Package: specProc 0.8.3.9000

specProc: Preprocessing Tools for Laser-Induced Breakdown Spectroscopy

Tools for exploring, preprocessing and modeling spectroscopic data, developed for laser-induced breakdown spectroscopy (LIBS) and also applicable to other techniques such as Raman, infrared, and inductively coupled plasma optical emission spectroscopy. Covers the workflow from raw spectra to calibrated results: peak finding and fitting, wavelength calibration, baseline correction, normalization and scaling, orthogonal signal correction including orthogonal partial least squares (OPLS; Trygg and Wold (2002) <doi:10.1002/cem.695>), wavelength selection (variable importance in projection, selectivity ratio, interval partial least squares), calibration transfer, calibration curves and figures of merit, and plasma diagnostics (Boltzmann and Saha-Boltzmann plots, Stark broadening). Includes robust tools for outlier detection and exploration: robust principal component analysis (Hubert, Rousseeuw and Vanden Branden (2005) <doi:10.1198/004017004000000563>), robust partial least squares regression (Hubert and Vanden Branden (2003) <doi:10.1002/cem.822>) and principal component regression (Hubert and Verboven (2003) <doi:10.1002/cem.783>) with their robust cross-validation (Engelen and Hubert (2005) <doi:10.1016/j.aca.2005.01.015>), robust classification by SIMCA (Vanden Branden and Hubert (2005) <doi:10.1016/j.chemolab.2005.03.002>) and discriminant analysis (Hubert and Van Driessen (2004) <doi:10.1016/S0167-9473(02)00299-2>), detection of deviating cells (Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909>), PCA with cellwise outliers and missing values (MacroPCA; Hubert, Rousseeuw and Van den Bossche (2019) <doi:10.1080/00401706.2018.1562989>), robust transformations to central normality (Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5>), and self-organizing maps (Kohonen (1982) <doi:10.1007/BF00337288>). Preprocessing steps are also available as 'recipes' steps, and the robust models as 'parsnip' models and engines, for 'tidymodels' workflows. Computationally intensive steps are implemented in 'C++' via 'Rcpp' and 'RcppEigen'.

Authors:Christian L. Goueguel [aut, cre]