Another very important challenge to be tackled during the processing is the signal alignment. The aligned signals are needed for the trend analysis and signal (feature) prioritization. Existing tools are limited to either a single batch or fully identified structures. Retention indices using a set of calibrant chemicals or retention mapping have been widely utilized for such alignments. However, both of these approaches need the presence of a set of chemicals (i.e. calibrants) in all the samples. Such solutions may be very effective for small scale chromatogram alignment but are not able to address the challenges associated with the alignment of archived data, given that these data rarely include the retention index calibrants or the same set of internal standards. Additionally, the few added internal standards are meant to represent all the chemicals present in those samples. These limita-tions imply that the alignment of datasets acquired on different instruments using different experimental conditions is still an open question and requires further development.