Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS

Abstract

The objective of the current research was to compare different data-driven multivariate statistical predictive algorithms for the quantitative analysis of Fe content in iron ore measured using Laser-Induced Breakdown Spectroscopy (LIBS). The algorithms investigated were Principal Components Regression (PCR), Partial Least Squares Regression (PLS), Multi-Block Partial Least Squares (MB-PLS), and Serial Partial Least Squares Regression (S-PLS). Particular emphasis was placed on the issues of the selection and combination of atomic spectral data available from two separate spectrometers covering 208-222 nm and 300-855 nm ranges, which include many of the spectral features of interest. Standard PLS and PCR models produced similar prediction accuracy, although in the case of PLS there were notably less latent variables in use by the model. It was further shown that MB-PLS and S-PLS algorithms which both treated available UV and VIS data blocks separately, demonstrated inferior performance in comparison with both PCR and PLS.

A sequential algorithm for multiblock orthogonal projections to latent structures

“…Of course, while both examples were interpreted in the light of spectroscopic datasets like those used in metabolomics [22, 23], MB-OPLS is a fully general algorithm that admits any multiblock dataset for the purposes of regression or discriminant analysis. For example, recent applications of MB-PLS for investigating food spoilage [28], iron-ore content [29], chemical toxicity [30], the evolution of human anatomy [31], and the assessment of cortical and muscle activity in Parkinson's disease patients [32] would benefit from our MB-OPLS algorithm. The presented algorithm admits either a vector or a matrix as responses, and is implemented in the latest version of the open-source MVAPACK chemometrics toolbox [24].…”

Prediction of 2-acetyl-1-pyrroline content in grains of Thai Jasmine rice based on planting condition, plant growth and yield component data using chemometrics

“…Several works have reported that an ordinary PLS with the combined data seemed to perform rather the same as the modified multiblock algorithms regarding the variance of the parameter estimates [26,27]. Yaroshchyk et al [28] reported that, in their work, the ordinary PLS performed the best when compared with the multiblock regressions (MB-PLS and S-PLS).…”

Application of Laser-Induced Breakdown Spectroscopy to Real-Time Elemental Monitoring of Iron and Steel Making Processes

“…The different data-driven multivariate statistical predictive algorithms, such as PCR, PLSR, multi-block PLSR (MB-PLSR), and serial PLSR (S-PLSR), were compared for the quantitative analysis of Fe content in iron ore measured using LIBS. 75) There were notably less latent variables in the case of PLSR. PLSR and PCR models, however, produced similar prediction accuracy.…”