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Advances in technology have resulted in a need for higher and faster sample throughput, with increasing number of measured analytes per sample – sometimes up into the 1000’s. A greater strain on necessary data analysis steps leads to a bottleneck of data processing and review for chromatographic peak integration, quality of calibration curve, and more. Analysts can spend hours just reviewing calibration curves for acceptability.
SCIEX OS-MQ software brings routine quantitation workflow to the next level for quicker turnaround time. The sleek, intuitive user interface overlies a sophisticated new peak integration algorithm and other new features, such as the automatic outlier removal for calibration curves, and new tools for calculating, flagging, and filtering results. These features combine to make data processing and review more streamlined and efficient.
The Automatic Outlier Removal feature allows the user to set criteria for the automatic removal of standard outliers from the calibration curve. The user controls what regression linearity, accuracy of standards, precision of replicates, and outlier tolerance are acceptable, and the algorithm then builds the calibration curve for each analyte to meet the defined criteria. The algorithm helps to alleviate the pain of manually establishing many acceptable calibration curves when working with many analytes varying in sensitivity and performance. In an example of 193 analytes with 9 calibrator levels acquired in triplicate, the processing took about 1 minute, compared to 180 minutes to manually review and approve all calibration curves for the same data set. This is a leap forward in time saving for data reviewers.
With higher throughput demands on today’s mass spec quantitative applications, the bottlenecks affecting sample turnaround time is moving from data acquisition towards data processing. Most lab environments like this can’t afford the time it takes to re-integrate missed peaks due to run to run retention time shifts, or bad integrations due to matrix interferences. As well, regulations put on data integrity has made the need for simplified, more automated methods and less manual review for peak integrations paramount.
To address this challenge, a new integration algorithm has been introduced with SCIEX OS software for data processing. AutoPeak is the second generation peak modeling integration algorithm which 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 application of the algorithm to samples with challenging chromatography. It is simpler to use because there are fewer parameters to adjust and less user input is needed, therefore 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 any optimization of 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 type. 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; whereas MultiQuant MQ4 algorithm left a lot of work to be done 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, log in to your SCIEXUniversity™ account to take the AutoPeak Essentials course now!
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