On-line monitoring of a granulation process by NIR spectroscopy

Quantitative analysis of moisture content and particle size in a fluidized bed granulation process using near infrared spectroscopy and acoustic emission combined with data fusion strategies

Monitoring granule property is essential for fluidization maintenance and product quality control in fluidized bed granulation (FBG). In this study, two non-invasive techniques, near-infrared (NIR) spectroscopy and acoustic emission (AE), were applied for quantitative analysis of moisture content (MC) and median particle size (D50) in a FBG process, combined with chemometrics and data fusion strategies. Partial least squares (PLS) and support vector machine (SVM) regression models were established based on NIR and AE spectral data. The optimal quantitative models were identified considering the effect of spectra preprocessing and variable selection. In the comparison study, the best separate models for MC and D50 quantification were based on NIR and AE, respectively. The NIR model exhibited the better prediction ability with the determination coefficient of validation set (R2v) of 0.9815, root mean square error of validation set (RMSEv) of 0.2226 %, and residual predictive deviation (RPD) of 7.4674 for MC. Meanwhile, the AE model presented the better prediction performance with R2v of 0.9710, RMSEv of 18.2643 μm, and RPD of 5.9740 for D50. Furthermore, among three data fusion strategies, the high-level fusion model achieved the best overall performance on D50 quantification with R2v of 0.9863, RMSEv of 12.5707 μm, and RPD of 8.6798. The results indicated that both NIR and AE are effective monitoring tools for MC and D50 analysis in fluidized bed granulation process. In addition, a more accurate and reliable analysis of particle size can be achieved by combining NIR and AE technology with high-level data fusion.

In-Line Detection of Bed Fluidity in Gas-Solid Fluidized Beds Using Near-Infrared Spectroscopy

A novel approach was developed to detect bed fluidity in gas-solid fluidized beds using diffuse reflectance near-infrared (NIR) spectroscopy. Because the flow dynamics of gas and solid phases are closely associated with the fluidization state, the fluidization quality can be evaluated through hydrodynamic characterization. In this study, the baseline level of NIR spectra was used to quantify the voidage of the fluidized bed. Two indicators derived from the NIR baseline fluctuation profiles were investigated to characterize bed fluidity, named bubble proportion and skewness. To establish a robust fluidity evaluation method, the relationships between the indicators and bed fluidity were investigated under different conditions firstly, including static bed height and average particle size. Then, a generalized threshold was identified to distinguish poor and good bed fluidity, ensuring that the probability of the α- and β-errors was less than 15% regardless of material conditions. The results show that both indicators were sensitive to changes in bed fluidity under the investigated conditions. The indicator of skewness was qualified to detect bed fluidity under varied conditions with a robust threshold of 1.20. Furthermore, the developed NIR method was successfully applied to monitor bed fluidity and for early warning of defluidization in a laboratory-scale fluidized bed granulation process.

A Study of the Reliability and Accuracy of the Real-Time Detection of Forage Maize Quality Using a Home-Built Near-Infrared Spectrometer

The current study was conducted to explore the real-time detection capability of a home-built grating-type near-infrared (NIR) spectroscopy online system to determine forage maize quality. The factor parameters affecting the online NIR spectrum collection were analyzed, and the results indicated that the detection optical path of 12 cm, conveyor speeds of 10 cm s−1, and number of scans of 32 were the optimal parameters. Choosing the crude protein and moisture of forage maize as quality indicators, the reliability of the home-built NIR online spectrometer was confirmed compared with other general research NIR instruments. In addition, an NIR online multivariate analysis model developed using the partial least squares (PLS) method for the prediction of forage maize quality was established, and the reliability, applicability, and stability of the NIR model were further discussed. The results illustrated that the home-built grating-type NIR online system performed satisfying and comparable accuracy and repeatability of the real-time prediction of forage maize quality.

Real-Time Monitoring of Critical Quality Attributes during High-Shear Wet Granulation Process by Near-Infrared Spectroscopy Effect of Water Addition and Stirring Speed on Pharmaceutical Properties of the Granules

To produce high-quality pharmaceuticals, a real-time monitoring method for the high-shear wet granulation process (HSWG) was developed based on near-infrared spectroscopy (NIRS). Samples consisting of lactose, potato starch, and hydroxypropyl cellulose were prepared using HSWG with varying amounts of purified water (80, 90, and 100 mL) and impeller speed (200, 400, and 600 rpm), which produces granules of different characteristics. Twelve batches of samples were used for the calibration and nine batches were used for validation. After drying, the median particle size (D50), tapped density (TD), and Hauser ratio (HR) were measured. The best calibration models to predict moisture content (MC), D50, TD, and HR were determined based on pretreated NIR spectra using partial least squares regression analysis (PLSR). The temporal changes in the pharmaceutical properties under different amounts of water added and stirring speed were monitored in real time using NIRS/PLSR. Because the most important critical quality attribute (CQA) in the process was MC, granule characteristics such as D50, TD, and HR were analyzed with respect to MC. They might be used as robust and simple monitoring methods based on MC to evaluate the pharmaceutical properties of HSWG granules.

Implementation of Dynamic and Static Moisture Control in Fluidized Bed Granulation

The application of process analysis and control is essential to enhance process understanding and ensure output material quality. The present study focuses on the stability of the feedback control system for a fluidized bed granulation process. Two strategies of dynamic moisture control (DMC) and static moisture control (SMC) were established based on the in-line moisture value obtained from the near-infrared sensor and control algorithm. The performance of these strategies on quality consistency control was examined using process moisture similarity analysis and principal component analysis. The stable moisture control performance and low batch-to-batch variability indicated that the DMC method was significantly better than other granulation methods. In addition, the investigation of robustness further showed that the implemented DMC method was able to produce predetermined target moisture values by varying process parameters. This study provides an advanced and simple control method for fluidized bed granulation quality assurance.

Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation

Soft sensors are powerful tools for the implementation of process analytical technology (PAT). They are categorized into white-box (first-principle), black-box (statistical), and gray-box models. Gray-box models integrate white-box and black-box models to address each drawback, i.e., prediction accuracy and intuitiveness. Although they have been applied to various industrial processes, their applicability to water content monitoring in fluidized bed granulation has not been reported. In this study, we evaluated three types of gray-box models, i.e., parallel, serial, and combined gray-box models, in terms of prediction accuracy using real operating data on a commercial scale with two formulations. The gray-box models were constructed by integrating the heat and mass balance model (white-box model) and locally weighted partial least squares regression (LW-PLSR) model (black-box model). LW-PLSR was utilized to cope with collinearity and nonlinearity. In the serial gray-box models, LW-PLSR models adjusted the fitting parameters of the white-box model depending on the process parameters for each query. In the parallel gray-box or combined gray-box models, LW-PLSR models compensated for the output error of the white-box or serial gray-box models, respectively. The results demonstrated that all three types of gray-box models improved the prediction accuracy of the white-box models regardless of the formulation. Besides, we proposed the assessment method based on Hotelling's T² and Q residual for gray-box models using LW-PLSR, which contributes decision support to select gray-box or white-box model. The accurate and descriptive gray-box models are expected to enhance process understanding and precise quality control in fluidized bed granulation.

Using a simple spectrophotometer to analyze cypress hydrolat composition

The Pure Dew (Cypress Hydrolat), which could be extracted from the waste material after the extracting essential oil from Taiwan cypress, has a good bactericidal effect. However, due to the high cost on quality control and concentration measurement of the Pure Dew, its application was restricted. This research tries to find suitable spectral frequencies through which the absorbance detected by the spectrometer could be used as the index of the pure dew concentration. This study used Gas Chromatography-Mass Spectrophotometer (GC-MS) to analyze the composition of Taiwan cypress hydrolat. After obtaining the composition, the raw liquor of cypress hydrolat was diluted to 100, 50, 25 and 0% v/v with pure water. The test samples were then tested by a simple spectrophotometer. After the spectrographic detection of absorbance using a simple spectrophotometer, it is confirmed that the spectrum of wavelength between 205-350 nm is the most representative. The absorptance and the pure dew concentration was roughly in linear relation which suggested that a simple spectrophotometer can be used to develop a low-cost and high.

Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model vs. NIRS-Based Model

Soft sensors play a crucial role as process analytical technology (PAT) tools. They are classified into physical models, statistical models, and their hybrid models. In general, statistical models are better estimators than physical models. In this study, two types of standard statistical models using process parameters (PPs) and near-infrared spectroscopy (NIRS) were investigated in terms of prediction accuracy and development cost. Locally weighted partial least squares regression (LW-PLSR), a type of nonlinear regression method, was utilized. Development cost was defined as the cost of goods required to construct an accurate model of commercial-scale equipment. Eleven granulation lots consisting of three laboratory-scale, two pilot-scale, and six commercial-scale lots were prepared. Three commercial-scale granulation lots were selected as a validation dataset, and the remaining eight granulation lots were utilized as calibration datasets. The results demonstrated that the PP-based and NIRS-based LW-PLSR models achieved high prediction accuracy without using the commercial-scale data in the calibration dataset. This practical case study clarified that the construction of accurate LW-PLSR models requires the calibration samples with the following two features: 1) located near the validation samples on the subspace spanned by principal components (PCs), and 2) having a wide range of variations in PC scores. In addition, it was confirmed that the reduction in cost and mass fraction of active pharmaceutical ingredient (API) made the PP-based models more cost-effective than the NIRS-based models. The present work supports to build accurate models efficiently and save the development cost of PAT.

Development of a Near Infrared Spectroscopy method for the in-line quantitative bilastine drug determination during pharmaceutical powders blending

The Food and Drug Administration (FDA)'s guidelines and the Process Analytical Technology (PAT) approach conceptualize the idea of real time monitoring of a process, with the primary objective of improvement of quality and also of time and resources saving. New instruments are needed to perform an efficient PAT process control and Near Infrared Spectroscopy (NIRS), thanks to its rapid and drastic development of last years, could be a very good choice, in virtue of its high versatility, speed of analysis, non-destructiveness and absence of sample chemical treatment. This work was aimed to develop a NIR analytical method for bilastine assay in powder mixtures for direct compression. In particular, the use of NIR instrumentation should allow to control the bilastine concentration and the whole blending process, assuring the achievement of a homogeneous blend. The commercial tablet formulation of bilastine was particularly suitable for this purpose, due to its simple composition (four excipients) and direct compression manufacturing process. Calibration and validation set were prepared according to a Placket-Burman experimental design and acquired with a miniaturized NIR in-line instrument (MicroNIR by Viavi Solution Inc.). Chemometric was applied to optimize information extraction from spectra, by subjecting them to a Standard Normal Variate (SNV) and a Savitzky-Golay second derivative pre-treatment. This spectra pre-treatment, combined with the most suitable wavelength interval (resulted between 1087 and 1217 nm), enabled to obtain a Partial Least Square (PLS) model with a good predictive ability. The selected model, tried on laboratory and production batches, provided in both cases good assay predictions. Results were confirmed by traditional HPLC (High Performance Liquid Chromatography) API (Active Pharmaceutical Ingredient) content uniformity test on the final product.

Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification

Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.

Use of near-infrared spectroscopy on predicting wastewater constituents to facilitate the operation of a membrane bioreactor

The use of near-infrared (NIR) spectroscopy in wastewater treatment has continuously expanded. As an alternative to conventional analytical methods for monitoring constituents in wastewater treatment processes, the use of NIR spectroscopy is considered to be cost-effective and less time-consuming. NIR spectroscopy does not distort the measured sample in any way as no prior treatment is required, making it a waste-free technique. On the negative side, one has to be very well versed with chemometric techniques to interpret the results. In this study, filtered and centrifuged wastewater and sludge samples from a lab-scale membrane bioreactor (MBR) were analysed. Two analytical methods (conventional and NIR spectroscopy) were used to determine and compare major wastewater constituents. Particular attention was paid to soluble microbial products (SMPs) and extracellular polymeric substances (EPSs) known to promote membrane fouling. The parameters measured by NIR spectroscopy were analysed and processed with partial least squares regression (PLSR) and artificial neural networks (ANN) models to assess whether the evaluated wastewater constituents can be monitored by NIR spectroscopy. Very good results were obtained with PLSR models, except for the determination of SMP, making the model qualitative rather than quantitative for their monitoring. ANN showed better performance in terms of correlation of NIR spectra with all measured parameters, resulting in correlation coefficients higher than 0.97 for training, testing, and validation in most cases. Based on the results of this research, the combination of NIR spectra and chemometric modelling offers advantages over conventional analytical methods.

Quantitative Microscopy: Particle Size/Shape Characterization, Addressing Common Errors Using 'Analytics Continuum' Approach

Particle size/shape characterization of active pharmaceutical ingredient (API) is integral to successful product development. It is more of a correlative property than a decision-making measure. Though microscopy is the only technique that provides a direct measure of particle properties, it is neglected for reasons like non-repeatability and non-reproducibility which is often attributed to a) fundamental error, b) segregation error, c) human error, d) sample randomness, e) sample representativeness etc. Using the "Sucrose" as model sample, we propose "analytics continuum" approach that integrates optical microscope PSD measurements complimented by NIR spectroscopy-based trending analysis as a prescreening tool to demonstrate sample randomness and representativeness. Furthermore, plethora of statistical tests are utilized to infer population statistics. Subsequently, an attribute-based control chart and bootstrap-based confidence interval was developed to monitor product performance. A flowchart to serve as an elementary guideline is developed, which is then extended to handle more complex situations involving API crystallized from two different solvent systems. The results show that the developed methodology can be utilized as a quantitative procedure to assess the suitability of API/excipients from different batches or from alternate vendors and can significantly help in understanding the differences between material even on a minor scale.

Effect of Polymers and Storage Relative Humidity on Amorphous Rebamipide and Its Solid Dispersion Transformation: Multiple Spectra Chemometrics of Powder X-Ray Diffraction and Near-Infrared Spectroscopy

This study aimed to investigate the effect of polymers and storage relative humidity on amorphous rebamipide (RB) and its solid dispersion phase transformation using chemometrics based on multiple datasets. The amorphous RB was prepared using particle mixture and grinding methods with hydroxypropyl cellulose, polyvinylpyrrolidone, and sodium dodecyl sulfate. Prepared amorphous RB and solid dispersion samples were stored under a relative humidity of 30% and 75% for four weeks. Infrared spectra of the dispersion samples suggested that the hydrogen bond network was constructed among quinolinone, carbonyl acid, and amide of RB and other polymers. The dataset combining near-infrared (NIR) spectra and powder X-ray diffractograms were applied to principal component analysis (PCA). The relationship between diffractograms and NIR spectra was evaluated using loadings and the PCA score. The multiple spectra analysis is useful for evaluating model amorphous active pharmaceutical ingredients without a standard sample.

Fluidised bed granulation of two APIs: QbD approach and development of a NIR in-line monitoring method

The study focused on the fluid-bed granulation process of a product with two active pharmaceutical ingredients, intended for coated tablets preparation and further transfer to industrial scale. The work aimed to prove that an accurate control of the critical granulation parameters can level the input material variability and offer a user-friendly process control strategy. Moreover, an in-line Near-Infrared monitoring method was developed, which offered a real time overview of the moisture level along the granulation process, thus a reliable supervision and control process analytical technology (PAT) tool. The experimental design's results showed that the use of apparently interchangeable active pharmaceutical ingredients (APIs) and filler sorts that comply with pharmacopoeial specifications, lead to different end-product critical attributes. By adapting critical granulation parameters (i.e. binder spray rate and atomising pressure) as a function of material characteristics, led to granules with average sizes comprised in a narrow range of 280–320 µm and low non-granulated fraction of under 5%. Therefore, the accurate control of process parameters according to the formulation particularities achieved the maintenance of product within the design space and removed material related variability. To complete the Quality by design (QbD) strategy, despite its limited spectral domain, the microNIR spectrometer was successfully used as a robust PAT monitoring tool that offered a real time overview of the moisture level and allowed the supervision and control of the granulation process.

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.

Process analytical technologies and injectable drug products: Is there a future?

Parametric release was the first subset of real time release testing (RTRT), applied to terminally sterilised injectable drug products. The objective was to offer the industry an alternative to the time and money consuming sterility testing, without compromising the sterility of the products. The rationale was that quality cannot be tested into products, instead it must be planned (the principle of quality by design, QbD). This can be implemented by setting appropriate in-process controls supported on process analytical technologies (PAT). Two of the most versatile and promising PAT tools are the near infrared spectroscopy (NIRS) and the Raman spectroscopy. However, their application to injectable drug product development and manufacturing has been scarce. This review has the objective to provide a framework for the practical implementation of the QbD approach to injectable formulations, including application of diverse risk assessment and factorial design tools. Finally, the actual application of PAT, namely NIRS and Raman spectroscopy, to injectable drug product analysis is addressed.

Accuracy Improvement of In-line Near-Infrared Spectroscopic Moisture Monitoring in a Fluidized Bed Drying Process

An exploratory analysis of a large representative dataset obtained in a fluidized bed drying process of a pharmaceutical powder has revealed a significant correlation of spectral intensity with granulate humidity in the whole studied range of 1091.8–2106.5 nm. This effect was explained by the dependence of powder refractive properties, and hence light penetration depth, on the water content. The phenomenon exhibited a close spectral similarity to the well-known stochastic variation of spectral intensities caused by the process turbulence (the so-called “scatter effect”). Therefore, any traditional scatter-corrective preprocessing incidentally eliminates moisture-correlated variance from the data. To preserve this additional information for a more precise moisture calibration, a time-domain averaging of spectral variables has been suggested. Its application resulted in a distinct improvement of prediction accuracy, as compared to the scatter-corrected data. Further improvement of the model performance was achieved by the application of a dynamic focusing strategy when adjusting the model to a drying process stage. Probe fouling was shown to have a minor effect on prediction accuracy. The study resulted in a considerable reduction of the root-mean-square error of in-line moisture monitoring to 0.1%, which is close to the reference method's reproducibility and significantly better than previously reported results.

A Multivariate Control Chart Approach for Calibration Transfer between NIR Spectrometers for Simultaneous Determination of Rifampicin and Isoniazid in Pharmaceutical Formulation

Background:

Multivariate transfer techniques have become a widely accepted concept over the past few years, since they avoid full recalibration procedures when instruments are changed to analyze a specific sample.

Objective:

This paper reports a multivariate control chart transfer approach between two near infrared (NIR) spectrometers for simultaneous determination of rifampicin and isoniazid in pharmaceutical formu-lation using Direct Standardization (DS).

Method:

The control charts are based on the calculation of Net Analyte Signal (NAS) models and the transfer samples are selected by the Kennard-Stone (KS) algorithm. Three control charts (NAS, interfer-ence and residual) transferred on both the master and slave instruments were measured.

Results:

As a result, a classification model for rifampicin and isoniazid developed on a primary instrument has been successfully transferred to a secondary instrument. The spectral differences after the standardiza-tion procedure were considerably reduced and errors values found in the charts for both analytes were comparable with the errors obtained for the original chart models.

Conclusion:

The proposed approach appears to be a valid alternative to the commonly used transfer of multivariate calibration models in simultaneous determination of isoniazid and rifampicin in pharmaceuti-cal formulation

Matrix systems for oral drug delivery: Formulations and drug release

In this current article matrix formulations for oral drug delivery are reviewed. Conventional dosage forms and novel applications such as 3D printed matrices and aerogel matrices are discussed. Beside characterization, excipients and matrix forming agents are also enlisted and classified. The incorporated drug could exist in crystalline or in amorphous forms, which makes drug dissolution easily tunable. Main drug release mechanisms are detailed and reviewed to support rational design in pharmaceutical technology and manufacturing considering the fact that R&D members of the industry are forced to obtain knowledge about excipients and methods pros and cons. As innovative and promising research fields of drug delivery, 3D printed products and highly porous, low density aerogels with high specific surface area are spreading, currently limitlessly. These compositions can also be considered as matrix formulations.

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.

Near-infrared spectroscopy monitoring and control of the fluidized bed granulation and coating processes-A review

The fluidized bed granulation and pellets coating technologies are widely used in pharmaceutical industry, because the particles made in a fluidized bed have good flowability, compressibility, and the coating thickness of pellets are homogeneous. With the popularization of process analytical technology (PAT), real-time analysis for critical quality attributes (CQA) was getting more attention. Near-infrared (NIR) spectroscopy, as a PAT tool, could realize the real-time monitoring and control during the granulating and coating processes, which could optimize the manufacturing processes. This article reviewed the application of NIR spectroscopy in CQA (moisture content, particle size and tablet/pellet thickness) monitoring during fluidized bed granulation and coating processes. Through this review, we would like to provide references for realizing automated control and intelligent production in fluidized bed granulation and pellets coating of pharmaceutical industry.

Overall uncertainty measurement for near infrared analysis of cryptotanshinone in tanshinone extract

This study presented a new strategy of overall uncertainty measurement for near infrared (NIR) quantitative analysis of cryptotanshinone in tanshinone extract powders. The overall uncertainty of NIR analysis from validation data of precision, trueness and robustness study was fully investigated and discussed. Quality by design (QbD) elements, such as risk assessment and design of experiment (DOE) were utilized to organize the validation data. An "I×J×K" (series I, the number of repetitions J and level of concentrations K) full factorial design was used to calculate uncertainty from the precision and trueness data. And a 2(7-4) Plackett-Burmann matrix with four different influence factors resulted from the failure mode and effect analysis (FMEA) analysis was adapted for the robustness study. The overall uncertainty profile was introduced as a graphical decision making tool to evaluate the validity of NIR method over the predefined concentration range. In comparison with the T. Saffaj's method (Analyst, 2013, 138, 4677.) for overall uncertainty assessment, the proposed approach gave almost the same results, demonstrating that the proposed method was reasonable and valid. Moreover, the proposed method can help identify critical factors that influence the NIR prediction performance, which could be used for further optimization of the NIR analytical procedures in routine use.

Real-time monitoring of granule properties during high shear wet granulation by near-infrared spectroscopy with a chemometrics approach

An in-line near-infrared spectroscopy monitoring method was developed for analyzing granule properties during a high shear wet granulation process.

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.

Near infrared spectroscopic transmittance measurements for pharmaceutical powder mixtures

This study describes the development of near infrared (NIR) calibration models using transmittance measurements in powder samples and compares the results obtained with those of tablet transmittance and diffuse reflectance of powders. Transmission near infrared spectroscopy is a method widely used for the analysis of tablets in the evaluation of drug concentration due to the larger sample volume analyzed, but not commonly used for the analysis of powder samples. Diffuse reflection near infrared spectroscopy is a method used in both powder and tablets for the evaluation of quality attributes. In this initial study NIR transmittance measurements were obtained using an off-line spectrometer equipped with a high intensity light source. Spectra were obtained with three different resolutions for the analysis of powder and tablet samples of 7.50-22.50% (w/w) acetaminophen. The Partial Least Squares (PLS) calibration models developed include pretreatments such as Standard Normal Variate (SNV) and first derivative in the region from 9500-7500 cm(-1). Transmittance in powder presented low Root Mean Square Error of Prediction (RMSEP) values that varied from 0.23-1.15% (w/w) APAP with resolution of 64 and 16 cm(-1). The lowest RMSEP values (0.23-0.39% (w/w) APAP) were obtained using a resolution of 64 cm(-1). The RMSEP values for powder transmittance measurements were 2.4-5.6 times lower than the diffuse reflectance measurements of the powder mixtures.

Discrimination and content analysis of fritillaria using near infrared spectroscopy

Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.