基于可见-近红外光谱技术快速检测水质酸度  

Rapid Detection of Water Acidity Based on Visible-Near Infrared Spectrometry Technique

作　　者：苏涵君 李丽娜[1] SU Hanjun;LI Lina(College of Mechanical Engineering and Automation,Huaqiao University,Xiamen 361021,China)

机构地区：[1]华侨大学机电及自动化学院,厦门361021

出　　处：《理化检验(化学分册)》2025年第3期249-256,共8页Physical Testing and Chemical Analysis(Part B:Chemical Analysis)

基　　金：国家自然科学基金青年科学基金项目(51805177)。

摘　　要：为了满足快速、准确、在线持续检测水质酸度(pH)的需求,基于可见-近红外光谱(Vis-NIRS)技术,结合化学计量学方法,提出了一种水质酸度的快速检测方法。采集60个不同酸度水溶液样本的Vis-NIRS原始数据,分别采用Kennard-Stone(K-S)算法和光谱-理化值共生距离(SPXY)算法进行样本集划分,运用Savitzky-Golay(S-G)卷积平滑、标准正态变量变换(SNV)、一阶导数(1D)、二阶导数(2D)和正交信号校正(OSC)等方法对原始光谱数据进行预处理,并使用连续投影算法(SPA)、竞争性自适应重加权(CARS)算法进行特征波长筛选,建立并比较了不同的偏最小二乘法(PLS)定量分析模型,以确定最佳模型效果。结果表明,利用SPXY算法划分样本集,并经过SNV预处理和CARS筛选出特征波长,建立的水质酸度PLS定量分析模型性能较优,其预测集决定系数和预测均方根误差分别为0.9786和0.3803。参与建模的波长变量数由2860个减少至45个,极大地提高了模型的运算速率,方法能够实现对水质酸度的快速、准确检测。In order to meet the requirements of rapid,accurate and continuous online detection of the water acidity(pH),a method for rapid detection of the water acidity was proposed based on visible-near infrared spectrometry(Vis-NIRS)technique in combination with chemometric methods.The original Vis-NIRS data were collected from 60 water solution samples with varying acidities.These samples sets were split by Kennard-Stone(K-S)algorithm and sample set partitioning based on joint X-Y distance(SPXY)algorithm.The original spectral data were pre-processed by the methods including Savitzky-Golay(S-G)convolution smoothing,standard normal variate(SNV),first derivative(1D),second derivative(2D)and orthogonal signal correction(OSC).Successive projections algorithm(SPA)and competitive adaptive reweighted sampling(CARS)algorithm were used for characteristic wavelength screening.Different partial least squares(PLS)quantitative analysis models were established and compared to determine the effect of the best model.As shown by the results,the SPXY algorithm for sample set partition,SNV for spectral data pre-processing,and CARS for characteristic wavelength screening produced better performance for water acidity PLS quantitative analysis model.The prediction set had a coefficient of determination of 0.9786 and a root mean square error of 0.3803,and the variable number of wavelengths involved in modeling was reduced from 2860 to 45,greatly speeding up model calculation.The proposed method could achieve rapid and accurate water acidity detection.

关 键 词：可见-近红外光谱(Vis-NIRS) 水质酸度 预处理 竞争性自适应重加权算法 偏最小二乘法(PLS) 定量分析模型 

分 类 号：O657.33[理学—分析化学]