Model that can determine components of mixtures

[jdchapplesokol@gmail.com]

I am asking a general question, using specifics as examples.  For instance, If I sampled flours – wheat, rye, millet, buckwheat,…. and created a good categorical model that could identify each.  If I then mixed two different flours together, how can I create a model that will say ” 50% rye, 50% millet”?  I think right now the models just try to find the best categorical fit and does not try to match linear combinations of categories, right?

[Ayelet]

Hi Jonathan,

That is correct!

If you wish to detect 50% rye content and 50 millet content in the same matter, this information can be detected by 2 separate estimation models.

Each one will be used in order to analyze the same spectral data based on each attribute.

 

I hope this is clear now.

 

Ayelet,

The Consumer Physics Team

 

[jdchapplesokol@gmail.com]

Thanks, Ayelet.  I think that is a problem.  If I had 5 different grains and mixed only some, I’d need to create and check the mixture against 5 models to determine composition?  If each grain had a unique spectral signature, I would expect a that single good model where each grain is well characterized should suffice to determine the composition of a mixture.  Think of a simple mixture, say baking soda and wheat flour. Or ethanol and water. Each should be clearly distinguishable in a spectrum.  The respective fraction of each component should be determinable from a single analysis by the relative peak (or peaks) areas.

 

I cannot believe I am the only person interested in such a capability, and I suspect the modeling is not too complicated.  Can Consumer Physics consider working on this?

 

Thanks

Jonathan

[Ayelet]

Hi Jonathan,

 

I apologize for the delayed response.

 

You can create this kind of a model using Scio Lab tools.

As you mentioned, for one collected data set, 5 different models should be created – each time the data should be filtered differently to create the required combination.

 

Our experience shows that due to different grains weight and particles size, it is difficult to preapre the samples so that the mixture will be homogenous.

As you may know, the more homogounes that sample is, the better results will be calculated.

 

Also note that different grains may have quite similiar spectral signatures so that it might be difficult to classify between some of them.

 

Ayelet

[Author]

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