Quick guide to PLS

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Dioxin and fatty acid data • •

Problem

To develop a PLS model that can predict the dioxin concentration (y) in ng/(kg fat) in fish meal samples from fatty acids profiles (X).

In total 64 samples were analysed and gas chromatography was used for the determination of 32 fatty acids (including an unidentified group). The results are given in % of all 32 fatty acids.

The very expensive and complex dioxin analyses were performed by a German laboratory.

Data

The dimension of the data structure is 64 objects x 34 variables.

The first two variables are the species code (herring, sprat, blue whiting, sand eel etc. in total 15 species) and the dioxin content.

Variables 3 to 34 are the chromatographic variables.

Source:

Rapid dioxin assessment in fish products by fatty acid pattern recognition, Marc Bassompierre, Lars Munck, Rasmus Bro and Søren Balling Engelsen, Analyst, 129, 553-558, 2004.

2

Get the data 3

The data in the WorkBench 4

Select the fatty acid variables Write 3:34 and press [

ENTER

] or select the fatty acid variables in the Variables listbox 5

Define a variable set 6

Give the set a name, e.g. X 7

Now select dioxin in the listbox The plot window always show the actual selection in the Objects and Variables listboxes 8

Plot dioxin as a function of the objects Click on this icon 9

Define a variable set containing only the dioxin variable 10

Give the set a name, e.g. y 11

Autoscale the X data Then select autoscale First select X in the Sets box 12

The effect is shown instantly in the plot Click Transform to see how the transformation affects the data 13

Make a PLS model Select PLS in the dropdown menu 14

Select the dependent variable(s) Then select y (or click on Dioxin in the listbox) First select y in the Sets box 15

Autoscale y 16

Choose cross validation Select CV: Syst123 (Venetian blinds) 17

Choose the number of segments In this case we use eight segments 18

Calculate the PLS model Then press OK Press Calculate 19

Plot the error 20

RMSEC & RMSECV Select both using CTRL 21

U vs T plot 22

U vs T plot

TIP: use the arrows keys to change the number of components

Change the markers & labels 23

Plot regression coefficicents 24

Regression coefficients

TIP: use the arrows keys to change the number of components

Change the markers & labels 25

Plot Actual vs Predicted 26

Actual vs Predicted for six components

TIP: use the arrows keys to change the number of components

THE END Change the markers & labels 27