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使用傅里叶变换中红外衰减全反射光谱法结合去卷积算法原位测定水中的硝酸盐。

In Situ Determination of Nitrate in Water Using Fourier Transform Mid-Infrared Attenuated Total Reflectance Spectroscopy Coupled with Deconvolution Algorithm.

机构信息

The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China.

College of Environment and Ecology, Jiangsu Open University, Nanjing 210017, China.

出版信息

Molecules. 2020 Dec 10;25(24):5838. doi: 10.3390/molecules25245838.

Abstract

Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy has been used to determine the nitrate content in aqueous solutions. However, the conventional water deduction algorithm indicated considerable limits in the analysis of samples with low nitrate concentration. In this study, FTIR-ATR spectra of nitrate solution samples with high and low concentrations were obtained, and the spectra were then pre-processed with deconvolution curve-fitting (without water deduction) combined with partial least squares regression (PLSR) to predict the nitrate content. The results show that the typical absorption of nitrate (1200-1500 cm) did not clearly align with the conventional algorithm of water deduction, while this absorption was obviously observed through the deconvolution algorithm. The first principal component of the spectra, which explained more than 95% variance, was linearly related to the nitrate content; the correlation coefficient () of the PLSR model for the high-concentration group was 0.9578, and the ratio of the standard deviation of the prediction set to that of the calibration set () was 4.22, indicating excellent prediction performance. For the low-concentration group model, and were 0.9865 and 3.15, respectively, which also demonstrated significantly improved prediction capability. Therefore, FTIR-ATR spectroscopy combined with deconvolution curve-fitting can be conducted to determine the nitrate content in aqueous solutions, thus facilitating rapid determination of nitrate in water bodies with varied concentrations.

摘要

傅里叶变换衰减全反射(FTIR-ATR)光谱法已被用于测定水溶液中的硝酸盐含量。然而,传统的水扣除算法在分析低浓度硝酸盐样品时存在明显的局限性。在本研究中,获得了高浓度和低浓度硝酸盐溶液样品的 FTIR-ATR 光谱,并对光谱进行了去卷积曲线拟合(无需扣除水)与偏最小二乘回归(PLSR)相结合的预处理,以预测硝酸盐含量。结果表明,硝酸盐的典型吸收(1200-1500cm)与传统的水扣除算法并不明显一致,而通过去卷积算法可以明显观察到这种吸收。解释了超过 95%方差的光谱的第一主成分与硝酸盐含量呈线性相关;高浓度组 PLSR 模型的相关系数()为 0.9578,预测集与校准集的标准偏差比()为 4.22,表明具有优异的预测性能。对于低浓度组模型,和分别为 0.9865 和 3.15,也表明预测能力显著提高。因此,FTIR-ATR 光谱法结合去卷积曲线拟合可以用于测定水溶液中的硝酸盐含量,从而能够快速测定不同浓度水体中的硝酸盐。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6456/7764078/4fbd6faa061a/molecules-25-05838-g001.jpg

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