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Coffeelover
Level I

How can I "autoscale" functional / spectroscopic data?

Hello,

I have some Raman spectroscopy data, i.e. continous data (x: wavenumer, y: intensity) that I would like to analyse in JMP.

I am interested in how different sample treatments affect the ratios between the first and second peak (see picture), but due to the measurement, the intensities around this region of interest differ quite a lot. I think that this random difference masks the potential effect of sample treatment and therefore I would like to somehow scale the signals in this region to the same level. 

Basically, I guess I am looking for a version of autoscaling but for continous data, so that the intensities are relative.

Does anyone have some insights on how to do this?

Thank you!

1 ACCEPTED SOLUTION

Accepted Solutions

Re: How can I "autoscale" functional / spectroscopic data?

Hi @Coffeelover,

 

Have you tried the Multiplicative Scatter Correction (MSC) command in the FDE tool? If you filter your X range to focus on the region of interest this may help:

Before

Ben_Ingham_2-1714720142654.png


After

Ben_Ingham_3-1714720153574.png

 

 

“All models are wrong, but some are useful”

View solution in original post

4 REPLIES 4
jthi
Super User

Re: How can I "autoscale" functional / spectroscopic data?

If you have JMP Pro, have you tried Functional Data Explorer 

-Jarmo
Coffeelover
Level I

Re: How can I "autoscale" functional / spectroscopic data?

Yes, that's what I'm using, but haven't found a way of relativise the intensities across samples?

Re: How can I "autoscale" functional / spectroscopic data?

Hi @Coffeelover,

 

Have you tried the Multiplicative Scatter Correction (MSC) command in the FDE tool? If you filter your X range to focus on the region of interest this may help:

Before

Ben_Ingham_2-1714720142654.png


After

Ben_Ingham_3-1714720153574.png

 

 

“All models are wrong, but some are useful”
Coffeelover
Level I

Re: How can I "autoscale" functional / spectroscopic data?

Yes, that I will do. Thank you so much