College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China.
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China.
Sci Total Environ. 2023 Feb 20;860:160545. doi: 10.1016/j.scitotenv.2022.160545. Epub 2022 Nov 28.
Minerals in rice leaves is a crucial indicator of plant health, and their concentrations can be used to guide plant management. It is important to predict mineral content in contaminated rice rapidly. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to quantify minerals (Ca, Cu, Fe, K, Mg, Mn, and Na) in rice leaves under chromium (Cr) stress. Two feature extraction methods, including principal component analysis (PCA) and extreme gradient boosting (XGBoost), were compared to identify important variables that related to mineral concentrations. Results showed that partial least square regression (PLSR) achieved good performance in Ca, Fe Mg, K, Mn, and Na, with correlation coefficient of 0.9782, 0.8712, 0.8933, 0.9206, 0.9856, and 0.9865, root mean square error of 219.25, 14.78, 1192.47, 385.12, 9.56, and 124.32 mg/kg, respectively. In addition, the correlation between different spectral lines were further analyzed. Cr exhibited a positive correlation with Ca, Mg, and Na, and a negative correlation with Mn, Cu, and K. The proposed method provides a high-accuracy and fast approach for minerals prediction in rice leaves under Cr stress, which is important for environmental protection and food safety.
水稻叶片中的矿物质是植物健康的关键指标，其浓度可用于指导植物管理。快速预测受污染水稻中的矿物质含量非常重要。本研究应用激光诱导击穿光谱（LIBS）定量分析铬（Cr）胁迫下水稻叶片中的矿物质（Ca、Cu、Fe、K、Mg、Mn 和 Na）。比较了两种特征提取方法，包括主成分分析（PCA）和极端梯度提升（XGBoost），以确定与矿物质浓度相关的重要变量。结果表明，偏最小二乘回归（PLSR）在 Ca、Fe、Mg、K、Mn 和 Na 方面表现出良好的性能，相关系数分别为 0.9782、0.8712、0.8933、0.9206、0.9856 和 0.9865，均方根误差分别为 219.25、14.78、1192.47、385.12、9.56 和 124.32mg/kg。此外，还进一步分析了不同谱线之间的相关性。Cr 与 Ca、Mg 和 Na 呈正相关，与 Mn、Cu 和 K 呈负相关。该方法为 Cr 胁迫下水稻叶片中矿物质的预测提供了一种高精度、快速的方法，对环境保护和食品安全具有重要意义。
