Spectral Calibration Research Articles

Raman spectral quantification of Li2SO4, Na2SO4, K2SO4 and Cs2SO4 at 25 to 250 °C and its potential implications for the determination of sulfate contents in natural fluid inclusions

The quantitative study of the sulfate content in natural fluid inclusions (FIs) will help our understanding of the processes of metal transport and related mineralization. The sulfate-rich FIs typically contain several types of sulfates with diverse cations such as Na+, K+, and so on. In order to get the sulfates contents in natural FIs, in situ Raman spectral quantitative calibration method is used to calibrate the Li2SO4, Na2SO4, K2SO4 and Cs2SO4 solutions at temperatures ranging from 25 °C to 250 °C in our study. The results show that the A(SO42−)/A(H2O) ratio increases linearly with increasing mSO42− in sulfate solutions containing single cation of Li, Na, K or Cs at the set temperatures, where A is Raman peak area and m is molality (mol/kg H2O), and a negative linear relationship exists between k (quantitative calibration coefficient) and temperature for sulfate solutions with specific cations up to 250 °C. Meanwhile, the slopes and the intercepts of the k-T correlation curves are obviously different among sulfate solutions with different cations, and the coefficients k of these sulfate solutions at certain temperature decrease with the increase of atomic number of the cation, in accordance with the following order: Li+ > Na+ > K+ > Cs+. As Na+ and K+ are the main cations in the natural sulfate-bearing FIs, the quantification of the multi-cation (Na ± K ± Li) sulfate solutions was evaluated by using the Raman quantitative calibration curves of Li2SO4, Na2SO4, K2SO4 and Cs2SO4 separately, the errors exist due to the use of calibration curves with different cations; the average concentration errors derived from the use of Na2SO4 or K2SO4 Raman quantitative calibration curves are within about 5 %, while those from the use of Li2SO4 or Cs2SO4 Raman quantitative calibration curves are within about 15 %. Therefore, the acceptable errors suggest that the calibration curves of individual sulfates can be applied to the determination of sulfate content of natural sulfate-bearing FIs.

Quantitative analysis of the illegal addition of Atenolol in Panax notoginseng based on NIR-MIR spectral data fusion and calibration transfer.

To address the issue of the common illegal addition of Atenolol in Panax notoginseng, we propose an approach that realizes multivariate calibration transfer between different particle sizes based on near-infrared (NIR) and mid-infrared (MIR) spectral data fusion. To achieve high prediction accuracy, we construct three data fusion schemes (full-spectrum fusion, feature-level fusion, and decision-level fusion) that combine NIR and MIR spectral data. Among three data fusion schemes, the feature-level fusion based on the UVE-SPA-PLS model for 120-mesh spectral data achieves optimal prediction accuracy. Here, a Piecewise Direct Standardization (PDS) algorithm has been applied to calibration transfer from 100-mesh and 80-mesh to 120-mesh to reduce the influence of particle size and improve the robustness of the model. The correlation coefficient (R2) of 100-mesh, and 80-mesh prediction sets can reach 0.9861 and 0.9823, respectively. The corresponding root mean square error (RMSE) are 0.1545 and 0.2045, respectively. This research provides a method for illegal additions in precious herbs and reduces the effect of particle size on spectral modeling, enabling high-precision quantitative detection. In addition, it has important application prospects in reducing experimental losses of precious medicinal materials and ensuring the safe use of Chinese and Western medicines, which provides an alternative method for non-destructive testing.

Facility for spectral radiance calibration at low light level

The spectral radiance measurement at daytime level can be realized with high accuracy, while it’s difficult when the spectral radiance is at nighttime level. We design a spectral radiance calibration facility which has the characteristics of completely unchanged spectrum over 3 orders of magnitude and approximately unchanged spectrum for about 6 orders of magnitude. It combines a spectral radiance light source, a precision aperture and a white diffuser together, make it easy to reproduce the spectral radiance at 380 nm from 4 × 10−9 W/(m2·sr·nm) to 4 × 10−3 W/(m2·sr·nm). The facility can be easily used to calibrate a spectroradiometer at nighttime level. When the spectral radiance from 380 nm to 780 nm is around 1 × 10−7W/(m2·sr·nm), the calibration uncertainty of the spectroradiometer is 0.87%∼1.0% (k = 1).

HyperScout-1 inflight calibration and product validation

ABSTRACT Earth observation aims at monitoring the Earth in increasingly more detail by improving the spatial, spectral and temporal resolution of image acquisitions. This can now be achieved in more cost-efficient ways by the miniaturization of instruments and platforms. New initiatives have emerged to accommodate hyperspectral instruments on CubeSats despite inherent platform limitations in terms of volume, power consumption, computing and downlink capacity. However, the adoption of CubeSat data for operational and scientific applications requires sufficient trust in dependability and data quality. Achieving reliable data quality from CubeSats remains challenging and requires advanced geometric, radiometric, and spectral calibration techniques. We present the successful in-orbit calibration of the HyperScout-1 mission. This In-Orbit Demonstration (IOD) mission, equipped with a miniaturized hyperspectral instrument based on Linear Variable Filter technology (LVF), was launched in 2018 by the European Space Agency (ESA). Rigorous inflight calibration has been applied to HyperScout-1 to ensure the accuracy of its data. The radiometric calibration of the HyperScout-1 instrument was performed vicariously based on modelled spectral radiances over a desert site. Independent validation using data from a CEOS RadCalNet site showed good radiometric accuracy with absolute radiometric errors <5%. Geometric errors due to inaccuracies in attitude measurements and sensor focal plane distortions were corrected using a bundle adjustment technique combined with matching to accurate Ground Control Points. This resulted in excellent geometric performance, with sub-pixel average absolute geolocation errors (~0.48 pixels across track and 0.51 pixels along track). The very limited data budget was a major challenge, forcing us to make some trade-offs in the calibration. Despite this, our robust vicarious calibration strategy allowed effective instrument calibration. As a result, the HyperScout-1 demonstrated the capability of an LVF Hyperspectral instrument on a CubeSat to deliver high-quality imagery and serve applications like land cover classification, change detection and disaster monitoring.

A high resolution prism-grating panoramic imaging spectrometer based on occultation observations

Occultation observation is an important technique for shadow region detection and a significant complement to the illuminated region detection method. This technology will significantly reduce the impact of background light pollution and provide clearer occultation signals. In order to achieve high-resolution all-round detection in the shadow region, this paper introduces a proposal for a prism-grating panoramic imaging spectrometer based on occultation observations. It utilizes a panoramic annular lens to enable 360-degree occultation observations within the shadow region, combines a grating for spectral separation to obtain spectral information, and achieves an overall straight-line design for the system by adjusting the prism-grating surface's deflection angle. This paper, by analyzing the non-uniformity of light rays from different fields of view after dispersion by the grating, obtains the coordinate information of the spectrum on the image plane, significantly improving the accuracy of subsequent spectral calibration. The design results indicate that the optical system of this imaging spectrometer achieves a spectral resolution of 0.67 nm in the wavelength range of 365–770 nm. At a Nyquist frequency of 37.04lp/mm, the MTF across the entire bandwidth exceeds 0.6. The RMS of the point spread function is less than 9 μm across the full field of view, and aberration correction is excellent. This imaging spectrometer utilizes stellar occultations to achieve real-time 360-degree observations in the shadow region, and at the same time obtains total information on atmospheric components and contour information that varies with occultation height. This scheme offers a more comprehensive information for scientists in related fields.

Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer). III. Spectral calibration.

The Moons And Jupiter Imaging Spectrometer (MAJIS) is the visible and near-infrared imaging spectrometer onboard the European Space Agency (ESA)'s Jupiter Icy Moons Explorer mission. Before its integration into the spacecraft, the instrument undergoes an extensive ground calibration to establish its baseline performances. This process prepares the imaging spectrometer for flight operations by characterizing the behavior of the instrument under various operative conditions and uncovering instrumental distortions that may depend on instrumental commands. Two steps of the on-ground calibration campaigns were held at the instrument level to produce the data. Additional in-flight measurements have recently been obtained after launch during the Near-Earth Commissioning Phase. In this article, we present the analyses of these datasets, focusing on the characterization of the spectral performances. First, we describe and analyze the spectral calibration datasets obtained using both monochromatic sources and polychromatic sources coupled with solid and gas samples. Then, we derive the spectral sampling and the spectral response function over the entire field of view. These spectral characteristics are quantified for various operational parameters of MAJIS, such as temperature and spectral binning. The derived on-ground performances are then compared with in-flight measurements obtained after launch and presented in the framework of the MAJIS performance requirements.

Transfer of near infrared calibration for gasoline octane number based on screening consistent wavelengths combined with direct standardization algorithm

In order to share multivariate calibration models of gasoline research octane number between different near infrared spectrometers, a novel calibration transfer method, namely combination of screening consistent wavelengths and direct standardization (SWCSS-DS) was proposed. Firstly, screening wavelengths with consistent and stable signals (SWCSS) between instruments was used to select the wavelengths with best stability, and then direct standardization (DS) further corrected the systematic errors that still exist after the SWCSS was implemented. The spectra of 120 standard gasoline samples collected on two near infrared spectrometers of the same type were investigated in detail to verify the validity of the new algorithm. Compared results of other transfer methods such as SWCSS, Slope/Bias (S/B), direct standardisation (DS), and piecewise direct standardization (PDS), the root mean squared error for prediction (RMSEP) of SWCSS-DS algorithm for target samples was decreased from 5.75 to 0.295, and the Akaike information criterion value decreased from 1516 to 640, which were better than those of the SWCSS, S/B, DS and PDS algorithms. Therefore, the joint algorithm of SWCSS-DS has not only improved the universality of the master model, but also reduced the dimension of the spectral matrix and calibration equation, that would provide a more efficient model transfer strategy for the practical applications.

Optimized combined spectral calibration model based on single-constant Kubelka–Munk theory for color prediction of pre-colored cotton fiber blends

There is a complex interaction between primary fibers in pre-colored fiber blends, which leads to the impracticality of Duncan’s additive theory in the single-constant Kubelka–Munk (KM-1) model, resulting in poor accuracy for color prediction. This paper builds an optimized combined spectral calibration model based on the KM-1 function to solve this issue. This proposed model involves colored and non-colored parts with trained spectral coefficients, and was established to achieve a good matching between the predicted and actual ratio of the absorption coefficient K to the scattering coefficient S, K/ S of samples for color prediction from the perspective of the spectrum. Experimentally, five primary cotton fibers were selected and prepared into fiber blends to demonstrate its validation, and its optimal number of training samples was found to be only 14, which was much less than the general training sampling method using half of all samples directly. Finally, a remarkable average color difference of only 0.63 CIEDE2000 units for 74 test specimens was achieved on this optimized model, which was significantly lower than that of the KM-1 model (∼1.16), two-constant KM model (∼1.03), Stearns–Noechel model (∼1.16) and Friele model (∼1.11). The results indicate that the optimized model behaves well and could be applied in the color prediction of pre-colored fiber blends.

Rapid and robust bacterial species identification using hyperspectral microscopy and gram staining techniques.

Gram staining can classify bacterial species into two large groups based on cell wall differences. Our study revealed that within the same gram group (gram-positive or gram-negative), subtle cell wall variations can alter staining outcomes, with the peptidoglycan layer and lipid content significantly influencing this effect. Thus, bacteria within the same group can also be differentiated by their spectra. Using hyperspectral microscopy, we identified six species of intestinal bacteria with 98.1% accuracy. Our study also demonstrated that selecting the right spectral band and background calibration can enhance the model's robustness and facilitate precise identification of varying sample batches. This method is suitable for analyzing bacterial community pathologies.

Calibration method of relative spectral response function of indirect imaging spectrometer

In imaging spectrometers, area array detectors are usually used as photoelectric conversion devices, but the inconsistency of the spectral response among pixels can distort the collected target spectra. To improve the spectral radiometric accuracy of imaging spectrometers, calibrating and correcting the inconsistency of the spectral response among pixels is essential. The signal received by each pixel of area array detector of the indirect imaging spectrometer is usually the superposition of the target multi-spectral radiation signals or full-spectral radiation signals. Therefore, its relative spectral radiometric calibration requires measuring the spectral response of each pixel at different wavelengths on the array detector. Under the ideal conditions, the response values of each pixel in the area array detector are different, so the indirect imaging spectrometer cannot simply calibrate the relative spectral response (RSR) function between pixels by using the method of “monochromator + integrating sphere”. In this work, taking the interferometric imaging spectrometer for example, we analyze the influence of the inconsistency of the RSR among pixels on the target spectral radiation measurement accuracy, and propose a system-level RSR function measurement method for the indirect imaging spectrometer based on the Fourier transform modulation calibration source. In addition, we establish a mathematical model for calibrating the RSR function,and provide guidelines for selecting calibration system parameters such as light source, spectral resolution, and OPD sampling interval. The simulation results show that under the ideal noise-free condition, the 1% spectral response inconsistency among pixels results in a relative error of 1.02% to the recovered spectra. After RSR correction, the relative error of the recovered spectra of different rows decreases to 0.08%. Furthermore, in this work we simulate and analyse the influence of spectral signal-to-noise ratio on the calibration accuracy of the RSR function, and point out that increasing the brightness of the calibration light source, extending exposure time, and combining multi-frame interferograms can enhance RSR function calibration accuracy in practical applications. The research result can provide a theoretical basis for realizing the relative spectral radiometric calibration of indirect imaging spectrometer, which is of great significance in promoting quantitative spectral remote sensing.

Advancements in solar spectral irradiance measurements by the TSIS-1 spectral irradiance monitor and its role for long-term data continuity

The first implementation of NASA’s Total and Spectral Solar Irradiance Sensor (TSIS-1) launched on December 15th, 2017, and was integrated into the International Space Station (ISS) to measure both the total solar irradiance (TSI) and the solar spectral irradiance (SSI). The direct measurement of the SSI is made by the LASP Spectral Irradiance Monitor (SIM) and provides data essential to interpreting how the Earth system responds to solar spectral variability. Extensive advances in TSIS-1 SIM instrument design and new SI-traceable spectral irradiance calibration techniques have resulted in improved absolute accuracy with uncertainties of less than 0.5% over the continuous 200–2400 nm spectral range. Furthermore, improvements in the long-term spectral stability corrections provide lower trend uncertainties in SSI variability measurements. Here we present the early results of the TSIS-1 SIM measurements covering the first 5 years of operations. This time period includes the descending phase of solar cycle 24, the last solar minimum, and the ascending phase of solar cycle 25. The TSIS-1 SIM SSI results are compared to previous measurements both in the absolute scale of the solar spectrum and the time dependence of the SSI variability. The TSIS-1 SIM SSI spectrum shows lower IR irradiance (up to 6% at 2400 nm) and small visible increases (~0.5%) from some previous reference solar spectra. Finally, initial comparisons are made to current NRLSSI2 and SATIRE-S SSI model results and offer opportunities to validate model details both for short-term (solar rotation) spectral variability and, for the first time, the longer-term (near half solar cycle) spectral variability across the solar spectrum from the UV to the IR.

Laboratory Thermal Emission Spectral Measurement and Calibration Methods for Planetary Science Research

Laboratory Thermal Emission Spectral Measurement and Calibration Methods for Planetary Science Research

Pre-Launch Spectral Calibration of FY-3F/HIRAS-Ⅱ

Pre-Launch Spectral Calibration of FY-3F/HIRAS-Ⅱ

Minimum Noise Fraction Analysis of TGO/NOMAD LNO Channel High-Resolution Nadir Spectra of Mars

NOMAD is a suite of spectrometers on the board of the ESA-Roscosmos Trace Gas Orbiter (TGO) spacecraft and is capable of investigating the Martian environment at very high spectral resolution in the ultraviolet–visible and infrared spectral ranges by means of three separate channels: UVIS (0.2–0.65 μm), LNO (2.2–3.8 μm), and SO (2.3–4.3 μm). Among all channels, LNO is the only one operating at infrared wavelengths in nadir-viewing geometry, providing information on the whole atmospheric column and on the surface. Unfortunately, the LNO data are characterized by an overall low level of signal-to-noise ratio (SNR), limiting their contribution to the scientific objectives of the TGO mission. In this study, we assess the possibility of enhancing LNO nadir data SNR by applying the Minimum Noise Fraction (MNF), a well-known algorithm based on the Principal Components technique that has the advantage of providing transform eigenvalues ordered with increasing noise. We set up a benchmark process on an ensemble of synthetic spectra in order to optimize the algorithm specifically for LNO datasets. We verify that this optimization is limited by the presence of spectral artifacts introduced by the MNF itself, and the maximum achievable SNR is dependent on the scientific purpose of the analysis. MNF application study cases are provided to LNO data subsets in the ranges 2.627–2.648 μm and 2.335–2.353 μm (spectral orders 168 and 189, respectively) covering absorption features of gaseous H2O and CO and CO2 ice, achieving a substantial enhancement in the quality of the observations, whose SNR increases up to a factor of 10. While such an enhancement is still not enough to enable the investigation of spectral features of faint trace gases (in any case featured in orders whose spectral calibration is not fully reliable, hence preventing the application of the MNF), interesting perspectives for improving retrieval of both atmospheric and surface features from LNO nadir data are implied.

Sharing calibration information among laser-induced breakdown spectroscopy instruments using spectral line binning and calibration transfer

The ability of a publicly accessible preexisting laser-induced breakdown spectroscopy (LIBS) database to train multivariate models to predict rock and mineral compositions from other laboratories or from remotely collected spectra is evaluated using LIBS spectra collected on >2500 unique geological targets using three different instruments and a range of collection protocols. Datasets collected under increasingly disparate conditions are utilized, including a single instrument with different resolution settings; two different instruments with very similar ablation and collection optics; and a benchtop instrument and portable instrument with different collection protocols, resolutions, and plasma conditions. Cross-calibration among datasets is performed for a range of scenarios designed to test the efficacy of post-processing techniques.Major element predictions are most accurate when instrument parameters match among training and test spectra. Use of a piecewise direct standardization-partial least squares (PDS-PLS) calibration transfer algorithm reduces major element prediction uncertainties when the resolution of training and test spectra do not match. Even when training and test spectra are collected on different instruments, reasonable predictions can be derived by binning peak areas prior to training calibration models. Finally, incorporating a large, preexisting database into a smaller dataset collected under test conditions has the potential to greatly improve the reliability of predicted compositions. With careful consideration to match plasma conditions and utilize post-processing, existing large-scale LIBS calibration databases like the one used in this study can be used to boost LIBS accuracy and expand the potential of LIBS as a widely-applicable quantitative geochemical tool.

Im-SCC-FRET: improved single-cell-based calibration of a FRET system.

We recently developed a SCC-FRET (single-cell-based calibration of a FRET system) method to quantify spectral crosstalk correction parameters (β and δ) and system calibration parameters (G and k) of a Förster resonance energy transfer (FRET) system by imaging a single cell expressing a standard FRET plasmid with known FRET efficiency (E) and donor-acceptor concentration ratio (RC) (Liu et al., Opt. Express30, 29063 (2022)10.1364/OE.459861). Here we improved the SCC-FRET method (named as Im-SCC-FRET) to simultaneously obtain β, δ, G, k and the acceptor-to-donor extinction coefficient ratio (ε A ε D), which is a key parameter to calculate the acceptor-centric FRET efficiency (EA), of a FRET system when the range of β and δ values is set as 0-1. In Im-SCC-FRET, the target function is changed from the sum of absolute values to the sum of squares according to the least squares method, and the initial value of β and δ estimated by the integral but not the maximum value spectral overlap between fluorophore and filter. Compared with SCC-FRET, the experimental results demonstrate that Im-SCC-FRET can obtain more accurate and stable results for β, δ, G, and k, and add the ratio ε A ε D, which is necessary for the FRET hybrid assay. Im-SCC-FRET reduces the complexity of experiment preparation and opens up a promising avenue for developing an intelligent FRET correction system.

Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 μg/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

Water vapor absorption spectroscopy and validation tests of databases in the far-infrared (50–720 cm−1). Part 2: H217O and HD17O

The present work is the second part of our systematic study of the absorption spectrum of the water vapor isotopologues by high resolution (≈ 0.001 cm−1) Fourier transform spectroscopy in the far infrared (50–721 cm−1). The room temperature spectra were recorded at the AILES beam line of the SOLEIL synchrotron with an absorption pathlength of 151.75 m. Here, we consider three spectra of a water vapor sample highly enriched in 17O. Line parameters retrieved from the three spectra were combined in a global list of 4432 water lines (assigned to 4651 transitions). The spectral calibration based on a statistical matching with about 370 accurate reference line positions of H216O allows for line center determinations with an accuracy of 5×10−5 cm−1 for well isolated lines of intermediate intensity. Six water isotopologues (H218O, H216O, H217O, HD18O, HD16O, and HD17O) were found to contribute to the spectrum. 460 and 99 of the measured H217O and HD17O transitions are newly observed by absorption spectroscopy. 69 H217O and 20 HD17O energy values of the ground (000) and first excited (010) states are newly determined.The present set of measured H217O line positions is combined with 24 literature sources to provide a list of 821 empirical energies for the first five vibrational states – (000), (010), (020), (100), and (001) – using the RITZ principle. A set of 332 rotational energies of the (000) and (010) states of HD17O is determined by merging the 465 HD17O transitions measured in the present study to eight literature sources.

RedEye-1: a compact SWIR hyperspectral imager for observation of atmospheric methane and carbon dioxide

We present the development of our first prototype of a compact short-wavelength infrared (SWIR) hyperspectral imager, RedEye-1, designed to observe and measure the concentrations of methane (CH4) and carbon dioxide (CO2) in the atmosphere. It operates in the spectral range of 1588 nm to 1673 nm with a nominal spectral resolution of 0.5 nm and has a total weight of approximately 1.8 kg. We outline the optical design of the instrument, including its fore-optics (telescope), and evaluate its performance using the OpticStudio ZEMAX software. In addition, we explain the spectral and radiometric calibration procedures and present the results. Our preliminary data reduction and analysis revealed a high quality SWIR image and a single path measurement of an atmospheric profile displaying the absorption lines of CH4 and CO2.

Solar Observation with the Fourier Transform Spectrometer. II. Preliminary Results of Solar Spectrum near the CO 4.66 μm and Mg i 12.32 μm

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky, relative to the visible wavelengths. Its accuracy is also influenced by the spectral resolution of the Fourier transform spectrometer. In the paper, we developed a CO2 gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared wavelengths. The measured spectral resolution is 0.00342 ± 0.00086 cm−1 and 0.0059 ± 0.00024 cm−1 at the wavenumbers of 798 cm−1 and 2136 cm−1, respectively. We also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66 μm and Mg i 12.32 μm. By quickly pointing the sunlight feeding system about 1 degree away from the solar disk center, we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing Station. After removing the background radiation, our observed solar spectrum at CO 4.66 μm is consistent with that from the National Solar Observatory. The Mg i 12.32 μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun (AIMS) project is also identified. Our method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.