近红外光谱谱线特性对物质浓度分析误差影响的研究

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，以及利用曲线进行近红外光谱法待测组分浓度可测度分析方法。结果表明所提出的ESNR和OC两个谱线特性参数的有效性，以及分析误差预估方法的有效性。为近红外光谱法待测组分浓度定量分析提供了有效、快捷的预估方法，完善了近红外光谱法成分含量可测度分析理论，对近红外光谱法物质浓度定量分析研究具有一定指导意义。

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

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 was estimated by theoretical analysis, and the RMSE of near infrared spectroscopy was 0.32%. The relative error was 6.67%. We have realized the quantitative calculation and experimental verification of the theoretical error of the content of the tested components based on near infrared spectroscopy under the conditions of the measurement conditions, the types of samples, the components to be measured, and the methods of modeling and analysis. This study identified two spectral parameters that have a clear and quantitative relationship with the concentration of the measured component in NIR spectroscopy. The analytical accuracy empirical curve was established when using the classical partial least-squares regression in spectral analysis. In addition，the analysis of the measurable degree of the concentration of the components could also be tested by near infrared spectroscopy. The results showed the effectiveness of the ESNR and OC in this paper, as well as the analytical method of error prediction. This study provided an effective and rapid prediction method for the quantitative analysis of near infrared spectroscopy, and optimized the theory of measurable analysis of near infrared spectroscopy, which has a good guidance for the quantitative analysis of the concentration of near infrared spectroscopy.