Soft classification scheme with pre-cluster-based regression for identification of same-base alloys using laser-induced breakdown spectroscopy

In this study, a novel soft classification scheme is proposed for metal scrap identification with laser-induced breakdown spectroscopy (LIBS) measurements. LIBS provides unique spectra for different metals that can be utilized for classifying metal scraps in real time. Despite its potential, LIBS-based metal classification is not yet fully implemented in practice due to the large shot-to-shot variation and non-linear relationships between spectral intensities and elemental concentrations. Particularly for recycling metal alloys of the same base, learning all candidate types is infeasible, and conventional classification approaches exhibit limited performance in classifying unknown samples of untrained types due to the variability and non-linearity in LIBS measurements. To overcome the limitations of LIBS-based metal scrap classification, the proposed scheme employs pre-cluster-based regression (PCBR) analysis. PCBR takes advantage of the joint relationships between the elemental concentration variations of the pre-clusters (p-clusters), which are pre-determined by prior information of the probability distributions. The variance of the regression of individual p-clusters can be significantly reduced compared to global regression by jointly taking into account common relationships between the elemental concentrations within a particular p-cluster. By combining the layered regression results with their estimated statistics, soft multi-label classification and extraction of the likelihood values of trained classes is possible even for samples of untrained types. For performance evaluation, a list of reference alloys from the National Institute of Standard and Technology (NIST) and Brammer databases was divided into finite sets of p-clusters based on the relationships of elemental concentrations, in particular, four p-clusters for Cu-based alloy tests with Cu and Zn concentrations. Then, PCBR were trained with LIBS-captured spectra of 35 certified reference materials for all and four individual p-clusters. The partial least squares (PLS) and random forest (RF) regression methods were employed, and the root mean square error (RMSE) of the estimation and soft classification measures was investigated. The evaluation results of same-base alloy regression revealed that the proposed PCBR reduced the RMSE of the major element concentration estimation compared to conventional regression schemes. In addition, the accuracy of the soft classification of same-base alloys by PCBR for untrained types was tangibly improved compared to that of prior approaches, such as PLS discriminant analysis and soft independent modeling of class analogy (SIMCA).