Online monitoring of small volume reactions using compact liquid chromatography instrumentation

A wide variety of analytical techniques have been employed for monitoring chemical reactions, with online instrumentation providing additional benefits compared to offline analysis. A challenge in the past for online monitoring has been placement of the monitoring instrumentation as close as possible to the reaction vessel to maximize sampling temporal resolution and preserve sample composition integrity. Furthermore, the ability to sample very small volumes from bench-scale reactions allows the use of small reaction vessels and conservation of expensive reagents. In this study, a compact capillary LC instrument was used for online monitoring of as small as 1 mL total volume of a chemical reaction mixture, with automated sampling of nL-scale volumes directly from the reaction vessel used for analysis. Analyses to demonstrate short term (~2 h) and long term (~ 50 h) reactions were conducted using tandem on-capillary ultraviolet absorbance followed by in-line MS detection or ultraviolet absorbance detection alone, respectively. For both short term and long term reactions (10 and 250 injections, respectively), sampling approaches using syringe pumps minimized the overall sample loss to ~0.2% of the total reaction volume.

Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process

When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.

Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.

Prediction of Dissolution of Sustained Release Coated Ciprofloxacin Beads Using Near-infrared Spectroscopy and Process Parameters: a Data Fusion Approach

The aim of the work is to develop a data fusion model using near-infrared (NIR) and process parameters for the predictions of drug dissolution from controlled release multiparticulate beads. Using a design of experiments, ciprofloxacin-coated beads were manufactured and critical process parameters such as air volume, product temperature, curing temperature, and curing time were measured; environmental humidity was monitored using a Pyrobuttons®. The NIR spectra were decomposed using principal component analysis (PCA). The PCA scores were fused with process measurements and all variables were autoscaled. The autoscaled variables were regressed against measured dissolution data at 1 h and 2 h time points; the PLS regression used quadratic and cross terms. The NIR spectra only model using data collected at the end of bead curing generated a PLS model using 5 latent variables with R² equal to 0.245 and 0.299 and RMSECV 13.23 and 13.12 for the 1 h and 2 h dissolution time points, respectively. The low R² and high root mean square error of cross validation (RMSECV) values indicate that NIR spectra alone were insufficient to model the drug release. Similar results were obtained for NIR model using data collected at the end of spraying phase. Models with fused spectral and process data yielded better prediction with R² above 0.88 and RMSECV less than 5% for the 1 h and 2 h dissolution time points. The data fusion model predicted dissolution profiles with an error less than 10%.

Predicting the drug concentration in starch acetate matrix tablets from ATR-FTIR spectra using multi-way methods

The amounts of drug and excipient were predicted from ATR-FTIR spectra using two multi-way modelling techniques, parallel factor analysis (PARAFAC) and multi-linear partial least squares (N-PLS). Data matrices consisted of dissolved and undissolved parallel samples having different drug content and spectra, which were collected at axially cut surface of the flat-faced matrix tablets. Spectra were recorded comprehensively at different points on the axially cut surface of the tablet. The sample drug concentrations varied between 2 and 16% v/v. The multi-way methods together with ATR-FTIR spectra seemed to represent an applicable method for the determination of drug and excipient distribution in a tablet during the release process. The N-PLS calibration method was more robust for accurate quantification of the amount of components in the sample whereas the PARAFAC model provided approximate relative amounts of components.

Examples of the application of optical process and quality sensing (OPQS) to beer brewing and polyurethane foaming processes

Optical methods play an important role in process analytical technologies (PAT). Four examples of optical process and quality sensing (OPQS) are presented, which are based on three important experimental techniques: near-infrared absorption, luminescence quenching, and a novel method, photon density wave (PDW) spectroscopy. These are used to evaluate four process and quality parameters related to beer brewing and polyurethane (PU) foaming processes: the ethanol content and the oxygen (O2) content in beer, the biomass in a bioreactor, and the cellular structures of PU foam produced in a pilot production plant.