2.5.4 Reliable Online-Prediction of Characteristic Process Parameters by FTNIR-Spectroscopic Analysis (original) (raw)
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Chemometrics in process analytical technology
Archives of Applied Science Research, 2015
The requirement for enhancing healthcare products is eternally increasing. In this regard, the requisite for monitoring all the physical and chemical attributes during the manufacturing of the health care products with a lesser time is truly essential. A new tool, Process analytical technology (PAT) is used to monitor and control critical process parameters in materials and in-process products to maintain the critical quality attributes and build quality into the product. Process Analytical Technology checks the quality of the materials on-line, which saves a huge amount of time and facilitates rapid testing through direct sampling without any destruction of sample. However, to successfully adapt PAT tools into pharmaceutical and biopharmaceutical environment, thorough understanding of the process is needed along with mathematical and statistical tools to analyze large multidimensional spectral data generated by PAT tools. Chemometrics is a chemical discipline which incorporates bot...
Applied Spectroscopy, 2003
A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advanta...
An infrared spectrometer for process monitoring II, chemometry and automatization
2010
We describe a hard and software architecture for process control applications in the chemical, pharamceutical and food industries based on spectroscopic measurements. We argue for the tight integration of the spectrometer itself, the data analysis software and the measurement automatization to achieve situation awareness and predictible real-time behavior and to be able to handle complicated sampling situations, while keeping the programming effort for an individual installation low.
Talanta, 2012
Process Analytical Technology (PAT) is playing a central role in current regulations on pharmaceutical production processes. Proper understanding of all operations and variables connecting the raw materials to end products is one of the keys to ensuring quality of the products and continuous improvement in their production. Near infrared spectroscopy (NIRS) has been successfully used to develop faster and non-invasive quantitative methods for real-time predicting critical quality attributes (CQA) of pharmaceutical granulates (API content, pH, moisture, flowability, angle of repose and particle size). NIR spectra have been acquired from the bin blender after granulation process in a non-classified area without the need of sample withdrawal. The methodology used for data acquisition, calibration modelling and method application in this context is relatively inexpensive and can be easily implemented by most pharmaceutical laboratories. For this purpose, Partial Least-Squares (PLS) algorithm was used to calculate multivariate calibration models, that provided acceptable Root Mean Square Error of Predictions (RMSEP) values (RMSEP API ¼ 1.0 mg/g; RMSEP pH ¼ 0.1; RMSEP Moisture ¼ 0.1%; RMSEP Flowability ¼ 0.6 g/s; RMSEP Angle of repose ¼1.71 and RMSEP Particle size ¼ 2.5%) that allowed the application for routine analyses of production batches. The proposed method affords quality assessment of end products and the determination of important parameters with a view to understanding production processes used by the pharmaceutical industry. As shown here, the NIRS technique is a highly suitable tool for Process Analytical Technologies.
Spectroscopic on-line monitoring of reactions in dispersed medium: Chemometric challenges
Analytica Chimica Acta, 2007
Emulsion and suspension polymerizations are important industrial processes for polymer production. The end-user properties of polymers depend strongly on how the polymerization reactions proceed in time (i.e. a batch or semicontinuous, rate of reagents feeding, etc.). In other words, these reactions are process dependent, which makes the successful process control a key point to ensure high-quality products. In several process control strategies the on-line monitoring of reaction performance is required. Due to the multiphase nature of the emulsion and suspension processes, there is a lack of sensors to perform successful on-line monitoring. Near infrared and Raman spectroscopies have been pointed out as useful approaches for monitoring emulsion and suspension polymerizations and several applications have been described. In such instance, the chemometric approach on relating near infrared and Raman spectra to polymer properties is widely used and has proven to be useful. Nevertheless, the multiphase nature of emulsion and suspension polymerizations also represents a challenge for the chemometric approach based on multivariate calibration models and demands the development of new methods. In this work, a set novel results is presented from the monitoring of 15 batch emulsion reactions that show the chemometric challenge to be faced on development of new methods for successful monitoring of processes taken under dispersed medium. In order to discuss these results, several chemometric approaches were revised. It is shown that Raman and NIR spectroscopic techniques are suitable for on-line monitoring of monomer concentration and polymer content during the polymerizations, as well as medium heterogeneity properties, i.e. average particle size. It is also shown that Hotteling and Q statistics, widely used in chemometrics, might fail in monitoring these reactions, while an approach based on principal curves is able to overcome such restriction.
Near-Infrared Spectroscopy as a Process Analytical Tool Part I: Laboratory Applications
2003
Background: The active ingredients and thus pharmacological efficacy of traditional Chinese medicine (TCM) at different degrees of parching process vary greatly. Objective: Near-infrared spectroscopy (NIR) was used to develop a new method for rapid online analysis of TCM parching process, using two kinds of chemical indicators (5-(hydroxymethyl) furfural [5-HMF] content and 420 nm absorbance) as reference values which were obviously observed and changed in most TCM parching process. Materials and Methods: Three representative TCMs, Areca (Areca catechu L.), Malt (Hordeum Vulgare L.), and Hawthorn (Crataegus pinnatifida Bge.), were used in this study. With partial least squares regression, calibration models of NIR were generated based on two kinds of reference values, i.e. 5-HMF contents measured by high-performance liquid chromatography (HPLC) and 420 nm absorbance measured by ultraviolet-visible spectroscopy (UV/Vis), respectively. Results: In the optimized models for 5-HMF, the root mean square errors of prediction (RMSEP) for Areca, Malt, and Hawthorn was 0.0192, 0.0301, and 0.2600 and correlation coefficients (R cal) were 99.86%, 99.88%, and 99.88%, respectively. Moreover, in the optimized models using 420 nm absorbance as reference values, the RMSEP for Areca, Malt, and Hawthorn was 0.0229, 0.0096, and 0.0409 and R cal were 99.69%, 99.81%, and 99.62%, respectively. Conclusions: NIR models with 5-HMF content and 420 nm absorbance as reference values can rapidly and effectively identify three kinds of TCM in different parching processes. This method has great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online analysis and quality control in TCM industrial manufacturing process.
Chemometrics and Intelligent Laboratory Systems, 2021
The Process Analytical Technology (PAT) initiative promoted by the Food and Drug Administration (FDA) encourages pharmaceutical companies to increase the use of new analytical technologies to perform constant monitoring of the critical quality attributes (CQA), allowing a better understanding and a better control of the process. This paper presents a practical framework based on different dimension-reduction methods as well as calibration methods aimed at following over time chemical experiments organized in batches. To illustrate it, this paper uses pharmaceutical data collected in a research and development context towards industrial production. This methodological framework aims to reach two objectives. The first objective is to visualize and interpret in real time, or off-line, the kinetics of chemical reactions using the following dimension-reduction methods: principal component analysis (PCA), non-negative matrix factorization (NMF) and multivariate curve resolution (MCR). The results show that, due to their additional constraints, NMF and MCR allow a better interpretability of chemical reactions than PCA with a comparable quality of fit. Moreover, eventough NMF and MCR come from different fields, their algorithms share many similarities and produce close results. The second objective is to predict chemical component concentrations over time. For this second objective, the partial least squares regression (PLSR) is used in a one-step approach and compared with a two-step approach combining multivariate regression with PCA, NMF or MCR. The results show that spectra or scores obtained from unsupervised approaches PCA, NMF or MCR can be used to predict concentrations of the main chemical compounds continuously over all the time of the reaction with a good precision and with a gain of interpretability. For both objectives, possible model validation indices are also discussed including a leave-onebatch-out approach.
Optical spectroscopic measurements in the UV-Vis and IR ranges were performed in model solutions of aqueous and organic phases of the PUREX process for spent nuclear fuel (SNF) reprocessing. Chemometric processing of the spectral data with PLS (partial least squares) regression allowed simultaneous quantification of several key components (uranium, neptunium, plutonium, nitric acid) in these mixtures in an effective and elegant way. The content of all key components was quantitively determined with mean relative errors not exceeding 10%. It was shown that the employment of the whole spectra or their certain continuous regions for a PLS calibration enables to decrease the analytical errors compared to the use of a single wavelength in an ordinary least squares approach. The results of this research imply that the development of on-line techniques for SNF reprocessing monitoring is fully possible and can be based on optical spectroscopy methods combined with multivariate data processing techniques.
A New Index to Detect Process Deviations Using IR Spectroscopy and Chemometrics Process Tools
Food and Bioprocess Technology, 2023
Process analytical technologies (PATs) have transformed the beverage production management by providing real-time monitoring and control of critical process parameters through non-destructive measurements, such as those obtained with infrared (IR) spectroscopy and enabling process readjustment if necessary. New requirements in the analysis of beverages call for new methods, so in this article, we propose a method based on the construction of multivariate statistical process control (MSPC) charts from a new dissimilarity index (the evolving window dissimilarity index, EWDI) to monitor fermentation processes. The EWDI was applied to monitor wine alcoholic fermentation, the biochemical transformation of sugars into ethanol. Small-scale fermentations were carried out and analyzed using a portable mid-infrared spectrometer. In some of them, process deviations due to nitrogen deficiency or temperature changes were intentionally promoted to evaluate the performance of the EWDI. The MSPC charts build by using the fermentations carried out under normal operating conditions allowed identifying deviations of the fermentation in its early stages. Furthermore, the shape of the EWDI curve over time provides insights about the specific type of deviation occurring. These results show the potential of this new approach to improve the monitoring and control of key process stages in biochemical processes in the food industry, which allows maximizing quality and minimizing losses.