PA 800 Plus

Analyze PA 800 Plus Capillary Electrophoresis System data with AI

High-resolution capillary electrophoresis system for biomolecule separation and purity analysis

Detection: UV detection with optional LIF
Temperature Control: Precise capillary temperature control
Sample Handling: Multi-sample carousel
Compliance: 21 CFR Part 11 software
Data Export: CSV, PDF reports
Get Started For Free Fully compatible with Sourcetable

Quick Start Integration

Get your PA 800 Plus data into Sourcetable in 3 simple steps

  • Step 1

    Export Data

    Export electropherograms and peak data from your PA 800 Plus via 32 Karat software to CSV

  • Step 2

    Upload to Sourcetable

    Import your capillary electrophoresis data into Sourcetable for analysis

  • Step 3

    Start Analysis

    Perform automated peak integration and purity assessment analysis

File Format Compatibility

Optimized for capillary electrophoresis analysis with automated peak integration and purity assessment

.csv

Raw Data

Full Support Electropherogram visualization, automated peak integration, molecular weight reporting

Pre-built Analysis Workflows

Advanced workflows for biomolecule separation and characterization

🔬

Protein Purity Analysis

High-resolution separation and purity assessment of proteins

Protein Analysis Purity Assessment High Resolution
📊

Charge Variant Analysis

Analysis of protein charge heterogeneity and variants

Charge Variants Protein Characterization Heterogeneity
🧬

Glycan Profiling

Detailed analysis of glycan structures and profiles

Glycan Analysis Structural Analysis Profiling

Technical Requirements

Software Dependencies

  • PA 800 Plus Capillary electrophoresis system with UV/LIF detection
  • Control Software 32 Karat software with 21 CFR Part 11
  • File Export Format CSV, PDF reports
  • Applications Biopharma, analytical chemistry

Sourcetable Libraries

  • Data Processing pandas, numpy
  • Statistical Analysis scipy, statsmodels
  • Visualization matplotlib, plotly
  • Electrophoresis Analysis scikit-learn, seaborn

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