In-line monitoring of the drug content of powder mixtures and tablets by near-infrared spectroscopy during the continuous direct compression tableting process

Järvinen, Kristiina; Hoehe, Wolfgang; Järvinen, Maiju; Poutiainen, Sami; Juuti, Mikko; Borchert, Sven

doi:10.1016/j.ejps.2012.12.032

Cited by 106 publications

(47 citation statements)

References 24 publications

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“…So, it is of great value to identify these differences in pharmaceutical supervision. Near infrared spectroscopy (NIRS) analysis technology can be used for quick and nondestructive detection of the drugs and has been widely used in the pharmaceutical industry, [1][2][3] for it has abundant frequency doubling and frequency vibration information in molecular groups.…”

Section: Introductionmentioning

confidence: 99%

Deep belief network-based drug identification using near infrared spectroscopy

Yang

Pan

et al. 2017

J. Innov. Opt. Health Sci.

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Near infrared spectroscopy (NIRS) analysis technology, combined with chemometrics, can be e®ectively used in quick and nondestructive analysis of quality and category. In this paper, an e®ective drug identi¯cation method by using deep belief network (DBN) with dropout mechanism (dropout-DBN) to model NIRS is introduced, in which dropout is employed to overcome the over¯tting problem coming from the small sample. This paper tests proposed method under datasets of di®erent sizes with the example of near infrared di®use re°ectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs, aluminum and nonaluminum packaged. Meanwhile, it gives experiments to compare the proposed method's performance with back propagation (BP) neural network, support vector machines (SVMs) and sparse denoising autoencoder (SDAE). The results show that for both binary classi¯cation and multi-classi¯cation, dropout mechanism can improve the classi¯cation accuracy, and dropout-DBN can achieve best classi¯cation accuracy in almost all cases. SDAE is similar to dropout-DBN in the aspects of classi¯cation accuracy and algorithm stability, which are higher than that of BP neural network and SVM methods. In terms of training time, dropout-DBN model is superior to SDAE model, but inferior to BP neural network and SVM methods. Therefore, dropout-DBN can be used as a §

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Section: Introductionmentioning

confidence: 99%

Deep belief network-based drug identification using near infrared spectroscopy

Yang

Pan

et al. 2017

J. Innov. Opt. Health Sci.

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“…Järvinen et al [41] monitored not only the individual tablets on the tablet press but also the powder stream coming from a continuous mixer prior to tableting. Both calibration and test-run spectra were collected inline.…”

Section: Direct Compressionmentioning

confidence: 99%

“…%API NIR In-line in the tablet press, measuring each individual tablet * Due to the large amount of data, only each 20th spectrum was taken into account [41] %API NIR Through a Plexi-glass observation window in the feed frame * NIR predictions were influenced by the paddle wheel rotational speed [42] …”

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confidence: 99%

Process Analytical Technology for continuous manufacturing of solid-dosage forms

Fonteyne

Vercruysse

Leersnyder

et al. 2015

TrAC Trends in Analytical Chemistry

144

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Currently, pharmaceutical production is making the switch from batch processing towards continuous processing. The quality of intermediate and end products produced by batch processes is assured by off-line testing. It is obvious that off-line tests in analytical laboratories cancel out the advantages of continuous processing, so the critical quality attributes of continuously produced pharmaceuticals need to be monitored in real time. In 2004, the US Food and Drug Administration launched the process analytical technology (PAT) concept to stimulate the pharmaceutical industry to change from off-line to real-time quality testing. This review explores the implementation of PAT tools within continuous pharmaceutical processes (i.e., blending, spray drying, roller compaction, twin-screw granulation and compression), focusing on both opportunities and challenges.

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“…There are many reports on drug uniformity of the pharmaceutical products during the ordinary mixing process by high performance liquid chromatography 23 and ultraviolet spectroscopy. 24 In particular, one of the most accurate and effective methods to characterize drug content uniformity during pharmaceutical manufacturing processes is partial least squares regression (PLSR) analysis using NIR spectra. However, in order to construct drug production processes with high uniformity, it is required to have an NIR calibration model based on many standard samples and with sufficient mixing time using an ordinary blend mixer.…”

Section: Introductionmentioning

confidence: 99%

In-line and Real-time Monitoring of Resonant Acoustic Mixing by Near-infrared Spectroscopy Combined with Chemometric Technology for Process Analytical Technology Applications in Pharmaceutical Powder Blending Systems

et al. 2017

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Resonant acoustic® mixing (RAM) technology is a system that performs high-speed mixing by vibration through the control of acceleration and frequency. In recent years, real-time process monitoring and prediction has become of increasing interest, and process analytical technology (PAT) systems will be increasingly introduced into actual manufacturing processes. This study examined the application of PAT with the combination of RAM, near-infrared spectroscopy, and chemometric technology as a set of PAT tools for introduction into actual pharmaceutical powder blending processes. Content uniformity was based on a robust partial least squares regression (PLSR) model constructed to manage the RAM configuration parameters and the changing concentration of the components. As a result, real-time monitoring may be possible and could be successfully demonstrated for in-line real-time prediction of active pharmaceutical ingredients and other additives using chemometric technology. This system is expected to be applicable to the RAM method for the risk management of quality.

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In-line monitoring of the drug content of powder mixtures and tablets by near-infrared spectroscopy during the continuous direct compression tableting process

Cited by 106 publications

References 24 publications

Deep belief network-based drug identification using near infrared spectroscopy

Deep belief network-based drug identification using near infrared spectroscopy

Process Analytical Technology for continuous manufacturing of solid-dosage forms

In-line and Real-time Monitoring of Resonant Acoustic Mixing by Near-infrared Spectroscopy Combined with Chemometric Technology for Process Analytical Technology Applications in Pharmaceutical Powder Blending Systems

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