【摘要】：近年來,飲用水安全問題引起社會的廣泛關(guān)注。采用紫外-可見光吸收光譜對水質(zhì)進(jìn)行異常檢測,具有現(xiàn)場原位、無需試劑、分析快速等優(yōu)點(diǎn),適合快速在線監(jiān)測。然而,紫外-可見光光譜數(shù)據(jù)量大,且易受儀器和水質(zhì)正常波動的干擾,從而影響水質(zhì)異常檢測結(jié)果。提出一種基于基線校正和主元分析的紫外-可見光光譜法來檢測污染物引起的水質(zhì)異常,該方法利用非對稱最小二乘校正基線,采用主元分析法從基線校正后的光譜矩陣中降維并提取特征,然后根據(jù)殘差子空間的Q統(tǒng)計量評估測試樣本的離群點(diǎn),最后采用累計概率來更新異常報告結(jié)果。通過苯酚注入的實(shí)驗(yàn),驗(yàn)證了該算法的有效性,實(shí)驗(yàn)結(jié)果表明,提出的方法與單波長法相比,有效地提高了污染物的檢出下限;與未經(jīng)基線校正采用主元分析進(jìn)行的異常檢測方法相比,提高了檢出率,降低了誤報率。
[Abstract]:In recent years, the safety of drinking water has attracted wide attention of the society. Using UV-Vis absorption spectrum to detect abnormal water quality, it has the advantages of in situ, no reagent, fast analysis and so on. It is suitable for rapid on-line monitoring. However, the UV-Vis spectrum has a large amount of data and is susceptible to the interference of instrument and water quality fluctuation, thus affecting the detection results of water quality anomalies. A UV-Vis spectral method based on baseline correction and principal component analysis (PCA) is proposed to detect water quality anomalies caused by pollutants. The method uses asymmetric least squares to correct baselines. The principal component analysis (PCA) is used to reduce the dimension and extract the feature from the baseline corrected spectral matrix. Then the outliers of the test samples are evaluated according to the Q statistics in the residual subspace, and the cumulative probability is used to update the anomaly report results. The experimental results show that the proposed method can effectively improve the detection limit of pollutants compared with the single wavelength method. Compared with the anomaly detection method without baseline correction using principal component analysis, the detection rate is improved and the false positive rate is reduced.
【作者單位】： 浙江大學(xué)控制科學(xué)與工程學(xué)院工業(yè)控制技術(shù)國家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金項(xiàng)目(61573313,U1509208) 浙江省科技廳公益項(xiàng)目(2014C33025) 浙江省重點(diǎn)研發(fā)計劃項(xiàng)目(2015C03G2010034)資助
【分類號】：O657.3;R123.1

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