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首页> 外文期刊>Journal of Remote Sensing & GIS >A Feasibility Study of NIR Spectra in Identifying Heavy Metal Contamination in Rice Around Abandoned Tailing Ponds: A Case Study inGuiyang County in South China
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A Feasibility Study of NIR Spectra in Identifying Heavy Metal Contamination in Rice Around Abandoned Tailing Ponds: A Case Study inGuiyang County in South China

机译：NIR光谱识别废弃尾矿中水稻重金属污染的可行性研究-以中国南方贵阳县为例

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Protecting people from heavy metal contamination is an important public-health concern and a major national?environmental issue in China. The purpose of this study is to explore the feasibility of the near-infrared (NIR) spectral?technique in identifying heavy metal concentration (HMC) in coarse rice. 28 rice samples were gathered from the?farmlands around four tailing ponds in Guiyang County of south China, and then were sieved by 2.0 mm plastic mesh for?the laboratory spectral measurement and the determination of protein, lead (Pb), and copper (Cu). Before constructing?the partial least square regression (PLSR) models for predicting HMC, all spectral data were treated by some methods,?including, logarithm (Log), baseline correction (BC), standard normal variate (SNV), multiple scatter correction (MSC),?first derivates (FD), and continuum removal (CR). In terms of enrichment coefficients (EC), Pb was accumulated in rice?at a high level (17.05). ???°ts relation to protein (P=0.77, r<0.01) is more significant than that of Cu (P=0.67, r<0.01). Protein?content was well predicted by MSC-PLSR model with higher coefficient of determination (R2=0.51) and lower root mean?square error (RMSE=0.17%). MSC-PLSR models were respectively built for Pb (R2=0.49, RMSE=2.01 mg/kg) and Cu (R2=0.29, RMSE=0.75 mg/kg). ???°t is feasible to identify Pb and Cu content in rice by using NIR spectral technique.?However, further investigation should be conducted on the application of spectral technique in discriminating the other?heavy metals in crops due to the limitations of few samples and particle size interruption.
机译：保护人们免受重金属污染是中国重要的公共卫生问题，也是重大的国家环境问题。这项研究的目的是探讨近红外（NIR）光谱技术在鉴定粗稻中重金属浓度（HMC）方面的可行性。从华南贵阳县四个尾矿池周围的农田中采集了28个水稻样品，然后用2.0 mm塑料筛网过筛，以进行实验室光谱测量以及蛋白质，铅（Pb）和铜（Cu）的测定。 ）。在构建用于预测HMC的偏最小二乘回归（PLSR）模型之前，所有光谱数据均已通过一些方法进行了处理，包括对数（Log），基线校正（BC），标准正态变量（SNV），多重散点校正（ MSC），“一阶导数（FD）”和“连续谱去除（CR）”。就富集系数（EC）而言，铅在水稻中的积累水平很高（17.05）。与蛋白质的关系（P ＝ 0.77，r ＜0.01）比Cu的关系显着（P ＝ 0.67，r ＜0.01）。 MSC-PLSR模型可以很好地预测蛋白质含量，测定系数较高（R2 = 0.51），而均方根误差较低（RMSE = 0.17％）。建立了分别针对Pb（R2 = 0.49，RMSE = 2.01 mg / kg）和Cu（R2 = 0.29，RMSE = 0.75 mg / kg）的MSC-PLSR模型。使用NIR光谱技术鉴定水稻中的Pb和Cu含量是可行的。但是，由于少数几种方法的局限性，应进一步研究使用光谱技术来鉴别农作物中的其他重金属。样品和粒度中断。

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