Received 3 May 2007. Revised 25 July 2007. Accepted 18 October 2007. Available online 26 October 2007.Abstract
Continuous process monitoring is desirable for many particulate processes such as the crystallization of active pharmaceutical ingredients. Only an in-line measurement technique can achieve such a continuous monitoring.
A popular in-line measurement technique, which can be applied without dilution, is the focussed beam reflectance measurement (FBRM). However, FBRM is at this point mainly used for qualitative measurements. The measured chord length distribution is different from a particle size distribution. For a quantitative measurement a sound understanding of the measurement principle is necessary. In this paper, an optical model of the FBRM probe and a three-dimensional simulation of the measurement are presented.
A three-dimensional particle field is generated with a Monte-Carlo approach. The back scattered light intensity is calculated as a function of the position of the laser beam with respect to this particle field. A vector of scattering intensities is obtained for a given laser path. This vector is processed with the simulated electronics of the Lasentec FBRM system. The output of this processing step is a chord length distribution which can be compared to the output of the Lasentec FBRM system.
Simulation studies with mono-disperse polystyrene particles of different sizes and concentrations are conducted and compared to measurements of a Lasentec D600L FBRM probe. With the presented model yet unexplained massive over-estimation of small particles and concentration-dependent changes in the chord length distribution can be described.Graphical Abstract
A popular in-line measurement technique, which can be applied without dilution, is the focussed beam reflectance measurement (FBRM). However, FBRM is at this point mainly used for qualitative measurements. For a quantitative measurement a sound understanding of the measurement principle is necessary. In this paper, an optical model of the FBRM probe and a three-dimensional simulation of the measurement are presented.
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Analytical Essay Format
February 7th, 2012Format For A Perfect Analytical Essays
Some cases that involve great thinking in writing an essay will also involve analysis of details in the article. When you use your very own analytical essay format. there are some things that you should consider so that your discussions are most appreciated. Here are some of the parts that are necessary for an analytical essay: Introduction, Body and Conclusion. Now the only thing that will concern you is to integrate analysis or analytical discussions in the body paragraphs.
An analytical essay format should have a thesis statement. This is the primary idea that you wish to incorporate in your essay. The thesis statement should first assert what you have found out or discovered in the process of researching. This will then be subject to analysis in the following paragraph discussions.
In the body paragraphs try to incorporate the details that will give the readers new information out of the discussions. When you say analytical, you are going to provide a clear analysis of the details pertaining to the facts you have assented in the introduction. It is necessary that you argue or persuade in the body discussions so that it will be clear to the audience what you are trying to convey in the research paper. Analysis of parts or the whole topic of interest will be done by simply providing more information than usual.
An analytical essay format should also adhere to the technical aspects of research paper writing. You need to cite references using either APA or MLA formats, include evidences to your claims, discuss the background of each sub-topic and then include in-text citation sentences or paragraphs. All of this will be necessary for a successful analytical essay writing task.
Analytical Essay Format. 6.0 out of 10 based on 1 rating
Laser light scattering technology, as applied in the Lasentec focussed beam reflectance measurement (FBRM) system, was used to characterise two morphologically dissimilar plant cell suspension cultures, Morinda citrifolia and Centaurea calcitrapa. Shake-flask suspensions were analysed in terms of biomass concentration and aggregate size/shape over the course of typical batch growth cycles. For the heavily aggregated C. calcitrapa, biomass levels [from 10â€“160 g fresh weight (fw) l-1)] were linearly correlated with FBRM counts. For M. citrifolia, which grows in unbranched chains of 2â€“10 elongated cells, linear correlation of biomass concentration with FBRM counts was applicable in the range 0â€“100 g fw l-1; at higher levels (100â€“300 g fw l-1), biomass was non-linearly correlated with FBRM counts and length-weighted average FBRM chord length. For both cell systems, particle morphology (size/shape) was quantified using semi-automated digital image analysis. The average aggregate equivalent diameter (C. calcitrapa) and average chain length (M. citrifolia), determined using image analysis, closely tracked the FBRM average chord length. The data clearly demonstrate the potential for applying the FBRM technique for rapid characterisation of plant cell suspension cultures.
Effect of different pH levels, temperature, light intensity and media were tested against the growth of A. alternata under in vitro conditions. The results of experiment indicated that the growth of A. alternata was maximum in pH range of 6.00- 6.50 and temperature range of 25 - 30Â°C. The.
A detailed study was conducted using lab scale spray dryer to produce micro particles using Îº-carrageenan (1 wt.%) as the encapsulation or binding agent by different ratio, Mcore/Mwall (1:1, 1:2, 1:4 and 1:6) at different temperature (90, 100, 120 and 140Â°C). The concentrated noni extract.
The cell suspension cultures were established from leaf, fruit and root explant's of Indian Mulberry (Morinda citrifolia) for the production of medicinally important secondary metabolites, anthraquinones, flavonoids and phenolics and the effect of subculture on these secondary metabolites was.
Synthesis of anthraquinones (AQs) involves the shikimate and 2- C-methyl-D-erythritol 4-phosphate pathways. The proline cycle is linked to the pentose phosphate pathway (PPP) to generate NADPH needed in the first steps of this pathway. The effect of two proline analogs, azetidine-2-carboxylic.
States that noni fruit is useful for preventing a number of health problems including sinus infections, menstrual problems and skin disorders. Countries in which the herbal plant, noni is grown; Traditional medicinal uses of the herb; Results of a number of studies conducted on noni. INSET: 10.
Morinda citrifolia Linn (Rubiaceae) (noni) is a small evergreen tree growing in coastal and forest regions. In traditional plant based medicine, the fruits have been used for diverse medicinal purposes. Detailed physicochemical evaluation of the plant was carried out to observe its microscopic.
To improve root growth and production of bioactive compounds such as anthraquinones (AQ), phenolics, and flavonoids by adventitious root cultures of Morinda citrifolia, the effects of aeration rate, inoculum density, and Murashige and Skoog (MS) medium salt strengths were investigated using a.
The response of plants to salt stress is an extremely complex phenomenon that involves morphological, physiological and biochemical changes, modifying the leaf contents of chlorophyll and carotenoids, among others and affecting plant growth, development and production. An experiment was carried.
The article presents an encyclopedia entry for corn flower or Centaurea cyanus, a member of Asteraceae.
Columbia, MD (PRWEB) January 22, 2009
METTLER TOLEDO invites you to attend a three part complimentary webinar series - Maximize Data Analysis with METTLER TOLEDO FBRM® - presented by Eric Dycus, Particle System Characterization Technology and Application Consultant, beginning on January 27, 2009.
Part I - Mechanisms of Particle Change - Tuesday, January 27
How does FBRM® track particle agglomeration, breakage, attrition, nucleation, growth, and shape. How can users extract a mechanistic understanding of these changes from FBRM® data? Part I will provide examples of particle size distribution changes as characterized by FBRM® and PVM®. Example of particle agglomeration, growth, breakage, and shape change will be presented providing you with a foundation on how various particle changes manifest in the data. By using the correct analysis tools, weighing, and statistics, specific particle changes can be understood and optimized with maximum precision.
Part II - Correlating FBRM® Directly to Process Efficiency and Product Quality - Tuesday, February 10
How can FBRM® be correlated to predict downstream process efficiency or product quality? How can FBRM® be correlated to an offline particle measurement such as laser diffraction, sieving, or microscopy? Part II will provide examples of direct correlations between:
FBRM® data and process efficiency such as filtration, flow properties, or dissolution rates; FBRM® data and product quality such as bulk density, stability, color, and particle size; FBRM® data and offline laser diffraction, the sieve, and image analysis; Caveats to correlation as well as successful correlations and case studies will be discussed.
Part III - Overcoming Pitfalls to FBRM® Data Interpretation - Wednesday, February 25
How do changes in particle system physics affect FBRM® data? The FBRM® measurement principle has inherent sensitivity which can affect results in ways expected by chemists and engineers. Understanding these particle system properties can significantly increase success with FBRM® data interpretation. Part III will discuss specific case studies to maximize data analysis with particles undergoing the following changes: concentration, dilution, segregation, index of refraction, smoothness, brightness, flotation, settling.
At the end of each webinar, there will be an interactive question and answer session providing you with the opportunity to ask questions relevant to your particular application.
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1 State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, China 2 Department of Chemical and Biochemical Engineering, the University of Western Ontario, London, ON N6A 5B9, Canada 3 Engineering Research Center of Seawater Utilization Technology of Ministry of Education, Hebei University of Technology, Tianjin 300130, China
* Author to whom correspondence should be addressed.
Received: 5 January 2012 / Revised: 20 January 2012 / Accepted: 31 January 2012 / Published: 9 February 2012
(This article belongs to the Special Issue Solid Dosage Forms )
Figure 1. X-ray diffraction patterns of carbamazepine Form II and III. "> Figure 2. Differential scanning calorimetric curves of carbamazepine. "> Figure 3. SEM pictures of polymorphic forms of carbamazepine: (a) Form II; (b) Form III. "> Figure 4. Comparison between actual and calculated concentrations. "> Figure 5. The solubility-temperature diagram and fitting curves for carbamazepine of Forms II and III. "> Figure 6. The metastable limits and fitting curves for Carbamazepine of Forms II and III. "> Figure 7. The Raman spectrum of polymorphs at different ratios. "> Figure 8. Development of chord length counts with time during Run 2. "> Figure 9. SEM picture of the sample taken at 20 min in Run 2. Crystals 1 and 2 are considered to be Form II and 3-6 as Form III. "> Figure 10. The comparison between the concentration profiles measured by ATR-FTIR during Runs 1, 2 and 3. "> Figure 11. The conversion ratio of the two forms measured by Raman during Runs 1, 2 and 3. ">
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The objective of this work was to study the polymorphic transformation of carbamazepine from Form II to Form III in 1-propanol during seeded isothermal batch crystallization. First, the pure Form II and Form III were obtained and characterized. Then their solubilities and metastable zone limits were measured by in-situ attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and focused beam reflectance measurement (FBRM). A transition temperature at about 34.2 °C was deduced suggesting the enantiotropic nature of this compound over the studied temperature range. To quantify the polymorph ratio during the transformation process, a new in-situ quantitative method was developed to measure the fraction of Form II by Raman spectroscopy. Successful tracking of the nucleation of the stable form and the transformation from Form II to Form III during isothermal crystallization was achieved by Raman spectroscopy and FBRM. The results from these three in-situ techniques, FBRM, FTIR and Raman were consistent with each other. The results showed a strong dependency on the amount of seeds added during isothermal crystallization.
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).Share & Cite This Article
MDPI and ACS Style
Zhao, Y.; Bao, Y.; Wang, J.; Rohani, S. In Situ Focused Beam Reflectance Measurement (FBRM), Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) and Raman Characterization of the Polymorphic Transformation of Carbamazepine. Pharmaceutics2012. 4. 164-178.
Zhao Y, Bao Y, Wang J, Rohani S. In Situ Focused Beam Reflectance Measurement (FBRM), Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) and Raman Characterization of the Polymorphic Transformation of Carbamazepine. Pharmaceutics. 2012; 4(1):164-178.
Zhao, Yingying; Bao, Ying; Wang, Jingkang; Rohani, Sohrab. 2012. "In Situ Focused Beam Reflectance Measurement (FBRM), Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) and Raman Characterization of the Polymorphic Transformation of Carbamazepine." Pharmaceutics 4, no. 1: 164-178.
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Particle size distributions (PSDs) measured by different techniques, including Sieve Analysis (SA), Image Analysis (IA), Laser Diffraction (LD) and Focused-Beam Reflectance Measurement (FBRM), were investigated. The two compounds selected for the study were spherical glass beads and non-spherical Sodium Chloride. It was observed that the results obtained by different techniques were affected by the particle shape and the type of particle. For spherical particles, the PSDs obtained by SA, IA and LD agreed well but there was a less consistent result among different particle measurement techniques for non-spherical particles. The number based PSDs obtained by IA were converted to equivalent spherical volume based PSDs. It is very complicated to convert the CLDs obtained from FBRM to their corresponding PSDs as it requires complex calculations. The use of different statistics was evaluated to find the most suitable statistics which resemble the PSD results obtained from other techniques. It was confirmed that the square weighted Chord Distribution gives most similar results to those obtained by other techniques. The results obtained from FBRM were complex as its measurements depend on many factors such as its PSDs, particle optical properties and shape.
Particle size distribution (PSD) is an important parameter of many particulate products and it is critical for controlling process efficiency such as filtration rates, flow properties or dissolution rates (Kempkes et al. 2008 ; Hareland, 1994). Within the pharmaceutical industry, the PSD characterization of particles is generally of great concern for evaluating the quality of pharmaceutical products (Tinke et al. 2008). A variety of characteri- zation techniques are used for the determination of PSD in current chemical process industry such as Sieve Analysis (SA), Laser Diffraction (LD), Ultrasonic Attenu ation Spectroscopy (UAS), Image analysis (IA) and focused beam reflectance measurement (FBRM). LD, FBRM and IA are among the most widely used in-process or off-line techniques (Li et al. 2005).
Each technique defines a size of particle in a different way. Sieving is the most common method for measuring size distributions of many types of materials because of its simplicity of use and preparation. Samples are sieved through several square meshes. The sieve defines a particle diameter as whether the particle can pass through the particular mesh size or not (Konert and Vandenberghe, 1997). The significant advantage of SA is that the sieves can test large amounts of particles without being very expensive. However, sieving is very labour-intensive and involves long measurement times. Furthermore, the sieving time for SA is one of several factors which can significantly affect the final results of measurement (Chapeau et al. 2008).
LD estimates volume-based PSD by measuring the forward light-scattering (diffraction) of light from the laser. It is one of the most widely used techniques because of its range of applicability, ease of implementation, broad dynamic range, high reproducibility and speed of measurement (Blott et al. 2006; Li et al. 2005; Tinke et al. 2008). However, a significant challenge in applying LD for on-line particle size measurement is to accommodate the multiple scattering that takes place at the high particle concentrations encountered in some processes (Malvern Instruments Ltd).
A large community of users successfully applies FBRM technology for monitoring, fault detection and quality control of dynamic processes (Kail et al. 2009). The operating principle of FBRM is shown in Figure 1. The measurement principle of FBRM is based on backward light scattering. It uses a focused beam of laser light that scans across a particle passing in front of the probe window to take measurements. When the laser beam intersects the edge of a particle, some of it backscatters to the detector installed in the same probe, and generates a rise in signal in the circuit until it reaches the opposite edge of the particle. The product of rise-time and tangential velocity of the rotating laser beam records the chord length – the straight line between two points on the edge of a particle (Yu et al. 2008). The instrument has benefits for on-line and in-situ measurements in systems with high solid concentrations (Hobbel et al. 1991). However, it is complicated to transform a measured CLD into its corresponding PSD accurately due to the lack of a theoretical analysis of the measurement principle. Empirical correlations between a measured CLD and moments of the PSD are often used (Li et al. 2005).
Figure 1. Measurement principle and schematic drawing of the FBRM probe (Kail et al. 2006). IA is a technique by which a few particles are captured, digitized into a pixel image and then processed so that information can be extracted in the form of PSD (Mora et al. 1998). IA can supply particle shape information and it can also be used to take on-line images. However, a significant number of particle images must be taken to generate good results, which can be time consuming.
Many research articles have reported the comparing of various techniques for PSD measurement using different particles (Hareland, 1994; Pierri et al. 2006; Fernlund, 1997). Nathier-Dufour et al. (1993) compared SA measurements of raw material used in foodstuff with those obtained using LD and found considerable discrepancies between the two methods which is attributable to the uncertainty of particle shape factors of different types of particle (fine, medium, coarse). As pointed out by Eshel et al. (2004), the lack of agreement between techniques could also be due to several sources of error, inherent in the design of different techniques. There is therefore considerable impetus to further study the effect of particle shape on PSD and compare the results obtained from each technique.
The aim of this work is to compare PSDs measured by different techniques and to assess the effect of particle shape on measured PSD results. To engineer this goal, four measurement techniques. (1) Image Analysis, (2) Laser Diffraction, (3) FBRM and (4) Sieve Analysis, were used.
Material and Methods
Sample Selection and Sieving
Two particulate products with different morphologies were selected: Sodium Chloride and Glass Beads. Figure 2 shows the shape of each material observed by microscopy. It can be seen that the Sodium Chloride appears to be in square shape (non-spherical) and Glass Beads are very regular with spherical shape.
Ten fractions of each material differing by their particle size distributions were obtained by sieving (106-125 µm, 125-150 µm, 150-180 µm, 180-212 µm, 212-250 µm, 250-300 µm, 300-355 µm, 355-420 µm, 420-500 µm and 106-500 µm). One more sample was prepared by mixing the same material smaller size particles with the larger size, by the ratio of 1:1 w/w (125-150 µm and 420-500 µm). The whole sample collection therefore included twenty- two samples in range 106-500 µm (two wide range samples, eighteen narrow fractions and two mixing range materials).
In theory, each sphere should just pass through a square mesh aperture which has internal side length dimensions equivalent to the diameter of the sphere, and therefore sieving should provide an accurate physical description of the size distribution of a population of spheres (glass beads).
Figure 2. Microscope images of Glass Beads (a) and Sodium Chloride (b) before image processing
Laser Diffraction (LD). The Beckman Coulter LS-130 was used to measure the PSD of the proposed samples. The instrument measures the forward scattering (diffraction) of light from a single laser which has a wavelength of 750 nm and it uses the newPIDS i.e. Polarization Intensity Differential Scattering method (Beckman Coulter LS-130 user manual). The instrument has 126 photodiode detectors and can measure particles in the size range 0.4 to 900 µm. In all the experiments, the measurements were carried out in suspensions which were prepared in Silicon Oil. The refractive index of 1.49 was used for Silicon Oil. The obscuration of all samples in the LD experiments was maintained in the range 9%- 18%.
Image Analyses (IA). Ernst Leitz, Wetzlar microscope was used for IA. There is an automated camera and a television camera port fitted with the instrument for digital imaging. Images were captured using Aver-TV software. The processing, analysis and measurements were carried out using Photoshop and Image-J software. In the IA experiments, the particles were distributed on the slides and were measured from random orientations. More care was required to make sure the particles are separated completely. Roughly 600- 800 particles of each material in the specified range were analys ed (125-150 µm, 420-500 µm and 106-500 µm) in this study.
The basic procedure followed during the experiment for image processing is explained in Figure 3. The aim of the image processing was to obtain binary images of the individual particles. To achieve this, initial processing was carried out in Photoshop, which included image filtering (sharpening), border killing and debris removing. The images were then processed in Image-J to produce number based PSD of the particles. The number based PSD was converted to the equivalent spherical volume based PSDs to facilitate comparison with the other techniques.
Figure 3 Procedure for Image Processing. The steps within the dashed box represent processing in Image-J. The procedure followed was: Start Image-J; Set Scale for individual lens; Set measurement (required parameters); select Image typeà8-bit; Select BinaryàMake binary; Fill hole; Analyse Particle to get the results.
Focused Beam Reflectance Measurement (FBRM).
A Lasentec (FBRM) was used to measure the chord length distribution (CLD) of samples using a Lasentec D600L FBRM probe. In all the experiments, the measurements were carried out in suspensions which were prepared in Silicon Oil. Sample suspensions were put into a 500 cm3 beaker and agitated using a pitched blade impeller at a speed of 400 rpm so that the sample was well dispersed in the solution. The measured chord count data were classified into 90 logarithmically spaced channels over the particle size range of 1-1000 µm. The data analysis software gives the CLDs in different formats. It has been reported that the conversion of measured CLD to its corresponding PSD is not straightforward (Kempkes et al. 2007; Wynn, 2003). Therefore the most suitable statistics of the measured CLD were directly compared with the PSDs measured by other techniques.
Results and Discussion
Two different particle samples, spherical Glass Beads and non-spherical Sodium Chloride, were used in the experiments. Shape factor is an effective means for the quantification of particle shape. Sphericity (S), a common shape factor, was calculated (for each particle) from image processing and analysed to consider the deviation of the shape of samples from ideal sphere. The means of sphericity are shown below for the Glass Beads (GB) and Sodium Chloride (NaCl), measured by IA.
Table 1. Means of sphericity (S) for Glass Beads (GB) and Sodium Chloride (NaCl) measured by IA.
Figures 4, 5 and 6 show the PSDs obtained from LD instrument in comparison with individual sieved samples. From the results, it can be seen that the two methods give similar particle size range for the spherical glass beads but the LD results shifts towards the larger particle size with non-spherical sodium chloride.
Figure 2. Microscope images of Glass Beads (a) and Sodium Chloride (b) before image processing
The SA does not measure the particle size of an individual particle and it is highly influenced by the particle form. During sieving, the particles in sieves rotate in all directions and hence the particle can pass through the sieves diagonally (along the least cross-sectional area of the particle). In contrast, the LD measures the particle size of an individual particle and hence LD results are shifted towards the larger particle size for non-spherical particles.
Figures 7, 8 and 9 summarize the variations in the PSDs obtained using IA and LD for different ranges of sieved samples. The results obtained from the LD instrument have been directly used for comparison, whereas the conversion has been done from a number-based PSD to volume-based PSD for the IA measurements. The IA and LD gave very similar results for spherical particles, as these can reasonably accurately show relative volume fractions for individual particle sizes. IA produced the narrowest PSD as compared to other techniques which could be the result of limitation of image processing and analysis software used.
The FBRM results were obtained by converting the number-based CLD to its volume-weighted CLD without considering the translation of CLDs into PSDs. The use of different statistics was evaluated to find the most suitable statistics which resemble the PSD results obtained from other techniques.
The distributions in Figures 10 and 11 show U n weighted (UW), Length-weighted (LW), Square- weighted (SW) and Cube-weighted (CW) Chord Distributions. The Weighted Chord Distribution (WCD) was obtained via:
w here n i is the average number of count in a channel and M i is midpoint of the channel size. T able 2 below shows how different types of weight procedures were used to calculate weighted distribution by varying γ:
By comparing, it can be confirmed that the square weighted Chord Distribution gives most similar results to those obtained by other techniques for the same individual sieved sample (125 -150 µm).
In Figures 12 and 13, the square-weighted Chord Length Distribution (SWCLD) by FBRM is plotted with the PSDs obtained by the other techniques, and it can be seen that the results still have some discrepancies between them. The parameters, such as the particles’ optical properties, dispersion of particles, the solvent used and the conditions of operation (such as stirring speed) can influence the results obtained from FBRM. It has also been proven that transparent particles (such as glass beads) do not possess either sufficient pulse strength or a short enough rise time, and therefore the FBRM instrument is proven to undersize the particles (Sparks and Dobbs, 1993; Tadayyon and Rohani, 1998).
Measuring the particle size is complex, because the techniques used to measure the PSD do not ‘directly’ measure the axial dimensions of the particles and the particle shape can affect the results obtained from these techniques. For spheres, the PSDs obtained by IA, LD and SA agree well, whereas the results are less consistent for non-spherical particles. SA is dependent on the overall particle shape, in particular the particles’ minimum projected area, and hence the resulting PSD can give a wider range than expected for non-spherical particles. In contrast to other techniques IA and LD give better estimation of several parameters that can be used to measure the PSD in a more refined way. Conversion among PSDs ob tained by these techniques (such as from number based to volume based in IA) is possible and can be done using different models/software.
CLD was obtained by FBRM, which is complex as it is dependent not only on PSD but also on particle optical properties. Therefore caution must be exercised when measuring the CLD of transparent particles (such as glass beads) as the instrument can undersize the particles. For simplicity, the conversion of CLD into its corresponding PSD was not done as it requires complex calculation and varying models. H owever this translation is necessary in many practical applications.
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