Agilent, 5190-1004, Captiva EMR-Lipid, 6 mL cartridges, with 600 mg sorbent mass, 50/pk

Captiva EMR-Lipid, 6 mL cartridges, with 600 mg sorbent mass, 50/pk. Highly selective, efficient lipid/matrix removal without analyte loss.

Specifications:
Brand: Captiva EMR
Cartridge Material: Polypropylene
Cartridge Size: 6 mL
Certificate of Analysis: Yes
Compound Types: AntibioticsDrugs of AbusePAHsVet Drugs
Loading Volume: 4.5 - 5.5 mL
Matrix Removal Mechanism: CombinedHydrophobicSize Exclusion
QC Tests Reported: Analyte Recovery and PrecisionCleanlinessFlow TestMatrix RemovalPhospholipid Removal
Sorbent Mass (mg): 600 mg
Sorbent Material: Captiva EMR-Lipid
Tabless: No
Typical Matrices: DairyEdible OilsEggsFatty Animal-Origin FoodsMeat
UNSPSC Code: 41122102
Volume: 6 mL

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Designed for the efficient removal of lipids from biological samples, these 6 mL cartridges incorporate 600 mg of Enhanced Matrix Removal-Lipid sorbent, delivering reliable sample cleanup prior to analytical workflows. The innovative sorbent technology selectively targets and removes phospholipids and other lipid components, minimizing matrix interferences and improving sensitivity for downstream analyses such as LC-MS and GC-MS.

Each pack contains 50 cartridges, supporting high-throughput laboratories in clinical, food safety, environmental, and pharmaceutical research applications. These cartridges are particularly well suited for sample preparation involving serum, plasma, milk, and other biological matrices where excessive lipid content may compromise analytical accuracy.

Integration into existing lab protocols is straightforward, with compatibility across a wide range of manual and automated solid phase extraction systems, including Agilent’s 12 and 24-position vacuum manifolds. The design ensures minimal analyte loss and reproducible results across batches. For complete workflows, these cartridges pair well with Agilent's solvent delivery systems, analytical columns, and mass spectrometers. By optimizing sample preparation, they help laboratories achieve lower detection limits, higher reproducibility, and improved data quality in demanding research environments.