Evaluation of supervised chemometric methods for sample classification by Laser Induced Breakdown Spectroscopy

In this work seven supervised chemometric methods have been evaluated in a real world application for the classification of human bone remains with similar elemental composition based on Laser Induced Breakdown Spectroscopy (LIBS) measurements. Bone samples belonging to five individuals were obtained from a local cemetery, exposed to uncontrolled conditions. LIBS data were processed with different linear and non-linear supervised chemometric approaches. The performance of each chemometric model was assessed by three validation procedures taking into account their sensitivity (internal validation), generalization ability and robustness (independent external validation). The accuracy of each method increased in the following order: 42% for Linear Discriminant Analysis (LDA), 48% for Classification and Regression Tree (CART), 56% for Support Vector Machines (SVM), 58% for Soft Independent Modeling of Class Analogy (SIMCA), 58% for Partial least Squares-Discriminant Analysis (PLS-DA), 66% for Binary Logistic Regression (BLR) and 100% for Artificial Neural Networks (NN). The results showed that NN outperforms in terms of sensitivity, generalization ability and robustness; whereas SIMCA, PLS-DA, LDA, CART, Logistic Regression and SVM did not show significant accuracy to discriminate the bone samples with a high degree of similarity.