College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
Food Chem. 2019 Oct 15;295:327-333. doi: 10.1016/j.foodchem.2019.05.119. Epub 2019 May 17.
Dual-pulse laser-induced breakdown spectroscopy (DPLIBS) and chemometric methods were used to predict chromium content in rice leaves, along with the purpose for increasing the detection sensitivity and accuracy. The influence of important parameters in DPLIBS were investigated and optimized. Then, partial least square (PLS) was used to establish chromium content prediction models, and the value of regression coefficient based on PLS was applied to determine feature variables. In addition, multivariate and univariate analysis were used to verify the modeling performance of selected feature variables. The results indicated that support vector machine model based on feature variables achieved the best performance, with correlation coefficient of 0.9946, root mean square error of 4.85 mg/kg and residual predictive deviation of 9.70 in prediction set. The proposed method provides a high-accuracy and fast approach for chromium content prediction in rice leaves, which could potentially be used for toxic and nutrient elements detection in food.
采用双脉冲激光诱导击穿光谱（DPLIBS）和化学计量学方法预测水稻叶片中的铬含量，旨在提高检测灵敏度和准确性。研究并优化了 DPLIBS 中的重要参数的影响。然后，采用偏最小二乘法（PLS）建立了铬含量预测模型，并应用基于 PLS 的回归系数来确定特征变量。此外，还采用多元和单变量分析验证了所选特征变量的建模性能。结果表明，基于特征变量的支持向量机模型表现最佳，预测集的相关系数为 0.9946，均方根误差为 4.85mg/kg，剩余预测偏差为 9.70。该方法为水稻叶片中铬含量的预测提供了一种高精度、快速的方法，有望用于食品中有毒和营养元素的检测。
