First of all: unless the variance between your repeated measurements is negligible, I'd definitively recommend to keep both spectra. One of the advantages of PLS over PCR (principal component regression) is that it can downweight ("learn to disregard") sources of variance that are not correlated to the information you're after. But it can only do so, if both wanted and unwanted variance are covered by the training data.
Secondly, yes if you have repeated measurements or technical replicates (as opposed to newly prepared samples of the same concentrations), you should take this into account in cross validation and all measurements of the same sample should always be together in the same set (training or test).
A number of software tools nowadays allow you to specify such grouping, but even if that is not the case, wrapping a cross validation by your own that excludes those pairs of spectra isn't that difficult. For 10x2 spectra I'd say that it is even reasonable to do it manually.
Are you talking about a regression problem (quantitiation) or classification? For classification, you won't be able to get anything reasonable out of a total of 10 samples, sorry. (Even if you have 5 cases x 2 classes with spectra so different that you can tell by "naked eye" where each case belongs, you have at most 10 samples for testing (e.g. via cross validation) - leading to huge uncertainty on your test results.
For regression, your situation may be better (with a bit of luck). In that case, I'd suggest a leave-sample-out cross validation may be a suitable choice. If you decide to go for fewer models, it may make sense to think about particular non-random ways of splitting like Venetian blinds.
Anyways, similar to what theGD suggests, I'd recommend to keep in mind that CV predictions of the outermost (e.g. in concentration) samples extrapolate the calibration range. It may be worth while to mark these predictions accordingly.