Fast detection of heavy metals in lettuce is significant for food market regulation and the control of heavy metal pollution. Advanced methods like laser-induced breakdown spectroscopy (LIBS) technology have been tried to determine the cadmium (Cd) content. To retard the negative effect of complex matrix composition from samples and improve quantitative performance of LIBS technology, the pyrolysis process combined with LIBS was adopted to determine the cadmium (Cd) content of lettuce. Adaptive iteratively reweighted penalized least squares (airPLS) was used to preprocess the LIBS spectra and solve the baseline drift. For multivariate linear regression based on the three selected Cd emission lines correlation coefficient in the prediction set Rp2 increased from 0.9154 to 0.9969, and the limit of detection (LOD) decreased from 9.1 mg/kg to 0.9 mg/kg after the pyrolysis process. The partial least squares (PLS) regression and support vector regression (SVR) were applied to construct calibration models based on full spectra. In addition, the least absolute shrinkage and selection operator (LASSO) was implemented to choose limited lines to predict the Cd content. The PLS model with the pyrolysis process obtained the best results with Rp2 = 0.9973 and LOD = 0.8 mg/kg. The results indicated that the pyrolysis method could enhance the spectral signal of cadmium and thus significantly improve the analysis results for all the models. It is shown in this experiment that proper sample preprocessing could effectively amplify the Cd signal in LIBS and make LIBS measurement an efficient method to assess Cd contamination in the vegetable industry.
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