[VMTBB-Based Spectral Radiometric Calibration of NIR Fiber Coupled Spectrometer]

The medium temperature black body (MTBB) is conventional high precision equipment used as spectral radiometric scale in infrared spectral region. However, in near-infrared (NIR) spectral region, there are few papers about spectral radiometric calibration by using MTBB, that is because NIR spectral region is the borderland of its effective spectral region. The main research of this paper is spectral radiometric calibration method by using MTBB in NIR spectral region. Accordingly, this paper is devoted mostly to a discussion of how the calibration precision could be affected by selecting different structural parameters of calibration model. The purpose of this paper is to present the results of research and provide technical reference for improving the traceability in NIR spectral radiometric calibration. In this paper, a NIR fiber coupled spectrometer, whose wavelength range covers from 950 to 1700 nm, has been calibrated by a MTBB with adjustable temperature range from 50 to 1050 °C. Concentrating on calibration process, two key points have been discussed. For one thing, the geometric factors of radiation transfer model of the calibration systems have been compared between traditional structure and fiber direct-coupled structure. Because the fiber direct-coupled model is simple and effective, it has been selected instead of traditional model based on the radiation transfer between two coaxial discs. So, it is an advantaged radiation transfer model for radiometric calibration of fiber coupled spectrometer. For another thing, the relation between calibration accuracy and structural parameters of calibration model has been analyzed intensively. The root cause is scale feature of attribute of calibration data itself, which is the nonlinear structure in scales of spectral data. So, the high precision calibration needs nonlinear calibration model, and the uniform sampling for scale feature is also very important. Selecting sample is an inevitable problem when the nonlinear model is explained by small sample dataset. As the analytic results, there are obviously influences for the calibration precision among different strategies of selecting model's structural parameters. The calibration precision, which is mathematical described by standard deviation of spectral data for calibration, could be from ±0.1% to ±1%.