Wu Guo-Qing, Bi Wei-Hong
Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Nov;34(11):3071-4.
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 x 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.
提出了一种基于多源光谱特征组合的化学需氧量检测新方法。首先，分别采集实际水样的紫外光谱和近红外光谱。对光谱数据进行预处理后，通过非负矩阵分解算法提取光谱特征并进行归一化训练。应用粒子群和最小二乘支持向量机算法对水样验证集的化学需氧量进行预测。讨论了光谱基数对预测结果的影响。实验结果表明，紫外光谱的最佳基数为5，近红外光谱的最佳基数为2；预测模型的验证集相关系数为0.999 8，预测均方根误差为3.26 mg·L⁻¹。实验结果表明，非负矩阵分解算法更适合光谱数据的特征提取，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，分别采用不同的特征提取方法（主成分分析、独立成分分析）、光谱方法（紫外光谱法、近红外光谱法）和不同的组合模式（数据直接组合、先组合数据再提取特征）都能获得较好的预测精度。
