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An Improved Algorithm for Measuring Nitrate Concentrations in Seawater Based on Deep-Ultraviolet Spectrophotometry: A Case Study of the Aoshan Bay Seawater and Western Pacific Seawater

机译:基于深紫色分光光度法测定硝酸盐浓度的改进算法 - 以奥山湾海水和西太平洋海水为例

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摘要

Nowadays, it is still a challenge for commercial nitrate sensors to meet the requirement of high accuracy in a complex water. Based on deep-ultraviolet spectral analysis and a regression algorithm, a different measuring method for obtaining the concentration of nitrate in seawater is proposed in this paper. The system consists of a deuterium lamp, an optical fiber splitter module, a reflection probe, temperature and salinity sensors, and a deep-ultraviolet spectrometer. The regression model based on weighted average kernel partial least squares (WA-KPLS) algorithm together with corrections for temperature and salinity (TSC) is established. After that, the seawater samples from Western Pacific and Aoshan Bay in Qingdao, China with the addition of various nitrate concentrations are studied to verify the reliability and accuracy of the method. The results show that the TSC-WA-KPLS algorithm shows the best results when compared against the multiple linear regression (MLR) and ISUS (in situ ultraviolet spectrophotometer) algorithms in the temperatures range of 4–25 °C, with RMSEP of 0.67 µmol/L for Aoshan Bay seawater and 1.08 µmol/L for Western Pacific seawater. The method proposed in this paper is suitable for measuring the nitrate concentration in seawater with higher accuracy, which could find application in the development of in-situ and real-time nitrate sensors.
机译:如今,商业硝酸盐传感器仍为挑战,以满足复杂水中高精度的要求。基于深紫外光谱分析和回归算法,本文提出了一种用于获得海水中硝酸盐浓度的不同测量方法。该系统由氘灯,光纤分离器模块,反射探针,温度和盐度传感器组成,以及深紫外光谱仪。建立基于加权平均核的回归模型与温度和盐度(TSC)的校正一起建立。之后,研究了中国西太平洋和奥山湾的海水样本,中国都研究了中国各种硝酸盐浓度的中国,以验证该方法的可靠性和准确性。结果表明,TSC-WA-KPLS算法显示与在4-25℃的温度范围内的多元线性回归(MLR)和Isus(原位紫外分光光度计)算法中相比的最佳效果,RMSEP为0.67μmol / L为奥山湾海水和西太平洋海水的1.08μmol/ L.本文提出的方法适用于在海水中测量具有更高精度的硝酸盐浓度,这可能在原位和实时硝酸盐传感器的开发中找到应用。

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