In-line Near-infrared Spectroscopy Research Articles

Near-infrared spectroscopy in resin transfer molding—determination of the degree of cure

Near-infrared (NIR) spectroscopy is employed to directly monitor the degree of cure of composite material during the curing phase in the resin transfer molding (RTM) process. The composite material consists of natural fibers and an epoxy-amine resin system. The quantitative determination of the degree of cure, α, from the NIR spectra is realized by partial least square (PLS) regression in conjunction with various pre-treatments of the spectral data. As a reference, the degree of cure is determined by isothermal differential scanning calorimetry (DSC) experiments. The best PLS model that could be obtained for α is characterized by a determination coefficient of prediction (R²P) of 0.980 and root mean square error of prediction (RMSEP) of 4.4 %. While the spectra change during curing according to the literature for each RTM trial, significant differences can be observed between the spectra of different RTM trials. The reasons for this are discussed in detail. The findings show the potential of inline NIR spectroscopy for monitoring α in the RTM process.

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.

The impact of marker selection, in-line near-infrared spectroscopy (NIR), and feed mix time on the coefficient of variation (mix uniformity), body weight uniformity and broiler growth performance during the starter, grower, and finisher periods

Most feed manufacturers in the United States use the same mixing time (and thus mix uniformity) throughout the growing period regardless of age and consumption patterns. However, research evaluating the optimum mixing time requirements and novel analysis methods, such as in-line near-infrared (NIR) spectroscopy, on the coefficient of variation (CV) and growth performance of broilers throughout the production phases is sparse. Two experiments were conducted to determine the effects of marker selection, in-line NIR, and varying mix times on mix uniformity, broiler growth performance, and body weight uniformity from 1 to 42 d of age. Feed was manufactured utilizing a 1,815-kg counterpoise ribbon mixer. In both experiments, feed was mixed for 4.5 min (3 min dry mix and 90 s of wet mix) and 30 s (0 s dry mix and 30 s wet mix) to obtain a standard mix (SM) and an abbreviated mix (AM), respectively. Experiment 1 constituted a 2×2×4 factorial arrangement of 2 mix times, (4.5 and 0.5 min), 2 batch sizes (908 and 1,815 kg), and 4 methodologies to evaluate mixer performance (sodium chloride, Microtracers (Red#40 and Blue#40), and in-line NIR). In experiment 2, broilers received different mix time combinations: 1) SM from 1 to 42 d, 2) SM from 1 to 28 d and AM from 28 to 42 d, 3) SM from 1 to 14 d and AM from 14 to 42 d, and 4) AM from 1 to 42 d. In both experiments, selecting a single source marker provided a more accurate estimation of mixer CV in SM and AM diets (P < 0.05). In experiment 2, mix time did not influence BW, feed intake (FI), FCR, or individual bird BW CV from 1 to 42 d of age (P > 0.05). These data indicated that mixer CV differed depending on total mix time and methodology used and diets with a reduced mix time may not necessarily influence growth performance and BW uniformity during the starter, grower, and finisher periods of broilers.

Near-infrared spectroscopy as a process analytical technology tool for monitoring performance of membrane filtration in a whey protein fractionation process.

A successful transition from a laboratory proof-of-principle to a functioning industrial Process Analytical Technology (PAT) application of spectroscopy and chemometrics is still an active area of research and development. A comprehensive understanding on how the design and implementation of the optical instrumentation affect the data quality, and how this will affect the performance of the prediction models is vital for the successful implementation of in-line monitoring. In a previous study, we have demonstrated that near-infrared spectroscopy (NIRS), combined with chemometric techniques, can be used to quantify α-lactalbumin and β-lactoglobulin in aqueous whey solutions. This work demonstrates the potential, both at-line and in-line, of monitoring a protein fractionation process in a full-scale production. An in-line near infrared spectrometer (NIRS) was used to monitor the individual protein concentrations in a protein fractionation process over 20 days. In addition, samples were extracted from the process and analysed with at-line NIRS and an in-house HPLC reference method. The developed models could predict β-lactoglobulin and α-lactalbumin concentrations with satisfactory precision and accuracy, yielding a root mean square error of cross-validation of 0.08 and 0.18 w/w% proteins, respectively. For the first time, the whey proteins concentrations were measured continuously in a production facility to demonstrate the potential of NIRS for at-line and in-line rapid monitoring of protein composition in industrial whey streams. A PAT tool was developed from NIRS data where Partial Least Squares regression modelling and an Exponentially Weighted Moving Average filtering were used to extract and visualize valuable information on the process dynamics and performance. The study demonstrates that continuous monitoring by in-line NIRS allowed elucidation of some process behaviours not readily detectable with the current sampling frequency and that it offers novel process optimisation opportunities by providing increased process understanding and decision support information concerning preventive maintenance and real-time process validation.

A case study on decentralized manufacturing of 3D printed medicines

Pharmaceutical 3D printing (3DP) is one of the emerging enabling technologies of personalised medicines as it affords the ability to fabricate highly versatile dosage forms. In the past 2 years, national medicines regulatory authorities have held consultations with external stakeholders to adapt regulatory frameworks to embrace point-of-care manufacturing. The proposed concept of decentralized manufacturing (DM) involves the provision of feedstock intermediates (pharma-inks) prepared by pharmaceutical companies to DM sites for manufacturing into the final medicine. In this study, we examine the feasibility of this model, with respect to both manufacturing and quality control. Efavirenz-loaded granulates (0–35%w/w) were produced by a manufacturing partner and shipped to a 3DP site in a different country. Direct powder extrusion (DPE) 3DP was subsequently used to prepare printlets (3D printed tablets), with mass ranging 266–371 mg. All printlets released more than 80% drug load within the first 60 min of the in vitro drug release test. An in-line near-infrared spectroscopy system was used as a process analytical technology (PAT) to quantify the printlets' drug load. Calibration models were developed using partial least squares regression, which showed excellent linearity (R2 = 0.9833) and accuracy (RMSE = 1.0662). Overall, this work is the first to report the use of an in-line NIR system to perform real-time analysis of printlets prepared using pharma-inks produced by a pharmaceutical company. By demonstrating the feasibility of the proposed distribution model through this proof-of-concept study, this work paves the way for investigation of further PAT tools for quality control in 3DP point-of-care manufacturing.

Real-Time Monitoring of a Sol-Gel Reaction for Polysilane Production Using Inline NIR Spectroscopy.

The sol-gel process is an effective method for the preparation of homogeneous structured nanomaterials whose physico-chemical properties strongly depend on the experimental conditions applied. The control of a three-component reaction with silanes showing multiple reaction sites revealed the need for an analytical tool that allows a rapid response to ongoing transformations in the reaction mixture. Herein, we describe the implementation of near-infrared (NIR) spectroscopy based on compact, mechanically robust, and cost-efficient micro-optomechanical system technology in the sol-gel process of three silanes with a total of nine reaction sites. The NIR-spectroscopically controlled reaction yields a long-time stable product with reproducible quality, fulfilling the demanding requirements for further use in coating processes. 1H nuclear magnetic resonance measurements are used as reference values for the calibration of a partial least squares (PLS) regression model. The precise prediction of the desired parameters from collected NIR spectroscopy data acquired during the sol-gel reaction proves the applicability of the calibrated PLS regression model. The determined shelf-life and further processing tests verify the high quality of the sol-gel and the produced highly cross-linked polysilane.

In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation—A Case Study from Commercial Cheese Production

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.

Continuous Twin-Screw wet granulation process with In-Barrel drying and NIR setup for Real-Time Moisture Monitoring

The purpose of this study was to evaluate if wet granule formation and drying could take place in a single operation by utilizing in-barrel drying. The drying kinetics of the formulation were studied in order to select appropriate processing parameters and assess feasibility with short residence times in the extruder. The 18-mm extruder was operated in a 40:1 L:D ratio with 8 zones. The first two zones were used for material feeding and wet granule formation and the remaining zones were used for drying at elevated temperature. The impact of screw configuration as well as screw speed, feed rate, and residence time were all studied to optimize the drying process. Due to limitations of temperature and residence time, vacuum was added to enable sufficient drying. In-line NIR spectroscopy was incorporated into the twin-screw wet granulation (TSWG) process to monitor the moisture content of wet granules in real-time. The set-up was optimized and a predictive model was developed for future experiments.This study demonstrated the success of this technique on a pilot-scale (18-mm) extruder for the first time. Granules were formed and dried to a target loss on drying (LOD) of less than 2 % at moderate temperatures (100 °C – 110 °C) with one single operation. Streamlining wet granulation and drying into one unit operation can have a profound impact on pharmaceutical manufacturing reducing time, footprint, and environmental exposure due to reduced product transfers.

Development of Inline Near-Infrared Spectroscopy Method for Real-Time Monitoring of Blend Uniformity of Direct Compression and Granulation-Based Products at Commercial Scales.

Blending is a critical intermediate unit operation for all solid oral formulations. For blend uniformity testing, API content in the blend must be quantified precisely. A detailed study was conducted to demonstrate the suitability of inline NIR (near-infrared) spectroscopy for blend uniformity testing of two solid oral formulations: existing direct compression (DC) product with a multistep blending process and granulation-based product with API granules. Both qualitative and quantitative methods were developed at a laboratory scale using statistical moving block standard deviation (MBSD) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. The qualitative MBSD method demonstrated that there was no need for multiple steps for the existing DC product. Hence, a simplified single-step process was developed for blending. Quantitative PLS models for blending processes of both the products were developed, validated, and successfully implemented at a commercial scale for the real-time release of blends. Results obtained from the validated model were in good agreement with the current method of sampling and chromatography.

Continuous-flow reactor setup for operando x-ray absorption spectroscopy of high pressure heterogeneous liquid-solid catalytic processes.

A continuous-flow reactor and a continuous-flow setup compatible with operando x-ray absorption spectroscopy (XAS) were designed for safely studying liquid-phase reactions on solid high atomic number transition metal catalysts (e.g., Au, Pd, and Pt) under pressures up to 100 bars with temperatures up to 100°C. The reactor has a stainless-steel body, 2mm thick polyether ether ketone (PEEK) x-ray windows, and a low internal volume of 0.31 ml. The rectangular chamber (6 × 5 × 1 mm3) between the PEEK x-ray windows allows us to perform XAS studies of packed beds or monoliths in the transmission mode at any position in the cell over a length of 60mm. A 146° wide-angle beam access also allows recording complementary x-ray fluorescence or x-ray diffraction signals. The setup was engineered to continuously feed a single-phase liquid flow saturated with one or more gaseous reactants to the liquid-solid XAS reactor containing no free gas phase for enhanced process safety and sample homogeneity. The proof of concept for the continuous-flow XAS cell and high-pressure setup was provided by operando XAS measurements during the direct synthesis of hydrogen peroxide at room temperature and 40 bars using a 35 ± 5 mg catalyst (1 wt. % Pd/TiO2) and inline near-infrared spectroscopy. The experiments prove that the system is well suited to follow the reaction in the liquid phase while recording high-quality XAS data, paving the way for detailed studies on the catalyst structure and structure-activity relationships.

Model-based design of secondary drying using in-line near-infrared spectroscopy data

This article deals with the design and optimization of the secondary drying stage, via design space calculation, of a freeze-drying process. A simple and well known mathematical model was used to this purpose: the kinetic parameters of the water desorption step were determined, either off-line or in-line, using the measurement of the residual amount of water (Cs ) in one of the processed samples through Near-Infrared spectroscopy. In the first approach, three tests, at different values of heating shelf temperature, are employed: the measurement of Cs versus time allows estimating the desorption rate and, finally, the kinetic constant at the given temperature. Arrhenius plot is used to get the parameters expressing the dependence of the kinetic constant on product temperature, thus allowing the calculation of the design space. In the second approach, the kinetic parameters are estimated in-line, focusing on the first part of the secondary drying stage, where the variation of product temperature is more relevant, hence allowing to track the evolution of the desorption rate (via Cs measurement) versus product temperature. A fitting procedure is then used, looking for the kinetic parameters that provide the best fit between calculated and measured values of Cs . By this way only one test is required to get the design space. Drying of sucrose and of sucrose – arginine mixtures were used as case studies, to point out the effectiveness of the proposed method. Examples of the design spaces that can be obtained are presented and discussed, focusing on the effect of operating parameters like the heating rate and the residual water content at the beginning of the secondary drying, as well as on the constraint about the maximum allowed temperature. Abbreviations: KF: Karl-Fischer; MVA: multivariate analysis; NIR: near-infrared; NIRS: near-infrared spectroscopy; PAT: process analytical technology; PLS: partial least square; PRT: pressure rise test; RM: residual moisture; RMSE: root mean squared error

Optimization of in-line near-infrared measurement for practical real time monitoring of coating weight gain using design of experiments

This study was conducted to develop an in-line near-infrared (NIR) spectroscopy approach that allows real time quantitative analysis of the coating weight gain on a moving tablet surface during a coating process where talc is used. A holder directly inserting a diffuse reflectance probe into a coating pan was designed, and the optimal measurement conditions were identified using the design of experiments (DoE). The surface of the probe was kept clean of coating droplets at a maximum distance between the probe and the holder of 272.5 mm, leading to the acquisition of accurate spectral data. Under this condition, partial least squares regression (PLSR) was developed using the spectra from 7197 to 6233 cm−1, which covers the specific peaks for the core tablet and the coating solution. Under the same conditions, least squares regression (LSR) was developed using the univariate predictive analysis of the single absorption spectrum of talc at 7181 cm−1. In a comparison of the accuracy of the two models, PLSR was found to be more accurate as a result of testing the significance of differences between these distributions in terms of the root mean square errors of prediction (RMSEP) using a randomization t-test. Additionally, it confirmed that the predicted weight gain using NIR spectroscopy was correlated with the coating thickness measured using micro-CT. In conclusion, this study developed an in-line NIR measurement approach for the real-time monitoring of the coating weight gain of tablets and optimized the conditions by evaluating the effect of various factors.

Elucidation of the Molecular Mechanism of Wet Granulation for Pharmaceutical Standard Formulations in a High-Speed Shear Mixer Using Near-Infrared Spectroscopy.

The granulation process of pharmaceutical standard formulation in a high-speed shear wet granulation (HSWG) was measured by in-line near-infrared spectroscopy (NIRS) and agitation power consumption (APC) methods. The F-1, F-2, and F-3 formulations (500 g) contained 96% w/w α-lactose monohydrate (LA), potato starch (PS), and a LA:PS = 7:3 mixture, respectively, and all the formulations contained 4% w/w hydroxypropyl cellulose. While adding purified water at 10 mL/min, the sample powder was mixed. The calibration models to measure the amount of binding water (Wa) and APC of the HSWG formulations were established based on NIRS of the samples measured for 60 min by partial least-squares regression analysis (PLS). Molecular interaction related to APC between the particle surface and binding liquor was analyzed based on NIRS. The predicted values of Wa and APC for all formulations were superimposed with the measured values on a straight line, respectively. The regression vector (RV) of the calibration model for Wa indicated the chemical information of all the water in the samples. In contrast, the RV for APC suggested that APC changes in the processes are related to powder aggregation because of surface tension of binding water between particles.

Stabilizer-Free Dispersion Copolymerization Monitoring by In-Line NIR Spectroscopy

Stabilizer-free dispersion copolymerization is a one-step technique to produce uniform polymer particles. Due to its fast kinetics and nucleation, assessing polymer conversion and particle size via...

NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry

Near-infrared spectroscopy (NIRS) is widely used for quality and process control in the food industry. In the meat industry, the method is still used mainly for determination of fat, water, and protein content, while new applications are emerging. In this paper, we report on how in-line NIRS can be used to detect and sort chicken breast fillets with the myopathies wooden breast and spaghetti meat from normal fillets. A total of 270 fillets were measured with 2 different near-infrared systems. The near-infrared spectra contained information about protein content and water-binding properties that showed systematic differences between the 3 quality classes. Wooden breast could be well separated fro m normal fillets (96% correct classification), while spaghetti meat was slightly more difficult to separate from both normal and wooden breast because properties measured by NIRS were overlapping between the 3 groups. Two quite similar NIRS instruments had quite different classification performance, which emphasizes the importance of optimizing spectroscopic instrumentation for different applications.

Analysis of granulation mechanism in a high-shear wet granulation method using near-infrared spectroscopy and stirring power consumption

The dynamic granulation process of high-speed shear wet granulation (HSWG) was measured by in-line near-infrared spectroscopy (NIRS) and agitation power consumption (APC) methods. Molecular interactions between powder particles and the binding liquid were analyzed based on both NIRS and APC data by multivariable regression analysis. The granulated sample used glass beads (d50 = 46 μm) with or without hydroxypropyl cellulose, and the binder solution used purified water. The HSWG granulator (2-L volume) with APC device and NIRS was used, and the agitator was rotated at 600 min−1 and the chopper at 2000 min−1 with glass beads to be granulated being 920 g (0.6 L), and a total of 360 mL of purified water was added at 10 mL/min. In order to establish calibration models to predict APC and amount of binding water of the granular formulations, NIRS spectra of the granular samples were recorded every 10 s for 40 min. The calibration models to predict moisture content and APC were constructed based on the corrected NIRS spectral data by partial least-squares regression (PLSR) analysis. The relationships between actual and predicted values for moisture content and APC produced a straight line, respectively. The regression vector (RV) of the PLS model to predict the water content showed the presence of free water between the bead powder particles. On the other hand, the RV for the APC showed the presence of bound water between the particles.

Real-time monitoring and fault detection of pulsed-spray fluid-bed granulation using near-infrared spectroscopy and multivariate process trajectories

Pulsed spray is a useful tool for granule size control in fluid bed granulation. To improve the quality control of pulsed-spray fluid bed granulation, a combination of in-line near-infrared (NIR) spectroscopy and principal component analysis was used to develop multivariate statistical process control (MSPC) charts. Different types of MSPC charts were developed, including principal component score charts, Hotelling's T2 control charts, and distance to model X control charts, to monitor the batch evolution throughout the granulation process. Correlation optimized warping was used as an alignment method to deal with the time variation in batches caused by the granulation mechanism in MSPC modeling. The control charts developed in this study were validated on normal batches and tested on four batches that deviated from normal processing conditions to achieve real-time fault analysis. The results indicated that the NIR spectroscopy-based MSPC model included the variability in the sample set constituting the model and could withstand external variability. This research demonstrated the application of synchronized NIR spectra in conjunction with multivariate batch modeling as an attractive tool for process monitoring and a fault diagnosis method for effective process control in pulsed-spray fluid bed granulation.

Combining convolutional neural networks and in-line near-infrared spectroscopy for real-time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins.

The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in-line near-infrared spectroscopy for notoginsenoside R1 , ginsenosides Rg1 , Re, Rb1 and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in-line near-infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products.

Development and validation of in-line near-infrared spectroscopy based analytical method for commercial production of a botanical drug product

Near-infrared (NIR) spectroscopy is one of the most successful pharmaceutical process analytical tools. For botanical drug products (BDPs), many studies have applied NIR spectroscopy for rapid analysis of botanical raw materials, extracts and formulations. However, the real-time process analysis reported for BDPs was still mainly conducted on lab- or pilot- scale equipment, where some essential conditions of the materials and process parameters can be easily controlled for NIR spectral measurement. Due to the chemical and physical characteristics of the commercial production of BDPs, it is challenging to develop in-line NIR methods with enough robustness for industrial-scale application. This is the first reported case study of the development and validation of the in-line NIR method for the commercial production of BDPs, taking Compound Danshen Dripping Pill (CDDP) as an example. An in-line NIR method was developed for simultaneous measurement of the three critical quality attributes, i.e. the relative density, the moisture content and the content of 3,4-dihydroxyphenyl lactic acid (danshensu, a key active compound), during the extract concentration process. The NIR spectra and sample collection lasted for three years (67 production batches) to cover the variability of raw materials and process conditions. NIR calibration models were established respectively, with determination coefficients (r2) of 0.9905, root mean square errors of prediction (RMSEP) of 0.004 for the relative density in the range of 1.042–1.184; r2 of 0.9870, RMSEP of 1.1% for the moisture content in the range of 50.8–83.0%; r2 of 0.9870, RMSEP of 0.461 mg/g for danshensu in the range of 2.563–8.869 mg/g. Then, all the method validation parameters (accuracy, precision, range, specificity, linearity, robustness, detection and quantitation limits) were discussed according to the characteristics of the commercial production of BDPs. The NIR method development and validation strategy proposed may also be applied in the future for the commercial production of other BDPs.

Real-time monitoring of particle size distribution in a continuous granulation and drying process by near infrared spectroscopy

In continuous granulation, it can be important to control granules particle size distribution (PSD), as it may affect final product quality. Near infrared spectroscopy (NIRS) is already a routine analytical procedure within pharmaceutical continuous manufacturing for the in-line analysis of chemical material-characteristics. Consequently, the extraction of additional information related to granules’ physical properties like particle size distribution is tempting, as it would enhance process knowledge without the need for new capital investments.Three in-line NIRS methods were developed via partial least squares regression, to predict dried granules PSD-fractions X10, X50, and X90 within a GMP-qualified continuous twin-screw wet granulation and fluid-bed drying process. Methods were developed for the size range of 20–234 µm (X10), 98–1017 µm (X50), and 748–2297 µm (X90) and assessed with one internal and three external validation datasets in agreement with current guidelines on NIRS. Internal validation indicated root mean square error of predictions (RMSEPs) of 17 µm, 97 µm, and 174 µm, for PSD X10, X50, and X90 respectively, with acceptable linearity, slope, and bias. Furthermore, the ratio of prediction to deviation (RPD), the ratio of prediction error to laboratory error (PRL), and the range error ratio (RER) were evaluated, with all values within the acceptance range for adequate to good NIR methods (1.75 > RPD < 3, PRL ≤ 2, RER ≥ 10). Methods applicability to in-line processes and their robustness towards water content and active pharmaceutical ingredient content was further demonstrated with three independent in-line datasets in real-time, showing good agreement between predicted and reference values. In summary, methods demonstrated to be sufficient for their intended purpose to monitor trends and sudden changes in dried granules PSD during continuous granulation and drying. Because of their fast response time, they are unique tools to characterize the dynamic behavior and navigate the agglomeration state of the material in static and transient process conditions during continuous granulation and drying.