Artificial Intelligence for Signal Quality Review
Southwest Research Institute® (SwRI®) offers non-targeted analysis using complex chromatography and Mass Spectrometry enhanced by artificial intelligence. With expertise in analytical chemistry, machine learning and data science, SwRI developed Highlight™, a novel tool to automate the signal quality review and data processing steps in a high-throughput manner. Along with extensive analytical chemistry experience of SwRI’s staff, Highlight™ works with existing instrumental acquisition software to perform non-targeted analysis.
Highlight™ builds upon the success of SwRI’s award-winning Floodlight™ software, providing high resolution, high-throughput screening (HTS) capabilities for high-quality signal interpretation. Highlight™ leverages machine learning algorithms for rapid pattern matching, allowing scientists to make faster data-informed decisions with non-targeted analysis and other complex datasets.
To inquire about licensing, contact Kristin Favela, Ph.D. or call +1 210 522 4209.
Highlight™ Software Functions
Highlight solves many challenges associated with complex data analysis and offers:
- Expanded features
- Chemical Data Analysis
- High-throughput Screening (HTS)
- Non-Targeted Analysis
- Targeted Analysis
- Pattern matching
- Signal interpretation
Instrumentation Applications
- Mass Spec Data Analysis
- GC/MS, LC/MS, MSI, Optical Spectroscopy, Spectroscopy, Tandem MS
R&D 100 Award for Floodlight™ Non-Targeted Analysis System
Cheminformatics tool that analyzes mass spectrometry data recognized by R&D World as one of the 100 most significant innovations of 2021.
Learn MoreNon-Targeted Analysis Tools
Traditional targeted analysis approaches will screen all the spectra in a sample against a library of known compounds. Unfortunately, these traditional library techniques do not cover the entire chemical space, and a hidden world waiting to be discovered is once again ignored. New developments in analytical chemistry, big data and artificial intelligence are helping to bridge that gap to the unknown, through development of non-targeted analysis (NTA) methods. These NTA methods attempt to reveal any chemical compound present in a sample, providing high-quality information on known and unknown compounds, important for chemical identification, characterization and property evaluation. Floodlight features AI and deep learning algorithms to perform non-targeted analysis (NTA) of datasets from mass spectrometry.
Related Links
- Mass Spec Data Analysis
- Complex Data Analysis
- Chemical Analysis Services
- Data Analytics & Machine Learning
- Environmental Analysis & Testing
- Materials & Bioengineering
- Papers & Presentations