Signal Processing Methods for Capillary Electrophoresis

Capillary electrophoresis (CE) is a separation technique that can be used as a sample pretreatment step in the analysis of ionic analytes (Grossman and Colburn 1992; Stewart et al. 2008). Compared with other separation technologies it can offer advantages such as higher speed and sensitivity, smaller injection volumes and reduced consumption of solvent and samples, the possibility of miniaturisation, and reduced cost (Issaq 2001; Jarmeus and Emmer 2008; Polesello and Valsecchi 1999; Wang 2005; Wee et al. 2008). CE is based on the difference of the electrical mobilities of molecules within a capillary tube filled with electrolyte solution. When an electrical field is applied between the two ends of a capillary and a sample is introduced at one end, analytes are separated as they migrate towards the other end under the influence of the electrical field. These separated analytes are detected near the outlet by methods such as optical or electrochemical techniques (Polesello and Valsecchi 1999; Guijt et al. 2004; Kappes and Hauser 1999; Kubaň and Hauser 2004, 2009; Kuhn and Hoffstetter-Kuhn 1993; Marzilli et al. 1997; Tanyanyiwa et al. 2002; Zemann et al. 1998). The signal from a detector is digitised and typically presented in the form of voltage versus time, i.e. an electropherogram. Peaks evident in an electropherogram typically correspond to analytes in the sample, and with optimisation of the system parameters, the peaks can usually be resolved sufficiently. Fig. 1 shows an example electropherogram of data obtained from a practical trial reported earlier (Petkovic-Duran et al. 2008) For analytical chemistry purposes, the operator's aim is to determine from the electropherogram what analytes are present and the corresponding concentrations. In this paper we assume that this is done by separating the task into two stages: Signal Processing, i.e., obtaining peak information from the electropherogram, and Pattern Matching, using this peaks' summary information to compare with established peak library of known chemicals. Whilst the process of identifying the peaks, removing the noise present and fitting curves for peak quantification is, to a large extent, done to-date manually by professionals, the operator would be greatly aided through fully automated techniques with little or no human input. This is particularly crucial for the development of field-deployable devices which could be operated by non-technical staff. Furthermore, automated signal processing techniques can allow results to be reproducible or consistent and can remove the subjectivity of a human evaluation. In addition, they can also detect features that may not