摘要： In the demand for renewable energy sources, algae are considered to have a good potential for biodiesel production. Fast detection of heavy metals in energetic algae is of vital importance for algal biomass and biodiesel production environment monitoring. This study aims to determine cadmium (Cd) and zinc (Zn) contents in algal pellets with laser-induced breakdown spectroscopy (LIBS) technology. Partial least squares (PLS) regression and extreme learning machine (ELM) were constructed after spectral preprocessing by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and Savitzky–Golay smoothing (SG). For univariate analysis, ELM models based on Cd II 226.45 nm and Zn II 206.19 nm of SNV preprocessed spectra achieved the best results for Cd and Zn content prediction respectively, with Rp, RMSEP and RPD values of 0.9866, 50.71 mg kg?1 and 6.95 for the former, and 0.9873, 30.08 mg kg?1 and 6.44 for the latter. Multivariate analysis of Cd based on the PLS model with global spectra achieved the best performance with Rc and Rp values of 0.9965 and 0.9972, RMSECV and RMSEP values of 25.57 mg kg?1 and 23.63 mg kg?1, and an RPD value as 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 the ELM model based on feature variables selected by regression coefficients, with Rc and Rp values of 0.9836 and 0.9920, RMSEC and RMSEP values of 34.25 mg kg?1 and 24.64 mg kg?1, and an RPD value of 7.87. The results indicated that the LIBS technique combined with appropriate preprocessing algorithms and multivariate chemometric methods could be a rapid and accurate way for quantitative analysis of Cd and Zn contents in algae and the aquatic environment.