1. Presentation of the main new features in
The Unscrambler® 9.0

2. New features presented:
3D Editor
3D Layouts and 3D file importation
Analysis
3-way PLS Regression
Automatic Pretreatments
User-friendliness
PC Navigation Toolbar
See the complete list of new features at the end of this presentation

3. 3D Data and Three-Way PLS Regression
New Feature 1:
3D Data and Three-Way PLS Regression

4. What is three-way data and Three-Way PLS Regression?
Three-Way PLS-R models the relationships between 3-dimensional X-data and 2-dimensional Y-data

5. Application examples of 3-way PLS Regression: Woodchip treatment
Samples: 32 fibres from steam treated and ground woodchips
X-Data: Fluorescence Excitation-Emission spectra
( nm) x ( nm) x
Y-Data: 32 fibres, 4 variables: Kind of wood (beech and spruce)
Age of wood (fresh and old)
Plate gap of grinding ( fine and coarse)
Severity of treatment (a combination of time and temperature)

6. Application examples of 3-way PLS Regression
Sensory Science
X-data: In a sensory experiment, eight assessors score on 18 different attributes on ten different sorts of apples. The data can consequently be arranged in 10×8×18 array.
Y-data: 7 chemical measurements on the apples (10x7 array)

7. Application examples of 3-way PLS Regression
Fluorescence spectroscopy
X-data: Seventy-two samples are measured using fluorescence excitation-emission spectroscopy with 100 excitation wavelengths and 540 emission wavelengths. The excitation-emission data can be held in 72×540×100 array.
Y-data: Water and Protein content of the samples (72x2 array)

8. Application examples of 3-way PLS Regression
Process Control
X-data: Twelve batches are monitored with respect to nine process variables every minute for two hours. The data are arranged as a 12×9×120 array.
Y-data: 5 quality parameters on the final product (12x5 array)

9. Application examples of 3-way PLS Regression
Quality control
X-data: Fifteen food samples have been assessed using texture-measurements (40 variables) after six different types of storage conditions. The subsequent data can be stored in a 15×40×6 array.
Y-data: Texture assessment by a sensory panel (15x1 array)

10. Application examples of 3-way PLS Regression
Environmental analysis
X-data: The concentrations of seven chemical species are determined weekly at 23 locations in a lake for one year in an environmental analysis. The resulting data is a 23×7×52 array
Y-data: Occurences of a specific diseases in fish (23x1 array)

11. Application examples of 3-way PLS Regression
Infrared Spectra
X-data: Infrared spectra (300 wavelengths) are measured on several samples (50). A spectrum is measured on each sample at five distinct temperatures. In this case, the data can be arranged as a 50×300×5 array.
Y-data: 4 chemical constituents of the samples (72x4 array)

12. Application examples of 3-way PLS Regression
Infrared Spectra
X-data: Infrared spectra (300 wavelengths) are measured on several samples (50). A spectrum is measured on each sample at five distinct temperatures. In this case, the data can be arranged as a 50×300×5 array.
Y-data: 4 chemical constituents of the samples (72x4 array)

13. 3D Data Editor functionalities
3D data Import: ASCII, Excel, JCAMP-DX, Matlab, F3D
Swapping: toggle freely between the 12 possible layouts of a 3D table
Matrix plots of the samples
Create Primary variable sets and Secondary Variables sets

14. 3D Data Importation
The Unscrambler reads the right 3D structure directly from the Matlab file

15. Swapping Layouts

16. Menu “Plot-Matrix 3D” Plots the horizontal slices of the table, i. e
Menu “Plot-Matrix 3D” Plots the horizontal slices of the table, i.e. the samples
Beech
Spruce
Treatment: low middle severe

17. Primary and Secondary Variable sets
Use menu ”Modify-Edit Set” to create Primary and Secondary variable sets on 3D tables

18. Steps for Three-Way PLS-R in The Unscrambler
Open your 3D X-data table
Menu Task-Regression

19. Three-Way PLS-R in The Unscrambler
6. Click OK
1. Select sample set
2. Select Primary Xs, Secondary Xs and Y Variables
4. Select Validation method and options
5. Select number of components
3. Select weight option

20. Regression Overview of Three-Way PLS-R

21. Numerous Pre-defined Plots Available

22. Advantages of 3-way PLS Regression
● Allows to analyse data of natural 3D structure with a mathematical model which respects and takes advantage of this natural 3D structure
● Results are easier to interpret than if you use a traditional 2-way model on a natural 3D data table
● In The Unscrambler, Three-Way PLS is easy and user-friendly! The dialogs, options and plots available are similar to those of PLS-regression

23. Automatic Pretreatments in Prediction and Classification
New Feature 2:
Automatic Pretreatments in Prediction and Classification
(Also supported in our On-Line software OLUP and OLUC version 9.0)

24. Use of Data Pretreatments
Data pretreatments are often required to correct raw data for various types of instrument errors and shifts.
They are available from menu ”Modify-Transform...”

25. Advantages of Automatic Pretreatments
● Data pretreatments allow the user to achieve better prediction and classification models
● But we must make sure to apply these pretreatments on new collected data each time we want to use our Prediction or Classification model, otherwise the predictions /classifications would be erroneous
● With Automatic pretreatments, no need to pretreat the new collected data by hand: The Unscrambler, OLUP and OLUC will do the job automatically prior to applying our model
● It becomes much faster and easier to use our models with no risk of errors

26. Steps for Automatic Data Pretreatments
Apply a data pretreatment or a combination of pretreatments on the raw data from menu “Modify-Transform”
Build your PCA, PCR or PLS-R model

27. Steps for Automatic Data Pretreatments
Register the relevant pretreatments in the model file (menu “File-Properties”) Note! If you don’t perform this step, it can be done at Classification or Prediction stage

28. Steps for Automatic Data Pretreatments
In the Classification or Prediction dialog, select the sample set, X-variable set and model you want to apply onto your new samples
Using the ”Pretreat” button, you can register not-yet-registered pretreatments which need to be applied onto the new samples

29. Steps for Automatic Data Pretreatments
The number of pretreatment variables can differ from the number of X-variables in the model.
Supported automatic pretreatments (alone or in combinations):
Smoothing
Normalize
Spectroscopic
MSC
Noise
Derivatives
Baselines

30. New Feature 3:
PC Navigation tool

31. Higher Flexibility in the PC Navigation Tool
Previous Vertical PC Next Vertical PC Suggested PC
(or Down keyboard arrow) (or Up keyboard arrow)
Previous Horizontal PC Next Horizontal PC
(or Left keyboard arrow) (or Right keyboard arrow)
The PC Navigation tool is avaliable on all PC-oriented plots in PCA, PCR, PLS-R, Three-Way PLS-R and Prediction (Scores, Loadings, Leverages, Predicted vs. Measured, Regression coefficients...)

32. Full list of new features (1/2)
Analysis
New analysis method: Three-Way PLS regression. Open a 3D data table, then use menu “Task-Regression”. The following key features can be named: Two validation methods available (Cross-Validation and Test Set), Scaling and Centering options, over 50 pre-defined plots to view the model results, over 60 importable result matrices.
The following data pretreatments are available as automatic pretreatments in Classification and Prediction: Smoothing, Normalize, Spectroscopic, MSC, Noise, Derivatives, Baselines. Combinations of these pretreatments are also supported.
3D Editor
Fill the name of a Primary Variable or a Secondary Variable at one place in the table; the same name will be populated automatically in every column for this variable
Toggle between the 12 possible layouts of 3D tables with submenus in the Modify menu or using Ctrl+3
Create Primary Variable and Secondary Variable sets for use in 3-Way analysis. Use menu “Modify-Edit Set” on an active 3D table.

33. Full list of new features (2/2)
User-friendliness
Optimized PC-Navigation toolbar. Freely switch PC numbers by a simple click on the “Next horizontal PC”, “Previous horizontal PC”, “Next vertical PC”, “Previous vertical PC” and “Suggested PC” buttons, or use the corresponding arrow keys on your keyboard. The PC-Navigation tool is available on all PCA, PCR, PLS-R and Prediction result plots.
A shortcut key Ctrl+R was created for “File-Import-Unscrambler Results”
Compatibility with other software
Importation of 3D tables from Matlab supported. Use menu “File-Import 3D-Matlab”
Importation of *.F3D file format from Hitachi supported. Use menu “File-Import 3D-F3D”
Importation of files from Analytical Spectral Devices software supported (file extensions: *.001 and *.asd). Use menu “File-Import-Indico”
Visualisation
Passified variables are displayed in a different color from non-passified variables on Bi-Plots, so that they are easily identified.
Plot header and axes denomination are shown on 2D Scatter plots, 3D Scatter plots, histogram plots, Normal probability plots and matrix plots of raw data.
Plus several bug fixes and minor improvements