Variable selection and data fusion for diesel cetane number prediction

Buendia-Garcia, J.; Lacoue-Nègre, Marion; Gornay, Julien; Mas-Garcia, S.; Bendoula, Ryad; Roger, JM

doi:10.1016/j.fuel.2022.126297

Cited by 3 publications

(1 citation statement)

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“…For improving the performance of the calibration model, spectral pretreatment and variable selection strategy need to be implied in the modeling process. Common spectral pretreatment methods for LIBS mainly consist of baseline correction, noise filtering, overlapping, peak resolution, and data compression, such as multivariate scattering correction (MSC), standard normal variation (SNV), Savitzky-Golay (SG) convolution derivation, wavelet transform (WT), first-order derivation (D1st), second-order derivation (D2nd), etc. − Similarly, feature selection refers to selecting the optimal feature from the original feature subset based on an evaluation criterion, which is an essential approach to improve the performance of machine learning algorithms . Variable importance measurement (VIM), variable importance projection (VIP), mutual information (MI), successive projections algorithm (SPA), genetic algorithm (GA), etc.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning

et al. 2023

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Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R 2 P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.

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