SPE-ls CSS – Membrane Particle Counting
USP <788> outlines methods used to measure "subvisible particulate matter" found in injectables. rap.ID contract testing laboratory conducts such analyses as well as white and gray standard nitrocellulose analyzation of membranes 25 mm and 47 mm in diameter. A 25mm standard sample can be analyzed in as little as 2 minutes using high throughput processing. The automated procedure is quick and requires little human interaction. Samples can be either sent to a central lab using rap.ID analytical equipment or straight to the rap.ID contract-testing lab.
SPE-ls raman.ID - Particle Counting and Statistical Particle Identification
This model identifies hundreds of particles ranging from 5 µm and larger per hour. The fully automated system provides statistically relevant data resulting in proactive quality assurance and the development of high quality biopharmaceuticals. Develop and control parenterals using high throughput Quality by Design principles without requiring additional human interaction.
SPE-ls raman.ID + metal.ID - Elemental Distribution and Chemical composition identification
The system identifies the exact chemical composition/elemental distribution, count, shape, and size of particles at a throughput rate of 100-1000 per hour. The equipment is designed to conduct parts cleanliness testing as well as identify foreign particles larger than 5 µm in medical devices and sterile pharmaceutical products such as parenterals.
SPE raman.ID 0.5 biopharma - Biopharmaceutical Solutions
The SPE 0.5 biopharmaceuticals identifies particles and agglomerates recovered from biopharmaceutical intrinsic particles. This device performs analyses with exceptional speed, a crucial factor when conducting silicone protein interaction studies or other formulation stability related investigations. Measuring and identifying nano particles is now as easy as a touch of a button.
SPE raman.ID 0.5 - Bio Particle Explorer(BPE) - Micro-Contamination solutions
The BPE is able to determine and identify the viability of a single microorganism. Microbial strains of micro-contamination can be distinguished from a single germ. The automated technology quickly determines the entire bacteria count and the hazardous potential of such germs with very little user interaction required. This model detects pathogenic bacterial contamination in troublesome pathogenic germs. Data is documented and stored in a results file in compliance with 21 CFR Part 11.
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