Near-infrared spectral imaging for quality assurance of pharmaceutical products: Analysis of tablets to assess powder blend homogeneity (original) (raw)
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Vibrational Spectroscopy, 2012
This study demonstrated a novel application of near-infrared chemical imaging (NIR-CI) for monitoring the blending process of Yinhuang powder. An experiment design was created including eight intermediates. The blending process was executed at different rotation velocities and ended at various time points. Baicalin (BAI) of Yinhuang powder could be spatially determined by the method of basic analysis of correlation between analytes (BACRA). However, starch (STA) and Lonicera japonica extract (LJE) could not be identified by the BACRA method due to high correlation coefficients of each other. Subsequently, characteristic wavenumbers were used to generate a RBG image, which indicated the distribution of the BAI (red), LJE (green) and STA (black). Furthermore, the homogeneity of BAI distribution in a ternary system during the blending process was measured by histogram analysis and moving block macropixel relative standard deviation (MBMRSTDEV). In the histogram analysis, the standard deviation decreased from 0.171 to 0.032 with increased blending time, which indicated that the Yinhuang powder gradually became homogeneous. However, other statistical parameters such as kurtosis and skewness were difficult to be used in understanding the blending process of Yinhuang powder. MBMRSTDEV was introduced as a suitable approach to evaluate the homogeneity. The result indicated that the blending process of the powder experienced different blending stages. The MBMRSTDEV parameter could provide an advantage in visualizing the trend of blending process. These results highlighted a promising technology to extract critical process information and provided essential process knowledge of the blending process of Yinhuang powder.
2007
This study investigates the use of a laboratory scale blender fitted with a near infrared probe to monitor lubricant uniformity in a granule blend. A software method was developed to monitor the change in absorbance at significant wavelengths for the granule and lubricant (magnesium stearate) as the blend proceeded in real-time. The standard deviation of the absorbance was plotted as a function of time to monitor the change in the blend. With near infrared spectra, when a process is complete, the spectra will not change, therefore the standard deviation will be small [6]. To verify this, the blend was sampled using a standard sampling method and analyzed with an atomic absorption method for magnesium stearate to ascertain the distribution in the blend. Blends sampled at the predetermined time intervals were well blended when the standard deviation of the absorbance was low and poorly blended when the standard deviation of the absorbance was high, thus verifying the near infrared pre...
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.
Analytical control of pharmaceutical production steps by near infrared reflectance spectroscopy
Analytica Chimica Acta, 1999
A method for the analytical control of different pharmaceutical production steps involving various types of sample (blended products, cores and coated tablets) is proposed. The measurements are made by using a near infrared (NIR) diffuse re¯ectance spectrophotometer furnished with a ®bre-optic module that enables expeditious,¯exible analyses with no sample manipulation.
Characterization of pharmaceutical powder blends using in situ near-infrared chemical imaging
Chemical Engineering Science, 2014
This study demonstrates the capabilities of NIR imaging as an effective tool for characterization of pharmaceutical powder blends. The powder system used in this small-scale powder blending study consists of acetaminophen (APAP, the model API), microcrystalline cellulose (MCC) and lactose monohydrate. Mixtures of these components were blended for different times for a total of ten time points (ten blending trials). Images collected from multiple locations of the blends were used to generate a qualitative description of the components' blending dynamics and a quantitative determination of both the blending end point and the distribution variability of the components. Multivariate analyses, including pure-component PCA and discriminate PLS, were used to treat the imaging data. A good correlation was observed between imaging results and a UV-Vis monitoring method for determining blend homogeneity. Score images indicated general trends of the distribution of blending constituents for all ten blending trials. The API distribution pattern throughout blending was detected and the API domain size for different blending trials was compared. Blending insights obtained from this study may be transferable to large scale powder blending. Blending process understanding obtained from this study has the potential to facilitate the optimization of blending process control in the future.