Here's an update from my end.
I returned form vacation to find CoreyG's helpful responses. It turns out that I was not using "value='into'". I changed this param, and now my data look much better.
I've been using the Brunius batchCorr package, because I already know how to use R. However, given the characteristics of my dataset, I wonder if it is adequate.
-- ~1950 files representing ~570 plant extracts (triplicate injection) + QC samples
-- 13 batches
-- All extracts are from the same species
-- The QC sample is an extract of about 40 accessions pooled together. However, it looks quantitatively different than most of the extracts in the sample set: the later eluting peaks of the QC sample are generally bigger while the early peaks are smaller. I don't think there are many qualitative differences between QC and other samples. However, I can imagine that these might translate into presence/absence differences in the peak table for minor compounds.
-- The extracts--other than QC--are not standardized by concentration or by equivalent weight of plant material. There is a range of weight of plant material that was extracted. Nonetheless, I do have for each sample the weight of plant material extracted and the weight of solvent used for extraction. From these values, I have generated a sample:solvent correction factor.
-- This is a pilot dataset and was not intended for publication.
My thinking is, now that the batch correction has been done, the next step is to apply the sample:solvent correction factor. The simplest thing to do would be, for each feature in a sample, divide the peak area value by the correction factor for that sample. However, I realize that detector response may not be linear in the range of interest for each feature; thus, the results may not be completely accurate. Nonetheless, I can't think of a better option. Any feedback on my approach?