Toward Prediction: Using Chemometrics for the Optimization of

9-phenanthrol, 9-PHOH. acenaphthene, ACNP. α-cyano-4-hydroxycinnamic acid, CHCA. α-cyano-4-phenylcinnamic acid, CPCA. all-trans retinoic acid, RA...
0 downloads 0 Views 174KB Size
Download PDF
Anal. Chem. 2010, 82, 8169–8175

Toward Prediction: Using Chemometrics for the Optimization of Sample Preparation in MALDI-TOF MS of Synthetic Polymers Heike Brandt* and Thomas Ehmann Wacker Chemie AG, Johannes-Hess-Strasse 24, D-84489 Burghausen, Germany Matthias Otto TU Bergakademie Freiberg, Institute of Analytical Chemistry, Freiberg, Germany In recent years, matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDITOF MS) has become a powerful tool for the study of synthetic polymers although its mechanism is still not understood in detail. Sample preparation plays the key role in obtaining reliable MALDI mass spectra, in particular, the proper choice of matrix, cationization reagent, and solvent. There is still no general sample preparation protocol for MALDI analysis of synthetic polymers. For known synthetic polymers, such as polystyrenes and other frequently investigated polymers, application tables in review articles might be a guide for selecting a MALDI matrix, cationization reagent, and solvent. For unknown polymers (polymers which were not analyzed by MALDI-TOF MS before but whose structures are in part known from the manufacturing process and from NMR analysis as well), the selection of matrix and solvent is based upon the polarity-similarity principle. Chemometric methods provide a useful tool for the investigation of sample preparation because huge data sets can be evaluated in short time, that is, for extracting relevant information and for classification of samples, as well. Furthermore, chemometrics provide a suitable way for the selection of a proper matrix, cationization reagent, and solvent. In this paper, a prediction model is presented using the partial least-squares (PLS) regression. By applying the model, the suitability of appropriate (nontested) combinations (matrix, cationization reagent, solvent) can be predicted for a certain synthetic polymer based upon the investigation of a few combinations. This model may help find suitable combinations in a short time and serve as a starting point for the investigation of unknown polymers. Results are exemplary presented for polystyrene PS2850. It is well-known that the matrix type, cationization reagent, their concentrations as well as the type of solvent or solvent * To whom correspondence should be addressed. E-mail: heike.brandt@ wacker.com. 10.1021/ac101526w  2010 American Chemical Society Published on Web 08/31/2010

mixture, and the spotting technique affect the polymer distribution.1-16 Many authors described an optimization of their sample preparation, but to our knowledge, there is no paper which treats a systematic investigation. Optimization of sample preparation had been based upon trial and error until now. Aside from sample preparation, the instrumentation parameters influence the determined molar mass distribution.17,18 For the investigation of the influence of the instrumentation parameter designs of experiments (DoE) are the chemometric methods of choice. Wetzel et al.19 studied significant instrument parameters for optimization of MALDI-TOF analysis employing a 25-1 fractional factorial design, while Liland et al.20 had used a 23 full factorial design and fractional factorial designs. (1) Weidner, S. M.; Trimpin, S. Anal. Chem. 2008, 80, 349–4361. (2) Arakawa, R.; Watanabe, S.; Fukuo, T. Rapid Commun. Mass Spectrom. 1999, 13, 1059–1062. (3) Danis, P. O.; Karr, D. E. Org. Mass Spectrom. 1993, 28, 923–925. (4) Guttman, C. M.; Wetzel, S. J.; Blair, W. R.; Fanconi, B. M.; Girard, J. E.; Goldschmidt, R. J.; Wallace, W. E.; Vanderhart, D. L. Anal. Chem. 2001, 73, 1252. (5) Jackson, A. T.; Yates, H. T.; MacDonald, W. A.; Scrivens, J. H.; Critchley, G. C.; Brown, J.; Deery, M. J.; Jennings, K. R.; Brookes, C. J. Am. Soc. Mass Spectrom. 1997, 8, 132–139. (6) Belu, A. M.; DeSimone, J. M.; Linton, R. W.; Lange, G. W.; Friedman, R. M. J. Am. Soc. Mass Spectrom. 1996, 7, 11–24. (7) Deery, M. J.; Jennings, K. R.; Jasieczek, C. B.; Haddleton, D. M.; Jackson, A. T.; Yates, H. T.; Scrivens, J. H. Rapid Commun. Mass Spectrom. 1997, 11, 57–62. (8) Hanton, S. D.; Owens, K. G. J. Am. Soc. Mass Spectrom. 2005, 16, 1172– 1180. (9) Yalcin, T.; Dai, Y.; Li, L. J. Am. Soc. Mass Spectrom. 1998, 9, 1303–1310. (10) Hoteling, A. J.; Erb, W. J.; Tyson, R. J.; Owens, K. G. Anal. Chem. 2004, 76, 5157–5164. (11) Meier, M. A. R.; Schubert, U. S. Rapid Commun. Mass Spectrom. 2003, 17, 713–716. (12) Montaudo, G.; Samperi, F.; Montaudo, M. S. Prog. Polym. Sci. 2006, 31, 277–357. (13) Rashidzadeh, H.; Wang, Y.; Guo, B. Rapid Commun. Mass Spectrom. 2000, 14, 439–443. (14) Wang, Y.; Rashidzadeh, H.; Guo, B. J. Am. Soc. Mass Spectrom. 2000, 11, 639–643. (15) Rashidzadeh, H.; Guo, B. J. Am. Soc. Mass Spectrom. 1998, 9, 724–730. (16) Trimpin, S.; Keune, S.; Ra¨der, H. J.; Mu ¨ llen, K. J. Am. Soc. Mass Spectrom. 2006, 17, 661–671. (17) Schriemer, D. C.; Li, L. Anal. Chem. 1997, 69, 4169–4175. (18) Schriemer, D. C.; Li, L. Anal. Chem. 1997, 69, 4176–4183. (19) Wetzel, S. J.; Guttman, C. M.; Flynn, K. M.; Filliben, J. J. J. Am. Soc. Mass Spectrom. 2006, 17, 246–252.

Analytical Chemistry, Vol. 82, No. 19, October 1, 2010

8169

Three questions had to be answered toward prediction: • Which is the most significant factor of MALDI-TOF analysis? • How should the mixing ratio be considered in the modeling process? • Is it possible to classify the combinations with increasing suitability for a special polymer? In a previous paper, we had studied the effect of mixing ratio employing a 23 full factorial experimental design varying the volumes of the polymer, matrix, and cationization reagent solutions.21 An analysis of variance (ANOVA) was used to investigate the main effects of mixing ratio, matrix type, cationization reagent, and solvent as well as the two-factor and three-factor interaction effects of matrix type, cationization reagent, and solvent. According to Wilks’ lambda of the multivariate ANOVA statistics, the most significant factor is the three-factor interaction effect of matrix, cationization reagent, and solvent; that is, for modeling the selection of matrix type, cationization reagent, and solvent, the entire combination has to be considered. Since the mixing ratio affects the molar mass distribution, as well, the 23 full factorial design was applied on each studied combination.21 In order to answer the third question, the MALDI results of the investigated combinations for a defined polymer were classified by applying various chemometrics, and the suitability of each combination was evaluated. For this purpose, ANOVA as well as principal component analysis (PCA) provided a suitable tool. Applying these chemometrics, the MALDI results were evaluated and an overall averaged grade representing the suitability of the studied combination for the MALDI analysis of the examined polymer was determined. The three questions toward prediction are answered: the entire combination has to be considered in the model as well as the mixing ratio, which is varied for each studied combination using a 23 full factorial design; the MALDI results could be classified and evaluated. For modeling, the methods of design and optimization in organic synthesis by Carlson et al.22,23 were applied, predicting the suitability of nontested combinations for one polymer using a partial least-squares (PLS) regression based upon eight training combinations. For this model, the three componentssmatrix, cationization reagent, and solventshad to be characterized by molecular descriptors. The results of our study are exemplary presented for PS2850, although more synthetic polymers had been investigated. EXPERIMENTAL SECTION Samples and Reagents. Polystyrene PS2850 (PDI ) 1.04) was obtained from Macherey-Nagel (Du¨ren, Germany). 2-[(2E)3-(4-tert-Butylphenyl)-2-methylpropenylidene]malononitrile (DCTB), 2′,6′-dihydroxyacetophenone (DHAP), 2,5-dihydroxybenzoic acid (DHB), 2,5-dihydroxy-1,4-benzoquinone (DHBQ), trans-trans-1,4diphenyl-1,3-butadiene (DPBD), dithranol (DT), nicotinic acid (NA), and 2′,4′,6′-trihydroxyacetophenone (THAP) were used as (20) Liland, K. H.; Mevik, B.-H.; Rukke, E.-O.; Almøy, T.; Skaugen, M.; Isaksson, T. Chemom. Intell. Lab. Syst. 2009, 96, 210–218. (21) Brandt, H.; Ehmann, T.; Otto, M. J. Am. Chem. Soc. Mass Spectrom. 2010, doi: 10.1016/j.jasms.2010.07.002. (22) Carlson, R.; Carlson, J.; Grennberg, A. J. Chemom. 2001, 15, 455–474. (23) Carlson, R. Org. Process Res. Dev. 2005, 9, 680–689.

8170

Analytical Chemistry, Vol. 82, No. 19, October 1, 2010

matrices. Silver trifluoroacetate, lithium trifluoroacetate, copper(II) trifluoroacetate, lithium chloride, and sodium nitrate were used as cationization reagents. MALDI matrices and cationization reagents were purchased from Sigma-Aldrich (Steinheim, Germany) and used as received. The solvents toluene (TOL), chloroform (TCM), dichlormethane (DCM), methyl ethyl ketone (MEK), methyl isobutyl ketone (MiBK), and tetrahydrofuran (THF), stabilized with 2,6-di-tert-butyl-4-methylphenol, were purchased from Merck (Darmstadt, Germany). MALDI-TOF MS. All experiments were carried out on an AXIMA Performance MALDI-TOF mass spectrometer (Shimadzu Biotech, Manchester, U.K.) equipped with a nitrogen laser (λ ) 337 nm). The mass spectra were obtained in linear mode with an accelerating voltage of 20 kV; at least 150 to 200 mass spectra were averaged for one spectra by scanning a raster. Four measurements of each sample were carried out for evaluating standard deviation. Although Wetzel et al.19 showed that detector voltage can have a high impact on S/N ratios, none of the voltage settings were manually changed. As shown in numerous papers, laser energy has a high impact on MALDI-TOF MS analysis. Thus, in this study, laser energy was optimized for each sample. The laser energy was set up for each measurement slightly above threshold of the analyte signal. Sample Preparation. All combinations of the eight matrices, five cationization reagents, and six solvents were analyzed (240 combinations). Polymer solutions were prepared at a concentration of 5 mg/mL, matrix solutions at a concentration of 10 mg/mL, and cationization reagent solutions at a concentration of 0.1 mol/ L. According to our previous paper dealing with the effect of mixing ratio, the solutions were mixed in the given volume mixing ratios of a 23 full factorial design (eight mixing ratios plus the triplicate center point).21 A 1 µL aliquot of the solution was hand-spotted on a stainless steel target and allowed to dry by air. Hardly soluble components in a specified solvent were made as saturated solutions at ambient temperature and did not correspond to the above-mentioned concentrations. Chemometrics. For each of the 240 combinations, the 23 full factorial design varying the volume mixing ratio was applied so that more than 2000 mass spectra are available for a reasonable application of chemometric methods. The employed chemometrics are not described in this paper; detailed explanations are found in the literature.24-27 For chemometrics, laser energy, the molecular weight at maximum of distribution (Mp), signal intensity, signal-to-noise (S/N) ratio, and spectroscopic resolution (R) were chosen as response variables; the latter three variables were taken from Mp. Additionally, two response variables were worked out describing the quality of the distribution: the number (values between 0 and 4) and intensity of interfering peaks (caused by clusters or further peak series besides the principal series; values between 0 and 5) were taken between Mp and Mp+ 1 repeat units. The higher the values of the number and the intensity of interfering peaks, the worse (24) Otto, M. Chemometrics: Statistics and Computer Application in Analytical Chemistry; Wiley-VCH: Weinheim, Germany, 1999. (25) Kessler, W. Multivariate Datenanalyse; Wiley-VCH: Weinheim, Germany, 2007. (26) Brereton, R. G. Applied Chemometrics for Scientists; John Wiley & Sons: Chichester, UK, 2007. (27) Eriksson, L.; Johansson, E.; Kettaneh-Wold, N.; Wold, S. Multi- and Megavariate Data Analysis; Umitrics Academy: Umea, 2001.

Figure 1. PCA scores plot of 70 matrices defined by molecular descriptors (81.97% explained variance by the first and second principal component). Abbreviations are listed in Table 1.

the mass spectrum. The chosen response variables show variations in molar mass distributions representing the visual nature of the spectra and the suitability of the studied combination (matrix, cationization reagent, solvent), as well. The chemometrics were calculated and evaluated using MATLAB (R2007b, The MathWorks Inc.) and SOLO (Eigenvector Research Inc.). RESULTS AND DISCUSSION Preface. Since the MALDI mechanism is still not understood in detail, the modeling of the sample preparation could not include all possible interactions in the desorption/ionization process. Any modeling would only be an approximation. The aim of our modeling is the prediction of the suitability of nontested combinations (matrix, cationization reagent, solvent) based upon a few tested and evaluated combinations for one single polymer. Applying the methods of design and optimization in organic synthesis by Carlson et al.,22,23 a polymer-specific model was established. Workflow of Modeling. According to the strategy of Carlson et al.,22 the following steps toward prediction were carried out: 1. Applying the dried droplet sample preparation method, a proper matrix, cationization reagent, and solvent, as well as the mixing ratio have to be selected. Since the

2.

3.

mixing ratio affects the molar mass distribution for every studied combination, the whole 23 full factorial design varying the volume mixing ratio was carried out so that the experimental space is only made up of the matrix type, cationization reagent, and solvent.21 The matrix, cationization reagent, and solvent had to be defined by pertinent descriptors. For the matrices and cationization reagents, molecular descriptors were applied while, for the solvents, solubility parameters were more useful. Molecular descriptors were developed for organic molecules, and we had used them to define the cationization reagents, too. Since there were not enough typical characteristics for ionic substances found in the literature (first, we did not found any data for describing the trifluoroacetates), the molecular descriptors were also applied on the cationization reagents. Initially, the solvents were also defined by molecular descriptors, but during the modeling improvement process, Hansen solubility parameters had been proved for description (the polar component δp and the hydrogen bonding component δh). Principal component analysis (PCA) was carried out using the molecular descriptors. The scores of the first and second principal component were determined for 70 Analytical Chemistry, Vol. 82, No. 19, October 1, 2010

8171

matrices (Figure 1, Table 1) as well as for 29 cationization reagents (Figure 2). From the score plots, subsets of test items were selected so that the desired spread of the experimental space is covered by the chosen test items. The selected solvents should completely dissolve the polymer. Figure 3 presents the 2D Hansen solubility map which provides a feasible tool for choosing appropriate solvents for a defined polymer.28 4. Steps 4-7 can be done by a totally mathematical workflow to select experiments for modeling.22 Since experiments (combinations) should be chosen which cover the experimental space (i.e., both well suitable and suboptimal combinations) for the selection of experiments, a fractional factorial design was created.23 For the screening design, one test item was chosen of each quadrant of the PCA score plots as well as four (appropriate) solvents. The created screening design including eight combinations is presented in Table 2. 5. The eight combinations of the screening design were analyzed by MALDI-TOF MS. The obtained response variables were evaluated, and an overall averaged grade was determined. A grade of 1 describes very well performing combinations for the studied polymer, while the grade of 4 represents a failed experiment. Notice that the whole evaluation is subjectively influenced by the operator and his aims of research. These eight combinations represent the training data set of the model. 6. Additionally, further eight combinations had to be analyzed and evaluated to be used for validation. 7. With the calibration data set, the model is built using the partial least-squares regression (PLS1, preprocessing autoscale, NIPALS (nonlinear iterative partial leastsquares) algorithm). After validation, the model can be used to predict the suitability of nontested combinations for the examined polymer. The matrix X of both data sets consists of 8 rows and 6 columns. The eight training combinations, respectively, the eight validation combinations make up the rows. The PCA scores of the first and second principal components of the matrices as well as the cationization reagents are listed in columns 1-4, whereas both solubility parameters of the solvents are given in columns 5 and 6. The averaged grades of the suitability of the tested combinations define vector y. For the prediction of the suitability of a new combination, the scores of the chosen matrix and cationization reagent as well as the solubility parameters of the solvent are needed. For this purpose, the high number of matrices and cationization reagents was considered in the PCA. Prediction Results. For PS2850, THF, MEK, DCM, and TOL were used as solvents for modeling; the results are shown in Table 3. The predicted suitability of the modeled combinations are, aside from the second combination, consistent with the measured ones. An absolute mean deviation of ±0.5 was determined for 16 combinations (eight training combinations and eight validation combinations). Table 4 lists a few predictions applying the PLS1 model for the polar matrix THAP and the nonpolar matrix DCTB (both matrices were not included in the model). For both matrices, (28) Brandt, H.; Ehmann, T.; Otto, M. Rapid Commun. Mass Spectrom. 2010, 24, 2439–2444.

8172

Analytical Chemistry, Vol. 82, No. 19, October 1, 2010

Table 1. The 70 Matrices and Their Abbreviations matrix

abbr.

1,4-bis(5-phenyl-2-oxazolyl)benzene 1,4-dihydroxy-2-naphthoic acid 2-(4-hydroxyphenylazo)benzoic acid 2′,4′,6′-trihydroxyacetophenone 2,5-dihydroxybenzoic acid 2,5-dihydroxy-p-benzoquinone 2′,6′-dihydroxyacetophenone 2-amino-3-methyl-5-nitropyridine 2-amino-4-methyl-3-nitropyridine 2-amino-4-methyl-5-nitropyridine 2-bromo-4,6-dinitroaniline 2-hydroxy-4-methoxybenzoic acid 2-hydroxy-4-methoxybenzophenone 2-mercaptobenzothiazole 2-nitrophenyl dodecyl ether 2-nitrophenyl octyl ether 2-nitrophenyl pentyl ether 2-octyldodecanol 3-aminoquinoline 3-hydroxypicolinic acid 4-benzyloxy-R-cyanocinnamic acid 4-nitroaniline 5-acetylamino-2-mercapto-1,3,4-thiadiazole 5-amino-2-mercapto-1,3,4-thiadiazole 5-chloro-2-mercaptobenzothiazole 5-chlorosalicylic acid 5-ethyl-2-mercaptothiazole 5-methoxysalicylic acid 6-aza-2-thiothymine 6-mercaptopurine 9-anthracenecarboxylic acid 9-bromoanthracene 9-chloroanthracene 9-nitroanthracene 9-phenanthrol acenaphthene R-cyano-4-hydroxycinnamic acid R-cyano-4-phenylcinnamic acid all-trans retinoic acid aminopyrazine anthracene anthranilic acid benzo[a]pyrene caffeic acid chrysene dipicolinic acid dithranol esculetin ethylen glycol monosalicylate ferulic acid isovanillin naphthalene nicotinic acid norharmane o-phenanthrolin o-vanillin p-cumaric acid phenanthrene picolinic acid pyrazinecarboxylic acid pyrene salicylamide sinapinic acid terthiophene tetraethylene glycol dimethyl ether thiourea trans,trans-1,4-diphenyl-1,3-butadiene trans-2-[3-(4-tert-butylphenyl)-2-methyl2-propenylidene]malononitrile trans-3-indoleacrylic acid vanillic acid

POPOP DHNA HABA THAP DHB DHBQ DHAP 2,3,5-AMNP 2,4,3-AMNP 2,4,5-AMNP BDNA HMB HMBP MBT NPDE NPOE NPPE 2-OD 3-AC 3-HPA CBCA 4-NA AAMT AMT CMBT 5-CSA EMT 5-MSA ATT MP 9-ACA 9-BA 9-CA 9-NA 9-PHOH ACNP CHCA CPCA RA APY ANT 2-AA BAP CA CHR DPA DT DHC EGS FA IVA NPH NA NOR PHAN VAN pCA PHEN PA PZA PYR SCA SA TTP TEGD TU DPBD DCTB IAA VANA

Figure 2. PCA score plot of 29 cationization reagents defined by molecular descriptors (76.63% explained variance by three principal components).

AgTFA and Cu(II)TFA should be proper cationization reagents due to low grades. For DCTB combinations, a slightly better feasibility is predicted than for THAP. These predictions can be confirmed by the MALDI results. A very important fact is also shown in Table 4: the influence of the solubility. Cu(II)TFA was selected for prediction to show the effect of solvent selection on the model. For this cationization reagent, THF and MEK are shown to be good solvents. Hence, the model will predict Cu(II)TFA combinations to perform well disregarding the chosen solvents as for chloroform, a nonsolvent for Cu(II)TFA. Almost all investigated combinations of Cu(II)TFA using chloroform failed. Solubility is the limiting factor of the model which is also the reason for the high absolute deviation of the second combination in Table 3. Taking this fact into account, the model predicts well and one can choose three or five or more combinations which are predicted to be suitable and investigate them. According to these facts, the combinations of DCTB and THAP with AgTFA or Cu(II)TFA using THF or MEK as solvents (Table 4) are predicted to be suitable due to their low averaged grade, and hence, these combinations should yield reliable MALDI mass spectra of PS2850. This workflow serves not only for predicting useful combinations but also as a starting point for analysis of unknown polymers;

that is, the eight matrices of the screening design have to be selected and investigated; the modeling step is omitted. It is possible to construct a screening design with eight training combinations for a unknown polymer which has not yet been characterized by MALDI-TOF MS. On the basis of the production process, the reactants, the solubility, and, hence, the polarity of the polymer are known so that a chemical structure can be assumed. Furthermore, NMR spectra provide useful information. Feasibility of the Model. The combinations of the screening design used as calibration experiments are not suitable for all synthetic polymers. The model (Table 2) predicts well for PS and PDMS of different molecular masses (recognize that the matrices and cationization reagents were always the same, but the solvents were chosen corresponding to the polymer). For other synthetic polymers, such as poly(vinyl alcohol), or polymers of higher molar masses, the PLS model has to be adjusted so that other matrices should be used to satisfy the purpose of step 3. Protocol of Modeling. The characterization of matrices, cationization reagents, and solvents by molecular descriptors and solubility parameters, respectively, has to be calculated once for a high number of reagents (70 matrices and 29 cationization reagents were defined by molecular descriptors in this study). So the investigation of a polymer applying the PLS modeling workflow starts with the construction of the screening design with Analytical Chemistry, Vol. 82, No. 19, October 1, 2010

8173

Figure 3. Two-dimensional Hansen solubility map including PS and PDMS and several solvents.

Table 2. Screening Design for Modeling (PS2850)a combination

QM

1 2 3 4 5 6 7 8

-+-+ ++ -+-+ ++

matrix

QCR

CR

DHBQ DPBD DHB DT DHBQ DPBD DHB DT

-+ ---+ ++ ++++

(II)

Cu TFA AgTFA AgTFA Cu(II)TFA LiTFA LiCl LiCl LiTFA

solvent THF TOL DCM MEK MEK DCM TOL THF

a Q is quadrant of PCA score plots of matrices (M) and cationization reagents (CR).

Table 3. Evaluated and Predicted Suitability and Their Deviation of the Training Combinations for PS2850 no.

combination

evaluated

predicted

deviation

1 2 3 4 5 6 7 8

DHAP-Cu(II)TFA-THF DPBD-AgTFA-TOL DHB-AgTFA-DCM DT-Cu(II)TFA-MEK DHAP-LiTFA-MEK DPBD-LiCl-DCM DHB-LiCl-TOL DT-LiTFA-THF

1 1 2 2 3 4 4 2

0.9 2.5 2.0 1.0 2.6 3.4 3.8 2.3

0.1 –1.5 0.0 1.0 0.4 0.6 0.2 –0.3

the eight combinations for calibration, which were chosen according to the chemical nature of the polymer. Further eight combinations for evaluation have to be selected. Subsequently, all of the chosen combinations have to be analyzed for the examined polymer, and the MALDI mass spectra have to be evaluated. For the evaluation, the operator has to select several 8174

Analytical Chemistry, Vol. 82, No. 19, October 1, 2010

Table 4. Predicted Suitability of Nontested Combinations Applying the PLS1 Model for PS2850 combination

predicted values

DCTB-AgTFA-TCM DCTB-AgTFA-MEK DCTB-AgTFA-THF DCTB-Cu(II)TFA-TCM DCTB-Cu(II)TFA-MEK DCTB-Cu(II)TFA-THF THAP-AgTFA-TCM THAP-AgTFA-MEK THAP-AgTFA-THF THAP-Cu(II)TFA-TCM THAP-Cu(II)TFA-MEK THAP-Cu(II)TFA-THF

1.9 1.9 1.7 0.9 0.9 0.7 2.1 2.1 1.9 1.1 1.1 0.9

response variables according to his aim of research. When all combinations are evaluated by grades, the PLS model can be designed and the suitability of a high number of nontested combinations can be predicted. The operator will chose the best predicted combinations (lowest grades) and investigate them. If a good PLS model was built, these combinations will yield better results for the selected response variables. Improvement of Matrix Selection without Modeling. Apart from its importance for constructing the prediction model, Figure 1 provides some useful information for sample preparation. On the left side, polar matrices are located, whereas on the right, nonpolar matrices were found. The bottom right quadrant contains nonpolar matrices such as polycyclic aromatic hydrocarbons and some sulfur-containing substances. Nonpolar matrices with polar functionalities are located in the top right quadrant. NPOE (2-nitrophenyl octyl ether), POPOP

(1,4-bis(5-phenyl-2-oxazolyl)benzene), DCTB, and RA (all-trans retinoic acid) are matrices that require low laser energies and are suitable for higher molecular mass polymers. These matrices are located close to each other. A cluster analysis of the MANOVA results had shown that DHB and THAP can be exchanged in most cases, confirming the experimental observations. In Figure 1, both matrices are also located at close quarters. However, several specific features of the matrices cannot be captured with the molecular descriptors. So the PCA score plots of Figure 1 do not provide detailed information of these features. DHB and THAP could be exchanged in the most samples in this study, but for some samples, it was not possible. In these cases, the acidic character of DHB or the nonacidic character of THAP was crucial for the MALDI analysis. We had successfully used Figure 1 for a huge number of experiments because the plot contains more information than the simple polarity-similarity principle of choice (i.e., the polarity of the chosen matrix should be similar to the one of the examined synthetic polymer).

CONCLUSIONS Applying the PLS1 regression, a polymer-specified model was built predicting the suitability of nontested combinations. An absolute deviation of ±0.5 was achieved, predicting the eight training and the eight validation combinations; that is, a predicted suitability of 1 ± 0.5 means that this combination should perform well for the examined polymer. However, a confirmation experiment of a combination predicted to be suitable could fail since several effects on the obtained molar mass distribution, such as solubility effects or the occurrence of a precipitate using defined matrices with silver or copper salts, were not well modeled employing a small number of training combinations. Nevertheless, the eight training combinations of the screening design offer the possibility to be used as a starting point for the analysis of unknown synthetic polymers.

Received for review June 9, 2010. Accepted August 17, 2010. AC101526W

Analytical Chemistry, Vol. 82, No. 19, October 1, 2010

8175