采用光谱技术检测水质参数是当前的一个研究热点, 提出了一种基于荧光发射光谱的水质化学需氧量(COD)的检测方法。 实验样本分为两组, 第一组为20份COD标准溶液, 第二组为63份实际水样。 实验样本的化学需氧量检测采用快速消解分光光度法, 利用三维荧光分光光度计采集水样在EX=275 nm激发波长下的荧光发射光谱(荧光发射光谱范围为EM=325450 nm), 并对两类水样的荧光发射光谱数据进行了处理和建模。 分别采用主成分回归(PCR)和偏最小二乘回归(PLSR)对两类水样的荧光发射光谱数据进行了预测模型的建立, 并对模型效果进行了对比。 为验证该方法的可行性和模型的预测能力, 将所建PLSR模型预测结果与标准方法的检测结果进行了对比。 结果表明, 对于COD标准液来说, PLSR和PCR的主成分数分别取5和8时所得的模型的交叉检验效果最优, 校正模型的决定系数分别为RPLS2=0.999 9和RPCR2=0.989 7, 校正模型对检验集数据的预测误差不超过10%, 且PLSR所建模型优于PCR模型。 对于实际水样而言, PLSR和PCR的主成分数分别为6和7时, 校正模型的交叉验证效果最优。 PLSR法和PCR法的校正集的交叉检验均方差RMSECV_PLS和RMSECV_PCR分别为0.932 2和0.976 4 mg·L^-1。 对于实际水样的检验集来说, PLSR法和PCR法的预测决定系数RPLS2和RPCR2分别为0.940 2和0.919 0, 说明PLSR法的预测效果更优, 基于荧光发射光谱数据的PLSR模型具有较高的预测能力和较强的适应性, 可以快速、 准确的检测出水质COD。 通过和传统检测方法的效果对比可知, 该方法可用于检测有机污染物浓度较低的水体, 有机物浓度较高时采用该方法时检测误差会变大。 该研究为水质检测光学传感器的研发提供了一种新的设计思路。

The detection of parameters for water quality with spectral technique is a research hotspot at present. This paper proposes a method for the determination of chemical oxygen demand (COD) based on the fluorescence emission spectrum. Two groups of experimental samples are provided in the experiment, among which the fundamental group is 20 COD standard solutions, and the remaining 63 are actual water samples as the other group. Rapid digestion spectrophotometry is utilized to detect the COD of experimental samples. Three dimensional fluorescence spectrophotometer is used to collect the fluorescence emission spectra of the water samples at EX=275 nm (all the range of fluorescence emission spectra are EM=325450 nm), then the data of fluorescence emission spectra of two kinds of water samples are processed and modeled. Principal component regression (PCR) and partial least squares regression (PLSR) are utilized to establish the prediction models based on fluorescence emission data respectively, and the effects of the models are compared. In order to verify the feasibility of the proposed method and the prediction ability of the model, the results of the PLSR mode are compared with the standard method. The comparison results show that, for the COD standard solution, when the number of principal component of PLSR and PCR is 5 and 8 respectively, the optimal results are obtained for both models, of which the determination coefficients of the correction model areRPLS2=0.999 9 andRPCR2=0.989 7, respectively. The prediction error of validation set data in the calibration model is less than 10%, and the PLSR model is better than the PCR model. While for the actual water samples, when the number of principal component of PLSR and PCR is 6 and 7 respectively, the cross-validation effect of the correction model is the best. Among them, root mean square error of cross-validation of the PLSR method is RMSECV_PLS=0.932 2 mg·L^-1, while for the PCR algorithm, RMSECV_PCR=0.976 4 mg·L^-1. For the validation set, the determination coefficient of PLSR is 0.940 2, while for PCR method, it is 0.919 0. It shows that PLSR method has better prediction effect. Consequently, the PLSR model based on fluorescence emission spectrum data has high prediction ability and strong adaptability, which can detect water COD quickly and accurately. Through the comparison of the method proposed in this paper and the traditional detection, we can see that proposed method can be used to detect the water with low concentration of organic pollutants, however, the detection error will increase if the concentration of organic pollutants in the detected water is high. This paper providesa new design idea for the research and development of water quality detection optical sensor.