Validation Kits
Product release from NLG Analytical...
Cut the cost of achieving regulatory compliance with full traceability by up to 90%.
How? With v:kit, a new idea to revolutionize the way you validate your analytical equipment.
A single box containing all the procedures, documentation and materials needed to perform OQ/PQ test on various instruments.
Kits are available for HPLC and GC with full traceability and without contravening 21 CFR Part 11 compliance regulations.
HPLC systems: isocratic and gradient pumps, UV/vis detectors (including PDAs), fluorescence detectors, refractive index detectors, column thermostats and autosamplers.
GC systems: including auto-injectors, headspace samplers, FIDs ECDs and TCDs.
Whether performing your regular validation checks, or for pre- or post-maintenance tests, v:kit offers you cost-effective, credible regulatory compliance for all your analytical systems.
v:kits allow a common validation standard across all your equipment, in all locations.
Tel: +44 (0) 1625 574633
Fax: +44 (0) 1625 574699
Investigating the Protective Effects of Frankincense Oil on Wound Healing with GC–MS
April 2nd 2025Frankincense essential oil is known for its anti-inflammatory, antioxidant, and therapeutic properties. A recent study investigated the protective effects of the oil in an excision wound model in rats, focusing on oxidative stress reduction, inflammatory cytokine modulation, and caspase-3 regulation; chemical composition of the oil was analyzed using gas chromatography–mass spectrometry (GC–MS).
Evaluating Natural Preservatives for Meat Products with Gas and Liquid Chromatography
April 1st 2025A study in Food Science & Nutrition evaluated the antioxidant and preservative effects of Epilobium angustifolium extract on beef burgers, finding that the extract influenced physicochemical properties, color stability, and lipid oxidation, with higher concentrations showing a prooxidant effect.
Rethinking Chromatography Workflows with AI and Machine Learning
April 1st 2025Interest in applying artificial intelligence (AI) and machine learning (ML) to chromatography is greater than ever. In this article, we discuss data-related barriers to accomplishing this goal and how rethinking chromatography data systems can overcome them.
