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Agilent Technologies Introduces New Software to Optimize Chemometric Profiling Workflows
Product News

Agilent Technologies Introduces New Software to Optimize Chemometric Profiling Workflows

Agilent Technologies Introduces New Software to Optimize Chemometric Profiling Workflows
Product News

Agilent Technologies Introduces New Software to Optimize Chemometric Profiling Workflows


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Agilent Technologies Inc. has introduced MassHunter Profinder software designed for batch processing of complex mass spectrometric data. The latest member of the MassHunter software family, MassHunter Profinder transforms chemometric peak-finding workflows with enhanced batch processing, robust re-mining and alignment capabilities.  

“Using Agilent’s MassHunter Profinder in our untargeted metabolomics and proteomics experiments, we can dramatically improve the quality of our differential analysis data sets,” said Dr. Rick Reisdorph, pulmonary disease researcher and assistant professor at National Jewish Health in Denver. “We can quickly and easily extract thousands of compounds from these complex data sets, then quickly determine differences between sample groups and select specific compounds for targeted analysis.” 

“To date, traditional processing of mass spectrometric data generated from complex biological samples has been restrictive and tedious, requiring excessive manual effort, time and expense,” said Steve Madden, Agilent’s product manager for LC/MS software. “With MassHunter Profinder, our most sophisticated differential peak-finding solution, researchers have a powerful tool for simultaneously processing multiple, large-volume data sets with minimal intervention, maximum flexibility and superior results.

Agilent’s MassHunter Profinder is powered by proprietary algorithms specially designed to identify differential compounds in highly complex data sets. The software significantly reduces the noise associated with data profiling by employing a new recursive feature extraction workflow, which eliminates additional manual editing tasks such as stacking, adding and deleting peaks. New user-defined peak re-integration capabilities enable significantly fewer false-positives/negatives, critical factors when performing subsequent statistical analyses.

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