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.