多源光谱特征组合的COD光学检测方法研究

水样的化学需氧量大小直接决定水质的污染程度，传统的检测方法都是源于氧化还原反应，对水样会造成二次污染。为此，提出一种基于多源光谱特征组合的水质化学需氧量光学检测方法，以不同地点实际水样为被测对象，分别采集其紫外和近红外光谱曲线，进行预处理后，通过非负矩阵分解算法进行光谱数据的特征提取、数据特征归一化，然后将组合特征输入训练集样本，通过粒子群最小二乘支持向量机算法对验证集水样的化学需氧量进行定量预测。讨论了非负矩阵分解算法中基光谱数目对预测模型的影响。实验结果显示，紫外光谱的最佳基光谱数目为5，近红外光谱的最佳基光谱数目为2;预测模型的验证集平方相关系数为0.999 8，预测均方根误差为3.26 mg·L-1;分别与不同特征提取方法(主成分分析, 独立成分分析)、不同光谱法(紫外光谱法, 近红外光谱法)以及不同的组合方式(数据直接组合, 先组合数据再提取特征)加以比较，表明非负矩阵分解算法更适合光谱数据的特征提取，粒子群最小二乘支持向量机算法作为实际水样的定量模型校正方法可以得到良好的预测精度。

Research on Chemical Oxygen Demand Optical Detection Method Based on the Combination of Multi-Source Spectral Characteristics

A novel method based on multi-source spectral characteristics of the combination is proposed for chemical oxygen demand detection. First, the ultraviolet and near infrared spectrum of the actual water samples are collected respectively. After pretreatment of the spectrum data, the features of the spectrum are extracted by the nonnegative matrix factorization algorithm for training after normalization. Particle swarm and least squares support vector machines algorithm are applied to predicting chemical oxygen demand of the validation set of water samples. The effect of spectrum’s base number on the predicted results is discussed. The experimental results show that the best base number of the ultraviolet spectrum is 5, the best base number of the near infrared spectrum is 2; The validation set correlation coefficient of the prediction model is 0.999 8, and the root mean square error of prediction is 3.26 mg·L-1. Experimental results demonstrate that the nonnegative matrix factorization algorithm is more suitable for feature extraction of spectral data, and the least squares support vector machines algorithm as a quantitative model correction method of the actual water samples can get good prediction accuracy with different feature extraction methods(principal component analysis, independent component analysis), spectroscopic methods(ultraviolet spectrum method, near infrared spectrum method) and different combination pattern (data direct combination, combining data first, then feature extraction) respectively.