Flowmicro In-Line Analysis-Driven Design of Reactions Mediated by Unstable Intermediates: Flash Monitoring Approach.

The direct observation of reactive intermediates is an important issue for organic synthesis. However, intermediates with an extreme instability are hard to be monitored by common spectroscopic methods such as FTIR. We have developed synthetic method utilizing flow microreactors, which enables a generation and reactions of unstable intermediates. Herein we report that, based on our flowmicro techniques, we developed an in-line analysis method for reactive intermediates in increments of milliseconds. We demonstrated the direct observation of the living and dead species of the anionic polymerization of alkyl methacrylates. The direct information of the living species enabled the anionic polymerization and copolymerization of oligo(ethylene glycol) methyl ether methacrylates, which is the important but difficult reaction in the conventional method.

Development of generic metabolic Raman calibration models using solution titration in aqueous phase and data augmentation for in-line cell culture analysis.

This study presents a novel approach for developing generic metabolic Raman calibration models for in-line cell culture analysis using glucose and lactate stock solution titration in an aqueous phase and data augmentation techniques. First, a successful set-up of the titration method was achieved by adding glucose or lactate solution at several different constant rates into the aqueous phase of a bench-top bioreactor. Subsequently, the in-line glucose and lactate concentration were calculated and interpolated based on the rate of glucose and lactate addition, enabling data augmentation and enhancing the robustness of the metabolic calibration model. Nine different combinations of spectra pretreatment, wavenumber range selection, and number of latent variables were evaluated and optimized using aqueous titration data as training set and a historical cell culture data set as validation and prediction set. Finally, Raman spectroscopy data collected from 11 historical cell culture batches (spanning four culture modes and scales ranging from 3 to 200 L) were utilized to predict the corresponding glucose and lactate values. The results demonstrated a high prediction accuracy, with an average root mean square errors of prediction of 0.65 g/L for glucose, and 0.48 g/L for lactate. This innovative method establishes a generic metabolic calibration model, and its applicability can be extended to other metabolites, reducing the cost of deploying real-time cell culture monitoring using Raman spectroscopy in bioprocesses.

Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM).

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

In-line Multidimensional NMR Monitoring of Photochemical Flow Reactions.

This work demonstrates the in-line monitoring of a flow photochemical reaction using 1D and ultrafast 2D NMR methods at high magnetic field. The reaction mixture exiting the flow reactor is flown through the NMR spectrometer and directly analyzed. In the case of simple substrates, suitable information can be obtained through 1D 1 H spectra, but for molecules of higher complexity the use of 2D experiments is key to address signal overlaps and assignment issues. Here we show the usefulness of ultrafast 2D COSY experiments acquired in 70 s or less, for the in-line monitoring of photochemical reactions, and the possibility to obtain reliable quantitative information. This is a powerful framework to, for example, efficiently screen reaction conditions.

Bioprocess in-line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model.

To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.

The Good pH probe: non-invasive pH in-line monitoring using Good buffers and Raman spectroscopy.

In bioprocesses, the pH value is a critical process parameter that requires monitoring and control. For pH monitoring, potentiometric methods such as pH electrodes are state of the art. However, they are invasive and show measurement value drift. Spectroscopic pH monitoring is a non-invasive alternative to potentiometric methods avoiding this measurement value drift. In this study, we developed the Good pH probe, which is an approach for spectroscopic pH monitoring in bioprocesses with an effective working range between pH 6 and pH 8 that does not require the estimation of activity coefficients. The Good pH probe combines for the first time the Good buffer 3-(N-morpholino)propanesulfonic acid (MOPS) as pH indicator with Raman spectroscopy as spectroscopic technique, and Indirect Hard Modeling (IHM) for the spectral evaluation. During a detailed characterization, we proved that the Good pH probe is reversible, exhibits no temperature dependence between 15 and 40 °C, has low sensitivity to the ionic strength up to 1100 mM, and is applicable in more complex systems, in which other components significantly superimpose the spectral features of MOPS. Finally, the Good pH probe was successfully used for non-invasive pH in-line monitoring during an industrially relevant enzyme-catalyzed reaction with a root mean square error of prediction (RMSEP) of 0.04 pH levels. Thus, the Good pH probe extends the list of critical process parameters monitorable using Raman spectroscopy and IHM by the pH value.

IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water.

The in-line determination of chemical parameters in water is of capital importance for environmental reasons. It must be carried out frequently and at a multitude of points; thus, the ideal method is to utilize automated monitoring systems, which use sensors based on many transducers, such as Ion Selective Electrodes (ISE). These devices have multiple advantages, but their management via traditional methods (i.e., manual sampling and measurements) is rather complex. Wireless Sensor Networks have been used in these environments, but there is no standard way to take advantage of the benefits of new Internet of Things (IoT) environments. To deal with this, an IoT-based generic architecture for chemical parameter monitoring systems is proposed and applied to the development of an intelligent potassium sensing system, and this is described in detail in this paper. This sensing system provides fast and simple deployment, interference rejection, increased reliability, and easy application development. Therefore, in this paper, we propose a method that takes advantage of Cloud services by applying them to the development of a potassium smart sensing system, which is integrated into an IoT environment for use in water monitoring applications. The results obtained are in good agreement (correlation coefficient = 0.9942) with those of reference methods.

Scale-Free Soft Sensor for Monitoring of Water Content in Fluid Bed Granulation Process.

In-line monitoring of granule water content during fluid bed granulation is important to control drug product qualities. In this study, a practical scale-free soft sensor to predict water content was proposed to cope with the manufacturing scale changes in drug product development. The proposed method exploits two key ideas to construct a scale-free soft sensor. First, to accommodate the changes in the manufacturing scale, the process parameters (PPs) that are critical to water content at different manufacturing scales were selected as input variables. Second, to construct an accurate statistical model, locally weighted partial least squares regression (LW-PLSR), which can cope with collinearity and nonlinearity, was utilized. The soft sensor was developed using both laboratory (approx. 4 kg) data and pilot (approx. 25 kg) scale data, and the prediction accuracy in the commercial (approx. 100 kg) scale was evaluated based on the assumption that the process was scaled-up from the pilot scale to the commercial scale. The developed soft sensor exhibited a high prediction accuracy, which was equivalent to the commonly used near-infrared (NIR) spectra-based method. The proposed method requires only standard instruments; therefore, it is expected to be a cost-effective alternative to the NIR spectra-based method.

Development of a NIR Method for the In-Line Quantification of the Total Polyphenolic Content: A Study Applied on Ajuga genevensis L. Dry Extract Obtained in a Fluid Bed Process.

This study describes an innovative in-line near-infrared (NIR) process monitoring method for the quantification of the total polyphenolic content (TPC) of Ajuga genevensis dry extracts. The dry extract was obtained in a fluidized bed processor, by spraying and adsorbing a liquid extract onto an inert powder support. NIR spectra were recorded continuously during the extract's spraying process. For the calibration of the in-line TPC quantification method, samples were collected during the entire process. The TPC of each sample was assessed spectroscopically, by applying a UV-Vis reference method. The obtained values were further used in order to develop a quality OPLS prediction model by correlating them with the corresponding NIR spectra. The final dry extract registered good flowability and compressibility properties, a concentration in active principles three times higher than the one of the liquid extract and an overall process yield of 85%. The average TPC's recovery of the NIR in-line prediction method, compared with the reference UV-Vis one, was 98.7%, indicating a reliable monitoring method which provided accurate predictions of the TPC during the process, permitting a good process overview and enabling us to establish the process's end point at the exact moment when the product reaches the desired TPC concentration.

In-Line Monitoring of a High-Shear Granulation Process Using the Baseline Shift of Near Infrared Spectra.

Although near infrared (NIR) spectra are primarily influenced by undesired variations, i.e., baseline shifts and non-linearity, and many applications of NIR spectroscopy to the real-time monitoring of wet granulation processes have been reported, the granulation mechanisms behind these variations have not been fully discussed. These variations of NIR spectra can be canceled out using appropriate pre-processing techniques prior to spectral analysis. The present study assessed the feasibility of directly using baseline shifts in NIR spectra to monitor granulation processes, because such shifts can reflect changes in the physical properties of the granular material, including particle size, shape, density, and refractive index. Specifically, OPUSGRAN®, a novel granulation technology, was investigated by in-line NIR monitoring. NIR spectra were collected using a NIR diffuse reflectance fiber optic probe immersed in a high-shear granulator while simultaneously examining the morphology, particle size, density, strength, and Raman images of the mixture during granulation. The NIR baseline shift pattern was found to be characteristic of the OPUSGRAN® technology and was attributed to variations in the light transmittance, reflection, and scattering resulting from changes in the physicochemical properties of the samples during granulation. The baseline shift also exhibited an inflection point around the completion of granulation, and therefore may be used to determine the endpoint of the process. These results suggest that a specific pattern of NIR baseline shifts are associated with the unique OPUSGRAN® granulation mechanism and can be applied to monitor the manufacturing process and determine the endpoint.

Using PAT to accelerate the transition to continuous API manufacturing.

Significant improvements can be realized by converting conventional batch processes into continuous ones. The main drivers include reduction of cost and waste, increased safety, and simpler scale-up and tech transfer activities. Re-designing the process layout offers the opportunity to incorporate a set of process analytical technologies (PAT) embraced in the Quality-by-Design (QbD) framework. These tools are used for process state estimation, providing enhanced understanding of the underlying variability in the process impacting quality and yield. This work describes a road map for identifying the best technology to speed-up the development of continuous processes while providing the basis for developing analytical methods for monitoring and controlling the continuous full-scale reaction. The suitability of in-line Raman, FT-infrared (FT-IR), and near-infrared (NIR) spectroscopy for real-time process monitoring was investigated in the production of 1-bromo-2-iodobenzene. The synthesis consists of three consecutive reaction steps including the formation of an unstable diazonium salt intermediate, which is critical to secure high yield and avoid formation of by-products. All spectroscopic methods were able to capture critical information related to the accumulation of the intermediate with very similar accuracy. NIR spectroscopy proved to be satisfactory in terms of performance, ease of installation, full-scale transferability, and stability to very adverse process conditions. As such, in-line NIR was selected to monitor the continuous full-scale production. The quantitative method was developed against theoretical concentration values of the intermediate since representative sampling for off-line reference analysis cannot be achieved. The rapid and reliable analytical system allowed the following: speeding up the design of the continuous process and a better understanding of the manufacturing requirements to ensure optimal yield and avoid unreacted raw materials and by-products in the continuous reactor effluent. Graphical Abstract Using PAT to accelerate the transition to continuous API manufacturing.

Use of near-infrared spectroscopy and multipoint measurements for quality control of pharmaceutical drug products.

Near-infrared (NIR) spectroscopy is a non-destructive analytical technique that enables better-understanding and optimization of pharmaceutical processes and final drug products. The use in line is often limited by acquisition speed and sampling area. This work focuses on performing a multipoint measurement at high acquisition speed at the end of the manufacturing process on a conveyor belt system to control both the distribution and the content of active pharmaceutical ingredient within final drug products, i.e., tablets. A specially designed probe with several collection fibers was developed for this study. By measuring spectral and spatial information, it provides physical and chemical knowledge on the final drug product. The NIR probe was installed on a conveyor belt system that enables the analysis of a lot of tablets. The use of these NIR multipoint measurement probes on a conveyor belt system provided an innovative method that has the potential to be used as a new paradigm to ensure the drug product quality at the end of the manufacturing process and as a new analytical method for the real-time release control strategy. Graphical abstract Use of near-infrared spectroscopy and multipoint measurements for quality control of pharmaceutical drug products.

Spectroscopic sensors for in-line bioprocess monitoring in research and pharmaceutical industrial application.

The use of spectroscopic sensors for bioprocess monitoring is a powerful tool within the process analytical technology (PAT) initiative of the US Food and Drug Administration. Spectroscopic sensors enable the simultaneous real-time bioprocess monitoring of various critical process parameters including biological, chemical, and physical variables during the entire biotechnological production process. This potential can be realized through the combination of spectroscopic measurements (UV/Vis spectroscopy, IR spectroscopy, fluorescence spectroscopy, and Raman spectroscopy) with multivariate data analysis to obtain relevant process information out of an enormous amount of data. This review summarizes the newest results from science and industry after the establishment of the PAT initiative and gives a critical overview of the most common in-line spectroscopic techniques. Examples are provided of the wide range of possible applications in upstream processing and downstream processing of spectroscopic sensors for real-time monitoring to optimize productivity and ensure product quality in the pharmaceutical industry.

Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic.

The implementation of automation in the multistep flow synthesis is essential for transforming laboratory-scale chemistry into a reliable industrial process. In this review, we briefly introduce the role of automation based on its application in synthesis viz. auto sampling and inline monitoring, optimization and process control. Subsequently, we have critically reviewed a few multistep flow synthesis and suggested a possible control strategy to be implemented so that it helps to reliably transfer the laboratory-scale synthesis strategy to a pilot scale at its optimum conditions. Due to the vast literature in multistep synthesis, we have classified the literature and have identified the case studies based on few criteria viz. type of reaction, heating methods, processes involving in-line separation units, telescopic synthesis, processes involving in-line quenching and process with the smallest time scale of operation. This classification will cover the broader range in the multistep synthesis literature.

In-line ATR-UV and Raman Spectroscopy for Monitoring API Dissolution Process During Liquid-Filled Soft-Gelatin Capsule Manufacturing.

Complete dissolution of the active pharmaceutical ingredient (API) is critical in the manufacturing of liquid-filled soft-gelatin capsules (SGC). Attenuated total reflectance UV spectroscopy (ATR-UV) and Raman spectroscopy have been investigated for in-line monitoring of API dissolution during manufacturing of an SGC product. Calibration models have been developed with both techniques for in-line determination of API potency. Performance of both techniques was evaluated and compared. The ATR-UV methodology was found to be able to monitor the dissolution process and determine the endpoint, but was sensitive to temperature variations. The Raman technique was also capable of effectively monitoring the process and was more robust to the temperature variation and process perturbations by using an excipient peak for internal correction. Different data preprocessing methodologies were explored in an attempt to improve method performance.

In-line imaging and recognition of flip chip fabrication defects by real-time photoacoustic remote sensing system.

Microscopic defects in flip chips, originating from manufacturing, significantly affect performance and longevity. Post-fabrication sampling methods ensure product functionality but lack in-line defect monitoring to enhance chip yield and lifespan in real-time. This study introduces a photoacoustic remote sensing (PARS) system for in-line imaging and defect recognition during flip-chip fabrication. We first propose a real-time PARS imaging method based on continuous acquisition combined with parallel processing image reconstruction to achieve real-time imaging during the scanning of flip-chip samples, reducing reconstruction time from an average of approximately 1134 ms to 38 ms. Subsequently, we propose improved YOLOv7 with space-to-depth block (IYOLOv7-SPD), an enhanced deep learning defect recognition method, for accurate in-line recognition and localization of microscopic defects during the PARS real-time imaging process. The experimental results validate the viability of the proposed system for enhancing the lifespan and yield of flip-chip products in chip manufacturing facilities.

Application of Raman spectroscopy during pharmaceutical process development for determination of critical quality attributes in Protein A chromatography.

Raman spectroscopy is considered a Process Analytical Technology (PAT) tool in biopharmaceutical downstream processes. In the past decade, researchers have shown Raman spectroscopy's feasibility in determining Critical Quality Attributes (CQAs) in bioprocessing. This study verifies the feasibility of implementing a Raman-based PAT tool in Protein A chromatography as a CQA monitoring technique, for the purpose of accelerating process development and achieving real-time release in manufacturing. A system connecting Raman to a Tecan liquid handling station enables high-throughput model calibration. One calibration experiment collects Raman spectra of 183 samples with 8 CQAs within 25 h. After applying Butterworth high-pass filters and k-nearest neighbor (KNN) regression for model training, the model showed high predictive accuracy for fragments (Q2 = 0.965) and strong predictability for target protein concentration, aggregates, as well as charge variants (Q2≥ 0.922). The model's robustness was confirmed by varying the elution pH, load density, and residence time using 19 external validation preparative Protein A chromatography runs. The model can deliver elution profiles of multiple CQAs within a set point ± 0.3 pH range. The CQA readouts were presented as continuous chromatograms with a resolution of every 28 s for enhanced process understanding. In external validation datasets, the model maintained strong predictability especially for target protein concentration (Q2 = 0.956) and basic charge variants (Q2 = 0.943), except for overpredicted HCP (Q2 = 0.539). This study demonstrates a rapid, effective method for implementing Raman spectroscopy for in-line CQA monitoring in process development and biomanufacturing, eliminating the need for labor-intensive sample pooling and handling.

In-Line Chemical Composition Monitoring for the Injection Molding Process of Biodegradable Polymer Blends Using Simultaneous Measurement of Near-Infrared Diffuse Reflectance and Transmission Spectra.

In the processing of polymer blends and composites, in-line near-infrared (NIR) spectroscopy enables monitoring of the composition and its composite uniformity and contributes to rapid process development and quality control. However, in the injection molding process, the study of the composition of polymer materials has been delayed due to high-pressure conditions. Our research group developed NIR probes for transmission and diffuse reflectance measurements that can withstand high-pressure and temperature conditions up to 130 MPa and 200 °C. In this research, transmission and diffuse reflectance spectra were measured inline during the injection molding process of polymer blends of poly(lactic acid) and polybutylene succinate adipate. The intensity of each polymer band in the second-derivative spectra exhibited a monotonic increase or decrease in response to changes in the blend ratio. Using transmission and diffuse reflectance spectra as explanatory variables of the partial least squares regression model simultaneously, the model showed high estimation accuracy for the entire region of the blend ratio. Finally, this model was applied to monitor the polymer changeover operation, and the change in the blend ratio in the molded product was successfully estimated in line.

Monitoring of the Homogeneity of Primer Layers for Ink Jet Printing on Polyester Fabrics by Hyperspectral Imaging.

Untreated polyester films and fibers can be hardly printed or coated, in particular if aqueous inks or lacquers have to be applied. Therefore, an adequate primer layer has to be applied first. A cationic polymer formulation based on poly(dimethylamine-co-epichlorohydrin-co-ethylenediamine) (PDEHED) was used as primer layer for digital printing on polyester fabrics. Because of the exceedingly high requirements on the homogeneity of such layers, hyperspectral imaging was used for qualitative and quantitative monitoring of the distribution of the primer layer on the textiles. Multivariate data analysis methods based on the PLS algorithm were applied for quantification of the NIR reflection spectra using gravimetry as a reference method. Optimization of the calibration method resulted in various models with prediction errors of about 1.2 g/m2. The prediction performance of the models was proven in external validations using independent samples. Moreover, a special ink jet printing technology was tested for application of the aqueous primer formulation itself. Since possible clogging of jet nozzles in the print head might lead to inhomogeneity in the coatings such as missing tracks, the potential of hyperspectral imaging to detect such defects was investigated. It was demonstrated that simulated missing tracks can be clearly detected. Consequently, hyperspectral imaging has been proven to be a powerful analytical tool for in-line monitoring of the quality of printability improvement layers and similar systems.

Soft sensor based on Raman spectroscopy for the in-line monitoring of metabolites and polymer quality in the biomanufacturing of polyhydroxyalkanoates.

Polyhydroxyalkanoates (PHA) are among the most promising bio-based alternatives to conventional petroleum-based plastics. These biodegradable polyesters can in fact be produced by fermentation from bacteria like Cupriavidus necator, thus reducing the environmental footprint of the manufacturing process. However, ensuring consistent product quality attributes is a major challenge of biomanufacturing. To address this issue, the implementation of real-time monitoring tools is essential to increase process understanding, enable a prompt response to possible process deviations and realize on-line process optimization. In this work, a soft sensor based on in situ Raman spectroscopy was developed and applied to the in-line monitoring of PHA biomanufacturing. This strategy allows the collection of quantitative information directly from the culture broth, without the need for sampling, and at high frequency. In fact, through an optimized multivariate data analysis pipeline, this soft sensor allows monitoring cell dry weight, as well as carbon and nitrogen source concentrations with root mean squared errors (RMSE) equal to 3.71, 7 and 0.03 g/L, respectively. In addition, this tool allows the in-line monitoring of intracellular PHA accumulation, with an RMSE of 14 gPHA/gCells. For the first time, also the number and weight average molecular weights of the polymer produced could be monitored, with RMSE of 8.7E4 and 11.6E4 g/mol, respectively. Overall, this work demonstrates the potential of Raman spectroscopy in the in-line monitoring of biotechnology processes, leading to the simultaneous measurement of several process variables in real time without the need of sampling and labor-intensive sample preparations.