RESUMEN

A new approach for process monitoring is described, the self-organizing map quality control (SOMQC) index. The basis of the method is that SOM maps are formed from normal operating condition (NOC) samples, using a leave-one-out approach. The distances (or dissimilarities) of the left out sample can be determined to all the units in the map, and the nth percentile measured distance of the left out sample is used to provide a null distribution of NOC distances which is generated using the Hodges-Lehmann method. The nth percentile distance of a test sample to a map generated from all NOC samples can be measured and compared to the null distribution at a given confidence level to determine whether the sample can be judged as out of control. The approach described in this paper is applied to online high-performance liquid chromatography (HPLC) measurements of a continuous pharmaceutical process and is compared to other established methods including Q and D statistics and support vector domain description. The SOMQC has advantages in that there is no requirement for multinormality in the NOC samples, or for linear models, or to perform principal components analysis (PCA) prior to the analysis with concomitant issues about choosing the number of PCs. It also provides information about which variables are important using component planes. The influence of extreme values in the background data set can also be tuned by choosing the distance percentile.

Asunto(s)

Cromatografía Líquida de Alta Presión/métodos , Cromatografía Líquida de Alta Presión/normas , Análisis Discriminante , Preparaciones Farmacéuticas/química , Preparaciones Farmacéuticas/normas , Análisis de Componente Principal , Control de Calidad

RESUMEN

A continuous process is monitored by on-line HPLC in four separate campaigns, ranging in duration from 10 to 104 h. Methods are reported that allow the study of variation over all four campaigns using Multilevel Simultaneous Components Analysis, which separates out the within- and between-campaign variation. In order to obtain control charts, Q- and D-statistics are combined with a within-campaign submodel (Simultaneous Components Analysis) to obtain a single model that is based only on within-campaign variation over all four campaigns.

Asunto(s)

Cromatografía Líquida de Alta Presión/métodos , Espectrometría de Masa por Ionización de Electrospray/métodos , Anaerobiosis/fisiología , Bacterias/química , Bacterias/metabolismo , Reactores Biológicos/microbiología , Análisis Multinivel

RESUMEN

A continuous process was studied over 83.32 h using on-line high-performance liquid chromatography, involving the acquisition of 252 chromatograms. A method for analysis of these data using multivariate statistical process control on peak tables, in real-time, is described. The normal operating condition (NOC) region of the process was identified using evolving principal components analysis to be between 5.77 and 8.13 h. 19 out of the 37 peaks detected throughout the process were found in the NOC region, the remainder representing undesirable contaminants found elsewhere in the process. A major challenge is to develop the peak table as the process evolves, which is dynamically updated as new peaks are detected after the NOC region: this approach involving an "unlocked" peak table is contrasted to an approach using a "locked" peak table where only peaks detected during the NOC region are included in the model. In addition, results are compared to those obtained using baseline corrected and aligned chromatograms, using a NOC region of 5.85-8.33h. D- and Q-charts were obtained. It is shown that the "unlocked" peak table detects out of control samples best and provides good diagnostic insight into problems with the process.

Asunto(s)

Cromatografía Líquida de Alta Presión/métodos , Análisis Multivariante , Sistemas en Línea , Análisis de Componente Principal , Control de Calidad

RESUMEN

On-line high performance liquid chromatography is used to monitor a steady state reaction over 35.2 h, with 197 chromatograms recorded as the reaction progresses. For each chromatogram, peaks are detected, baseline corrected, aligned and integrated to provide a peak table consisting of the intensities of 19 peaks, two corresponding to the reactants, one to the product and one to the solvent, the remaining being impurities, by-products or intermediates. D-charts and Q-charts from multivariate statistical process control are applied to the data to determine which samples are out of control and also provide diagnostic insight into why these samples are problematic. The D-chart is best at looking at overall performance issues such as problems with mixing or difficulties with instrument operation, whereas the Q-charts are best at detecting impurities during the reaction.

Asunto(s)

Cromatografía Líquida de Alta Presión/estadística & datos numéricos , Análisis Factorial , Sistemas en Línea

RESUMEN

The use of Principal Components plots in the exploratory investigation of reactions monitored by UV-Vis spectroscopy is described. The effects of different types of pre-processing (raw, mean-centred, and standardised) are illustrated. Four types of plot (scores versus time, loadings versus wavelength, scores versus scores, and loadings versus loadings) are considered. The approach is used to investigate the reaction between phenylhydrazine and benzophenone to give a hydrazone. Observable deviations from ideal behaviour indicate differential crystallisation of the product, and the presence of small quantities of an intermediate during the reaction. Additional information about the reaction is gained by comparing selected components from several batches. PCA is easily performed at- or on-line, and the information gained can be used to help decide upon a suitable harder model for further analysis.