RESEARCH ARTICLE

Near Infrared Method Development for a ContinuousManufacturing Blending Process

Yleana M. Coln & Miguel A. Florian & David Acevedo &Rafael Mndez & Rodolfo J. Romaach

Published online: 30 August 2014# Springer Science+Business Media New York 2014

AbstractPurpose This study describes the validation of a near infraredspectroscopic method for monitoring a continuousmanufacturing system. The achievement of this goal requiresdetermining the near infrared (NIR) methods accuracy, pre-cision, obtaining an estimate of the sample volume analyzed,and the frequency with which measurements should beobtained.Methods Five calibration blends were prepared spanning aconcentration range from 7 to 13 % w/w ibuprofen to preparea partial least squares (PLS) calibration model for a fivecomponent formulation. NIR spectra were obtained after pow-ders passed through feeders and a custom-made tumble mixer.The calibration models precision and accuracy were deter-mined with the prediction of three validation blends.Results NIR concentration predictions obtained from spectracollected during the validation runs showed relative standarderrors of predictions of 2.8 % at target concentration withstandard deviations NMT 0.2 % w/w. At certain time points,blend samples coming out of the blender were collected forUV analysis. Results from the NIR and UV analysis werecomparable with the largest difference being 0.36 % w/wbetween the methods. A continuous blending process wasmonitored for 3 min after steady-state conditions wereachieved. All individual predictions were within 3 standarddeviations of the average reference value. The standard devi-ation for the NIR concentration predictions was 0.49 % w/w.Conclusion The use of the standard normal variate (SNV)transform significantly reduced the effect of differences in

powder flow on the NIR predictions. The use of variogramsprovided valuable insight into the frequency of measure-ments needed for the continuous manufacturing system.Additional research is needed to investigate the differencesobserved in the NIR and UV results for the continuousmanufacturing run.

Keywords Continuous powdermixing . Near infraredspectroscopy . Validation . Sampling . Pharmaceuticals .

Particulate processes . Theory of sampling . In-linequantification . Calibration

Introduction

Current pharmaceutical manufacturing processes primarilyinvolve batch processes. These batch processes require thatconsiderable development efforts be dedicated to productscale-up and transference of the process from developmentto manufacturing facilities [14]. The use of a continuousmanufacturing process, as described in this work, will elimi-nate scale-up efforts as the same mixing unit used in develop-ment could be used in product manufacture.

The continuous manufacturing process described in thispaper uses a small custom-made mixer that offers significantflexibility at a maximum of 80 kg/h at 70 rpm [5]. Theexcipients and active pharmaceutical ingredient (API) areadded to the mixer through volumetric feeders. The size of alot will not be restricted by the manufacturing equipmentavailable at a pharmaceutical site. Lot size will be adjustedby the amount of time employed during manufacture. Thus,the systemwill be used for a certain time to fulfill a customersorder of 100,000 tablets and for a longer period for a 500,000tablet order. The continuous manufacturing system is also avery compact system, thereby reducing product hand-offs andtransfers, reducing the potential for product cross-

Y. M. Coln :R. J. Romaach (*)Department of Chemistry, University of Puerto Rico at Mayagez,PO Box 9000, Mayagez Campus, Mayagez 00682, Puerto Ricoe-mail: rodolfoj.romanach@upr.edu

M. A. Florian :D. Acevedo :R. MndezDepartment of Chemical Engineering, University of Puerto Rico atMayagez, Mayagez, Puerto Rico

J Pharm Innov (2014) 9:291301DOI 10.1007/s12247-014-9194-1

contamination and simplifying cleaning validation efforts.Due to the small size of continuous manufacturing systems,product isolation could be facilitated for potent drugs, al-though the design presented in this study is not designed forthese purposes. The small size of continuous manufacturingmixers is also an advantage in early product developmentwhen supplies of a new API are often limited. The increasedflexibility may be achieved within a smaller manufacturingarea leading to considerable savings in energy and sitemaintenance.

Near infrared (NIR) spectroscopy has been extensivelyused for monitoring batch processes [68], but this workdescribes the monitoring of a continuous mixing process withNIR spectroscopy. The NIR methods for batch manufacturinghave been used to evaluate the endpoint of the mixing process[9, 10]. The NIR methods for continuous blending need tomonitor the variation in the mixing after steady state isachieved [11, 12]. The evaluation of method precision isextremely important to discern between the variation fromthe mixing process and the variation associated to the NIRmethod that is monitoring the blending process. Thus, in thiswork, the precision and accuracy of the NIR method arethoroughly evaluated. A five-component blend is analyzedand significant efforts to incorporate previous knowledgefrom this field which has shown the importance of carefullyconstructing calibration sets capable of predicting the futuremanufacturing process [12].

Materials and Methods

Materials

Ibuprofen (90 grade, BASF Corporation) was chosen as arepresentative cohesive API, lactose monohydrate (Tablettose70, Mutchler Inc., NF, EP, JP) and microcrystalline celluloseType 102 (JRS Pharma, PhEur, JP, USP, NF) were chosen asmain excipients and accounted for roughly 90 % of the targetblend. Colloidal silicon dioxide (99.0100.8 % (w/w), CabotCorporation) and magnesium stearate (Mallinckrodt Inc., NF)were also included as minor excipients to enhance ease offlow and final product parameters.

Manufacturing Setup

Figure 1 illustrates the experimental continuous manufactur-ing setup used for these experiments (model development,validation runs, and continuous process). Pilot-scale feedersallowed flow rate control of powder to the mixer. The NIRinstrument was placed on a holder over the conveyor belt.Preblended formulations were placed in a Gericke feederinitially filled up to 80 % of its maximum capacity andoperated in volumetric mode (the screw operated at constant

speed, 76.5 kg/h). For the continuous blending process run,API preblend was placed in feeder #1 and the excipientpreblend in feeder #2 (the screw operated at constant speed,9.96 and 66.5 kg/h, respectively).

NIR Spectral Acquisition

A Control Development, Inc. (CDI) spectrometer series 1402NIR analyzer (South Bend, IN) governed by CDI Spec32 dataacquisition package version 1.7.1.3 was used to obtain theNIR spectra. This spectrometer is equipped with an indiumgal l ium arsen ide ( InGaAs) d iode a r ray tha t i sthermoelectrically cooled and has 256 elements covering aspectral response from 905 to 1,681 nm and includes a diffusereflectance measurement probe that illuminates a 2.5-cm di-ameter. The NIR instrument was placed approximately 1 cmover the sample. The systems integration time was set at6.6 ms per scan. A total of 36 spectra were averaged.

Reference spectra were obtained with a CDH 50Standard White disk, made with Albrillon, an organicmicrocrystalline fluorinated polymer that provides astrong reflectance. This disk was also used to character-ize the spectral noise. The spectrum of this material wasused for both background and sample spectra, and theratio of the spectra was used to estimate the instrumentnoise. The standard deviation of the mean signal amongdays ranged from 1.7 to 2.0104. Spectra were ac-quired during five consecutive days. The system wasturned off and on for periods of 20 min each day. A15-min wait time for lamp stability was employed asrecommended by the manufacturer prior to collectingspectra. This result represents the minimum standarddeviation estimated to be observed throughout theexperiments.

The sample volume analyzed by the NIR was estimated byplacing layers of different width of sample on top of an acrylicsurface. As the sample thickness on top of the acrylic surfacewas decreased, the distinct absorption bands of the acrylicsurface around 1,331 and 1,344 nm were observed. Thesecharacteristic bands were first observed at approximately0.5 mm of powder sample thickness at a constant probe-sample distance of approximately 1 cm. These experimentsshowed that the signal returning to the detector comes approx-imately from the top 0.5 mm of the powder sample. Theapproximate sample volume when 12 scans are averaged is312 mg and was calculated from the equation below whichtakes into consideration that the sample is moving while thespectra are being obtained.

M d2

2 d x

" #H 1

292 J Pharm Innov (2014) 9:291301

where is the sample bulk density (0.60 g/cm3), d is theNIR beam diameter,x is the sample displacement, and H isthe experimental depth of penetration of the NIR beam.

Preparation of Calibration and Validation Blends

A total of five calibration blends and three validationblends were prepared spanning a concentration rangefrom 7 to 13 % w/w ibuprofen (Ibu). The Solver Exceltool was used to create an experimental design thatreduced correlation between the concentrations of themain excipients and ibuprofen.

All eight blends were individually prepared by placingibuprofen over a standard sieve (USA Standard Testing SievesASTM Specification: Sieve Designation No. 30). Colloidalsilicon dioxide (SiO2) was placed over the ibuprofen alongwith a portion of preweighed lactose in order to prevent thecohesivemixture from sticking to the sieve. Components wererubbed together through the sieve [13]. This preliminary mix-ture was used as the API preblend. A 16-qt PK Shell V-Blender was charged with the components in the followingorder: remaining preweighed lactose (Lac), API preblend, andmicrocrystalline cellulose (MCC). The V-Blender was operat-ed at 15 rpm for a total time of 60 min. Afterward, magnesiumstearate (MgSt) was added and the blend mixed for an addi-tional 4 min. The API content in each sample was calculatedfrom the values obtained by weighing. The blending endpointof the calibration blends was monitored by calculating thestandard deviation of the 1,202-nm peak intensity at 12 dif-ferent points of the blend after first derivative pretreatment forevery 15 min during the 60-min preblending process [7].

Preparation of Continuous Blending Process Preblends

Two preblends were individually prepared for the continuousblending process: the API preblend and the Excipientpreblend. The API preblend was prepared as described aboveand placed in the 16-qt PK Shell V-Blender operated at 15 rpmfor a total time of 30 min. The excipient preblend was pre-pared by charging the excipient components in the followingorder: preweighed lactose (Lac), API preblend, and MCC.The V-Blender was operated at 15 rpm for a total time of60 min. Afterward, magnesium stearate (MgSt) was addedand the blend mixed for an additional 4 min.

NIR Calibration Model Development and Data Analysis

In-line NIR spectra of the calibration blends were obtained asthe powder moved underneath the NIR beam on the movingconveyor belt as shown in Fig. 1. Data pretreatment and NIRcalibration model development was performed using SIMCA-P (Umetrics Software Prediction Engine) and the partial leastsquare (PLS) algorithm. The NIR calibration model was de-veloped after evaluation of spectral regions, data pretreat-ments, and number of PLS components. The calibrationmodels performance was first assessed in terms of root meanstandard error for calibration (RMSEC). The calibrationmodels performance was also evaluated in terms of root meanstandard error of prediction (RMSEP) using a prediction sam-ple set (Eq. 3).

RMSEC

Xmi1 byiyi 2

N f 1

vuut 2

Fig. 1 Schematic of theexperimental setup for thecontinuous mixing experiments: atwo pilot plant powder feeders, bcustom-made continuous mixer, cconveyor belt, d NIRspectrometer, and e samplingpoint for reference method

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RMSEP

Xmi1 byiyi 2N

vuut3

where byi is the API concentration predicted by the NIRcalibration model, yi is the reference (gravimetric) concentra-tion,N is the number of samples in the prediction or validationsets, and f is the number of factors used in the model. Allspectra were mean centered. The spectral pretreatments testedfor model construction included first and second derivativeand standard normal variate (SNV). Derivatives were obtainedby applying the Savitzky-Golay (SG) algorithm using a 21-point moving window [14, 15]. The effect of different spectralregions was also evaluated.

Variograms were calculated using in-house developed soft-ware with MATLAB (The MathWorks, Natick, MA) version7.10 (R2010a). Fast Fourier transform (FFT) analysis wasperformed with a MATLAB discrete Fourier transforms(DFT) to study the variability observed in the continuousmanufacturing results.

Ibuprofen UV Method

To confirm the prediction of the NIR calibration model,an in-house validated UV method was used as thereference method for the quantification of the ibuprofen.The absorbance of samples and standards was measuredat 266 nm with a 10-mm optical path length. Eachpowder sample collected was weighed and transferredto 500-mL volumetric flasks using a solution of 75 %methanol: 25 % distilled water as diluent. After samplepreparation, the solution was analyzed using a ThermoElectron Corp. UV spectrometer (Marietta, OH). A dayof use accuracy assessment was performed at the begin-ning of the analysis over three concentration levels toevaluate the systems and the analysts performance.Readings were made in duplicate for accuracy standardsand samples. A standard deviation of 0.9 % and anaverage recovery of 100.7 % (n=36) for ibuprofen wereobtained. The methods linearity study showed a corre-lation coefficient (r2) of 1.000 over the range of 0.050.70 mg/mL ibuprofen.

Blend Sampling

Samples were retrieved at the end of the conveyor beltfor each test sample set and continuous mixing run. Theflowing powder coming out at the end of the conveyorbelt was sampled every 5 s using a plastic cup tosample the flowing stream obtaining approximately 3 gof powder per time point [16]. Afterward, API quanti-fication of each sample was performed off-line by thein-house validated UV method.

Results and Discussion

Development of NIR Calibration Model

NIR spectra for the main components of the formulation (Ibu,Lac and MCC) were measured off-line as illustrated in Fig. 2.Ibuprofen (API) is characterized by a group of absorptionbands at low wavelengths corresponding to the first andsecond overtone vibrations, while the major excipients haveabsorbance bands at higher wavelengths. The API concentra-tion is the greatest source of variation in the 1,0901,428-nmwavelength region as shown in Fig. 3. Well-defined clusterscan be seen in the PCA score plot for each API concentration

Fig. 2 Raw NIR spectra for the three major components in the pharma-ceutical blend: ibuprofen, lactose, and microcrystalline cellulose. Theshaded area corresponds to the spectral region associated with the APIspectral bands

Fig. 3 PCA score plot for the calibration sample set in the 1,0901,428-nm wavelength region

294 J Pharm Innov (2014) 9:291301

level when using first derivative pretreatment, capturing93.8 % of the spectral variation between the first two factors.

Random sample selection was used to select 40 % of thecalibration sample set spectra to develop the NIR calibrationmodel and the remaining 60 % as the prediction sample set.Calibration models developed with several spectral regionsand number of PLS factors were evaluated to optimize theability of the NIR calibration model to predict the predictionsample set. This first assessment was based on the RMSEC(Eq. 2) and the RMSEP (Eq. 3). Table 1 shows the fraction ofthe variation of the Y variables explained by the model(R2Ycum) and the error of prediction for the prediction sampleset for some of the NIR calibration models evaluated. Not allNIR calibration models were included for clarity purposes.

Results showed that when a wide spectral region (modelno. 1) was used to develop the NIR calibration model, the firstPLS factor describes less variation of the Y variable (APIconcentration) than models developed using shorter spectralrange (model nos. 2, 4, 6, and 8). This is likely related with the

inclusion of high wavelengths into the model, which areassociated with the excipients of the formulation. The calibra-tion model with 2 PLS factors provided a better prediction ofthe Y variable as seen by the R2Ycum. Therefore, when com-paring shorter spectral regions, 1,0901,428-nm spectral re-gion and 2 PLS factors (model nos. 3 and 5) are determined asthe optimal parameters to develop the preliminary NIR cali-bration model with an average RMSEP of 0.3 % (w/w) asshown in Table 1.

Method Robustness

The amount of blend over the conveyor may change over timein the continuous manufacturing setup used in this process(Fig. 1). The distance between probe and sample will vary as aconsequence of powder flow rate changes in the process. Theprobe distance to the powder will be reduced when morepowder is accumulated or increased if less powder is presentin the conveyor belt. The effect of this variation was assessed

Table 1 Parameters of the NIR calibration models constructed and evaluated

NIR calibration model no. Spectral range(nm)

Data pretreatment Principal Components (#) Percent of variation RMSEC(%)

RMSEP(%)R2Ycum

1 1,0901,605 SNV+1st derv 1 87.1 0.77 0.79

2 1,0901,428 1st derv 1 99.0 0.22 0.27

3 1st derv 2 99.5 0.15 0.24

4 SNV+1st derv 1 93.6 0.54 0.55

5 SNV+1st derv 2 97.9 0.31 0.37

6 1,0901,255 1st derv 1 95.0 0.48 0.59

7 1st derv 2 98.9 0.22 0.40

8 SNV+1st derv 1 94.6 0.50 0.59

9 SNV+1st derv 2 98.5 0.26 0.42

NIR near infrared, RMSEC root mean standard error for calibration,RMSEP root mean standard error of prediction, derv derivative, SNV standard normalvariate

Table 2 Probe to sample distance effects in NIR model predicted concentration

Sample-probe distance (cm) NIR calibration model response

Model no. 3 (1st derivative) Model no. 5 (SNV/1st derivative)

Average predicted concentration % (w/w) SD (n=6) Average predicted concentration % (w/w) SD (n=6)

0.79 8.90 0.04 8.29 0.16

0.83 9.04 0.05 8.27 0.17

0.90 9.24 0.09 8.41 0.07

0.97 9.51 0.08 8.48 0.08

1.10 9.89 0.09 8.40 0.16

1.19 10.20 0.12 8.38 0.16

1.29 10.56 0.04 8.48 0.14

NIR near infrared, SNV standard normal variate

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by varying the probe to sample distance to a maximum of0.5 cm and determining its effect on the predictions. Table 2presents the NIR predicted concentrations of API and repeat-ability assessment for both preliminary NIR calibrationmodels at different probe to sample distance.

The results show that using first derivative to pretreat thedata lead to concentration predictions varying up to 1.66 % w/w when the probe to sample distance varied 0.5 cm. On thecontrary, when using SNV prior to first derivative, the con-centration predictions varied as little as 0.2 %w/w as the probeto sample distances varied. Figure 4 illustrates the drug con-centration predictions as a function of probe to sample dis-tance. The predictions remained practically invariant whenSNV-first derivative is performed. All remaining predictionswere performed with SNV-first derivative because of therobustness of this NIR calibration method to variations inpowder level and powder distance to NIR probe.

The effect of instrument noise on PLS predictions was alsoevaluated as part of method robustness. The background noisespectra acquired from the polymer standard were multipliedby a factor of 3 and added to the spectra obtained from a 10 %(w/w) API target blend. These new spectra were then predicted

by the NIR calibration model. The additional error did notaffect the NIR predictions.

Prediction of Validation Blends

The NIR calibration model was further evaluated for precisionand accuracy. Three independent validation blends of knownconcentrations ranging from 9 to 11 % (w/w) API were pre-dicted as they passed through the continuous manufacturingsetup. For each run, the feeder was filled with a validationblend. The blend was passed through the blender, and NIRspectra were obtained for approximately 4 min as the powdermoved over the conveyor belt. Blend samples were obtainedevery 5 s for the determination of API by UV analysis [17].Table 3 shows the excellent results obtained. Figure 5 furtherillustrates the NIR predictions obtained from the validationblends and the UV results measured at certain time points.

The largest difference between UV and NIR results was0.36 % (w/w), for the 11 % (w/w) blend. Close evaluation ofthese experimental runs showed that after steady-state condi-tion (approx. 80 to 90 s), average NIR predictions from the 9and 10 % (w/w) validation blends were within the 95 %conflict of interest (CI) of the average target concentrationrange. The average NIR prediction of the 11 % validationblend is within the 99 % CI (0.36) of the average targetconcentration range. Therefore, results between the twomethods were comparable.

The validation blends also provided valuable informationin terms of method precision. The standard deviations obtain-ed by the NIR method were NMT 0.2 % and with an RSEPbelow 4 %. The standard deviation for the validation blends istwo to three times larger than those observed for the repeat-ability studies shown in Table 3. The repeatability studieswere performed by obtaining six consecutive spectra withoutmoving the sample (same sample) and ranged from 0.08 to0.17 % (w/w) (Table 2). Thus, the repeatability studyrepresents the method error, the minimum error thatmay be expected for the NIR method. Since the blendswere well mixed before they were placed in the feeder,the 0.2 % (w/w) standard deviation is considered theminimum variation that could be expected in the

Fig. 4 Comparison of NIR calibration model response when using SNVpretreatment

Table 3 NIR model prediction of validation blends

API target concentration % (w/w) NIR UV

Predicted API mean % (w/w) SD RSEP (%) CI (95 %) Measured API mean % (w/w) SD CI (95 %)

9 8.89 0.24 2.81 0.02 8.78 0.47 0.27

10 9.61 0.20 2.82 0.02 9.81 0.43 0.25

11 11.17 0.21 3.93 0.02 10.81 0.49 0.28

NIR near infrared, API active pharmaceutical ingredient, RSEP relative standard error of prediction

296 J Pharm Innov (2014) 9:291301

continuous manufacturing setup discussed in this study.However, the 0.2 % (w/w) standard deviation was ob-tained when the blends were preblended, passed throughone feeder, through the mixer, and deposited over theconveyor belt. The next phase of the study included theentire system where the two feeders were used.

Real-time Monitoring of Continuous Blending Process

The validated calibration model was used to monitor a con-tinuous blending process for 3 min. Figure 6 shows the real-time monitoring of a continuous powder blending operation.The system was set up to target a 10 % w/w ibuprofen blend.Blend samples were collected for the corresponding off-lineUV analysis. The NIR method predicted the API concentra-tion approximately every 0.24 s. The UV samples were col-lected approximately every 60 s, and the results obtained areshown in Table 4.

All individual predictions were within 3 standard devia-tions of the average after steady-state conditions wereachieved. The standard deviation for the NIR predictionswas 0.49 % w/w. The standard deviation was approximatelytwo times greater than obtained for the validation blends.Since the validation blends were premixed before being mon-itored in the continuous manufacturing setup, the increase inthe standard deviation in the continuous blending process was

expected. These are considered excellent preliminary resultssince the choice and operation of the custom-made mixingequipment still require optimization [5].

Table 4 also reveals that NIR predicts a higher drug con-centration than that determined by the UV method. Thisdifference was not observed in the validation blends (Table 3)

Fig. 5 NIR predictions obtained from the monitoring of three validation blends. UV measurements of API content for each run are also illustrated

Fig. 6 NIR predictions obtained from real-time monitoring of a contin-uous blending process. UV measurements of API content are alsoillustrated

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and is not understood at the present time. The main differencein the experimental setup is that the full continuousmanufacturing system used two feeders (for API and excipi-ent), while the setup for the validation blends used only onefeeder. These results are in the same range of predictionconcordance as the results obtained by monitoring real-timeon-line blend uniformity by Sulub et al., which obtained anaverage prediction difference of 2.2 % between API NIRpredictions (%) and HPLC (%) measurements [18]. This isfurther evidence of the difficulty of analyzing blends. Acareful review of previous publications revealed that veryfew studies have compared real-time NIR measurements and

off-linemeasurements [6, 1921]. This comparison is not easysince the NIR method is providing the concentration of asample volume that is approximately 300mgwhile the sampleanalyzed by UV is about 3 g. The authors recognize thatadditional experimental work is required to evaluate the dif-ference between UVand NIR results.

The variation after steady state was also evaluated with theuse of a variogram. The theory of sampling defines one-dimensional (1-D) heterogeneity for processes where samplesare obtained along one dimension in space, and there is anatural linear order among the increments [22]. In continuousmanufacturing, the drug concentration in the blend may vary

Table 4 Evaluation of the continuous blending process

NIR UV

Predicted API mean % (w/w) SD CI (95 %) Measured API mean % (w/w) SD CI (95 %)

10.5 0.49 0.04 9.3 0.71 0.23

NIR near infrared, API active pharmaceutical ingredient

Fig. 7 Variographic analysis of the validation blends and the real-time monitoring of the continuous blending process

298 J Pharm Innov (2014) 9:291301

over time, and time constitutes the dominant dimension. Thevariogram was used to optimize the NIR sampling rate for theprocess. The variogram was used to identify trends in theprocess data by calculating the correlation between NIR pre-dictions [23]. Thus, a lag of 1 measures the correlation be-tween consecutive NIR predictions, and a lag of 2 measuresthe correlation between every other NIR prediction.

V j 12 N j

XN1

N

a N j a N 2 4

where N is the number of data points in the time series (orlot), j is the lag (which refers to the distance in time betweentwoNIR predictions, and a is the concentration. Low values ofV(j) result from similar drug concentration predictions andindicate high correlation between the NIR predictions.

Figure 7 shows the variograms obtained for the NIR pre-dictions from the continuous blending process and the valida-tion blends. The lag (inter-distance) between any two mea-surements is measured in seconds (equidistant intervals). Thevariogram function, V(j), was evaluated from 1 to 100 incre-ments. Each variogram shows a cyclic behavior as well as aslight increase in the variogram function (V(j)) as the lagbecomes increasingly larger. A cyclic variogram is typicallyobserved when the variability of the process is influenced by afactor causing this behavior. This is representative of thequasi-steady state of the system and demonstrates that the

variogram analysis is an effective tool to determine if thesystem is operating in steady state.

In the validation blend runs, results for V(j) and the changein the autocorrelation function are minimal (approx. 0.04) forconsecutive lag times since these blends are less heteroge-neous (previously blended) when compared to the continuousblending run. In the validation runs, only one feeder was used,and the ascending tendencies in the variograms were related tosmall changes in powder feed rate affecting the distancebetween the NIR sensor and the powder bed.

During the continuous blending run, the autocorrelationfunction varied approximately ten times (approx. 0.4) morethan for the validation blends describing more heterogeneitybetween consecutive measurements. In addition, the continu-ous blending run variogram increases less rapidly with in-creasing lag time compared to the validation blend runs. Theautocorrelation function varies from 0.4 and 0.9, but withminimal change from a lag time of 10 to values as high as100. This low variation may be a result of steady state. Duringthe continuous blending run, two feeders were used; therefore,more powder remained in the feeder extending the time to getto the emptying stage, but additional studies are necessary toconfirm this observation. This run also shows a minimum atlag (j)=21 and additional minima at j=35, 48, 61, 74, 84. Thisindicates a period of 13 NIR measurements (approximately3.1 s). According to the Nyquist theorem, it is recommendedto sample at a higher frequency than two per period to capturethe process variation. NIR measurements of the continuous

Fig. 8 FFT analysis of the validation blends and the real-time monitoring of the continuous blending process

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process could be taken every 1.4 s, more than half the ob-served frequency of 3.1 s. In future experiments, the samplingrate will be set to the optimized parameters found. Thus, thevariogram is a useful tool for determining how frequently tosample.

FFT analysis was performed to study the variability ob-served in the continuous manufacturing results. Figure 8shows the frequencies associated to the measurements ofNIR API prediction concentration obtained through time. Inthis continuous manufacturing setup, the screw feeders [24]and the mixer influence in the observed cyclic behavior. Twodominant frequencies at approximately 0.98 and 1.32 Hz wereobserved in the validation runs. These frequencies could beassociated to the mixer and screw feeder speed cyclic behav-ior, respectively. These units were operated under the sameconditions in all validation runs. Three dominating frequen-cies were observed in the continuous blending run. One ofdominating frequencies (0.98 Hz) was also observed in thevalidation runs. This observation is not unexpected since themixer conditions were the same as for those runs. The othertwo dominating peaks (0.62 and 0.96 Hz) were unique for thecontinuous run. This could probably be associated to the useof two feeders in the continuous blending process. Moreover,both peaks are at lower frequencies compared to the 1.32 Hzassociated with the screw feeder speed in the validation runs.This is in agreement with the fact that during the continuousblending process, the screw feeder speed in feeder #2 was setat a lower RPM to maintain the total feed rate since anadditional feeder (feeder #1) was used. Therefore, the two0.62 and 0.96 Hz peaks can be associated to the two differentscrew feeder speeds used in the continuous blending run.Thus, the use of FTT complemented the variogram results inidentifying the root causes of the cyclic behavior observed.

Conclusions

The sources of error in the NIR method used to monitor acontinuous mixing process have been investigated. Repeat-ability studies measured the instrumental error that rangedfrom 0.07 to 0.17 % (w/w). The precision and accuracy indetermining well-mixed blends were also determined bymon-itoring previously mixed blends as they passed through thecontinuous manufacturing setup. The standard deviation ofthe well-mixed blends was about 2.5 times greater than thevariation observed in the repeatability study. This increase instandard deviation is due to variations in the composition ofthe powder mixture and powder flow. Powder flow differ-ences affect the level of powder over the conveyor belt andvary the distance between the powder and the NIR instrument.NIR predictions were affected by the probe to powder distancein the initial calibration models. However, the use of the SNV

transform significantly reduced the effect of differences inpowder flow on the NIR predictions. The standard deviationof the continuous mixing experiment ranged from 0.47 to0.53 % (w/w) about twice the variation observed for thevalidation mixtures. The increase is associated with the useof two feeders and the mixing of excipients and API. Thisthorough characterization of the precision of the NIR methodis necessary in continuous manufacturing where the NIRsystem is used to monitor drug concentration after steady stateis achieved.

The cyclic variation observed in the variograms and theresults from the analysis of the FFTcan identify the units in anoperation causing variation in the measurements in this con-tinuous manufacturing setup. The variogram and the FTTshowed to be effective tools to investigate the variability ofthe blend under study and present an approach to understandhow frequently a continuous process has to be sampled.

Acknowledgments This study was financially supported by the NSFEngineering Research Center for Structured Organic Particulate Systems(EEC-0540855). The authors would like to thank Jesus Torres for assis-tance with the Fast Fourier Transform analysis that greatly improved themanuscript.

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Near Infrared Method Development for a Continuous Manufacturing Blending ProcessAbstractAbstractAbstractAbstractAbstractIntroductionMaterials and MethodsMaterialsManufacturing SetupNIR Spectral AcquisitionPreparation of Calibration and Validation BlendsPreparation of Continuous Blending Process PreblendsNIR Calibration Model Development and Data AnalysisIbuprofen UV MethodBlend Sampling

Results and DiscussionDevelopment of NIR Calibration ModelMethod RobustnessPrediction of Validation BlendsReal-time Monitoring of Continuous Blending Process

ConclusionsReferences