf
mRf
mRf
mRfmRfmRfmRf
mRf
mRfmRfmRfmRf
mRf mRf
mRfmRf
mRfmRfmRf
mRfmRf
mRfmRfmRf
mRg
mRgmRg
mRg
mRg
mRgmRg
mRgmRgmRg
mRgmRg
mRgmRgmRgmRgmRgmRg
mRgmRgmRgmRgmRg
mRgmRgmRgmRgmRg
mRgmRg
mRg
mRg
mRg
mRgmRg
mRgmRgmRg
PC1
PC2 Scores
Principal Component Analysis Separate the Overlapping Information
PC1 = kind of wood: Spruce Spruce with bark
PC
2 =
Fin
enes
sco
arse
fin
e
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-0.1
0
0.1
500 1000 1500 2000 RESULT9, PC(X-expl): 1(72%)
X-variables
X-loadings
Principal Component Analysis Scores and Loadings for PC1 and PC2
PC1:Kind of wood
-0.10
-0.05
0
0.05
06:21:49_13.11. 11:43:52_13.11. 07:05:02_14.11. 14:15:56_14.11. RESULT2, PC(X-expl): 1(95%)
Samples
Scores
-0.1
0
0.1
500 1000 1500 2000 RESULT9, PC(X-expl): 2(24%)
X-variables
X-loadings
-0.05
0
0.05
06:21:49_13.11. 10:19:54_13.11. 14:37:28_13.11. 08:47:24_14.11. 14:06:48_14.11.
RESULT5, PC(X-expl): 2(24%)
Samples
Scores
PC2:FinenessSprucewith bark
Spruce
fine
coarse
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PLS Regression Degradation of Lignin for Spruce
-0.02
-0.01
0
0.01
0.02
-0.06 -0.03 0 0.03 0.06
RESULT16, X-expl: 80%,5% Y-expl: 88%,6%
2.22.22.2
2.2
2.52.5
2.52.5
2.52.52.52.52.52.52.52.5
2.5
2.9 2.92.92.9
2.9
2.9
2.9 2.9
2.9
2.92.92.9
2.9
PC1
PC2 Scores
2.1
2.4
2.7
3.0
2.1 2.4 2.7 3.0
RESULT16, (Y-var, PC): (SFC,2)
Elements:Slope:Offset:Correlation:RMSEP:SEP:Bias:
300.9100250.2379150.9416800.0850690.0865170.000981
Measured Y
Predicted Y
0
0.5E-06
0.0000010
0.0000015
0.0000020
10 20 30
RESULT16, PC: 2 2
Samples
X-variance Residual Sample Variance
0
0.01
0.02
0.03
0.04
10 20 30
RESULT16, PC: 2 2
Samples
Y-variance Residual Sample Variance
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Analysing Three-Way Data
Mode 2
Mod
e 1
I
L
KMode 2
Mod
e 1
I
L
K
Two different types of modesare distinguished:
Sample mode -usually first modeVariable mode -usually second and/or third mode
• Sample mode - O• Variable mode - V
OV2 or O2V
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Substructures in Three-way Arrays
L frontal slices I horizontal slices
K vertical slice s
L frontal slices I horizontal slices
K vertical slice s
Three-way arrays can be divided into different slicesDecide which slices are put together to form a two-dimensional array
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• Samples: 32 fibres from steam treated and ground woodchips • X-Data: Fluorescence Excitation-Emission spectra (250 - 575 nm) x (300 - 600 nm)
• Y-Data: Kind of wood (beech and spruce) Severity of treatment (a combination of time and temperature) Age of wood (fresh and old) Plate gap of grinding ( fine and coarse).
Three-way Data Example: Fluorescence Excitation Emission Spectra
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Three-way Data Example: Fluorescence Excitation Emission Spectra
Beech
Spruce
Treatment: low middle severe
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• 3D Data Import: ASCII, Excel, JCAMP-DX, Matlab
• Swapping: toggle freely between the 6 OV2 and 6 O2V layouts of a 3D table
• Matrix plots: Contour and landscape plots of the samples
• Variable sets: Create Primary variable sets and Secondary Variables sets
Possibilities for Three-way Data in The Unscrambler®
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• Easy to make models• Easy to interpret results• High user-friendliness• Less time spent doing data analysis,
more information extracted from your data• Faster decision making
The Unscrambler® Benefits
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Fully functioning version
Includes the Unscrambler user manual
Includes 7 tutorial exercises and associated files
Includes 3 demonstration tours
Try The Unscrambler® 9.2 for 30 days
CAMO Software India Pvt. Ltd.,14 -15, Krishna Reddy Colony, Domlur Layout,Bangalore - 560071, [email protected]
Free trial version available on www.camo.com
For details, contact: