近红外光谱谱线特性对物质浓度分析误差影响的研究  

Influence of Spectral Characteristics on the Accuracy of Concentration Quantitatively Analysis by NIR

作　　者：赵喆 王慧[1] 王慧泉 何鑫伟[1] 缪竟鸿 王金海[1,2] ZHAO Zhe;WANG Hui;WANG Hui-quan;HE Xin-wei;MIAO Jing-hong;WANG Jin-hai(School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China;Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China;School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China)

机构地区：[1]天津工业大学电子与信息工程学院,天津300387 [2]天津市光电检测技术与系统重点实验室,天津300387 [3]天津大学精密仪器与光电子工程学院,天津300072

出　　处：《光谱学与光谱分析》2019年第4期1070-1074,共5页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61705164);中国博士后科学基金项目第61批资助

摘　　要：为解决近红外光谱法分析物质浓度过程中缺乏可测度分析而导致测量过程存在一定盲目性问题,研究在已知测量条件、样品种类、被测组分以及建模分析方法的条件下,利用近红外光谱谱线特性作为参数,在大量样品近红外光谱采集和标准法测得浓度数据等工作前,对被测物质浓度的分析误差做大致估算。经过大量尝试和试验提出等效信噪比(ESNR)和谱线重叠系数(OC)两个重要参数,其中ESNR反映待测组分吸光度占总吸光度的比重,而OC则反映待测组分近红外光谱曲线间的重叠程度。通过理论仿真得到光谱分析中用经典的偏最小二乘回归建立定量分析模型时谱线特性与物质浓度分析误差的关系,分别计算ESNR和OC与被测组分浓度分析误差(RMSE)的关系,并且研究两个谱线参数的独立性。利用理论分析得到结果对浓度为8%～12%乙醇水溶液进行可测度分析,并与近红外光谱法分析的实际结果进行比较。研究通过理论仿真得到使用光谱分析中经典的偏最小二乘回归建立定量分析模型时谱线特性与物质浓度分析误差的关系,其中ESNR与RMSE成反比关系,而OC与被测组分分析误差成非线性的单调关系,并且验证了ESNR和OC两个参数的独立性。通过理论计算和乙醇水溶液近红外光谱检测实验对等效信噪比和谱线重叠系数与光谱分析浓度误差的定量关系进行讨论,通过理论分析得到的乙醇浓度RMSE预估值为0.30%,近红外光谱分析实际RMSE为0.32%,相对误差6.67%,二者结果相符。实现了在测量条件、样品种类、被测组分以及建模分析方法已知的条件下基于近红外光谱分析的待测组分含量理论误差的定量计算和实验验证。该研究明确了对近红外光谱法分析物质浓度有明确定量关系的两个谱线参数,给出了使用光谱分析中经典的偏最小二乘回归建立定量分析模型时的分析误差经验曲线,以及利�In order to solve the problem of measurement blindness caused by the lack of measurable analysis in the the near-infrared spectroscopy, we can roughly estimate the analytical error of the concentration of the tested substances using the spectral characteristics of near-infrared spectroscopy under the known conditions of measurement, sample types, components under analysis and modeling and analysis methods, before a large number of samples were collected by near-infrared spectroscopy and concentration data measured by standard method. In the research, two important parameters, ESNR and OC, were proposed and tested. ESNR reflects the proportion of the component absorbance to the total absorbance, while OC reflects the overlap degree between near-infrared spectral curves of the components. We got the relationship between spectral characteristics and concentration analysis error when using the classical partial least squares regression in spectral analysis to establish quantitative analysis model through theoretical simulation. The relationship between ESNR and OC and the concentration of analyte(RMSE) was calculated respectively, and the independence of the two spectral parameters was also studied. The results of theoretical analysis were used to measure the concentration of aqueous ethanol solution between 8% and 12%, and compared with the actual results of near infrared spectroscopy. The relationship between the spectral characteristics and the concentration analysis errors when using partial least squares regression to establish a quantitative analysis model was obtained through theoretical simulation. ESNR is inversely proportional to RMSE, and OC is in a non-linear monotonic relationship with the measured component analysis error, and the independence of ESNR and OC was verified. The quantitative relationship between ESNR and OC and spectral concentration error was discussed by theoretical calculations and near-infrared spectroscopy of ethanol aqueous solution. The RMSE of ethanol concentration was 0.3% which w

关 键 词：光谱重叠系数 等效信噪比 近红外光谱 可测度分析 

分 类 号：O443.4[理学—电磁学]