Comparison of partial least squares and lasso regression techniques as applied to laser-induced breakdown spectroscopy of geological samples

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• 作者：M.D. Dyar^a ; ^{mdyar@mtholyoke.edu} ; M.L. Carmosino^a ; E.A. Breves^a ; M.V. Ozanne^a ; S.M. Clegg^b ; R.C. Wiens^b
• 关键词：LIBS ; ChemCam ; Partial least square ; Lasso ; Multivariate analysis
• 刊名：Spectrochimica Acta Part B ; Atomic Spectroscopy
• 出版年：2012
• 期刊代码：101_05848547
• 类别：ch
• 出版时间：April, 2012
• 卷：70
• 期：Complete
• 页码：51-67
• 文件大小：2158 K

摘要

A remote laser-induced breakdown spectrometer (LIBS) designed to simulate the ChemCam instrument on the Mars Science Laboratory Rover Curiosity was used to probe 100 geologic samples at a 9-m standoff distance. ChemCam consists of an integrated remote LIBS instrument that will probe samples up to 7 m from the mast of the rover and a remote micro-imager (RMI) that will record context images. The elemental compositions of 100 igneous and highly-metamorphosed rocks are determined with LIBS using three variations of multivariate analysis, with a goal of improving the analytical accuracy. Two forms of partial least squares (PLS) regression are employed with finely-tuned parameters: PLS-1 regresses a single response variable (elemental concentration) against the observation variables (spectra, or intensity at each of 6144 spectrometer channels), while PLS-2 simultaneously regresses multiple response variables (concentrations of the ten major elements in rocks) against the observation predictor variables, taking advantage of natural correlations between elements. Those results are contrasted with those from the multivariate regression technique of the least absolute shrinkage and selection operator (lasso), which is a penalized shrunken regression method that selects the specific channels for each element that explain the most variance in the concentration of that element. To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units. Results demonstrate that the lasso, PLS-1, and PLS-2 all yield comparable results in terms of accuracy for this dataset.

However, the interpretability of these methods differs greatly in terms of fundamental understanding of LIBS emissions. PLS techniques generate principal components, linear combinations of intensities at any number of spectrometer channels, which explain as much variance in the response variables as possible while avoiding multicollinearity between principal components. When the selected number of principal components is projected back into the original feature space of the spectra, 6144 correlation coefficients are generated, a small fraction of which are mathematically significant to the regression. In contrast, the lasso models require only a small number (< 24) of non-zero correlation coefficients (尾 values) to determine the concentration of each of the ten major elements. Causality between the positively-correlated emission lines chosen by the lasso and the elemental concentration was examined. In general, the higher the lasso coefficient (尾), the greater the likelihood that the selected line results from an emission of that element. Emission lines with negative 尾 values should arise from elements that are anti-correlated with the element being predicted. For elements except Fe, Al, Ti, and P, the lasso-selected wavelength with the highest 尾 value corresponds to the element being predicted, e.g. 559.8 nm for neutral Ca. However, the specific lines chosen by the lasso with positive 尾 values are not always those from the element being predicted. Other wavelengths and the elements that most strongly correlate with them to predict concentration are obviously related to known geochemical correlations or close overlap of emission lines, while others must result from matrix effects. Use of the lasso technique thus directly informs our understanding of the underlying physical processes that give rise to LIBS emissions by determining which lines can best represent concentration, and which lines from other elements are causing matrix effects.