External knowledge such as the fact that your instrumental set-up cannot measure reliably outside certain spectral ranges, or you're working on a substrate/in a solvent that renders certain regions unsusable, or that for the type of sample you have, no bands at all can appear in certain regions is very valuable knowledge.
PLS is fairly good at recognizing such regions itself provided you supply training data that exemplifies this knowledge, but IMHO you'd usually be better off to feed in this knowledge manually and focus your experiments on variation that will occur in practice rather than on artificial situations.
I'd say you're in a no-free-lunch situation here: you can ask your PLS model to do this, but you'll have to pay for it.
Having PLS paying less attention to region
You can scale that region to downweight it:
yarn.pls <- plsr(density ~ NIR, 6, data = yarn)
yarn.dw <- yarn
yarn.dw$NIR[, 100:150] <- yarn.dw$NIR[, 100:150] / 10
yarn.dw.pls <- plsr(density ~ NIR, 6, data = yarn.dw)
plot (coef (yarn.dw.pls), type = "l", col = 2)
lines (coef (yarn.pls), type = "l", col = 1)
rect (100, -10, 150, 10, border = NA, col = "#00000020")
abline (h = 0, lty = 2)
Get a model that uses only bands
IMHO this request is not sensible: Identity of the analyte is established both by present Raman bands as well as by the absence of bands characteristic for other structural elements. Think of how you conclude qualitatively that you got an alkane sample: you find C-H and C-C (stretching and deformation) vibrations, and no vibrations due to other functional groups.
So in order to reduce cross sensitivities you need to establish the whole spectral pattern.
PLS is an inverse calibration (regression) technique, so it is meant to be as robust as possible wrt. to the presence of other, even unknown, substances. In that sense, your request is not in line with what PLS is supposed to do.
Get a model that uses only a few wavenumbers
PLS is a regularization that is meant to produce latent spectra which cover basically the whole spectral range. However, there are other regularization techniques which basically provide variable selection, e.g. the LASSO.
For the first few components (3 in this example), pick the wavenumbers that have the highest scores in the loadings. (Having a high score = contributing significantly to the variance in data between concentrations)
Yes, but unless your data is variance scaled (which is usually not a good idea for spectroscopic data) you need to take into account also the intensities you have at the respective wavenumber. You may get a huge weight/coefficient for a small but important band while an equally important strong band will get far smaller weights and coefficients.
Update: A few thoughts about identifying substances in a possible mixture
Please read up on the advantages and disadvantages of ordinary vs. inverse calibration: your choice in that respect should depend on your actual application, in particular whether you can reasonably assume that you can collect reference spectra of all substances that can possibly be in the mixture or not.
I'm not even sure whether regression/calibration is the right technique. If you aim at qualitative identification of substance (classes) rather than at concentration measurements, you may be better off with one-class classification.
To establish identity of a substance, I insist that you need presence as well as absence of bands/peaks, at least if there is no external information that helps narrowing down the choices.
Here's an example. Say, you measure some carboxylic acid and your training spectra contain some alkane. Looking for presence of alkane bands only, you'd get a hit on alkane (meaning, really, that vibrations of C-C and C-H bonds are detected)?
(That SERS due to the local and directed enhancement makes things somewhat more complicated doesn't help here, neither...)
Note that this is a very basic caution to the interpretation of mixture vibrational spectra.
In the framework of one-class classification, you'd be much closer to the usual interpretation of spectra: you'd detect presence of C-C, C-H, and COOH/COO⁻ for the carboxylic acid, all of which are correct. And in this framework absent bands are not needed (other than saying which part of the spectrum is not yet explained).