With higher throughput demands on today’s mass spec quantitative applications, the bottlenecks affecting sample turnaround time are moving from data acquisition to data processing. Most lab environments like this can’t afford the time it takes to re-integrate missed peaks due to run retention time shifts or bad integrations due to matrix interferences. Also, regulations put on data integrity have made the need for simplified, more automated methods and less manual review for peak integrations paramount.

To address this challenge, a new integration algorithm for data processing has been introduced with SCIEX OS software. AutoPeak is the second-generation peak modeling integration algorithm that improves data quality and ease of use for targeted analysis. It provides improved peak finding, more consistent peak integration, and better integration of poorly resolved peaks, which allows the algorithm to be applied to samples with challenging chromatography.  It is simpler to use because there are fewer parameters to adjust and less user input is needed, reducing the time spent performing data review and manual re-integration.

The peak model is constructed based on a 3 Gaussian peak model. All chromatograms in the selected batch for processing are evaluated to determine which sample is the best peak model for each transition. An additional step is performed when processing groups. The chromatographic characteristics of the peaks (RT, shape, peak start and stop) should be the same for the fragments of the same precursor ion. This is leveraged to provide consistent peak integrations within the group and to be able to detect interferences. The peak models for each component in the group are evaluated and the best peak model is selected as the peak model for all components in the group. Internal Standards are treated as a sub-group due to potential shifts in retention time.

The result is this: across multiple data types, AutoPeak consistently and accurately integrates peaks without optimizing integration parameters. A comparison of 3 different integration algorithms was performed to see what the first pass rate of success would be when using the default processing method parameters for each algorithm, that is – with no parameter optimization or expected retention times across multiple data sets of varying data types. The AutoPeak integration algorithm showed a significant improvement for multiple data types compared to the other algorithms, with over 90% of peaks integrated correctly in the first pass without parameter optimization. In contrast, the MultiQuant MQ4 algorithm left a lot of work to optimize the integrations.

To learn more about how the AutoPeak integration algorithm can reduce the need for re-integrations, and to see the algorithm in action, watch the short video below or log in to your SCIEX Now Learning Hub account to take the AutoPeak Essentials course now!

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RUO-MKT-18-10329-A