Toward Prediction: Using Chemometrics for the Optimization of Sample Preparation in MALDI-TOF MS of Synthetic Polymers

Abstract

In recent years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF 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.