The data for these calculations are:
knowns.xyz contains the spectra of twenty known samples, concentrations.xyz contains the concentrations of a target chemical T in each of the twenty samples, and unknowns.xyz gives the spectra of fifteen more samples whose concentrations of T you will predict. Concentrations2xyz is just to compare values afterwards.

Also, no particular reason for long rows vs. long columns.

Data to apply to regression is
*I thought I was on the right track here, but my plot is not really comparable to what it should be* Any help is greatly appreciated!

The fundamental relationship between a measured spectral data matrix S and the associated analyte concentration matrix C is
S = CP + N; spectra are represented as matrix rows
The ordinary least squares (OLS) estimate of the concentration values is
C = SP^T(PP^T)^-1:
If we want to predict the concentration of just one target chemical T in a new sample, we could simply use the first column of Pt(PPt)-1, call it a vector b^t, and calculate
c = sb^t (1)
where s is the sample's spectrum.

1. Inverse least squares. The simplest thing you can do is to say, I can just solve
Eq. 1 by least squares to get an estimate of b." This is called Inverse Least Squares (ILS).

(a) Solve Eq. 1 for an algebraic expression for b.

(b) There is an inverse in your answer. State what constraint that places on your ex-perimental design, i.e. , what balance is required between numbers of samples, wave-lengths, pure components, etc. to guarantee that the inverse will exist?

(c) Select the intensities at the discete pixels 100, 200...1000 from the spectra in knowns and call this your S matrix. (In Matlab, this would mean typing something like
>>S = S(100:100:end).) Use ILS to generate b, and then predict the concentration
of T in unknowns. Scatter-plot your predictions versus the true values, which are
provided in concentrations2.xyz.