A New Rapid On-Line Imaging Method to Determine Particle Size Distribution of Granules

Närvänen, Tero; Seppälä, K.; Antikainen, Osmo; Yliruusi, Jouko

doi:10.1208/s12249-008-9043-y

Cited by 38 publications

(18 citation statements)

References 17 publications

(13 reference statements)

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“…As a result, research has been done on at-line, on-line and in-line particle size measuring tools. The use of on-line image analysis for granule size determination was initiated by Watano et al [4][5][6][7][8][9] and more recently investigated by Naervanen et al [10]. As a near infrared (NIR) spectrum is dependent on the chemical and physical composition and properties of a measured sample, NIR has been extensively investigated for the fast and non-destructive (at-line, online or in-line) particle size monitoring during granulation, usually combined with moisture content determination [11][12][13][14][15][16].…”

Section: Evaluation Of In-line Spatial Filter Velocimetry As Pat Monimentioning

confidence: 99%

Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation

Burggraeve

Kerkhof²,

Hellings³

et al. 2010

European Journal of Pharmaceutics and Biopharmaceutics

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In this study, the feasibility of spatial filter velocimetry (SFV) as Process Analytical Technology tool for the in-line monitoring of the particle size distribution during top spray fluidized bed granulation was examined. The influence of several process (inlet air temperature during spraying and drying) and formulation variables (HPMC and Tween 20 concentration) upon the particle size distribution during processing, and the end product particle size distribution, tapped density and Hausner ratio was examined using a design of experiments (DOE) (2-level full factorial design, 19 experiments). The trend in end granule particle size distributions of all DOE batches measured with in-line SFV was similar to the offline laser diffraction (LD) data. Analysis of the DOE results showed that mainly the HPMC concentration and slightly the inlet air temperature during drying had a positive effect upon the average end granule size. The in-line SFV particle size data, obtained every 10 s during processing, further allowed to explain and better understand the (in)significance of the studied DOE variables, which was not possible based on the LD data as this technique only supplied end granule size information. The variation in tapped density and Hausner ratio among the end granules of the different DOE batches could be explained by their difference in average end granule size. Univariate, multivariate PLS and multiway N-PLS models were built to relate these end granule properties to the in-line measured particle size distribution. The multivariate PLS tapped density model and the multiway N-PLS Hausner ratio model showed the highest R 2 values in combination with the lowest RMSEE values (R 2 of 82% with an RMSEE of 0.0279 for tapped density and an R 2 of 52% with an RMSEE of 0.0268 for Hausner ratio, respectively).

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Section: Evaluation Of In-line Spatial Filter Velocimetry As Pat Monimentioning

confidence: 99%

Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation

Burggraeve

Kerkhof²,

Hellings³

et al. 2010

European Journal of Pharmaceutics and Biopharmaceutics

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show abstract

“…Dynamic digital image processing (Camsizer, Retsch Technology) was used by Patchigolla and Wilkinson [9] for the size and shape monitoring during glutamate-crystallization. Naervanen et al [10] reconstructed threedimensional topographic images by means of red, green and blue led light illumination. These topographic data were then used to calculate the size of separate granules.…”

Section: Introductionmentioning

confidence: 99%

Prediction of quality attributes of continuously produced granules using complementary pat tools

Fonteyne

Soares

Vercruysse

et al. 2012

European Journal of Pharmaceutics and Biopharmaceutics

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Manufacturers of pharmaceutical solid dosage forms aim for a reduced production time and a shorter 'time-to-market'. Therefore continuous manufacturing gains increasing interest in the pharmaceutical industry. For continuous manufacturing, the quality of produced pharmaceuticals should be assessed in real-time (in-line, on-line and at-line) and not via the traditional off-line, often destructive and time-consuming analysis methods which supply the desired information only hours after sampling. This research paper evaluates three Process Analytical Technology (PAT) tools for the real-time at-line analysis of granules, which were produced using a continuous wet twin-screw granulator being part of a from powder-to-tablet production line (ConsiGma™-25). A Raman and NIR spectrometer were used together with a photometric imaging technique in order to acquire solid state information and granule size data. These multivariate data were then used to predict the granules' moisture content, tapped and bulk density and flowability. The three PAT tools provided complementary information for predicting these quality attributes of the continuously produced granules. The residual moisture content was mostly correlated with the spectroscopic data, whereas the imaging-data had the highest predictive capability for the flowability of the granules.

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“…Hence, the change in PSD during granulation gives a direct indication of the batch progress and batch quality. Several techniques have been evaluated to monitor PSD during granulation in real-time: near infrared spectroscopy (Alcala et al, 2010;Findlay et al, 2005;Frake et al, 1997;Goebel and Steffens, 1998;Kaddour and Cuq, 2009;Luukkonen et al, 2008;Tok et al, 2008), focused beam reflectance method (Hu et al, 2008;Huang et al, 2010;Tok et al, 2008), spatial filter velocimetry (SFV) (Burggraeve et al, 2010;Lipsanen et al, 2008;Narvanen et al, 2008Narvanen et al, , 2009), image analysis (Laitinen et al, 2004;Naervanen et al, 2008;Watano and Miyanami, 1995;Watano et al 1996Watano et al , 1997Watano et al , 2000Watano et al , 2001Watano, 2001) and acoustic emission monitoring (AE) (Gamble et al, 2009;Halstensen et al, 2006;Matero et al, 2009;Tok et al, 2008;Whitaker et al, 2000). Each technique has its own advantages and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements

Burggraeve

Kerkhof²,

Hellings³

et al. 2011

European Journal of Pharmaceutical Sciences

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Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model.

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A New Rapid On-Line Imaging Method to Determine Particle Size Distribution of Granules

Cited by 38 publications

References 17 publications

Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation

Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation

Prediction of quality attributes of continuously produced granules using complementary pat tools

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements

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