On-Line Process Control of Gradient Elution Liquid Chromatography

• Published in: Analytical chemistry (Washington) Vol. 76; no. 16; pp. 4875 - 4880
• Main Authors: Schweitz, Leif, Fransson, Magnus, Karlsson, Lars, Torstensson, Arne, Johansson, Erik
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 15.08.2004
• Subjects: Analytical chemistry; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography; Chromatography, Liquid - instrumentation; Chromatography, Liquid - methods; Computer Simulation; Equipment Design; Exact sciences and technology; Fluids; Laboratories; Methods; Online Systems; Other chromatographic methods; Pharmaceutical Preparations - analysis; Pharmaceuticals; Samples; PLS regression; Elution; Gradient; Chemical analysis; Process control; HPLC chromatography; Real time; Chemometrics; Monitoring
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac0497050

The typical measure of the stability of analytical HPLC methods in the pharmaceutical laboratory is standards injected repeatedly throughout the sample sequence. To obtain improved control of the analysis and reduction of the number of standards and replicates, a novel approach to treat the analytical run as a process, where the chromatographic data is the product, is proposed. Thus, an alternative and continuous system suitability test procedure is described. This is obtained by continuous monitoring of several parameters of the chromatographic system such as pressure, temperature, and conductivity. The data are analyzed in real time with chemometrics to produce easily interpreted control charts. Gradient elution LC is extensively employed in pharmaceutical analysis. A gradient elution system is inherently dynamic due to the mobile-phase composition being changed during the chromatographic run. To handle the dynamics, suitable chemometric tools are needed. In this report, we extend the use of liquid chromatography process control (LCPC) to gradient elution LC by creating partial least-squares regression batch models of the data collected. The gradient elution LCPC system was evaluated by inducing disturbances, and it was shown to easily detect any real or simulated deviation.