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.