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作 者:刘恩华 吴德操 王杰[1] 谭万尧 蒲君豪 罗彬彬[1] 汤斌[1] 龙邹荣 赵明富[1] Liu Enhua;Wu Decao;Wang Jie;Tan Wanyao;Pu Junhao;Luo Binbin;Tang Bin;Long Zourong;Zhao Mingfu(Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection,Chongqing University of Technology,Chongqing 400054,China)
机构地区:[1]重庆理工大学光纤传感与光电检测重庆市重点实验室,重庆400054
出 处:《光学学报》2021年第22期228-235,共8页Acta Optica Sinica
基 金:重庆市自然科学基金面上项目(cstc2019jcyj-msxmX0243);重庆市教委科学技术研究计划青年项目(KJQN202001117);重庆市教委科学技术研究计划重点项目(KJZD-K201905601);重庆市研究生创新项目(CYS20350);重庆理工大学研究生创新项目(clgycx20202034、ycx20192054)。
摘 要:提出一种多场景优化的光谱分类建模解算方法:首先,通过颗粒物Mie散射仿真分析,构建幂函数修正方程,以直接拟合法对样本光谱进行精确浊度校正;然后,利用吸光度归一化法获取不同场景的线性特征光谱,形成场景特征库;使用偏最小二乘法(PLS)为每个场景建立解算模型,形成化学需氧量(COD)解算模型库。对未知水样本进行COD检测时,先通过杰卡德(Jaccard)相似性理论将其归一化光谱与场景库线性特征谱进行匹配,识别其归属场景,再获取解算库中的最优解算参数来计算COD浓度。实验结果表明,所提方法可获得较高的场景匹配精度,有效降低多场景条件下的COD解算误差,具备良好的实用价值。This paper proposed a solution method of spectral classification modeling for multi-scene optimization.Firstly,a modified power function equation was constructed through a simulation analysis of the Mie scattering of particles,and the direct fitting method was used to achieve accurate turbidity correction of the sample spectra.Then,the absorbance normalization method was employed to obtain the linear feature spectra of different scenes and develop a scene-based feature library.Subsequently,the partial least-squares(PLS)method was applied to build a solution model for each scene and thereby establish a chemical oxygen demand(COD)solution model library.When an unknown water sample went through the COD detection,its normalized spectrum was first matched with the linear feature spectra of the scene-based library through the Jaccard similarity theory for the identification of the scene it belonged to.Then,its COD concentration was calculated with the optimal solution parameters obtained from the solution library.The experimental results show that the proposed method holds application value in that it delivers high scene-matching accuracy and reduces the COD solution error under multi-scene conditions.
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