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bubudumbdumb t1_j9rfqhp wrote

Spectral analysis has established methods that are exact and won't benefit from ML. As far as I understand the field that studies approximated or constrained spectral analysis is compressed sensing : that might have overlaps with ML.

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thecuteturtle t1_j9rkmwo wrote

There are some chemicals and mixtures whose bands could overlap and make it difficult to distinguish between active sites (IE multiple O-H bonds etc.). Still agree that ML with spectral analysis is unnecessary, but that is a possible niche it could have.

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Ferocious_Armadillo t1_j9rlhzx wrote

I might be off base here but my first thought was there might be something there with integrating the full area of peaks and sorting out peaks from specific elements in the spectral analysis of a heterogeneous mixture (possibly through a Fourier transform or convolution? This is ringing bells for me as feeling similar to signal processing…)

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NotSoChildishRubino OP t1_j9t91yd wrote

I was thinking the same way, but then came to me the idea of, once I have detected peaks in a spectrum, I could distinguish peaks of different nature (eg. gaussian vs lorentzian) knowing the peak symmetry, the FWMH, or similar characteristics. I wouldn't be able to quantify the elements but i could use ML at a certain point, i guess.

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