基于基线校正和主元分析的紫外-可见光光谱在线水质异常检测方法

近年来,饮用水安全问题引起社会的广泛关注。采用紫外-可见光吸收光谱对水质进行异常检测,具有现场原位、无需试剂、分析快速等优点,适合快速在线监测。然而,紫外-可见光光谱数据量大,且易受仪器和水质正常波动的干扰,从而影响水质异常检测结果。提出一种基于基线校正和主元分析的紫外-可见光光谱法来检测污染物引起的水质异常,该方法利用非对称最小二乘校正基线,采用主元分析法从基线校正后的光谱矩阵中降维并提取特征,然后根据残差子空间的Q统计量评估测试样本的离群点,最后采用累计概率来更新异常报告结果。通过苯酚注入的实验,验证了该算法的有效性,实验结果表明,提出的方法与单波长法相比,有效地提高了污染物的检出下限;与未经基线校正采用主元分析进行的异常检测方法相比,提高了检出率,降低了误报率。

Online Detecting Water Quality Anomaly from UV/Vis Spectra Using Baseline Correction and Principal Component Analysis Method

In recent years,water quality security issue has aroused widespread social concerns.Ultraviolet and visible absorption spectrum for real-time monitoring of water quality has the advantages of in-situ detection as well as no need for reagents and rapid analysis,which makes it suitable for online detection.However,the ultraviolet and visible spectra are of large size and easily interfered by instrument and the normal water quality fluctuation,which affect the water quality anomaly detection result.In this paper,the baseline correction and principal component analysis method for UV/Vis spectra is proposed to detect abnormal water quality.The asymmetric least squares algorithm is used to correct the baseline and the principal component analysis for UV/Vis spectral matrix is adopted to reduce dimensions and extract features.Afterwards the outliers in the test samples are evaluated according to the Q statistic in the residual subspace.Finally,anomalies warning is updated by calculating the cumulative probability.The performance of the proposed method is evaluated by using data from phenol injection experiments.Results show that the proposed method effectively improves the detection limit of pollutants and has a higher detection rate and lower false alarm rate compared with the result without baseline correction.