Transcript of Quick guide to pre-processing Use [Alt-Tab] to go to LatentiX (if running) Press [Page Down] or...
Quick guide to pre-processing
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Sugar mixture data
• Purpose
To show how to use pre-processing in LatentiX. A PCA model will be developed on near infrared (NIR) spectroscopy measurements on 231 mixtures of three constituents: glucose, fructose and sucrose.
The sample set was made according to a mixture design where the concentrations of all three constituents are varied between 0% (w/w) and 100% (w/w):
The crystalline samples were measured in a NIR Systems 6500 instrument (reflection) furnished with a sample cup (3 cm in diameter). The spectral range covered is 1100 nm to 2496 nm (every 4th nm); in total 350 data points per spectrum.
• Data
The dimension of the data structure is 231x350 variables. Three classes (as well as labels) are assigned the concentrations of fructose, glucose and sucrose.
100 %Fructose
100 %Sucrose
100 %Glucose
100 %Fructose
100 %Sucrose
100 %Glucose
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Plot the spectra
Select the predefined sets: Samples and NIR spectra
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Use wavelengths as the x-axis
First click on Variable number
Then select Wavelengths (nm)and click OK
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Now the x-axis is in nm
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Mean center the data
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Calculate a PCA model
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Plot the scores
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Color according to fructose
First select ContinuousThen select Fructose
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Name the PCA model
Click on Name
Then type name and click OK
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Make an SNV transformation
Note: you can select Show plot to see the effect on the data
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Calculate a PCA model
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Name the model
Click on Name
Then type name and click OK
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Plot the scores
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Scores plot – color by fructose concentration
First select ContinuousThen select Fructose
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Scores plot – color by glucose concentration
Then select Glucose
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Scores plot – color by sucrose concentration
Then select Sucrose
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Remove SNV transformation
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Make an MSC transformation
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Calculate a PCA model
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Name the model
Click on Name
Then type name and click OK
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Remove MSC transformation
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Calculate first derivatives
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Input to first derivatives
Window size
Polynomial order
Derivative calculated
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Calculate PCA model
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Name the model
Click on Name
Then type name and click OK
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The four models with different pre-processing can now be investigated and compared