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On-Line Classification of Mixed Co-Crystal and Solute Suspensions using Raman Spectroscopy Fei Sheng, Pui Shan Chow, Zai Qun YU, and Reginald B. H. Tan Org. Process Res. Dev., Just Accepted Manuscript • DOI: 10.1021/acs.oprd.6b00123 • Publication Date (Web): 26 May 2016 Downloaded from http://pubs.acs.org on June 1, 2016

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Organic Process Research & Development

On-Line Classification of Mixed Co-Crystal and Solute Suspensions using Raman Spectroscopy Fei Sheng, †,*, Pui Shan Chow†, Zai Qun Yu†, Reginald B. H. Tan†‡,* †

Institute of Chemical and Engineering Sciences, A*STAR (Agency for Science, Technology and Research), 1 Pesek Road, Jurong Island, Singapore 627833, Singapore

‡

Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 119260, Singapore

Key Words, co-crystal, PAT, Raman spectroscopy, crystallization, principal component analysis

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Abstract

This work seeks to develop a calibration-free statistical approach based on Raman spectroscopy for on-line identification of impurities during co-crystallization. Caffeine-glutaric acidacetonitrile was employed as the model system, which forms co-crystals from solution at appropriate conditions. Raman spectra were collected in three classes of suspensions with a solid mixture of caffeine crystals and co-crystals, pure co-crystals, and a mixture of glutaric acid crystals and co-crystals, respectively at different temperatures. These suspensions were used to represent the possible products of co-crystallization processes during which single component could crystallize out concomitantly with the desired co-crystal. A statistical model combining principal component analysis (PCA) and discriminant analysis (DA) was developed to classify these suspensions. PCA was first performed for these spectra and the resulting first few principal components were subjected to DA. It was found that the three classes of suspensions can be distinguished clearly by DA.



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Introduction Pharmaceutical co-crystals are defined as stoichiometric molecular complexes comprising an active pharmaceutical ingredient (API) and a co-former in a crystal lattice bound by noncovalent interactions, predominantly hydrogen bonds. 1 In contrast to salts, co-crystals can be formed by API with neutral components. Compared with APIs themselves, pharmaceutical co-crystals have advantages in solid-state properties, such as solubility, melting point, chemical interaction, stability and bioavailability.

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Co-crystals thus offer the pharmaceutical industry an alternative

route to obtain the desired physicochemical properties of products. 3 Pharmaceutical co-crystals can be obtained by various approaches, such as melt crystallization, solid-state grinding,

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solution-mediated phase transformation

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and solution crystallization.

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Solution crystallization is an important preparation method that can be implemented in existing crystallizers and is suitable for large-scale production. Typical solution co-crystallization modes include solvent evaporation, 7 cooling 8 and anti-solvent addition. 9 Among these crystallization processes, cooling crystallization is the most widely used due to its relative simplicity. However, crystals of constituent components, either API or co-former, may crystallize out concomitantly with co-crystals due to operational fluctuations in starting composition or temperature, compromising the purity of co-crystals. 10-11 Therefore, it is desirable to detect crystals of single components in a real time manner, whereby corrective measure can be taken to ensure co-crystal purity. Raman spectroscopy is a fast and reliable process analytical technology (PAT) that can be used to identify crystal forms in drug products and processes. It has many advantages in terms of operational simplicity, such as non-destructiveness, little sample preparation and instantaneous


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response. Raman spectroscopy has been successfully used for on-line monitoring of polymorphic transformation processes of single component crystal. 12-15 A lot of efforts have been spent on the application of Raman spectroscopy to polymorphic crystallization processes. It has been found that Raman spectra can be influenced by particle size, temperature, solid concentration, etc. 16 A few groups have tried to calibrate Raman spectra to measure polymorphic composition of crystal suspensions. Wang et al. quantitatively determined the polymorphic transformation of progesterone using in-line Raman spectroscopy.

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Some research groups combined Raman

spectroscopy with different PATs to monitor and control the process of polymorphic transformation, such as combining Raman and ATR-FTIR spectroscopies Raman and ATR-UV/Vis spectroscopies.

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14

or combining

With the help of multivariate analysis, Cornel et al.

successfully determined solute concentration and solid phase transformation simultaneously using Raman spectroscopy.

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However, there are only a few reports of co-crystallization

monitoring using Raman. Lee et al. monitored the co-crystallization of salicylic acid-4,4’dipridyl in solution using Raman spectroscopy based on a univariate model. 8 An anti-solvent cocrystallization process was successfully monitored by combining NIR and Raman spectroscopies.

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Soares et al.

2

proposed the utilization of multivariate curve resolution for

Raman signals interpretation to monitor the synthesis process of ibuprofen-nicotinamide cocrystals at fixed temperatures. This method efficiently obtained the concentration profiles of solids during process. However, under a cooling crystallization process, temperature effects need to be considered. Furthermore, in some crystallization cases, Raman signals from liquid phase highly overlap with signals from solid phase in slurry, which makes it difficult to keep the accuracy of results.



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This study is aimed to develop an industrially amenable method for on-line investigation of cocrystal purity using Raman spectroscopy. The first and foremost task is to identify and separate the co-crystal/constituent compound mixtures with the pure co-crystals from Raman signals. Due to the similar composition, Raman spectra from each sample give highly overlapped results. It is not easy to obtain accurate results from univariate analysis. Therefore, two multivariate analysis methods, i.e. principal component analysis (PCA) and discriminant analysis (DA) were employed for the separation of different co-crystal/constituent mixtures and the pure co-crystals. PCA is a useful mathematical analysis method often used with PAT tools. It was applied to monitor polymorphic transformation

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and to analyze crystallization process kinetics.

contrast, DA is normally used for the classifying tasks.

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In

To confirm the composition of

suspensions, the benchmark technique for crystal phase identification, i.e. PXRD was employed to quantify the constituent solids in suspensions. Caffeine-glutaric acid-acetonitrile was used as a model system. In acetonitrile (ACN), caffeine (CA) and glutaric acid (GA) can form a 1:1 co-crystal (CO) in two polymorphs, which includes one stable form and one metastable form. 4, 26 Since the metastable form transforms rapidly to the stable form in acetonitrile, only the stable form was considered here. Materials, Instrumentation and Methods Chemicals Anhydrous caffeine (≥99% purity) was obtained from Sigma-Aldrich, glutaric acid (99% purity) from Alfa Aesar and HPLC grade acetonitrile from Fisher Scientific. Caffeine-glutaric acid cocrystals were prepared by cooling co-crystallization following the procedure described by Yu et al. 27 6 ACS Paragon Plus Environment

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Experimental setup The experimental setup is shown schematically in Figure 1. The main vessel was a 150-ml flatbottomed jacketed glass crystallizer. A marine-blade impeller rotating at 600 rpm was used to provide agitation. Crystallizer temperature was controlled by a Thermo Haake circulator (C25P). Experimental procedures To obtain CA/CO mixture, GA/CO mixture and pure CO suspensions, a well established cocrystallization process

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was conducted. The experiments were operated at three fixed

temperatures, i.e. 15, 25 and 35 °C. 100 g of acetonitrile were put into the crystallizer followed by adding appropriate amounts of CA and GA to form an equilibrium solution. The concentrations of CA and GA in equilibrium solutions at certain temperatures were obtained from Yu et al. 28 To prepare a CA/CO mixture suspension, excess CA solids were added into the equilibrium solution, nucleation and crystal growth of CO would occur spontaneously until the system reached the eutectic point of CA and CO and both solids were in equilibrium with the liquid phase. GA/CO mixture and pure CO suspensions were prepared using the same method by adding excess GA or CO solids into equilibrium solutions. Raman spectroscopy A Kaiser Raman RXN4 system from Kaiser Optical System Inc. (Ann Arbor, MI) equipped with a light-emitting diode laser operating at 785 nm and 450 mW was employed. Spectra of solvent, pure solutions and suspensions were collected by an immersion probe sealed with a sapphire window in the range from 260 to 1700 cm-1. Spectra of solids were collected by a non-contact probe in the same range of Raman shift. Each spectrum was acquired with a 1 s exposure time



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and the average value of five accumulations was used to smooth system oscillation. Raman spectra of CA/CO, GA/CO and pure CO suspensions were collected after sufficient standing time to ensure the suspensions were in equilibrium state. 20 spectra were collected for every suspension at the same condition. Baselines of spectra were corrected using the asymmetric least squares smoothing algorithm. 29 Principal component analysis (PCA) Raman signals arise from both liquid and solid phases. Signals from liquid phase often partially overlap with, and dominate over those from solid phase. Furthermore, the peaks in the Raman spectra are highly correlated. In addition, intensity and position of peaks, and even baseline may change with varying liquid composition, solids concentration, solids composition and temperature. Consequently, it is very difficult to monitor changes in solids composition based on single peaks. In order to determine co-crystal purity, we need to separate and identify components of Raman signals specific to solids. PCA is employed to discriminate Raman signals that belong to solids. PCA generates a new set of variables, called principal components (PCs). Each PC is a linear combination of the original variables, and all the PCs are orthogonal to each other, which present no redundant information. The first few PCs can normally reveal over 80% of the total variance of the original data. In this study, PCA was used to investigate the differences in the Raman spectra that are susceptible to the changes of composition in suspensions. PCA was programmed in Matlab statistics toolbox, the first two PCs, i.e. PC1 and PC2 were used to visualize the classification of different suspensions. Discriminant analysis (DA)


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The Raman spectra collected from CA/CO mixture, GA/CO mixture and pure CO suspensions were first divided into three groups, i.e. CA/CO group, GA/CO group and CO group. DA is used to determine whether the entire set of means in one group of data is different to the others. Canonical variables C are linear combinations of the original variables calculated in DA, which is used to maximize the separation between groups. The first canonical variable contains the most discriminative information and maximizes the separation between the groups. The second canonical variable is orthogonal to the first canonical variable and gives the second maximum separation between the groups. DA was programmed in statistics toolbox of Matlab in this study. Because of the large number and the highly correlated nature of the variables, direct application of DA to separate the spectra was ill-conditioned and failed.

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Instead, the projection-based

method, i.e. PCA was combined to use with DA. Powder X-ray diffraction A D8-ADVANCE powder X-ray diffractometer (Bruker AXS GmbH, Germany) with Cu Kα radiation was used. The voltage and current were 30 kV and 40 mA, respectively. Final products and calibration samples were scanned in the range of 5 to 35 ° (2θ) at a scan rate of 2 °/min. Solids samples were taken from the suspensions to determine the solid composition by PXRD quantitative analysis. Measurement of solid composition by off-line PXRD quantitative analysis Figure 2 shows the PXRD patterns of CA, GA, and CO solids. The differences in diffraction patterns are distinct. Peaks at 11.2° and 14.6° of 2θ are characteristic of CO. Peaks at 11.9 and 13.9 of 2θ are characteristic of CA and GA, respectively.



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Sample preparation for PXRD analysis To determine the composition in a solid sample by PXRD, it must be milled to reduce the particle size for analysis. The particle size of all samples should be close enough to obtain consistent patterns for quantification. At the same time, part of the material may turn amorphous during milling, and the amorphous content should be accounted for should that happen. In this study, the milling procedure including frequency and milling duration were kept constant for all the samples, which are expected to produce similar size reduction and amorphous transformation outcome. Spiking

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method was used to offer a correction factor for amorphous content. In the

spiking method, a known amount of diamond powder is mixed with a sample to estimate the amorphous content in it. The resulting amorphous contents in samples milled under a given set of condition are list in table 1. A correction factor was then computed as the inverse of crystalline content in a sample. It can be seen in Table 1 that the correction factor was determined only for pure CA, GA and CO. For the CA/CO or GA/CO mixture suspensions, we assumed that the correction factors remain the same even in the presence of the other component, and this assumption is valid as demonstrated below. Validation of PXRD quantitative analysis Ten GA/CO mixtures and ten CA/CO mixtures with known GA/CO and CA/CO ratios were milled under the same conditions. The PXRD patterns of pure CA, GA, CO and their correction factors along with the PXRD patterns of the ten samples were analyzed using Topas V3 (Bruker AXS GmbH, Karlsruhe, Germany). Quantitative Rietveld

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analysis of all samples was

performed using the convolution approach to calculate the solid composition, and the results are 10 ACS Paragon Plus Environment

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graphed in Figure 3, which shows that there is good agreement between actual and measured CO content in both GA/CO mixtures and CA/CO mixtures. This quantitative analysis method was used to determine the solid composition in samples taken from the suspensions of cocrystallization. Results and discussion Solid composition of suspensions 27 batches of co-crystallization processes were carried out to prepare three types of suspensions with CA/CO solid mixture, GA/CO solid mixture and pure CO solids, respectively. Each class consists of 9 batches with different solid compositions and Raman spectra were collected at different temperatures ranging from 15 to 35 °C. The experimental conditions and the final solid compositions calculated from PXRD are listed in Table 2. It can be seen that the CO purity in CA/CO mixtures ranges from 27.40% to 97.48%, and the GO purity in GA/CO mixtures ranges from 8.08% to 98.08%. Qualitative Analysis of Raman Spectroscopy Figure 4 presents the Raman spectra of CA, GA and CO in solid and solution together with ACN. In solids, CA, GA and CO could be separated from their specific peaks. In solution, CA and GA solutions both show their corresponding peaks and the spectrum of CO solution is a simple combination of spectra of CA and GA solutions. However, it is a complicated and timeconsuming task to separate the specific peaks of CA, GA and CO solids from CO solution in suspension due to peak overlapping. Furthermore, Raman signals are known to be affected by other factors including solid composition, solid density solution concentration and temperate



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vary constantly during crystallization process. Univariate method based on any specific peak was not reliable to monitor the co-crystal purity. Thereby, multivariate analysis methods were introduced in this study for spectral separation. Figure 5 shows the evolution of Raman spectra of CO suspensions at different solid concentrations collected at 25 °C. Gradual addition of CO solids into the saturated CO solution generated an increment in Raman intensities of the entire spectra. Figure 6 shows Raman spectra of CO suspensions at different temperatures. The same solid concentration was prepared for all the saturated solutions. In a solid suspension, solution solubility changes at different temperatures, so the evolution of Raman spectra shown in Figure 6 involves the effects from temperature and solution concentration at the same time, and it is difficult to decouple the effects from them. Principal Component Analysis of Raman Spectra Collected in Suspensions The spectra collected in the 27 suspensions were analyzed by PCA, and their score plot in the space spanned by the first two principal components is displayed in Figure 7. It can be seen that these three classes of suspensions can be distinguished from each other to some degree, for example, the CA/CO suspension can be separated from the other two classes. However, the boundary between the GA/CO and pure CO class is unclear. The PC scores of samples in these two classes highly overlap even though some GA/CO suspensions contained high solid content of GA. Upon examination of the Raman spectra collected at 25 °C (Figure 8), it is obvious that there is significant difference between the spectra of CA/CO mixture and that of pure CO; whereas the Raman spectra for GA/CO mixture and that of pure CO are relatively close. This explains the ambiguous boundary between GA/CO mixture and pure CO on the PC 12 ACS Paragon Plus Environment

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score plot. Despite the significant difference between the spectra of CA/CO mixture and pure CO, PC scores of CA/CO mixture would still shift towards the pure CO class if the mixture contained high solid content of CO, as evident in batches 4, 5 and 7. Discriminant analysis (DA) While PCA looks for the linear combination of the original variables that has the largest possible variation, DA looks for the linear combination of the original variables that has the largest separation between groups. However, the number of variables is required to be less than the number of samples in DA. If we classify the 27 original Raman spectra using DA directly, only a maximum of 26 Raman shift positions can be selected for analysis, which is not sufficient to reveal the variations between the spectra. Therefore, a method combining PCA and DA was developed in this work. PCA was first performed on these spectra to reduce the dimensions of the variation and the resulted PCs were put into DA to distinguish the suspensions. In PCA analysis discussed earlier on, the first two PCs explained only 72.51% of the variation among the suspensions and the suspensions were not fully separated due to their high similarity. In contrast with two PCs, five, ten and twenty PCs presented 90.68%, 96.63% and 98.34% of the variation, respectively. It is obvious that more details of the differences from the suspensions can be described by using more PCs. Table 3 lists the calculated p values in a 2-Dimensional space among the three classes of suspensions with different number of PCs in DA. The p values are less than the critical p value of 0.05 from null hypothesis if more than five PCs are used as input. This means the three classes of suspensions are judged different and separated from a statistical perspective. If only two PCs are used, the p value is larger than the critical p value and these three classes of suspensions cannot be separated.



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Different numbers of PCs were put into DA to classify the suspensions and the results are presented in Figure 9. The ordinate is the first canonical variable, and the abscissa is the second canonical variable. The first canonical variable, c1, dominates the separation of these three classes. The second canonical variable, c2, reveals some separation between pure CO class and the other two classes. If only two PCs are used in DA, the result is exactly the same as PCA treatment due to their similar mathematical algorithm. More PCs input in DA yielded a better separation of the three classes of suspensions. The degree of separation among the three classes can be indicated by the distances between the group centers of each two classes (see Figure 10). Similarly, the distances from each point to its group centre in each class present the degree of separation within the class (see Figure 11). It is easy to understand that the distances between different classes and the distances within each class both increased with the increment of PCs used in analysis. However, the increment of distance between classes was much larger than the increment of distance within each class, which led the separation between the three classes of suspensions in DA. With the help of twenty PCs in analysis for this case, there was no overlapping in scatter plot among the three classes of suspensions because the three group distances were all more than ten times over all the inner group distances. This model was used to separate suspensions of co-crystals with different composition, which provides an easy method for the industry to determine co-crystal purity on-line based on Raman spectroscopy. There is no need to carry out a large number of experiments for model calibration because PCA is used to reduce the dimensions of variation. For simple cases, PCA is sufficient to separate different compositions. For the cases that are difficult to separate, combining of PCA and DA is required. With the appropriate number PCs used in DA, sufficient differences among samples will be revealed for separation. 14 ACS Paragon Plus Environment

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Conclusion Co-crystal purity of caffeine-glutaric acid-acetonitrile system was determined on-line by Raman spectroscopy. Three classes of solid suspensions, caffeine-co-crystal, glutaric acid-co-crystal and pure co-crystal, were prepared to represent possible outcomes of solution crystallization, during which single solute components could crystallize concomitantly with the desired co-crystal. A calibration-free statistical approach using a combination of PCA and DA was developed to classify the three classes of suspensions. It was found that the suspensions can be clearly classified using twenty PCs as input to DA. This offers a simple approach for identification of impurity in the form of pure component during co-crystallization that is easily implementable in industrial process without the need for elaborate calibration. *Corresponding author: E-mail address: [email protected] (F. Sheng) E-mail address: [email protected] (R.B.H. Tan) Acknowledgements This work was supported by project grant ICES/14-220A01 from A*STAR (Agency for Science, Technology and Research) of Singapore. The authors wish to thank one colleague, Dr. Martin Karl Schreyer, for his help on PXRD analysis.



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20. Lee, M.-J.; Chun, N.-H.; Kim, M.-J., et al., In Situ Monitoring of Antisolvent Cocrystallization by Combining Near-Infrared and Raman Spectroscopies. Cryst. Growth Des. 2015, 15 (9), 4385-4393. 21. Simone, E.; Saleemi, A. N.; Nagy, Z. K., In Situ Monitoring of Polymorphic Transformations Using a Composite Sensor Array of Raman, NIR, and ATR-UV/vis Spectroscopy, FBRM, and PVM for an Intelligent Decision Support System. Org. Process Res. Dev. 2015, 19 (1), 167-177. 22. Lee, M. J.; Seo, D. Y.; Wang, I. C., et al., Quantitative in‐line monitoring of solvent‐mediated polymorphic transformation of sulfamerazine by near‐infrared spectroscopy. J. Pharm. Sci. 2012, 101 (4), 1578-1586. 23. Wu, H.; Khan, M. A., Quality-by-Design (QbD): An integrated process analytical technology (PAT) approach for real-time monitoring and mapping the state of a pharmaceutical coprecipitation process. J. Pharm. Sci. 2010, 99 (3), 1516-1534. 24. Falcon, J. A.; Berglund, K. A., In Situ Monitoring of Antisolvent Addition Crystallization with Principal Components Analysis of Raman Spectra. Cryst. Growth Des. 2004, 4 (3), 457-463. 25. Yu, Z. Q.; Chow, P. S.; Tan, R. B., Quantification of particle morphology by boundary Fourier transform and generic Fourier transform. Chem. Eng. Sci. 2007, 62 (14), 3777-3786. 26. Trask, A. V.; Motherwell, W. D. S.; Jones, W., Pharmaceutical Cocrystallization: Engineering a Remedy for Caffeine Hydration. Cryst. Growth Des. 2005, 5 (3), 1013-1021. 27. Yu, Z. Q.; Chow, P. S.; Tan, R. B. H., Operating Regions in Cooling Cocrystallization of Caffeine and Glutaric Acid in Acetonitrile. Cryst. Growth Des. 2010, 10 (5), 2382-2387. 28. Yu, Z. Q.; Chow, P. S.; Tan, R. B. H., et al., Supersaturation Control in Cooling Polymorphic CoCrystallization of Caffeine and Glutaric Acid. Cryst. Growth Des. 2011, 11 (10), 4525-4532. 29. Boelens, H. F. M.; Dijkstra, R. J.; Eilers, P. H. C., et al., New background correction method for liquid chromatography with diode array detection, infrared spectroscopic detection and Raman spectroscopic detection. J. Chromatogr., A 2004, 1057 (1–2), 21-30. 30. Gunatilaka, H.; Till, R., A precise and accurate method for the quantitative determination of carbonate minerals by X-ray diffraction using a spiking technique. Mineral. Mag. 1971, 38 (296), 481-487. 31. Rietveld, H., A profile refinement method for nuclear and magnetic structures. J. Appl. Crystallogr. 1969, 2 (2), 65-71. 32. Hill, R. J.; Howard, C. J., Quantitative phase analysis from neutron powder diffraction data using the Rietveld method. J. Appl. Crystallogr. 1987, 20 (6), 467-474.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

3

1

4 2

1Computer 2 Raman Probe 3 Overhead stirrer 4 Thermocouple

Figure 1. Schematic of experimental setup


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Relative intensity

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CO CA

GA 5

10

15

20

25

2θ θ

Figure 2. PXRD patterns of GA, CA and CO


30

35

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Calculated CO contents in CA/CO mixtures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Calculated CO contents in GA/CO mixtures


y = 1.0125x R² = 0.9966

0 0.2 0.4 0.6 0.8 Actual CO contents in GA/CO mixtures

1

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

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y = 1.0121x R² = 0.9992

0 0.2 0.4 0.6 0.8 Actual CO contents in CA/CO mixtures

(a)

1

(b)

Figure 3. Relation between actual and PXRD calculated CO contents in (a) GA/CO mixtures, and (b) CA/CO mixtures


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(g) (f) Intensity (a.u.)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(e) (d) (c) (b) (a) 600

800

1000 1200 Raman shift (cm-1)

1400

1600

Figure 4. Raman spectra of (a) CA solids, (b) GA solids, (c) CO solids, (d) CA solution, (e) GA solution, (f) CO solution, and (g) ACN at ambient conditions



16000 3g 12000 Intensity (a.u.)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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6g 9g 12g

8000

4000

0 1260

1360

1460 Raman shift

1560

1660

(cm-1)

Figure 5. Raman spectra of CO suspensions at different solid concentrations at 25 °C


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16000 15 °C

12000 Intensity (a.u.)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


25 °C 35 °C 8000

4000

0 1260

1360

1460 Raman shift

1560

1660

(cm-1)

Figure 6. Raman spectra of CO suspensions with a same solid concentration at 15, 25 and 35 °C



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 7. Principal component (PC) score plot of CO purity classification on 27 batches of suspensions


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Page 25 of 31

16000

12000 Intensity (a.u.)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


CO/CA CO/GA

8000

CO

4000

0 1260

1360

1460 Raman shift

1560

1660

(cm-1)

Figure 8. Raman spectra of CA/CO mixture, GA/CO mixture and pure CO suspensions at 25 °C



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(a)

(b)

(c)

(d)

Figure 9. Scatter plot of DA for CO purity classification: results from (a) two PCs input, (b) five PCs input, (c) ten PCs input and (d) twenty PCs input.


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2000 1800 Distances between groups

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Between CA/CO and GA/CO

1600 1400

Between CA/CO and CO

1200 1000

Between GA/CO and CO

800 600 400 200 0 0

5

10

15

PCs

Figure 10. Distances between the every two classes of suspensions


20


2 PCs Distances within each group

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

5 PCs

10 PCs

15 PCs

Page 28 of 31

20 PCs

90 80 70 60 50 40 30 20 10 0 1

6

11

16

Sample number

Figure 11. Distances within each class of suspensions


21

26

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Table 1. Amorphous content in pure CA, GA and CO after ball mill Material CA GA CO

Crystalline, w % 80.0 34.8 80.9

Amorphous, w % 20.0 65.2 19.1


Correction Factor 1.25 2.87 1.24


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Page 30 of 31

Table 2. Initial experimental conditions and final solid compositions of the 27 batches of cocrystallization

No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Temperature, °C 15 15 15 25 25 25 35 35 35 15 15 15 25 25 25 35 35 35 15 15 15 25 25 25 35 35 35

Solid mass added to saturated solution, g CA GA CO 10 0 0 20 0 0 30 0 0 5 0 0 10 0 0 20 0 0 5 0 0 10 0 0 20 0 0 0 10 2 0 10 4 0 10 6 0 5 0 0 10 0 0 20 0 0 5 0 0 10 0 0 20 0 3 6 9 3 6 9 3 6 9

Solid composition of suspensions, wt% CA GA CO 26.11 0 73.89 64.53 0 35.47 72.60 0 27.40 2.52 0 97.48 24.07 0 75.93 56.72 0 43.28 16.34 0 83.66 40.95 0 59.05 64.75 0 35.25 0 72.69 27.31 0 47.95 52.05 0 44.42 55.58 0 2.86 97.14 0 20.64 79.36 0 91.92 8.08 0 1.92 98.08 0 2.76 97.24 0 3.73 96.27 0 0 100.00 0 0 100.00 0 0 100.00 0 0 100.00 0 0 100.00 0 0 100.00 0 0 100.00 0 0 100.00 0 0 100.00


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Table 3. P value calculated among the three classes of suspensions with different PCs input PCs in put

2

5

10

20

P values in a 2-Dimensional space

0.5057

0.0215

0.00028

0.0036


Online Classification of Mixed Co-Crystal and ... - ACS Publications

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