With replicates - three is not many, but you can compute variance for each analyte for each sample in each lab and then look at pooled variance to compair variability of results lab to lab. Look at what is going on in samples. If the matrix has an interference that distorts results in one lab in a sample, but not the other lab, you may want to do the analysis with and without that sample. While everything is the "same" between labs. The chromatography may not match exactly for a number of reasons - including differences in columns, even if each is new.
If your sample have a range of values for results, take narrow bands in the ranges and see how variance compares at concentration levels.
With a set of 50 samples, I hope that there were instrument controls or calibration checks in the set of samples. With 50 samples run in triplicate (150 injections) there needs to be a fair number of control samples or calibration checks run in the sequence - I hope. These should have many more than three replciates at a high, low and perhaps even medium level? If so, start the analysis on these. (These are the instrument QC samples which show if the instrument is even being consistant in the particular laboratory.)
Now, I have taken you off into digging for details in the data - but we do come back to the purpose of the experiment. Wtih statistical techniques you can ask two questions - 1) can I say that there is no difference that can be determined between set A and set B (or a data set and a model). And 2) (the one people forget to ask) with the data I have, can I detect a difference as small as I need to be able to find? If you can not detect a difference as small as you need to detect and your experiment shows no significant difference between A and B - your experiment fails to answer the question.
Be sure you have the question clearly stated. If you want to compare reproducability of computed results within laboratories against each other you are measuring variance within laboratories and doing a comparison of variance beween laboratories. (F test at the simplest) If you are testingto see if one laboratory reproduces the other, you are also testing mean values. (t-test at the simplest). The trick is pooling data in a meaningful way. Because the mean values are expected to scatter across a box of samples, a regression may work, but you need to be aware of the distribution of means- this weights the fit.
I would suggest that with the ideas you have from here, you start tumbling the data to see what it looks like. There are some good books that help with a look at experimental data. I have a couple at home - the one I reached around and picked up is John Mandel's "The Statistical Analysis of Experimental Data" - availalbe from Dover books (old classic, but at Dover inexpensive!) the other has wndered off... And, at work I my favorite is an older version of Box, Hunter and Hunter: "Statistics for Experimenters" The current is "Statistics for Experimenters: Design, Innovation, and Discovery" and you can get a used copy at an almost affordable price.