CN114414519B - 一种水体重金属种类及浓度检测的方法 - Google Patents
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Abstract
本发明公开了一种水体重金属种类及浓度检测的方法,包括利用微藻的生物吸附原理,基于太赫兹光谱技术检测出重金属对微藻体内物质成分的改变,进而建立预测重金属浓度的PLS模型;结合主成分分析的结果来看,对铅离子的最佳预测时间为6小时,对镍离子的最佳预测时间为18小时,最佳波段为15‑18THz;利用真实地表水的铅镍浓度设置不同混合离子浓度,建立预测两种重金属离子的模型,并用真实水体的实验来验证模型的准确度,实验中铅离子的预测准确度为100%,对镍离子的预测准确度为93.2%,本发明实现了基于太赫兹技术以微藻为载体的水体重金属种类及浓度检测,避免了每次检测前繁杂的预处理,缩短了检测时间,提高了检测精度,检测消耗样本量也减少了。
Description
技术领域
本发明涉及太赫兹光谱技术领域,尤其涉及一种水体重金属种类及浓度检测的方法。
背景技术
水体重金属元素超标是自然界中危害人类健康的主要威胁之一。随着城市的发展和工业水平的进步,水体中重金属的含量也不断增加。由于城市发展和工业水平的进步,水体中重金属主要包括镉(Cd)、铅(Pb)、镍(Ni)、铜(Cu)等的含量不断增加,对动植物以及人类健康造成严重威胁。重金属危害的持久性、不易降解以及随着食物链的富集危害性逐渐增强的特点,使得我们不得不重视水体重金属污染的问题。
近年来,为了解决传统水质评价方法无法直接、全面地反映其对环境的综合影响的问题,生物监测水环境的污染成为了一个新的研究方向。生物监测指通过检测指定生物受水体污染的变化来反映水体的污染状况,因为生物与其生存的环境具有协同性,其变化可以直接反映出污染物的危害程度,微藻作为水环境中重要的光合生物,其生长快、成本低、易操作、无污染、具有吸附重金属的能力,使得微藻成为水体生物检测的理想选择。微藻对重金属吸附有两种机制,一种是物理吸附,一种是生物吸附,生物吸附机制是微藻将重金属吸收进体内,重金属引起微藻内主要物质成分如蛋白质,淀粉,脂质,色素的含量改变。利用其生物吸附机制可以推断重金属污染水体的情况。
近年来,太赫兹光谱技术的应用研究也扩展到了越来越多的领域。太赫兹(THz)波位于红外和微波之间,频率范围覆盖0.1-10THz。由于太赫兹辐射的光子能量较低,因此可对待测样品进行无损检测。相比于其他的光谱技术,太赫兹光谱技术在大分子震动和转动能级方面更占有优势。很多生物样本如氨基酸、蛋白质等的分子振动范围在太赫兹范围内,它们的太赫兹特征峰明显,因此可以用太赫兹光谱技术来检测微藻体内受重金属影响的物质成分变化。
发明内容
本发明的目的是为了解决上述问题,提供一种水体重金属种类及浓度检测的方法,包括如下步骤:
S1:根据待测金属种类和浓度,分别配置多种单重金属溶液;
S2:在每一种所述单重金属溶液中均加入微藻藻泥样本;
S3;设置不同浓度和种类的所述单重金属溶液对所述微藻藻泥样本进行不同时间的胁迫;
S4:将胁迫后的微藻溶液进行离心、取上清液、研磨以及干燥处理形成藻膜;
S5:使用傅里叶红外光谱仪采集藻膜的光谱,每个所述藻膜样本采集21条光谱;使用unscramble软件对采集到的光谱进行数据处理,先用相邻平均平滑算法进行窗口数为5的平滑,后对数据进行基线矫正,继续使用unscramble中偏最小二乘法对光谱数据建立分类预测模型,其PLS模型中建模集样本数为14,预测集样本数为7,并将7种浓度标注为“1”、“2”、“3”、“4”、“5”、“6”、“7”;后用主成分分析法对光谱数据进行降维分析;获得待测重金属的最佳胁迫时间和波段;
S6:基于最佳胁迫时间和波段,配制多组重金属混合溶液,并且对微藻藻泥进行最佳时间的胁迫,利用傅里叶红外光谱仪对胁迫后的藻膜进行最佳波段的光谱采集;
S7:利用S6采集的光谱数据作为建模集建立PLS模型,以实际地表水样本光谱数据作为PLS模型的预测集对模型进行检测,判断模型检测的精准度。
进一步地,所述S1中,所述待测金属为铅和镍,铅金属溶液的多种浓度分别为:1ng/ml,10ng/ml,50ng/ml,0.1×103ng/ml,0.5×103ng/ml,1×103ng/ml和5×103ng/ml;镍金属溶液的多种浓度分别为:1ng/ml,10ng/ml,50ng/ml,0.1×103ng/ml,0.5×103ng/ml,1×103ng/ml和3×103ng/ml。
进一步地,所述S2中,加入所述微藻藻泥的所述每一种单金属溶液的溶液量至少有10ml;微藻藻泥样本的具体制作包括如下步骤:
S21:取稳定生长两个月的斜生栅藻;
S22:使用离心机以6500r/分钟的转速对上述斜生栅藻进行10分钟的离心;
S23:对离心后的斜生栅藻去除上清液,剩余藻泥;
S24:将藻泥分别装入10ml试管中,每支试管装入1g藻泥。
进一步地,所述S3中,不同的时间分别为6h、12h和18h。
进一步地,所述S4中,所述离心、研磨和干燥处理程序的具体参数设置中,离心为:离心机以6500转/分钟离心10分钟;研磨为:研磨机以70Hz频率研磨3分钟;干燥为:干燥机以30℃干燥3个小时。
进一步地,所述S5中,所述傅里叶红外光谱仪的参数设置波段为30-680cm-1,分辨率为4cm-1;铅和镍的最佳胁迫时间分别为6h和18h,最佳波段为15-18THz。
进一步地,所述S6中,所述多组重金属混合溶液中,铅和镍分别为:50ng/ml和10ng/ml、0.5×103ng/ml和10ng/ml、50ng/ml和0.1×103ng/ml、0.5×103ng/ml和0.1×103ng/ml。
进一步地,所述S7中,所述PLS模型的阈值设为0.5。
与现有技术相比,本发明的有益效果主要体现在:实现了基于太赫兹技术以微藻为载体的水体重金属种类及浓度检测,避免了每次检测前繁杂的预处理;缩短了检测时间,提高了检测精度,也将检测消耗样本量减少了。
附图说明
图1为在铅离子溶液胁迫下的斜生栅藻的光谱图;
图2为在0.5×103ng/ml的铅离子胁迫下的斜生栅藻光谱PCA分析图。
具体实施方式
下面将结合示意图以及实施例对本发明一种水体重金属种类及浓度检测的方法进行更详细的描述,其中表示了本发明的优选实施例,应该理解本领域技术人员可以修改在此描述的本发明,而仍然实现本发明的有利效果,因此,下列描述应当被理解为对于本领域技术人员的广泛知道,而并不作为对本发明的限制。
斜生栅藻(Scenedesmus obliquus)采用市售产品,购自中国科学院野生生物种质库-淡水藻种库,株系276。将购买来的斜生栅藻使用BG11培养基进行扩养至稳定生长两个月。期间收集华东地区五种实际地表水样本,分别来自巢湖、太湖、阳澄湖、洪泽湖与宿迁电池厂处理废水,提前用原子吸收光谱法测定这五种样本地表水的铅与镍的含量(均为:镍离子:50ng/ml,铅离子:10ng/ml)。
本发明一种水体重金属种类及浓度检测的方法以重金属铅和镍为例。
首先配置不同浓度的铅离子溶液(1ng/ml,10ng/ml,50ng/ml,0.1×103ng/ml,0.5×103ng/ml,1×103ng/ml,5×103ng/ml)和镍离子溶液(1ng/ml,10ng/ml,50ng/ml,0.1×103ng/ml,0.5×103ng/ml,1×103ng/ml和3×103ng/ml)。设置其中不同浓度,涵盖国家排污标准,自来水饮用标准。然后取稳定生长两个月的斜生栅藻,用离心机以6500r/分钟的转速进行10分钟的离心,去除上清液,将藻泥分别装入10ml试管中,每支离心管装入1g藻泥。将两种不同重金属离子溶液分别装入装有藻泥的试管,每个试管装入8ml溶液。不同种类和浓度的重金属离子溶液分别胁迫斜生栅藻6小时、12小时、18小时,将胁迫好的微藻溶液用离心机以6500转/分钟离心10分钟,去上清液,将藻泥放入研磨机以70Hz频率研磨3分钟,后倒入2cm*2cm的称量盘,送入干燥机以30℃干燥3个小时。干燥后形成藻膜,用傅里叶红外光谱仪(FTIR)采集光谱。设置波段为30-680cm-1,分辨率为4cm-1,每个样本采集21条光谱,如图1所示。
得到光谱后,使用unscramble软件对采集到的光谱进行数据处理,先用相邻平均平滑算法进行窗口数为5的平滑,后对数据进行基线矫正。继续使用unscramble中偏最小二乘法(PLS)对光谱数据建立分类预测模型,其PLS模型中建模集样本数为14,预测集样本数为7,并将7种浓度标注为“1”、“2”、“3”、“4”、“5”、“6”、“7”。后用主成分分析法(PCA)对光谱数据进行降为分析,如图2所示。基于我们实际应用的数据,根据PLS分析结果得出铅离子最佳检测时间是胁迫6小时,镍离子最佳检测时间是胁迫18小时(见表1)。PCA分析最佳的检测波段为15-18THz,即β胡萝卜素对应的太赫兹波段。
表1铅离子和镍离子胁迫下光谱数据的PLS模型
基于上述的最佳时间与波段,配置四组混合溶液(镍离子:50ng/ml,铅离子:10ng/ml;镍离子:0.5×103ng/ml,铅离子:10ng/ml;镍离子:50ng/ml,铅离子:0.1×103ng/ml;镍离子:0.5×103ng/ml,铅离子:0.1×103ng/ml),用混合溶液胁迫至6小时与18小时,FTIR采集光谱波段为15-18THz。同时用收集的真实地表水培养斜生栅藻后按上述参数采集光谱数据。将采集的实验组光谱数据以14条作为建模集建立PLS模型,实际地表水样本光谱数据以7条作为PLS模型的预测集,将四种混合溶液浓度分别标注为“1”、“2”、“3”、“4”,预测在标注为“1”的样本为预判成功,阈值设定为“0.5”。预测结果显示铅离子的预测准确率为100%,镍离子的预测准确率为93.2%,见表2。
表2预测铅离子与镍离子准确性
铅离子 | 镍离子 | |
太湖 | 100% | 83% |
洪泽湖 | 100% | 83% |
宿州电池厂处理水 | 100% | 100% |
阳澄湖 | 100% | 100% |
巢湖 | 100% | 100% |
总计 | 100% | 93.2% |
上述仅为本发明的优选实施例而已,并不对本发明起到任何限制作用。任何所属技术领域的技术人员,在不脱离本发明的技术方案的范围内,对本发明揭露的技术方案和技术内容做任何形式的等同替换或修改等变动,均属未脱离本发明的技术方案的内容,仍属于本发明的保护范围之内。
Claims (7)
1.一种水体重金属种类及浓度检测的方法,其特征在于,包括如下步骤:
S1:根据待测金属种类和浓度,分别配置多种单重金属溶液;
S2:在每一种所述单重金属溶液中均加入微藻藻泥样本;
S3;设置不同浓度和种类的所述单重金属溶液对所述微藻藻泥样本进行不同时间的胁迫;
S4:将胁迫后的微藻溶液进行离心、取上清液、研磨以及干燥处理形成藻膜;
S5:使用傅里叶红外光谱仪采集藻膜的光谱,每个所述藻膜样本采集21条光谱;使用unscramble软件对采集到的光谱进行数据处理,先用相邻平均平滑算法进行窗口数为5的平滑,后对数据进行基线矫正,继续使用unscramble中偏最小二乘法对光谱数据建立分类预测模型,其PLS模型中建模集样本数为14,预测集样本数为7,并将7种浓度标注为“1”、“2”、“3”、“4”、“5”、“6”、“7”;后用主成分分析法对光谱数据进行降维分析;获得待测重金属的最佳胁迫时间和波段;
S6:基于最佳胁迫时间和波段,配制多组重金属混合溶液,并且对微藻藻泥进行最佳时间的胁迫,利用傅里叶红外光谱仪对胁迫后的藻膜进行最佳波段的光谱采集;
S7:利用S6采集的光谱数据作为建模集建立PLS模型,以实际地表水样本光谱数据作为PLS模型的预测集对模型进行检测,判断模型检测的精准度;
所述S2中,加入所述微藻藻泥的所述每一种单重金属溶液的溶液量至少有10ml;微藻藻泥样本的具体制作包括如下步骤:
S21:取稳定生长两个月的斜生栅藻;
S22:使用离心机以6500r/分钟的转速对上述斜生栅藻进行10分钟的离心;
S23:对离心后的斜生栅藻去除上清液,剩余藻泥;
S24:将藻泥分别装入10ml试管中,每支试管装入1g藻泥。
2.根据权利要求1所述的方法,其特征在于,所述S1中,所述待测金属为铅和镍,铅金属溶液的多种浓度分别为:1ng/ml, 10ng/ml, 50ng/ml, 0.1×103ng/ml, 0.5×103ng/ml, 1×103ng/ml和5×103ng/ml;镍金属溶液的多种浓度分别为:1ng/ml, 10ng/ml, 50ng/ml,0.1×103ng/ml, 0.5×103ng/ml, 1×103ng/ml和3×103ng/ml。
3.根据权利要求2所述的方法,其特征在于,所述S3中,设置不同浓度和种类的所述单重金属溶液对所述微藻藻泥样本分别进行6h、12h和18h的胁迫。
4.根据权利要求3所述的方法,其特征在于,所述S4中,所述离心、研磨和干燥处理程序的具体参数设置中,离心为:离心机以6500转/分钟离心10分钟;研磨为:研磨机以70Hz频率研磨3分钟;干燥为:干燥机以30℃干燥3个小时。
5.根据权利要求4所述的方法,其特征在于,所述S5中,所述傅里叶红外光谱仪的参数设置波段为30-680cm-1,分辨率为4cm-1;铅和镍的最佳胁迫时间分别为6h和18h,最佳波段为15-18THz。
6.根据权利要求5所述的方法,其特征在于,所述S6中,所述多组重金属混合溶液中,铅和镍分别为:50ng/ml和10ng/ml、0.5×103ng/ml和10ng/ml、50ng/ml和0.1×103ng/ml、0.5×103ng/ml和0.1×103ng/ml。
7.根据权利要求6所述的方法,其特征在于,所述S7中,所述PLS模型的阈值设为0.5。
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