chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

Contents lists available at ScienceDirect

Chemical Engineering Research and Design

journa l h om epage: www.elsev ier .com/ locate /cherd

Application of quantitative Raman spectroscopy forthe monitoring of polymorphic transformation incrystallization processes using a good calibrationpractice procedure

E. Simonea, A.N. Saleemia, Z.K. Nagya,b,∗

a Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UKb School of Chemical Engineering, Purdue University, West Lafayette, IN 47907-2100, USA

a b s t r a c t

Polymorphism is the property of a substance to have more than one crystalline form. Polymorphic forms of the same

chemical compound can have different physical and chemical properties that can strongly affect the manufacturing

process. For this reason, determining and monitoring polymorphic transformations have become very important,

especially in pharmaceutical industry. Significant work has been developed for the calibration of Raman spectroscopy

to monitor the presence and amount of solid polymorphs in suspensions during crystallization, as well as the liq-

uid concentration. Nevertheless, a clear and systematic approach to Raman calibration is missing in the literature.

The present work has the aim of developing a methodical strategy for Raman calibration, taking into account the

principal factors that can affect the Raman spectra of a specific compound in solution, such as solid type, solute

concentration, temperature, crystal size and suspension density. Univariate and multivariate calibration techniques

were investigated using pre-processing techniques to optimize the signal. The results are combined in a systematic

“good calibration practice” (GCP) procedure, proposed for the first time in this work. The approach has been applied

for the quantitative monitoring of the polymorphic transformation of ortho-aminobenzoic acid (OABA).

© 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Keywords: Raman spectroscopy; Polymorphism; Crystallization; OABA; Process analytical technologies; Calibration

Fourier transform infrared (FTIR) and Raman spectroscopy.

1. Introduction

The term process analytical technology (PAT) became widelypopular in 2004 when the Food and Drug Administrationpublished the “Guidance for industry”. A process analyticaltechnology is there describe as a tool that “enable processunderstanding for scientific, risk-managed pharmaceuticaldevelopment, manufacture, and quality assurance” (U.S.Department of Health and Human Services, 2004). Thesetools can provide effective and efficient means for acquiringinformation to facilitate process understanding, contin-uous improvement, and development of risk-mitigationstrategies. Process analysers and instrumentation include

on- and in-line equipment. Those allow obtaining instant

∗ Corresponding author at: School of Chemical Engineering, Purdue Unfax: +1 765 494 0805.

E-mail address: zknagy@purdue.edu (Z.K. Nagy).Received 30 July 2013; Received in revised form 22 October 2013; Acce

0263-8762/$ – see front matter © 2013 The Institution of Chemical Engihttp://dx.doi.org/10.1016/j.cherd.2013.11.004

measurements avoiding the time delay typical of off-lineanalysis. The measured properties can be univariate (scalar)quantities or multivariate (vector and matrix). In the lastdecades advanced spectroscopic instrumentation progressedtremendously: UV–visible, near- and mid-infrared and Ramanspectroscopy are studied and began to be implemented inmanufacturing (Chew and Sharrat, 2010; Nagy et al., 2013).For the crystallization process PATs have been widely usedin the last decade. In particular focused beam reflectancemeasurement (FBRM), particle vision and measurement(PVM), nuclear magnetic resonance (NMR), attenuated totalreflectance (ATR)-UV/vis, attenuated total reflection (ATR)-

iversity, West Lafayette, IN 47907-2100, USA. Tel.: +1 765 494 0734;

pted 3 November 2013

Together with the more complex instrumentation (FBRM or

neers. Published by Elsevier B.V. All rights reserved.

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 595

Nomenclature

ATR-UV attenuated total reflection ultravioletATR-Mid-IR attenuated total reflection mid-infraredNIR near-infraredFBRM focused beam reflectance measurementPVM particle vision measurementPCA principal component analysisPCR principal component regressionPLSR partial least squares regressionRMSEC/P/CV root mean square error of calibra-

tion/prediction/cross validation

stt2R(ic

fcatutscoitphaaceptitttsutiptfommp(g22cu

pectroscopic techniques) low-cost on-line sensors, such asurbidity probes or endoscopy based video imaging, wereested during crystallization processes (Simon et al., 2009,012). A comparison between those low cost tools and FT-aman, FBRM and ATR-FTIR was performed by Simon et al.

2011). The study demonstrated the validity of video imagingn monitoring the pseudopolymorphic transformation ofitric acid form the anhydrous form to its monohydrate.

Raman spectroscopy enables in situ, non-destructive andast quantitative measurements of solid samples without spe-ific sample preparation, making it a potential tool to monitornd control crystallization processes. In particular, its abilityo distinguish between different polymorphic forms enablessing it during crystallization of chemical species with morehan one polymorphic form (Fevotte, 2007). Traditional analy-es used to detect polymorphs (such as differential scanningalorimetry, DSC, or X-ray diffraction, XRD) are off-line andften require sample preparation. This leads to a time delay

n the identification of the polymorphic form and error relatedo polymorphic transitions during the preparation of the sam-le and the transport. In recent years, Raman spectroscopyas become one of the fastest, most reliable and most suit-ble techniques to identify crystals forms in drug productsnd can be easily exploited routinely for monitoring phasehange in drug products and quality control assays (Auert al., 2003). Most organic molecules present clear and resolvedeaks in Raman spectra, offering the possibility to do quanti-ative and qualitative analysis. The disadvantages of Ramannclude the fluorescence that can disturb the measurements,he sample heating generated by the probe that can degradehe sample and the presence of strong peaks in Raman spec-ra for organic solvents that can interfere with those of theolid (Fevotte, 2007). Raman spectroscopy has been alreadysed in qualitative identification of polymorphic transforma-ion for solvent mediated crystallization processes as well asn the determination of the polymorphic form in dry solidowders and tablets. The potential of Raman spectroscopyo quantitatively determine the concentration of polymorphicorms in solutions or solid mixtures depends on the possibilityf building a good calibration model using a robust experi-ental approach. Both univariate and multivariate calibrationodels have been successfully used for dry solid mixture of

olymorphs of progesterone (Wang et al., 2000), famotidineNémet et al., 2009), paracetamol (Kachrimanis et al., 2007), l-lutamic acid (Ono et al., 2004; Alatalo et al., 2008; Qu et al.,009), d-mannitol (Braun et al., 2010), carbamazepine (Qu et al.,008; O’Brien et al., 2004; Salameh and Taylor, 2006) and cal-

ium carbonate (Agarwal and Berglund, 2003). Raman was alsosed to identify impurities in solid capsules (Hargreaves et al.,

2011), mixtures of sulfamerazine polymorphs (Li et al., 2011)and ranitidine hydrochloride (Pratiwi et al., 2002). Principalcomponent regression (PCR) was used to quantify the ratiobetween different polymorphs of carbamazepine (Strachanet al., 2004). Univariate models seem to be sufficient to ana-lyse solid mixtures. The problem of using solid samples is themixing. Obtaining a homogeneous mixture of two or three dif-ferent powders can be very difficult, so slurries are often used.Water is the preferred solvent for the slurries because it doesnot show peaks in Raman. Raman has been applied for quanti-tative measurement of water-based slurries of flufenamic acid(Hu et al., 2007), theophylline (Wikstrom et al., 2005), citric acid(Caillet et al., 2006, 2007, 2008), carbamazepine (Tian et al.,2006), ibuprofen (De Beer et al., 2006). Some calibration mod-els were developed also in solvents like toluene (O’Sullivanet al., 2003), 1-propanol (Zhao et al., 2012), ethanol and water(Su et al., 2010), isopropyl-acetate (Starbuck et al., 2002), ace-tone and water (Falcon and Berglund, 2004) and lactose (DeSpiegeleer et al., 2005). Kobayashi et al. (2006) have developeda multivariate calibration function to monitor the isother-mal polymorphic transformation of timepidium bromide in asolution of water and 10% acetone. More recently, the crystal-lization of Carvedilol at different temperatures and mixturesof solvents was studied (Pataki et al., 2012a, 2012b). A multi-variate approach was already used for Raman in the evaluationof solid composition of mixtures of l-glutamic acid in water(Cornel et al., 2008) and progesterone in acetone/water (Falconand Berglund, 2004). Generally, Raman spectroscopy is used tomonitor the solid composition, but it was also used to mea-sure the total solid concentration (Caillet et al., 2006, 2008) andthe liquid concentration (Qu et al., 2008; Hu et al., 2006).

Many parameters can affect calibration models, such astemperature, crystal size and solution density. The effectof temperature on calibration was studied but not in detail(Fevotte, 2007; Wong et al., 2008a), most of the experimentswere performed under isothermal conditions or neglecting theeffect of temperature in the calibration. The effect of crystalsize, solution density, sample positioning or excitation inten-sity on the spectra is often a change in intensity that canpartially be compensated using a pre-processing technique(Vankeirsbilck et al., 2002; Huang et al., 2010). A different cali-bration approach, considering the effect of particle size on thespectra was proposed by Hu et al. (2007) and Chen et al. (2008,2012). They explained how crystal size affects Raman spectra,which may lead to errors in the calibration for polymorphicratio measurement. Chen et al. (2012) proposed a new methodof calibration to distinguish the effect of crystal size from thepolymorphic ratio. It is interesting to notice that Hu et al. (2007)found that increasing particle size generates a decrease in theRaman intensity. In our work the opposite trend was found,which is reported by Chen too (Chen et al., 2008). An attemptto introduce other parameters in the Raman calibration wasdone by Wong et al. (2008a, 2008b). They included tempera-ture and slurry density in a partial least squares (PLS) model,which was then validated with a cooling crystallization exper-iment. The results for validation were greatly improved by theintroduction of these two values even if the error was still con-siderably high. It can be that calibration experiments werenot conducted in an optimized way (using design of exper-iment) and this led to a higher error. A suitable calibrationplan can generate more accurate model even without includ-ing additional variable (especially if they have a linear effecton the spectra) because the effect of these variables is already

included in the spectra (Mark and Workman, 2007). Also a

596 chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

Fig. 1 – (a) Chemical structure of anthranilic acid, (b)

prismatic OABA form I and (c) needle like form II.

nonlinear factor can still be modelled by PLS with a good cal-ibration set of experiments and a more complicated model(Wulfert et al., 1998, 2000).

A better analysis was done by Cornel et al. (2008) for thetransformation of l-glutamic acid. They studied the effect oftemperature, crystal size, solute concentration and solid den-sity and then planned a set of calibration experiment thatled to a final error in polymorphic ratio estimation of about3%. A similar approach was used in this work but a design ofexperiment technique was applied to reduce the number ofexperiments. Furthermore, the effects of the different factorshave been studied in more details to determine if they can beeffectively modelled with a linear calibration approach.

The final aim is to determine a systematic procedure thatcan be applied to different systems for the calibration of theRaman instrument for the determination of polymorphic ratiodesigned for cooling crystallization. This procedure is definedas the good calibration practice (GCP) and allows obtaininghigh quality measurement with a reduced amount of experi-ments.

2. Methods and materials

2.1. Materials and equipment

The model compound used for the experiments waso-aminobenzoic acid (OABA), which has three different poly-morphic forms I, II and III. Forms I and II are relatedenantiotropically (transition temperature for a solid transfor-mation between 80 ◦C and 98 ◦C), while forms II and III aremonotropic (Jiang et al., 2010). OABA is generally used as anintermediate for the production of pigments, dyes and sac-charin, as well as in preparing perfumes and pharmaceuticals(Jiang et al., 2008). The molecular structure of OABA is shown inFig. 1(a), while Fig. 1(b) and (c) represent micrographs of formI and form II, respectively. Form II is the metastable form atambient temperature and it has a characteristic longed hexag-onal shape. Form I instead has a prismatic shape. OABA (>98%Form I) was purchased from Sigma–Aldrich. Isopropyl alcohol(Fisher Scientific) was used, and ultrapure water was obtainedvia a Millipore ultra-pure water system. OABA form II wasprepared in laboratory by cooling crystallization in a 300 mljacketed vessel stirred by a pitched blade turbine and fittedwith a PT-100 temperature probe connected to a Huber Mini-stat CC3 thermoregulator that adjusted the temperature of theprocess using the Crystallization Process Informatics System(CryPRINS). Form I of OABA was dissolved in pure isopropylalcohol and heated up to 60 ◦C. The temperature was kept con-stant for 15 min in order to reach a complete dissolution. Anin situ focused beam reflectance measurement (D600L Lasen-tec FBRM probe Mettler Toledo with FBRM software V 6.7.0) was

used to monitor the solid phase during dissolution. PVM V819probe (Mettler Toledo with PVM on-line image acquisition

software) was also used to check in real time the formobtained. After that the solution was cooled down to 20 ◦C.Few minutes after the crystallization started the solution wasextracted from the vessel and the crystals were filtrated anddried for 24 h. DSC and a Raman NCO-1.3-VIS/NIR (Kaiser opti-cal systems) non-contact probe were used to verify which formwas obtained. Calibrations of slurries were conducted usingthe same 300 ml stirred and jacketed vessel with temperaturecontrol via the CryPRINS software. The vessel was providedwith a condenser to avoid loss of solution via evaporation dur-ing experiments. A schematic of the equipment used is shownin Fig. 2. The thermocouple and an immersion Raman probewere inserted in the vessel. The Raman probe was connectedto a RamanRXN1 analyser (785 nm laser Kaiser optical sys-tems). The Raman measurements were conducted using theiC Raman 4.1 software. The processing of the data has beendone both with Excel and specific software for multivariateanalysis (TQ Analyst by Thermo Fisher Scientific) as well asMatlab 2012 using the PLS toolbox. The solid samples havebeen analysed with an optical non-contact NCO-1.3-VIS/NIR(Kaiser optical systems) Raman probe at a distance of 2.5 cm.For the validation experiment a MSC621 Carl Zeiss ATR-UV/Visprobe (with an in-house LabView software) was used.

2.2. Methodology for the study of the effect of soluteconcentration on the Raman spectra

Clear solutions at different concentrations of solute havedifferent Raman spectra. To study the effect of solute concen-tration on Raman signal a solution of 10% IPA and 90% of water(w/w) was prepared and held at 40 ◦C during all experiment.Solid OABA form I was added in steps and dissolved in the sol-vent. The maximum concentration was below the saturationpoint, so all the measurements were taken in clear solution.Three Raman spectra were collected for all concentrations. Atime exposure of 10 s was used and the number of scans was10.

2.3. Methodology for the study of the effect of crystalsize on the Raman spectra

Raman spectra are affected by the crystal size as previouslystudied by Chen et al. (2012). A more detailed study on thisphenomenon has been done in this work using OABA form Isolid powder with different dimensions. Metallic sieves wereused to produce four different size fractions: (1) 63–75 �m, (2)75–125 �m, (3) 125–150 �m, and (4) 150–250 �m.

For each mixture, three measurements were taken using10 s exposure time, 2.5 cm distance between the optical probeand the sample and 17 scans. Every sample had the samemass and approximately the same volume (some mixtureswere compressed in order to have always the same volumeof sample); in this way the only parameter which was dif-ferent between samples was the size range of the crystals.Multiple series of experiments were conducted to evaluate theconsistency of the results.

Further experiments were carried out to investigate theeffect of crystal size in slurries. A solution of IPA and water(10%, w/w IPA) was prepared and OABA form I was added inorder to obtain a saturation temperature of 40 ◦C. The temper-ature was kept at 38 ◦C for 30 min, then the solution was cooleddown slowly to 33 ◦C, kept for 1 h, cooled down again to 28 ◦C

and kept for an additional hour. The temperature effect onthe Raman signal within a range of ten degrees is negligible.

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 597

Fig. 2 – Schematic of the rig used for the experiments.

Tnugs

2c

TspaetRRtistat

2t

Ttto

he seeds were added at a very low supersaturation so thatucleation was avoided and growth was negligible. FBRM wassed to check the presence of nucleation and to confirm therowth of the crystals during the experiment. Two differenttirring speeds were used (400 rpm and 200 rpm).

.4. Methodology for the study of the effect of solidoncentration on the Raman spectra

he experiment was conducted in water at 20 ◦C, at low OABAolubility (0.35 g OABA/100 g water). OABA form I was addedrogressively in the solution and 5 measurements were takenfter each addition in order to have Raman spectra at differ-nt solid concentration in water. For every new addition theime of exposure had to be reduced in order to have a clearaman spectrum (a larger amount of solids generates moreaman scattering with the effect of a possible saturation ofhe instrument). The number of scans was adjusted accord-ngly with the decrease of the exposure time to have the sameignal to noise ratio in all the measurements. The most impor-ant variables of the experiment are shown in Table 1. Thesere amount of solid added in each measurement, exposureime and solid concentration of the suspension.

.5. Methodology for the study of the effect ofemperature on the Raman spectra

he effect of temperature on Raman spectra is more difficulto study independently because solubility of OABA varies with

emperature. In the case of suspensions of solids, an increasef the temperature will provoke the dissolution of part of the

Table 1 – Solid added, exposure times and solidconcentration during the experiments performed tocheck the effect of solid density on Raman spectra.

Sample Solidadded (g)

Exposuretime (s)

Solid concentration(g solid/100 g

solution)

1 4.68 10 1.212 6.25 10 3.293 10.95 5 6.944 10.49 5 10.445 8.68 4 13.33

solids and thus a change in the concentration of solids. More-over, solids in suspension can grow or nucleation can occur,making more difficult to distinguish between the effect of tem-perature and crystal size or suspension density. For this reasonthe effect of temperature was studied on clear solutions inabsence of solid particles.

Clear solutions at constant concentration of OABA form Iin a mixture of ethanol and water (50/50, w/w) were used. Thechoice of a different solvent was to avoid overlapping peakswith OABA. Ethanol has characteristic peaks which are lessoverlapping with the typical peaks of OABA. Five measure-ments were taken for each temperature: 5 s time of exposureand 20 scans. Five temperatures were examined: 20 ◦C, 30 ◦C,40 ◦C, 50 ◦C and 60 ◦C.

2.6. Methodology for the study of the effect of soluteconcentration on the Raman spectra

A solution of IPA (10%) and water (90%) was used for the exper-iment. Temperature was kept constant and form I was addedin 4 steps (+1.01 g, +1.13 g, +1.25 g, +1.60 g). Five measurementswere taken for each concentration using 10 s time of exposureand 10 scans.

2.7. Methodology for the calibration of solid samplesof OABA forms I and II

The aim of these experiments was to build a calibration modelfor the quantification of the mass fraction of solid OABA form IIusing Raman spectroscopy in powder mixtures of both formsI and II. Dry mixtures were prepared using different propor-tions of forms I and II (0, 25, 50, 75 and 100%). The crystal sizeof the two forms was the same 75–150 �m (sieves were used toprepare the samples of each form). Each sample was agitatedby hand for 5 min before the first measurement and then agi-tated again for about 1 min before each other measurement.A Raman non contact NCO-1.3-VIS/NIR (Kaiser optical sys-tems) probe was used. The samples were put on a paper andarranged in a circular shape of 3 cm of diameter. Five measure-ments were taken and for each measurement three sampleswere collected from different points of the samples. A compar-ison with a NIR Q412/A-Ex (Bruker) emission probe was also

performed to investigate the effect of the slot area and theaccuracy of the Raman measurement.

598 chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

Table 2 – Box–Behnken design of experiment for thecalibration of polymorphic concentration in solutions ofwater and IPA.

Experiment Temperature(◦C)

Polymorphicratio (%)

Total solid for100 g solvent (g)

1 10 0 0.7752 10 100 0.7753 40 0 0.7754 40 100 0.7755 10 50 0.36 10 50 1.257 40 50 0.38 40 50 1.259 25 0 0.3

10 25 0 1.2511 25 100 0.312 25 100 1.2513 25 50 0.77514 25 50 0.775

500 1000 1500-2

-1

0

1

2

3

4

Raman shift (c m-1)

Ram

an in

tens

ity

Fig. 3 – Effect of solute concentration of OABA on Ramanspectra. The intensity of the signal increases with increase

iC Raman with 17 points and a window of 16 cm−1. To find acorrelation with the solute concentration the area under the

Table 3 – Raman peaks analysed for the univariate studyof solute concentration effect on spectra.

Peak number Region of the spectrum (cm−1)

1 1175–11642 1046–10253 777–7594 570–556

15 25 50 0.775

2.8. Methodology for the calibration of solid mixturesof forms I and II in suspensions of IPA and water

A Box–Behnken design of experiment approach was usedto plan the experiments considering the factors that affectRaman spectra mostly and the ones that could be easilycontrolled: solute concentration, solid concentration and poly-morphic ratio. Solute concentration is dependent only ontemperature for saturated solutions so temperature was con-sidered as the changing factor. This type of design allowsconsidering more factors with a restricted number of experi-ments. It is a typical three level approach. The upper and lowerlevels for each factor must be chosen in this approach. Becausethe calibration is for a cooling crystallization it was decidedto work in a range of temperature from 10 to 40 ◦C, and themaximum solid content was chosen as the amount of solidsneeded to produce a 40 ◦C saturated solution. The minimumwas chosen arbitrary considering a 10 ◦C saturated solution.Table 2 reports the experiment planning.

Each experiment was conducted at constant temperature(10, 20, 30 and 40 ◦C). OABA form I or II was dissolved in thesolvent until saturation of the solution and kept for at least30 min. Then, the other form was added in order to reach thedesired polymorph ratios and measurements were taken (2samples for each measurement). Each sample was the resultof 10 scans with an exposure time of 10 s.

2.9. Methodology for the polymorphic transformationstudy

The calibration function determined in this work was used tomonitor the polymorphic transformation of OABA from formII into form I during a cooling crystallization. ATR-UV/vis wasused to measure the solute concentration and a supersatura-tion control strategy was implemented in CryPRINS, in orderto follow a constant supersaturation profile in the phase dia-gram (Nagy and Aamir, 2012; Nagy and Braatz, 2012; Saleemiet al., 2012; Nagy et al., 2008; Fujiwara et al., 2005).

Solid form I was added to a solution of IPA and water 10:90(w/w) at 30 ◦C and the solution was heated up to 55 ◦C and keptfor 30 min at this value for complete dissolution. After this thesolution was cooled down at a constant rate of −1 ◦C/min until

nucleation. After nucleation started the temperature controlwas switched to supersaturation control. The set point chosen

of the concentration.

was slightly below the solubility of form II, in order to allowthe dissolution of the metastable form II, and promote thetransformation and formation of pure stable form I. Ramanwas used to monitor the polymorphic transformation. A com-mon practice for the monitoring of polymorphic form is tofollow one specific peak for form I and one for form II. A peakat 770 cm−1 was chosen for form II and a peak at 1038 cm−1

for form I. Those two peaks are present in both the Ramanspectra of the two forms of OABA but the peak at 770 cm−1 issignificantly stronger in form II, while the peak at 1038 cm−1

is much more relevant in the spectrum of form I. Experimentsgenerally show a rapid decrease of the intensity of the form IIpeak and an increase in the one for form I during transforma-tion. FBRM was also used during the experiment to check thenucleation and complete dissolution of the starting material.

3. Results and analysis

3.1. Effect of solute concentration

The effect of solute concentration was studied using both aunivariate and multivariate approach. In both cases it wasfound that the effect of solute itself on Raman is linear, and anincrease in concentration generates an increase in the char-acteristic peaks of solid OABA (as shown in Fig. 3).

While for some systems specific peaks for the solute in thesolution can be identified (e.g. Hu et al. (2005) found specificpeaks for flufenamic acid), a specific peak for the solute wasnot identified for this system. For the univariate analysis sixdifferent peaks were studied as reported in Table 3. Secondderivative was calculated in order to distinguish more peaksand eliminate baseline shift. Smoothing was performed using

5 1496–14846 1573–1561

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 599

Table 4 – Results of univariate interpolation performedin Excel. C is the solute concentration while A is the areaof each chosen peak.

Peak number Linear function ofpeak area (cm−1)

Correlationcoefficient

1 C = 0.2709A + 0.0062 0.98432 C = 0.6707A − 0.0006 0.99673 C = 0.7031A − 0.0129 0.99754 C = 0.6583A − 0.012 0.99655 C = 0.4616A − 0.0013 0.9881

ca

taS2wUmsRmtc

3

Mp2ctstwtstpsnc

Fs

500 1000 1500-4

-2

0

2

4

6

8

Raman shift (cm-1)

Ram

an in

tens

ity

Fig. 5 – Spectra of OABA form I for samples with different

6 C = 0.4306A − 0.0008 0.9884

hosen peaks was considered. Results are shown in Table 4nd Fig. 4.

The trend is linear from the univariate analysis and allhe peaks show a similar trend. A multivariate approach waslso used considering the region between 200 and 1800 cm−1.pectra were only smoothed with 11 points window and and order polynomial function. Both PCR and PLS approachesere used in Matlab 2012, with PLS giving better results.sing 5 factors the correlation coefficient is 1.0000 with rootean square error of calibration (RMSEC) and root mean

quare error of prediction (RMSEP) of RMSEC = 2.64E−09 andMSEP = 3.47E−08, respectively. With both the univariate andultivariate approaches the effect of solute concentration on

he Raman spectra is linear and in particular, increasing theoncentration generates an increase in the signal.

.2. Effect of crystal size

any attempts were conducted to find a correlation betweenarticle size and Raman spectra (Hu et al., 2006; Cornel et al.,008; Allan et al., 2013). However, a characteristic trend andlear explanation of the effect has not been found, partly dueo the fact that the dependence on the type of probe and theampling conditions create difficulty in studying the effect ofhe size of particles on the Raman spectra. Due to this in thisork a study of the effect of crystal size on the Raman spec-

ra was conducted using the probes available and applyinguitable pre- and post-processing of the data to try to reducehe effect. Fig. 5 shows the spectra for different solid sam-les after a baseline correction. It can be noticed that thepectra are very similar to each other. A specific trend was

ot found in the two series of experiments. The results indi-ate that pre-processing can reduce the differences between

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0 0.0 05 0.01 0.015

Peak

Are

a

Solute concentra tion (g/g sol vent)

peak1 peak 2peak 3 peak 4peak 5 peak 6

ig. 4 – Trends of the chosen peak areas as a function ofolute concentration (peak specifications shown in Table 3).

size of the crystals (autoscaled and smoothed).

the spectra in the range considered. Different pre-processingtechniques, such as standard normal variate (SNV), normal-ization, baseline corrections of different order, 1st and 2ndorder derivatives, etc. were evaluated to find the best com-bination that reduces the effect of crystal size on Raman. Thecombination of SNV, 1st derivative and normalization wasfound to be the most effective in reducing the crystal sizeeffect on Raman spectra.

Eight peaks were analysed, and the corresponding regionof the spectrum are shown in Table 5. The difference in inten-sity in the raw spectrum is not large but it is not clear if thesignal increases or decreases with the particle size. The effectof size and packing is not clear from the literature and proba-bly minor differences in the preparation of the sample as wellas in the type of probe used can strongly affect the measure-ment considering the high sensitivity of Raman. In this work,by applying simple pre-processing of the signal the effect ofpacking and particle size was practically eliminated.

The signal intensity of the different peaks, for samples withdifferent particle size, and different pre-processing techniquesis shown in Fig. 6. For the first series of experiments a slightincreasing in the signal is associated with the increase in crys-tal size. The use of SNV correction partially eliminates thistrend, as well as the second derivative and the normalizationof all the spectra for the same peak at 801 cm−1. The com-bined used of SNV and derivative for eliminating scatteringeffect was already known to be effective (Huang et al., 2010).

The second series of data still shows a slightly increasingtrend apart from the data at 125 �m which seems to be an

outlier. With the same pre-processing technique, the differ-ences between the samples are almost completely eliminated

Table 5 – Peaks analysed for the evaluation of the effectof crystal size on Raman spectra.

Peak number Region of the spectrum (cm−1)

1 809–7952 170–16253 1625–15724 1050–10205 1381–10506 1220–11807 1180–11578 681–650

600 chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

Fig. 6 – Signal intensities of the chosen peaks for the first set of samples at different crystal size (peak specifications inTable 5). Pre-processing was performed in iC Raman: (a) 3rd order baseline correction; (b) 3rd order baseline correction andSNV; (c) 3rd order baseline correction, SNV and 2nd derivative; (d) 3rd order baseline correction, SNV, 2nd derivative andnormalization with peak at 801 cm−1.

even for this series of experiments (see Fig. 7). As shown inFigs. 6 and 7a good pre-processing can eliminate the effectof crystal size in the solid sample using the optical probeavailable. A further set of experiments was carried out toinvestigate the effect of crystal size on the measurementsobtained in the slurry using the immersion probe during cool-ing crystallization experiments. Specific peaks were trackedduring the experiments and FBRM and PVM were used to checkthe crystal growth. The material at the beginning and end werefiltered and analysed with an optical microscope to check theincrease in size. Experiment 1 was conducted at high stirringrate (400 rpm) and no agglomeration was observed with PVM.The final size of the crystals was considerably higher than theinitial one as shown by the FBRM data. Fig. 8 shows the totalcounts registered by FBRM and the temperature profile. Thetotal counts go down rapidly in the first 25 min as the seedspartially dissolved in the solution; then it remains constantfor the rest of the experiment. The trend of the total countsshows that during the experiment there was no nucleation, soonly growth was happening in the system.

The growth of the crystals is well visible in the crystal size

distribution trend recorded by FBRM at different times dur-ing the batch (Fig. 9). The initial CSD is characterized by a

maximum square weighted mean chord length (SWMCL) ofabout 85 �m (in good correlation with the initial sieve fractionof 63–75 �m). During the batch the distribution shifts towards ahigher SWMCL and becomes narrower, as results of the growthof the crystals. Samples from the initial and final crystals werealso analysed using an optical microscope. The micrographsin Fig. 10 also clearly indicate the growth of the crystals.

The Raman spectra were analysed by tracking differentpeaks. Fig. 11 indicates the peaks followed during the exper-iment: (i) peak 1 between 1051 and 1023 cm−1, (ii) peak 2between 777 and 760 cm−1, and (iii) peak 3 between 1175 and1154 cm−1. The signal is relatively noisy but no significantchange in the intensity of all the three peaks can be observedalthough the change in the crystals size is remarkable.

Principal component analysis (PCA) was performed on theRaman autoscaled spectra and it was found that the first prin-cipal component covers 95.8% of the variance in the signal. Forthis reason it is the only component shown in this study. Itstrend is shown in Fig. 12. The variations during all experimentsare minimal and mainly confined during the first 25 min wherethere was still dissolution of the seeds.

In conclusion crystal size does not affect the Ramanspectra in the slurry with the immersion probe utilized. A

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 601

Fig. 7 – Signal intensities of the chosen peaks for the second set of samples at different crystal size (peak specifications inTable 5). Pre-processing was performed in iC Raman: (a) 3rd order baseline correction; (b) 3rd order baseline correction andSNV; (c) 3rd order baseline correction, SNV and 2nd derivative; (d) 3rd order baseline correction, SNV, 2nd derivative andnormalization at 801 cm−1.

FF

0

200

400

600

800

1000

1200

1400

1600

1800

2000

25

27

29

31

33

35

37

39

0 50 10 0 15 0 20 0 250

Tem

pera

ture

(°C

)

Time (m in)

Tempera tureTota l co unt s

Totalcounts

ig. 8 – Temperature profile and total counts recorded byBRM during experiment 1 (400 rpm).

0

2

4

6

8

10

12

14

1 10 10 0 10 00

Squa

re w

eigh

t cho

rd le

ngth

di

strib

utio

n (#

/sec

)

Diameter (µm)

10 minutes90 minutes130 mi nutes160 mi nutes190 mi nutes

Fig. 9 – Crystal size distribution during experiment 1(400 rpm) during the study of the effect of crystal size onRaman spectra.

602 chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

Fig. 10 – (a) Seeds used for experiment 1 (400 rpm) and (b)crystals at the end of the experiment.

0.99

0.992

0.994

0.996

0.998

1

1.002

0 50 10 0 15 0 20 0 250

Nor

mal

ized

firs

t prin

cipa

l co

mpo

nent

sco

re

Time (m in)

Fig. 12 – Trend of the first principal component (normalizedto 1) during experiment 1. PCA performed on smoothed

second experiment was conducted using a lower stirringrate (200 rpm). In this case the growth of crystals is lessbut significant agglomeration was observed with PVM. Sinceagglomeration is a competing phenomenon with growth lessgrowth is observed. Part of the supersaturation was used todevelop the crystal bonds between the particles in the agglom-erates. The FBRM signal is different, the total counts/s startsdecreasing as the temperature start going down (see Fig. 13).The decrease in the total counts is associated to the forma-tion of agglomerates as shown by the PVM images in Fig. 14.

In experiment 1 the stirring rate was high enough to prevent

0

5000

10000

15000

20000

25000

30000

0 10 0 200

Ram

an In

tens

ity

Time (min)

peak 2 peak 1 peak 3

Fig. 11 – Trend of three different Raman peaks of OABAform I during experiment 1 (400 rpm). Peak 1:1051–1023 cm−1. Peak 2: 777–760 cm−1. Peak 3:1175–1154 cm−1.

and autoscaled spectra.

agglomeration and the total counts remains constant. Theagglomerates in experiment 2 start appearing after the firstcooling step and that is related to the kinetics of agglomera-tion. In fact, agglomeration depends strongly on the surfaceof the particles and the probability of collision between eachother. The initial solution is characterized by small parti-cles highly dispersed in the solution. Small particles tend toagglomerate easier than big ones because of the higher spe-cific surface energy, but because the particles are dispersed inthe solution the probability of collision and the kinetic energyof collisions are lower. During the cooling step supersatura-tion increases and the particles start to grow. At some pointthe particles are large enough to have high collision kineticenergy but still small enough to maintain strong inter-particleforces that generate agglomerates. As a result the total countsdecreases and agglomerates start to appear as shown in thePVM images (see Fig. 14).

The formation of agglomerates decreases the growth of thesingle crystals. The images with the optical microscope andthe PVM confirm that the final size of the crystal is not muchdifferent from the initial one. The CSD trend, shown in Fig. 15,is also different from experiment 1. The SWMCL increasessuddenly significantly during the batch as the number ofcounts per size bins decrease, also indicating the formation

of agglomerates.

0

500

1000

1500

2000

2500

25

27

29

31

33

35

37

39

0 10 0 200

Tem

pera

ture

(°C

)

Time (min)

Tempera turetotal counts Totalcounts

(#/sec)

Fig. 13 – Temperature profile and total counts recorded byFBRM during experiment 2 (200 rpm).

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 603

20 m

s(tescs

3

TiF

F(i

Fig. 14 – PVM images at (a) 10 min, (b) 90 min, (c) 1

Even in this experiment the Raman signal does not changeignificantly as shown by the principal component analysissee Fig. 16). The first principal component covers the 96.8% ofhe variance and it stays relatively constant during the entirexperiment showing that agglomeration of crystals, henceignificant change in size, does not affect the Raman signalonsiderably in the case of the immersion probe used in thistudy.

.3. Effect of solid concentration

he gradual addition of solid in the solution generates an

ncrease in the intensity of the Raman signal as shown inig. 17. The increase characterizes the entire spectrum.

0

1

2

3

4

5

6

7

8

9

1 10 10 0 10 00

Squa

rre

wei

gth

of th

e ch

ord

legn

th

(#/s

ec)

Diameter (µm)

40 minutes90 minutes96 minutes102 mi nutes120 mi nutes288 mi nutes

ig. 15 – Crystal size distribution during experiment 2200 rpm) for the determination of the effect of crystal sizen solution on the Raman spectra.

in and (d) 172 min during experiment 2 (200 rpm).

The data was analysed with both univariate and multi-variate techniques in order to find a correlation between thesolid concentration of the solution and the intensity of specificpeaks. Three peaks were analysed (data were smoothed withusing a 31 point smoothing window) for univariate analysis.The increase in the signal compared to the increase in the solidconcentration is significant. Table 6 indicates the increases inthe signal of three Raman peaks of OABA for an increase ofabout 10 times of the solid concentration. It can be seen thatthe increase is between 50 and 200% and varies depending onthe Raman shift region.

The best univariate regression result was obtained usingthe first derivative spectra of the third region. In particular

the height of the peak at 928 cm−1 was considered. The found

0.996

0.9965

0.997

0.9975

0.998

0.9985

0.999

0.9995

1

1.0005

0 100 200

Nor

mal

ized

firs

t prin

cipa

l co

mpo

nent

sco

re

Time (m in)

Fig. 16 – Trend of the first principal component (normalizedto 1) during experiment 2. PCA performed on smoothedand autoscaled spectra.

604 chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

500 1000 1500-2

0

2

4

6

8

Rama n shift (cm-1)

Ram

an in

tens

ity

Fig. 17 – Spectra of suspensions of OABA form I in water atdifferent solid concentrations.

Table 6 – Increase in the signal intensity for threespecific Raman peaks of OABA, associated with anincrease in the solid concentration by 10 times.

Region of the spectrum (cm−1) Increase in thepeak intensity

680–650 50%1059–1000 201.3%

964–900 215%

0 10 20 30 40 500

10

20

30

40

50

Actual solid densit y (g soli d/100g wat er)Cal

cula

ted

solid

den

sity

(g s

olid

/100

g w

ater

)

Fig. 19 – Multivariate analysis result for the effect ofsuspension density on the Raman spectra.

changes considerably it is worth taking into account the effect

relation is parabolic with a correlation coefficient of about 0.96(see Fig. 18).

A linear multivariate approach was also used consideringthe raw spectrum with linear baseline correction (average inrange 4000-0 cm−1) in the regions between 124 and 117, 575and 423, 843 and 831, 1608 and 1597, 1671 and 1656 cm−1 usinga PLS technique with 3 factors. The result show a correlationcoefficient of 0.9913 with RMSEC = 1.73. The calculated andactual values are shown in Fig. 19.

The multivariate approach is more reliable than theunivariate. A PLS regression can easily capture the suspen-sion density effect with reasonable accuracy. The strongdependence of the signal on the solid concentration of thesolution requires considering this parameter during thedesign of the calibration experiments and in the calibration.Although from the univariate analysis the variation seems to

be non-linear, the linear PLS model can still predict quite wellthe variation of the spectra. Also an increase in the error at

0 10 20 30 40 50-0.5

0

0.5

1

Solid densit y (g soli d/100 g wat er)

Ram

an s

igna

l at 9

28 c

m-1

y = -1 E-4x2 + 0.03636x -0.5959

R² = 0.96

Fig. 18 – Univariate analysis of the effect of solidconcentration on the Raman signal.

very high solid concentration can be observed, which is dueto the non-linearity effect. This indicates that a nonlinearcalibration model may be required in the case of calibrationwith very high solid concentration.

3.4. Temperature effect

The effect of temperature on the Raman spectra for both thesolvent and the solution was studied. Characteristic peakswere chosen for the solid as well as the solvent and the datawere analysed with univariate and multivariate techniques.Fig. 20 shows spectra of OABA solutions at different tempera-tures, from 10 to 40 ◦C. The difference between the spectra isminimal; temperature does not seem to have a strong effect onthe Raman signal. For the univariate analysis of the dissolvedsolute three regions were chosen. The effect of temperatureis not significant, a +200% increase of temperature generatesless than 15% decrease in intensity in the 1st derivative Ramanspectra for the peaks reported in Table 7 (characteristic ofthe solid form of OABA). The first derivative was calculatedand spectra were smoothed (31 points). Fig. 21 shows thetrend of the Raman intensity as a function of the tempera-ture. Because during a cooling crystallization the temperature

500 1000 1500-2

0

2

4

6

8

Raman shift (c m-1)

Ram

an in

tens

ity

Fig. 20 – Raman spectra of clear solutions of OABA atdifferent temperatures but same concentration.

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 605

Table 7 – Decrease in the signal intensity for threespecific Raman peaks of OABA, associated with anincrease in the temperature of 4 times.

Region of spectrum (1/cm) Decrease in intensityof the peaks in first

derivative

1190–1148 −13%1365–1345 −14%1587–1567 −7.45%

oi

so(CsUtpc

rr

ltftmTtbaf(

cia

Fsa

20 30 40 50 600.6

0.8

1

1.2

1.4

1.6

1.8 x 104

Tempera ture (°C)

Ram

an in

tens

ity

Peak 2Peak 1Reg. lin e 2Reg. lin e 1

Fig. 22 – Trend of the two chosen peaks for the solvent as afunction of the temperature of the solution. Peakspecifications and regression equations are shown in

the intensity of peak 2 increases as well. At the same timethere is a decrease in the intensity of peak 1. An attempt to

f temperature on the Raman signal during calibration, toncrease the accuracy of the model.

Regarding the solvent itself the temperature dependenceeems to be stronger; the intensity of the characteristic peaksf ethanol decreases with −32% (1479–1444 cm−1) and −23%

1117–1079 cm−1) with an increase in temperature of +200%.onsidering the 1st derivative, instead, the decrease in inten-ity is −22% (1479–1444 cm−1) and −19% (1117–1079 cm−1).nivariate analysis for solvent peaks was done choosing

wo peaks at 1479–1444 and 1117–1079 cm−1. Data were pre-rocessed with a 31 points smoothing and a 3rd order baselineorrection. Results are graphically shown in Fig. 22.

The data of Figs. 21 and 22 were interpolated using a loga-ithmic function in Excel. The results of the interpolation areeported in Table 8.

It must be noticed that the trend of each peak is not simi-ar, both for the solid and for the solvent. An equal decrease ofemperature generates different changes in intensity for dif-erent peaks. Thus, considering the peaks ratio to compensatehe temperature effect can lead to an error in the calibration. A

ultivariate approach was also used with PCR (4 components).he 1st derivative was calculated and a Norris derivative fil-

er was applied to the data (segment length of 7 and gapetween segments of 5) in the region 1066–166 cm−1. Temper-ture was chosen as the interested variable, and the resultingunction has R2 = 0.9999 with RMSEC = 0.166 and RMSEP = 0.349see Fig. 23).

Similarly to the solid concentration effect, PLS regressionan capture the variation of Raman spectra due to the changen temperature with reasonable precision although the systemppears to be non-linear using a univariate approach.

20 30 40 50 601200

1400

1600

1800

2000

2200

2400

Temperature (°C)

Ram

an in

tens

ity

Peak 1Peak 2Peak 3

ig. 21 – Trend of the three chosen peaks for solid OABA inolution as a function of temperature. Peak specificationsre shown in Table 7.

Table 8.

3.5. Calibration of Raman for solid dry mixtures offorms I and II

This set of experiments is necessary to be sure that Raman caneffectively discriminate between the two polymorphic formsof OABA. A preliminary attempt with solid mixtures shouldalways be done, even if the desired calibration function is ina specific solvent. It is very useful to study the most differ-ent regions of the spectra that will be observed during theexperiments in the solution. In addition it allows understand-ing which calibration technique is better. From a first study ofthe spectra it was found that in the case of OABA, form I andII solids have two characteristic peaks in the region 817–786(peak 1) and 786–755 cm−1 (peak 2), respectively.

Fig. 24 shows the spectra of solid samples of the twoforms. Fig. 24(a) represents solid OABA form I while Fig. 24(b)represent the spectrum of solid form II. Looking at othersamples at different concentration of forms I and II it can beobserved that when the concentration of form II increases

20 30 40 50 60 7020

30

40

50

60

70

Actual Tempera ture (°C)

Cal

cula

ted

Tem

pera

ture

(°C

)

Fig. 23 – Multivariate calibration approach to study theeffect of temperature on the Raman spectra. Calculatedversus actual temperature values.

606 chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

Table 8 – Univariate analysis results for temperature effect on different peaks of Raman spectra. The variable x representsthe peak intensity in the region of spectrum chosen while the variable y represents temperature.

Peak Region of spectrum (cm−1) Interpolating function Correlation coefficient (R2)

Peak 1 (OABA) 1190–1148 y = −269.7 ln(x) + 2995.9 0.917Peak 2 (OABA) 1365–1345 y = −216.8 ln(x) + 2208.2 0.951Peak 3 (OABA) 1587–1567 y = −96.86 ln(x) + 2196.1 0.769Peak 1 (solvent) 1479–1444 y = −50.443x + 9894.9 0.987

y

Peak 2 (solvent) 1117–1079

build a univariate calibration curve using dry mixtures of thetwo forms was done but the result was not satisfactory. Amultivariate approach is more reliable and the pre-processingtechniques can compensate for the changes in intensities dueto variation of compactness and size of the samples (Cornelet al., 2008; Chen et al., 2012).

Other factors that were analysed were the amount of sam-ple and its compactness. Two measurements for the validationdata were taken, one with 3 g of sample, the other with dou-ble amount. A difference in the intensity of the backscatteredsignal was noticed. In particular the larger the amount in thesample the weaker is the signal. However, the pre-processingtechniques can compensate for this effect. The size of thecrystal can affect the Raman spectra, generating a change inintensity as shown in this work and by Cornel et al. (2008). Asystematic approach was not used in this case but the pow-ders used for the calibration did not have a uniform CSD.In particular, form II was produced through a crystallizationexperiment, so the crystals are larger and less homogeneous

than form I. However, as shown before, the effect of crystal

200 400 600 800 1000 1200 14 00 16000

0.5

1

1.5

2

2.5

3

3.5

4

Raman shift (c m-1)

Ram

an in

tens

ity o

f for

m II

200 400 600 800 100 0 1200 14 00 1600 18000

0.5

1

1.5

2

2.5

3

Raman shift (1 /cm)

Ram

an in

tens

ity o

f for

m I

(a)

(b)

Fig. 24 – (a) Raman spectrum of OABA solid form I and (b)Raman spectrum of OABA solid form II.

= −139.46 + 20,270 0.9941

size is negligible. The most significant effect on the accuracy ofthe calibration model is given by the non-homogeneity of themixtures. To optimize the calibration and minimize the effectof non-homogeneity another series of samples were preparedand a different approach was used during sample measure-ments. Four samples were prepared at different concentrationof form II (0%, 25%, 50%, 75% and 100%). Every sample wasmixed by hand for 5 min. For each sample 5 measurementswere taken, between each of those the sample was mixed for1 min. Every measurement was repeated three times changingslightly the position of the optical probe.

As an additional comparison an NIR optical probe was usedwith the same samples and procedure. Both NIR and Ramancan discriminate forms I and II of OABA but the two probeshave different spot areas (100 �m for Raman, 1 cm for the NIRprobe). A larger spot area can capture a larger amount of sam-ple so that non homogeneities in the sample have less effecton the measurement. TQ Analyst was used to pre-process thedata and perform both PCR and PLS regression. Results areshown in Tables 9 and 10.

Compared to the previous analysis this procedure gives agreater accuracy, although the error is still higher that in thecase of the NIR. The RMSEPs of PLSR and PCR are compara-ble but PLSR needs a smaller number of components. It isclear than a better mixing can lead to better results for theRaman calibration function. In addition, a bigger spot areacould increase even more the accuracy of the model. To deeplyinvestigate the effect of non-homogeneity on the model the5th measurement for the sample at 75% of form II was per-formed 7 times instead of three. The position of the probe waschanged every time following a clockwise trajectory over thesample. For the same sample, varying the position of the probecauses a variation of the calculated value of form II contentusing PCR of around ±5%. Using the NIR with same samplethe variation is only ±1.4%. This means that the precision ofthe Raman calibration model could be improved using an opti-cal probe with a bigger spot area. However, considering thatthe Raman probe’s spot area is 100 times smaller than for theNIR but the error is only 4 times higher it can be affirmed thatthe Raman calibration model works better in evaluating thecomposition of form I and II solid mixtures of OABA.

3.6. Calibration in solution

To build a good calibration model that can be used duringthe crystallization process a set of experiments must be care-fully planned in order to include all the parameters that canaffect the Raman spectra during the process itself. A solutionof 90% water and 10% IPA was chosen because solubility datafor both forms I and II was provided and cooling crystallizationwas considered. Therefore the following parameter should beconsidered during calibration: (i) temperature, (ii) suspension

density, (iii) ratio of forms II and I. As shown in the analy-sis of this work solute concentration, suspension density and

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 607

Table 9 – Optimized parameter used for a multivariate calibration with NIR and Raman for dry solid mixtures of OABAforms I and II.

Probe Region Pre-processing techniques PLS factors PCR components

NIR 4300–10,000 nm 2nd derivative, Norris filter (5,5), SNV 4 5Raman 200–1700 cm−1 2nd derivative, Norris filter (5,5), SNV 2 5

Table 10 – Results of a multivariate calibration with NIR and Raman for dry solid mixtures of OABA forms I and II.

Probe R2 PLS R2 PCR RMSEC PLS RMSEC PCR RMSEP PLS RMSEP PCR

NIR 0.9997 0.9998 0.584% 0.497% 0.634% 0.567%Raman 0.9892 0.9894 3.74% 1.98% 3.24% 4.11%

Table 11 – PCR and PLSR comparison for the DoE experiments only.

Technique Region Factors/components R2 RMSEC RMSEP

PLS 500–3400 cm−1 8 0.9990 0.0185 0.0436PCR 500–3400 cm−1 6 0.9589 0.1200 0.0611

pal

pwpcbcomnptTcpcdwbnpsaTba

(ca(

calibrations.

0 0.5 1 1.5

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Observed Respo nse

Fitte

d R

espo

nse

PLS

Calibrati on dataVali dati on data

1

1.5

nse

PCR

(b)

(a)

olymorphic ratio can strongly affect the Raman spectrumnd those parameters can change during a cooling crystal-ization.

Second derivative (with a 2nd order polynomial and 25oints for smoothing) was applied to the spectra togetherith autoscaling. Partial least squares regression (PLSR) andrinciple component regression (PCR) were both investigatedonsidering all the samples and it was found that PLSR worksetter for this specific system as reported in Table 9. K-foldross validation was performed to choose the correct numberf components or factors, as the one which minimizes the rootean square error of cross validation (RMSECV). It was also

oticed that using the concentration of form II instead of theolymorphic ratio gives better results. This is probably due tohe introduction of solid concentration as a calibration factor.wo spectra at the same polymorphic ratio but different solidoncentration are usually very different and the model cannotredict the polymorphic ratio correctly. Also a large region washosen to take into account all the possible changes due to theifferent factors considered. Temperature and solid densityere also added to the input matrix together with the spectraut the prediction and calibration error does not change sig-ificantly. This means that the calibration experiments wereerformed in an optimized way that takes into account pos-ible factors that affect the spectra. Fig. 25 shows the PLSRnd PCR multivariate calibration and prediction points andable 11 shows the numerical results. PLS regression worksetter than PCR in predicting the concentration of form IIlthough the number of factors used is 8 instead of 6.

It must be noticed that because the design of experimentsDoE) was applied considering polymorphic ratio instead ofoncentration of form II, some calibration points are missingt high concentration. Therefore two additional experiments

with the conditions shown in Table 12) were performed to

Table 12 – Conditions of the additional two calibrationexperiments.

Experiment Temperature(◦C)

Polymorphicratio (%)

Total solidfor 100 g

solvent (g)

16 25 50 117 40 100 1.2

improve the reliability of the calibration model. The resultswith the new calibration points are shown in Table 13. Addingthe two new points does not considerably affect the correla-tion coefficient. However, the root mean squared error of bothcalibration and validation are different. The error on calibra-tion is higher but the one on prediction is lower. Comparedto the first set of data the two errors of prediction and vali-dation are very similar to each other, which is typical of good

0 0. 5 1 1.50

0.5

Obse rved Respo nse

Fitte

d R

espo

Calib ration dataVali dation da ta

Fig. 25 – PLSR and PCR calibration and validation responseswith data obtained from the DoE.

608 chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611

Table 13 – PCR and PLS comparison for the DoE experiments plus two additional calibration samples.

Technique Region Factors/components R2 RMSEC RMSEP

PLS 500–3400 cm−1 8 0.9987 0.0253 0.0258PCR 500–3400 cm−1 6 0.9650 0.1333 0.059

0

1000

2000

3000

4000

5000

6000

Tota

l cou

nts

(#/s

ec)

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Con

cent

ratio

n of

form

II (g

/100

g so

lven

t)

-0.2

0

0.2

0.4

0.6

0.8

1

1.2C

once

ntra

tion

of fo

rm I

(g/1

00g

solv

ent)

2

4

6

8

10

12

14

16x 10-3

Solu

te c

once

ntra

tion

(g/g

sol

vent

)

0 20 40 60 80 100 12 00

10

20

30

40

50

60

Time (min)

Tem

pera

ture

(°C

)

Tempera tureSolute conc.Form I conc.Form II con c.Counts

10 15 20 25 30 35 40 45 50 550

0.005

0.01

0.015

0.02

Temperature (°C)

Solu

te c

once

ntra

tion

(g/g

sol

vent

)

Solute conc.Solubil ity Form ISolubil ity Form II

(a)

(b)

Fig. 26 – (a) Evolution in time of solute concentration, temperature, total counts, and concentration of OABA forms I and IIduring the validation experiment and (b) solute concentration plotted versus temperature during the validation experiment.

3.7. Application of the calibration model forquantitative monitoring of the polymorphictransformation during the crystallization of OABA

A validation experiment was performed to check the cal-ibration model developed. In this experiment form II wasnucleated and then a supersaturation control was imple-mented to control the crystallization along a trajectory inthe phase diagram that promotes the transformation of themetastable form II into form I. Fig. 26(a) shows the signalsfrom the ATR-UV/Vis, Raman and FBRM. Nucleation occurredafter 23 min and was detected by all three probes. The nuclea-tion of form II is confirmed by the Raman measurement. FormII continued to nucleate until 60 min as shown by the FBRMcounts and the Raman signal. Around 60 min form I appearsand form II starts disappearing. Due to the formation of form Ithe solute concentration crossed the solubility of curve of formII which then starts dissolving simultaneously with the forma-tion of form I, as shown in Fig. 26(b) at around 27 ◦C. Between80 and 100 min, both the temperature and the solute concen-tration remain constant, while the concentration of form II

measured from the Raman is decreasing. That means that therate of dissolution of form II and the rate of nucleation of form

I are similar and the solute concentration can remain con-stant without changing the temperature. At around 100 minthe nucleation of form I became faster than the dissolutionof form II and the solute concentration started to decreaserapidly. The supersaturation control was not able to maintainsuch a high set point and the temperature went down rapidlyto 10 ◦C, which was considered the lower limit for the control.However the Raman signal shows that the complete transfor-mation happened just before the end of the supersaturationcontrol. Optical microscopy and Raman were also used at theend of the run to check the polymorphic forms and confirmedthe absence of form II.

4. Conclusions

The effect of crystal size, temperature, solute and solid con-centration on Raman spectra were analysed systematically inorder to design a good calibration strategy for polymorphicform quantification of OABA in solution. Solute and solid con-centration were found to strongly affect Raman spectra whilecrystal size and temperature cause only minor changes. Exper-

iments on dry solid mixtures were performed to investigatethe sensitivity of Raman with OABA forms I and II. Finally

chemical engineering research and design 9 2 ( 2 0 1 4 ) 594–611 609

cic

(

(

(

pp

ptm

A

FCPCBaha

iffi

R

A

A

A

A

B

C

C

alibration in solution was performed using design of exper-ments with three levels. In conclusion the steps for a goodalibration of polymorphic ratios are:

1) Identify the parameter that can change during a crys-tallization experiment (in cooling crystallization they aretemperature, solid concentration and crystal size).

2) Verify the sensitivity of Raman with solid samples beforestarting working with suspensions.

3) Study the effect of those parameters on Raman spectrawith the probe available and select calibration experi-ments accordingly using a design of experiment (DoE).

The calibration model was used to monitor the polymor-hic composition during a cooling crystallization experimenterformed at constant supersaturation.

The systematic investigation and results presented in thisaper represent basis of the proposed “good calibration prac-ise” (GCP) framework for quantitative analysis of polymorphic

ixtures using Raman spectroscopy.

cknowledgements

inancial support is acknowledged by the European Researchouncil under the European Union’s Seventh Frameworkrogramme (FP7/2007-2013)/ERC grant agreement no. [280106-rySys]. We would like to acknowledge Thermo Fisher UK andruker UK for the loan of NIR instruments. Sheelagh Halseynd Dr. Ali Gahkani from Thermo Fisher and Bruker for theirelp and assistance with setting up NIR instruments and datanalysis.

The second author would like to acknowledge Engineer-ng and Physical Science Research Council (EPSRC) centeror Continuous Manufacturing and Crystallization (CMAC) fornancial support.

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