528 Volume 54, Number 4, 2000 APPLIED SPECTROSCOPY0003-7028 / 00 / 5404-0528$2.00 / 0

q 2000 Society for Applied Spectroscopy

On-Line Monitoring of a Latex Emulsion Polymerization byFiber-Optic FT-Raman Spectroscopy. Part I: Calibration

CORINNE BAUER, BRUNO AMRAM, MATHIAS AGNELY,DOMINIQUE CHARMOT, JURGEN SAWATZKI, NATHALIE DUPUY, andJEAN-PIERRE HUVENNE*Laboratoire de Spectrochimie Infrarouge et Raman, LASIR, CNRS, BaÃt. C5 et Ecole Universitaire D’ IngeÂnieurs de Lille BaÃt. D,

UniversiteÂdes Sciences et Technologies de Lille, F-59655 Villeneuve d’Ascq cedex, France (C.B., N.B., J.-P.H.); RHODIA Centre

de Recherches d’Aubervilliers, 52, rue de la Haie Coq, F-93308 Aubervilliers Cedex, France (B.A., M.A., D.C.); and BRUKER

OPTIK GmbH, Wikingerstr. 13, D-76189 Karlsruhe, Germany (J.S.)

The on-line monitoring of a styrene/butadiene latex emulsion po-

lymerization was completed. Our aim was to determine in real time

the dry extract percentage or the amount of styrene monomer inthe reactor. We constructed two partial least-squares (PLS) cali-

bration models with mean-centered spectra over the 3500± 2700 <1800± 500 cm 2 1 spectral range. Four factors were required to modelthe evolution of the dry extract, and a two-factors model calibrated

the amount of free styrene. The results were very satisfactory: the

dry extract was predicted with a root mean squared error of pre-diction (RMSEP) lower than 1% over the 2± 50% dry extract range,

and for the amount of styrene monomer, the RMSEP was lower

than 5000 ppm over the 0± 70 000 ppm range. The nonlinear effectsof temperature and water reabsorption were considered, and they

did not prevent PLS from giving good-quality results.

Index Headings: Emulsion polymerization; FT-Raman; PLS.

INTRODUCTION

The need to be able to rapidly and accurately monitorthe composition of reacting polymerization systems hasbecome a serious industrial preoccupation. The maindriving forces behind current research and developmentin this area are the need (1) to improve existing technol-ogy, and reduce manufacturing costs; (2) to maintain/im-prove product quality; and (3) to reduce the amount ofthe remaining polluting volatile organic compounds.

Conventional analysis is time consuming and is noteasily adapted to on-line real-time measurements. Vibra-tional spectroscopy provides an abundance of chemicalinformation, and spectra are rapidly acquired. Due to theabundance of chemical information, the concentration ofindividual species can be determined, even in a complexsystem, with the use of a multivariate calibration andanalysis technique.

The system we are interested in consists of a styrene/butadiene latex during an emulsion polymerization pro-cess. Emulsion polymerization kinetics has been exten-sively studied with the use of Fourier transform (FT)-Raman scattering spectroscopy because of several mainadvantages over infrared absorption spectroscopy.1±3 Theintensity in the Raman spectrum of the n (C 5 C) stretchingvibration gives this technique a particular advantage forthe study of polymers containing insaturations. On theother hand, water is a very weak Raman scatterer, andRaman spectroscopy is particularly well adapted to the

Received 24 May 1999; accepted 12 November 1999.* Author to whom correspondence should be sent.

study of aqueous heterogeneous systems such as emul-sions. Moreover, FT-Raman, resorting to long wavelengthexcitation (1064 nm), avoids sample ¯ uorescence, whichmay completely obscure the very weak Raman scatteringphenomenon.1,2,4 This is a great advantage even if, due tothe inverse dependence on the wavelength raised to thepower of 4 of the scattering process, the scattering inten-sity strongly decreases.

Several papers have been reported in the literaturedealing with on-line and off-line monitoring of emulsionpolymerization systems using FT-Raman spectrosco-py.3,5,6 Raman spectroscopy has proved to be an ideal toolfor monitoring the monomer consumption during thecourse of a polymerization. Finally, the excitation lightof FT-Raman spectroscopy is compatible with the use ofsilica ® bers, enabling the remote control of the process.1,7

MATERIALS AND METHODS

Emulsion Polymerization Reaction. The reactor wasinitially loaded with water, initiator, surfactants, and anacidic monomer. The system was heated. Monomer feed-ing started when an adequate temperature reaction wasreached. The polymerization was carried out under con-trolled temperature as the continuous feeding in mono-mers lasted. Then the temperature was increased andmost of the remaining monomers were converted duringa ® nal cure. As the temperature decreased, the reactionwas completed.

Analytical Reference Methods. Samples were with-drawn under pressure from the reactor for off-line anal-ysis. Hydroquinone was added, and the samples were leftunder stirring in ice to quench the reaction. The dry ex-tract (% DE) was measured by gravimetry, and theamount of styrene monomer (ppm styrene) was deter-mined by gas chromatography (GC) using ¯ occulation-extraction in acetonitrile.

Spectroscopic Method. All spectra were recorded ona Bruker RFS100 FT-Raman spectrometer. The on-linemeasurements were made with the use of a Raman probe(Ramprobe, Bruker), connected to the spectrometer via 23 15 m optic ® bers, through a 6 mm-thick quartz windowset in the reactor wall. The interface FT-Raman/reactorsetup is shown in Fig. 1. Compressed air was continu-ously passed around the probe head to cool it. A rubbergasket prevented the re¯ ected light from coming out. Aninterlock system was set to ensure very tight security. ANd:YAG laser (1064 nm) was used for Raman excitation.

APPLIED SPECTROSCOPY 529

FIG . 1. Scheme of the interface FT-Raman/reactor setup.

Raman signal was detected with a liquid nitrogen-cooledGe detector. The Bruker OPUS 2.2 software operated theinstrument. Each interferogram was averaged over 200scans acquired with a 8 cm 2 1 resolution using a Black-mann±Harris apodization function. For each spectrum,the Stokes 3500±500 cm 2 1 spectral region was calculated.

Evaluation of Repeatability. The repeatability of thelatex spectra was estimated on several bands thanks tothe relative standard deviation (RSD) presented in Eq. 1.

1/2æ ö2ç ÷(Y 2 Y )O ix x1 ç ÷iç ÷RSD 5 3 3 100 (1)

Y n 2 1è øx

where Y ix is the intensity of band x of spectrum i, YÅ x isthe average intensity of band x for the n spectra, and nis the number of spectra.

Data Treatments. Chemometrics. The Unscrambler(CAMO Inc., Norway) statistical software package wasused to process all spectroscopic data. Those parametersthat led to the best calibration model were then trans-ferred on OPUS Quant 2 (Bruker) software. The spectraldata analysis consisted of partial least-squares (PLS1) re-gression using the mean-centered spectra, with or withoutmathematical preprocessing.

The validity and the accuracy of a calibration modelwere estimated by the following two parameters: (1) theroot mean squared error of calibration (RMSEC),

1/2M12RMSEC 5 (c 2 c 9 ) (2)O i i1 2M 2 1 2 p i 5 1

where M is the number of calibration samples, p is thenumber of independent variables in the regression, c i isthe measured concentration for the ith calibration sample,and c 9 i is the corresponding calculated value. TheRMSEC is expressed in the reference concentration unit.A low RMSEC value is a necessary but not suf® cientcondition for a good-quality prediction model. (2) theroot mean squared error of prediction (RMSEP),

1/2N12RMSEP 5 (c 2 c 9 ) (3)O i i1 2N 2 1 i 5 1

where N is the number of prediction samples differentfrom those used to construct the calibration equation, c i

is the measured concentration for the ith prediction sam-ple, and c 9 i is the corresponding value calculated with thecalibration model. The RMSEP is expressed in the ref-erence concentration unit. The RMSEP gives an estima-tion of the prediction performance during the step of val-idation of the calibration model; estimating the RMSEPamounts to an external validation of the calibration step.

Data Preprocessing. Among the available data prepro-cessing methods, the most interesting for us were the der-ivations (® rst and second order) and the multiplicativescatter correction (MSC). First and second derivatives al-low one to correct for the baseline effects, and the MSCis a general preprocessing technique to correct for line-arity problems between the signal and the concentration,by correcting additive and multiplicative effects.8,9 In theUnscrambler software, we used the following prepro-cessing options: ® rst derivative (Savitzky±Golay, aver-aging on 11 points, second-order polynomial); second de-rivative [Savitzky±Golay, averaging on 13 points (% DE)or 9 points (ppm styrene), second-order polynomial]; andthe MSC.

Optimization of a Model. As the RMSEP is a standarddeviation, the (RMSEP)2 can be considered as a varianceand, in a ® rst approximation, can be analyzed as such.The Fisher±Snedecor test enables one to determinewhether an RMSEP is signi® cantly better than anotherRMSEP. If (RMSEP)2

0 is the smallest (RMSEP) 2, everyRMSEP such as

2(RMSEP)F 5 , F (4)c2(RMSEP)0

where Fc is the critical value provided by the Fisher±Snedecor tables for the number of degrees of freedom ofthe RMSEP, is not statistically signi® cantly different (%risk) from the RMSEP0. Fc depends on this percentage ofrisk, generally set to 10%. This test of variance allows acomparison between the predictive qualities of an n fac-tors model and an n 1 1 factors model, and between nfactors models with or without spectral pretreatment.

RESULTS AND DISCUSSION

Validation of FT-Raman. The repeatability of a spec-trum obtained for a given latex over several measure-ments was estimated, the latex being stirred and main-tained at 40 8 C in the reactor. One hundred spectra wereregularly collected over a duration characteristic of thatfor a polymerization, with respect to several Ramanbands. The results of the calculation of the RSD associ-ated with each band lead to a value of RSD equal to 2%,which is an acceptable value in terms of the duration ofthe measurements. The repeatability of our spectra wasthus suitable for quantitative analysis.10

Position of the Probe. The probe was set on its standbefore the monomer feeding had started, and it was im-possible to optimize its position on the signal of the re-actor content, as it was essentially water. Therefore, theprobe was placed at a reference distance with respect to

530 Volume 54, Number 4, 2000

FIG. 2. View of a Raman spectrum of a latex during the polymeriza-tion process.

TABLE I. Vibrational wavenumbers and assignments for a Ramanspectrum of a latex during the polymerization process.

n (cm 2 1) Assignmentsa Componentsb

3056 n (C±H)arom. S, PS

3001 n (C±H)arom.

n (C±H)ethyl.

S, PSS, PBcis

2979 n (C±H)ethyl. S, PBvinyl

2912 n (C±H): n a (CH2) PBtrans; PS, PB

2848 n s (CH2) PS, PB

1664 n (C 5 C) PBcis, PBtrans

1634 n (C 5 C) S, B, PBvinyl

1601 n (C±C)arom. S, PS

1581 n (C±C)arom. S, PS

1439 n (C±C)arom; d ip (C±H)ethyl.;d (CH2) S, PS; B; PS, PB

1413 d ip (C±H)ethyl. S, PB

1310 d ip (C±H)ethyl. S, B, PB

1278 d ip (CH2) B, PB

1198 n (C±X) S, PS, B, PB

1179 d ip (C±H)arom. S, PS

1156 d ip (C±H)arom. S, PS

1031 d ip (C±H)arom. S, PS

1000 n (C±C)arom. S, PS

907 d op (C±H)arom. S, PS

774 d op (C±H)arom. S

750 d op (C±H)arom. PS

620 d ip (C±C±C)arom. S, PS

a De® nitions: n 5 stretching; d 5 deformation; ip 5 in-plane; op 5 out-of-plane; s 5 symmetric; a 5 antisymmetric; X 5 a substitute (vinylor polymer).

b De® nitions: S 5 styrene monomer; B 5 butadiene monomer; PS 5polystyrene; PB 5 polybutadiene.

FIG. 3. View of the 1800±1550 cm 2 1 spectral region as the reactionprogresses.

the Raman spectrum of the quartz window. The intensityof this spectrum was very weak, but the glass windowwas the only standard that could be taken into account ina semi-batch process, and this procedure allows one toreach the previously mentioned level of spectral repro-ducibility.

Qualitative Analysis. The Raman signal collected dur-ing a polymerization contained useful qualitative infor-mation; an example of a spectrum is presented in Fig. 2.One can observe that the Raman spectra are absolutelynot obscured by ¯ uorescence. In previous measurementson a Raman spectrometer with 514.5 nm laser excitation,severe ¯ uorescence was observed. Moreover, the thermalemission is insigni® cant at the temperature of interest,which justi ® es the FT-Raman technology with near-infra-red (NIR) excitation in our case. Another Raman analyzercould have been used, such as a dispersive spectrometerwith a visible red laser source, which would be helpfulfor the charge-coupled device (CCD) detector.11 We cansee that the spectral information is especially located be-tween 3500 and 2700 cm 2 1 and between 1800 and 500cm 2 1. The principal vibrational wavenumbers and assign-ments12,13 are reported in Table I. The typical spectra overthe 1800±1550 cm 2 1 spectral range collected over the du-ration of a polymerization are shown in Fig. 3. The arrowindicates the order of spectrum collection, i.e., the pro-gression of the reaction. The intensity of the 1635 cm 2 1

Raman band of the monomers vinyl C 5 C stretching islinked to the amount of free monomers, and the quantityof polymer can be qualitatively estimated by the intensityof the 1660 cm 2 1 band, which corresponds to the stretch-ing of the cis and trans C 5 C of polymerized butadiene.

As the spectra proved to be a reliable tool for acquiringqualitative information concerning the progress of the re-action, we tried to extract the most relevant quantitativechemical information from them by chemometrics. As theRaman spectra collected during a polymerization havemany overlapping peaks, univariate interpretation of thebands was precluded. Indeed, the complexity of the spec-tra required the use of a powerful multivariate method toextract the chemical information from the spectral data.PLS1 regression was used to correlate the dry extractpercentage and the amount of styrene monomer to thespectra.

The Training and the Validation Sets. Seven poly-merization reactions were conducted and are cited in Ta-ble IIA and IIB. The samples removed throughout theC1, C2, C3, C4, and C5 reactions (Table IIA) formed thetraining set, used to construct the calibration models. TheC1, C2, and C3 reactions were carried out according tothe same experimental conditions. The C4 and C5 reac-

APPLIED SPECTROSCOPY 531

TABLE II. Polymerizations used to construct the multivariatemodels (A) and to validate them (B).

Polymerization Sample

Dryextract

(%)

Freestyrene(ppm)

A

C1 123456789

101112131415

10.0016.7320.7424.9328.2330.8934.3136.5338.1840.6942.4744.5946.8947.7247.74

14 97825 38628 29232 17936 16038 50740 37742 07343 03043 47844 73040 16416 560

72604458

C2 161718192021222324252627282930313233

1.92.44

12.0117.3222.3426.4030.9333.4136.0538.7640.9042.6744.3647.0648.7849.3249.3449.57

0

46 46447 75745 56146 01346 78945 38146 17645 79945 10227 19510 463

494138122919

C3 343536373839404142

1.901.785.90

12.8420.2624.7528.4032.1834.75

0

66 00060 43556 47753 88054 576

4344454647484950

37.2839.8241.8343.8346.7748.4248.9349.30

54 73552 13951 65351 44526 79010 758

57584332

C4D T 5 2 5 8 C

5152535455565758596061626364656667

1.902.102.224.729.70

17.5021.8726.7030.8933.9536.9039.6241.5044.1049.3550.6751.00

0

144 705130 390109 320102 21093 77588 95683 70578 40973 55760 39518 167

58902885

TABLE II. Continued

Polymerization Sample

Dryextract

(%)

Freestyrene(ppm)

C5D T 5 1 5 8 C

6869707172737475767778798081828384

1.909.35

14.6319.0124.0427.3130.5834.2136.1438.8841.8143.0644.8147.4248.4348.5448.94

0

22 48523 91124 90526 84326 95628 33729 46829 78330 44311 812

486233193349

B

P1 123456789

1011121314151617

1.901.899.09

16.0522.8126.6430.4834.1236.9638.9041.8843.5245.4548.2949.6949.8950.02

0

68 37862 65252 68048 99846 56745 30848 59347 53445 62744 84743 83916 720

656337852950

P2D T 5 6 2 8 C

18192021222324252627282930313233

1.93.09

12.9818.0022.3926.8729.4431.9635.1038.1641.8443.6746.1548.8749.3949.87

0

49 24742 86741 46741 67446 63849 86253 16952 37151 32950 17834 379

830642032605

tions simulated differences that might accidentally occurbetween the set and the real temperatures during the pro-cess. We imposed D T 5 2 5 8 C compared with the ref-erence temperatures for C4, and D T 5 1 5 8 C for C5. Avarying temperature in¯ uenced not only the intensity andthe shapes of the Raman bands but the reaction kinetics.Our models had to be able to give an account of, on onehand, the normal increase of temperature during the pro-cess and, on the other hand, the exceptional ¯ uctuationsaround the standard temperatures, which explained whyC4 and C5 polymerizations were integrated in the train-ing set.14 The P1 and P2 polymerizations (Table IIB), thevalidation sets, were used to validate the models. P1 wasa standard reaction, and P2 simulated temperature regu-

532 Volume 54, Number 4, 2000

TABLE III. Optimization of the calibration model of the dry extract.

Range (cm 2 1)Preprocessing

1800±500No

3500±2700 < 1800±500

No 1st derivative 2nd derivative MSC

FactorsRMSEC (%)RMSEP (%) P1

P2

21.210.961.31

41.300.980.94

21.631.251.13

21.691.331.11

61.463.111.41

FIG. 4. Dry extract four-factors model over 3500±2700 < 1800±500 cm 2 1. Prediction ( 3 ) and comparison with the analytical reference values(v) of P1 and P2.

lation problems ( D T 5 6 2 8 C compared with the refer-ence temperatures) which were not supposed to preventthe models from giving accurate predictions. Each modelwas tested by predicting these two polymerizations sep-arately, in order to come close to the real conditions ofon-line monitoring. The training set consisted of 84 sam-ples, and the two validation sets consisted of 17 samplesfor P1 and 16 samples for P2. Those sets included spectraassociated with the samples withdrawn and analyzed byreference techniques (gravimetry and gas chromatogra-phy), and spectra corresponding to the monomer feedingstarting time, with a dry extract calculated value of 1.9%and an amount of styrene monomer equal to zero. Duringa polymerization, the dry extract spanned a 2±50% range,and the amount of styrene monomer varied from 0 to 150000 ppm.

Spectral Range. The preliminary study of the spectrashowed that the spectral information which explained theevolution of the dry extract and of the amount of styrenemonomer was located in the 3500±2700 cm 2 1 and 1800±500 cm 2 1 ranges. Nevertheless, in order to simplify theproblem, we tried to work on the smallest spectral region,and we compared the results given by the signi® cant3500±2700 cm 2 1 < 1800±500 cm 2 1 range with the onesprovided by the reduced 1800±500 cm 2 1 range.

Optimization of the Calibration for the Dry Ex-tract. The smallest RMSEP values obtained concerningthe spectral range to consider are presented in Table III.Information in the high wavenumbers region seemed tobe worth being taken into account. That region was notsensitive for the prediction of the standard P1 polymeri-

zation, but the prediction of P2, which presented varia-tions compared with the calibration polymerizations, wasmore accurate when this spectral region was included inthe calibration.

Over the 3500±2700 < 1800±500 cm 2 1 spectral rangeand with the different mathematical pretreatments, we ob-tained the RMSEC and RMSEP values listed in Table III.The results acquired from the 1800±500 cm 2 1 range arenot presented as they did not improve the former ones.The different derivatives did not signi® cantly improve thecalibration, but we can observe that, in these cases, onlytwo factors were enough to model the reaction. The MSCcorrection complicated it, showing that this type of pre-processing was not relevant in our case.

Consequently, the dry extract calibration was con-structed from mean-centered spectra without any othermathematical pretreatment, over the 3500±2700 < 1800±500 cm 2 1 range and with four factors. The RMSEP wasthen 1%, on the 1.9±50% dry extract range. The resultsof prediction compared with the analytically measuredvalues are illustrated by Fig. 4. The predictions remainedcorrect whatever the temperature was.

Optimization of the Calibration for the Amount ofStyrene Monomer. The results obtained with the initialcalibration set exhibited great dif® culties in calibratingthe samples withdrawn at the very beginning of the re-action. Indeed, they appeared to be inhomogeneous andcould not be accurately analyzed by GC. As a conse-quence, less reliable reference values were available, and72 samples formed the training set. The P1 reaction pro-vided a 16-samples validation set, and the P2 polymeri-

APPLIED SPECTROSCOPY 533

TABLE IV. Determ ination of the spectral range to use in the cal-ibration of the amount of styrene monomer.

Range (cm 2 1) Factors RMSEC (ppm) RMSEP (ppm)

3500±2700 <1800±500

123456789

10

382431671882185114291080

832666494336

2404110324002540269620782329184518972152

1800±500 123456789

10

12 03021151905146411961033

815583381257

12 793201025383239259920801697182819512118

3500±2700 <1800±500

123456789

10

382431671882185114291080

832666494336

3674480461806315504647824125429946504117

1800±500 123456789

10

12 03021151905146411961033

815583381257

15 290563859904652503147465837499649334780

TABLE V. Determ ination of the mathematical preprocessing to use in the calibration of the amount of styrene monomer.

Preprocessing No. 1st derivative 2nd derivative MSC

FactorsRMSEC (ppm)RMSEP (ppm) P1

P2

2316711034804

2272319324685

2217819474772

46419

13 5187376

zation another 15-samples validation set. In contrast tothe dry extract, the optimal number of factors was dif-ferent from one polymerization to the other, as is shownin Table IV. In order to overcome this dif® culty, we re-sorted to classical statistical tools and performed a test ofthe variance of the RMSEP. In our study, the RMSEPslinked to the P1 and P2 polymerizations had, respective-ly, 14 and 15 degrees of freedom. The critical values Fc

(14;14) and Fc (15;15) given by the Fisher±Snedecor ta-bles for 10% risk were, respectively, 2.03 and 1.97. Thesmallest RMSEPs were, respectively, 1103 ppm for theprediction of P1 (3500±2700 < 1800±500 cm 2 1; two fac-tors) and 3674 ppm for the prediction of P2 (3500±2700< 1800±500 cm 2 1; one factor). We considered that theRMSEPs verifying, respectively, F , 2.03 or F , 1.97were statistically equivalent to those values. The calcu-

lations of F and their comparison with the critical valuesled to the following result: as far as the prediction of P1was concerned, the two-factors model over the 3500±2700 < 1800±500 cm 2 1 spectral region showed markedlybetter predictive qualities than the others, whereas for theprediction of P2, a large number of models were statis-tically equivalent. Thus the amount of free styreneseemed to be best modeled with two factors over the3500 ±2700 < 1800 ±500 cm 2 1 spectral range; theRMSEP of this model was then about 5000 ppm. We cannote that the RMSEC unceasingly decreased as the num-ber of factors increased. The RMSEC was not a relevantparameter to evaluate a model as it did not account forthe over® tting. The RMSEC appeared to be superior tothe RMSEP because the calibration set contained variousand extreme polymerization cases, whereas the P1 vali-dation set was a standard polymerization. Besides, wetested the preprocessing options of ® rst and second de-rivative, along with the MSC. The results are presentedin Table V. The reported number of factors was the oneleading to the smallest RMSEP values. As the derivativeprocessings did not improve the calibration model, wekept only the mean-centered spectra. As for the dry ex-tract, the MSC correction did not seem to be appropriateto calibrate our spectra. Therefore, a two-factors modelwas constructed with the use of only the mean-centeredspectra, over the 3500±2700 < 1800±500 cm 2 1 range.The RMSEP was approximately 5000 ppm over the 0±70 000 ppm range.

Figure 5 shows the predicted free styrene values withthe former model, and the analytical reference measure-ments are marked for each sample. This model succeededin predicting standard P1 polymerization. The P2 poly-merization, which had no standard temperature values,was not so satisfactorily predicted, but the predictionswere not aberrant.

The two factors calibrating the amount of styrenemonomer are represented in Fig. 6. The ® rst loading (Fig.6a) exhibited characteristic bands of styrene monomer,but this information was counterbalanced by the secondloading (Fig. 6b) corresponding to the copolymer spec-trum. That meant that the model properly differentiatedthe information relative to the monomer from that relativeto the copolymer. We can observe that these two loadingswere particularly complementary to the two ® rst loadingsof the calibration of the dry extract (Fig. 6c and Fig. 6d).

Thus the PLS1 regression succeeded in overcomingnonlinearity problems such as the water reabsorption andthe effects of temperature on the spectra. Near-infraredwater absorption may alter the linearity of the spectrawith the concentrations in the high wavenumbers spectralregion,3 and we wondered whether this spectral regionwas useful or not to improve the calibrations. We testeddifferent calibrations considering only the mean-centeredspectra and the two following spectral ranges: 3500±2700

534 Volume 54, Number 4, 2000

FIG. 5. Styrene monomer predicted with a two-factors model over 3500±2700 < 1800±500 cm 2 1 ( 3 ). Comparison with the reference analyticalvalues (v) of P1 and P2.

FIG. 6. Calibration of the amount of styrene monomer: ® rst factor (a); second factor (b). Calibration of the dry extract: ® rst factor (c); secondfactor (d ).

cm 2 1 < 1800±500 cm 2 1 and 1800±500 cm 2 1. Our resultsproved that the information contained in the 3500±2700cm 2 1 range improved the calibration. Moreover, our cal-ibration model had to be able to predict quantities duringa nonisothermal process. Spectra are temperature in¯ u-enced in a nonlinear way, essentially due to variations inthe refractive index of the sample.15 PLS is indeed a lin-ear regression method; i.e., it requires that the spectraincrease linearly with the concentration. Nonlinear effectscan compromise the quality of the calibration models andconsequently the results of the predictions. Often it is

found that more latent variables take care of the nonli-nearities in a satisfactory way.8,16,17 That observation wascon® rmed with our results, especially for the dry extractwhose calibration required four factors, at least two morethan the chemical dimension of the copolymer in the po-lymerizing system. Thus, with an important calibrationset, the PLS regression may overcome nonlinearity phe-nomena such as the water reabsorption and moderatethermal in¯ uence on the spectra. Concerning the spectralpretreatment, the Raman spectra might be modi® ed byvariations in particle diameter and ¯ uctuations in the op-

APPLIED SPECTROSCOPY 535

tical pathlengths that happened during a polymerization.The in¯ uence of this irrelevant information might be re-duced by mathematical preprocessings. The pretreatmentsthat would improve the quality of the spectra for thequantitative analysis could not be foreseen. The only wayto determine the best approaches was to test them. Thesuccessive derivatives allow one to eliminate the baselineshifts. Moreover, when the bands are larger than the res-olution used to record them, shoulders may appear. A® rst-order derivative corrects a constant shift of the base-line, but the derivatized spectrum is dif® cult to interpret.A second-order derivatized spectrum presents distinctminima for each shoulder of the raw spectrum. Thefourth-order derivative may extract more useful infor-mation from the shoulders, but the signal-to-noise ratiomay suffer serious deterioration in this higher order de-rivatized spectrum. Our spectra did not suffer from anybaseline shift. Nevertheless, the overlappings were nu-merous, and we could expect the derivatives to improvetheir readability. That was not the case. The MSC couldhave corrected for linearity problems between the signaland the concentration, but it proved not to be an adequatecorrection. We can note that, as the polymerization con-cerned was a semi-batch process, no internal valuablestandard was available for any normalization of the spec-tra. The Raman bands of all components moved withtime, and introducing another component was not ac-ceptable for industrial applications. Due to those prob-lems of the reference band, we preferred not to use anynormalization pretreatment.

Thus, we came to the conclusion that, for the two com-ponents, the best predictive calibration equations wereobtained with the use of mean-centered Raman spectrawithout other reprocessing and a spectral region spanningthe 3500±2700 < 1800±500 cm 2 1 range. In these con-ditions, the dry extract (%) was predicted with a four-factors model with an RMSEP lower than 1%, and theamount of free styrene (ppm) was modeled by two factorswith an RMSEP lower than 5000 ppm.

Thus PLS1 regression enabled us to predict the pro-gress of a polymerization without any analytical infor-mation about the free butadiene, which necessarily in¯ u-enced the Raman spectra. Those models were very usefulto predict standard polymerizations such as P1, and theyreasonably accounted for nonconforming, exceptionalcases such as P2. Further calibration polymerizationswould enrich the training, resulting in more robust mod-els.

CONCLUSION

The on-line monitoring of an emulsion polymerizationreaction by ® ber-optic FT-Raman spectroscopy was per-fected. The multivariate models provided an accurate on-line estimation of the dry extract or of the amount ofstyrene monomer far more rapidly than any analyticaltechnique. This instantaneous information may enable ac-tion on the process in the case of deviation from referencepro ® les. In addition, the stability of the models must beensured. The instrumental characteristics change withtime, and the models may be transferred on data mea-sured on different Raman spectrometers. The study of thestandardization procedures will be the subject of futurepapers.

ACKNOWLEDGMENTS

The authors wish to thank the European Community, which sup-ported this work (part of the BRITE Project No. BE 3558).

1. M. J. Roberts, A. A. Garrison, S. W. Kercel, and E. C. Muly, Pro-cess Control Qual. 1, 281 (1991).

2. W. D. Hergeth, in Optical Spectroscopy on Polymeric Dispersions:Principles and Applications, J. M. Ajua, Ed. (Kluwer AcademicPublishers, Amsterdam, 1997).

3. M. Claybourn, T. Massey, J. Highcock, and D. Gogna, J. RamanSpectrosc. 25, 123 (1994).

4. K. P. J. Williams and S. M. Mason, Spectrochim. Acta, Part A 46,187 (1990).

5. T. OÈ zpozan, B. Schrader, and S. Keller, Spectrochim. Acta, Part A53, 1 (1997).

6. G. Ellis, M. Claybourn, and S. E. Richards, Spectrochim. Acta, PartA 46, 227 (1990).

7. K. P. J. Williams, J. Raman Spectrosc. 21, 147 (1990).8. H. Martens and T. N ñ s, Multivariate Calibration (John Wiley and

Sons, New York, 1991).9. P. Geladi, D. MacDougall, and H. Martens, Appl. Spectrosc. 39,

491 (1985).10. F. T. Walder and M. J. Smith, Spectrochim. Acta, Part A 47, 1201

(1991).11. A. Al-Khanbashi, M. Dhamdhere, and M. Hansen, Appl. Spectrosc.

Rev. 33, 115 (1998).12. W. M. Sears, J. L. Hunt, and J. R. Stevens, J. Chem. Phys. 75, 1589

(1981).13. S. W. Cornell and J. L. Koenig, Macromolecules 2, 540 (1969).14. V. Bellon-Maurel, C. Vallat, and D. Gof® net, Appl. Spectrosc. 49,

563 (1995).15. P. Fayolle, D. Picque, B. Perret, E. Latrille, and G. Corrieu, Appl.

Spectrosc. 50, 1325 (1996).16. F. WuÈ lfert, W. T. Kok, and A. K. Smilde, Anal. Chem. 70, 1761

(1998).17. T. F. Cullen and S. R. Crouch, Mikrochim. Acta 126, 1 (1997).