Evaluation and optimization of the robustness of a multivariate analysis methodology for identification of alloys by laser induced breakdown spectroscopy

In this study, laser induced breakdown spectroscopy (LIBS) with chemometrics was used for classification and identification of alloys, with a particular focus on the issue of the model robustness. A supervised classification model, Soft Independent Modeling of Class Analogy (SIMCA) was calculated with calibration spectra of 13 representative materials. These measurements were reproduced, with the same samples and using the same LIBS instrument, on two different dates (seven and eight months after the calibration measurements): during this period, instrumental variations occurred and the robustness of sample classification was assessed by the prediction error rate. Then, the optimization of SIMCA model parameters, including spectral preprocessing and wavelength selection, was performed using a full factorial experimental design, and a prediction error rate of 0% with a robustness of 100% was achieved for this period extending until eight months after the model calibration. The study was completed two and a half years later by a test of the robustness of the previously optimized model, carried out with an additional series of measurements on test samples with the same LIBS instrument. The predictive ability of the model on spectra acquired more than two years after validation remained good.