铀矿区超富集植物可见及短波近红外光谱判别研究

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中文摘要：随着各国对核能需求的剧增,铀矿勘探成为核能供给中关键环节.铀矿勘探方法主要有放射性物探测量、地球化学测量等传统方法,大都存在探测数据不准确、效率低等的不足.采用近红外光谱技术,结合化学计量学,探索铀超富集植物筛选及确定的可行性.通过铀矿区植物生长情况及特性调查挑选超富集植物,利用近红外光谱分析仪获取不同区域其叶片综合光谱,比较分析光谱响应关系.发现两种超富集植物的吸收峰位于650～700和950～1 050 nm两个波段内,前一个波段为叶绿素的吸收峰主要由C—O与C—H键伸缩振动的组合频产生.后一个波段是水的吸收峰主要由O—H键弯曲振动5级倍频导致.通过PCA与SPA选择特征变量,将两种样本分别按照3∶1的比例随机划分为训练与预测两部分,结合PLS和LSSVM两种方法构建超富集植物铀富集检测模型,并对比预测效果.发现基于PLS的狗尾草铀富集检测模型效果最佳,其判别正确率高达100％,RMSEP为0.115,R2为0.946.两种建模方法中狗尾草检测模型均优于辣蓼,可能是狗尾草富集系数高于辣蓼,导致其铀矿区叶片铀含量浓度高于非铀矿区.结果表明,近红外光谱技术联合偏最小二乘法建立的超富集植物铀富集检测模型效果最好,很好的对铀超富集植物进行无损识别,从而筛选并确定之是可行的.该方法为废铀矿区生态修复提供重要参考,同时为利用特异性、指示性植物寻找铀矿提供新思路.

外文标题：Identification of Visible and Short Wave Near Infrared Spectra of Super-Enriched Plants in Uranium Ore Area

外文摘要：With the increasing demand for nuclear energy,uranium exploration has become a key link in the supply of nuclear energy.Uranium exploration methods mainly include radioactive geophysical surveys,geochemical surveys,and other traditional methods,most of which have the shortcomings of inaccurate detection data and low efficiency.This study used near-infrared spectroscopy and stoichiometry to explore the feasibility of screening and identifying uranium super enriched plants.Through the investigation of the growth and characteristics of plants in the uranium mining area,the ultra-enriched plants were selected,the leaf comprehensive spectra in different regions were obtained by a near-infrared spectroscopy analyzer,and the spectral response relationship was compared and analyzed.It was found that the absorption peaks of the two hyper-enrichedplants were located in two bands:650～700 and 950～1 050 nm.The absorption peak of chlorophyll in the former band was mainly generated by the combined frequency of C—O and C—H bond stretching vibration.In the latter band,the absorption peak of water is mainly caused by the 5-order frequency doubling of O—H bond bending vibration.The feature variables were selected by principal component analysis(PCA)and successive projections algorithm(SPA),and the two samples were randomly divided into training and prediction parts according to the ratio of 3∶1,respectively.The detection model of uranium enrichment in super-enriched plants was constructed by combining the two methods of partial least squares(PLS)and least square support vector machine(LSSVM),and the prediction effect was compared.Obtained the detection model of Setaria uranium enrichment based on PLS had the best effect,with a discrimination accuracy of up to 100％,RMSEP of 0.115,and R2 of 0.946.The Setaria detection model is superior to the coverage in the two modeling methods.It may be that the enrichment coefficient of setaria morifera is higher than that of ciderage.The results show that the detection model of uranium enrichment in super-enriched plants established by near-infrared spectroscopy combined with the partial least squares method has the best effect,and it is feasible to screen and identify uranium super-enriched plants.This method provides an important reference for the ecological restoration of the spent uranium ore area and a new idea for the use of specific and indicative plants to search for uranium ore.

外文关键词：

Uranium depositSuper-enriched plantUranium enrichmentNear infrared spectroscopyPartial least squares

作者：

肖怀春、刘阳、魏冰雪、高家榕、刘燕德、肖慧

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作者单位：

东华理工大学江西省核地学数据科学与系统工程技术研究中心,江西南昌 330013

华东交通大学智能机电装备创新研究院,江西南昌 330013

关键词：

铀矿超富集植物铀富集近红外光谱偏最小二乘

基金：

国家自然科学基金项目江西省核地学数据科学与系统工程技术研究中心开放基金项目东华理工大学博士启动研究基金项目江西省质谱与仪器重点实验室开放基金项目东华理工大学江西省大气污染成因与控制重点实验室开放基金项目江西省大学生创新创业训练计划项目东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室开放基金项目

项目编号：

31760344JETRCNGDSS202102DHBK2019074JXMS202011AE2109S202110405004XMEMI-2021-2022-18

出版年：

2024

DOI：

10.3964/j.issn.1000-0593(2024)07-1813-07

光谱学与光谱分析

中国光学学会

光谱学与光谱分析

CSTPCD北大核心

影响因子：0.897

ISSN：1000-0593

年,卷(期)：2024.44(7)