College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
Molecules. 2019 Jul 9;24(13):2517. doi: 10.3390/molecules24132517.
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 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 = 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.
快速检测生菜中的重金属对于食品市场监管和重金属污染控制具有重要意义。已经尝试了诸如激光诱导击穿光谱（LIBS）技术等先进方法来确定镉（Cd）的含量。为了减缓样品中复杂基质组成的负面影响并提高 LIBS 技术的定量性能，采用了与 LIBS 相结合的热解过程来测定生菜中的镉（Cd）含量。自适应迭代重加权惩罚最小二乘法（airPLS）用于预处理 LIBS 光谱并解决基线漂移问题。对于基于预测集中三个选定的 Cd 发射线相关系数的多元线性回归，相关系数从 0.9154 增加到 0.9969，并且在热解过程后，检测限（LOD）从 9.1 mg/kg 降低到 0.9 mg/kg。偏最小二乘（PLS）回归和支持向量回归（SVR）被应用于基于全谱构建校准模型。此外，实施最小绝对收缩和选择算子（LASSO）以选择有限的线来预测 Cd 含量。具有热解过程的 PLS 模型得到了最佳结果， = 0.9973，LOD = 0.8 mg/kg。结果表明，热解方法可以增强镉的光谱信号，从而显著改善所有模型的分析结果。实验表明，适当的样品预处理可以有效地放大 LIBS 中的 Cd 信号，使 LIBS 测量成为评估蔬菜工业中 Cd 污染的有效方法。
