The Unscrambler ® A Handy Tool for Doing Chemometrics Prof. Waltraud Kessler Prof. Dr. Rudolf Kessler Hochschule Reutlingen, School of Applied Chemistry.

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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Fluorescence Excitation Emission SpectraResults of N-PLSR

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Fluorescence Excitation Emission Spectrax1 and x2 Loading Weights

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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: