The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography–mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. “Background correction” methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.

Collagen peptides are analyzed using a low-cost, high-throughput method for assessing deamidation using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). For each chosen peptide, the theoretical distribution is calculated and the measured distribution for each sample compared with this to determine the extent of glutamine deamidation. The deamidation of glutamine (Q) to glutamic acid (E) results in a mass shift of +0.984 Da. Thus, from the resolution of our data, the second peak in the isotope distribution for a peptide containing one glutamine residue coincides with the first peak of the isotope distribution for the peptide in which the residue is deamidated. A genetic algorithm is used to determine the extent of deamidation that gives the best fit to the measured distribution. The method can be extended to peptides containing more than one glutamine residue. The extent of protein degradation assessed in this way could be used, for example, to assess the damage of collagen, and screen samples for radiocarbon dating and DNA analysis.