中文摘要：

植物体内重金属能与树脂内功能基团发生络合作用,所形成的络合物具有拉曼光谱信息,因此可借助该类有机分子基团对植物体内重金属含量作间接检测分析。提出了一种应用拉曼光谱技术快速检测香根草根内重金属铜含量的方法,采用不同光谱预处理方法,结合偏最小二乘法建立了最优香根草根内重金属铜含量定量分析模型。试验结果为,经过一阶微分处理的光谱建模效果较理想,其建立的预测相关系数为0.78,预测均方根误差为23.46%。研究结果表明,基于拉曼光谱技术,并结合D113树脂吸附技术应用于快速定量检测香根草根内重金属铜含量的具有可行性。

英文摘要：

Heavy metals are harmful to the vast majority of organisms including human. However, heavy metals can be gradually accumulated in plants and animals by the food chains. So the heavy metal pollution in the environment will threaten human health if we do not control the pollution. Heavy mental ions in plants always have chelation with the organic molecular groups in resin, and the complex has the Raman spectroscopy. Therefore heavy mental ions in plants can be indirectly detected by using Raman technique basing on the chelation. Vetiver grass can grow up in the soil with heavy metal pollution. It has the strong tolerance of heavy metals. Because of that, vetiver grass becomes one of the ideal plants for the conservation and phytoremediation of heavy metal pollution in soil and water. After preprocessing resin by HCl and NaOH, we compared copper adsorption rate for different types of resins. Based on the results of comparison, we chose ion exchange resin D113 to absorb copper. We then compared different oscillation time, solution pH, solution temperature for their impact on copper adsorption so that the best adsorption conditions could be determined. It showed that changing the temperature of solution had a little impact on resin adsorption rate. Therefore, experiments for copper was conducted at the conditions: at room temperature, pH value from 5 to 7, and the oscillation time 80 min. The adsorption rate can reach 99.54% in these conditions. An application of confocal microprobe Raman spectroscopy fast detecting heavy metal copper in vetiver grass roots was proposed, and partial least squares (PLS) regression combined with different data preprocessing methods (Savizky-golay smoothing, Baseline correction, first derivative, second derivative, Detrended fluctuation analysis) was used to develop quantitative models of heavy metal copper in vetiver grass roots. Calibration models were evaluated by an independent predictor of adaptive sample set. With the first derivative preprocessing, the best prediction model of hea