Photoacoustic spectroscopy is a novel gas detection technology. Photoacoustic spectroscopy is an indirect absorption spectroscopy technology. Gas concentration inversion is performed by detecting acoustic signals. It has the advantages of simple structure, response speed, and small system size. It is one of the widely used high-sensitivity trace gas sensing methods currently under development and is becoming one of the research focus in detecting toxic gases, explosives and chemical warfare agent simulants. This paper focuses on the research work of photoacoustic spectroscopy in the rapid detection of chemical warfare agent simulants and toxic gases. Photoacoustic spectroscopy can use different laser systems. When the laser emission range is in the mid-infrared band, chemical warfare agent simulants will have characteristic absorption peaks in these bands, so they can also be used to detect chemical warfare agent simulants. This paper introduces the methods of photoacoustic spectroscopy in foreign countries for the detection of chemical warfare agents and simulants. It introduces the method information such as the optical path configuration, laser light source, etc. in the method, as well as the structural characteristics of the newly built gas absorption cell. The characteristic absorption wavelength range of photoacoustic spectroscopic detection of chemical warfare agents and simulants was analyzed. In addition to the traditional detection of chemical poisons in gas absorption cells, methods and systems for the open optical path telemetry of photoacoustic spectroscopy in the range of several centimeters to tens of meters have been continuously developed in recent years to detect chemical warfare agent simulants. The article also reported the method of photoacoustic spectroscopy in detecting ammonia, hydrogen sulfide, HF, sulfur dioxide and other toxic and harmful gases. This paper analyzes the related work on light sources and methods, acoustic wave transducers, characteristic spectra of chemical poison simulants, and detection performance. The foreign short-range telemetry photoacoustic spectroscopy prototype is reported, and the potential development of photoacoustic spectroscopy short-range spectral telemetry technology is reviewed.

When obtaining the real temperature by multi-spectral radiometric thermometry, the target emissivity information is the key to calculating the temperature. The general solution is to establish an emissivity model based on the function between emissivity and wavelength or temperature. However, when the assumed model deviates from the actual situation, it can cause large temperature measurement errors. Therefore, eliminating the interference of the unknown emissivity of the target, reducing the reliance on the emissivity model, and increasing the universality of the temperature measurement algorithm are the urgent challenges to be solved in multi-spectral radiometric thermometry. This paper propose an improved hybrid optimization algorithm, particle swarm optimization and genetic algorithm(HPSOGA). The core idea of the algorithm is to transform the multi-wavelength radiometric thermometry problem into a constrained optimization problem. Firstly, a group of spectral emissivity satisfying the constraint is initialized, constituting a population. The fitness value is calculated after taking the emissivity into the objective function established by the reference temperature model of multi-spectral radiometric thermometry. The population continuously evolves and iterates in the feasible domain by HPSOGA algorithm until the fitness value is the smallest. The corresponding temperature of each spectral channel is approximately equal. In this algorithm, the spectral emissivity and the real temperature of the target can be inverted simultaneously without assuming an emissivity model. Simulating six typical emissivity models verifies the new algorithms adaptability to the inversion of spectral emissivity with different distribution trends. The results show that the average relative error of the inversion temperature is less than 0.73% for the cases of true temperature 800 and 900 K. Finally, the algorithm is applied to process rocket motor plume flame temperature measurement data. The results show that when the design temperature is 2 490 K, the relative errors of the inverse temperature are less than 0.65%. Both simulation and experiment show that the new algorithm can solve the emissivity and true temperature to meet certain accuracy requirements. Therefore, the HPSOGA algorithm proposed in this paper is reliable and effective and provides a new way formulti-spectral radiometric thermometry to measure the true temperature of the target.

The physical temperature of shock is an important parameter for weapon ammunition performance testing and the characterization of material states in extreme environments. Accurate acquisition of temperature has vital significance in the fields of national defence and industrial manufacturing. The shock process has characteristics of short duration, difficulty in contact-based measurements, and a wide temperature range, which can cause the failure of conventional temperature measurement methods. This paper proposes a temperature measurement method based on multi-spectral radiometry, which obtain the values of material radiation intensity. The inversion model based on Plancks radiation law can obtain the shock physical temperature value. In practice, the randomness of different target emissivity can cause large errors using the temperature inversion model. The emissivity model of the material during the shock process is more difficult to obtain. Meanwhile, the materials structure under shock may change in phase, which leads to a change in the emissivity model. Therefore, it is difficult to accurately obtain the shock physical temperature value by directly assuming the emissivity model. In this paper, the temperature calculation in multi-spectral temperature measurement experiments turned into a constrained optimization problem based on the constrained optimization theory. The temperature value obtained for each channel should be the same, limiting the object emissivity to a specific range. The constraint optimization algorithm calculates the target temperature and emissivity, which can overcome unknown emissivity for a shock physical temperature solution. At the same time, the combination of Equilibrium Optimizer and Sequential Quadratic Programming is applied to the solution of the temperature model, which avoids the shortcomings of poor stability and slow speed of a single algorithm. It improves the efficiency and accuracy of temperature inversion. The emissivity data of four common emissivity models at 3 000 K are simulated and verified. The results show that the temperature inversion error is less than 1%, and an inversion time is within 3 seconds. Finally, the temperature inversion of copper under shock compression is carried out using this algorithm. Compared with the Least Squares Method and Interior Point Penalty Function Method, the results indicate that the method proposed in this paper obtains the impact physical temperature value of copper more closely to the theoretical calculation. Therefore, this method provides an effective inversion approach for obtaining the physical temperature of other targets with unknown emissivity models.

Solar cells are widely used as an efficient photo-converter in photovoltaic power generation systems. Laser, as a kind of high light source, will cause damage to the battery. The surface scattering spectrum characteristics of the battery can be used to determine the damage degree. This paper used the target surface scattering spectrum measurement system to measure the scattering spectrum of three junction GaAs cells irradiated by laser, and the bidirectional reflection distribution function (BRDF) was calculated. The measurement system comprised an FX 2000 and NIR 17 optical fiber spectrometer. Based on the strong mirror reflection characteristics of the battery surface, a geometric model of incident and reflection angle of 30° was adopted in the experiment.The structure of the original GaAs solar cells mainly consists of the antireflection film DAR layer, the top cell GaInP layer, the middle cell GaAs layer and the bottom cell Ge layer. The scattering spectrum characteristics of GaAs solar cells include absorption characteristics in the visible spectrum (500~900 nm) and period-like oscillation characteristics in the near-infrared spectrum (900~1 200 nm). When the cell was damaged by continuous laser irradiation, the change of the spectral BRDF of the damaged ones was obtained. The characteristics of the damaged film layer were analyzed.The results showed that the function of the DAR layer was to reduce the spectral reflection energy, and it had little effect on the characteristics of the spectral curve.The Ge layer had little effect on the change of the spectral curve, too.The scattering spectrum characteristics of the battery were mainly caused by the GaInP and GaAs layer. The GaInP layer mainly affected the absorption characteristics of the visible spectrum and modulated the interference characteristics of the near-infrared spectrum, while the GaAs layer mainly affected the interference characteristics of the near-infrared spectrum. As the damage of the GaInP layer reached a certain extent, it would lead tothe interference characteristics in the visible spectrum.Finally, based on the analysis of the experimental results, the influence of each layer of the battery on the scattering spectrum characteristics was studied according to the provided model, and the analysis of the battery damage based on the characteristics was discussed. The results can provide the basis for the damage degree determination of the battery.

A multi-structure needle-ring electrode argon plasma jet device was designed to grasp the quantitative influence of the reactor structure parameters and discharge parameters on the jet length of the atmospheric pressure non-equilibrium plasma jet (N-APPJ). The effects of discharge voltage, electrode gap, the distance between the discharge end of the high-voltage electrode and the ground electrode, and the volume flow of argon on the jet length were calculated. The results show that the maximum length of the plasma jet can reach 80mm; the longer the distance between the discharge end of the high voltage electrode and the ground electrode, the longer the jet length but not linearly; the jet length first increases and then decreases with the increase of the electrode gap, and the jet reaches the maximum length when the electrode gap is 4.5 mm; with the increase of the volume flow of argon, the length of the plasma jet also shows a trend of first increasing and then decreasing, and the decreasing amplitude is low; the electron excitation temperature It is higher at the high-voltage electrode and the ground electrode, and the part between the two electrodes is second, and there will be a more obvious drop at the outlet of the quartz tube.

Acetonitrile, widely used in pharmaceutical and chemical industries, is a flammable and explosive chemical which can cause fire accidents with great harm. It is of great practical value to explore the fire pollution characteristics of acetonitrile combustion by studying its temperature and concentration fields and flame radiation spectra. In this paper, the spatial concentration values of NO, a product of acetonitrile pool fire, at the 20, 40, 60, and 80 s on a 5 cm-diameter scale were obtained using Planar Laser Induced Fluorescence (PLIF) and Fluent numerical simulation methods, and the temperature and concentration fields of acetonitrile combustion at different times were obtained by combining CFD and FDS simulations. Secondly, data from the temperature field and concentration field of the acetonitrile flame (the flame was divided into six thermodynamic equilibrium regions) were used to construct an acetonitrile flame spectral radiation model based on absorption coefficients of high-temperature gas molecules and overall radiative transfer equation of the flame in HITRAN database. Again, data from the concentration field and temperature field of the acetonitrile flame were substituted into the flame spectral radiation model, and the model simulation results were compared with the measured acetonitrile flame spectral data under the same conditions to verify the model accuracy and compare with the Radcal model. Finally, the concentration inversion of NO, a characteristic pollution product of combustion, was performed using the self-built flame spectral radiation model. The results showed that: (1) the flame temperature range of 5 cm-diameter acetonitrile pool fires was 400～1 000 K, and the temperature was higher in the region of 60～80 mm above the pool fire with the highest temperature of 945 K; (2) the volume fractions of combustion products of 5 cm-diameter acetonitrile pool fire at 20, 40, 60 and 80 s moments were 0.005%～0.025 5% for NO, 0.034 5%～0.062 5% for H2O, and 0.055 5%～0.085 5% for CO2; (3) an acetonitrile flame spectral radiation model was built by ourselves, and comparison between the model simulation value and the actual measured value showed that: in combustion products, the CO2 characteristic peak accuracy was 86.8% min and 88.7% max; NO was 79.6% min and 84.9% max; and H2O was 84.6% min and 89.1% max. Compared with spectral radiation values calculated by the Radcal model, the calculation accuracy of our model was improved by about 10%; (4) the inversion accuracy of the concentration of NO, the characteristic product of acetonitrile combustion, in dominant band of 5.62～5.66 μm at moments of the 20, 40, 60, and 80 s was 76.9%, 78.5%, 94.7%, and 81.3%, respectively. This study can provide a basis and reference for detecting combustion field information of large-scale acetonitrile chemical fires and the quantitative inversion of combustion pollution product concentrations by remote sensing.

Faience is a glass-like material composed of quartz, fusing and coloring agents. It is one of the earliest artificial materials, first appearing in West Asia 5 000 years ago and is usually made into beads. After being introduced to Central China via the Grassland Silk Road, the Faience beads grew in popularity there during the Western and Eastern Zhou Dynasty. Chinese ancient people learned and kept improving the manufacturing technology of faience beads for hundreds of years. This article selected the faience beads of the warring states period excavated from Zenghou Yi tomb and Xiongjiazhong tomb in Hubei province for testing. Various methods were used to determine the spectroscopy characteristic and manufacturing technology of the samples, including conventional gemological tests, microscopic imaging, Fourier infrared spectroscopy (FTIR), laser Raman spectroscopy (LRS), scanning electron microscopy-energy dispersive X-ray spectrometry (SEM-EDS) and Laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS). The results revealed that the brown faience beads of the Zenghou Yi tomb differed from the blue and green faience beads of the Xiongjiazhong tomb in their composition. The boundary between the glaze and main body, porosity and fragmentation of the beads could be seen in the microscopic image. In addition, there is a small amorphous part on the glaze layer of the bead from the Xiongjiazhong tomb. LRS was used on the blue glaze layer of the beads from the Xiongjiazhong tomb and the spectrums composite. 128, 207, 362, 468, 692, 797, and 1 188 cm-1 were the characteristic peaks of quartz, and 979 cm-1 was the characteristic peak of calcium phosphate. The formation of calcium phosphate may be due to the plant ashes in the fusing agent. The peaks at 590 and 1 066 cm-1 were related to CuSiO3·H2O. 3 346 and 3 435 cm-1 were characteristic peaks of water. The results of SEM-EDS and LA-ICP-MS showed that there were more impurity substances in the material of the faience beads from Zenghou Yi tomb, and they had a higher content of S element, which indicated the glaze layer might use copper sulfide ores. Meanwhile, the material of the faience beads from the Xiongjiazhong tomb was pure, and the main body was composed of a large number of pure quartz sands, with white quartz weathered layer on the surface. It can be indicated that the faience beads from Zenghou Yi and Xiongjiazhong tombs were not from the same production center, and the manufacturing technology of the latter was advanced. There could be multiple production centers of faience beads in China during the Warring States period.

Spot disease is a common foliar disease with outbreaks in maize production areas worldwide, seriously affecting maize yield and quality. Fluorescence spectroscopy can reflect the physiological information of crops quickly and accurately without loss, and dynamically detect its response pattern to adversity. In this study, we investigated the response patterns of maize physiological parameters to different degrees of spot diseases based on the fusion analysis of fluorescence spectra and physiological parameters (SPAD and Fv/Fm) and constructed a fluorescence spectral inversion model. Firstly, the sensitive bands of fluorescence spectra were screened by correlation analysis and peak analysis, and multivariate scattering correction (MSC), standard normal variable transformation (SNV), polynomial smoothing (Smoothing), and the inversion model were used. Savitzky-Golaay (S-G), FD spectral first-order derivative, SD spectral second-order derivative, and four modeling combinations such as MSC-SG-FD, MSC-FD-SG, SNV-SG-FD, SNV-SG-SD, etc. The correlation coefficient R2 and the root mean square error RMSE were used as the evaluation indexes to determine the optimal method for fluorescence spectral inversion. The results showed that modeling the different spot disease levels was not as effective as modeling the physiological parameters. The results showed that the overall trend of fluorescence spectral properties under different spot disease degrees was consistent, but the intensity varied significantly, and the spectral reflectance would show an obvious peak center and reach the extreme value in the band 600.000～800.000 nm. After the band 900.000 nm, the reflectance leveled off and the features decreased significantly. For latent phase leaves, the modeling optimal method for both SPAD and Fv/Fm is SNV-SG-FD with Rc of 0.985 2 and 0.976 8 and RMSEP of 1.59 and 0.015 0. For early onset leaves, the modeling optimal method for SPAD is SNV-SG-FD with Rc of 0.949 7 and RMSEP of 3.79, and the Fv/Fm The modeling optimal method was SNV-SG-SD with Rc of 0.943 8 and RMSEP of 0.011 7. The high predictive accuracy of the model indicates that accurate prediction of SPAD and Fv/Fm for early spot diseased maize leaves can be achieved, providing a reference basis for monitoring physiological information during the latent and early disease stages of maize spot disease. The results of this paper can be applied to field operations, which improves the level of fine and intelligent management in the field and provides the theoretical basis and technical support for high yield, high quality and eugenics of maize.

The study aimed to clarify the VIS/NIR spectral characteristics of Cabernet Sauvignon leaves with phosphorus deficiency, then to construct a rapid and nondestructive diagnosis model, which is expected to help the vineyard management and disease control. Firstly, the grape leaves in healthy, early and later stress by phosphate deficiency were analyzed by VIS/NIR micro fiber spectrometer. In order to remove noise interference, four preprocessing methods, including Savitzky-Golay convolution smoothing (S-G Smoothing), moving average smoothing (MAS), standard normal variate (SNV) and multiple scattering corrections (MSC), were used to optimize spectral signals. Then, the successive projections algorithm (SPA) was used to select the feature wavebands of leaf phosphate deficiency. Finally, the support vector machine models were constructed based on four different kernel functions, including linear kernel function (Linear), polynomial kernel function (Poly), radial basis function (RBF) and Sigmoid tanh function (Sigmoid), to diagnose the phosphate deficiency of leaves. The sensitivity (SEN) and accuracy (CCR) were cited to assess the availability and effectiveness of those models. Experimental results proved that S-G Smoothing was the best preprocessing method because of the better signal-to-noise ratio of spectrum processed by it and the good availability of the model based on it. Principal component analysis (PCA) was used to find outliers with a confidence interval of 95%. 22 samples were identified with outliers and removed. Eleven wavebands (402.6, 404.6, 409.0, 411.5, 539.4, 691.9, 729.9, 838.7, 1 011.9, 1 017.5 and 1 020.5 nm) were selected by SPA to consider as reflecting the information of phosphate deficiency and be the input variables of the diagnosis model. After the contrast of four models with different kernel functions, it can be known that the SVM model with Linear showed better sensitivity and accuracy than others. Its SEN was 81.08%, and CCR was 100% for healthy leaves, its SEN was 100%, and CCR was 84.78% for early-stage diseased leaves, and its SEN and CCR were 100% for late-stage diseased leaves. In this study, A rapid and nondestructive diagnosis method was proposed based on VIS/NIR spectroscopy for phosphate deficiency of the Cabernet Sauvignon leaves, which is expected to improve the management and disease control of the vineyard and the intelligence of wine grape cultivation.

Rapid and accurate detection of the acidity of fermented grains can significantly improve the yield of Baijiu and the quality of finished liquor. Near infrared spectroscopy (NIR) mainly contains information on octave and ensemble frequencies of molecules, i. e., the vibrations of hydrogen-containing groups (C-H, N-H, O-H) in organic matter. It is usually used for qualitative and quantitative analysis of hydrogen-containing compounds in samples. The NIR can be used to determine the acidity of fermented grains in a simple, rapid overcoming the shortcomings of traditional chemical analysis methods, such as long detection cycles, large reagent consumption, and human errors. As NIR is an indirect analysis technology, establishing a calibration model is the key to accurately detecting the acidity of fermented grains. As a typical model in deep learning, convolutional neural networks (CNN) have the advantages of local area connection and weight sharing. It can not only extract critical features from complex spectral data, but also reduce the complexity of network models. Therefore, a quantitative analysis method for the acidity of fermented grains based on CNN and NIR is proposed in this work. The research object is the spectral data of 545 fermented grains samples collected in the production line of a wine enterprise, and the original spectra are preprocessed using a combination of three algorithms: standard normal variation (SNV), Savitzky-Golay(SG) filtering and first derivative (1stD); uninformative variable elimination (UVE) is used to select the characteristic wavelength of spectral data; CNN is used to establish the acidity model of fermented grains. The results show that: (1) The pre-processed spectral data eliminated the baseline shift and noise problems in the original spectra, increased the prediction set coefficient of determination by 22.85%, and decreased the root mean square error by 0.049 5 compared with the original spectral modeling, which improved the correlation between the acidity of fermented grains and spectral reflectance. (2) The model established after wavelength screening of spectral data increased the determination coefficient of the prediction set by 2.04% and decreased the root mean square error of the prediction set by 0.004 8 compared with full-wavelength modeling. (3) The acidity prediction model based on CNN had a determination coefficient of 0.955 5 and a root mean square error of 0.039 1. Compared with the partial least squares model, the determination coefficient of the prediction set is increased by 1.03%, and the root mean square error of the prediction set is reduced by 0.097 6. Compared with the backpropagation neural network model, the determination coefficient of the prediction set is increased by 1.16%, and the root mean square error of the prediction set is reduced by 0.099 4. The research results can realize the rapid and accurate measurement of the acidity content of fermented grains and provide method support for subsequent online detection of the acidity of fermented grains.

The industrial culture of microalgae provides an important way to produce natural carbohydrates and proteins. The high cost of cultivation is one of the main “bottlenecks” that influences microalgae industrialization development. Due to the high growth rate, the biomass and nutrients of microalgae vary rapidly. Therefore, a method that can -monitor the growth of microalgae and the dynamic information in the culture of microalgae would be of great necessity for the timely optimization of environmental parameters, thereby ensuring the efficient and quality production of microalgae. However, studies on the rapid and non-destructive detection of microalgae growth and metabolism information were mostly focused on lipids and their characteristics, with the other important components neglected, such as carbohydrates and proteins.In this study, T. obliquus was used as the research objects. A fast and nondestructive approach to estimate the carbohydrates and proteins concentration of T. obliquus in situ in a living environment was proposed based upon visible/near-infrared (VIS/NIRS) HSI system. Twelve data preprocessing approaches e.g. autoscaling and standard normal variate transform (SNV) etc., 3 feature selection methods including competitive adaptive reweighted sampling algorithm (CARS), interval random frog algorithm (iRF) and simulated annealing algorithm (SA), and 4 calibration models including multiple linear regression (MLR), partial least squares (PLS), support vector machine regression (SVR) and random forest regression (RFR) were applied to establish and optimize the estimation models. The results showed that vector normalization (VN) pretreatment combined with the CARS-MLR algorithm got the best performance on the biomass prediction of T. obliquus, with an R2p of 0.967 and RPD of 6.212. Raw spectra followed by the IRF-RFR algorithm performed the best for the carbohydrate of T. obliquus (R2p=0.996, RPD=36.156). Wavelet transform (WT) with SA-RFR obtained the best results for protein detection (R2p=0.909, RPD=10.116). Moreover, the visualization maps of these components spatial distribution and abundance in the microalgal liquid were obtained based on the optimal models. The overall results show that VIS/NIRS HSI is expected to be applied for efficient and high-quality production in microalgae industries.

Research on efficient, accurate and convenient soil heavy metal detection methods is of great significance for understanding soil pollution and carrying out pollution prevention and control. Because X-ray fluorescence spectrometry (XRF) technology has the advantages of being fast, accurate and non-destructive, it has been widely used in detecting element content. The XRF method obtains the concentration of the sample to be tested by measuring the fluorescence intensity of the sample to be tested, and establishing a corresponding relationship using the fluorescence intensity of the standard sample of the calibration curve and the corresponding concentration. However, due to the existence of matrix effect and spectral line overlapping interference, the element spectral line intensity obtained in the actual XRF analysis test and its corresponding concentration do not show a relatively perfect linear relationship. In order to solve the above problems, this paper uses wavelet transform and asymmetric weighted penalized least squares (arPLS) to denoise the spectrum and correct the baseline, which improves the determination coefficient of the calibration curve to a certain extent. The characteristic energy spectral line selection model of different heavy metal elements was constructed by the Competitive Adaptive Reweighting Algorithm (CARS) algorithm to explore the aggregation performance of the characteristic spectral lines. Further, based on the selected features, the particle swarm optimization (PSO) optimized support vector machine regression (SVR) model is used to predict the element content, and the generalization ability of the quantitative analysis model is improved. Partial least squares regression (PLSR) and SVR models are used to compare. The results show that: after pretreatment, the coefficients of determination of the calibration curves of Cr, Cu, Zn, As, Pb are improved from 0.965, 0.979, 0.971, 0.794, 0.915 to 0.979, 0.987, 0.981, 0.828, 0.953; the characteristic lines selected by CARS In addition to the elements to be analyzed, some also correspond to the soil matrix effect elements and the corresponding spectral line interference elements, which shows the effectiveness of the CARS algorithm in feature selection, and the number of variables has changed from 2 048 to 9~29, which is 0.43%~1.42% of the original number of variables, which makes the variables of feature selection more statistical and intelligent; the content prediction using the PSO-optimized SVR model is higher than the accuracy of SVR and PLSR, training set and test set. The coefficients of determination are above 0.99 and 0.89, respectively.

The chemical determination method of quality traits is cumbersome, destructive and time-consuming. The spectral determination method has the advantages of high efficiency, speed and low cost, but the accuracy is affected by different instruments and models. In order to establish and optimize the model for rapid determination of crude protein (CP), ether extract (EE), acidic detergent fiber (ADF) and neutral detergent fiber (NDF) using near-infrared diffuse reflectance spectra of alfalfa samples, and better determine the quality traits of alfalfa. A total of 147 samples of 25 alfalfa materialswere selected. The scanning spectral values of the spectral range of 4 000～10 000 cm-1 are obtained by scanning with Fourier transform near-infrared spectroscopy (NIRS). The software TQ Analyst V9 adopts partial least squares (PLS), and OPUS 7.0 adopts the quantitative 2 methods to establish and optimize the quantitative model and further carry out cross-validation and external test to evaluate the effect of the model. The results showed that the models for determining CP content were also through two software. Two modeling coefficient of determination (R2cal) were 0.999 and 0.984 8, the root mean square error (RMSECV) of cross-validation was 2.121 and 0.471, respectively. The coefficient of determination (R2) of external validation is greater than 0.97, and the ratio of standard deviation to SEP (RPD) was greater than 6.0. The model established by TQ analyst V9 was better for EE with R2cal of 0.999 7, RMSECV of 1.502, R2 of external verification of 0.929 3 and RPD value of 3.89. The models established by OPUS 7.0 were better for ADF and NDF with R2cal of 0.944 1 and 0.978 8, RMSECV of 1.040 and 0.514, R2 of external verification of 0.914 5 and 0.911 8, and RPD of 3.66 and 3.43, respectively. The modeling results of four quality traits showed that the models of TQ Analyst V9 are more accurate for CP and EE with relatively simple molecules structure, while the models of OPUS 7.0 are more accurate for ADF and NDF with relatively complex molecular structures.

In recent years, the fluorescence excitation-emission matrix (EEM) technique has become common for chemical analysis, but fluorescent organic matters with similar structures may exhibit extremely similar EEMs, which may mislead the analysis results. Thus, precisely distinguishing the organic matters with similar EEMs is an important problem to solve. Fluorescence quantum yield (FQY) and fluorescence lifetime (FL) are two important optical parameters for EEM, which are more sensitive to the difference in molecular structure. This study investigated the EEM, FQY, and FL of indole, 3-methylindole, and L-tryptophan. The result showed that their EEMs all displayed one emission (Em) maxima corresponding to two excitation (Ex) maxima, and the position is very close. The fluorescence peaks of indole and L-tryptophan were roughly located at Ex/Em=［275, 340～350］ and ［220, 340～350］ nm, the fluorescence peaks of 3-methylindole were roughly located at Ex/Em=［280, 365］ and ［225, 365］ nm. At the same concentration, these three compounds maximum fluorescence intensity (MFI) at Ex = 275~280 nm followed this sequence: indole>3-methylindole>L-tryptophan. The integral sphere technique evaluated the FQYs of indole, 3-methylindole and L-tryptophan as 0.264, 0.347, and 0.145, respectively. The FLs of indole, 3-methylindole and L-tryptophan were determined as 4.149, 7.896 and 2.715 ns, respectively. This study indicated that the FQY and FL can distinguish fluorescent organic matters with similar EEMs. The results are of great value in accurately identifying fluorescent organic matters.

Rapid and accurate identification of soybean varieties play an important role for identifying seed quality, purifying the seed market and ensuring food security. The traditional identification methods of crop varieties have the problems of poor accuracy and low efficiency. Therefore a PLS identification model was established by Raman spectroscopy combined with characteristic wavelength extraction to fast identify four high-oil soybean varieties (Heinong 87, Heinong 89, Suinong 38 and Suinong 77) in Heilongjiang Province. RF is a new characteristic wavelength selection algorithm that determines the importance of variables by iteratively calculating the selected probability, which can remove redundant information to a great extent in the full spectrum. However, this method has the disadvantages of the random initial variable set, a large number of iterations and uncertain threshold selection. Therefore, an improved random frog (MRF) algorithm based on LASSO regression was proposed. In order to get rid of the randomness of the initial variable set in the RF algorithm, LASSO was used to extract the characteristic wavelength point most related to the attribute variable as an initial variable set F0. On this basis, iterative calculations were carried out to reduce the number of useless iterations and improve the models prediction accuracy. In addition, RF selects variables by setting a threshold, which leads to the uncertainty of the extracted characteristic wavelength. The improvements were as follows: Firstly, the variables with the selected probability of 0 were removed, taking 10 wavelength points as intervals for the sorted variables. Then, the partial least squares discriminant analysis model between the characteristic wavelengths and soybean varieties was built by adding one interval each time, and taking the wavelength subset with the smallest RMSECV as the selected characteristic wavelengths. The PLS-DA model was established with the selected characteristic wavelengths of MRF as the input variables and compared the prediction performance with full spectrum and other characteristic wavelength selection methods of RF, LASSO and ElasticNet algorithms. The results indicated that the MRF algorithm selected 300 characteristic wavelength points, accounting for only 9.37% of the full spectrum, which effectively screened the key characteristic variables and simplified the complexity of the model. The RMSEP and R2p were 0.246 9 and 0.951 2 respectively, and the identification accuracy reached 100%, which was the best among all models. Therefore, Raman spectroscopy combined with MRF algorithm could achieve the fast identification of soybean varieties and provide a new technique for the fast identification of other crop varieties.

With its unique technical advantages, such as non-destructive detection, high sensitivity, simplicity and speed, Raman spectroscopy has shown good application potential in food safety and other fields. Liquor is a very popular drink, and everyone has highly valued its quality and safety. There are always some inferior blended liquors in the market that seriously affect everyones health, and there is no clear detection method in the current national inspection standards to identify them. If these inferior blended liquors are quickly identified, it will effectively protect everyones safety. We measured the Raman spectra of 56 bulk liquors and 7 bottled branded liquors using Raman spectroscopy. The fluorescence background of all spectra was analyzed with the C-C-O stretching vibration peak of ethanol at 886 cm-1 in the spectrum as the internal standard. The results show that the fluorescence background of bottled brand liquor is smaller than that of bulk liquor, which can clearly distinguish the two types of liquor samples. For the measurement and analysis of Raman spectra, it is generally believed that the fluorescence background will affect the experimental results, so various methods have been developed to reduce or subtract the fluorescence background. Our work shows that retaining the fluorescence background may be more beneficial to the quality detection of liquor. This detection method is very simple and quick to operate and analyze if this method is combined with a portable Raman spectrometer, an effective and rapid detection method can be provided for the quality and safety of liquor.

Two-dimensional correlation spectroscopy (2D-COS) and three-dimensional excitation-emission matrix fluorescence technologies coupled with parallel factor analysis (EEM-PARAFAC) are characterized by separating overlapping peaks and insight into different component variations. Therefore, the 2D-COS and EEM-PARAFAC technologies can be used to probe into the compositions and spectral changes of dissolved organic matter (DOM). Here, the compositions and variations of DOM in solar ponds isolated from three representative salt lakes, i. e, Qarham Xitaijinaier and Mahai salt lake, were investigated using dissolved organic carbon UV-Visible absorption spectrum (UV) and EEM coupling with 2D-COS and PARAFAC. The results indicated that the contents of DOM and color DOM (CDOM) increased with the prolongation of sunshine times, and they increased 1.5 vs. 1.0, 8.2 vs. 5.3 and 15.7 vs. 11.0 times for DOM and CDOM they originated from Qarham Xitaijinaier and Mahai, respectively. Moreover, the values of SUVA254 and HIX decline in solar ponds suggested that the relative contents of aromatic compounds were decreased. The 2D UV-COS results indicated that the DOM with absorption peaks at 230, 217 and 235 nm were susceptibility in solar ponds, and following the sequence: 228>229>230>231>232 nm &235>234>233>232 nm, 200>216>300 nm and 201>203>231>232>237>238>281>217 nm for Qarham Xitaijinaier and Mahai, respectively. The EEM-PARAFAC results revealed that the salt lake DOM is mainly composed of four humic-like substances, i. e., marine humic-like component C1(Ex/Em: 320/400 nm), humic-like acids C2 (Ex/Em: 250/400 nm) and C3 (Ex/Em: 260/400 nm), hydrophobic humic acid C5 (Ex/Em: 280, 360/430 nm) and one protein-like substance C4 (Ex/Em: 280/350). The percentages of humic-like substances were 84.0%, 87.2% and 93.1% in total fluorescent contents in Qarham, Xitaijinaier and Mahai, respectively. Along with the sunshine extent, the relative contents of C1, C2 and C3 exhibited an initial decrease followed by a gradual decline or stabilization, especially C2 absence from the tail brine, indicating its lability. C3 and C4 exhibited an initially decrease, followed by the gradual increase in the solar ponds of Qarham and Mahai. Compared to other components, C3 and C4 were more refractory to degrade, i. e., 6.7%<C3/C4<75.2% vs. 52.8%<C1/C2/C5<100%.

Ternary cocrystal is a new cocrystal design strategy developed based on binary cocrystal, which can improve the physicochemical properties of drugs without affecting their biological and pharmacological activities. Therefore, ternary cocrystal shows much potential in the development and research of drugs. Since ternary cocrystal involves the complex assembly of three different molecules, the complexity of which increases with the type and number of molecules involved in pharmaceutical cocrystal, and the number of potential cocrystal hydrogen-bonding sites also increases, it is difficult to obtain a specific ternary cocrystal. There are few reports on the microscopic molecular structure of a specific ternary cocrystal system. In order to understand the hydrogen-bonding form of the ternary cocrystal structure, it is crucial to obtain the molecular structure information of the related binary and ternary cocrystals in the ternary cocrystal system by detecting means to understand the complex formation process of the ternary pharmaceutical cocrystal. In this paper, the isonicotinamide-glutaric acid, pyrazinamide-glutaric acid binary cocrystals and isonicotinamide-glutaric acid-pyrazinamide ternary cocrystal were successfully synthesized by mechanical grinding. The binary and ternary cocrystal structures were studied by terahertz time domain spectroscopy (THz-TDS) and density functional theory (DFT). The experimental results of THz spectroscopy showed that both binary and ternary cocrystals showed their unique spectral characteristics. The crystal structure analysis showed that in the isonicotinamide-glutaric acid-pyrazinamide ternary cocrystal structure, the hydroxyl group in the carboxyl group on the glutaric acid side and the pyridine N in isonicotinamide formed a carboxyl-pyridine N hydrogen bond heterosynthon, while the amide in isonicotinamide forms an amide-amide hydrogen bond homosynthon with the amide in pyrazinamide. Finally, the theoretical THz spectra calculated by DFT were compared with the experiment, and it was found that the superposition of hydrogen bond forms of isonicotinamide-glutaric acid and pyrazinamide-glutaric acid binary cocrystals is not completely consistent with the hydrogen bond form of isonicotinamide-glutaric acid-pyrazinamide ternary cocrystal. However, the hydrogen bond forms of the two binary cocrystals are of great reference value for predicting the hydrogen bond forms of the ternary cocrystal. These results provide a wealth of information and unique methods for the emerging field of pharmaceutical cocrystals to study the molecular assembly and intermolecular interactions of specific ternary cocrystals at the molecular level.

Carbon Quantum Dots (CQDs), a class of zero-dimensional carbon nanomaterials with significant fluorescence properties, have become popular in biosensing application research in recent years. Lead comes from many sources in environments such as cosmetics and industrial pollution, and inhalation or ingestion of lead adsorbed on particulate matter can cause lead poisoning, which can cause various diseases, so point of care detection of lead ion (Pb2+) content is extremely important in clinical medical applications. Based on the fluorescence characteristics of carbon quantum dots, a new blue and red dual-emission ratio fluorescent probe was proposed for rapid detection of lead ion content, and the morphological structure and properties of the probe were characterized, detected and analyzed by transmission electron microscope, fluorescence spectroscopy and other means, and the optical properties and application feasibility of Pb2+ response probe were studied in depth. The double emission carbon point is calibrated by comparison with itself to improve the detection effect and sensitivity of the analyte concentration, and effectively avoid the interference of the external environment. This proportional fluorescent uses aqueous synthesis, which is simple and reproducible, and can rapidly respond to Pb2+ in just a few seconds. The detection process can be observed naked-eye from blue to red with only an ultraviolet lamp, which can be used for clinical point-of-care detection. In the concentration range of Pb2+ in line with current medical applications of 0~0.5 mg·L-1, the fluorescence intensity IBCDs/IRCDs has a good linear relationship with concentration, R2=0.987 44, and the detection limit is 0.013 6 mg·L-1. The progressive fluorescence sensing of the probe with Zn2+, Fe3+, K+ and other ten metal interfering ions showed that the probe had a good specific selection for Pb2+. The lead response was measured at different pH environments and incubation times to investigate probe stability.

1,1,1,3,4,4,4-heptafluoro-3-(trifluoromethyl)-2-butanone (C5-PFK) gas has attracted widespread attention at home and abroad due to its excellent electrical insulation properties and good environmental protection properties. Preparing high-precision C5-PFK mixed gas and accurately detecting the mixing ratio is conducive to the scientific demonstration of C5-PFK mixed gas and minimizes potential power hazards. The FTIR experiment, combined with the B3LYP method for spectral theoretical calculation, was used to study the infrared spectral absorption characteristics of C5-PFK gas. For CO2 and micro-water gas that may exist in the test environment, the cross-interference analysis of spectral lines was carried out under the same conditions as temperature, pressure and optical path; The C5-PFK gas mixture ratio sensor was simulated and tested based on NDIR technology, and the overall design of the sensor hardware system was carried out. According to the output characteristics of the sensor, a BP neural network temperature compensation model is established, and the repeatability and indication error of the sensor are tested. The results show that the strong absorption peak positions of C5-PFK gas are 1 200, 1 262 and 1 796 cm-1, respectively, and the molecular theoretical calculation agrees with the gass measured infrared spectrum. Under the background of synthetic air, the absorbance of CO2 gas at 1 262 cm-1 position is 6.04×10-7, the influence factor of micro water peak area in 150 nm filter bandwidth is about 3.15×10-3, and the cross-interference of spectral lines can be ignored. It is feasible to realize mixing ratio detection at a 1 262 cm-1 position. The range tracks the optical path, and the sensor simulation test results show that the 6.5 mm optical path can realize the detection of the C5-PFK mixture ratio of 0~15%. The output characteristics of the sensor show that the value of the absorption variable SA/SB decreases with the increase in temperature, showing a nonlinear relationship. The maximum indication error of 10% C5-PFK/Air mixture before and after temperature compensation by the BP neural network algorithm is 29.23% and 1.29%, respectively, and the output absorption variable SA/SB value remains unchanged after compensation. The sensor repeatability test shows that the RSD is 0.27%, less than 3%. The linear fitting coefficient R2 of the sensor indication corresponding to different concentrations is 0.999, and the maximum indication error is 2.47%. In summary, the advantages of the detection method and its sensor in the detection range of C5-PFK gas mixture ratio, anti-interference ability and reliability are verified, and a feasible solution for detecting the mixture ratio of C5-PFK gas mixture electrical equipment is provided.

The effects of different varieties of tea are different because of their different organic chemical components. Therefore, it is essential to find a technical method that can accurately and quickly identify tea varieties. Near-infrared (NIR) spectroscopy is a nondestructive detection technology correctly identifying tea varieties. Due to noise signals in the NIR spectra of tea samples collected by the NIR spectrometer, a fuzzy linear discriminant QR analysis method was proposed to accurately identify the NIR spectra of tea samples containing noise signals. After the dimensionality of NIR spectra was compressed by principal component analysis (PCA), it was reduced using fuzzy linear discriminant analysis (FLDA). The discriminant vector matrix was constructed from the eigenvectors obtained by FLDA. The discriminant vector matrix was decomposed by QR decomposition to obtain a new discriminant vector matrix. Then, the K-nearest neighbor (KNN) algorithm was used for classification, which has the advantage of high accuracy. Four kinds of tea samples, namely Yuexi Cuilan, Luan Guapian, Shiji Maofeng and Huangshan Maofeng, were taken as the experimental samples. There were 65 tea samples in each category, and the total number of tea samples was 260. Firstly, the NIR spectral data of tea samples were collected by the Fourier NIR spectrometer Antaris Ⅱ. Secondly, the obtained NIR spectral data of tea were preprocessed, and the scattering effect of spectral data was reduced through multiple scattering correction. Thirdly, the dimensionality of NIR data is 1 557, so PCA was used to reduce the dimensionality of the spectra to 7. Then, fuzzy linear discriminant QR analysis was performed to extract the identification information from the compressed NIR spectra, and the dimensionality of the data was further reduced to 3 dimensions. Finally, KNN was used to classify tea samples and achieved the accurate classification of tea varieties. Furthermore, the experimental results were compared including three algorithms, which are PCA combined with KNN, PCA and linear discriminant analysis (LDA) combined with KNN, PCA and fuzzy linear discriminant QR analysis combined with KNN. Under the weight index m=2 and K=1, the final classification accuracies of the three algorithms were 83.89%, 87.78% and 98.33%, respectively. The experimental results showed that fuzzy linear discriminant QR analysis provided a method for the identification of NIR spectra of tea, and its effect was better than PCA and LDA.

Near-infrared spectroscopy has the characteristics of high dimension, high redundancy, and nonlinearity, which seriously affects the similarity measurement results between samples. This paper proposes a t-distributed stochastic nearest neighbor embedding algorithm (Wt-SNE) based on Wasserstein divergence. Based on the idea of manifold learning algorithm, Gaussian distribution is used to convert the distance of high-dimensional data into a probability distribution, and t-distribution is used to represent the probability distribution of corresponding data points in low-dimensional space, which is more inclined to long-tailed distribution. The probability distribution embedding of high-dimensional data is mapped to the low-dimensional space. The low-dimensional manifold structure is reconstructed, the Wasserstein divergence is introduced to measure the difference between the probability distributions in the two spaces, and the similarity of the two distributions is improved by reducing the divergence value. In this way, the dimensionality reduction processing of high-dimensional data is realized. In order to verify the effectiveness of the Wt-SNE algorithm, this paper first performs dimensionality reduction projection on tobacco NIR spectral data and compares it with PCA, LPP, and t-SNE algorithms. The results show that the sample category boundaries in the low-dimensional space are more obvious after the dimensionality reduction of the Wt-SNE algorithm. Secondly, the KNN, SVM, and PLS-DA classifiers were used to predict the tobacco origin of the reduced-dimensional data, and the accuracy rates were 93.8%, 91.5%, and 92.7% respectively, indicating that the reduced-dimensional data not only reconstructed the spatial structure of the original spectrum but also retained the similarity relationship between samples. Finally, tobacco from a particular cigarette formula was selected for single material target tobacco replacement, and the replacement samples were selected based on the Marginal distance between the candidate samples and the target samples. The experiments showed that the replacement tobacco selected by Wt-SNE had the highest similarity to the target tobacco, the chemical composition contents such as nicotine and total sugar were less different from those of the target tobacco, and the aroma, smoke, and taste scores showed high consistency. The method can effectively measure the similarity between the NIR spectra of the tobacco and provide a strong basis for the maintenance of the cigarette formula.

MC-LR possessing strong hepatotoxicity, potential carcinogenicity, and biological toxicity could have an intensive influence on human health and the aquatic ecological environment and need to be monitored critically. For the specific and highly sensitive analysis performance, agold-magnetic composite nanoparticlemodified by aptamer-cDNAfluorescent hybridization probe (Fe3O4@MIL-101-NH2@Au@aptamer) was prepared and applied for sensing detection of MC-LR in water with laser-induced fluorescence (LIF). This work studied the feasibility, optimization of analysis conditions, specificity, stability of the method, and sample analysis application. As a result, a high Brunauer-Emmett-Teller (BET）specific surface area of 114.02 m2·g-1 was achieved in Fe3O4@MIL-101-NH2@AuNPs, which resulted in a high modification rate of up to 98.8% achieved for aptamer-cDNA fluorescenthybridization probe. With the excitation wavelength of 497 nm, the aptamer-cDNA fluorescent probe has a strong fluorescence response with the emission wavelength of 512 nm to the trace MC-LR. Under the optimal conditions (pH 7.5, NaCl concentration 500 mmol·L-1, nanogold size 20 nm, analytical time 30 min), a good linear relationship between MC-LR concentration and fluorescence intensity was achieved in a wide linear range (0.020~3.000 μg·L-1) with the limit of detection (LOD) as low as 0.006 μg·L-1, which was 1.6~22.3 folds better than that of the most fluorescence methods reported previously. The relative standard deviation (RSD) of intra-day, intra-day, and inter-batch was gained in 1.7%~8.8%, and the relative error (RE) of -4.3%~4.1%. High specific selectivity and low cross-reactivity for MC-LR (1.0 μg·L-1) were also achieved using theas-prepared fluorescent probe, even if the concentration of interfering toxins (MC-RR, MC-YR, OA) was 100 folds higher than that of MC-LR. The fluorescence responses of MC-LR in these mixtures werewell consistent with that of MC-LR in the standard solution. Applied to the analysis of samples from the Minjiang River, Lake Water, and the inland river, the identification of MC-LR in water samples was satisfactory. The recovery yields of MC-LR for three concentrations (0.050, 0.100 and 1.000 μg·L-1) rangedin (90.1%±6.4%)~(104.2%±7.0%) (n=3). The results were consistent with those obtained by the LC-MS method ［（91.3%±7.0%)~(104.4%±2.0%), n=3］. It might light a new technique for the specific and rapid monitoring technology for tracing MC-LR in the environmental water.

Raman spectroscopy has the advantages of high resolution, fast analysis, simple sample preparation, nondestructive, online measurement, etc. It is widely used to analyze the composition and molecular structure information of a wide range of organic and inorganic substances for qualitative and quantitative analytical measurement. At present, it is mainly used for quantitative analysis. When used for quantitative analysis, the poor reproducibility and the imperfect analysis theory of Raman spectroscopy are two main factors that limit its application. In the quantitative analysis based on intensity ratios, the emergence of Raman intensity normalization theory has provided a theoretical basis for its application. The choice of the reference peak/internal standard peak and the fitting method have a great influence on the measurement accuracy and stability. In this paper, the relative intensities of the characteristic peaks (C-C-O symmetrical stretching, 874 cm-1) of the Raman spectra of ethanol and other reference/internal standard peaks with different ethanol concentrations were investigated using a laser Raman system. A peak ratio method based on ethanol intrinsic peak and an internal standard method based on the characteristic peak position of CCl4 were developed, and both methods can effectively eliminate the effects of mutation noise and strong fluorescence background in the system through normalization. The reference peak/internal standard peak with the best accuracy and stability of the two methods was determined by statistical methods such as joint hypothesis testing for data differences within and between different groups. The F-test and t-test showed that the standard curve established with the characteristic peak at 1 446 cm-1 (CH3-asymmetric deformation) as the reference peak could more accurately invert the concentration of ethanol when the calibration was performed by the self-peak ratio method, while the standard curve with the Raman characteristic peak at 446 cm-1 as the internal standard peak had higher stability and accuracy when CCl4 was used as the internal standard. The retest within 30 days eliminates the need to measure and plot the standard curve again. The linear regression model established according to the two calibration methods can provide an experimental basis for the quantitative analysis of ethanol solution concentration. The ethanol concentration can be inverted more accurately in real-time, through the application of the model in the ethanol solution concentration detection system, to achieve accurate, rapid and real-time quantitative analysis of ethanol solutions in the high concentration range with strong fluorescence background interference.

Salt damage is a common and serious disease in porous cultural relics. Complex water and salt activities make the salt continuously destroy the cultural relics. Accurate identification of soluble salts in cultural relics is anessential prerequisite for studying the salt damage mechanism and management, and the design and development of appropriate protection materials for cultural relics. In this study, an operational procedure for the extraction and identification of soluble salts in cultural relics was established. Through the optimization of ion chromatography (IC), optical microscopy, infrared spectroscopy (FTIR), Raman spectroscopy (Raman), and scanning electron microscopy (SEM-EDS), accurate identification of common soluble salts in cultural relics has been achieved. The research shows that NaCl can be identified by IC (Na+ and Cl- in leachate), microscopic observation (cubic crystal) and SEM-EDS (mainly containing Na and Cl elements); CaCl2 can be identified by IC (Ca2+, Cl-) and SEM-EDS (Ca, Cl elements); Na2SO4 can be identified by IC (Na+, SO2-4), microscopic observation (clumped or cluster crystal), FTIR (main peaks 1 134, 637, 615 cm-1), Raman (main peak 993 cm-1) and SEM-EDS (Na, S, O elements); CaSO4 can be identified by IC (Ca2+, SO2-4), microscopic observation (hexagonal prism, needle or rod-shaped crystal), FTIR (1 144, 668, 603 cm-1), Raman (1 017 cm-1) and SEM-EDS (Ca, S, O elements); NaNO3 can be identified by IC (Na+, NO-3), FTIR (1 379, 1 353, 837 cm-1), Raman (1 071 cm-1) and SEM-EDS (Na, N, O elements); Ca(NO3)2·4H2O can be identified by IC (Ca2+, NO-3), FTIR (1 437, 1 367, 1 047 cm-1), Raman (1 059 cm-1) and SEM-EDS (Ca, N, O elements). In addition, cubic NaCl, clumped, cluster or flake Na2SO4, hexagonal prism, needle or rod-shaped CaSO4can be directly identified through microscopic morphology observation. CaCl2, NaNO3 and Ca(NO3)2·4H2O have strong hygroscopicity and have rapid deliquescence when exposed to room temperature, according to which it can be determined. This identification technology combines common instruments, has relatively low cost, is simple and easy to operate, and has reliable results. It can be used to accurately identify of soluble salts in cultural relics of different materials, with good prospects for application and promotion.

Copper-based artefacts are the important material manifestations of the development of ancient Chinese civilization and possess multiple types of values. Copper-based artefacts suffer from corrosion in burial and storage environments, resulting in the formation of multiple types of corrosion products on the surface of the artefacts. Different varieties of corrosion products would have different influences on the stability of the objects. The chlorine-containing corrosion is the most concerning one since it is mostly related to “bronze disease”. The “bronze disease” is a corrosion phenomenon induced by chloride ions with a high developing rate and would cause severe damage to the metallic body of copper-based artefacts. Therefore, the accurate and rapid identification of corrosion products and the determination of their stability are of great importance for copper-based artefacts. This study focuses on the corrosion products of gold-painted copper-based bodhisattva (Guanyin) in half lotus position collected in the National Museum of China. Multiple analytical methods, including macro X-ray fluorescence imaging (MA-XRF), fiber optics reflection spectroscopy (FORS), scanning electron microscope and energy dispersive X-ray spectroscopy (SEM-EDS) and laser confocal micro Raman spectroscopy (Raman) were applied to study the composition, structure and distribution of its corrosion products. The results indicate that the corrosion products on the statues surface mainly include copper trihydroxychlorides (at acamite and clinoatacamite) and chalconatronite. A type of copper-zinc hydroxychlorides was also identified on the statue, which was seldomly found on copper-based artefacts before. The chemical formula is calculated as Cu3.52-3.64Zn0.36-0.48(OH)6Cl2. The result provides a new reference for future researchstudy of copper-zinc hydroxychlorides. The distribution of various types of corrosion products on this statue was revealed comprehensively through the current analytical work. Copper trihydroxychlorides are mainly located at the statues head, face, hands, legs, feet and lotus base. This study builds a scientific basis for accurately assessing the influence of “bronze disease” on the deterioration degree of the statue. The results are important for making proper preservation plans for the artefact. Combining macro and micro analysis methods could provide a new and effective way to investigate corrosion products on copper-based artefacts.

Exosomes are nano-sized phospholipid bilayer-enclosed vesicles secreted by all cells into the extracellular milieu. Released exosomes contain cell-specific proteins, membrane lipids, mRNA, DNA and microRNA that can perform versatile roles in normal or diseased processes. Exosomes are ideal biomarkers of cancer, which have important application potential in liquid biopsy and are expected to become one of the means for rapid cancer detection. Surface enhanced Raman spectroscopy (SERS) is a molecular vibration spectrum, which can detect the fine structure and information changes of substances at the molecular level, and has the characteristics of a “fingerprint spectrum”. In this study, the exosomes were isolated via differential centrifugation combined with ultracentrifugation of the supernatants of breast cells. The SERS profiles of breast cancer cells and their exosomes were collected with colloidal Au nanoparticles as the enhanced substrate, and multivariate statistical analysis was used to identify and distinguish breast cancer cells. The results showed that breast cancer cells and their exosomes had characteristic Raman signals in the range of 500～1 600 cm-1. Their Raman phenotypes obtained by non-labeled detection were the presentation of all the signals of the“whole-organism fingerprint” of the sample. The accuracy rate reached 100% by using exosome-SERS detection and OPLS-DA analysis. Single-cellular SERS detection combined with PCA-LDA analysis showed that the accuracy of differentiating breast cancer cells was 83.7%. Breast cancer cells and their exosomes showed similarity at the bands of 506～569 and 1 010～1 070 cm-1, but the characteristic Raman peaks of exosomes at 735, 963 and 1 318 cm-1 were significantly higher than those of cells. It may be because the structure of exosomes is simpler than that of cells, and the information of biological macromolecules such as nucleic acids and proteins can be characterized more easily, indicating the feasibility of rapid identification of breast cancer by detecting exosomes by SERS technology. In summary, this study established a non-labeling and direct detection method for rapidly detecting single cells and their exosomes by SERS analysis. Combined with multivariate statistical analysis, different types of breast cancer cells could be quickly identified, and the relationship between exosomes and maternal cells was explored from the perspective of Raman omics. This method has the advantages of non-labeling, rapidness, sensitivity, accuracy and simplicity, which provides an effective technical means for rapid diagnosis and screening of breast cancer in vitro, and lays a foundation for clinical application.

Above-ground biomass and chlorophyll are important indexes in alfalfas growth process, which can effectively help the dynamic monitoring and management of alfalfa growth. As the most important forage crop, how to effectively and accurately predict the status of alfalfa by using modern spectral intelligence technology is an important issue in the planting process of alfalfa. However, in the development process of spectroscopy, its progress in agriculture is relatively slow. Therefore, establishing a rigorous and accurate prediction model based on spectroscopy knowledge requires certain algorithms, training, testing and verification. Therefore, this experiment studied the estimation results of above-ground biomass and chlorophyll content of different alfalfa varieties based on UAV multi-spectrum and established the prediction model. In this experiment, a total of 21 alfalfa varieties were studied. The UAV equipped with a multi-spectral camera was used to take images in sunny weather without wind, and the images captured by the UAV were analyzed by ENVI 5.3 software. NDVI,EVI, SAVI, Green NDVI,NDGI, DVI, NGBDI, OSAVI, NDRE and MSR. These 10 vegetation indexes and 5 based bands (blue, green, red, red edge and near-infrared) which UAV cameras were analyzed, and then Matlab 2020b software was used to analyze these indexes. A support vector machine (SVM) was used to build the prediction model of above-ground biomass and chlorophyll content in the different alfalfa varieties. In the actual operation, it was found that the accuracy of the prediction model built by SVM was not ideal. Therefore, this experiment used intelligent algorithms whale (WOA) and Gray Wolf (GWO) to optimize the SVM prediction model. The results showed that all prediction models could roughly predict the above-ground biomass and chlorophyll content of different varieties of alfalfa. Among the three models, the SVM prediction model optimized by WOA intelligent algorithms had the highest accuracy in estimating above-ground biomass and chlorophyll content of different alfalfa varieties. Therefore, this experiment can provide certain guidelines for the selection of alfalfa varieties with better quality in the future agriculture. It also provides effective help and reasonable reference for the UVA multi-spectral estimation of alfalfa biomass and its related physiological and ecological indicators in the future.

Soil hyperspectral technique could estimate soil organic carbon content efficiently. Continuous wavelet transform had unique advantages in noise removal and effective information extraction of hyperspectral data. However, the spectral data after continuous wavelet transform was decomposed into multiple scales, and the information of a single decomposition scale could not represent the information of different decomposition scales. Making full use of the wavelet coefficients of multiple decomposition scales becomes a difficult problem for hyperspectral estimation of soil organic carbon content. Lake Bosten was the largest inland freshwater lake in China, and the lakeside oasis, as an important interlacing zone between land and water, had a unique spatial and temporal structure and played an important role in maintaining and restoring the health of the lake ecosystem. The study area was the lakeside oasis of Bosten Lake. 138 surface soil samples were collected in September 2020 at a depth of 0~20 cm, 3 outlier samples were excluded to obtain 135 useful samples, soil sample spectra were collected outdoors, and soil organic carbon content was determined by potassium dichromate-external heating method. The continuous wavelet transform was then performed with Gaussian4 as the wavelet basis function to convert the soil hyper spectrum into wavelet coefficients at 10 decomposition scales, and the correlation coefficient method, Stability Competitive Adaptive Reweighted Sampling, Competitive Adaptive Reweighted Sampling, Successive Projections Algorithm, Genetic Algorithm, five special wave filtering methods to further reduce noise and eliminate redundant information, calculate the root mean square of wavelet coefficients as wavelet energy feature scale by scale, and form a wavelet energy feature vector (Energy Feature Revector) with 10 scales of wavelet energy features, and build a BP neural network model (BP neural network model) based on the wavelet energy feature vector. The result showed that wavelet continuous transform could effectively improve the correlation between spectral reflectance and soil organic carbon content, with poor correlation at the 1~3 decomposition scale and good correlation at the 4~10 decomposition scale, with an average increase of 43.66% in the correlation coefficient and an average increase of 67.93% in the maximum value of the correlation coefficient. The feature band screening CC algorithm was mainly distributed in 400~1 500 nm visible and NIR short wavelength; sCARS and CARS algorithms were concentrated in 1 500~2 500 nm NIR long wavelength; SPA algorithm was concentrated in 760~2 500 nm NIR band; GA algorithm was uniformly distributed in 400~2 500 nm. The hyperspectral wavelet energy feature could better estimate the organic carbon content of the surface soil of the lakeshore oasis, and the mean R2 values of the training and validation sets of the six models were 0.73 and 0.74, respectively; the mean RMSE values were 7.64 and 7.28, respectively; and the mean RPD value was 1.95. The model accuracy showed that CC-EFV-BPNN＞sCARS-EFV-BPNN＞Full-spectrum-EFV-BPNN＞CARS-EFV-BPNN＞GA-EFV-BPNN＞SPA-EFV-BPNN. The continuous wavelet transform combined with the feature variable screening method to extract the wavelet energy feature vector effectively reduces the spectral data dimensionality and hyperspectral wavelet energy feature model complexity, an important reference value for rapidly estimating surface soil organic carbon content.

Leaf area index (LAI) is an important parameter for evaluating crop growth, rapid, accurate and low-cost acquisition of LAI has great significance for guiding crop field management. To achieve low-cost acquisition of LAI for multiple crops, the general LAI prediction models were built based on multi-source information and deep learning. The field experiments were carried out in six growth periods of soybean, wheat, peanut, and maize to obtain multi-source information for modeling. In addition, relevant one-dimensional data were collected, including UAV flight attitude angles, image capture height, crop growth states and environmental illumination. With the help of the excellent image and data processing ability of deep learning, the LAI prediction models were built accurately based on complex input information. Considering that the one-dimensional data also participated in the training process of the models, the combined network architecture was adopted in the design of the models. Based on extracting image depth features by the convolutional neural network (CNN) algorithm, the LightGBM (Light Gradient Boosting Machine Method) algorithm was added to realize the final prediction of crop LAI by combining image features and one-dimensional data. Four common network structures, VGG19, ResNet50, Inception V3 and DenseNet201, were used in four CNN models. In order to better illustrate the ability of CNN models to extract image features, the crop classification results of the four models under different image inputs were analyzed. The results showed that the classification accuracyof the four models with inputs using multispectral images was better than that of inputs using visible images only. The classification accuracy of the models based on Inception V3 and DenseNet201 was more than 99%, which proved the validity of the CNN model in extracting multispectral image features. Taking the image features as inputs of the LightGBM model to predict LAI, the results were shownthat the maximum R2 betweenthe measured value and the predicted value of LAI is 0.819 2. After one-dimensional data were addedtothe inputs, the R2 of the models can reach more than 0.9, which indicates that multi-source information inputs play an important role in improving the accuracy of the LAI prediction models. The models established in this study can predict LAI for multiple crops without the complex processing for multispectral images. Therefore, this study can realize the low-cost and rapid prediction of LAI and have high LAI prediction accuracy.

Soil phosphorus is one of the most important nutrients for plants. Phosphorus is highly dynamic in soil, and it is not easy to detect it. It has no obvious absorption band in the visible-near infrared range. Therefore, rapid phosphorus detection methods based on other spectral methods are of great significance for developing precision agriculture and/or smart agriculture. Raman spectroscopy has the characteristics of interference-free from water, less sample pretreatment, and complementary to infrared spectral information. Many researchers have tried to use Raman spectroscopy to detect soil phosphorus. However, the weak Raman signal and poor stability restrict its application in soil sensing. To clarify the quantitative relationship between Raman spectra and phosphorus, water-soluble phosphorus (KH2PO4) was used as a research target, and the effects of KH2PO4 solutions with different phosphorus concentrations on Raman spectra were studied. The raw spectra (RS) were processed by moving average (MA), MA+baseline correction (BL), MA+standard normal variable (SNV), and MA+multivariate scattering correction (MSC). Low concentrations (0.02~5 g·L-1) and high concentrations (5.21~93.87 g·L-1) of KH2PO4 and their relationships with Raman spectrum variation characteristics were analyzed. A prediction model for phosphorus concentration content was established. The results show that: (1) the coefficient of variation of the spectra in the low concentration range and the high concentration range were significantly different, and the dispersion degree of the spectra in the high concentration range was larger; (2) No obvious Raman peaks were detected in the Raman spectra of the low concentration range. Concentration changes exhibited significant baseline shifts. The coefficient of Determination (R2) of partial least squares regression (PLSR) models was 0.28~0.36; (3) Characteristic Raman peaks at 863 and 1 070 cm-1 were identified in the high concentration range, and PLSR modeling results were R2=0.65~0.7. The MA+SNV and MA+MSC treatments had higher prediction accuracy than the MA alone, indicating that the Raman characteristic peaks of phosphate radicals are the main contributing factors of the model; (4) PLSR modeling using the full concentration range can increase the prediction accuracy (R2=0.73~0.89). The modelling accuracy of using RS was the highest, indicating that the baseline shift has a positive effect on the PLSR results; (5) Through the PLSR regression coefficient, 645, 863, 1 070, 1 412 cm-1 were selected as characteristic bands to establish a multiple linear regression (MLR) model, and the coefficient of determination R2 was close to 1. It shows that the selection of characteristic bands can filter out the interference of background light and extract the effective features of phosphate variation. It can be seen from the above results that it is feasible to detect the content of water-soluble phosphorus by Raman spectroscopy quantitatively. Besides reducing the interference of background light and improving the stability of the Raman signal, a method for selecting characteristic bands is important to improve the repeatability and anti-interference ability of the model for high-resolution detection of Raman spectroscopy.

The resource utilization of magnesium slag is an urgent problem. This paper uses magnesium slag and modified magnesium slag as the main cementing materials, and magnesium slag based cementing materials (UCGB) and modified magnesium slag based cementing materials (MCGB) are prepared by adding fly ash. The flow characteristics, mechanical properties, microstructure and hydration characteristics of the filling materials are compared and analyzed. The results show that when the mixing ratio of modified magnesium slag and fly ash is 4/1, the prepared MCGB sample has excellent mechanical properties, and the uniaxial compressive strength is up to 4.213 MPa after curing for 28 days. With the growth of curing age, MCGB sample hydration produces a large number of hydration products such as C-S-H gel, Ca(OH)2 crystal and filamentous Ettringite, which are interwoven and agglomerated with other silicate oxides (［Fe, Mg, Al］2.5［Si, Al］2O5［OH］4). Filling in the pores and holes inside the sample is helpful in improving the mechanical properties and durability of the MCGB sample. Compared with the MCGB sample, the mechanical properties of the UCGB sample are not ideal. The early strength of the UCGB sample is low, and only a small amount of Ettringite and Ca(OH)2 crystals are produced by hydration, forming a porous microstructure. In addition, the infrared spectrum curve analysis shows that the characteristic frequency of β-C2S appears near 997 cm-1 of the modified magnesium slag, while the characteristic identification spectrum band of γ-C2S appears near 820 cm-1 of the magnesium slag. Combined with the X-ray spectrum analysis, the mineral phase of the modified magnesium slag mainly changes to β-C2S. However, the mineral phase of the original magnesium slag is mainly γ-C2S, which has almost no hydration activity, so it is unsuitable for mining as a cementitious material. Therefore, this study aims to provide scientific basis and guidance for the preparation of new mine cementitious materials based on modified magnesium slag.

Temperature is an important parameter for assessing the thermal radiation damage of the ammunition. After the detonation of the ammunition will compress the surrounding air in a very short time and violently release a large amount of energy to the surrounding area. Along with the release of energy, ammunition media will be sharply warmed up, and the formation of the flame field, by measuring and analyzing the true temperature value of the flame field, it is possible to obtain the spatial thermal radiation damage effect of the explosion flame. Due to the explosion processs strong destructive and transient nature, the measurement of the explosion flame is mainly dependent on radiation pyrometer. In previous studies, scholars have developed corresponding radiometric devices for the measurement of the explosion flame. However, the developed devices can only measure the bright temperature field of the explosion flame at a single wavelength, a single wavelength bright temperature field cannot achieve the calculation of the true temperature value. This paper developed a multi-spectral thermal imager, the imager uses multi-amplitude spectroscopy technology that can realize the explosion flame true temperature field at the same time, different wavelengths of spectral imaging, and the use of a high-speed CCD camera for data acquisition, and finally based on multi-spectral radiometric temperature theory inverse performance of the explosion flame true temperature field. The multi-amplitude spectroscopy technology is accomplished by the long-range multi-aperture spectroscopy lens, divided into two main parts: the main imaging lens and the multi-aperture spectroscopy lens. The function of the main imaging lens is to image the long-range ammunition explosion field, the image which converges through a single convex lens to the rear of the multi-aperture spectroscopy lens. The multi-aperture spectroscopy lens has a built-in spectral light bar, the light bar can be set with different wavelength narrowband filters. When the image is through the narrowband filter on the light bar, the transmitted light will be the measured target of single-wavelength radiation energy, the use of multiple single-wavelength radiation energy can be through the multi-spectral radiation thermometry theory for the calculation of the true temperature value. In this paper, the long-range multi-aperture spectroscopic lens can image the explosion flame up to 500 m, and according to the actual demand of the lens light bar design for the four-split structure, while the light bar for the convenience of filter replacement into the pluggable form. The lens weighs about 0.75 kg and can be mounted directly on the high-speed CCD camera by the flange, which fully meets the requirements of field experiments. In order to verify the validity of the instrument, the explosion flame true temperature field test was conducted on 1.660 9 kg of TNT. The test results show that the maximum temperature value is 3 251 K at 0.1 ms after the explosion, and the true temperature field gradually expands with time passing, but the corresponding maximum temperature value gradually decreases; when the time is 0.6 ms, the maximum temperature is 2 483 K.

Online monitoring of the aircraft skin laser paint removal process is an important means to achieve layered and controllable paint removal and meet airworthiness maintenance requirements. It is also the key technology to promote the industrial application of laser paint removal and aircraft maintenance automation. Currently, the main monitoring methods include surface imaging and process performance parameter measurement methods. However, these methods have inherent limitations, making it difficult to be online and real-time. Laser-induced plasma breakdown spectroscopy (LIBS) technology has the advantages of equipment simplicity, flexibility, quickness and sensitivity, which has been widely used in online monitoring and research of laser cleaning of artworks and cultural relics. Based on the established high-frequency nanosecond infrared pulsed laser paint removal LIBS online monitoring platform, three LIBS spectra (100 frames each) were collected during the removal of topcoat, primer and aluminum alloy substrate under different laser powers. The changes of characteristic spectral lines of various spectral tracer elements under different laser powers were analyzed, and 12 characteristic spectral lines were preliminarily screened as the characteristics of spectral identification. Principal component analysis (PCA) was further performed on these 12 characteristics. The data set composed of the first three principal components (PC1, PC2 and PC3) was used as the input of the support vector machines (SVM) identification model, and the identification model of three types of spectral data was established. A LIBS online monitoring and judgment rule for the controllable removal process of laser layering of multi-paint-layer structure was formed, and the rules validity was experimentally verified. It can be seen from the results that, compared with the needle-like LIBS spectra collected based on low-frequency pulsed laser single-point action, in general, the LIBS spectra collected based on this platform show a strong continuous background (greater than 5 000 a.u.) and a full width at half maximum of about 1.5 nm; an improved mean smoothing filtering algorithm was designed for this type of spectrum, which effectively avoids the intensity distortion of the characteristic spectral line while removing the background spectrum; under different laser powers, the characteristic spectral line of the tracer element is unstable; the contribution of the first three principal components, i.e., PC1, PC2, and PC3 in the principal component analysis to the explanation of the spectral data reaches 95%. The same type of spectra is clustered regionally in the three-dimensional space formed by them. The recognition accuracy of the PCA-SVM model on the training set and test set is 99.44% and 100%, respectively; the verification experimental results show that the identification models of the three types of spectra and the online monitoring and judgment rules are effective. The established identification model and judgment rules can provide key technical support for the monitoring and automation solutions of the aircraft skin laser layered paint removal process.

Photoacoustic spectroscopy gas detection technology is the most typical application of photoacoustic technology. Compared with other methods, photoacoustic gas detection technology has the advantages of a simple structure, wavelength free detector, zero background noise and low cost. This technology has been widely used in various fields. The photoacoustic cell is the core component of the photoacoustic spectrum gas detection system, and its performance greatly impacts the detection results. At present, the optimization of the photoacoustic cell is mainly carried out under static conditions, and there are few reports on the gas flow performance and dynamic time response in the photoacoustic cell cavity. Because the gas disturbance and system detection noise of the photoacoustic cell under dynamic detection conditions have a certain impact, the relevant parameters of the photoacoustic cell are further explored and optimized to improve the gas flow field distribution in the photoacoustic cell cavity. Dynamic pressure characteristics and gas concentration equilibrium time are of great significance to improve the gas detection performance of photoacoustic spectroscopy. Therefore, based on the traditional cylindrical photoacoustic cell, the steady-state and transient simulation models of the flow field in the photoacoustic cell cavity are established based on the three-dimensional flow field numerical simulation method, and the gas flow field distribution and gas concentration balance response law in the photoacoustic cell cavity are calculated. The results show that reducing the flow velocity in the photoacoustic cell and optimizing the transition structure in the photoacoustic cell will improve the dynamic pressure fluctuation caused by the flow and shorten the gas concentration regulation time in the cavity. Taking the five parameters of the transition corner between the buffer cavity and the resonator of the photoacoustic cell, the number of auxiliary holes, the radius of the auxiliary hole, the radius of the center circle of the auxiliary hole and the air inlet speed as factors, and taking the dynamic pressure value at the midpoint of the resonator axis and the gas concentration adjustment time as inspection indexes, the method of combining numerical simulation, orthogonal experimental design and entropy weight method is adopted, The primary and secondary order of the influence of the relevant parameters of the photoacoustic cell on the dynamic pressure value is obtained as follows: the radius of the auxiliary hole>the number of auxiliary holes>air inlet speed>transition fillet>the radius of the center circle of the auxiliary hole; The primary and secondary order of influence on the adjustment time is: inlet speed>auxiliary hole radius>number of auxiliary holes=auxiliary hole center circle radius>transition fillet. In order to balance the influence of indicators, this paper transforms the multi-objective parameter optimization problem into a single objective optimization problem. It objectively gives the weight of dynamic pressure value as 0.49 and the weight of adjustment time as 0.51 respectively. Within the range of parameters studied in this paper, the best combination of parameters is obtained: the circle at the transition between the buffer cavity and the resonator is 3.0 mm, the number of auxiliary holes is 8, the radius of auxiliary holes is 3.5 mm, the radius of the center circle of auxiliary holes is 22.5mm, the air inlet speed is 0.06 m·s-1, the dynamic pressure value at the midpoint of the axis of the optimized photoacoustic cell resonator is 9.4×10-4 Pa, and the gas concentration adjustment time in the cavity is 141 s. Compared with the indicators before the optimization of the photoacoustic cell, the dynamic pressure value is relatively reduced by 88.1%, and the adjustment time is relatively reduced by 17.5%. Both indicators have been optimized and improved, and the optimization effect is relatively ideal. The research methods and conclusions can provide important references for photoacoustic cell optimization design and expansion.

Mangrove ecosystem is one of the most productive ecosystems on the earth, and it is one of the important components of the coastal "blue carbon" ecosystem. As an important part of mangrove blue carbon, obtaining mangrove aboveground biomass accurately and quickly has become one of the hot issues in mangrove ecosystem research. Analyzing spatial distribution pattern and magnitude of Aboveground biomass (AGB) of mangroves in the Maowei Sea of Beibu Gulf can provide the scientific basis for the protection of the mangrove ecological environment and the ecological restoration of “South Red and North Willow” in this area. As a domestic civil hyperspectral satellite independently developed by China, the hyperspectral data of ZY-1-02D provides a new opportunity to research mangrove aboveground biomass. Because of its high performance and efficiency, machine learning algorithms are increasingly used in mangrove-related research. It has become an important means to obtain mangrove parameter information. How accurate is the retrieval of hyperspectral data in mangrove aboveground biomass, whether the domestic hyperspectral satellite data and machine learning algorithm can be applied to the estimation of mangrove aboveground biomass needs further verification. Based on ZY-1-02D Satellite hyperspectral data, three different machine learning algorithms, eXtreme Gradient Boosting (XGBoost), Random Forest Regression (RFR) and k-nearest neighbor regression (KNNR), were used to estimate the biomass of mangrove forests in the Maowei Sea. On this basis, the performance of different machine learning algorithms was compared. The results showed that: (1) The average aboveground biomass of Sonneratia apetala mangrove was the highest (90.93 Mg·ha-1), followed by Aegiceras corniculatum mangrove(52.63 Mg·ha-1), and Kandelia candel mangrove was the lowest (20.27 Mg·ha-1). (2) XGBoost, RF and KNN machine learning algorithms are used to model mangrove aboveground biomass and mangrove spectral variables. The XGBoost model based on log reciprocal first-order transformation has the highest accuracy and is the best machine learning model. In the testing phase, R2=0.751 5, RMSE=27.494 8 Mg·ha-2. (3) Based on the ZY-1-02D Satellite hyperspectral data, the XGBoost algorithm is used to retrieve the aboveground biomass of mangroves in Maowei Sea, which is between 4.58 and 208.35 Mg·ha-2, with an average value of 51.92 Mg·ha-2. The aboveground biomass shows a spatial distribution pattern of low in the middle and high on both sides. In a word, this paper demonstrates that the combination of domestic hyperspectral data and XGBoost machine learning algorithm has a good application prospect in the estimation of mangrove biomass, which can provide scientific basis and technical support for the ecological restoration and protection of Maowei Sea mangroves.

Aiming at the ambiguity in explaining the color mechanism of fiber aggregates by the existing color-spinning color matching models, the color change mechanism of dope dyed viscose fiber aggregates during spinning was explored. In the experiment, 36 groups of sliver andyarn samples with different color mixing ratios were spun, and the Euclidean distance and spectral angular distance were used to quantify the spectral amplitude difference and shape difference between the fiber aggregates of different states in each group. The chromatic aberration, which was calculated based on the CMC(2∶1) formula was evaluated as a control, and the quantified spectral difference and chromatic aberration were compared horizontally based on the discrepancy criterion based on the category separable ratio (CSR) formula. Among them, between the raw sliver and the drawn sliver, the mean values of CSRED and CSREcmc of the samples were 2.87 and 2.17, the dED of the sample is similar to the performance of ΔEcmc, and the spectral radiation difference is more stable than the color difference. Between the sliver and the yarn, the dED and Ecmc have the same trend of greater changes, and all the samples CSRED and CSREcmc are greater than 1; in the above process, the shape difference of the sample spectrum keeps a small scale, before and after drawing and spinning, there are 20 and 22 samples corresponding to CSRSAD>1, and their mean values are 1.69 and 1.60, respectively, which did not change significantly with processing. It can be concluded that during the spinning process, due to the remodeling of fiber arrangement and aggregation state, the amount of reflected light(brightness) tends to decrease, and the spinning stage is the main stage of change, while the light quality(hue) not significantly changed during this process. Further, according to the light reflection theory of the fiber, that is, the amount of the reflected light of the fiber is mainly affected by the externally reflected light, and the chromaticity is mainly affected by the internally reflected light. It can be concluded that the spinning process mainly affects the external reflected light of the fiber. However, it has little explanatory power for internally reflected light. On the other hand, it also verified the ability of the SAD-ED formula, which integrates spectral amplitude and shape features, to characterize the color difference of colored yarns. CSRSAD-ED can distinguish 31 groups of sliver and drawn sliver samples and can distinguish all drawn sliver and yarn samples, this formula shows more sensitive and stable than the CMC(2∶1) formula, which is the color difference. This scheme also provides a new idea for the color evaluation of colored yarns.

Obtaining the spectral reflectance of an object is the key to accurately reproducing an objects true color under various lighting conditions, which plays an important role in industries with high color requirements, such as textiles and clothing, publishing and printing, online e-commerce, telemedicine, etc. The purpose of spectral reflectance reconstruction is to use training samples to establish the mapping relationship between RGB trichromatic values and high-dimensional vector of spectral reflectance obtained by general equipment such as digital cameras to avoid the problems of high cost, complex operation and low resolution caused by the use of a spectrophotometer and other professional equipment. Due to the limitation of uneven or inconsistent training sample distribution, the selection of training sample sets greatly impacts the spectral reflectance reconstruction processes. The representative color samples selection for local learning-based spectral reflectance reconstruction are discussed in this paper. From a physical point of view, the spectral reflectance vector is a smooth curve, and the selection of training samples should consider both the spatial distance and the similarity of the shape. A method based on improved weighted Euclidean distance is proposed for sample selection. The weighted Euclidean distance between the testing sample and the training sample is combined with the vector angle distance, and different weights are given as the similarity measure, which aims to ensure the similarity between training samples and target samples. The experimental results show that the proposed method can significantly reduce the chromaticity error while ensuring the minimum root mean square error. Moreover, after adding noise, it maintains the minimum root mean square error and chromaticity error, showing the method has good generalization performance.

Silk cultural relics in museum collections have high historical and artistic value, but silk materials are easy to crack and embrittle in the exhibition process. Since infrared light and ultraviolet light do not contribute to the illumination of cultural relics, the basis for effective lighting protection of silk cultural relics is to find an effective method to evaluate visible light damage to silk materials. Mechanical damage is the main photochemical damage form of silk cultural relics, which is essentially the change of its internal molecular structure. However, due to their limitations, the commonly used chromatic aberrationand Raman spectroscopy cannot effectively measure the microstructure of silk cultural. Studies have shown that infrared spectroscopy is suitable for measuring the microscopic changes of silk cultural relics, and the characteristic signals characterizing tyrosine and crystallinity in the spectrum can effectively reflect the influence of ultraviolet light on the structure and properties of silk fibroin. However, the effectiveness of the two in characterizing the damage of visible light to silk materials remains to be clarified. This study obtained the photoaged samples of silk cultural relics through irradiation experiments of ten narrow-band light sources. Two damage evaluation parameters, tyrosine index TFTIR and crystallinity index CFTIR,were defined based on the infrared spectral analysis of the samples. Based on using TFTIR and CFTIR to describe the damage law of visible light to silk samples, their performance to characterize the coupling response of silk materials to visible light wavelength λ and exposure Q was further analyzed. The results of correlation analysis between Q and TFTIR showed that, except for the 595 nm irradiation group (Sig0.05), indicating that TFTIR is not suitable for characterizing the light responsivity of silk. The results of correlation analysis between Q and CFTIR showed that, Q has a significant effect on CFTIR (Sig＜0.01). They are strongly correlated (|Pearson|＞0.9), indicating that CFTIR can effectively characterize the response characteristics of silk materials to Q. The results of linear regression analysis between Q and CFTIR showed that although they are not suitable for a linear regression model (R20.8, sum of squares due to error SSE and root mean squared error RMSE tend to 0). Finally, the lighting damage model D=∫780380S(λ)f(λ,Q)dλ of silk cultural relics was proposed, and a typical exhibition lighting source was selected to verify it. The results of paired sample T-test (Sig>0.05) indicated that the model could accurately calculate the lighting damage of silkcultural relics. This study can provide effective help for evaluating lighting damage, determining light source damage, and formulating lighting standards of silk cultural relics in museum collections.

The study of the Spectroscopic characterization of dissolved organic matter (DOM) in fresh snow is conducive to exploring its response to atmospheric pollutants. In this research, ultraviolet-visible absorption spectroscopy (UV-Vis) three-dimensional excitation-emission matrix spectroscopy (3DEEMs) combined with parallel factor analysis (PARAFAC) were applied to analyze the spectral characteristics and sources of DOM in fresh snow samples from Harbin. Chromophoric dissolved organic matter (CDOM) content in fresh snow showed the same trend as the intensity of fluorescent dissolved organic matter (FDOM), CDOM content was different from that in other environmental media due to different DOM sources, atmospheric cloud transport, air pollution and chromophore photobleaching properties, however, the intensity of FDOM was lower than that of soil and ocean owing to the differences in salt content and DOM degradation kinetics. The absorption spectroscopy of DOM in the fresh snow showed an exponentially decreasing trend, similar to the absorption spectroscopy of water-soluble organic chromophores in atmospheric particles in winter. Obviously absorption peaks at 200~220 nm (affected by water molecules and dissolved oxygen) indicated that DOM had more unsaturated double-bond conjugated structures. E2/E3 values (the ratio of absorbance at 250 and 365 nm) indicated that DOM in fresh snow possessed the characteristics of simple structure, small molecular weight and weak aromaticity. Furthermore fulvic-like acid was the main component of DOM in fresh snow. PARAFAC obtained two types of fluorescent components (humic-like and protein-like), and their contributions to fluorescence intensity were 66.78% and 33.22%, respectively. Fluorescence parameters analysis showed that DOM in fresh snow in the present research was affected by both terrestrial input and microbial activity and had strong autogenic characteristics (BIX>1) and weak humification characteristics (HIX<0.8). The correlation analysis between fluorescence components and atmospheric pollutants revealed that the sources of components of fresh snow in Harbin are similar, the sampling time is during the heating period, and the sampling point is near the factory and the railway. Thus fine particulate matter (PM2.5) emitted from fossil fuels, biomass combustion, transportation, and industry were the main sources of DOM in fresh snow. The maximum fluorescence intensity of humic-like components preliminarily established the concentration prediction equation of PM2.5. The analysis of DOM spectral characteristics in fresh snow provided a reference value for revealing its source composition and further exploring its carrier behavior mechanism. Meanwhile, it provided a new research idea and technical support for rapid diagnosis and identification of atmospheric environmental pollution.

Large-scale hyperspectral remote sensing monitoring is an important means of environmental supervision in rare earth mining areas, and the analysis of characteristic variation of reclaimed vegetation under environmental stress in mining areas provides a necessary basis for accurate dynamic monitoring of ecological restoration in mining areas. The original spectra of six typical reclaimed vegetation and their corresponding normal environment vegetation leaves in rare earth mining areas were collected on the spot, and their spectral variations were compared and analyzed. In addition to subjecting the original spectra to the usual derivative transform (DT), fractal dimension (FD) calculations in signal processing, discrete wavelet transform (DWT) analysis techniques, and short-time Fourier transform (STFT) processing are applied to amplify the detailed information of vegetation leaf spectra to investigate the spectral characteristics of reclaimed vegetation under environmental stress in the rare earth mining area. The results show that: (1) In the first-order derivative spectra, all vegetation except wetland pine show a blue shift in the “red edge position”, indicating that the reclaimed vegetation is affected by external factors such as environmental stress to varying degrees in the mining area. (2) By calculating the FD of vegetation spectral curves in mining areas, the FD of the same species of reclaimed vegetation is higher than that of normal vegetation, indicating that the influence of multiple conditions of environmental stress in mining areas will cause the waveforms of reclaimed vegetation spectral curves to become complex. (3) The vegetation leaf spectra are discrete wavelet transformed, where the best detail coefficient of the original spectral DWT is d5, the best detail coefficient of the first-order derivative spectral DWT is d6, and the first-order derivative spectral DWT amplifies the difference in spectral feature details at a smaller scale, achieving better results. (4) The spectra are localized in the null-frequency diagram by the STFT, with the original spectral null-frequency features appearing at the “red edge” and the first “trough” in the mid-infrared, while the first-order derivatives amplify and increase the spectral curve null-frequency features at smaller scales and in more bands. In general, applying signal processing methods to spectral processing can obtain more spectral features than the DT, where the STFT is superior to FD calculation and DWT analysis techniques in terms of spectral mapping into null-frequency features. The study results provide technical support for the inversion of physiological parameters of the reclaimed vegetation in rare earth mining areas and the monitoring of reclamation effects, which will help the ecological reconstruction of reclaimed mining areas.

In this paper, a pulsed cavity-ring down spectroscopy (CRDS) is employed to measure the quantitative concentration of the OH radical in a plane flame burner with premixed methane/air. By analyzing the cavity ring-down absorption spectrum theory, we select the P1(2) absorption line spectrum of the electronic transition band OH A2Σ+-X2Π(0,0) and build a set of the pulsed CRDS experimental device with a laser wavelength of 308.6 nm. The device of the pulsed CRDS is composed of a pair of mirrors with a reflectance of 99.7%, the cavity length of the ring-down cavity is 270 cm, and the ring-down time of the empty cavity (without a flame in the optical cavity) is 2.33 μs. By analyzing the experimental parameters that affect the precise measurement of concentration, we use Planar Laser Induced Fluorescence (PLIF), Coherent Anti-Stokes Raman Scattering (CARS), and the pulsed CRDS to measure the effective absorption length of OH, high temperature of the flame, and cavity ring-down time. When the premixed methane (1.1 L·min-1) and air (15 L·min-1) are burned in a flat flame burner, and at the height of 6 mm from the burner surface, the precisely measured effective absorption length by PLIF is 7.1% higher than that of directly choosing the diameter of the burner surface as the absorption length, the measured precision of the temperature by CARS is increased by 45% than that measured by the thermocouple under room temperature, the measured precision of the optical cavity ring-down time with flame in the cavity and non-OH absorption wavelength is improved by 21.6% than that measured time of cavity ring-down without a flame in the cavity. By combining the above measurement techniques to measure all experimental parameters precisely, we obtain that the number density of OH molecules (3.59×1013 molecules·cm-3) can reach the maximum value when the height from the furnace burner is 6 mm, and the precision of OH concentration is 35.6% higher than that of the unmodified OH concentration. Under different equivalence ratios (Φ=0.7～1.1), with the increase of the height from the burner surface, the number of OH particles gradually decreases, and the curve fitting shows that the OH concentration decreases in an e-exponential decay. At the same combustion height, the concentration of OH increases with the increase of equivalent ratios. When the methane flow rate is kept constant, the OH concentration in the oxygen-rich combustion condition is higher than in the low-oxygen combustion condition. In the combustion field, the precise measurement method with the multi-spectral technology (CRDS-CARS-PLIF) can achieve the precise quantitative measurement of OH concentration and provide technical support for the quantitative measurement of the concentration of other combustion product molecules, which plays a crucial role in the study of combustion chemical reactions.