Combining self-modeling curve resolution methods and partial least squares to develop a quantitative reaction monitoring method with minimal reference data.
An example of combining self-modeling curve resolution (SMCR) methods and partial least squares (PLS) to construct a quantitative model using minimal reference data is presented. The objective was to construct a quantitative calibration model to allow real-time in situ ultraviolet-attenuated total reflection (UV/ATR) measurements to determine the end-point during a chlorination reaction. Time restrictions for development combined with difficult reaction sampling conditions required the method to be developed using only a few key reference measurements. Utilizing evolving factor analysis (EFA) and the orthogonal projection approach (OPA), initial estimates of the concentration and spectral profiles for the intermediate and product were obtained. Further optimization by multivariate curve resolution-alternating least squares (MCR-ALS) led to refined estimates of the concentration profiles. A PLS2 model was then constructed using the calculated concentration profiles and the preprocessed UV spectra. Using a standard PLS model compatible with the spectrometer's standard process software facilitated real-time predictions for new batches. This method was applied to five 45 liter batches in a large-scale laboratory facility. The method successfully predicted the product concentration of batch 1 but exhibited larger prediction error for subsequent batches. The largest prediction error was attained during batch 3, for which a final concentration of 0.22 mole L(-1) was predicted, while the true measured value was 0.271 mole L(-1) (an error of 18.8%). However, the qualitative real-time profiles proved to be extremely useful as they allowed the end-point to be determined without sampling or performing off-line analysis. Furthermore, the concentration profile of the intermediate species, which could not be observed by the offline method, could also be observed in real-time and gave further confidence that the process was approaching the end-point. Another benefit of real-time reaction profiles was encountered during the manufacture when the formation of product in batch 3 appeared to be progressing slower than was observed in previous batches. This prompted a check of the batch temperature and it was found to be 10 degrees C lower than the required set-point. The temperature was corrected and the batch successfully reached completion in the expected time.
Pedge
Nicholas I
NI
Process Research and Development Department, AstraZeneca R&D Charnwood, Loughborough, Leicestershire, United Kingdom. nicholas.pedge@astrazeneca.com
Walmsley
Anthony D
AD
D000465:Algorithms; D002713:Chlorine / Q000032:analysis*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016018:Least-Squares Analysis; D008956:Models, Chemical; D015233:Models, Statistical; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013056:Spectrophotometry, Ultraviolet / Q000379:methods* / Q000592:standards; D006113:United Kingdom
D002713:Chlorine