Process Analytical Technique Research Articles

Decoupling Protein Concentration and Aggregate Content Using Diffusion and Water NMR.

Protein-based biopharmaceutical drugs, such as monoclonal antibodies, account for the majority of the best-selling drugs globally in recent years. For bioprocesses, key performance indicators are the concentration and aggregate level for the product being produced. In water NMR (wNMR), the use of the water transverse relaxation rate [R2(1H2O)] has been previously used to determine protein concentration and aggregate level; however, it cannot be used to separate between them without using an additional technique. This work shows that it is possible to "decouple" these two key characteristics by recording the water diffusion coefficient [D(1H2O)] in conjunction with R2(1H2O), even in the event of overlap in either D(1H2O) or R2(1H2O). This method is demonstrated on three different systems, following appropriate D(1H2O) or R2(1H2O) calibration data acquisition for a protein of interest. Our method highlights the potential use of benchtop NMR as an at-line process analytical technique.

Addressing raw material variability: In-line FTIR sugar composition analysis of lignocellulosic process streams

For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.

Coaxial Dielectric Spectroscopy as an In-Line Process Analytical Technique for Reaction Monitoring.

The suitability of broadband dielectric spectroscopy (DS) as a tool for in-line (in situ) reaction monitoring is demonstrated. Using the esterification of 4-nitrophenol as a test-case, we show that multivariate analysis of time-resolved DS data-collected across a wide frequency range with a coaxial dip-probe-allows reaction progress to be measured with both high precision and high accuracy. In addition to the workflows for data collection and analysis, we also establish a convenient method for rapidly assessing the applicability of DS to previously untested reactions or processes. We envisage that, given its orthogonality to other spectroscopic methods, its low cost, and its ease of implementation, DS will be a valuable addition to the process chemist's analytical toolbox.

Near infrared‐based process analytical technology module for estimating gelatinization optimal point

AbstractThis research article focusses on the implementation of process analytical technique for the rapid prediction of gelatinization points during the process of steaming of Komal Chawal, an indigenous rice variety from the state of Assam, using the technique of near‐infrared spectroscopy. The experimentation involves soaking Komal Chawal at 60°C for 90 min, then steaming at 0, 0.05, 0.1, 0.15, and 0.2 MPa for different time viz. 3, 6, 9, 12, 15, and 18 min, simultaneously Near Infrared (NIR) spectra were also acquired after every treatment. The degree of gelatinization (DG) and moisture gain were measured, and their change pattern was studied using first‐order kinetics and Fick's law of diffusion. For first‐order rate equation, the goodness of fits were obtained with R2 values between .964 and .927. The partial least square model developed with 10 latent variables showed a training accuracy in terms of statistical metrics were: R2 of .882 and RMSE of 2.56. Thus, NIR‐based estimation process had shown reliability for the prediction of the DG.Practical ApplicationsKomal Chawal, an indigenous variety to the state of Assam with a GI tag. It has a unique property of no cooking required, processed by the method of parboiling. So, a rapid prediction method of optimal gelatinization point estimation during the process can save a lot of funds and energy, thus making it more efficient for mass production in industries for the commercialization of the variety.

Process analytical technique (PAT) miniaturization for monoclonal antibody aggregate detection in continuous downstream processing

AbstractThe transition to continuous biomanufacturing is considered the next step to reduce costs and improve process robustness in the biopharmaceutical industry, while also improving productivity and product quality. The platform production process for monoclonal antibodies (mAbs) is eligible for continuous processing to lower manufacturing costs due to patent expiration and subsequent growing competition. One of the critical quality attributes of interest during mAb purification is aggregate formation, with several processing parameters and environmental factors known to influence antibody aggregation. Therefore, a real‐time measurement to monitor aggregate formation is crucial to have immediate feedback and process control and to achieve a continuous downstream processing. Miniaturized biosensors as an in‐line process analytical technology tool could play a pivotal role to facilitate the transition to continuous manufacturing. In this review, miniaturization of already well‐established methods to detect protein aggregation, such as dynamic light scattering, Raman spectroscopy and circular dichroism, will be extensively evaluated for the possibility of providing a real‐time measurement of mAb aggregation. The method evaluation presented in this review shows which limitations of each analytical method still need to be addressed and provides application examples of each technique for mAb aggregate characterization. Additionally, challenges related to miniaturization are also addressed, such as the design of the microfluidic chip and the microfabrication material. The evaluation provided in this review shows why the development of microfluidic biosensors is considered the key for real‐time measurement of mAb aggregates and how it can contribute to the transition to a continuous processing. © 2021 The Authors.Journal of Chemical Technology and Biotechnologypublished by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

Raman Spectroscopy as a PAT-Tool for Film-Coating Processes: In-Line Predictions Using one PLS Model for Different Cores.

Although Raman spectroscopy has been described as a potential process analytical technique for tablet coating, it has rarely been transferred from academic studies to commercial manufacturing applications. The reasons for this are probably not only the high level of process understanding and experience with multivariate data analysis required, but also the product-dependent elaborate model-building. Hence, this study represents a feasibility study to investigate, whether subtraction of core spectra is a suitable approach to generate versatile models for one specific coating that can be applied on a multitude of different tablet cores. Raman spectroscopy was used to predict the application of coatings on three different tablet cores using PLS regression. The obtained spectra were preprocessed, and differential spectra were calculated by subtraction of the core spectrum from each inline spectrum. Normalization ensured comparability between the spectral data of the different cores. It was shown that in general it is possible to build models for a specific coating suspension that can predict the application of this suspension on different cores. In the presence of a strong Raman marker (TiO2), promising results were obtained. Without the presence of a strong Raman marker this modeling approach is to be considered critical.

Calibration transfer of a Raman spectroscopic quantification method from at-line to in-line assessment of liquid detergent compositions

The industrial production of liquid detergent compositions entails delicate balance of ingredients and process steps. In order to assure high quality and productivity in the manufacturing line, process analytical technology tools such as Raman spectroscopy are to be implemented. Marked chemical specificity, negligible water interference and high robustness are ascribed to this process analytical technique. Previously, at-line calibration models have been developed for determining the concentration levels of the being studied liquid detergents main ingredients from Raman spectra. A strategy is now proposed to transfer such at-line developed regression models to an in-line set-up, allowing real-time dosing control of the liquid detergent composition under production. To mimic in-line manufacturing conditions, liquid detergent compositions are created in a five-liter vessel with an overhead mixer. Raman spectra are continuously acquired by pumping the detergent under production via plastic tubing towards a Raman superhead probe, which is incorporated into a metal frame with a sapphire window facing the detergent fluid. Two at-line developed partial least squares (PLS) models are aimed at transferring, predicting the concentration of surfactant 1 and polymer 2 in the examined liquid detergent composition. A univariate slope/bias correction (SBC) is investigated, next to three well-acknowledged multivariate transformation methods: direct, piecewise and double-window piecewise direct standardization. Transfer is considered successful when the magnitude of the validation sets root mean square error of prediction (RMSEP) is similar to or smaller than the corresponding at-line prediction error. The transferred model offering the most promising outcome is further subjected to an exhaustive statistical evaluation, in order to appraise the applicability of the suggested calibration transfer method. Interval hypothesis tests are thereby performed for method comparison. It is illustrated that the investigated transfer approach yields satisfactory results, provided that the original at-line calibration model is thoroughly validated. Both SBC transfer models return lower RMSEP values than their corresponding original models. The surfactant 1 assay met all relevant evaluation criteria, demonstrating successful transfer to the in-line set-up. The in-line quantification of polymer 2 levels in the liquid detergent composition could not be statistically validated, due to the poorer performance of the at-line model.

Real-Time Image Processing Based Online Feedback Control System for Cooling Batch Crystallization

Direct nucleation control (DNC) is a process analytical technique (PAT) based model free feedback control strategy for batch and continuous crystallization processes, which has been successfully applied in numerous cases. The basic principle of DNC is the use of controlled dissolution cycles to control a measurement directly related to the particle number in the system. During DNC, in the case of cooling crystallization fines are dissolved by repeated heating–cooling loops. In this context, the controlled variable is the (relative) particle number, which is manipulated using a feedback control approach through the temperature. The particle number is traditionally measured with focused beam reflectance measurement (FBRM); however, other PAT tools can also be employed in a similar feedback control setup. Often, crystallization processes are also monitored by real-time imaging systems. In the current work a novel DNC setup is proposed in which microscopy images are captured and processed by means of image an...

Process analytical quality control of tailored drug release formulation prepared via hot-melt extrusion technology

The objective of the present study was to compare the influence of Eudragit® RS PO and RL PO blends on the release of water-soluble and insoluble drugs from hot-melt extruded formulations. In addition, we aimed to evaluate drug content uniformity and distribution by Fourier transform-infrared (FT-IR) chemical imaging. Theophylline (TP) and carbamazepine (CBZ) were selected as the water-soluble and insoluble model drugs, respectively. Eudragit® RS PO and RL PO were selected as the polymeric matrices. FT-IR chemical imaging clearly demonstrated the content uniformity and distribution for both drugs in the extrudates, which was confirmed by HPLC. Increasing the ratio of Eudragit® RL PO led to an increase in the in vitro drug release, whereas an increase in the ratio of Eudragit® RS PO sustained the drug release for up to 12 h. The hot-melt extrusion of TP and CBZ with varying ratios of Eudragit® RS PO and RL PO can be employed to tailor the drug release profiles. In this study, we demonstrated, for the first time, the use of FT-IR chemical imaging as a process analytical technique to determine the drug content uniformity and distribution. Our data correlated well with the results of HPLC analysis in the study of tailored drug release from the prepared hot-melt extruded formulation.

Studying a gas–solid multi-component adsorption process with near-infrared process analytical technique: Experimental setup, chemometrics, adsorption kinetics and mechanism

The study on multi-component or competitive gas–solid adsorption process is a challenge to current theories as well as experimental methods. In the paper, a methodology composed of near-infrared spectroscopy, process analytical technique and chemometrics has been tried to investigate the adsorption process of orthoxylene/aniline onto active alumina surface. The adsorption process took place in a differential adsorption bed, which was non-invasively monitored by a near-infrared spectrometer via optical fiber probe, and spectra were collected at every minute during the whole adsorption process. After treating these spectra with chemometrics algorithms, including wavelet analysis, partial least squares and artificial neural network, the adsorption process has been investigated thoroughly as well as clearly: not only the adsorption rates of each adsorbates at various adsorption times but also a great deal of information about the mechanism of the adsorption process. For example, orthoxylene was adsorbed evenly on the active alumina surface, while aniline was adsorbed uprightly; aniline was adsorbed to fabricate the first and the second adsorption layer on the adsorbent surface simultaneously rather than to construct them in sequence or step by step; and some parts of the adsorbent surface was more active for adsorption.

Measurement of fermentation parameters of Chinese rice wine using Raman spectroscopy combined with linear and non-linear regression methods

Effective fermentation monitoring is a growing need during the manufacture of wine due to the rapid pace of change in the wine industry. Ethanol and reducing sugar are two most important process variables indicating the status of Chinese rice wine (CRW) fermentation process. In this study, the potentials of Raman spectroscopy (RS) as a rapid process analytical technique to monitor the evolution of these two chemical parameters involved in CRW fermentation process and to group samples according to different fermentation stages were investigated. The results demonstrated that compared with the PLS model using all wavelengths of Raman spectra, the prediction precision of model based on the spectral variables selected by competitive adaptive reweighted sampling (Cars) was significantly improved. In addition, nonlinear models outperformed linear models in predicting fermentation parameters. After systemically comparison and discussion, it was found that for both ethanol and glucose, Cars-support vector machine (Cars-SVM) models gave the best results with the highest prediction precisions. Moreover, the results obtained from discriminant partial least squares analysis (DPLS) showed that good performances were obtained with an average correct classification rate of 94.9% for different fermentation stages. The overall results indicated that RS combined with efficient variable selection algorithm and nonlinear regression tool could be utilized as a rapid method to monitor CRW fermentation process.

Chemical imaging and solid state analysis at compact surfaces using UV imaging

Fast non-destructive multi-wavelength UV imaging together with multivariate image analysis was utilized to visualize distribution of chemical components and their solid state form at compact surfaces. Amorphous and crystalline solid forms of the antidiabetic compound glibenclamide, and microcrystalline cellulose together with magnesium stearate as excipients were used as model materials in the compacts. The UV imaging based drug and excipient distribution was in good agreement with hyperspectral NIR imaging. The UV wavelength region can be utilized in distinguishing between glibenclamide and excipients in a non-invasive way, as well as mapping the glibenclamide solid state form. An exploratory data analysis supported the critical evaluation of the mapping results and the selection of model parameters for the chemical mapping. The present study demonstrated that the multi-wavelength UV imaging is a fast process analytical technique with the potential for real-time monitoring of critical quality attributes.

Rapid, Nondestructive Estimation of Surface Polymer Layer Thickness Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy and Synthetic Spectra Derived from Optical Principles

We have developed a rapid, nondestructive analytical method that estimates the thickness of a surface polymer layer with high precision but unknown accuracy using a single attenuated total reflection Fourier transform infrared (ATR FT-IR) measurement. Because the method is rapid, nondestructive, and requires no sample preparation, it is ideal as a process analytical technique. Prior to implementation, the ATR FT-IR spectrum of the substrate layer pure component and the ATR FT-IR and real refractive index spectra of the surface layer pure component must be known. From these three input spectra a synthetic mid-infrared spectral matrix of surface layers 0 nm to 10,000 nm thick on substrate is created de novo. A minimum statistical distance match between a process sample's ATR FT-IR spectrum and the synthetic spectral matrix provides the thickness of that sample. We show that this method can be used to successfully estimate the thickness of polysulfobetaine surface modification, a hydrated polymeric surface layer covalently bonded onto a polyetherurethane substrate. A database of 1850 sample spectra was examined. Spectrochemical matrix-effect unknowns, such as the nonuniform and molecularly novel polysulfobetaine-polyetherurethane interface, were found to be minimal. A partial least squares regression analysis of the database spectra versus their thicknesses as calculated by the method described yielded an estimate of precision of ±52 nm.

Impurity detection in solid and molten silicon by laser induced breakdown spectroscopy

The application of Laser Induced Breakdown Spectroscopy (LIBS) for the analysis of both solid and molten silicon has been developed. This technique provides fast and reliable chemical characterization of silicon. This work will present the investigation of experimental parameters such as buffering gas nature and pressure in order to find the most suitable conditions to quantify boron in solid silicon. These results show that the signal to background ratio (SBR) is improved by both the use of helium and argon instead of air and by reducing the pressure to 500mbar. Using calibrated samples, calibration curves were prepared for boron and limits of detection of the order of 0.2ppm were obtained working at a distance of 50cm from the sample. Additionally, the capabilities of LIBS to analyze molten silicon (1410°C) was demonstrated, opening the way for LIBS to be used as a process analytical technique.

Optical monitoring of milk fat phase transition within homogenized fresh milk by Photon Density Wave spectroscopy

Photon Density Wave (PDW) spectroscopy was applied for temperature dependent monitoring of melting and crystallization of milk fat within homogenized fresh milk. As an in-line process analytical technique, PDW spectroscopy quantifies continuously the optical properties of turbid material, providing an insight into its structural processes. Here, the measured absorption coefficients reflect temperature as well as fat content of milk and the reduced scattering coefficients probe physical changes of the light scattering fat droplets and casein micelles. Thermal processing reveals breakpoints within the temperature trend of the reduced scattering coefficient of fat containing milk. Found at 16 °C and 24 °C while cooling and heating, respectively, they are associated to the phase transitions of milk fat. Continuous isothermal measurement of the optical coefficients showed that the crystallization process requires several hours. The strongly changing reduced scattering coefficient implies that the thermal history of milk will have a major impact on any method based on light scattering as quantitative analytical technique.

Double-pulse laser-induced breakdown spectroscopy for analysis of molten glass

A mobile double-pulse laser-induced breakdown spectroscopy system for industrial environments is presented. Its capabilities as a process analytical technique for the recovery of metals from molten inorganic wastes are investigated. Using low-melting glass doped with different amounts of additives as a model system for recycling slags, the optimum number of shots, laser inter-pulse and acquisition delay times are optimized for solid and liquid (1200 °C) glass. Limits of detection from 7 ppm (Mn) to 194 ppm (Zn) are achieved working at a distance of 75 cm from the sample. To simplify the quantification of molten samples in an industrial furnace, the possibility is examined of using solid standards for analysis of molten material.

Raman imaging analysis of pharmaceutical tablets by two-dimensional (2D) correlation spectroscopy

Chemical properties of active substances and insoluble excipient within tablets such as crystalline structures can be seen as an important index for solubility of ingredients. Spectroscopic imaging can potentially be a solid solution to understanding mechanisms at the molecular level and it may bring useful insight in terms of process analytical technique. In the present study, generalized two-dimensional (2D) correlation spectroscopy is utilized for the Raman image analysis of pharmaceutical tablets to reveal molecular interactions between chemical components. By using a spatial distance as a perturbation variable in 2D correlation scheme, synchronous and asynchronous correlation analysis becomes possible. Two kinds of pharmaceutical tablets, pentoxifylline (PTX) as an active substance and palmitic acid (PA) as an insoluble excipient, are prepared with different grinding times, 0.5 and 45 min. The 2D correlation analysis of Raman images of the tablets clearly reveals both physical and chemical effects of grinding process on the properties of the tablets. Asynchronous correlations indicate that a specific molecular structural change of PTX related to the crystallinity is induced by the grinding process. Namely, the crystallinity of PTX based on CH 2 structure is a key factor to control the solubility of the tablets. Some properties of pharmaceutical tablets, i.e. solubility or distribution of components in turn may become possible by the simple grinding process. Detailed analysis of Raman images becomes possible by the 2D correlation spectroscopy.

A Novel Method for Studying Multicomponent Gas Uptake on Solid Adsorbent with Near-Infrared Process Analytical Technique

In this paper, we have studied the uptake of orthoxylene/isoamyl alcohol on silica gel as an example to demonstrate a novel methodology for studying multicomponent gas adsorption on solid adsorbent. In the method, the solid adsorbent was filled into a differential adsorption bed, and the bed was inline monitored with near-infrared diffuse reflectance spectroscopy continuously when the adsorption process was taking place. The spectral data recorded during the process were treated with algorithm of locally weighted regression, which constructed a series of partial least-squares models to more accurately predict concentrations of each adsorbate on the adsorbent. These efforts made the method feasible to obtain more thermodynamic and kinetic information about the adsorption process in a more convenient, rapid, economical, as well as straightforward way. With the method, we obtained the isothermal lines of the multicomponent system at 293.15 K, and the instantaneous adsorption rate of each component during the whole adsorption process.

Application of process analytical technology in tablet process development using NIR spectroscopy: Blend uniformity, content uniformity and coating thickness measurements

Near-infrared (NIR) spectroscopy was employed as a process analytical technique in three steps of tabletting process: to monitor the blend homogeneity, evaluate the content uniformity of tablets and determine the tablets coating thickness. A diode-array spectrometer mounted on a lab blender (SP15 NIR lab blender) was used to monitor blend uniformity using a calibration-free model with drug concentration ranging from 2.98 to 9.25% (w/w). The method developed accurately depicted the changes in concentration of the drug during blending and the positive effect of a delumping step in the production process. Blend homogeneity was reached within 2 min of the blending step post-delumping, with relative standard deviation (R.S.D.) values varying from 1.0 to 2.5% depending on the drug concentration of the blend. A Fourier-transform spectrometer (Bruker MPA) was used to analyze content uniformity and coating thickness with calibration based models. Prediction of a validation set with tablets compacted at pressures not present in the calibration set yielded an root mean square error of cross validation (RMSEP) of 1.94%; prediction of tablets compacted at pressures present in the calibration set yielded a RMSEP of 1.48%. Performance of the model was influenced by several physical tablet properties, which could be reduced by spectral pre-processing. A model based on reflectance spectra predicted coating thickness and its variation more accurately than the model based on transmission spectra. Inter-tablet coating variation was predicted with NIR and compared to reference thickness measurements. Both methods gave comparable results. Initial inter-tablet variation of tablets sampled in-process during coating was high, but stabilized after 30 min into the process.

Integrated Sensing and Processing Acoustic Resonance Spectrometry (ISP-ARS) for Sample Classification

This research introduces a novel process analytical technique, integrated sensing and processing acoustic resonance spectrometry (ISP-ARS), and compares ISP-ARS with conventional full-spectrum ARS for the characterization of solid fuel premixes used in a new pill safe designed to protect narcotics. In ISP-ARS, the acoustic excitation waveform is the analog implementation of the chemometric weight function, encoded for this work on an MP3 player and used to distinguish between fuel samples, sparing post-collection multivariate computation. In effect, the detector directly outputs the sample identity. Repeated measurements of batches of similar fuel mixtures over time produce an automatic projection of similar spectra into corresponding three-dimensional probability density contours, thus, forming the analyte classification directly. For the characterization of ten different fuel mixtures, full spectral ARS resulted in a median intermixture distance in multidimensional standard deviations (MSDs) of 185.1, while ISP-ARS resulted in a median MSD distance of 81.3. The median cross-validation MSD was 1.41 for the ARS and 1.58 for the ISP-ARS. Distances in MSDs greater than three are considered separable, and MSDs less than three are indistinguishable. The classification procedure correctly identified all samples for both analytical techniques. ISP-ARS is an effective, simpler, and more rapid alternative to full spectrum ARS that can be implemented with a commercial MP3 player and used as an inexpensive spectrometric sensor for dynamic data-driven application simulations (DDDAS) and process analytical applications.