Approximately 90% of the worlds energy supply today is generated through combustion. In a combustion field, the flames temperature affects the pathways and concentrations of various component chain reactions.Obtaining temperature information from the combustion field can provide important support for improving the fuel combustion efficiency and the design of combustion devices. With the continuous development of Chinas high-end manufacturing industries, such as aviation, aerospace, and navigation, the research and development of various large gas turbines and supersonic engines has entered an accelerated ph. Flames generated by such combustion devices have characteristics such as high temperature, high pressure, turbulent supersonic flow, and short duration. The traditional contact temperature measurement method makes it difficult to measure the temperature of this kind of turbulent combustion field. In recent years, the non-contact temperature measurement technologies, represented by laser spectroscopy, have gradually matured and have been widely applied. Femtosecond coherent anti-Stokes Raman scattering spectroscopy temperature measurement technology combined with an ultrashort pulse has been applied to the temperature diagnosis of various high temperature,turbulence and other complex combustion scenarios due to its advantages of high time resolution(thousands of temperature measurement data per second), high-temperature measurement accuracy and high-temperature measurement sensitivity. This article outlines the basic principles of femtosecond CARS temperature measurement technology and summarizes the research progress of femtosecond coherent anti-Stokes Raman scattering temperature measurement technology in steady-state flames, heated gases, simulated gas turbines, and other combustion environments. The research and application of femtosecond time-resolved CARS spectroscopy are briefly introduced. The article focuses on introducing the basic principles and application progress of femtosecond chirp probe pulse CARS temperature measurement technology and hybrid femtosecond/picosecond CARS temperature measurement technology, which can millisecond-level instantaneous temperature. The advantages and disadvantages of the three temperature measurement techniques are pointed out, and the technical problems that need to be paid attention to and solved in the femtosecond CARS temperature measurement technology are proposed. It is mainly to achieve high-precision CARS spectrum high-fidelity inversion temperature information in extreme conditions of high-temperature and high-pressure turbulent combustion fields and how to obtain high signal-to-noise ratio CARS signals in extreme conditions of high-temperature and high-pressure turbulent combustion fields. The future development trend is expected to use high temporal resolution measurement to obtain the instantaneous evolution information database for various complex flames to support researching various engine mechanisms.

Metamaterials have electromagnetic properties not found in natural materials, and terahertz (THz) metamaterial functional devices have wide application prospects in biomolecular detection, medical imaging, and security inspection. In this paper, we used CiteSpace software to visually analyze references on THz metamaterial functional devices on the Web of Science from 2016—2023 to review the research progress and hot areas and predict their development prospects to provide a reference for staff engaged in related research. A total of 2159 closely related literature were retrieved for this analysis. The papers are analyzed and discussed separately in terms of the published literatures country, institution, author contribution, citation number, and keyword clustering. The research areas of THz functional devices in recent years are obtained, mainly including absorbers, filters, electromagnetically induced transparency, modulators, asymmetric transmission, wavefront modulation, coding metasurfaces, machine learning, biosensing, quantum entangled metasurfaces, etc. In the field of THz metamaterial functional devices, China publishes more than 50% of all articles, and three countries, the UK, China, and the USA, had the highest impact. By analyzing the collaboration network of research institutions, it is found that the top ten research institutions in terms of the number of publications are all Chinese institutions, among which Tianjin University occupies the first place regarding the number of publications. Through the co-occurrence analysis and co-citation analysis of the papers authors, the authors number of publications and influence can be counted respectively. To further study the research hotspots of THz functional devices, we conducted a cluster analysis of keywords and obtained 11 clusters, summarizing 5 key research areas. Finally, a pie chart of the distribution of research fields is given, and the development status of each field is analyzed in the form of a knowledge tree. The fields of absorbers, filters, modulators, and electromagnetically induced transparency have been studied maturely, the fields of biosensing, asymmetric transmission, and antennas are in the development stage and gradually matured, and machine learning and quantum entangled metasurfaces will become hot research topics in the future.

The clustered regularly interspaced short palindromic repeats (CRISPR and CRISPR-associated system,CRISPR-Cas system) is an acquired immune system found in most bacteria and archaea,consisting of Cas enzyme and guide RNA (guide RNA,gRNA),which recognizes and shears target DNA or RNA based on the specificity of gRNA sequence. In recent years,thanks to the excellent enzymatic activity of the CRISPR-Cas system,a variety of biosensing technologies (biosensor) have been established,and optical signal sensing strategies are simple,portable,and widely used in scientific research and practical applications. We summarized in detail the basic principles and representative results and applications of various optical sensing strategies based on the CRISPR-Cas system in the last five years. Also,we provide an outlook on the current application prospects and challenges.

With the new technology of large area high temperature fixed point, a 14 mm diameter WC-C high temperature fixed point blackbody source was established at NIM. National primary scales of spectral irradiance in the wavelength range from 250 to 2 500 nm, correlated color temperature and distribution temperature were realized at 3 020.11 K. This is the first time that the large area high temperature fixed point blackbody technology has been successfully applied to the field of spectral radiometry. A selective multiple fit methods for calculating the point of inflection temperature of fixed pointmelting temperature plateau was proposed to solve the fluctuation problems of large diameter fixed point blackbody temperature plateau curve. The Akima fitting method was used to restore the missing part of the melting plateau curve. Using a double grating spectral comparison measurement system, a halogen tungsten lamps realized and preserved the spectral irradiance scale. Measurement uncertainty of the temperature of the blackbody was reduced to 0.36 K using the new method, and the traceability chain was shortened. The best measurement uncertainty of spectral irradiance is 0.25% (k=2). Spectral irradiance was realized respectively based on fixed point and variable temperature blackbody, and the average divergence between the two methods is 0.42% from 250 to 2 500 nm. By combining the two methods based on fixed-point blackbody and variable temperature blackbody, the expanded uncertainty of spectral irradiance national primary scale was realized as 1.9% at 250 nm, 0.43 % at 400 nm, 0.25% at 1 000 nm, 0.76% at 2 200 nm, and 2.4% at 2 500 nm respectively. The spectral irradiance scale realization technology based on fixed point blackbody has been applied to improve the calibration accuracy of the remote sensing spectroradiometers.

Due to the short operation time (~hundreds of milliseconds to seconds) and high laser energy requirement (>1 mJ) of the large-scale scramjet model engine, the conventional ultraviolet laser system cannot meet the fine measurement requirements of the combustion flow-field. The ultraviolet laser system used for high-speed planar laser-induced fluorescence (PLIF) measurements is required to meet the demands of short pulse interval and high laser output energy simultaneously, and the system possesses high reliability and high environmental adaptability. In the paper, a burst-mode ultraviolet laser system for high-speed PLIF measurement of a real engine ground test bench is designed, and it can obtain the effective flame dynamics data. The burst-mode ultraviolet laser system adopts the self-developed burst-mode laser to pump the dye laser, which has the functions of energy monitoring, wavelength monitoring and sheet distribution monitoring to correct the influence of laser parameters on the PLIF measurement results. The pump laser employs electro-optic Q-switch, burst-mode and MOPA technology, allowing the pump laser to have high pulse energy output (~50 mJ@532 nm), short pulse width (~10.8 ns) and high the burst frequency (20 Hz). The time interval of burst is 50 ms, which is 1/200 of the burst interval compared with the foreign burst-mode laser. The overall conversion efficiency is 6%, and the ultraviolet output energy is 2.95 mJ@283 nm, which is 7 times the typical value of foreign continuous laser output. The engineering-available 10 kHz PLIF system is self-integrated. It has anti-vibration, moisture-proof and dust-proof functions, improving the environmental adaptability of the high-speed PLIF system. At the same time, the system adopts the model design to improve the efficiency and reliability, making the system “adjust-free”. It solves the problems of rapid installation, debugging, movement and test operation of high-speed PLIF systems in engine ground tests. For the first time in China, the long-distance and large field-of-view (~15 cm) measurement of the scramjet model combustor was conducted successfully in the CARDCs pulsed combustion wind tunnel. The results obtained hydrogen and ethylene fuels high dynamic flame evolution process with high signal-to-noise ratio (SNR). In the future, the combustion condition and dynamic process can be studied with the spectral image feature extraction and analysis methods, which supports the study of complex flow-combustion mechanisms, CFD simulation and enginedesign improvement.

The Hyper-Cam medium-wave hyperspectral imager designed by Telops was used to measure the infrared hyperspectral imaging of a tank model on the cement floor in the 3~5 μm band. Atmospheric correction is performed on a pixel-by-pixel basis of the experimental data. Finally, according to the corrected experimental data, the effects of instrument, random, and atmospheric correction transmission errors on the test uncertainty were analyzed. The luminance distribution of the tank model on different spectral bands and the spectral distribution characteristics of different parts of the model were also analyzed. The results show that the relative uncertainty of the wavenumber in the band of 2 000~3 000 cm-1 has been stable within 10%, but in the band greater than 3 000 cm-1, the error rises rapidly. The error is mainly because the medium-wave radiation of the normal temperature object is low, and the target radiation in this band is close to the atmospheric radiation of the distance, and the noise caused by the increase of the noise caused by the decrease of the test signal-to-noise ratio is reduced. The average relative uncertainty of the tank models spectral radiation brightness test data is within 17%, and the overall error is low, which has reached the advanced level in China. In terms of spectral distribution, the difference in the spectral radiation brightness of the characteristic points of each part of the tank and the cement surface in the wavelength of 4.2~5 μm is greater than that in the wavelength of 3.0~4.2 μm, and the spectral radiation brightness in the 4.2~5 μm band emitted by the detector is greater than the spectral radiation brightness of 3.0~4.2 μm in the shorter band. Since the 4.3 μm band is in the atmospheric absorption band, the average transmittance calculated by Modtran is almost 0, so a true measurement of the spectral radiation brightness on the target cannot be obtained. Regarding spatial distribution, the radiation brightness of the model is mainly concentrated on the side coating, and the radiation brightness at the muzzle and track is small. The radiant brightness distribution of typical target parts is significantly different from that of the surrounding cement surface. Among them, 4.4~4.8 μm occupies a high radiation brightness ratio, and this bands imaging effect is the best. The proportion of radiation brightness occupied by 3.2~3.8 μm is low, and the imaging effect in this band is poor. In the 4.2~4.4 μm band under the atmospheric absorption zone, because the atmospheric transmittance is almost 0, the accurate spatial radiation brightness distribution cannot be obtained. The results show that the extended medium-wave hyperspectral imager has the characteristics of accurate target discrimination, small error and “image spectrum integration” in the study of the medium-wave infrared hyperspectral imaging characteristics of ground targets, and the test data can be applied to the infrared hyperspectral stealth design of the target.

Neutral Beam Injection (NBI) is an important auxiliary heating and driving method for Tokamak devices. The ionization of neutral atoms determines the neutral beam heating(energy and particle deposition profile) andcurrent drive efficiency. In general, the attenuation characteristics of the neutral beam are simulated by using the background plasma parameters and neutral beam parameters, and then the heating and current driving effects of the Tokamak neutral beam are analyzed. Beam emission spectroscopy is a series of characteristic spectral lines radiated by the excitation and deexcitation process of electron and ion collision of neutral beam-injected plasma. The strength of Beam emission spectroscopy is affected by plasma density, temperature, energy beam, beam density and other factors, so neutral beam attenuation can be acquired using the beam emission spectrum. This paper analyses neutral beam attenuation under different plasma densities and different neutral beam energies on EAST. Comparing the experimental and Simulation of Spectra(SOS) results,the experimental and simulation results are in good agreement. The feasibility of obtaining neutral beam attenuation characteristics by measuring the Beam emission spectroscopy is verified.

Time-resolved measurement of laser-induced breakdown spectrum (LIBS) is valuable in many applications. However, it is of high technical challenges. Thus, a new LIBS method based on multiple channel fiber bundle is developed. In this method, a fiber bundle consisting of many fibers with different lengths was first used to delay the signal of LIBS. Then, the spectra from different channels were recorded by an intensified charge-coupled device (ICCD) in different areas. For demonstration, a fiber bundle with 19 channels was constructed. The length of the fiber was in an arrangement of arithmetic sequence with a difference of 10m, corresponding to an interval time of 50 ns. The full recording length was 0.9 μs. The system was used to measure the breakdown spectrum of Si. The results showed multiple spectrum lines at 390.52 nm (Si Ⅰ),385.51 nm (Si Ⅱ),and 413.12 nm(Si Ⅱ) at 19 moments of 898 ns, which clearly exhibited the dynamic development of the breakdown spectrum. This new method can acquire the spectra at different moments in a single measurement. Therefore, it has a very high measurement efficiency. In particular, for experiments with expensive costsor low operation frequencies, this method could reduce the cost, efficiently increasing the ability of time-resolved spectrum measurement.

For a single-type particle system, the particle sizing model vialight extinction spectroscopy is generally established based on Mie scattering theory and Lambert-Beer law. However, the extinction characteristic of mixed particles composed of various types of particles is becoming rather complicated, whereby particle size and mixing ratio can make a combined contribution to the extinctionspectrum. Thus, a novel extinction model of mixed particles with the Monte Carlo method has been proposed, in which theincident lightbeamis assumed as discrete photons to account for the photon destinations and explore the extinction characteristics of the mixture by tracking all events experienced by photons from emission, reception to escape. The extinction spectra of the single-particle system with polystyrene and glass beadswere computed numerically, respectively. The resultshowsa 2% errorafter being compared with the extinction spectrum predicted by the Lambert-Beer law. The model was then extended to the mixed particle system consisting of polystyrene and glass beads. The extinction spectrum of the mixturecan be observed to increase sequentially with the growing proportion of glass beads (mixing ratio)until it is eventually converted into a single-particle system as the mixing ratio approaches 100%. When the wavelength reduces, the extinction value changes from linear to nonlineargrowth with the increase in mixing ratio, and the greater the difference in particle extinction characteristics, the more obvious the nonlinear trend. It can be interpreted that the extinction value of the mixture is determined by the particle type, mixing ratio, particle size, and light wavelength, and their contributions are coupled with each other. With the computed light extinction spectra, three global optimization algorithmswere employed to implement inversions of mixed particle size and/or mixing ratio, which yields the relative errors of mixing ratio all within 1.5% in the single parameter inversion cases. When performing a two-parameter inversion for particle size, the relative errors for two types of particlesare less than 3%. As to simultaneous inversion for two particle sizes and mixing ratio, the relative errors can obviously increase but do not exceed 10%.Regarding the three inversion algorithms, the PSO algorithm takes several times longer than other algorithms for each inversion, and the IGA has greater results from accuracy to stability. Through the preliminary verification of this work, the Monte Carlo- based model can be applied to predict the light extinction of mixed particle systems, and the simultaneous inversion of the mixing ratio and the two particle sizes in particle systems can be realized.

Spatial Heterodyne Spectroscopy (SHS),a new hyperspectral analysis technology, has been widely used in atmospheric detection, satellite remote sensing and other fields. However, because the fabrication of spatial heterodyne spectroscopy is not ideal or the change of working environment will change the instrument parameters and introduce errors, the interferogram data is not accurate, so error correction is needed. Due to the huge difference between the satellite platform and the ground environment, the correction parameters measured on the ground are no longer applicable to the spatial heterodyne interferogram data, especially the change of modulation errors (phase errors and non-uniform errors), which greatly affects the accuracy of the spectrum. Based on spatial heterodyne modulation of the remote sensing data error, from two aspects of spectrum and the interference figure, separation and analysis of the causes of error, think mainly comes from the CCD size scale spatial heterodyne remote sensing data error caused by spectral frequency change and the CCD response to a change in intensity of interference pattern change, spectral interferogram bidirectional correction method is proposed. Twelve O2 absorption spectra measured by the Greenhouse Gases Monitoring Instrument (GMI) on GF-5 were selected for calibration. Onewas taken as the calibration spectrum, and the error-free spectrum simulated by SCIATRAN was compared with the calibration spectrum. The frequency deviation of the two spectra in the spectral dimension was analyzed, and the frequency of the characteristic peak determined the frequency transformation relationship between the two. Then, the simulated spectrum is stretched in frequency according to the transformation relationship so that the simulated spectrum after stretching coincides with the measured spectrum peak. The interferogram of the stretching simulation spectrum and the measured spectrum is calculated, and the changing relationship of the interferogram intensity is obtained by comparing the interferogram of the two. Finally, the intensity variation relation of the interferogram is used to correct the other 11 spectra, and the corrected spectra are obtained. To measure the correction effect, The standard deviation(STD), mean square error(MSE) and signal-to-noise ratio(SNR) of the corrected spectra were calculated; results show that both STD and MSE were significantly lower,with a significant increase in SNR, the basic and STD are below 0.07, the SNR can reach more than 20. The STD of the spectrum with the best correction effect decreased by 0.376 7, SNR increased by 25.101 6, and MSE decreased by 0.158 7. The STD of the spectrum with poor correction effect decreased by 0.229 6, SNR increased by 9.632 8, and MSE decreased by 0.104 9. To sum up, it shows that the spectral interferogram bidirectional correction method proposed in this paper has a good effect on error correction of spatial heterodyne remote sensing data, and the processing process is simple, providing a new direction for similar data processing.

Excitation-emission matrix spectroscopy (EEMs) was widely used in food science, analytical chemistry, biochemistry and environmental science because of its wealth of information and high sensitivity. However, the Rayleigh and Raman scatterings bring difficulties to display data and quantitative analysis of EEMs. Therefore, in the early stage of data processing, eliminating the scatterings interference is significant for the popularization and application of EEMs. In previous studies, few researchers aimed to deal with the low-concentration solution and the scattering peaks overlap with the material fluorescence signals. In order to solve this problem, the paper proposed a self-adapting method to remove the scatterings of EEMs. First, the method corrects the Raman scatterings and background interference by subtracting the spectral baseline of the solvent obtained in each experiment. Then, according to the intensity and overlap degree of fluorescence signals and scattering peaks. EEMs are divided into five categories corresponding to three overlap levels: no overlap, weak overlap and serious overlap. The Rayleigh scatterings are corrected by setting to zero, piecewise cubic Hermite interpolation polynomial algorithm and Gaussian-piecewise cubic Hermite interpolation polynomial coupling algorithm, respectively. The method is based on one-dimensional interpolation, and only emission spectrums are studied, reducing the algorithms complexity and computation time. The methods effectiveness was demonstrated by the experiments on four typical organic compounds: Tyrosine, Fulvic acid, Naphthalene acetic acid and Rhodamine B. Moreover, compared with the Delaunay interpolation method, which is most used in current research. The concentration regressions are performed with the EEMs after the scatterings correction; the average of -R-squared obtained by the method is 0.9962, which is 5.04% higher than the Delaunay interpolation method. At the same time, by comparing the EEMs of Fulvic acid corrected by the method and Delaunay interpolation method, it is proved that this method can better maintain the structural characteristics of the fluorescence spectral regions and effectively avoid the occurrence of overfitting. Finally, this method is used to monitor the simulated sudden pollution water samples, which verifies that the method has good universality and practical application value. It provides a novel idea for removing scatterings interference of EEMs.

Aluminum alloy is an important aerospace equipment material, and its element content is an important factor determining the quality and performance of aluminum alloy materials. The Mn is an important element in aluminum alloy, which can stop the recrystallization process of aluminum alloy and increase the recrystallization temperature. Quantitative determination of aluminum alloy composition is an important part of on-line detection of alloy composition. The signal fluctuation (laser energy fluctuation, plasma instability, sample inhomogeneity, etc.) and self-absorption effect influence the determination of trace elements in aluminum alloys by laser-induced breakdown spectroscopy (LIBS). In order to eliminate the bias caused by the self-absorption effect and signal fluctuation, a new method for detecting alloy content using LIBS technology combined with the LASSO-LSSVM machine learning method is proposed. The Least Absolute Shrinkage and Selection Operator (LASSO) model is used to select the spectral eigenvectors, reducing the dimension of the spectral data to match the training samples, reducing the risk of overfitting, and effectively extracting the most important features that characterize LIBS spectra. The Least squares support vector machine regression (LSSVM) model is used to train the characteristic spectra selected by LASSO. Compared with the internal standard method and partial least squares regression (PLSR), the analysis results show that the model accuracy and accuracy of LASSO-LSSVM were improved. The Mn element regression curves correlation coefficient (R2) of Mn element regression curve increased from 74.62% to 99.29%. The mean relative error (ARE) decreased from 22.38% to 3.56%, the root mean square error (RMSEC) of the training set decreased from 0.66 wt% to 0.040 wt%, and the root mean square error (RMSEP) of the test set decreased from 0.58 wt% to 0.042 wt%. The LASSO-LSSVM regression model is suitable for complex and high-dimensional spectral data with high uncertainty, and can greatly reduce input spectral datas dimension and redundant information. Therefore, the model reduces the overfitting problem of LSSVM. The results show that LIBS technology and the LASSO-LSSVM regression model can effectively improve the quantitative analysis performance of aluminum alloy materials by LIBS technology, which is a simple, reliable and high-precision method to detect alloy content.

The Altar of Agriculture in Beijing is an important historical site of the royal sacrificial buildings in the Ming and Qing Dynasties. It is the carrier of the traditional Chinese thoughts of emphasizing agriculture,which bears witness to the long history of Chinese culture. Under the long-term influence of natural and human factors,various diseases such as smoke and peeling have appeared in the architectural color paintings of the Altar of Agriculture,which need to be protected urgently. In order to obtain the material composition information in the architectural colored paintings of the Altar of Agriculture to support the protection and restoration of the colored paintings,portable Raman spectrometer and a portable X-ray fluorescence (XRF) were used to conduct a non-destructive analysis of three representative architectural colored paintings in the Precious Clothing Hall of the Altar of Agriculture. A fragment was analyzed by combining micro Raman spectroscopy,scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS),X-ray diffraction (XRD) and other methods,the main components of pigments used in colored paintings were successfully obtained. The results showed that the priming coat material of the three paintings all contained calcium carbonate,and gold foil remained on the surface of the gold dragon pattern. In addition,cinnabar,red lead,lead white,atacmite,and indigo were used in the original colored paintings. In addition to the above cinnabar,red lead and lead white,the mixed use of red lead and orpiment was detected in the areas of the golden dragon pattern,and the blue and green pigments were ultramarine and Emerald green respectively,which were different from the pigments used in the original colored paintings. Various modern synthetic pigments were identified in the repainted color paintings,such as titanium dioxide,chrome yellow,Sudan I,phthalocyanine green,ultramarine blue,bright red β-naphthol,etc. According to the use of pigments and historical records,the painting periods of the three colored paintings were determined to be around 1754,1860 and 1997. Due to the lack of knowledge of the pigments used in the original colored paintings,the pigments used in the later repaired color paintings are different from the original colored paintings. This study not only helps to judge the preservation history of architectural color painting but also provides a reliable basis for the protection of color painting,which has an important guiding significance for the subsequent protection and restoration work.

Aronia melanocarpa is a small berry listed in the list of new food raw materials,rich in anthocyanins and other ingredients,which has been widely used in alcohol,beverages,functional food,cosmetics and other fields,with high economic value. Due to the influence of environmental factors such as climate and planting conditions in different areas,the fruit quality of A. melanocarpa is significantly different. Therefore, to standardize the market management of A. melanocarpa fruit,the fruit of A. melanocarpa from different places of origin was identified by mid-infrared spectroscopy combined with chemometrics. 750 infrared spectral data of A. melanocarpa fruit from 15 production areas were collected. After spectral pretreatments,such as multiple scattering corrections (MSC),standard normalization (SNV),moving smoothing (SG),first derivative (FD),second derivative (SD),and so on,the optimal spectral pretreatment method was determined by comparing the recognition effect of support vector machine (SVM) modeling with the original spectrum. The K-S sample division method divides the samples into training sets and test sets at a ratio of 4∶1,and then the samples are normalized. The competitive adaptive reweighting algorithm (CARS) and continuous projection algorithm (SPA) are used to extract the spectral feature information,and the best model is determined by modeling and comparing with random forest (RF),extreme learning machine (ELM) and support vector machine (SVM). The results show that MSC is the best spectral preprocessing method;the recognition rate of the MSC-SVM training set is 93.33%,and the recognition rate of the test set is 92.67%,which can effectively reduce the random error generated during spectral acquisition. After extracting the MSC characteristic spectral wavenumber by CARS and SPA,the modeling results of the three algorithms are compared,and the SPA-SVM model is determined to be the best recognition model. The recognition rate of its training set and test version is 100%,and only 16 wavelength points are needed to complete the accurate recognition. Therefore,the combination of mid-infrared spectroscopy and chemometrics,especially SPA-SVM model,can accurately identify the origin of A. melanocarpa fruit,provide a fast and simple method support for the origin traceability and quality evaluation of A. melanocarpa fruit,and provide a technical basis for building a unique brand with regional characteristics.

Chemical oxygen demand (COD) is an important surface water quality evaluation index. The traditional COD detection method needs to use toxic reagents, easily cause secondary pollution and other shortcomings; hyperspectral method can avoid the above shortcomings so that it has a broad application prospect in COD detection. In order to explore the feasibility of indoor inversion of COD concentration of surface water by hyperspectral technology, this paper takes 129 surface water samples in Jilin Province as research objects, divides the sample set into the training set and test set with sample number ratio of about 3∶1, and uses hyperspectral imaging system to collect DN values of samples and calculate the corresponding spectral reflectance of water bodies. The derivative method is used for data preprocessing. Pearson correlation analysis is used to judge the correlation degree between spectral data and measured COD concentration, and the characteristic spectral data is extracted. A least square support vector machine (PSO-LSSVM) inversion model optimized by particle swarm optimization was established using full and characteristic spectrum data respectively. These models prediction accuracy and reliability were compared by analyzing the coefficient of determination R2, root mean square error RMSE, and relative percent deviation RPD. The results show that the correlation between COD concentration and spectral reflectance of surface water is significantly enhanced after derivative pretreatment. The prediction results based on derivative spectral data are better than those based on original spectral data. The model based on extracting characteristic spectral data has a better prediction effect than the model based on full spectral data. Among them, the inversion model of surface waters COD concentration established using the first derivative preprocessing method and the characteristic spectrum has the best prediction results. The determination coefficient of verification set R2=0.856 7, the root mean square error RMSE=3.822 9, and the relative percent deviation RPD=2.641 4. The above research preliminarily confirms the feasibility of indoor inversion of COD concentration in surface water based on hyperspectral data. It provides a new method and idea for applying hyperspectral technology in the detection of COD in surface water.

An improved two-step heat of hydration method was used to prepare a core-shell structured ZnO/ZnSe composite material. Zinc oxide nanoparticles were prepared with zinc acetate and diethanolamine,and a selenium-containing anion solution was prepared with selenium powder and sodium borohydride. The optimal condition of Zn∶Se=1.6∶1 was obtained by adjusting the ratio of raw materials,and a spherical core-shell structure was prepared. ZnO/ZnSe,combined with transmission electron microscope,X-ray powder diffractometer,ultraviolet-visible spectrophotometer,photoluminescence spectroscopy,X-ray photoelectron spectroscopy to characterize the material composition and morphological structure,and its photocatalytic activity is analyzed and studied.

Investigations on the characteristic of dissolved organic matter released from sludge after agricultural use could help to evaluate and predict the environmental behaviors and ecological effects of the concurrent pollutants in soils. The characteristics change in concentration,molecular weight,composition,structure and other properties of DOM released from aerobic composted municipal sludge sampled from a sludge treatment plant in Luoyang City,Henan Province were characterized with Scanning Electron Microscope (SEM),total organic matter analyzer (TOC),UV-Vis absorbance spectroscopy,gel permeation chromatography (GPC),three-dimensional excitation-emission matrix (3D-EEM),Fourier transformation infrared spectroscopy (FTIR) and 1H nuclear magnetic resonance (NMR). Results showed a significant change in the microscopic morphology of the released DOM, varying from dense lumps to irregular loose material within 60 d of the release process. Dissolved organic carbon released from sludge was in the range of 4.25 to 6.22 mg·g-1 dry sludge, presenting an early increase and later decrease trend. Significant changes in molecular weight and aromaticity of DOM were seen during the release process. The measured molecular weight of DOM increased from 2 674 g·mol-1 on the 5th day to 129 026 g·mol-1 on the 60th day. The aromatic compounds in DOM gradually accumulate during the release process. 3D-EEM combined with the parallel factor analysis (PARAFAC) model was used to analyze the fluorescent substances in the released DOM,and it was found that the main fluorescent substances in DOM were fulvic-like and humic-like compounds. The humic-like compounds gradually accumulated during the process,resulting in the humification of the released DOM. The reduction of aliphatic alkane compounds and formation of aromatic compounds in DOM were indicated in FTIR. Variations in the quantity and properties of DOM released from sludge after agricultural use might significantly change the environmental behaviors and ecological effects of the coexisting pollutants in soils. These results may provide insights into the evaluation and prediction of sludge for agricultural use and have guiding significance to the utilization of municipal sludge resources.

Dendrobium officinale has high commercial value and nutritional value. In this study,130 samples were taken as research samples from Wenshan in Yunnan,Jinxiu in Guangxi,Huoshan in Anhui and Taizhou in Zhejiang. The Raman spectrawere obtained by a portable Raman spectrometer under a 785 nm laser. Then, the total flavonoid content of Dendrobium officinale was determined by NaNO2-Al (NO3)3-NaOH colorimetry. With each normalized Raman spectral data as input,different preprocessing methods included SG,SNV and MSC are used to preprocess the spectral data. Partial least squares (PLS),support vector machine (SVM) and convolution neural network short and long-term memory neural network (CNN-LSTM) models are used as a comparison,and competitive adaptive reweighting sampling (CARS) is used as wavelength selection method to compare different machine learning models. In addition,the following prediction quality indicators were used: correction set and correlation coefficient of the test set (Rc，Rp),root mean square error of correction set and root mean square error of prediction set (RMSEC,RMSEP) to evaluate the performance of the prediction model of total flavone content in Dendrobium officinale. The results showed that the prediction accuracy of the CNN-LSTM method was the highest,with Rc and Rp of 0.983 and 0.964,RMSEC and RMSEP of 0.032 and 0.047 mg·g-1,respectively. The SNV-CNN-LSTM deep learning model based on Raman spectroscopy is accurate,reliable,and robust,superior to traditional machine learning models (PLS,SVM). In this study,Raman spectroscopy combined with the CNN-LSTM model was used to predict the content of total flavonoids in Dendrobium officinale with the characteristics of fast and non-destructive,which overcame the shortcomings of traditional physical and chemical identification methods. This method can distinguish the quality of Dendrobium officinale,accelerate the industrialization of Dendrobium officinale in the market of medicinal and edible homologous plants,build its brand and increase its influence. At the same time,this technology can also be applied to consumers and market supervision departments,with broad prospects.

Biomethanation may be the most favourable method for low carbon conversion of peat. In order to explore the reuse of peat methane fermentation residue to extract humic acid and realize the new process of peat methane fermentation coupled with humic acid extraction, it is necessary to analyze the influence of alkali liquor on the yield, purity and structure of humic acid from methane fermentation residue. This paper uses herbaceous peat as raw material to conduct sodium hydroxide pretreatment and methane fermentation. The residue is extracted from humic acid by alkali dissolution and acid precipitation and from potassium hydroxide, sodium hydroxide, lithium hydroxide, and sodium pyrophosphate, respectively. The humic acid samples were characterized by yield, purity, UV-Vis spectrum, infrared spectrum and fluorescence spectrum. The results showed that the yield, content and structure of humic acid were significantly affected by extraction with different extractants and anaerobic fermentation with sodium hydroxide pretreatment. The yield and purity of humic acid in the residue of anaerobic fermentation and blank group pretreated by lithium hydroxide extraction and sodium hydroxide were the highest, with a yield of 43.59% and 41.07% and purity of 70.4% and 70.6%, respectively. The content of hydroxyl, methyl, methylene and ether bonds in peat humic acid extracted by sodium pyrophosphate is higher, and the degree of aromatization is low; the extraction degree of aromatic carboxylic acid and humic acid is high, and the content of aromatic aldehyde and humic acid is high. The contents of hydroxyl, methyl, methylene and ether bonds of humic acid in anaerobic fermentation residue pretreated with sodium hydroxide decreased, carbonyl and benzene ring contents were higher, and the degree of aromatization increased. The study on the change characteristics of extracting peat humic acid from anaerobic fermentation residue shows that sodium hydroxide pretreatment anaerobic fermentation has a significant impact on the structure of extracting peat humic acid by alkali dissolution and acid precipitation, and the process of reusing peat anaerobic fermentation residue to extract humic acid is feasible.

Oil spill pollution is a typical form of environmental pollution in todays era of rapid development, which harms biodiversity and human safety through multiple channels. Therefore, given the composition and characteristics of oil pollutants, it is particularly critical to improve the ecological environment and ensure the steady development of the economy and society by using multi-method cross-fusion to detect them in real-time, accurately and efficiently. Three-dimensional fluorescence spectroscopy is widely used in the substance detection field with fluorescence characteristics with its advantages of high detection accuracy, good real-time performance, simple operation and small interference. Three-dimensional fluorescence spectroscopy combined with a support vector machine and other algorithms have achieved good results in material classification and identification and concentration prediction, but there are still defects, such as slow convergence speed and easy fall into local optimum. A new method for the classification and identification of oil pollutants was proposed by combining a three-dimensional fluorescence spectrum with a support vector machine algorithm ( IGOA-SVM ) optimized by an improved grasshopper algorithm. Firstly, with 0.1 mol·L-1 sodium dodecyl sulfate as a solvent, 0# diesel oil, 95# gasoline and kerosene were prepared into 20 and 18 mixed samples of 0# diesel oil and 95# gasoline, 0# diesel oil and kerosene, and 20 mixed samples of three components. Half of each was taken as a training set and a test set. The fluorescence data of the mixed solution were collected by an F-7000 fluorescence spectrometer. Matlab analyzed the standard solution of the three oils and the mixed solution. It was found that the fluorescence spectra had different degrees of overlap within a certain range, and it could not be accurately identified by spectral detection alone. Finally, the grasshopper optimization algorithm is improved by combining chaotic initialization, elite optimization, and differential evolution algorithms. The fluorescence peak data in the excitation wavelength 270 nm and emission wavelength 270~450 nm are extracted as the input value of training. With three kinds of classification labels as output, the data are input into the grasshopper optimization algorithm support vector machine (GOA-SVM), particle swarm optimization support vector machine (PSO-SVM) and genetic algorithm optimization support vector machine (GA-SVM) for training. The IGOA-SVM model is superior to GOA-SVM, PSO-SVM and GA-SVM in convergence speed, stability and ability to jump out of local optimum, providing a new idea for accurately identifying oil contaminants.

In the process of mine water inrush disaster prevention and control,it is very important to accurately and quickly identify the type of water inrush sources for coal mine safety production. However,the traditional hydrochemical method has the disadvantages of time-consuming and complex detection. Therefore,a new idea of identifying mine water inrush sources using Raman spectroscopy is proposed. First of all,the water samples of goaf water,roof sandstone fissure water,Ordovician limestone water,Taiyuan limestone water,surface water and their mixture were collected from the Huainan mining area as experimental objects,and the Raman spectral data of water samples were collected with the help of Raman spectroscopy system. Then,the common spectral pretreatment method is used to reduce the noise of the original Raman spectrum. Then,the whale optimization algorithm (WOA) is used to screen the characteristic information of the water sample,and the characteristic information that best represents the mine water sample is obtained. Finally,the filtered characteristic Raman information is used as input to construct BP neural network (BPNN),K-nearest neighbor algorithm (KNN),support vector machine (SVM),decision tree (DT) and naive Bayesian (NB) classification models respectively,to verify the feasibility of Raman spectrum combined with WOA screening characteristic Raman information for mine water source identification. Experiments show that 102 characteristic Raman information can be filtered from 2 048 Raman data points by WOA,reducing the number of Raman information to 4.98%,and the modeling accuracy of characteristic Raman information filtered by WOA is higher than that of full Raman data. In addition,when the characteristic Raman information filtered by WOA is used to build BPNN,KNN,SVM,DT, and NB water source identification models,the analysis speed has been improved to varying degrees. The research results show that using WOA to screen the characteristic information of the Raman spectrum of the mine water source can effectively reduce the redundancy of the Raman spectrum data and significantly improve the speed of the Raman spectrum analysis,which can provide a reference for the rapid detection of the mine water source.

A new round of domestic strategic mining search operation is in full swing,gold mineral resources with their unique rarity and strategic with a special significance,its analysis and detection technology affects the accurate testing of gold elements directly. Taking gold element in ore as the research object,the orthogonal test design scheme is used to test the method of aqua regia concentration,oscillation time and thiourea concentration in the experimental elements,and the relative error of determination results is quantified；In accordance with the hierarchical analysis method AHP to determine the element indicators,establish the matrix,consistency judgment steps to calculate the element weights as (0.252, 0.159, 0.589), calculate the contrast strength and conflict of orthogonal test data through the objective weighting CRITIC method,the element weights are calculated as (0.452, 0.172, 0.377), and propose the combined analysis of element weights based on AHP-CRITIC hybrid weighting algorithm,the results are (0.314, 0.075, 0.611); Using particle swarm algorithm to construct particle multidimensional space,design algorithm flowchart by iterative position of particle velocity and direction attributes,combine the results of hybrid weighting algorithm to correct inertia weights by linear decreasing in the iterative process,optimize the learning factor of particles at the beginning and end of iteration,combine the results of orthogonal test to establish the target fitness function using particle swarm algorithm,improve the algorithm flow,applying MATLAB software simulate the iterative process of particle swarm,the optimized particle swarm algorithm is obtained converging to the optimal combination from each global position and direction by gradually,and the optimal condition parameters for finding the gold elements by atomic absorption spectrometry are 10.62% concentration of aqua regia,32.8 min oscillation time,and 9.5 g·L-1 concentration of thiourea. The validation results of the particle swarm optimization algorithm show that the gold standard analytes GAu-15a,GAu-16b,GAu-17b,GAu-18b,GAu-19b and GAu-22a have been tested in 11 parallel tests under the optimized parameters of the analytical conditions. The average value,relative error,and relative standard deviation indicatorsare calculated,and all of them satisfied the “Geology and Mineral Laboratory Test Quality Management Specification”. It is shown that the particle swarm optimization algorithm based on an orthogonal test is scientifically feasible for the optimization problem of gold elemental parameters analyzed by atomic absorption spectrometry,and the correctness and stability of the optimization algorithm are verified,which provides new research ideas for the new round of strategic mineral search business in domestic. The method proposes a hybrid weighting algorithm combined with evolutionary computational techniques to find optimal solutions for multi-objective parameters,which is expected to be extended to test environments in other fields of analytical laboratories and more prospective applications in scientific research seeking the direction of parameter optimization.

Nasal mucus is the primary barrier affecting the nasal absorption of vaccines and drugs. In vivo assessments are difficult to perform due to the complexity and variability of the confounding factors,whereas in vitro evaluations are mostly used. The existing in vitro assays for monitoring nasal mucus permeability of drugs,such as cell model assays and multiparticle tracer techniques,have the disadvantages of a long cell culture cycle,cumbersome operation,high cost,little available information,and the need for fluorescent labelling,which have great limitations for the in vitro evaluation of nasal mucosal formulations. Therefore,there is an in urgent need to establish a rapid,simple and sensitive method for the evaluation of mucus permeability of nasal mucosal formulations. Based on the sensitivity of ATR-FTIR spectroscopy to changes in drug structure and mucin secondary structures,the nasal mucus permeability of liposomes, typical lipid nanoparticles,with different properties (particle size and charge) was studied in this paper,and the interaction of different liposomes with mucin in mucus by FTIR spectra to establish an in vitro evaluation method for mucus permeability of nasal mucosal preparations. Methodological studies showed that for PEG10000,chitosan,and sodium alginate liposomes,the linear relationships of the method were Y=2.386 6X+2.154,Y=1.870 3X+0.278 9,Y=1.130 14X+0.060 9,the linear correlation coefficients were 0.995 8，0.994 5，0.990 9,and the precision RSD values were 0.62%,0.73%,and 0.95%,respectively; the RSD values in the repeatability experiment were 0.83%,0.97%,and 0.88%,respectively. It is indicated that the method has a good linear relationship,high precision,and good repeatability and can be used to evaluate the permeability of pharmaceutical preparations in mucus in vitro. The results showed that the sample absorption bands with increasing intensity of different liposome formulations could be obtained by scanning the samples at different times in interaction with mucus using ATR-FTIR. For PEG liposomes with different particle sizes,the smaller the particle size,the stronger the mucus permeability; for liposomes with different charges,chitosan liposomes with positive charges have the weakest mucus permeability,followed by sodium alginate and PEG liposomes have the strongest mucus permeability. Further studies have shown that the difference in mucus permeability of liposomes with different charges stems from their interaction with mucins,and this conclusion can be obtained by analyzing the information on each secondary structure (α-helix,β-sheet,β-turn,irregular turn) contained in the mucinamide Ⅰ band (1 600～1 700 cm-1). In summary,the in vitro evaluation method established in this paper based on ATR-FTIR is sensitive and simple and can be used as a rapid assay of nasal mucus permeability for various preparations. And with improved applicability,it can also be used to evaluate the permeability of pharmaceutical preparations in other mucus,which has a broad application prospect.

Microbeam X-ray fluorescence imaging (μ-XFI) is an important tool to obtain the distribution information of elements in the sample without destructive sample preparation. It is widely used in the analysis of elements in the micron region. Although synchrotron radiation is an ideal light source for μ-XFI,it is unsuitable for conventional applications because of its huge device,high cost and tense user time. Based on a laboratory X-ray tube,poli-capillary focusing lens,high-precision sample stage and silicon drift detector,the micro-beam X-ray fluorescence imaging system with element resolution imaging has been established; the performance test results show that the system has an element detection limit of 0.001% and a spatial resolution better than 20 microns. Based on the μ-XFI system,the spatial distribution of various main elements and trace elements in mouse brain,chip and ancient ceramic samples were obtained. The comprehensive results showed that the system could meet the needs of micro area X-ray fluorescence imaging and could provide help for biomedicine,electronic component detection,porcelain color composition identification and other fields.

Potassium aluminum sulfate ［KAl(SO4)2］,also known as alum,is often added to the production process of fans to improve the characteristics of poor tenacity and easy fracture of fans,and it can also improve the taste of vehicles to a certain extent. The aluminum element in aluminum potassium sulfate has neurotoxicity,reproductive toxicity,and developmental toxicity. Long-term excessive intake of aluminum will do great harm to human health. In the conventional determination of aluminum potassium sulfate in vermiculate,the sample must be digested,and the treatment method is tedious and time-consuming. The portable X-ray fluorescence spectrometer (XRF) has the advantages of small size,simple operation,fast detection speed,and high sensitivity,and it can also determine a variety of metal elements at the same time. Therefore,taking vermin as the research object and using Inductively coupled plasma mass spectrometry (ICP-MS) as the reference,this paper established a new method for XRF to quickly detect and identify KAl(SO4)2 in vermin. XRF technology was used for the first time to identify whether KAl(SO4)2 was added to fans. Firstly,the matrix effect of the instrument was corrected by the equation,and the linear curve of peak area and concentration was established. The R2 of the corrected working curve was >0.99,which could be used to determine vermicular samples. Secondly,the detection conditions of the instrument are optimized. When the samples were directly tested on the machine,the relative error of XRF test results was 0.65%～24.33%. For vernacular samples with aluminum content less than 100 mg·kg-1 in KAl(SO4)2,rapid carbonization treatment can be used to determine the low content of aluminum in vernacular samples after enrichment. The relative error of XRF test results is 1.36%～15.39%,and carbonization treatment greatly reduces the relative error of direct determination of samples. In addition,it was found that there was a correlation between Al,S,and K elements in the standard samples with aluminum potassium sulfate,and the ratio of the three elements was in the range of 1∶3∶1~1∶4∶1,while the ratio was not satisfied in the samples without aluminum potassium sulfate. Portable XRF can efficiently complete the bulk samples; single test process needs time for about 5 min; method is rapid,simple,and economical,with high precision and can quickly identify the fans whether adding KAl(SO4)2,food quality,and safety hidden danger in time,to the quality of the fan control provides a new way of thinking,has extensive practical popularization significance.

Baseline correction, one of the extremely critical steps in Raman spectroscopy pre-processing, is of great significance for further Raman spectroscopy data analysis, Raman imaging, etc. Currently, the most common baseline correction algorithm is based on polynomial fitting; due to its manual or semi-manual form, manual experience, a high level of user expertise, and a tedious processing process are required, leading to large differences in processing results. At the same time, the polynomial order and the moving segmentation window are difficult to select in the process, so the processed results are often under-fitted or over-fitted. This paper improves the Numlocal Piecewise Polynomial Fitting (NPPF) algorithm for accurately calibrating Raman spectral baselines. Firstly, an improved segmentation-based local optimum algorithm is used to select the approximate lateral width of the bottom contour of the widest peak in the spectrum as the background point window width; the minimum and second minimum values within the window, in turn, are selected as the background baseline points to be fitted, avoiding the difficulty of selecting background points, and achieving more accurate selection of each background contour baseline point. Then, the three fitted curve functions are obtained by iterative coverage of each window three times, and each point in the selected window corresponds to three curve function values, which are calculated with the previous fitted absolute value separately. The curve function value with the minimum absolute value is taken as the fitted curve value at this point. Thismethod better avoids the underfitting and overfitting phenomenon of the Piecewise Polynomial Fitting(PPF) algorithm and also determines the order and segmentation window in the fitting process. In this paper, two Raman spectra with different background types are simulated, and the NPPF and PPF algorithms are compared to process the two simulated spectra separately. The Root Mean Square Error (RMSE) of NPPF processing results is found to be smaller, which confirms the superiority of NPPF over PPF. Finally, the Raman spectra of the actual samples (alizarin and rhodamine 6G) are processed by comparing NPPF and PPF, and it is found that the fitted baseline of NPPF is more accurate, which confirms that the NPPF algorithm in this paper has wide practical application value and prospect in the baseline correction pretreatment of Raman spectra.

It is very important to use the differences in blood components between species to identify species in biomedicine, medical health, customs, criminal investigation, food safety, wildlife protection and so on. However, the current research is carried out on population cells, ignoring the heterogeneity of single cells. Therefore, it is very urgent to develop a single-cell-based blood fluorescence spectral classification method. A single-cell blood classification method is proposed based on fluorescence optical tweezers and machine learning. The optical tweezers are used to achieve single-cell capture, and the single-cell fluorescence spectrum data is obtained through the fluorescence spectrum detection system. The accurate classification is realized based on the machine learning method. First, a fluorescent optical tweezers system was designed and built to realize single-cell capture, fluorescence imaging and spectral detection were obtained. Then, the whole blood solutions of horses, pigs, dogs and chickens were prepared, and using 440 nm laser light as the fluorescence excitation light source, 100 pieces of fluorescence spectrum data for each of 4 species, including horse, pig, dog and chicken, totalling 400 pieces of fluorescence spectrum data were obtained, and the preprocessing of background removal, smoothing and normalization was carried out to eliminate instrument noise and environmental interference in the signal. Subsequently, a classification model of the random forest was established, and the relationship between the number of trees in the model and the prediction accuracy was analyzed when the number of extracted features k=20, and it was found that when the decision tree was m=500, the classification accuracy tended to be stable, and at the same time obtaining a high classification accuracy and operating efficiency. Further, 30% of the sample data was set as the test set and the rest as the training set. The relationship between different wavelengths and feature importance was calculated, 10 classification accuracy rates were obtained, and the average as the model classification accuracy rate was taken. The final average accuracy rate of the test set reaches 93.1%, and the variance is 0.31%. Finally, the confusion matrix was calculated, and the models prediction accuracy was evaluated. Chickens had the highest classification accuracy, and horses had the lowest accuracy. The analysis showed that the important contributions to the classification were porphyrins, heme and flavin adenine dinucleotide. In conclusion, the study shows that the combination of fluorescent optical tweezers and machine learning methods can achieve blood classification at the single-cell level, and the high classification accuracy validates the feasibility and efficiency of the optical tweezers-based single-cell fluorescence spectroscopy detection method. At the same time, this method can meet the modeling needs without too many samples and can avoid problems such as low fluorescence self-absorption intensity caused by low concentration. It has the advantages of fast and accurate classification and has very important potential application value.

Because the 1.27 μm O2(a1Δg) airglow radiation has the advantages of strong radiation signal,large space span and weak self-absorption effect,it is an important target source for near-space atmospheric remote sensing. In addition,it has important scientific significance and application value,such as research on the dynamics and thermal characteristics of the middle and upper atmosphere,global greenhouse gas detection,and three-dimensional tomography of ozone concentration. Firstly,based on the photochemical model of O2(a1Δg),the generation and annihilation mechanisms of O2(a1Δg) airglow were studied. The volume emission rate profile of O2(a1Δg) airglow was calculated on this basis. Based on the spectral intensity and Einstein coefficients given by HITRAN,two methods for calculating the spectral distribution of O2(a1Δg) airglow were proposed. Using the latest molecular spectral parameters,photochemical reaction rate constant and F10.7 solar ultraviolet flux,combined with the volume emission rate profile information of O2(a1Δg) airglow calculated by photochemical reaction model. The radiative transfer theoretical model of the 1.27 μm O2(a1Δg) airglow in limb-viewing was developed by using a line-by-line integration algorithm. The influence of the self-absorption effect on the spectral intensity of airglow radiation at different tangent heights is analyzed. Then,the O2(a1Δg) airglow radiation spectrum of the target layer is obtained by processing the airglow radiation of the O2 molecule near the infrared atmospheric band measured by scanning imaging absorption spectrometer for atmospheric chartography (SCIAMACHY) under the limb-viewing by onion peeling algorithm. Spectral integration algorithm is used to retrieve the volume emission rate profile of O2(a1Δg) airglow. Finally,the reliability and rationality of the radiative transfer theoretical model of the 1.27 μm O2(a1Δg) airglow in limb-viewing is verified by comparing the radiation spectrum and the volume emission rate profile obtained from the theoretical calculation and retrieval of the SCIAMACHY instrument. Regarding the comparison results,factors that contribute to the limb radiation intensity and volume emission rate of O2(a1Δg) airglow are analyzed. Analyses show that theoretical calculations agree with measured satellite results in the altitude region above 50 km. However,the deviation between the two increases gradually with the decrease of altitude because the satellite remote sensing in the middle and low altitude regions are seriously affected by the self-absorption effect and atmospheric scattering effect in limb-viewing. Additionally,compared with the spectral line intensity parameter given by the HITRAN database,the O2(a1Δg) airglow limb radiation model based on Einstein coefficients is more consistent with the measured satellite results. Establishing the radiative transfer theoretical model of the 1.27 μm O2(a1Δg) airglow in limb-viewing provides a theoretical foundation for atmospheric remote sensing in near space.

As an efficient marine environment monitoring method,remote sensing can simultaneously monitor the distribution and state changes of various substrates on the seabed. Currently,the observation technology of the reflectivity of the bottom substrate is mainly a single angle observation. The accuracy of remote-sensing models to detect the shallow water substrate would be improved significantly by studying and analyzingthe characteristics of multiple-angles reflectance spectra as well as BRDF information of substrates of the seabed in shallow water regions. Using the two-channel simultaneous measurement method,a simple two-way reflectivity measurement system of the seabed is designed,then the characteristic data of fine sandy bottom and Underwater coral were acquired. The results showed that (1) The standard deviation of the reflectance values measured at different azimuth angles for the constant zenith angle (θ=20°,40° and 60°) of the homogeneous sand on the shore,is less than 1.5% at 400～700 nm. The standard deviation of the reflectance at different zenith angles was analyzed at a constant azimuth angle,and the standard deviation was not more than 1.7%. It indicates that the sand substance on the shore has isotropic characteristics. BRDF characteristics of submarine sandy demonstrated that the standard deviation of the reflectance values,measured at different azimuth angles for the constant zenith angle (θ=20°),showed a relevance increase with the wavelength in the 400～700 nm. The standard deviation of reflectance is 3.8% at 400 nm,while it reaches 12% at 700 nm. It indicates that the submarine sandy also has isotropic characteristics. (2) The standard deviation of the reflectance values measured at different azimuth angles for the constant zenith angle (θ=20°) of corals,gradual increases from 1.1% to 2% at 400～605 nm,then decrease from 2% to 1.2% at 400～605 nm and increases from 1.2% to 4.9% at 675～700 nm. The field test data analysis results show that this underwater bidirectional reflection measurement system can effectively measure the reflection spectrum of various seabed substrates. Based on the measurement data obtained in the field,this study conducts a preliminary analysis of the reflectance spectra of two typical substrates,sandy and coral,which provides spectral characteristics of reflectance at multiple observed angles of various substrates for the subsequent use of remote sensing means to invert the seabed substrates in shallow water areas and dynamically monitor the dynamic changes of the substrates (e. g.,coral growth state).

The discovery of dinosaursfrom the Early Cretaceous Pterosaur Fauna in Xinjiang has increased the diversity of the fauna and provided new information for the phylogenetic evolution of dinosaurs, which is of great scientific significance.However, when the Hami dinosaur fossils were preserved off-site from their original burial environment, they showed severe weathering due to changes in the preservation environment. In order to better preserve, study, and display the Hami dinosaurfossils, this paper used a variety of analytical techniques to examine the Hami dinosaur fossils that have shownsignificant weathering and to analyze the causes of weathering and fragmentation of the fossils.X-ray diffraction (XRD) showed that the severely weathered area of the Hami dinosaur fossils was the cancellous part of the bone. The main mineral components were quartz and calcite, containing less apatite, feldspar and clay minerals. Quartz and feldspar are foreign clastic particles in the cancellous pores of the filled bone.Calcite is a cement formed in the later diagenesis or fossilization process, and a small amount of apatite is mainly from the bone. The results of ion chromatography (IC), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy (Raman) and scanning electron microscopy(SEM-EDS) showed that the soluble salts in the weathering area of dinosaur fossilswere mainly NaCl, CaCl2 and Ca(NO3)2·4H2O, and a small amount of CaSO4. The soluble salt content was as high as 2.63%, and the preliminary conclusion is that the high soluble salt content was the main cause of the severe weathering of fossils.The mercury intrusion test (MIP) results show that the porosity of the weathering area of dinosaur fossilis 21.272 2%, which is significantly increased compared with the porosity of 16.420 6% in the unweathered area. In addition, there are two kinds of pore size distribution in the weathering area of dinosaur fossils, with the sizes of 0.005~0.04 and 17.3~283.2 μm, respectively. Compared with the unweathered area, the weathering area produces microcracks, and the number of large pore-sized pores increases sharply.It is concluded that the fossils contain a large number of deliquescent salts represented by CaCl2 and Ca(NO3)2·4H2O, which are easily affected by the changes intemperature and humidity in Beijingduring four seasons recycles. The deliquescent salts have a great destructive effect, thus increasing the porosity of the weathered areas of the Hami dinosaur fossils and increasing the pores. The internal filling structure is flimsy and heterogeneous, which eventually leads to the fragmentation of the Hami dinosaur fossils.This research work is of scientific significance for preserving and protecting dinosaur fossils from Hami in ex situ.

Flavonoid is an important index affecting the nutritional value of peanut seed. The spectrophotometry and chromatography to detect flavonoid content are time-consuming and labor-intensive,so they are not suitable for mass detection in breeding process,and constructing its near-infrared measurement model can provide an important technical guarantee for rapid detection of flavonoid content in peanut seed. This study used 290 peanut germplasms with different flavonoid content to construct the model. The flavonoid content determined by the Al3+ chromogenic method was between 46.96 and 140.18 mg RT（RT: rutin）·(100 g)-1. The near-infrared spectrum of peanut seed was scanned and collected by Perten DA7250 near-infrared analyzer (950～1 650 nm). The partial least squares regression (PLSR) in the full wavelength spectrum range was used to compare the single and compound pretreatment methods,and the correlation coefficients and errors of different models were compared to predict the best model. The best spectral pretreatment method to determine the NIR calibration model of flavonoid content was “Savitzky-Golay derivative+baseline+de-trending”. The correlation coefficient (Rc) of calibration set was 0.884,and the root mean square error (RMSEC) of correction was 4.998. 50 peanut samples verified the model. The predicted correlation coefficient Rp was 0.904,while the predicted RMSEP was 1.122. The near-infrared spectroscopy model constructed in this study can be used to determine the content of flavonoids in peanut seeds non-destructively and efficiently. It could be effectively used to breed peanut varieties with high flavonoid content and can help construct near-infrared spectroscopy models of substances with low content (μg·g-1).

To improve the accuracy of the photo chlorophyll content estimation model, the remote sensing images of different growth stages of potatoes under control and drought treatments were obtained using a multi-spectral camera on a UAV platform. Thirteen vegetation indices were selected as input variables of the chlorophyll content inversion model, and the estimation model of potato chlorophyll content was constructed by using multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR) and decision tree regression (DTR). Correlation analysis between vegetation index and chlorophyll content showed that at the tuber formation stage of the control treatment, the absolute values of correlation coefficients between CIre, GNDVI, NDVIre, NDWI, GRVI, LCI and chlorophyll content were above 0.5, and their were significant (p<0.05) or highly significant (p<0.01) correlations. In other growth stages of potato, the absolute values of correlation coefficients between 13 vegetation indexes and chlorophyll content were all above 0.5, which was a highly significant correlation (p<0.001). In addition, the accuracy of MLR, SVR, RFR and DTR models were compared. The results showed that the SVR model has the best prediction effects in the tuber formation stage, tuber expansion stage and starch accumulation stage of the control treatment. The control treatments R2 and RMSE were 0.89 and 2.11 in the tuber formation stage, 0.59 and 4.03 in the tuber expansion stage, and 0.80 and 3.18 in the starch accumulation stage. The RFR model produces the best prediction effects in the tuber formation, tuber expansion, and starch accumulation stages of the drought treatment. The outcomes of R2 and RMSE on drought treatment were 0.90 and 1.57 in the tuber formation stage, 0.87 and 2.16 in the tuber expansion stage, and 0.63 and 3.01 in the starch accumulation stage. This study presents a new approach for monitoring the chlorophyll content of potatoes, and a corresponding estimating model can be selected based on the specific potato growth stage and different experimental treatments in future.

The quality and yield of leafy vegetables are closely related to the net photosynthetic rate. Affected by particulate matter pollutionespecially in autumn and winter,the photosynthesis of leafy vegetables in greenhouses is restricted,which is unfavorable for accurate prediction of physiological information. Under the growth environment of particle pollution,The lettuce during the harvest period was the test object. The optimal method for establishing the inversion model of lettuce net photosynthetic rate based on hyperspectral technology was studied. The net photosynthetic rate and hyperspectral data of lettuce were Obtained. The Ratio Vegetation Index (RI),Difference Vegetation Index (DI),Normalized Difference Vegetation Index (NDVI),visualization Visualize Atmospherically Resistive Vegetation Index (VARI),Renormalized Difference Vegetation Index (RDVI),Perpendicular Vegetation Index (PVI) and Vegetation Attenuation Index (PSRI) ,were selected. The original and first-order transformations of the 14 spectral vegetation indices were studied. The correlation matrix method was used to optimize the optimal vegetation index,which was related to the spectral position variable (Red edge magnitude,Dr) and spectral area variables,namely red edge area (SDr),the ratio of red edge area to blue edge area (SDr/SDb),the ratio of red edge area to yellow edge area (SDr/SDy) as spectral feature variables. The inversion model of the net photosynthetic rate about lettuce was established by using a combination of polynomial fitting and multivariate scatter correction (MSC),standard normal variable transformation (SNV),partial least squares (PLS) and principal component regression (PCR) . The results showed that PSRI (515,499) and DI (515,499) at the optimal wavelength position had the greatest correlation with net photosynthetic rate,which could reflect more physiological information of lettuce under particulate pollution. The combined modeling method of SG+MSC+PCR had the highest accuracy,the coefficient of determination Rc was 0.901 1,and the Rp was 0.945 8. The modeling effect based on the optimal spectral vegetation index was the best. The spectral area variable SDr/SDb had the highest fitting accuracy (R2=0.936 5),which could reliably predict the net photosynthetic rate of lettuce. It was the optimal method to establish the net photosynthetic rate of lettuce based on the spectral position and area variables. This work could provide a certain reference value for the inversion of plant physiological information using hyperspectral technology under a particulate pollution environment.

Tea is a high value-added economic crop with extremely high economic value. It is the main starting point for rural revitalization in mountainous areas of China. However, due to destructive behaviors such as deforestation and planting tea, forest resources are destroyed, and ecological and environmental problems such as soil erosion are caused. Acquiring the spatial distribution of tea plantations quickly and accurately is very important for government supervision and the planning and development of the tea industry. However, due to the rainy weather in the study area and the scattered distribution of tea plantations, which are close to the spectrum of vegetation such as forests, the extraction based on satellite imagery has become a problem. Tea plantations are challenging. In order to find out the spatial distribution of tea plantations in Yingde, this paper systematically analyzes the application potential of medium and high-resolution multispectral Sentinel-2 image data combined with multi-time-series and multi-feature information in tea garden extraction. Taking the whole territory of Yingde as the research area, this paper selects 9 phases of Sentinel-2 image data from 2019 to 2021 to analyze the phenological characteristics of tea tree growth in detail and further explore the characteristics changes of tea plantations and other land types in multiple time series, using the Relief algorithm to sort the importance of all features. According to the result of feature sorting, the feature factors weighted by 90% of the feature weight value are selected, namely 7 vegetation index features and 2 texture features, and 9 kinds of tea garden classification scenes are constructed through different combination rankings, and the RF algorithm is used to evaluate the accuracy of all classification scenes. To select the best classification scene and further discuss the feasibility of the RF classification algorithm and SVM classification algorithm for tea garden extraction. The results show that: (1) When extracting tea plantations in Yingde, February and October are the best combinations to construct multiple characteristics of tea plantations using multi-temporal phases. (2) Compared with the SVM classification method, the RF classification method has high accuracy. Its overall accuracy reaches 91.56%, the Kappa coefficient is 0.89, and the producer accuracy and user accuracy are 80.22% and 84.56%, respectively. This study provides an efficient method for quickly and efficiently obtaining the spatial distribution information of tea plantations in Yingde and provides data support for the government to plan and manage the tea industry.

Micro-spectrometers have become a significant trend in the development of spectrometer technology because of the advantages of portability, low cost, and integration. The NIR micro-spectrometer is a class of micro-spectrometers operating in the near-infrared band, which has an extensive range of applications in the field of optical fiber sensing and demodulation, fiber-optic communication, etc. However, contemporary NIR micro-spectrometers typically are low resolution, expensive, bulky, and impractical for portability. A unique micro-spectrometer structure with dual gratings and a cylindrical lens is proposed and carriedout theoretically and experimentally. Three major alterations are adopted in the new design compared to the traditional micro-spectrometer structure: optical fibers are used to reduce light energy loss, dual gratings are used to split the beam by secondary diffraction, and the cylindrical lens is used to change the imaging size on the surface of the line-array CCD. The optical path is decreased to a volume of 66 mm×40 mm×24 mm with a spectral resolution of 0.2 nm in the wavelength range from 1 525 to 1 570 nm according to the simulation analysis with Zemax, which is 2.5 times better than that of spectrometers without a cylindrical lens. Based on the theoretical analysis, suitable optoelectronic devices are selected for system packaging and combined with the irmatching circuit module to realize the spectral detection function of the micro-spectrometer.The optical system of the micro-spectrometer can be installed in a volume of 66 mm×40 mm×30 mm, and the spectral resolution is measured to 0.215 nm, which is in agreement with the theoretical results. Furthermore, a Fiber Bragg Grating (FBG) temperature sensing system based on the micro-spectrometer used as a demodulator is built. Four FBG with central wavelengths of 1 534, 1 538, 1 542, and 1 545 nm were selected as sensors. The temperature varies continuously at 1 ℃ intervals in the 0~50 ℃, resulting in real-time temperature measurement and signal demodulation with a system temperature sensitivity of 9.58,9.68,9.69 and 9.6 pm·℃-1 respectively. So, the micro-spectrometer with-high resolution and reliability is verified. The subcomponents of the micro-spectrometer can be fixed to the shell, which is small in size, high in resolution and good instability. It is expected to be applied to other occasions requiring high-resolution spectral analysis, such as substance concentration analysis, sensing signal measurement, etc.

Real-time fluorescence quantitative PCR is a commonly used detection method in molecular biology, mainly applied to detect DNA or RNA. However, the fluorescence data obtained by this method may feature crosstalk between fluorescence channels since there are overlapping fluorescence spectra and limitations of filter bandwidth. Such crosstalkcomplicates the PCR analysis and may ultimately affect the interpretation of detection results. Crosstalk between fluorescence channels can be reduced or eliminated by choosing appropriate filter combinations and using fluorescence crosstalk correction.Currently, the fluorescence crosstalk matrix is mostly estimated through aniterative algorithm, which is a complex method to obtain fluorescence crosstalk matrix from mixed multi-channel fluorescence data. A single dye experiment is carried out on the hardware platform to quickly calculate the fluorescence crosstalk matrix and reduce the computation. The principal component analysis (PCA) method is applied to estimate the distribution of dye fluorescence signals in each detection channel, and then the fluorescence crosstalk matrix is obtained. The crosstalk matrix shows that, for the built hardware platform, the Cy5 dye has a considerable crosstalk to the Cy5.5 channel with a crosstalk ratio of 8.76%; the Cy5.5 dye has a 6.2% crosstalk ratio to the Cy5 channel; the ROX dye has a 2.68%crosstalk ratio to the HEX channel; the crosstalk ratio of HEX dye to FAM channel is about 1.58%; the crosstalk ratio of FAM dye to HEX channelis relatively small, with only about 0.25%, and the other channels have no apparent crosstalk between each other, which is consistent with the fluorescence spectrum. The fluorescence crosstalk matrix is used to process the raw fluorescence data from the single dye experiment, which effectively removes the fluorescence data from the non-target channel and realizes the decoupling of the fluorescence channel data. The feasibility of the method is thus confirmed. Subsequently, a fluorescence separationexperiment is designed by randomly mixing various dyes of different concentrations to evaluate the quality of the crosstalk matrixs fluorescence correction. The experimental data are subject to fluorescence correction, and the linearity of the fluorescence for each dye is analyzed. The result demonstrates that the linear correlation of each fluorescent channel is high, and each linear correlation coefficient r of the five fluorescence channels exceeds 0.99, further validating the methods effectiveness.

For long-distance space targets moving at high speeds, temperature is one of the important parameters to characterize their working state and performance. Accurately obtaining the temperature of the target has an important reference value for judging its motion state and predicting its situation development. At present, the commonly used processing method of surface target or point target is no longer applicable to the measurement of the radiation characteristics of small targets. At the same time, spectral detection increases the distinguishable information of the target in the wavelength dimension, which can accurately obtain the distribution of the target energy with wavelength, providing a possibility for the inversion of the target temperature, and has great application potential. The slitless spectrometer can reduce the requirements for tracking and stabilization accuracy of space targets, has the characteristics of simple structure, high frame rate and fast response speed, and has high application value in astronomical observation and spacecraft observation. In this paper, we analyzed the spectral calibration model of target infrared radiation characteristic measurement and determined the main parameters in the linear response model of infrared detector pixels. In order to reduce the influence of imaging distance on temperature measurement accuracy, we proposed a target temperature inversion model based on distance correction. The improved temperature measurement accuracy meets the accuracy requirements in practical engineering applications and greatly affects infrared radiation spectrum temperature measurement. Certain guiding significance.

Maize is one of the three major food crops in the world, and the use of substandard seeds that do not meet the national standards will seriously affect the yield of maize crops, so how to identify substandard maize seeds quickly, accurately and efficiently is particularly important. The hyperspectral image system to obtain the 900~1 700 nm spectral curves of 900 “Yuan 3” corn seeds, in which the training set and test set ratio was 3∶2, 540 and 360 seeds respectively. The seeds were treated with an electric blast dryer to obtain corn seed samples with different degrees of damage, and the germination test was completed after collecting the spectra to determine the viability of the seeds. In order to improve the signal-to-noise ratio, the spectral bands of maize seeds in the range of 963.27~1 698.75 nm were intercepted as the effective bands. Standard Normal Variation (SNV) and Multiplicative Scatter Correction (MSC), were used to pre-process the raw spectral data. The Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to extract feature bands from the pre-processed spectral data, with wavelength reflectance as input matrix X and preset sample categories as output matrix Y. The Support Vector Machine (SVM) was used to model and analyze the data, and the results showed that the MSC-CARS-SVM model was the best model, with a model recognition success rate of 98.33% and a Kappa coefficient of 0.985. Genetic Algorithm (GA) was used to optimize the penalty coefficient c and kernel function parameter g in the SVM, and the model accuracy was improved to 100% for the identification of heat-damaged counterfeit and poor-quality maize seeds. This study provides a new idea and method for rapidly identifying the pseudo-inferior quality of maize seeds and seeds of other crops.

In order to meet the increasing demand for the transmission capacity of Dense Wavelength Division Multiplexing (DWDM) systems due to the rapid development of communication networks,the performance requirements for the core device of the DWDM system,Erbium Doped Fiber Application Amplifier (EDFA),are also getting higher and higher. Tellurite glass has become an ideal material to replace the traditional erbium-doped silica fiber because of its high solubility of rare earth ions,low phonon energy and high refractive index. Rare earth-doped tellurite glass can be used as the ideal gain medium of broadband fiber amplifiers to achieve effective signal amplification. Therefore,improving the spectral performance of erbium-doped tellurite glass and expanding its amplification bandwidth are of great significance for the expansion of the DWDM system. In this paper,Er3+,Nd3+and Tm3+are co-doped to improve the amplification bandwidth of tellurite glasses to obtain ultra wideband luminescence. Er3+,Nd3+, and Tm3+ ions can generate luminescence in 1.55,1.34 and 1.85 μm bands through the transition,and these three near-infrared emission bands are adjacent to each other. The luminescence of tellurite glass in a continuous spectrum is realized through energy transfer (ET) between ions by means of co-doping of three ions. In TeO2-WO3-ZnO-Na2O-Er2O3 tellurite glass,Er3+/Nd3+ ions were doped first,and the energy transfer mechanism between Er3+/Nd3+ ions was analyzed. It was found that the glass had a better luminous intensity when the concentration of Er2O3 and Nd2O3 was 1 and 0.1 mol%, respectively. On this basis,Er3+/Nd3+/Tm3+ doped tellurite glasses with good thermal stability were prepared by high-temperature melting annealing. Energy transfer occurs between Er3+,Nd3+ and Tm3+ ions,and three luminescence bands with luminescence centers of 1.3,1.5 and 1.8 μm are generated in the range of 1 250～2 100 nm,covering the whole O,E,S,C,L and U bands. The Full Width at Half Maxima (FWHM) increases to 131.68 nm at 1.5 μm,and the FWHM reaches 251.75 nm at 1.8 μm. The mechanism of energy transfer between rare earth ions during doping of three kinds of rare earth is analyzed in detail. The spectral results show that Er3+/Nd3+/Tm3+ triple tellurite glass is an effective material for the design of ultra-wideband fiber amplifiers when the doping concentrations of Er2O3,Nd2O3 and Tm2O3 are 1,0.1 and 0.2 mol%,respectively.

Calcium carbonate is an important inorganic filler in rubber production, with capacity building and reinforcement functions.With the growing demand for rubber in China, calcium carbonate usage is expanding, and its production costs and energy consumption are becoming increasingly problematic.Steel slag is a byproduct of the steelmaking process and has problems such as low utilization and large accumulation.The main components of steel slag are CaO, SiO2, FexOy, Al2O3, etc. The physical and chemical properties of steel slag are similar to those of traditional fillers, and the wear resistance and porosity of steel slag are excellent, so the use of steel slag to replace some calcium carbonate as a rubber filling material not only has important research significance but also can alleviate the problems of energy consumption and environmental pollution. Based on this, this study used the combined treatment of steel slag with homemade functional compound and ultra-fine vertical mill to obtain a sample shield powder with a particle size of 800 and prepared shield powder/rubber bulking composites with different dosage ratios of active calcium carbonate to shield powder.The prepared shield powder/rubber bulking composites were tested for their properties using an intelligent volcano meter, an electronic tensiometer, a rubber hardness tester and a microcalorimeter (MCC), and the shield powder/rubber bulking composites were characterized and analyzed by thermogravimetric analysis-Fourier transform infrared spectrometer (TG-FTIR) and Raman spectrometer (Ram).The results show that replacing part of active calcium carbonate by shield powder has a certain promotion effect on the vulcanization performance of the rubber system and can improve the vulcanization speed of shield powder/rubber capacitated composites.The addition of shield powder can improve the compound rubber systems mechanical and flame retardant properties. Analysis of the shield powder/rubber bulking composites by microcalorimetry and Raman spectroscopy revealed that the heat release capacity and total heat release of the rubber system after the replacement of part of active calcium carbonate by shield powder decreased, and the graphitization of carbon residue increased, further indicating that the replacement of part of active calcium carbonate by shield powder also has certain flame retardant properties while ensuring the physical properties of the shield powder/rubber bulking composites. Thermogravimetric-infrared analysis shows that the gas produced during the cracking of the shield powder/rubber capacitated composite is mainly hydrocarbons. Adding a home-made functional compound can alleviate the incompatibility of the steel slag interface with the rubber interface and optimise the rubber composite.The above study provides data and theoretical support for preparing shield powder/rubber bulking composites with better performance.

Humic substances (HS) are formed by the polycondensation of various precursors, and the precursors are the key to regulating the formation of HS. Whether the Maillard reaction precursors can promote the microbial transformation of lignin and the formation of HS remains to be verified. Given this, the method of liquid shake flask culture was adopted, the lignin culture medium serving as the research object, and the liquid shake flask culture of 120 d was started by adding single and combined solutions of catechol, glucose and glycine, and collected the supernatant (cell metabolites) and precipitate (microbial residues) by the centrifugation method. The properties of the cell metabolites and microbial residues were analyzed, and an in-depth study of the FTIR spectral characteristics of bacterial residues was necessary, which was used to evaluate the contribution of each precursor to the microbial transformation of lignin to HS more systematically. The results showed that: (1) Adding glycine into the lignin culture medium after the liquid shake flask culture was more conducive to the condensation of cell metabolites organic molecules, making their structures more complex, while the treatments involving catechol and the addition of glucose alone could promote the degradation of cell metabolites and make their molecular structures simpler. Compared with CK, exogenous addition of Maillard reaction precursors could promote the TOC content loss of cellular metabolites, and single catechol could make the TOC content of cellular metabolites always higher than other treatments; (2) The addition of Maillard reaction precursors could significantly increase the recovery rate of microbial residues formed from the microbial transformation of lignin, in which the recovery rate of microbial residues formed from the treatment of single catechol was the largest. In contrast, the combined solution of catechol, glucose and glycine had the smallest effect on the improvement of the recovery rate of microbial residues. The combined solution of glucose and glycine could keep the recovery rate of microbial residue at the highest level throughout the culture period. Adding Maillard reaction precursors could result in a smaller increase in the TOC content of microbial residues than CK. However, at the end of the culture, the four treatments involving catechol and the addition of a combination solution of glucose and glycine could enhance the TOC content of microbial residues significantly higher than CK; (3) The microbial residues formed from the microbial transformation of lignin had hydroxyl O—H, asymmetric aliphatic —CH3, symmetrical aliphatic —CH2—, aromaticCC and polysaccharides, which had similar FTIR characteristics to soil humic acid, but its molecular condensation degree could not reach the complexity of soil humic acid. After the completion of the culture, the hydroxyl content in the microbial residues was increased to different degrees, while the polysaccharide content decreased. The addition of a single glycine and the combined solution of glucose and glycine could increase the aliphatic degree of the microbial residues while the content of polysaccharides decreased. The four treatments involving catechol and a single addition of glucose could further increase the proportion of aromatic C in the microbial residues. In conclusion, the addition of Maillard reaction precursors could reduce the TOC content of cell metabolites, improve the recovery rate of microbial residues, and, at the same time, increase the content of hydroxyl groups and reduce the content of polysaccharides. Different precursors had different effects on the aliphatic and aromatization of microbial residues. The effects of aromatization were different. Four treatments involving catechol and a single addition of glucose could increase the proportion of aromatic C in microbial residues.

Chlortetracycline and oxytetracycline have high detection rates in wastewater treatment plants, usually coexisting in wastewater. Microbial extracellular polymeric substances (EPS) are the protective layers of the microbial cells against harmful substances, which play an important role in microbial life activities. Chlortetracycline and oxytetracycline may have a certain impact on microbial EPS. Nevertheless, there are few studies on the effects of chlortetracycline and oxytetracycline on microbial EPS during biological phosphorus removal. Three mixtures with different rates (L1, L2 and L3) were designed by direct equalization ray method to investigate the effects of chlortetracycline and oxytetracycline on microbial EPS. The effects of chlortetracycline and oxytetracycline and their mixtures on proteins and polysaccharides of EPS were studied. Three-dimensional fluorescence spectroscopy (3D-EEM) and Fourier transform infrared spectroscopy (FT-IR) was used to analyze the effects of chlortetracycline and oxytetracycline on the composition and structure of EPS. The results showed that, with the increase in concentration and the prolongation of reaction time, the performance of the biological phosphorus removal system gradually deteriorated. The contents of protein and polysaccharide of EPS increased first and then decreased, and the protein content was higher than that of polysaccharide. 3D-EEM analysis showed that, with the increase of chlortetracycline, oxytetracycline and their mixtures, the fluorescence intensity of protein-like substances of EPS first increased and then decreased, and humic and fulvic acid-like substances were produced when the concentration was higher. There was antagonism between chlortetracycline and oxytetracycline, so the fluorescence intensity of the mixture was weaker than that of the single action, and only the ratio L3 did not appear as a humic acid and fulvic acid substance. FTIR analysis showed that the CO stretching vibration of 1 653 cm-1 amido Ⅰ band,the CO symmetric stretching of 1 403 cm-1 amido Ⅱ band,the PO stretching vibration of 1 266 cm-1 and the carbohydrate C—OH stretching vibration of 1 100 cm-1 in EPS were affected by chlortetracycline,oxytetracycline and their mixtures. The antagonism between chlortetracycline and oxytetracycline causes differences in functional groups. The P—OH stretching vibration of 1004 cm-1 only exists in oxytetracycline,ratio L2 and ratio L3,The P—O stretching vibration of 900 cm-1 only exists in chlortetracycline,oxytetracycline,ratio L2 and ratio L3. It was absent for 770 cm-1 amino acid COO variable angle vibration only in chlortetracycline. This study will provide a theoretical basis for the scientific evaluation of the mechanism of effects of chlortetracycline and oxytetracycline and their mixed pollutants on microbial EPS during biological phosphorus removal.