Anal. Chem. 2002, 74, 4334-4342
Process Analysis of Recycled Thermoplasts from Consumer Electronics by Laser-Induced Plasma Spectroscopy Herbert Fink, Ulrich Panne,* and Reinhard Niessner
Institute of Hydrochemistry, Technical University of Munich, Marchioninistrasse 17, D-81377 Munich, Germany
An experimental setup for direct elemental analysis of recycled thermoplasts from consumer electronics by laserinduced plasma spectroscopy (LIPS, or laser-induced breakdown spectroscopy, LIBS) was realized. The combination of a echelle spectrograph, featuring a high resolution with a broad spectral coverage, with multivariate methods, such as PLS, PCR, and variable subset selection via a genetic algorithm, resulted in considerable improvements in selectivity and sensitivity for this complex matrix. With a normalization to carbon as internal standard, the limits of detection were in the ppm range. A preliminary pattern recognition study points to the possibility of polymer recognition via the line-rich echelle spectra. Several experiments at an extruder within a recycling plant demonstrated successfully the capability of LIPS for different kinds of routine on-line process analysis. Thermoplastic polymers are employed in many of today’s industrial designs where in the past only materials such as metals or wood were used. The chemical and mechanical properties can be tuned via a huge variety of additives.1,2 Among these additives are several inorganic compounds,3 for example, coloring pigments (TiO2, ZnO, Fe2O3, and soot), flame retardants (Sb2O3 combined with brominated organics), and various thermal and photochemical stabilizers (organometallic compounds of Ba, Sn, and Zn). Corresponding to desired properties, the mass concentration of the additives varies from a few ppm to several mass percentages. The extended use of rather expensive thermoplasts in shortlived consumer electronic products calls for an efficient recycling, while at the same time a down-cycling or an energetic reuse (waste burning) is strongly discouraged.4 Process analysis of simple polymers from household waste is easily possible via molecular spectroscopy (e.g., NIR spectroscopy). However, the multitude of additives in thermoplasts requires both, the elemental analysis of inorganic materials and the assessment of the bulk material * To whom correspondence should be addressed. Phone: ++49 89 7095 7987. Fax: ++49 89 7095 7999. E-mail:
[email protected]. (1) Domininghaus, H. Plastics for Engineers: Materials, Properties, Applications; Hanser Gardner Publ.: Cincinnati, OH, 1993. (2) Strong, A. B. Plastics: Materials and Processing; Prentice Hall: Englewood Cliffs, NJ, 1999. (3) Saechtling, H. J. International Plastics Handbook; Hanser Gardner Publ.: Cincinnati, OH, 1995. (4) Bisio, A. L.; Xanthos, M. How to Manage Plastics Waste; Hanser/Gardner Publications: Mu ¨ nchen, Germany, 1994.
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via molecular spectroscopy. For an economical recycling, only fast analytical methods with on-line and high-throughput capability qualify for process analysis. While several conventional methods for the elemental analysis of polymers and their respective additives were reported in the past,5-11 none of them can be currently employed for continuous analysis at an extruder system in a recycling process. In this study, we report the application of laser-induced plasma spectroscopy (LIPS, or laser-induced breakdown spectroscopy, LIBS) for on-line elemental characterization of recycled thermoplasts from consumer electronics. The major advantages of LIPS here are that no sample preparation is needed and no contact with the sample is necessary. Furthermore, the data are instantly available in digital form and can be used for process control. Although LIPS is studied for a growing number of analytical problems,12-14 only a few working demonstrations were reported in the past. Earlier reports on LIPS analysis were limited to laboratory investigations of rather pure polymers,15-17 which are rarely used in this form. Our approach was targeted to develop a flexible experimental setup for direct analysis of recyclates and can be used in an industrial environment. The recent employment of high-resolution echelle systems for LIPS18 promisessin combination with appropriate multivariate chemometrical methodss improvements in both selectivity and sensitivity. This is the first study to demonstrate this advantage in a real-world application with a complex matrix. (5) Hemmerlin, M.; Mermet, J. M. Spectrochim. Acta 1997, B52, 1687-1694. (6) Kenneth Marcus, R. J. Anal. At. Spectrom. 2000, 15, 1271-1277. (7) Dobney, A. M.; Mank, A. J. G.; Grobecker, K. H.; Conneely, P.; de Koster, C. G. Anal. Chim. Acta 2000, 423, 9-19. (8) Schelles, W.; van Grieken, R. Anal. Chem. 1997, 69, 2931-2934. (9) Simmross, U.; Fischer, R.; Du ¨ wel, F.; Mu ¨ ller, U. Fresenius J. Anal. Chem. 1997, 358, 541-545. (10) Vandenburg, H. J.; Clifford, A. A.; Bartle, K. D.; Carroll, J.; Newton, I.; Garden, L. M.; Dean, J. R.; Costley, C. T. Analyst 1997, 122, R101-R115. (11) Wang, F. C. Y. Anal. Chem. 1999, 71, 2037-2045. (12) Radziemski, L. J. Microchem. J. 1994, 50, 218-234. (13) Rusak, D. A.; Castle, B. C.; Smith, B. W.; Winefordner, J. D. TrAC, Trends Anal. Chem. 1998, 17, 453-461. (14) Sneddon, J.; Lee, Y. I. Anal. Lett. 1999, 32, 2143-2162. (15) Anderson, D. R.; McLeod, C. W.; Smith, T. A. J. Anal. At. Spectrom. 1994, 9, 67-72. (16) Sattmann, R.; Monch, I.; Krause, H.; Noll, R.; Couris, S.; Hatziapostolou, A.; Mavromanolakis, A.; Fotakis, C.; Larrauri, E.; Miguel, R. Appl. Spectrosc. 1998, 52, 456-461. (17) Anzano, J. M.; Gornushkin, I. B.; Smith, B. W.; Winefordner, J. D. Polym. Eng. Sci. 2000, 40, 2423-2429. (18) Haisch, C.; Panne, U.; Niessner, R. Spectrochim. Acta 1998, B 53, 16571667. 10.1021/ac025650v CCC: $22.00
© 2002 American Chemical Society Published on Web 07/31/2002
Figure 1. (a) Experimental setup of the LIPS system. (b) Experimental setup for employment of the LIPS system at an industrial extruder.
EXPERIMENTAL SECTION Apparatus. A schematic of the experimental setup is given in Figure 1a. A frequency-quadrupled (λ ) 266 nm) Nd:YAG laser (Surelite I, Continuum, Mu¨nchen, Germany) with a repetition rate of 10 Hz was utilized for plasma generation. The superior precision (∼5% RSD for the LIPS signal) for the UV ablation of polymers in comparison with the fundamental wavelength was established in an earlier study.19 To ensure a constant spatial and temporal pulse profile, the pulse energy, monitored via a beam splitter and a pyroelectric probe (PHD25, Laser 2000, Wessling, Germany), was adjusted by a variable attenuator (Newport, Irvine, CA). In addition, apertures of different diameters were introduced in the beam path for adjustment of the sample irradiance. In all experiments, an irradiance of 6.1 GW cm-2 was applied for plasma ignition. The laser beam was focused with a single plano-convex lens (f ) 100 mm) onto the sample, while the plasma emission was collected vertically through a pierced mirror and transmitted with a quartz/quartz fiber optic (550-µm diameter) to an echelle spectrograph (ESA 2000, LLA, Berlin, Germany). The echelle allows a high spectral resolution (λ/∆λ > 10000 at 250 nm) in combination with a spectral range of 200-780 nm. All optical (19) Fink, H.; Panne, U.; Niessner, R. Anal. Chim. Acta 2001, 440, 17-25.
setups were realized with microbank components (Linos Photonics, Go¨ttingen, Germany). The LIPS experiment, i.e., delay to the laser pulse and gate width, was timed through a delay generator integrated into the echelle controller. The timing parameter and laser pulse energy were optimized for three representative lines (C at 247.86 nm, Sb at 252.85 nm, and Ti at 252.56 nm) via a S/N approach describe in ref 19 and considering the C(I) line at 247.856 nm for normalization. For all measurements, a gate width of 400 ns, a delay to plasma ignition of 850 ns, was utilized. For static experiments, 10 laser pulses from 10 different sample areas were integrated on the detector, while for on-line experiments only 5 pulses were integrated. Data analysis of echelle spectra consisted of (using Origin software, Microcal, Northampton, MA) automatical averaging, smoothing (Savitzky-Golay algorithm), fitting emission lines with Gaussian profiles, and integration of the data. Typically, from the 50 000 points of an echelle spectrum, ∼1000 peaks were picked and evaluated. The elements included were Cd, Zn, Sn, Ti, Pb, Al, Ca, Mg, Ba, Sr, Cr, Si, Na, and C. All further multivariate calibrations and procedures were developed under MATLAB (version 5.2, The Mathworks, Natick, MA). Analytical Chemistry, Vol. 74, No. 17, September 1, 2002
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Table 1. LIPS Detection Limits (LODs) of Additives in Recycled Plastics element
emission (nm)
LOD (ppm)
Ti(II) Sb(I) Zn(I) Sn(I) Al(I) Cd(I) Cr(I) Pb(I)
308.802 259.805 213.856 283.999 308.215 228.802 359.349 405.781
50 730 190 250 100 30 80 70
The on-line experiments were performed on an double-chain extruder of laboratory scale (LSM 30.34, Leistritz AG, Nu¨rnberg, Germany) equipped with two differential batching scales (LWFD5S, K-Tron Soder GmbH, Gelnhausen, Germany). A construction of profiled aluminum rods (item Bayern GmbH, Vohburg, Germany) was used to support the laser system above the extruder; the complete arrangement is depicted in Figure 1b. Procedure. For preliminary studies, a sample lot, comprising 96 different shredded thermoplasts (acrylonitrilebutadienestyrene (ABS), polyamide (PA), polycarbontae (PC), polystyrene (PS), styrenebutadiene (SB), polyphenylene oxide (PPO), thermoplastic polyester (TPO), poly(vinyl chloride) (PVC), and commingled mixtures of them such as PPO/PS) from consumer electronics (i.e., TVs, computers, etc.), was analyzed. Reference analysis was performed by total X-ray flurescence analysis (TXRF) after dissolution of small amounts of the samples in suitable organic solvents and, alternatively, by instrumental neutron activation analysis (INAA). The procedure was described in detail elsewhere.20 For the doping experiments during extrusion, fine powders of the inorganic pigments lithopone (a mixture of ZnSO4 and BaSO4) and ultramarine blue (Na1.3[AlSiO4]S0.5) were used (Kremer Pigmente, Aichstetten, Germany). To image the homogeneity and chemical composition of individual additives in polymers, a scanning electron microscope (SEM; Cambridge S360, Leica, Bensheim, Germany) equipped with an energy-dispersive X-ray Si(Li) detector (EDX; Ro¨ntec UHV Dewar Xflash detector system, EasyUse/EDR288 M, Berlin, Germany) and a backscattered electron detector was employed. The working distance for the microanalysis was 25 mm; an accelerating voltage of 25 kV and a beam current of ∼1 nA (∼1300 counts/s) were used. Mapping was performed on an area of 136 × 100 pixels with a pixel size of 0.65 µm and a integration time of 3 s per pixel. RESULTS AND DISCUSSION Calibration. Plastic samples from consumer electronics consist of different polymers and additive mixtures. This implies a varying efficiency of the laser ablation process and varying properties of the generated plasma. In preliminary laboratory experiments, a normalization approach based on the C(I) line at 247.856 nm was established, resulting in detection limits summarized in Table 1. The calculation of the limit of detection (LOD) was based on the 3s-IUPAC definition; the mean background (20) Fink, H.; Panne, U.; Theisen, M.; Niessner, R.; Probst, T.; Lin, X. Fresenius J. Anal. Chem. 2000, 368, 235-239.
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signal was determined next to the spectral line evaluated. For calculation of the LODs, 10 laser pulses from 10 different areas on a polymer sample were averaged on the detector. The highresolution echelle system allows the extraction of multiple emission lines which can be utilized with multivariate calibration. Due to the size of the echelle spectrum (50 000 data points in a onedimensional spectrum originating from a 1024 × 1024 pixel image) only small sections can be visualized. Figure 2 reveals a small section (80 × 140 pixels) from the two-dimensional echelle spectrum of a SB sample that contains, among others, TiO2 pigments. A further improvement in accuracy and sensitivity can be obtained if a multivariate approach to calibration is employed, utilizing the line-rich echelle spectra. Table 2 summarizes the results for Sb, Sn, and Ti, which were investigated. In each case, the available numbers of samples containing the element under study were divided into a calibration and a test subset. The optimum calibration set was determined via a principal component analysis (PCA) and selecting the samples with the highest multivariate leverage. In addition to the integrated and normalized line intensities, the complete raw spectra were also used in a “whole spectrum” approach, suggested also by other authors.21,22 The data were only corrected for detector noise; no additional scaling was used. For calibration, a simple linear regression of single emission lines versus concentration was compared with an inverse calibration approach based respectively on principal component regression (PCR) and partial least squares (PLS).23,24 PLS and PCR models were cross-validated with random subsets, which provided a better idea about the robustness of the model than the usual leave-one-out procedure. The number or relevant latent variables (LV) were determined with the lowest, crossvalidated root-mean-square error of calibration (RMSECV). In case of ambiguities, the model with the lowest numbers of LVs was selected following the parsimony principle.25 To compare all models and the prediction “average” performance, the root-meansquare error of calibration (RMSEC), the RMSECV, and the rootmean-square error of prediction (RMSEP) were calculated. The RMSEC describes the average fit of the model to the calibration data, while the REMSECV gives an indication of the model’s ability to predict new samples and can be used to fine-tune the model. If the model is applied to new data and the concentration is know via reference analysis, a RMSEP can be calculated to evaluate the actual predictive performance. It should be noted here that the RMSEC(V) evaluates the fit and the model, while only the RMSEP permits a comparison of the prediction capabilities.30 Although all indicators have a unit of concentration, their absolute values (21) Goode, S. R.; Morgan, S. L.; Hoskins, R.; Oxsher, A. J. Anal. At. Spectrom. 2000, 15, 1133-1138. (22) Gornushkin, I. B.; Smith, B. W.; Nasajpour, H.; Winefordner, J. D. Anal. Chem. 1999, 71, 5157-5164. (23) Wold, S.; Sjostrom, M.; Eriksson, L. Chemom. Intell. Lab. Syst. 2001, 58, 109-130. (24) Wold, S.; Trygg, J.; Berglund, A.; Antti, H. Chemom. Intell. Lab. Syst. 2001, 58, 131-150. (25) Seasholtz, M. B.; Kowalski, B. Anal. Chim. Acta 1993, 277, 165-177. (26) Leardi, R. J. Chemom. 2000, 14, 643-655. (27) Jouan-Rimbaud, D.; Massart, D.-L.; Leardi, R.; De Noord, O. E. Anal. Chem. 1995, 67, 4295-4301. (28) Pearse, R. W. B.; Gaydon, A. G. The Identification of Molecular Spectra, 3rd ed.; Chapman & Hall Ltd.: London, 1986. (29) Westerhuis, J. A.; de Jong, S.; Smilde, A. K. Chemom. Intell. Lab. Syst. 2001, 56, 13-25.
Figure 2. (a) Echelle spectrum (1024 × 1024 pixel image) of an ABS/PA polymer , (b) detailed view of small section (80 × 140 pixels), and (c) conventional one-dimensional spectrum of (b).
are dependent on the concentration range under study. In this way, they should be only employed for a comparison of different calibration methods. Not surprisingly, Table 2 reveals that PCR and PLS outperforms a simple linear regression in modeling and prediction, while PLS gave better results for prediction and modeling than PCR. For Sn, the significance of the analysis is limited by the small number of samples in the studied sample set. The whole spectrum approach was significantly inferior to simple linear regression and PLS/PCR of the pretreated line intensities. Obviously, a suitable normalization and variable subset selection is necessary to have good predictions from the whole spectrum approach. The normalization of the whole spectrum to the carbon emission (not shown in Table 2) did not improve the figures of merit significantly. The need for variable selection is further supported by the improved modeling and prediction for Ti with a subset of 22 variables from the available 87 variables. This “best” variables were isolated via a genetic algorithm.26,27 A PLS model with three latent variables was used to determine RMSECV as a fitness criterion; 50% of the models with the lowest RMSECV were allowed to “live”. Randomly selected models from this population were then selected (30) Massart, D. L.; Vandeginste, B. G. M.; Buydens, L. M. C.; De Jong, S.; Lewi, P. J.; Smeyers-Verbeke, J. Handbook of Chemometrics and Qualimetrics: Part A and B; Elsevier: Amsterdam, 1997.
for double crossover breeding. The final subset selection is based on multiple random initializations to avoid artifacts from single runs. Figure 3 compares the single line regression, normalized and not normalized, for Ti with the results from PLS calibration. The multivariate approach provides a better sensitivity and improved accuracy. For Ti, we estimated via an improved detection limit of 8 ppm from the slope of the calibration plot and the average standard deviation of the signal from the sample with the lowest analyte content established by TXRF analysis. The relative prediction error for all elements under study was typically in the order of 15-25% even with multivariate calibration. This error reflects the differences in the matrix and resulting different ablation behavior, which cannot completely be compensated via normalization. Due to the different dispersion of the additives in the polymeric material, errors due to heterogeneity of the material are additionally expected for some of the additives. Figure 4 shows a SEM/EDX mapping of an ABS polymer, which underlines this effect for Al and Sb. The signals for Al and Sb can be easily correlated with different moieties in the backscattered image. Despite the moderate limits of detection obtained here in comparison to LIPS analysis of matrixes such as metals, LIPS was still more sensitive than the reference analysis obtainable by TXRF analysis (generally not below 150 ppm). For example, Cd was Analytical Chemistry, Vol. 74, No. 17, September 1, 2002
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Table 2. Comparison of the Results from the Multivariate Calibration RMSEC LR λ ) 206.83 nm PCR/PLS 9 variables PLS/PCR 205-310 nm 16 472 variables LR λ ) 310.48 nm PCR/PLS 87 variables PLS 22 variablesa PLS/PCR 200-500 nm 35 067 variables LR λ ) 284.00 nm PCR/PLS 9 variables PLS/PCR 200-400 nm 26 472 variables a
REMSECV
REMSEP
no. of LVs
Sb (39 Samples, 30 Calibration Samples, 9 Test Samples) 7686 17470 4560/5993
23727/28078
16522/15437
3/2
11022/3905
73175/52388
13671/15428
9/9
Ti (39 Samples, 30 Calibration Samples, 9 Test Samples) 10665 4428 7406/4918
32855/32662
5045/3429
4/3
4317
18777
3779
3
5929/1509
44987/9089
12757/12630
8/9
Sn (11 Samples, 7 Calibration Samples, 4 Test Samples) 223 613 144/116
1131/954
503/471
2/2
328/198
1813/1705
711/743
3/3
Variable subset determined via a genetic algorithm.
Figure 3. Comparison of different calibration methods for Ti determination via LIPS. (a) Correlation between the raw, not-normalized LIPS signal with the reference analysis. (b) Calibration with a single line (Ti (II), λ ) 308.802 nm) via linear regression (solid line, dashed line: 95% confidence interval of regression line). (c) Predicted Ti concentration vs measured concentration for calibration model with a single emission line (see (b), solid line: 45° intersection). (d) Predicted Ti concentration vs measured concentration for a PLS calibration model with three LVs (see Table 2, solid line: 45° intersection). 4338
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Figure 4. SEM/EDX mapping of an ABS polymer. (a) Backscattered image, (b) Al (KR1 at 1.487 eV), and (c) Sb (LR1 at 3.604 eV) elemental map.
identified and quantified by LIPS in far more samples than by TXRF, and for Cr, only an INAA reference was available (see ref 20). With the calibration obtained from reference data, the concentrations were calculated for the samples where no reference was available. Figure 5 shows a concentration diagram for the elements Cd and Cr with a threshold line that corresponds to the current legal limit value of these elements in packaging and durables (250 ppm in Germany). The diagrams show that the limit value is exceeded for both elements in about one-third of the investigated subset. Discrimination of Polymers. The identification of different polymer types based on LIPS was reported earlier by other authors16,17 but was always devoted to discrimination of the raw polymers or simple polymers from domestic waste. In both cases, rather pure polymers with a negligible concentration of additives were subjected to LIPS analysis. For these raw polymers, a discrimination based on the C/H ratio (C(I) at 247.856 nm and H(I) at 656.285 nm) was evaluated in a preliminary experiment. Although the ratio of similar polymer blends was not reproducible for all samples, a discrimination between the aliphatic polymers (PP, PA) and the aromatic polymers (ABS, SB, PS, PPO, TPO, PC) was possible (Figure S1, Supporting Information). To explore the possibility of polymer identification for the recycled thermoplasts, a principal component analysis of the samples with known
polymer type was performed utilizing the whole spectra (data set 78 × 48568). Each spectrum was normalized to the integrated emission intensity. With no further scaling, a model with three LVs described ∼96% of the variance. A scores plot (Figure S2, Supporting Information) revealed the clustering of similar materials (PC1 vs PC2) and that the third principal component is mainly used for the discrimination of the sole PVC sample. A loading plot (Figure S3, Supporting Information) showed that atomic emission lines and molecular bands (e.g., CN, C2)28 are utilized for the differences in the scores plot. However, no distinct groups of polymers could be identified in this way, pointing to a severe heterogeneity of this industrial polymer blends. Further studies are under way to determine the possibility of this approach. On-Line Experiments. The practicability of LIPS for the elemental monitoring of recycled plastics was tested during a campaign at a double chain extruder (see Figure 1b) within a recycling plant. In addition to the elemental analysis via LIPS, the color of the extruded polymer was registered on-line and the impact strength was also tested on-line. In this way, a complete characterization of the recycled material for later reuse is possible. To evaluate whether the LIPS system can detect a concentration gradient during a load change at the extruder, the emission signals of Sb and Ti were monitored during the transition between the extrusion of unused polymeric material (ABS) and recycled Analytical Chemistry, Vol. 74, No. 17, September 1, 2002
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Figure 5. Calculated concentrations of Cd and Cr via LIPS in samples for which no reference analysis was available.
plastic (granulate from casings of electronic waste). For both elements, the signal increases steadily as the extrusion of recycled material proceeds and reaches a plateau after the transition is complete (see Figure S4, Supporting Information). This allows us to recognize contaminations with unwanted additives and to control the polymer composition through automatic discarding of undesirable fractions of recycled material.
Further, doping experiments were performed with the pigments lithopone (a mixture of ZnSO4 and BaSO4) and blue ultramarine (Na1.3[AlSiO4]S0.5) in different concentrations. The concentration was adjusted with the batching scales of the extruding system (see Figure 1b). Figure 6 shows an experiment where blue ultramarine in concentrations of 2.4 and 4.8% was mixed for a short period with unused ABS polymer. The signals for Si and Al were recorded on-line and showed the expected two plateaus for each concentration. Their baseline-corrected mean values corresponded to the ratio of 2:1 and confirmed the trueness and reproducibility of the results obtained with this measuring system. After the mixing was stopped and the extrusion of pure ABS continued, the signals dropped off again to the mean background level. In many cases, new polymers are diluted with recycled material, while the mechanical properties, color, and chemical composition of the new polymer should not be altered. Different polymers (ABS, PS) were therefore continuously diluted with recycled material (mixing ratio of 0, 20, 50, 75, and 100%), while the relevant elemental emissions were monitored. Figure 7b demonstrates for Ti in SB that LIPS can be used to follow this kind of change in the signal. Figure 7a gives an example for an at-site recalibration (here Ti(I) at 334.188 nm), which is sometimes necessary due to transport misalignments of the instrument. Reference analysis for a mixing ratio of 100%, i.e., the “pure” recycled polymer, via TXRF gave a Ti concentration of 2.75%, while Figure 7b gives a final concentration of 2.56%. Problems can arise in the on-line analysis from the reduced number of averaged laser pulses, which results in a reduced signalto-noise ratio (about x20) and heterogeneous mixing of recycled materials and new material. Figure 8 shows the extrusion of an ABS polymer with a recycled material containing 250 ppm Cd. Although this is still above the estimated detection limit of Cd for the on-line measurement (150 ppm), no distinct step function was observed in comparison to Figure 7b, but rather a slow increase in the Cd emission. While this is a limitation of the
Figure 6. On-line LIPS signals of Al and Si during a doping experiment of ABS with 4.8 and 2.4% blue ultramarine. 4340
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Figure 7. (a) At-site calibration for Ti and (b) change in Ti concentration during the extrusion of different raw/recycled polymer mixtures (SB).
Figure 8. LIPS signal of Cd (in ABS) during the during the extrusion of different raw/recycled polymer mixtures.
current system, a higher repetition rate of the laser could easily overcome this sampling problem. CONCLUSIONS LIPS is a suitable method for on-line analysis of the elemental composition of recycled complex thermoplasts from consumer electronics. The employed echelle spectrograph in combination with suitable multivariate methods allows one to make full use of the line-rich emission spectra without loss of selectivity and multielement capability. With multivariate calibration methods
such as PLS, the precision and accuracy of the approach was improved considerably. Selection of variables via a genetic algorithm can improve the model further. In the future, pattern recognition methods based on echelle spectra could even permit the recognition of the polymer type. However, more work is needed for spectral normalization (for example, direct orthogonal normalization29) and fast variable selection as the results from the whole-spectrum approach revealed. The realistic experiments at an extruder machine within a recycling plant demonstrated successfully the capability of LIPS for routine on-line process Analytical Chemistry, Vol. 74, No. 17, September 1, 2002
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analysis. For a future complete characterization of thermoplasts, Raman spectroscopy is most attractive as no problems from black or darkly colored thermoplasts are expected in contrast to conventional NIR spectroscopy. Further, with a suitable echelle, a single setup could be utilized for both types of analysis. ACKNOWLEDGMENT We thank the Bayerisches Staatsministerium fu¨r Landesentwicklung und Umweltfragen (BayFORREST program) for financial support. We also acknowledge Dipl.-Ing. T. Schubert for assistance during the on-line campaign as well as N. Gerhardt and J. Leiterer for programming of the Origin and LabVIEW
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routines. Further, we are indebted to C. Sternkopf for the reference analysis and SEM pictures. SUPPORTING INFORMATION AVAILABLE Results from the discrimination of polymers via LIPS, PCA analysis of the LIPS spectra, and some results from on-line LIPS analysis of recycled thermoplasts. This material is available free of charge via the Internet at http://pubs.acs.org. Received for review March 25, 2002. Accepted June 14, 2002. AC025650V
