This multiauthor review article aims to bring readers up to date with some of the current trends in the field of process analytical technology (PAT) by summarizing each aspect of the subject (sensor development, PAT based process monitoring and control methods) and presenting applications both in industrial laboratories and in manufacture e.g. at GSK, AstraZeneca and Roche. Furthermore, the paper discusses the PAT paradigm from the regulatory science perspective. Given the multidisciplinary nature of PAT, such an endeavour would be almost impossible for a single author, so the concept of a multiauthor review was born. Each section of the multiauthor review has been written by a single expert or group of experts with the aim to report on its own research results. This paper also serves as a comprehensive source of information on PAT topics for the novice reader.

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The in situ measurement of solution supersaturation associated with the batch cooling crystallization of l-glutamic acid (LGA) at 500 mL and 20 L scale sizes is assessed via ATR-FTIR spectroscopy. A partial least squares chemometric calibration model was developed for the online prediction of LGA concentration from measured FTIR absorbance spectra overcoming some significant challenges related to the low sensitivity of LGA in the mid-IR frequency range, its low solubility in water, and its complex speciation chemistry. The solubility data of LGA in water over the temperature range from 40 to 80 °C, using ATR-FTIR, reveals excellent agreement with those obtained both from using a gravimetric method and literature data. The metastable zone width determined using the turbidimetric methods as a function of heating/cooling rates and solute concentration is found to increase with increasing cooling rate while it decreases with increasing solution concentration. Monitoring online crystallization via both spontaneous and seeded in 500 mL and 20 L crystallizers reveals good concentration predictions for seeded crystallization, while fouling of the ATR crystal prevents its routine use for unseeded crystallization studies. Higher supersaturation levels are found for the larger crystallizer scale-size consistent with enhancement of secondary nucleation at the smaller scale-size.

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This paper presents a novel nonlinear hybrid modeling approach aimed at obtaining improvements in model performance and robustness to new data in the optimal control of a batch MMA polymerization reactor. The hybrid model contains a simplified mechanistic model that does not consider the gel effect and stacked recurrent neural networks. Stacked recurrent neural networks are built to characterize the gel effect, which is one of the most difficult parts of polymerization modeling. Sparsely sampled data on polymer quality were interpolated using a cubic spline function to generate data for neural network training. Comparative studies with the use of a single neural network show that stacked networks give superior performance and improved robustness. Optimal reactor temperature control policies are then calculated using the hybrid stacked neural network model. It is shown that the optimal control strategy based on the hybrid stacked neural network model offers much more robust performance than that based on a hybrid single neural network model.

When analyzing complex mixtures that exhibit sample-to-sample variability using spectroscopic instrumentation, the variation in the optical path length, resulting from the physical variations inherent within the individual samples, will result in significant multiplicative light scattering perturbations. Although a number of algorithms have been proposed to address the effect of multiplicative light scattering, each has associated with it a number of underlying assumptions, which necessitates additional information relating to the spectra being attained. This information is difficult to obtain in practice and frequently is not available. Thus, with a view to removing the need for the attainment of additional information, a new algorithm, optical path-length estimation and correction (OPLEC), is proposed. The methodology is applied to two near-infrared transmittance spectral data sets (powder mixture data and wheat kernel data), and the results are compared with the extended multiplicative signal correction (EMSC) and extended inverted signal correction (EISC) algorithms. Within the study, it is concluded that the EMSC algorithm cannot be applied to the wheat kernel data set due to core information for the implementation of the algorithm not being available, while the analysis of the powder mixture data using EISC resulted in incorrect conclusions being drawn and hence a calibration model whose performance was unacceptable. In contrast, OPLEC was observed to effectively mitigate the detrimental effects of physical light scattering and significantly improve the prediction accuracy of the calibration models for the two spectral data sets investigated without any additional information pertaining to the calibration samples being required.