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. 2018 Nov 9;23(11):2930. doi: 10.3390/molecules23112930.
Quick access to cadmium (Cd) contamination in lettuce is important to supervise the leafy vegetable growth environment and market. This study aims to apply laser-induced breakdown spectroscopy (LIBS) technology for fast determination of Cd content and diagnosis of the Cd contamination degree in lettuce. Emission lines Cd II 214.44 nm, Cd II 226.50 nm, and Cd I 228.80 nm were selected to establish the univariate analysis model. Multivariate analysis including partial least squares (PLS) regression, was used to establish Cd content calibration models, and PLS model based on 22 variables selected by genetic algorithm (GA) obtained the best performance with correlation coefficient in the prediction set ² = 0.9716, limit of detection () = 1.7 mg/kg. K-Nearest Neighbors (KNN) and random forest (RF) were used to analyze Cd contamination degree, and RF model obtained the correct classification rate of 100% in prediction set. The preliminary results indicate LIBS coupled with chemometrics could be used as a fast, efficient and low-cost method to assess Cd contamination in the vegetable industry.
快速获取生菜中的镉(Cd)污染情况对于监督叶菜类蔬菜的生长环境和市场非常重要。本研究旨在应用激光诱导击穿光谱(LIBS)技术,快速测定生菜中的 Cd 含量并诊断 Cd 污染程度。选择 Cd II 214.44nm、Cd II 226.50nm 和 Cd I 228.80nm 发射线建立单变量分析模型。采用偏最小二乘(PLS)回归等多元分析方法,建立 Cd 含量校准模型,基于遗传算法(GA)选择的 22 个变量的 PLS 模型表现最佳,预测集中的相关系数²=0.9716,检出限(LOD)=1.7mg/kg。K-最近邻(KNN)和随机森林(RF)用于分析 Cd 污染程度,RF 模型在预测集中的正确分类率达到 100%。初步结果表明,LIBS 结合化学计量学可用于快速、高效、低成本地评估蔬菜行业中的 Cd 污染情况。