Quantitative analysis of cadmium and zinc in algae using laser-induced breakdown spectroscopy
In the demand for renewable energy sources, algae are considered to have a good potential for biodiesel production. Fast detection of heavy metals in energy algae is of vital importance for algal biomass and biodiesel production environment monitoring. This study aims to determine cadmium (Cd) and zinc (Zn) content in algae pellets with laser-induced breakdown spectroscopy (LIBS) technology. Partial least squares (PLS) regression and extreme learning machine (ELM) were constructed after spectra preprocessed by standard normal variate (SNV), multiplicative scatter correction (MSC) and Savitzky-Golay smoothing algorithm (SG). For univariate analysis, ELM models based on Cd II 226.45 nm and Zn II 206.19 nm of SNV preprocessed spectra achieved best results for Cd and Zn content prediction respectively, with Rp, RMSEP and RPD value of 0.9866, 50.71 mg/kg and 6.95 for the former, and 0.9873, 30.08 mg/kg and 6.44 for the latter. Multivariate analysis of Cd based on PLS model with global spectra achieved best performance with Rc and Rp value of 0.9965 and 0.9972, RMSECV and RMSEP of 25.57 mg/kg and 23.63 mg/kg, and RPD value high as 13.27, showing the excellent robustness and effectiveness of the model for Cd detection. For Zn analysis, the best performance was achieved by ELM model based on feature variables selected by regression coefficients, with Rc and Rp value of 0.9836 and 0.9920, RMSEC and RMSEP of 34.25 mg/kg and 24.64 mg/kg, and RPD value of 7.87. The results indicated that LIBS technique combined with proper preprocessing algorithms and multivariate chemometric methods could be a rapid and accurate way for quantitative analysis for Cd and Zn content in algae and aquatic environment.