Application of chemometric tools for automatic classification and profile extraction of DNA samples in forensic tasks

In this paper a method for the automatic DNA spots classification and extraction of profiles associated in DNA polyacrylamide gel electrophoresis is presented and it integrates the use of image processing techniques and chemometrics tools. A software which implements this method was developed; for feature extraction a combination of a PCA analysis and a C4.5 decision tree were used. To obtain good results in the profile extraction only DNA spots are useful; therefore, it was necessary to solve a two-class classification problem among DNA spots and no-DNA spots. In order to perform the classification process with high velocity, effectiveness and robustness, comparative classification studies among support vector machine (SVM), K-NN and PLS-DA classifiers were made. The best results obtained with the SVM classifier demonstrated the advantages attributed to it in the literature as a two-class classifier. A Sequential Cluster Leader Algorithm and another one developed for the restoration of pattern missing spots were needed to conclude the profiles extraction step. The experimental results show that this method has a very effective computational behavior and effectiveness, and provide a very useful tool to decrease the time and increase the quality of the specialist responses.