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Some effort have been put into the area, mostly centered around defining metrics that can be extracted from raw data files and used to monitor the different aspect of the instrumentation. The first iteration of this effort \cite{19837981} defined 46 different metrics that could be extracted from a standard run and used to trace subtle variations back to different parts of the instrumentation. Recently QuaMeter \cite{22697456} refined these and used this to compare data from several different laboratories \cite{24494671}. For the latter study robust PCA was used to investigate the different samples but apart from this no foray into more advanced computational data analysis algorithms has been attempted.  This paper will investigate the use of other algorithms on the same data as employed in \cite{24494671} \citep{24494671}  with the aim of finding a data-analytical approach that will be well-suited for automatic continuous quality control of LC-MS/MS equipment for both within- and between-run monitoring. As such it will forego the problem of instrument-to-instrument variation since this has already been adressed and because this would normally not be done in an automated manner anyway.