Acquisition-Time Quality Control Workflow for FT-ICR MS Complex Mixture Analysis
EMSL Project ID
51668
Abstract
High throughput complex mixture analysis is hindered by challenges in automating data acquisition, namely ensuring that the data generated is of consistently high quality as required for automatic data analysis pipelines. Therefore, the proposed project's main two objectives are to 1) develop and implement an automated data processing routine for acquisition-time quality control (QC) assessment of complex mixture mass spectra acquired on the FT-ICR mass spectrometers, 2) automate instrument and data processing workflows parameterization.
Acquired mass spectra raw data will be automatically sent to a dedicated computational analysis node for a pre-defined analysis, with summary results made available to the operator via a web browser interface within minutes of the data acquisition. The results will allow the spectrometer operator to intervene as required whenever an acquisition job behaves as an outlier. The QC analysis results will also provide necessary input to an adaptive instrument, and workflow parameterization of samples acquired in between QC runs. Thus, enabling full automation of data acquisition and processing of small molecule mixture analysis. Furthermore, this developed platform lends itself to future QC applications with other instruments and data types, i.e., metabolomics on LC-MS systems.
High throughput complex mixture analysis is hindered by challenges in automating data acquisition, namely ensuring that the data generated is of consistently high quality as required for automatic data analysis pipelines. Therefore, the proposed project's main two objectives are to 1) develop and implement an automated data processing routine for acquisition-time quality control (QC) assessment of complex mixture mass spectra acquired on the FT-ICR mass spectrometers, 2) automate the parameterization of instrument and data processing workflows.
Acquired mass spectra raw data will be automatically sent to a dedicated computational analysis node for a pre-defined analysis, with summary results made available to the operator via a web browser interface within minutes of the data acquisition. The results will allow the spectrometer operator to intervene as required whenever an acquisition job behaves as an outlier of acceptable quality control metrics. The QC analysis results will also provide necessary input to an adaptive parameterization of subsequent analysis of real samples acquired between QC runs and provide a feedback loop for instrument parameter optimization. Thus, enabling full automation of data acquisition and processing of small molecule mixture analysis. Furthermore, this developed platform lends itself to future QC applications with other instruments and data types, i.e., metabolomics on LC-MS systems.
High throughput complex mixture analysis is hindered by challenges in automating data acquisition, namely ensuring that the data generated is of consistently high quality as required for automatic data analysis pipelines. Therefore, the proposed project's main two objectives are to 1) develop and implement an automated data processing routine for acquisition-time quality control (QC) assessment of complex mixture mass spectra acquired on the FT-ICR mass spectrometers, 2) automate the parameterization of instrument and data processing workflows.
Acquired mass spectra raw data will be automatically sent to a dedicated computational analysis node for a pre-defined analysis, with summary results made available to the operator via a web browser interface within minutes of the data acquisition. The results will allow the spectrometer operator to intervene as required whenever an acquisition job behaves as an outlier of acceptable quality control metrics. The QC analysis results will also provide necessary input to an adaptive parameterization of subsequent analysis of real samples acquired between QC runs and provide a feedback loop for instrument parameter optimization. Thus, enabling full automation of data acquisition and processing of small molecule mixture analysis. Furthermore, this developed platform lends itself to future QC applications with other instruments and data types, i.e., metabolomics on LC-MS systems.
All complex mixture FT-MS projects at EMSL, a large proportion of the EMSL user base, will benefit from improved data quality and throughput. This project aligns tightly with the EMSL roadmap for automating NOM characterization and enhancing data quality standards. By combining a feedback loop with the instrument computer and machine learning methods, we will maximize our instrument's functionality and performance in the most efficient and technologically advanced ways possible, enabling complete automation of complex mixture analysis. We expect full automated QC analysis deployment by the end of FY22 with improvements to EMSL user projects during FY21 and FY22.
Project Details
Start Date
2020-10-26
End Date
2023-10-03
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members