Based on a high-temperature blackbody and a double grating spectral comparison measurement system, the new primary standard apparatus of spectral irradiance was developed at the National Institute of Metrology (NIM) in the spectral wavelength from 230 to 2 550 nm. Stable 1 000 W tungsten halogen lamps are the secondary primary standard for spectral irradiance values. The temperature measurement of the high-temperature blackbodies was traced to the Pt-C and Re-C fixed-point blackbodies and checked against the WC-C fixed-point blackbody at 3 021 K. From 2017 to 2019, NIM participated in the new international comparison of spectral irradiance CCPR-K1 using the newly developed primary standard apparatus. An organized by BIPM. The comparison was piloted by VNIIOFI from Russia, with 12 laboratories participating. The comparison results show that, except for a few laboratories, the consistency of the participated laboratories is ±2.0%, ±1.0%, and ±2.0% at wavelengths of 250 to 290 nm, 300 to 1 300 nm, and 1 500 to 2 500 nm. The average relative deviation between NIM and the KCRV(key comparison reference values)is 0.13%, 0.28% and 0.15%, respectively, in the ultraviolet, visible, and near-infrared wavelengths. The whole wavelength from 250 to 2 500 nm, the average relative deviation between NIM and the KCRV is 0.17%. Compared with the average relative deviation of 0.9% in 2004, the measurement capability of spectral irradiancehas significantly improved.

Spatial Heterodyne Spectroscopy (SHS) is a hyperspectral analysis technique applied in weak matter detection, planetary exploration, and atmospheric remote sensing. Influenced by the complex experimental environment and the weak signal of the measured target, the measurement results of the Spatial Heterodyne Spectrograph are prone to tilted interference fringes and speckle noise in the interferograms, which ultimately affect the accuracy of the measured spectra. To solve these problems, this paper proposes a wavelet transform-based interferogram error analysis method, which effectively evaluates the interferogram's speckle noise level and stripe tilt error, thus providing a theoretical basis for the subsequent error correction. In this paper, Gaussian noise with different standard deviations is added to the error-free simulated interferograms of potassium lamps. The complex wavelet coefficients are extracted by using wavelet decomposition. Three noise estimation methods are used to compute the interferogram data. The results show that the noise estimation results of the spatial correlation wavelet transform method have the smallest deviation from the real value. The Donoho estimation method matches the real value better after the intercept correction. Subsequently, the Db2 wavelet decomposes the potassium lamp interferograms with different noise levels in one wavelet layer and extracts the detail coefficients in the horizontal, diagonal, and vertical directions. Donoho estimation is used to analyze the interferograms for two-dimensional noise analysis, which effectively characterizes the noise levels of the three interferograms in each direction. The research results show that the three directions of the wavelet detail coefficient valuation change respectively in the decomposition to different layers have a similar rule of change: the wavelet detail coefficient valuation and the stripe tilt direction, and the wavelet detail coefficient belongs to the direction of the angle is inversely proportional to the direction of the stripe tilt direction and the wavelet detail coefficient of the direction of the smaller the angle of the stripe tilt direction and the direction of the wavelet detail coefficient of the valuation of the larger, and vice versa, the smaller, and the horizontal direction of the first layer of the decomposition, the diagonal decomposition of the direction to the second layer of the vertical direction of the third layer of the hierarchy conforms to the rule of change. This study is a useful attempt to utilize wavelet decomposition for spatial outlier spectral error analysis and also provides a new way for subsequent correction of interferogram streak tilt error.

The photolysis rate and its measurement technology were first proposed by German scientists; Metcon developed the photolysis rate measuring instrument in the early 21st century, and then the technology received attention in China, and some universities and companies in China carried out research, and development on the photolysis rate measurement system. However, foreign products are generally larger and more expensive. Core devices such as spectrometers and receivers in domestic products still rely on imports and are vulnerable to foreign technology blockade. Under the guidance of the key project of “Comprehensive Control of Atmospheric, soil, and Groundwater Pollution” in China's “14th Five-Year Plan” National Key Research and Development plan, a spectrograph system for measuring the photolytic rate of atmospheric substances was developed and met the requirements of localization of its core components. The system uses a small spectrometer designed by ourselves as the core component and an optical receiver with uniform response in all directions to measure the photolysis rates of various substances after laboratory optical calibration. The operating band of the system is 285~430 nm, and the time resolution can reach 0.1 s. The measured substances include NO2, HONO, HCHO, H2O2，etc. Compared with the imported measuring instruments, the measurement error of the photolysis rate of each gas substance is less than 5%, and the correlation is more than 0.93. This system has continuously monitored the photolysis rate of gases in the atmosphere on Dongpu Island, Hefei City. The trend of NO2 photolysis rate and the correlation between NO2 photolysis rate and concentration were analyzed. It was found that the photolysis reaction was closely related to the energy driving the photochemical reaction, and the photolysis rate value was significantly higher on sunny days than on cloudy and rainy days. At the same time, it is found that there is an empirical formula between the integration curve of NO2 photolysis rate and concentration; the correlation is greater than 0.9, and the accumulation of NO2 concentration can be estimated by measuring the NO2 photolysis rate. The system realizes the localization of core components, the overall structure is simple and portable, convenient for field test and installation, and reduces cost and improves economy. This study provides data support for comprehensive monitoring and control of air pollution and has reference significance for environmental monitoring and pollution prevention in various regions.

Against the background of dual-carbon policies and fossil energy shortages, hydrogen energy, as a highly efficient and environmentally friendly clean energy source, has attracted the attention of a wide range of researchers. Hydrogen/air hybrid combustion is essential to promote the large-scale application of hydrogen energy. Hence, the combustion mechanism and measurement technology of key hydrogen/air hybrid combustion parameters have become the current research hotspots. The combustion-air equivalence ratio is a pivotal parameter characterizing the degree of mixing of fuel and oxidant in the combustion process, which directly affects the start of combustion, self-sustainability, and the active degree of combustion chemical reactions. Therefore, real-time online monitoring of the local equivalence ratio in the combustion process is of great significance for improving combustion efficiency and controlling combustion. Currently, relatively few methods can measure the real-time hydrogen/air flame equivalence ratio. As a non-invasive measurement method, Laser spectroscopy can perform real-time online measurement and the characteristics of fast, in-situ, remote analysis, and simultaneous online monitoring of multiple elements. Hence, it has been widely used in the field of combustion measurement. Here, we propose to adopt nanosecond laser-induced breakdown spectroscopy to measure the equivalence ratio of hydrogen/air flames. The experiments were carried out in a premixed hydrogen/air jet flame. The laser was focused into the jet combustion field to generate breakdown and hence plasma, and a spectrometer monitored the plasma emission spectrum. We found that the spectra of H(at 656.3 nm) and O(at 777 nm)are strong, and their intensity shows a regular trend with the change of the equivalence ratio. Therefore, we used the spectra of H(at 656.3 nm) and O(at 777 nm) to label hydrogen and air, respectively, and used their ratios as the measurement coefficients of the fuel-air equivalence ratio. The ratios at different equivalence ratios were measured in a low-velocity flow field with a homogeneous mixture of hydrogen and air, and a calibration curve was established, which we found to be equally applicable to both combustion and non-combustion environments. Based on the calibration curve, we investigated the local combustion equivalence ratios of the jet combustion field in the presence of external doping. In a jet combustion field with an equivalence ratio of 1.0 and a flow velocity of 20 m·s-1, the variation of the combustion-to-air equivalence ratio at different heights at the jet's center was measured and numerically simulated under the same conditions. The simulations agree with the measurements, and the feasibility of nanosecond laser-induced breakdown spectroscopy for measuring the fuel-air equivalence ratio of hydrogen/air mixtures is verified. This method can provide technical support for the basic research and numerical simulation of hydrogen combustion.

Liquid crystal whispering-gallery-mode (WGM) sensing technology combines the high sensitivity of liquid crystal materials with the high precision characteristics of optical WGM, holding broad application prospects in substance detection, environmental monitoring, and biomedical fields. To enhance the accuracy and sensitivity of sensing measurements, accurate calibration of the WGM spectral lines is crucial. However, the irregular distribution of liquid crystal WGM spectra poses challenges to spectral calibration in practical applications. This paper proposes a method for calibrating irregular liquid crystal WGM spectra based on Savitzky-Golay filtering and Gaussian smoothing, advocating three principles of symmetry, high linearity, and rationality for spectral calibration. Spectral calibration is categorized into regular and irregular spectra. For regular spectra with good Lorentzian line shapes and Gaussian envelope profiles, the highest intensity spectral line, central wavelength, or spectral lines of adjacent modes with the same free spectral range can be selected as the resonance wavelength. For irregular spectra, three types are identified: (1) spectral missing peaks and intensity variations mainly caused by loss variations in resonance conditions; (2) spectral irregular splitting due to small defects or impurities in the liquid crystal microcavity, leading to asymmetric splitting of spectra; and (3) mixed-type variable spectra affected by complex measurement environments, resulting in various irregular changes. Through Savitzky-Golay filtering and Gaussian smoothing, spectral symmetry is effectively restored, and fitting linearity is improved, facilitating spectral calibration. To validate the effectiveness of the proposed method, experiments are designed for sensing different biological molecules using liquid crystal WGM mode, including trypsin (concentration range of 0.75～2.00 g·mL-1), pH (4.55～6.86), and DNA (concentration range of 90～490 g·mL-1). Experimental results demonstrate that using the proposed method and three principles for liquid crystal WGM mode spectral calibration and resonance peak selection, the linearity of sensing sensitivity exceeds 0.99, indicating good reliability and stability. This paper innovatively proposes an effective calibration method and three principles for irregular liquid crystal WGM mode spectral distribution, providing practical solutions and significant theoretical and methodological support for enhancing the accuracy and effectiveness of liquid crystal WGM mode sensing measurements, thereby holding important application and theoretical significance.

Bangpu deposit in Tibet is an important porphyry-skarn deposit in the Gangdise metallogenic belt, and the research degree of the skarn ore body in the eastern part of its mining area is relatively low. Core hyperspectral imaging technology can quickly obtain the hyperspectral data of core samples. In this paper, the typical core samples of skarn in the eastern part of the Bangpu deposit are selected, and the short-wave infrared (SWIR) hyperspectral imaging test and comparative analysis are carried out by using the core scanner independently developed by the Beijing Research Institute of Uranium Geology and the foreign SPECIM core imager. At the same time, the SWIR spectral characteristics of typical minerals are revealed, and the potential and advantages of advanced hyperspectral imaging technology in skarn exploration are discussed. We carried out hyperspectral imaging tests on the same batch of core samples with domestic and foreign instruments. We extracted the end members using the minimum noise separation transform and pure pixel index. By consensus, this study identified six minerals: calcite, epidote, chlorite, hornblende, quartz, and muscovite. The mineral mapping results by the two instruments were generally consistent, reflecting the combination and distribution of different altered minerals. The mapping accuracy of SPECIM for ZK0010 and ZK0011 is 91.5%, and the Kappa coefficient is 0.87. The mapping accuracy of the samples by the Beijing Research Institute is about 75%, and the Kappa coefficient is 0.66.The signal-to-noise ratio of hyperspectral data obtained by the two instruments is high. However, due to domestic instruments' relatively low spatial resolution, there are more mixed pixels in the image, which affects mapping accuracy. Further improving its spatial resolution is the key point of the next research and development.The hyperspectral imaging data show that the wavelength of the Fe—OH absorption peak of chlorite in the ZK0010 drill hole ranges from 2 252.3~2 260 nm, and that of chlorite in the ZK0011 drill hole ranges from 2 251.7~2 258.5 nm. The wavelength shifts from top to bottom in the short wave direction, indicating that chlorite is far from the mineralization center and relatively rich in Mg. Its high value of the Fe—OH wavelength indicates the center position of the ore body. Hyperspectral imaging technology can provide more abundant spatial and spectral information than the traditional point measurement method, which provides technical support for extracting spectral characteristic parameters of indicator minerals and analyzing their variation rules.Based on a large number of spectral data provided by hyperspectral imaging, the Fe—OH peak position of chlorite is statistically analyzed, and the exploration of the skarn deposit can be indicated by its high wavelength position.

As the key parameters of marine water quality monitoring, nitrate and nitrite are important references for studying the marine nitrogen cycle. The ultraviolet (UV) absorption spectrum is simple to operate and fast in response, and it is suitable for monitoring various water quality parameters. However, the measurement has aliasing interference between nitrate and nitrite and seawater ion interference. This paper proposes a rapid measurement method for nitrate and nitrite. First, the spectrum is preprocessed by first-order difference. Then, the correlation coefficient search is used to obtain the optimal modeling band. Finally, the BP neural network is used for concentration inversion. The linear correlation coefficient between the predicted value and the real value of the model is 0.998. The comparison test was conducted with similar equipment, and the linear correlation coefficient was 0.982. The application test was carried out in the 2023 summer voyage of Yantai Marine Environment Monitoring and Forecasting Center, and the linear correlation coefficient between the two methods was 0.902. The results show that this method can realize the rapid measurement of nitrate and nitrite in seawater and provide a reference for developing subsequent in-situ monitoring instruments.

Preprocessing is an important step in constructing a near-infrared (NIR) spectroscopy detection model, which significantly affects the accuracy of the detection process. Various preprocessing methods are available, each designed to address specific types of noise and irrelevant information, thereby improving the signal-to-noise ratio. Optimizing the preprocessing combination is essential for achieving the desired model results. This study proposes a strategy for the combinatorial optimization of pre-processing methods for the calibration of near-infrared spectroscopy models, which includes selecting eight commonly used preprocessing methods to establish a library of preprocessing methods, building a quantitative model using the partial least squares (PLS) method.Then selecting preprocessing combinations from the library that have an excellent calibration capability for the model simply and efficiently, using the root-mean-square-error-of-cross-verification of the model (RMSECV) as an iterative criterion.The strategy's structural design employs the greedy algorithm for optimization and achieves global optimization by searching for the optimal preprocessing method at each step. This enables the selection of preprocessing combinations for spectral data to be completed simply and efficiently. Tests were conducted on publicly available datasets such as wheat and meat, and the proposed strategy was compared with a similar stacked strategy (Stacked) and sequential orthogonal fusion of multi-block data strategy (SPORT). The results show that on the wheat dataset, the proposed strategy reduced the root mean square error of calibration (RMSEC) by 12%, 6%, and the root mean square error of prediction (RMSEP) by 32%, 17% compared to the Stacked and SPORT strategies, respectively. On the meat dataset, the proposed strategy reduced the RMSEC compared to the Stacked and SPORT strategies by 49% and 48%, and RMSEP was reduced by 46% and 41%, respectively.These results demonstrate good calibration performance.Finally, this analysis examines the contribution of the preprocessing methods selected by the strategy in model calibration. It also discusses the strategy's potential in terms of model interpretability and prevention of overfitting. The strategy presents a new approach to selecting preprocessing methods for NIR spectroscopy.

The triuranium octoxide (U3O8) exhibits exceptional kinetic and thermodynamic stability among various uranium oxides. The study of the high-pressure phase stability and high-pressure phonon behaviour of U3O8 is an important reference for its application in the nuclear industry, catalysts, and other fields. Due to the complexity of the electron structure outside the nucleus of the uranium atoms (［Rn］5f36d17s2), compared to the oxygen atoms (［He］2s22p4), significant differences exist between their electron configurations. Therefore, synchrotron X-ray diffraction makes it difficult to detect subtle changes in U—O bonding for uranium oxides under high pressure. However, Raman spectroscopy is highly sensitive to changes in U—O bonding at high pressure, and it can reveal some important information about substances at high pressure, including bonding or stoichiometry. To date, the investigations on the high-pressure structural phase transition for U3O8 have typically focused on exploring its evolution in hydrostatic environments. However, a deep investigation of the effects of non-hydrostatic environments on the high-pressure phase transition of U3O8 has not been conducted yet. In this work, the effects of hydrostatic and non-hydrostatic environments on the high-pressure phase transition and phonon behaviour for orthorhombic -U3O8 have been investigated using a high-pressure Raman scattering technique based on the diamond anvil cell. Our results show that initialization transition pressures of -U3O8 under hydrostatic (8.1 GPa) and non-hydrostatic (8.2 GPa) conditions are very close. However, the significant micro zonation bias stress present within the sample in the non-hydrostatic environments leads to the completion transition pressure (16.4 GPa) being approximately 2 to 3 GPa lower compared to the corresponding values in the hydrostatic environments (18.5 GPa). The first-order pressure coefficients and mode-Grneisen parameters of the main Raman modes in two comparison experiments were given. The results show that before the high-pressure phase transition, the absolute values of the zero-pressure first-order pressure coefficients |d/dP| for the main Raman modes under the hydrostatic environments are generally greater than those under the non-hydrostatic environments, which indicates that the Raman modes exhibit an insignificant response to the pressure under the non-hydrostatic environments. However, the absolute values of the first-order pressure coefficients under the non-hydrostatic environments are significantly larger once the high-pressure phase transition begins. This may be caused by significant micro zonation bias stress that greatly strengthens the mutual coupling between the outer electrons of the uranium-oxygen atoms. The B2（6） mode exhibits the smallest value at zero pressure, indicating a more positive response to external perturbations. Moreover, the first-order pressure coefficients of the A2(3) and A2(4) modes (representing the vibrations of the oxygen atoms along the a-axis) are typically smaller than those of the B2(4) and B2（6） modes (representing the displacements of the oxygen atoms in the bc-plane) when pressurized at room temperature, suggesting that the a-axis is less sensitive to pressure than the b-axis and c-axis before the high-pressure phase transition for -U3O8. The effect of hydrostatic and non-hydrostatic environments on the high-pressure phase transition for -U3O8 was investigated deeply for the first time in this work, which offered valuable insights into the high-pressure phase stability and lattice dynamics behavior of -U3O8.

The inductively coupled plasma (ICP) spectrometer is a widely used elemental analyzer, mainly for qualitative and quantitative elements analysis. The ICP source is the core component of the spectrometer, playing a crucial role. By exciting the target elemental atoms in the sample, the ICP source generates corresponding characteristic spectra, which are then analyzed and measured by the spectrometer, achieving rapid and accurate detection of target elements in the sample. Currently, there are two mainstream types of ICP sources: self-excited and external-excited, each with its advantages and disadvantages. The external-excited ICP source generally consists of Radio Frequency (RF) amplification and impedance matching. The two are designed separately, with a complex circuit and a relatively large volume. The impedance matching uses a mechanical capacitance matching method, with response times in milliseconds or even seconds, resulting in slow matching speeds. At the same time, the design mostly employs Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFET) with low power amplification, and the maximum output power is limited. This paper presents a self-excited ICP source based on a gold-metalized silicon n-channel RF power transistor and variable frequency impedance matching. The ICP source uses an integrated design of RF amplification and impedance matching, comparing the phase difference between the frequency signal f1 sampled from the load coil and the amplified frequency phase f0, and controlling the frequency change based on the phase difference to achieve impedance matching. The design uses gold plating technology to amplify power with high-power silicon n-channel RF power transistor, providing greater power density and smaller volume, with a maximum output power exceeding 2 400 W.The paper conducted electrical static tests on the developed ICP source, providing the characteristics of the Phase Detector(PD), Voltage-Controlled Oscillator(VCO), and power output, verifying the performance of each section under corresponding working conditions. Additionally, tests were carried out with standard solutions containing Ba, Na, and Li elements using the spectrometer, obtaining an ICP spectrogram of the target elements. Furthermore, the obtained ICP spectrograms were processed using the Ensemble Empirical Mode Decomposition (EEMD) method to enhance the spectral signals of the target elements effectively. The R2 coefficient of determination (R2) of the signal is increased from 0.97 to 0.99, and the relative RSD Standard Deviation (RSD) is increased from 6.47% to 1.07%, enhancing the signal's accuracy and usability. The self-excited ICP source developed in this paper has been reduced in size through the integrated design of RF amplification and impedance matching. The frequency conversion technology has improved the impedance matching speed from the millisecond level to the nanosecond level, and the MOSFET technology of gold plating technology has increased the maximum output power from 1 800 to 2 400 W, which has laid a foundation for further optimization and application of ICP source and has important scientific research and engineering practice significance.

This paper restored the formula of the Ding ware at different times for the first time by analyzing the raw materials for porcelain making that had been unearthed at the kiln site. Spectroscopic examinations such as ED-XRF compositional analysis and XRD structural analysis were performed on over twenty types of ceramic raw materials recovered from the Ding Ware workshop site archaeological excavations spanning the Five Dynasties to the Song and Jin periods in 2009. ED-XRF analysis revealed that these materials could be categorized as either Singular raw materials or composite raw materials, including rocks, clay, and plant ash for the former, and body and glaze materials for the latter. XRD analysis demonstrated that the kaolin-type raw materials utilized for producing Ding Ware white porcelain are feldspar-type mineral raw materials comprising montmorillonite and calcite-associated quartz. High-iron clay raw materials, characterized by their exceptionally high iron content, are presumably used for black glaze. The analysis of calcareous raw materials concurs with the findings of scholars such as Kang Baoqiang, who postulated that the glaze materials for Ding Ware might have been composed of 40% lime and 60% pine ash. AdditionGlaze ash was also identified, ash produced by calcining limestone with plants. In the analysis of the glaze of Ding Ware white porcelain from the Five Dynasties, it was inferred that glaze ash was utilized, thus the use of glaze as. Thusing Ware could be traced back to as early as the Five Dynasties, marking the earliest documented instance of using glaze ash in glazing. XRD crystalline phase analysis of the body raw materials indicated that aside from quartz and kaolinite being predominant quartz and kaolinite, they also contained feldspar, montmorillonite, and calcite, erals. X-ray diffraction analysis of singular raw materials showed that quartz is a mineral found in both clay and rocks, suggestint quartz may not be an essential additive for the porcelain body. Quartz and kaolinite are the principal components of clay raw materials, therefore body raw materials shou. Therefore, consist of rock-type and clay raw materials. The body materials for Ding Ware fine white porcelain likely utilized a formulation that combined clay raw materials with the first type of rock-type raw materials, with the proportion during the Jin period approximately 1∶1, and about 7∶3 (clay∶rock) during the Northern Song period, and roughly 6∶4 during the Five Dynasties. The analysis of glaze raw materials demonstrated that the glazing materials for Ding Ware were formed by combining rock-type raw materials with glaze ash, with a ratio of rock to ash about 9∶1 during the Five Dynasties and Northern Song periods, which decreased by half to 9.5∶0.5 in the Jin period. This has provided a more precise understanding of the body and glaze formulations during the peak period of Ding Ware, thereby furnishing robust evidence for the re-firing of Ding Kiln.

In recent years SnO has been increasingly used in optical and electrical applications. Raman spectroscopic in situ tests and first-principle calculations were applied to investigate the structural and electronic properties of SnO under high-pressure conditions to broaden the application scope of SnO. The results of the characterization of SnO are as follows: the scanning electron microscopy results show that the selected SnO samples are lamellar stacks with transverse dimensions, and the whole is in the shape of a flower; the X-ray diffraction patterns indicate that the crystal structure of the SnO samples is a tetragonal crystal system structure (space group P4/nmm) at room temperature and pressure. The structural properties of SnO samples under high pressure have been investigated using Mao-Bell Diamond anvil cell and in situ Raman spectroscopy, and the results show that there are four Raman vibrational modes (A1g, B1g, E1g and E2g) of SnO at atmospheric pressure. A1g characterizes the vibration parallel to the z-axis in the plane of the Sn—Sn bond; B1g characterizes the vibration parallel to the z-axis in the plane of the O—O bond; and Eg characterizes the vibration of Sn—O atoms in the plane of the intra-layer polarization, which are located near the wave numbers 211, 350, 113, and 460 cm-1, respectively, with the peaks 113 and 211 cm-1 being SnO characteristic peaks. During the pressurization of the SnO sample system to 12.5 GPa, the pressure causes Sn's intermolecular and atomic spacing to decrease, resulting in the shortening of the Sn—O bond length. When the atoms undergo telescopic vibration, the shortened bond length increases bond energy. Thus, the active Raman vibrational modes (E1g and A1g) shift toward the high-frequency direction. As the system pressure continues to increase, the lattice is distorted, the inelastic scattering intensity decreases, and the peaks broaden; when the pressure is increased to 8 GPa, the vibrational mode peaks of E1g and A1g near 125 and 216 cm-1 decrease dramatically; when the pressure is increased to 10 GPa, the two characteristic peaks disappear completely, and it is inferred that amorphization of the substance occurs in the non-hydrostatic pressure environment at 8~10 GPa. When the system was pressurized to 12.5 GPa, no new peaks still appeared in the spectra, indicating that the amorphous state was stable under high pressure. Subsequently, the system was depressurized, and theE1g and A1g modes of SnO reappeared after depressurization to 3 GPa, indicating that the sample regained the crystal structure at low pressure. The intensity of the unloading to atmospheric pressure characteristic peaks are located at 110 and 209 cm-1, respectively, in agreement with the unpressurized data, proving that the high-pressure phase transition behavior of SnO is reversible. To further understand the effect of pressure on the electrical properties of SnO, the electronic properties of SnO at atmospheric pressure and experimentally speculated amorphization pressure (8 GPa) were calculated using the first-principles approach. The effect of pressure on the electrical conductivity of SnO was investigated through the change in band gap width of SnO before and after amorphization. The results show that SnO is an indirect bandgap semiconductor with a bandgap of 0.43 eV at atmospheric pressure, there is no overlap of the density of states near the Fermi energy level, and SnO displays metallic properties at 8 GPa when the material is metalized due to the overlap of the density of states of the O-p, Sn-s, and Sn-p orbitals of SnO at the Fermi energy level, which leads to the closure of the bandgap. In this paper, the Raman spectroscopic and electrical properties of SnO under high-pressure environments have been investigated, enriching the study of the physicochemical properties of this material under extreme conditions. The results of this paper further improve the investigation of the structural and electrical properties of SnO under high pressure, expanding the scope of its research in the field of high pressure, and the results will be helpful for the experimental study of SnO under high-pressure and its application under high pressure.

GC-MS and FTIR analysis, combined with multivariate analysis methods such as PCA and PLS, were used to evaluate and identify moldy tobacco leaves. GC-MS analysis screened 9 markers of tobacco leaf mold, including 2-ethylhexanol. The linear discriminant equation constructed by 9 key compounds, such as 4-hydroxybutyrolactone, can accurately identify tobacco leaf mold, with an initial validation accuracy of 100% and a cross-validation accuracy of 98.7%. FTIR studies have shown that the moldy process of tobacco leaves consumes a large amount of carbohydrates, proteins, and lipids. GC-MS and FTIR, combined with PCA and PLS-DA respectively, can effectively distinguish moldy tobacco leaves.

Aiming at the difficulty of the absolute quantum yield measurement of weak luminescence samples, a Four-Line method is proposed, mainly using the neutral density attenuator to realize the relative enhancement of the sample emission signal. The fluorescence spectrometer and integrating sphere measure the absolute quantum yield. To investigate the feasibility of the Four-Line method, the quantum yields of standard samples rhodamine 6G and quinine sulfate were measured. The results show that the relative errors are 1.3% and 1.1%, respectively, far less than 5.0%, indicating this method has high accuracy. The influence of experimental conditions on quantum yield measured by the Four-Line method is discussed. It is found that the best experimental condition is that the neutral density attenuator is placed at the excitation position, and the attenuated light source intensity reaches the upper limit of the linear response interval of the detector. They used rhodamine 6G solution with different concentrations to simulate weak, medium, strong, and strong luminescence samples. The quantum yields of the solutions were measured by the Two-Line method (conventional method) and the Four-Line method, respectively, and the results were compared with those reported in the literature. The research shows that the Four-Line method is more suitable for the absolute quantum yield measurement of weak and medium luminescence samples, and the relative error is reduced from 10% by the Two-Line method to 0.32%.

The content and ratio of metal elements V and Ni in crude oil play an important role in understanding the characterizing depositional environment and organic matter types of crude oil. However, in the process of crude oil refining, V and Ni, as harmful metal elements in the catalytic cracking process of crude oil, will reduce the activity of the catalyst, and even lead to poisoning of the catalyst and serious corrosion of equipment. Before refining, V and Ni must be removed from the crude oil. In addition, V and Ni are mutagenic and carcinogenic, and it is becoming increasingly important to identify or monitor the potential danger of V and Ni in crude oil entering the environment. Therefore, conducting highly sensitive and accurate determination of V and Ni content in crude oil is of great significance. This paper proposes a new strategy for determining V and Ni in crude oil by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). ICP-MS/MS directly analyzes the crude oil sample after diluting aviation kerosene. For the spectral interferences of V and Ni, in the MS/MS mode, the reaction gas mixture NH3/He/H2 is composed of NH3/He (He is the buffer gas), and H2 is used as the reaction gas. Based on the fact that V+ does not react with NH3/He/H2, but the interfering ions of V+ react with NH3/He/H2, the on-mass method was used to eliminate the interference, and V+ was selected for determination. Based on the mass shift reaction between Ni+ and NH3/He/H2, the interference was eliminated by the mass shift method, and the interference-free cluster ion NiNH33+ was selected for determination. By comparing it with the NH3/He reaction mode, it is found that for the determination of V, adding H2 as a reaction gas can quickly remove the interference of large ions and cooperate with NH3 to eliminate the interference of oxide ions. For the determination of Ni, adding H2 as reaction gas can promote the formation of fully hydrogenated adducts (—NH3), thus increasing the yield of NiNH33+. In the NH3/He/H2 reaction mode, the background equivalent concentration (BEC) of V and Ni is lower, the sensitivity of Ni is higher, and the limit of detection (LOD) of V and Ni is as low as 0.83 and 3.76 ng·kg-1, respectively. The limit of quantification (LOQ) is 2.77 and 12.5 ng·kg-1, respectively. The accuracy and precision of the method were evaluated by analyzing the standard reference material NIST SRM 1634c and the spiked recovery experiment, respectively. The results showed that the method's determination results were consistent with the certified values of the standard reference material. The spiked recoveries of V and Ni were 97.7% and 103%, respectively, and the relative standard deviation (RSD) was 1.8% and 1.9%, respectively. The developed method avoids the complicated sample pretreatment process and has the characteristics of simple and rapid operation, with high sensitivity, accuracy, and good precision. It is suitable for detecting V and Ni in crude oil and can provide a theoretical basis for removing V and Ni in the deep processing of crude oil.

A natural plant named vine (Ampelopsis grossedentata) has been proven to exsert various bioactivities due to its major component of dihydromyricetin (DMY), but there is little information on its hypolipidemic function. In this study, the inhibition behavior of DMY based on pancreatic lipase (PL) assay was investigated by ultraviolet spectroscopy followed by a series of multiple-spectroscopy measurements including fluorescence spectroscopy, synchronous fluorescence spectroscopy and 3D fluorescence spectroscopy as well as the DMY-PL interaction mechanism by molecular docking. The half inhibitory concentration (IC50) of PL detected by UV spectroscopy was 2.6×10-4 mol·L-1, showing its satisfactory lipid-lowering capacity on PL. The calculation of the Lineweaver-Burk equation indicated their interaction type was competitive inhibition with the inhibition constant of 6×10-4 mol·L-1. The Stern-Volmer equation and static quenching double logarithmic formula analyzed the fluorescence spectra. The results suggested that DMY could significantly quench PL's self-luorescence and its fluorescence quenching constant KSV was negatively sensitive to temperature, revealing that the fluorescence quenching process belonged to static quenching. The value of 1 for the binding site and positive relation of Ka to temperature demonstrating PL might combine one DMY to produce a stable complex, which was further evidenced by Kq values exceeding 2.0×1010 L·mol-1·s-1. According to the Van't Hoff equation, the results of thermodynamic parameters S=0.201 4 J·mol-1·K-1, H=32.311 kJ·mol-1 and G<0 under 293 and 310 K elaborated that the binding force was mainly hydrophobic force and a spontaneous and exothermic process. The binding distance r=1.475 nm reflected the possible non-radiative energy transfer from PL to DMY based on the theory of Frster's non-radiative energy transfer. Both synchronous fluorescence spectroscopy and UV spectroscopy results ascertained that the amino acid residue microenvironment and secondary structure of PL changed after the interaction with DMY. The former displayed DMY could bind to the surroundings of tryptophan (Trp) residue in PL by 2 nm red-shifts of the spectrum (=60 nm), while the latter uncovered the →* transition of PL interacted with DMY. 3D fluorescence spectroscopy found the polarity of PL increased after hydrophobic interaction with DMY by 10 nm red-shifts of peak1 causing 51.38% decrease of fluorescence intensity and 5 nm bathochromic shifts of peak2 with 41.93% loss of fluorescence intensity. Moreover, the molecular docking results showed that the DMY binding site was located in the pocket of PL, which was formed by PHE77, PHE215, TYR114, ILE209, and PRO180 amino acids. The hydrogens in amino acids of HIS263, TYR114, SER152, and PHE215 could be linked to —OH of DMY on A ring C5, B ring C4′, C ring C3 and C ring —C＝O via hydrogen bonds, and other amino acids including THR78, LRU213, GLU179 and ALA178 may formed van der Waals forces with DMY. The experimental data obtained to a deeper understanding of the lipid-lowering molecular mechanism of DMY and its unique structure provides a theoretical basis for drug synthesis and screening of natural inhibitors.

In the current research on mixed gas detection, various mathematical algorithms for classifying and predicting data of multiple gas components have emerged. Rapid and accurate gas composition and concentration detection has gradually become a hot topic. However, in some studies, the features of gas data are difficult to capture and judge, and the classification and prediction of gas data exhibit poor accuracy and efficiency due to data bias and unbounded generalization errors. In response to challenges like data bias and unbounded generalization errors, this paper proposes a KNN-SVM algorithm. This algorithm performs secondary classification on ambiguous gas data that is challenging to classify. It combines K-nearest neighbors and Support Vector Machine algorithms to make more comprehensive data feature assessments. The algorithm determines the weights of each algorithm based on experiments, thereby improving the accuracy of discriminating gas categories. The integration of the two algorithms also enhances the efficiency of the overall algorithm, providing stable adaptability to different types of gases. The experimental gas composition consists of cylinders containing C2H2, NO2，and SF6 at concentrations of 12 mg·L-1, NO2, SF6 at 10 mg·L-1, and C2H2 at 5 mg·L-1(all diluted with N2 as a background gas), as well as two bottles of pure N2. The experiment involves mixing these gases and adjusting their ratios to set the required gas concentrations for detection. By detecting individual gases,60 sets of training data are obtained for each of the three gases. Linear fitting of these 60 data sets yields fitted lines for each gas, establishing the relationship between gas concentration and absorption peak. The accuracy of gas detection is confirmed through the adjusted R-squared values for the fitted lines: 0.991 for C2H2, 0.981 for NO2, and 0.987 for SF6. Subsequently, 40 sets of training data are obtained by detecting mixed gases. The KNN-SVM algorithm is then applied to classify and predict mixed gases, and the concentrations of each gas in the mixed gas are inferred from the fitted lines. Comparisons with traditional SVM algorithms using various classification metrics demonstrate the effectiveness and superiority of the proposed algorithm. Experimental results indicate that the KNN-SVM algorithm exhibits outstanding performance in gas classification and prediction, achieving an accuracy of 99.167% and an Area Under the Curve index of 99.375%. This algorithm enhances the accuracy of gas detection and improves generalization capabilities to adapt to diverse gas compositions, providing robust support for real-time gas detection systems.

Identifying different parts of Solanaceae plants is crucial for their product formulation design and quality control. Hyperspectral technology, which can quickly and non-destructively acquire rich information, has become a widely used tool in plant research and monitoring. As important economic crops, Solanaceae plants have great research potential when combined with hyperspectral technology. This study employs hyperspectral technology to classify different parts of Solanaceae plant leaves after initial roasting. Firstly, hyperspectral sampling was conducted on 293 powder samples from different parts of Solanaceae plants using the Field Spec 3 spectroradiometer. Subsequently, data preprocessing was performed using S-G smoothing and first-order and second-order derivatives to enhance information and remove noise. To minimize redundant features, partial least squares (PLS) were then used for data dimensionality reduction. Finally, based on the dimensionality-reduced data, six machine learning classification models-support vector machine (SVM), logistic regression, K-nearest neighbors (KNN), decision tree, random forest, and gradient boosting decision tree—were used for modeling and analysis. The results showed that for the classification task, the SVM model performed best after first-order derivative processing, achieving an accuracy of 100.0% on the training set and 84.7% on the test set. After grid parameter optimization, the optimal parameters were determined: no restriction on maximum depth, a minimum sample split of 4, and 200 estimators. The accuracy of five-fold cross-validation after parameter optimization was 88.1%, with the training set accuracy at 100% and the test set accuracy at 86.4%. The study results indicate that preprocessing methods combined with dimensionality reduction can enhance data information, enabling classification models to capture the characteristics of Solanaceae plant samples better. This study is of great significance for the rapid, accurate, and non-destructive differentiation of parts of Solanaceae plants.

The near-infrared spectrometer based on a digital micromirror device (DMD) has the advantages of good wavelength repeatability, high resolution, and good vibration resistance. It is widely used in the fields of food safety and agricultural production. With the development of micro near-infrared spectrometers based on DMD becoming more and more mature, the cost and performance of the instrument are still the key to research and development. Although most researchers have focused on software development and detection methods, the processing speed of the instrument hardware is also crucial. The spectral analysis can be effectively realized only by ensuring that the spectrometer can collect and transmit data quickly and accurately. In addition, in most studies, the generation and decoding of the Hadamard matrix is usually completed by the host computer, and the template is imported by FLASH storage. However, this method may limit the time efficiency of collecting complete spectral data. A method of high-speed driving DMD and fast data acquisition is proposed to improve the spectral acquisition speed and signal-to-noise ratio. A hardware circuit system is designed based on a DMD miniature near-infrared spectrometer. The system adopts the architecture of Field Programmable Gate Array (FPGA) and ARM and innovatively realizes the generation and decoding process of odd-even Hadamard template at the bottom of the embedded system, which accelerates the speed of spectral analysis and improves the signal-to-noise ratio. By comparing with the DMD spectrometer on the market, the research results show that the spectrometer developed in this paper only takes 214 ms to complete the acquisition time of a single spectrum, which is 4 times higher than that of the commercial DMD spectrometer. In the same 3 s acquisition time, the signal-to-noise ratio of the spectrometer developed in this paper is 4 600, which is 1.5 times higher than that of the commercial DMD spectrometer. Furthermore, the spectral scanning of rapeseed samples was carried out by the spectrometer developed in this paper. The contents of fat, protein, and moisture in rapeseed were analyzed, and the corresponding models were established by partial least squares regression (PLSR). The correction correlation coefficient of rapeseed fat content was 0.986 5, and the prediction correlation coefficient was 0.967 2. The protein content correction correlation coefficient was 0.985 4, and the prediction correlation coefficient was 0.963 6. The correction correlation coefficient of moisture content was 0.987 5, and the prediction correlation coefficient was 0.961 4. The model evaluation results show that the spectrometer can meet the needs of rapeseed component detection and verify that the spectrometer has important application value in the commercial field.

Traditional Chinese medicine is a treasure of Chinese culture and the crystallization of Chinese civilization, playing an important role in the health of the whole nation. Due to the limited growth environment of traditional Chinese medicine and the increasing market demand, numerous substandard and falsified traditional Chinese medicines have emerged in the market, which is extremely detrimental to the development and quality assurance of traditional Chinese medicine. Therefore, it is of utmost importance to detect and identify traditional Chinese medicine. Four medicinal herbs, angelica dahurica, Sappanwood, yam, and sanguinaria officinalis, were selected for classification and detection research to explore traditional Chinese medicine's fluorescent and Raman spectral characteristics. On the one hand, in this paper, we analyzed the possibility of angelica dahurica and sappanwood, traditional Chinese medicinal herbs, to exhibit fluorescence from the perspectives of chemical composition and structure. An F-4600 fluorescence spectrophotometer (200~750 nm) was used to measure the fluorescence spectra of aqueous extracts of angelica dahurica and sappanwood at different excitation wavelengths and concentrations, investigating the fluorescence spectral characteristics of aqueous extracts of angelica dahurica and sappanwood, and discussing the relationship between fluorescence intensity and different concentrations and excitation wavelengths. On the other hand, a confocal micro-Raman spectrometer (100~4 000 cm-1) was used to conduct Raman spectroscopy tests on herbal slices of yam from four different regions and sanguisorba officinalis from two different regions, and then the Raman spectra were obtained. The results showed that the aqueous extract of angelica dahurica exhibited strong fluorescence under excitation wavelengths ranging from 260 to 350 nm. The optimal excitation wavelength is 340 nm, and the peak fluorescence wavelength is 420nm. The fluorescence intensity increased with the concentration increase, following a linear relationship at lower concentrations. The aqueous extract of sappanwood exhibited strong fluorescence under excitation wavelengths ranging from 200 to 290 nm, with the optimal excitation wavelength at 220 nm and the peak fluorescence wavelength at 345nm. With the increase inconcentration, the fluorescence intensity first increased and then decreased, reaching its maximum at 0.175 mg·mL-1. Moreover, the fluorescence intensity of angelica dahurica and sappanwood aqueous extracts followed a Gaussian distribution about the excitation wavelength, followed by the fluorescence law. After analyzing the Raman spectra of yam and sanguisorba officinalis, it was found that the Raman characteristics of different producing areas were the same. The Raman characteristic peaks of yam were mainly concentrated at 477, 862, 939, 1 080, 1 258, 1 337, and 1 457 cm-1. The Raman characteristic peaks of sanguisorba officinalis were mainly concentrated at 862, 1 337 cm-1, consistent with the existing research results on chemical composition. Differences in Raman activity at certain characteristic peaks can be used to distinguish the origin of different traditional Chinese medicines. The research results provide experimental data and method references for applying fluorescence spectroscopy in the identification and quality analysis of traditional Chinese medicine, and they also lay the foundation for the rapid and accurate detection and origin attribution of traditional Chinese medicine by using Raman spectroscopy techniques.

In the modern metallurgical industry, online analysis of converter flue gas is commonly conducted by laser absorption spectroscopy, which can only measure a single gas component by each sensor. Therefore, a combination of multiple sensors is usually needed to monitor multiple gases simultaneously, including CO，CO2，O2，H2, etc. The whole analysis system is complicated and expensive, with a slow response speed. Daily calibration and maintenance are also time-consuming and labor-intensive. This manuscript established a novel Raman spectroscopy-based methodology for real-time and multi-component analysis of converter flue gas to simplify the online analysis system and improve the efficiency of online measurement and the steelmaking process. A multi-gas analysis system for fuel gas measurement was also developed for the first time. The content of five CO, CO2, N2, O2, and H2 components could be quantified simultaneously based on the characteristic peak intensities. With a detection range of 0.1%~100%, the RSD value of our method was ≤0.1%. In the analysis system, the sample gas processing module adopted a high-temperature resistant probe and a high-power suction pump, which can effectively and quickly remove moisture and dust from the sample gas entering the analyzer, ensuring stable testing conditions. The analysis module employed a narrow-linewidth 532 nm laser, a transmission grating spectrometer, and a free space light path design to improve the detection sensitivity for gas analysis. Compared with single-component analysis techniques, the multi-component analysis method proposed in this manuscript could provide quantitative results for five components simultaneously without being affected by gas pressure variation. Sample gas processing and system maintenance were simple, while the detection accuracy was high. When N2 standard gas was measured, the intensity of the characteristic peak was stable, with fluctuation less than 0.48% within 24 hours. The detection error of our method was less than 0.3% when quantifying standard gas samples, meeting the requirements of real-time analysis of converter flue gas. The multi-component gas analysis method by Raman spectroscopy could be widely applied in both the metallurgical industry and other fields by simple model expansion.

An ancient mine site for extracting multicolored pigments was discovered at the Flame Gorge of the SanWei Mountain, Dunhuang, which holds academic value for researching the sources of ancient mineral pigments in northwestern China. To determine the mineral species and spectral characteristics of pigments discovered there, we employed polarized light microscopy, X-ray diffraction, Laser Raman spectroscopy, and X-ray fluorescence spectroscopy to analyzethe pigment production. The results indicate that the mineral pigments produced from SanWei Mountain are natural minerals, primarily Fe2O3 (average 66%) and SiO2, etc. Some of the samples are rich in silica-aluminum, and all of them have different degrees of loss-on-ignition. The main phases are hematite, goethite, and some jarosite. The gangue minerals include quartz, kaolinite, and clay minerals. The color phase of pigment minerals was further analyzed by Raman spectroscopy. The color phase of bright No.2 minerals was consistent with the main phase discussed above, namely jarosite and natrojarosite. All the reddish-brown samples except for No.4 contain -Fe2O3, hematite, which causes the reddish-brown color. Sample No.4 contain sgoethiteand become sreddish-brown due to the presence of hematite, which is associated with yet is much stronger in color than hematite and, therefore, changes the color of sample No.4. In conclusion, the red mineral pigment produced at this ancient minesite is mainly hematite. The yellow mineral pigment is jarosite and natrojarosite. As an excellent pigment, jarosite is used in rock paintings and murals, but it has not been found in the frescoes studied in the Mogao Caves.This discovery has extended the dimension of yellow mineral pigments in northwestern China. It provides basic support for the subsequent pigment flow of the pigments produced in this mining and metallurgical site.

Measurement of the Doppler shift information of the O2(a1g) day glow using the satellite-borne spectral imaging interferometer is currently the state-of-the-art technological means to realize the detection of the atmospheric wind field in the global adjacent space. Observing the Doppler shift of the spectral line O19P18 (7 772.030 cm-1) allows high-precision and high-sensitivity wind speed measurements in the 40～80 km spatial region. However, the specific effect of atmospheric scattering on its detection precision is unknown. This paper aims to quantitatively assess wind measurement errors due to the optical dilution effect. First, the spectral properties of the O2(a1g) spectrum and the atmospheric scattering spectrum are introduced. The contributions of different reaction mechanisms of O2(a1g) were calculated using the latest HITRAN spectral parameters, photochemical reaction rate constants, and NRLMSIS 2020. The volume emission rate (VER) of O2(a1g) was calculated based on the photochemical reaction rate, and the effect of the solar zenith angle on the VER distribution was analyzed. Spectral radiation models of O2(a1g) at different temperatures and self-absorption effects were obtained based on the Einstein coefficient and spectral line intensity, respectively. The effects of different geographical and meteorological factors on the atmospheric scattering spectrum were also analyzed. Secondly, the principles of the measurement technique of the Doppler Asymmetric Spatial Heterodyne spectroscopy (DASH) for limb-viewing are introduced. Describes removing the atmospheric scattering component to produce a pure airglow interferogram. The forward process for acquiring interferometric images is explained based on the DASH instrument concept. Thirdly, the “onion peeling” algorithm was introduced for the retrieval problem. The contribution of the atmosphere above the target layer is eliminated while considering the influence of the self-absorption and optical dilution effects. The problem of extracting target layer information in interferometric images is solved. Finally, the atmospheric wind field detection precision profiles and their changing laws with the influence of geographical and meteorological factors are obtained by error analysis. It is demonstrated that the optical dilution effect reduces the interferogram visibility and increases the measurement noise, adversely affecting the limb-viewing weights and the effective signal-to-noise ratio. In the tangent altitude range of 45~80 km, the wind measurement precision is less affected by atmospheric scattering, with an error of about 2～3 m·s-1. Below 45 km, the wind measurement precision is affected by the optical dilution effect that increases sharply with decreasing altitude and is significantly increased by the surface albedo, aerosol, and cloud. When the effects of all three factors are considered simultaneously, the minimum lower detection limit is about 40 km.

The study of atmospheric temperature fields in the near space is of great scientific significance. According to the study of the radiation mechanism and distribution characteristics of the full-time atmospheric temperature tracer, oxygen, as one of the main components of the atmosphere, is widely distributed in the near space with obvious radiation intensity, so oxygen detection is an important basis for the study of temperature change in the near space. When the short-wave infrared part of solar radiation passes through the atmosphere, it is absorbed by oxygen molecules to obtain the oxygen absorption spectral line, which carries important information such as oxygen content. The oxygen spectral form obtained by the detector is used for high-precision inversion to obtain the temperature and other information. Due to the effects of solar zenith Angle, azimuth Angle, edge cutting height, and aerosol parameters on oxygen during the detection process, this paper analyzes oxygen sensitivity to different parameters based on the SCIATRAN model. Based on the SCIATRAN model, this paper will study the impact of environmental factors in the near space on oxygen detection and analyze and calculate the sensitivity of oxygen to the changes in radiation brightness caused by detection methods and environmental factors. The results show that the radiation brightness of oxygen increases with the zenith Angle below 60°, but the change is opposite when the zenith Angle exceeds 60°. The influence of solar zenith Angle on the radiation brightness of oxygen in one day is less than 0.01 W/m2/nm/sr, and the difference is less than 7%. Oxygen radiance decreases with the increase of azimuth Angle; the oxygen radiation brightness decreases as the tangent height increases, and the magnitude of the decrease gradually decreases. The difference ratio of aerosol type to oxygen radiation brightness is less than 10%. The influence of aerosol season on oxygen radiation intensity is greater in spring and summer than in autumn and winter. Under different aerosol types, the difference ratio of aerosol optical thickness multiples on the oxygen radiation brightness is less than 30%. The optical thickness multiples are proportional to the radiation brightness under rural and Marine aerosols. Still, the opposite is true under urban aerosols. The study also compares the simulation data with the measured data to verify the validity and feasibility of the simulation data of the SCIATRAN model. The research results of this paper provide a theoretical basis for oxygen detection in near space and a reference for the inversion of near space and other related research fields.

Clay minerals are essential components of clay rocks and soils, forming the primary constituents of various terrestrial surface coverings. Studying their structural attributes, particle dimensions, and moisture content variations is crucial for understanding environmental dynamics in clay mineral-rich regions and guiding mineral industry applications. Due to diverse geological applications and practical requirements, traditional mineral characterization methods often have limited applicability for mining minerals with distinct physical properties. Terahertz spectroscopy technology, a novel non-contact coherent testing method, utilizes fingerprint spectra, wide spectra, and water sensitivity within this frequency band. This technology enables the non-destructive detection of clay minerals, providing optical information to differentiate their crystal structure and composition. This article primarily focuses on using terahertz time-domain spectroscopy, thermogravimetric analysis, and Fourier transform infrared spectroscopy to study the thermal decomposition characteristics of clay minerals. The composition, particle size, and calcination products of kaolin, a vital raw material significantly influence the quality of ceramics. Experimental results confirm substantial variations in the absorption coefficient and refractive index of different states of kaolin within the terahertz frequency range. Differences in the crystal structure of talc and vermiculite, belonging to the 2∶1 type layer silicate, result in significant disparities in their terahertz spectra, effectively indicating thermal decomposition byproducts and moisture content in conventional electrically neutral hydrous minerals. Contrary to conventional understanding, vermiculite exhibits peaks at 1.10 THz without chemical interventions. The appearance of this can facilitate substance characterization and advance optical devices while enhancing the understanding of minerals in terahertz spectroscopy. This offers a fresh research perspective for the interdisciplinary investigation of terahertz spectroscopy and geology.

Raman spectra, infrared spectra, chemical composition analysis, and UV-Vis NIR spectra analysis were carried out for new turquoise imitations in gem wholesale markets. The main spectral characteristics were summarized. The results show that the imitations are mainly composed of a single component or multiple mixed components of barite, gibbsite, and calcite powder, which are pressed, cemented by epoxy resin or acrylate, and dyed. The infrared spectra results are as follows: The imitations of a single mineral component are significantly different from turquoise. The imitation of barite shows a 1 078 cm-1 absorption peak caused by［SO4］2- asymmetric stretching vibration and the imitation of calcite shows a 1 476± cm-1 absorption peak caused by［CO3］2- symmetric stretching vibration. The multi-component imitations can be seen to integrate the absorption spectra of various mineral characteristics, the spectral characteristics are complex. According to the missing water peak, or only showing the absorption peaks of 3 623, 3 528, 3 472, and 3 388 cm-1 caused by the stretching vibration of OH- of gibbsite, and there are［SO4］2- and［CO3］2- groups correlated absorption peaks in the fingerprint region, we can roughly distinguish the imitations from turquoises. The micro-Raman spectra show that the mineral compositions of the multi-component imitations are different at different test locations, and the characteristic peaks of gibbsite (3 360，3 432，3 522，3 614 cm-1), barite (989 cm-1）and calcite (1 088 cm-1）can be measured respectively. Combined with the results of micro-Raman spectra and chemical analysis, the specific mineral composition of the imitation product can be further confirmed. The UV-Vis-NIR spectra show that the samples are dyed and showed 633± nm, 713± nm or 610± nm, and 675± nm broad absorption peaks.

Dissolved organic matter (DOM) and microbial communities are closely related, DOM provides nutrients to microorganisms, and microbial metabolism can transform DOM. Their interactions affect the material cycle and energy flow of the ecosystem. In this study, surface (0~15 cm) sediment samples were collected from natural wetlands (river wetland, HL; lake wetland, HP) and artificial wetlands (paddy field, ST; fish pond, YT) in northern cold regions of China. Three-dimensional fluorescence spectroscopy and high-through put sequencing techniques were employed to reveal the DOM fluorescence spectra and fungal community characteristics of the different types of wetland surface sediment, and their correlation was further analyzed. The results indicated that four kinds of fluorescent components were identified from the surface sediment DOM fluorescence spectrum, including fulvic-like acid component(C1), humic-like acid component (C2), and protein-like component［tryptophan-like component (C3) and tyrosine-like component (C4)］. C1 and C2 were significantly positively correlated, while no significant correlations existed among them and the protein-like components. The relative concentration of C1 was higher than that of C2, while the relative concentrations of C3 and C4 were close.The concentration of DOM in YT was relatively higher than that in the other samples. At the same time, there were significant differences in the relative concentration of C2 and protein-like components in HL and HP and significant differences in the relative concentrations of C1, C2, and C3 in ST and YT. The dominant species of fungal communities in wetland surface sediment (excluding unclassified groups) were Ascomycota, Basidiomycota, and Rozellomycota. The Chao1 richness index and Shannon diversity index of fungal communities in YT were significantly lower, while the Simpson dominance index was significantly higher. The Shannon diversity index in HP was significantly higher than the other samples.C1 and C2 were significantly correlated with fungal community diversity, C2 was significantly correlated with fungal community composition, while protein-like components had no significant correlation with fungal community. C1 and C2 were significantly positively correlated with Ascomycota, C1 was significantly negatively correlated with Chyridiomycota and Monoblepharomycota, and C2 was significantly negatively correlated with Basidiomycota, Rozellomycota, Chytridiomycota, and Monoblepharomycota. Therefore, fungal communities significantly impacted the relative concentration of fluorescent components with relatively large molecular weights of DOM. This study analyzed the DOM fluorescence spectral characteristics and its correlation with fungal communities in the surface sediment of typical wetlands in the cold region of northern China, providing fundamental data for wetland environmental monitoring and evaluation and theoretical references for the rational utilization of wetlands.

This study aimed to effectively monitor the changes in soil properties after Spartina alterniflora invasion on coastal wetland ecosystems. The study area is a typical Spartina alterniflora wetland in the Yancheng Wetland Rare Birds National Nature Reserve of Jiangsu Province. A total of 15 sites were identified by a stratified-random sampling method, and 45 soil samples were collected from three depth intervals (0~30, 30～60, and 60~100 cm). The visible-near infrared spectral reflectance and 10 soil physicochemical properties were measured. The performance of partial least squares regression (PLSR) and random forest (RF) was studied, spectral transformation forms' influence on prediction accuracy was analyzed, and the potential of invasion years and soil depth as auxiliary predictors were discussed. The results show that: (1) the visible-near infrared spectral reflectance can be used to predict organic carbon, inorganic carbon, total nitrogen, water content, pH, bulk density, salinity, and clay contents in soils with reasonable accuracy; (2) the method of partial least squares generally outperform random forest algorithm, the R2 of prediction models developed using the PLSR method was between 0.341 and 0.979, and the biggest R2 of random forest models was 0.722; (3) Differential transformation and reciprocal transformation of spectral reflectance can substantially improve the model performance. The optimal prediction model of full nitrogen can be obtained based on the original spectra (R2 is 0.769 and RMSE is 0.091 g·kg-1). In contrast, the optimal models for other soil properties are mostly based on differential or reciprocal transformation of the original spectra. (4) In general, the model performance can be improved by adding variables of invasion years and soil depth, and the prediction accuracy of organic carbon, total nitrogen, salinity, pH and bulk density models are more sensitive to the two variables. The prediction model accuracy (R2) for estimating soil organic carbonincreased from 0.794 to 0.806, the accuracy (R2) of the pH model increased from 0.838 to 0.884, and the accuracy (R2) of the salt optimal model increased from 0.978 to 0.997. To sum up, visible-near infrared spectroscopy can be applied to predict key soil physicochemical properties in Spartina alterniflora wetlands, and soil change monitoring of invaded Spartina alterniflora wetlands can be achieved through appropriate spectral transformation, and variable selection and model selection.

Soil organic matter (SOM) content is a key index of soil quality and plays an important role in the global carbon cycle system. Rapid and accurate estimation and spatial mapping of SOM content are significant for soil carbon pool estimation, crop growth monitoring, cultivated land planning, and management. It is time-consuming and difficult to use traditional methods to monitor regional SOM content, and it is a reasonable and effective method to establish an SOM estimation model based on hyperspectral remote sensing images. However, the SOM content estimation model for hyperspectral remote sensing images has some problems, such as spectral data redundancy, low feature extraction accuracy, and weak generalization ability of a small sample model. In this paper, a total of 67 soil samples were collected in Huangzhong County, Qinghai Province. The ZY1-02D hyperspectral remote sensing image was obtained and preprocessed to obtain pixel spectral data of the sample points. The fractional-order differential transform (FOD) method explored the sensitive bands with a response relationship with SOM content. With 0.2 as a step, the correlation threshold method was used to compare and analyze different order differential processing data mining capabilities. The stable competitive adaptive reweighted sampling algorithm (sCARS) removes hyperspectral redundant data to obtain the modeling feature bands. Random forest (RF), extreme gradient lifting tree, extreme learning machine, and ridge regression machine learning are selected as modeling algorithms. The SOM estimation model is constructed using the spectral data of the full band and the characteristic band as input variables. The results show that the FOD transform can greatly improve the correlation between the band and the SOM content compared with the integer order, and more subtle spectral bands with a response relationship with SOM content can be mined. The 0.8th-order differential transform has the best effect, and the maximum correlation coefficient is increased by 0.546. Compared with full-band spectral data, the estimation accuracy of the model constructed with the sCARS feature extraction method is greatly improved, indicating that sCARS can effectively improve the quality of modeling data and the model's prediction accuracy. In the modeling algorithm, RF performance is the best, with Rp2 (determination coefficient) reaching 0.766 and RPD reaching 1.86, which is about 7.58% higher than the Rp2 of the full-band modeling result. Regional SOM content estimation mapping was realized based on FOD-sCARS and RF. This study further verifies that space-borne hyperspectral remote sensing images are a reliable way to achieve regional SOM estimation mapping, and the research results can provide a new idea for estimating regional SOM content and provide data support for mapping spatial distribution map of SOM content using space-borne hyperspectral remote sensing images.

The commonly used evaluation indexes of multivariate models lack the ability to evaluate many important predictive performance indicators of near-infrared quantitative analysis software. This has become a pain point in evaluating the predictive performance of near-infrared instrument selection and the applicability of models in practical near-infrared analysis applications. Therefore, this study aims to develop an evaluation method for the predictive performance of near-infrared quantitative analysis software. 192 national VI gasoline samples, including 92#, 95#, and 98#, were collected for determination of olefin concentration of gasoline using near-infrared spectroscopy; their near-infrared spectra collected and olefin concentrations were measured as a reference value according to GB/T 30519—2014, and two different multivariate software(one is partial least squares (PLS) modeling software, and the other is non-PLS software) were used to study. It has been found that compared to the reference value, the PLS model has a positive bias in predicting low-concentration samples and a negative bias in predicting high-concentration samples, which is known as the phenomenon of “averaging”. The commonly used performance evaluation indicators for model prediction cannot yet evaluate the degree of the averaging, nor can evaluate (1) the proportion of samples with deviation from the reference value greater than the limit value for the reproducibility of the reference method and (2) the model's generalization ability. In this paper, four new evaluation indicators are proposed to address the above issues, including Averaging Index (AE), Ratio of samples with prediction bias exceeding the limit value (Ratio), Deviation of Abnormal Sample (DAS), and Deviation of Isolated Sample (DIS). The comprehensive use of commonly used evaluation indicators and new ones (12 items) has practical significance in evaluating the predictive performance of near-infrared quantitative analysis software for instrument selection and the applicability of models in practical near-infrared analysis applications. It also has reference significance for academic research in near-infrared analysis.

Many elements' most sensitive spectral lines are mainly distributed in the ultraviolet band. Some important elements commonly used as detection targets, such as C, P, and S, even have spectral lines in the vacuum ultraviolet range below 200 nm. However, radiation in the vacuum ultraviolet band is easily absorbed by air and cannot be transmitted through air. Therefore, to detect the spectral lines in the ultraviolet range, a short optical path, and a streamlined Rowland circle optical system are commonly used as the dispersion system of the spectrometer. During the detection and analysis, it is also necessary to establish a relative vacuum environment inside the equipment cavity to reduce the weakening effect of the experimental environment on the intensity of the spectral lines in the vacuum ultraviolet range. However, even under these conditions, the spectral sensitivity of elements P and S in the vacuum ultraviolet range remains relatively weak. This study designed a cylindrical mirror focusing system at 30 mm in front of the image plane to achieve high sensitivity within the vacuum ultraviolet range. The system utilizes a cylindrical mirror with a material of CaF2, a central thickness of 5mm, and a sagittal radius of curvature of 22.06 mm as the core component. The optical system design was simulated and optimized using the simulation software Zemax. Furthermore, the simulated radiance results under non-sequential mode revealed that the cylindrical mirror focusing system significantly improved the imaging sensitivity for various wavelengths with minimal impact on the resolution. In the final experiment, a medium-low alloy steel detection sample was used, and the various spectral lines of elements in the ultraviolet range were excited using an electrical spark excitation light source to verify the spectrometer's performance. Mathematical models were established for the peak intensity of each element's spectrum and its actual content through linear fitting, reference fitting, and quadratic fitting, with fitting goodness offit (R2) values above 0.99. The cylindrical mirror focusing system significantly improved the sensitivity of the equipment in the vacuum ultraviolet range, with the response increased by more than 1 fold. The experimental results demonstrate that the design approach of the cylindrical mirror focusing system has a certain reference value for enhancing the sensitivity of the Rowland circle dispersion system.

When air is entrained into the engine lubricating oil system to form an oil and gas two-phase flow, it will seriously affect the normal operation of the lubrication system. Therefore, it is very important to realize the accurate and rapid measurement of the void fraction of the oil-gas two-phase flow in the engine lubricating oil system. This paper determines the void fraction in the engine lubrication system at the elbow based on the spectral matching method. Firstly, the absorption data of oil-gas two-phase flow were obtained at two flow rates and five temperature conditions, covering a gas content range of 0.10% to 1.00% (with an interval of 0.06%). This was accomplished by utilizing near-infrared, visible, and ultraviolet spectrophotometers, followed by rigorous analysis. It was ascertained that the oil-gas two-phase flow demonstrates absorption across all three wavelengths, with the intensity of absorption being correlated to the gas content. Secondly, the data preprocessing method combined with spectral similarity measure is proposed and applied to the gas content spectral analysis of bent pipe, significantly reducing the maximum relative error of gas content prediction based on the original spectrum. Using data enhancement methods such as center and autoscaling combined with Spectral Angle Cosine methods in the near-infrared spectrum, the maximum relative error of the gas content of the new lubricating oil was reduced from 48% to 36% relative to the original spectrum. The method predicts the void fraction of gas-oil two-phase flow in three bands respectively. The experimental conditions include two flow rates and five temperatures, and the influence of the temperature and flow rate of the two-phase flow on the void fraction prediction is analyzed. At the temperature of 30.0 ℃ and the flow rate of 5.1 m·min-1, the gas content information in the ultraviolet band (193.5～413.8 nm) is more closely related to the spectral feature of the direction or shape difference of the spectral vector and the maximum relative error of the gas content prediction is only 6%. In the near-infrared and visible bands, the maximum relative error decreases with the increase of temperature or velocity when the flow rate or temperature is constant. There is no specific effect of temperature on the prediction of gas content in the ultraviolet band. With the increase of the two-phase flow rate, the maximum relative error of gas content prediction tends to increase. The results show that for new lubricating oil with good light transmittance, the gas content data is collected by the ultraviolet spectrometer, and the maximum relative error of gas content prediction is minimum by using standardized pretreatment combined with spectral Angle cosine method.

In addressing the issue of rapid loss of sulfurin the lubricating oil in high-speed train traction system gearboxes, it is imperative to monitor the sulfur content in in-service gear oil to determine the optimal oil change timing. The attenuation mechanism of the characteristic absorption peaks of sulfur-containing additives in the gear oil infrared spectra was analyzed. The peak height of the 1 164 cm-1 characteristic peak with the highest correlation to sulfur content was selected as the input variable for setting a univariate linear regression (LR) model. The optimal number of principal components was determined using the tesrset R2 after 5-fold cross-validation, and the partial least squares regression (PLSR) model was constructed. Finally, six characteristic absorption peaks were selected based on the partial least squares regression coefficients, and the reduced feature PLSR (RF-PLSR) model was established after the optimization of the characteristic peaks. The proposed models predicted 39 oil samples in the prediction set. The results indicated that both the univariate linear regression and partial least squares regression models exhibited good predictive capabilities. The predicted R2 of the univariate linear regression model was 0.961, the RMSE was 973.1, and the RPD was 5.084. The predicted R2 of the PLSR model and the characteristic peak-optimized RF-PLSR model were 0.997 and 0.994, the RMSE was 250.1 and 376.3, and the RPD was 19.780 and 13.149, respectively. This indicated that the RF-PLSR model built after characteristic peak optimization could still maintain high accuracy and prediction ability while reducing model complexity, and the model is more interpretable because the information of irrelevant variables was eliminated. Thus, infrared spectroscopy technology offers a reliable and precise method for detecting sulfur content in gear oil, providing a feasible solution for monitoring the condition of gear oil.

Paper cultural relics are important for heritage transmission as they record human history and humanities in different periods. However, they are highly susceptible to microorganisms such as mold during preservation. Mold can accelerate the degradation of cellulose, generating mold on the surface of paper. Scattered spores can spread widely with airflow, increasing the risk of mold on other paper cultural relics. Regular mold spot detection is crucial for understanding paper artifacts' status and restoration. Hyperspectral imaging technology is a non-contact and non-destructive detection method that simultaneously obtains spatial and spectral data. This technology can be combined with computer technology to enable large batches of real-time, non-destructive testing of paper cultural relics. This paper proposes a method for reducing the dimensionality of hyperspectral data for Aspergillus niger, a commonly occurring mold. The method is based on the attention mechanism and allows for adaptive preprocessing of hyperspectral redundant data. This paper reports on the collection of 20 samples of Aspergillus niger, mold spots on paper artifacts provided by the Chongqing China Three Gorges Museum. The average spectral curves of the infected and healthy areas are analyzed using ENVI software in the 413~855 and 855~1 021nm bands. The results showed a significant difference in average reflectance between the two areas. The paper compares the proposed method with traditional principal component analysis and independent component analysis preprocessing methods for processing original hyperspectral data. The results are then experimented on four semantic segmentation networks: classical U-Net, SegNet, DeepLabV3+, and PSPNet. The experimental results demonstrate that the preprocessed data produced by the algorithm presented in this paper exhibit significant advantages over the classical U-Net and SegNet networks. Furthermore, compared to the principal component analysis method and independent component analysis method, the accuracy of mold spot identification has improved significantly by 89.49% and 88.46%, respectively. These results confirm the effectiveness of the proposed algorithm and provide valuable support and new ideas for the field of cultural relics protection.

Determining cold resistance physiological indicators is an important way to evaluate the cold resistance of tea plants. Traditionally, methods of evaluating the cold tolerance of tea trees are mainly through the determination of physicochemical parameters of tea trees under low-temperature stress. However, these methods are not only time-consuming and labor-intensive but also destructive. This study established a prediction model for REC, SPAD, and MDA of tea tree cold resistance based on multispectral and hyperspectral imaging techniques. Firstly, multispectral and hyperspectral images of 32 breeding materials under low-temperature stress were collected, and the REC, SPAD, MDA, SP, and SS contents of the corresponding tea tree leaves were determined. Secondly, the hyperspectral image data among them were spectrally pre-processed using five methods, namely, MSC, SNV, S-G, 1-D, and 2-D, and the characteristic bands were screened using two methods, UVE and SPA. Finally, the REC, SPAD, and MDA prediction models of tea tree cold resistance were established using SVM, RF, and PLS algorithms for multispectral and hyperspectral data. The results showed that (1) the spectral curves were more stable, the peaks and valleys were more prominent, and the accuracy and reliability of the models were higher after the joint preprocessing of MSC, SNV, S-G, 1-D and 2-D; (2) the UVE algorithm screened the largest number of characteristic bands, while the SPA algorithm screened the smallest number of characteristic bands, which was more suitable for establishing regression models with hyperspectral data; (3) The RF model has the highest accuracy in predicting leaf REC (Rp=0.735 2，RMSEP=0.077 1), SPAD (Rp=0.502 9，RMSEP=6.681 8), and MDA (Rp=0.784 6，RMSEP=8.885 3) content under multispectral imaging techniques; the SPA-SVM model has the highest accuracy in predicting leaf SPAD (Rp=0.734 9，RMSEP=4.154 6) and MDA (Rp=0.685 8，RMSEP=8.548 8) under hyperspectral imaging techniques, and the SPA-PLS model has the highest accuracy in predicting REC (Rp=0.629 8，RMSEP=0.066 9). Therefore, the REC, SPAD, and MDA prediction models based on multispectral and hyperspectral imaging and machine learning algorithms provide an accurate, non-destructive, and efficient method, which is of great significance for evaluating tea tree cold resistance.

A maize pot experiment with different copper stress gradients was designed in an outdoor greenhouse to explore the sensitive leaf types and spectral ranges of crop pollution response under heavy metal stress. Taking maize leaves as the research object, the hyperspectral reflectance data and heavy metal content data of maize leaves during the heading period were measured using instruments, providing basic data for research. This paper designed the Leaf Spectral Detection Method (LSDM) from the frequency domain perspective, combined with time-frequency analysis, to obtain sensitive leaf shapes and spectral bands under heavy metal copper stress, providing technical support for heavy metal monitoring in crops. Based on the growth process of maize, this study explores the full spectrum and sub-spectrum of the old leaf (O), middle leaf (M), and new leaf (N) spectra from 350 to 1 300 nm. Firstly, the hyperspectral reflectance data of maize leaves under copper stress were subjected to double differentiation (SOD) and envelope removal (CR) and transformed into the frequency domain. The Daubechies wavelet 6-layer decomposition was performed using time-frequency analysis methods. Then, based on the signal anomaly points, wavelet high-value points, and SODCR curve high-value points, the spectral anomaly parameters SAP (Spectral Anomaly Parameters) of maize leaves are defined, namely: Abnormal Changes Reflectivity (ACR), which is the absolute value of the difference between the abnormal reflectance and the reflectance of the next adjacent band; Abnormal Wavelet Coefficients (AWC), which is the absolute value of the difference between the abnormal wavelet coefficients and the wavelet coefficients of the next adjacent band; Abnormal SODCR value (ASR), which is the absolute value of the difference between the abnormal point SODCR value and the SODCR value of the next adjacent band. Finally, by examining the correlation between spectral anomaly parameters and heavy metal content in maize leaves, we aim to explore the leaf types and spectral segments sensitive to copper pollution. The results showed that the leaf spectral detection method LSDM can efficiently enhance weak information in maize leaves and accurately locate the spectral anomaly caused by heavy metal copper stress, with the anomaly range concentrated within 350 to 800 nm; Spectral anomaly parameters can quantitatively measure the spectral anomalies of maize leaves under heavy metal copper stress; Under different copper stress gradients, maize new leaves (N) exhibit sensitive leaf types, with sensitive spectral segments including blue edges, green peaks, yellow edges, and red valleys. This paper can provide technical support for monitoring heavy metals in other cereal crops and their canopy scales.

In recent years, some scholars have applied the fractional order differential theory to the hyperspectral inversion of the conductivity of saline soils and achieved more significant results. However, the Grnwald-Letnikov fractional-order differential definition form has been used in most of the existing studies. The application of the Riemann-Liouville and Caputo fractional-order differential definition form has been less studied. The applicability of the Riemann-Liouville and Caputo fractional-order differential definition form to the hyperspectral inversion of saline soil conductivity is still unclear. In this study, based on the measured soil conductivity and hyperspectral data, we consider the common Grnwald-Letnikov, Riemann-Liouville, and Caputo fractional-order differential definitions and realize the Grnwald-Letnikov, Riemann-Liouville and Caputo fractional-order differential processing functions through software programming. The differences in the hyperspectral data of the soil samples in different fractional-order differential definitions are compared and analyzed after the same-order differential processing, and the characteristics of the changes with the increase of the order. The results show that the spectral reflectance curves of soil samples under different fractional-order differential definitions show significant differences after the same-order differential treatment; in the range of 0.1~1 order, the number of highly variable bands of Grnwald-Letnikov, Riemann-Liouville, and Caputo after fractional-order differential treatment shows an increasing tendency as the number of differential orders increases; When the differential order tends to 1, the differential value of spectral reflectance gradually decreases and approaches 0, and the fluctuation range gradually decreases, while the variability of the spectral data is enhanced with the decrease of the fluctuation range; Grunwald-Letnikov fractional differential processing increased the correlation coefficients by 9.5% and 6.7% at the 0.6 and 0.7 orders; after Riemann-Liouville and Caputo fractional differential processing, the correlation coefficients increased by about 1% at the 0.8~0.9 orders and 0.7~0.9 orders respectively. This study provides a new research idea for hyperspectral data preprocessing and a better reference for applying fractional differential theory to soil salinization remote sensing inversion.

Spectroscopy and molecular simulation technologies investigated The interaction mechanism between ellagic acid (EA) and urolithin A~D (UA~D) with HSA, which helped explore its pharmacotoxicity and efficacy. The results indicated that EA and UA~D could bind with HSA at a molar ratio of 1∶1 and quench its fluorescence via a static mechanism. The binding of UA and UC with HSA was exothermic and driven by hydrogen bonding and van der Waals force. The binding of EA and UD with HSA was endothermic and driven by hydrophobic interaction. The three-dimensional fluorescence spectrum analysis exhibited that the addition of EA and UC, UD, UA, and UB enhanced the hydrophilicity and hydrophobicity of tyrosine and tryptophan microenvironments of HSA, respectively. The molecular simulation analysis showed that EA and UA~D formed hydrogen bonds with active amino acid residues Lys436, Asp187, Lys432, Arg485, Leu430, Leu4, Ile388 and Tyr411, and formed hydrophobic interaction with active amino acid residues Ala191, Val456, Lys199 and Trp214, which proved that they were majorly bound to HSA by hydrogen bonds and van der Waalsforce, and then screened glycation sites and inhibited HSA glycation. This study could provide a theoretical basis for developing EA and UA~D as non-enzyme glycation inhibitors to treat diabetic complications.

The freshness of chilled mutton is influenced by various factors and can be comprehensively evaluated through multiple physical, chemical, and microbiological indicators. Traditional testing methods are complex and inefficient. Hyperspectral imaging technology, as a rapid and non-destructive detection technique, can effectively detect the changes in different components during the freshness variation of chilled mutton. To study the feasibility of using hyperspectral imaging technology for the multi-indicator evaluation of chilled mutton freshness, this paper proposes an improved artificial neural network (ANN) algorithm that enhances the correlation between labels by redefining the loss function and fully utilizes multiple freshness indicators to classify the freshness of chilled mutton. Experimental high-spectral images were collected for chilled mutton samples from 0 to 14 days in the 400 to 1 000 nm range. Laboratory methods were used to determine the values of total volatile basic nitrogen (TVB-N), pH value, total aerobic count (TAC), and an approximate number of coliforms (ANC) indicators. The original spectral data of chilled mutton samples were preprocessed using the S-G smoothing filter and multivariate scatter correction. The continuous projection algorithm (SPA) was used to select 18 feature bands of the spectral data as input data, and the proposed improved ANN algorithm was employed to establish a multi-indicator chilled mutton freshness grading model. The results showed that the improved ANN achieved a classification accuracy of 96% on the test set. The recognition rates for the three freshness levels of the samples were 100%, 89.28%, and 98.68%, respectively. The model was evaluated using four multi-label model evaluation metrics: Hamming loss, one-error, ranking loss, and coverage. The corresponding evaluation scores were 0.008, 0.002, 0.002 5, and 4.048, respectively. The accuracy and various model evaluation metrics of the improved ANN classification model were superior to those of traditional ANN, demonstrating the feasibility of using the improved ANN for non-destructive detection of multi-indicator chilled mutton freshness.