Two-dimensional correlation spectroscopy (2D-COS) is a versatile technique to sort out important information in spectral variations under various external perturbations. A series of dynamic spectra exhibiting perturbation-induced changes can be readily transformed into two-dimensional correlation spectra based on cross-correlation analysis. 2D-COS has many advantages, such as enhanced spectral resolution, identification of inter- or intramolecular interactions, and chemical bond changes. It has been well-accepted as a powerful analytical technique in many fields of spectroscopic studies, such as biomedicine, pharmacy, food science, environmental science, and polymer materials. Firstly introduced by Noda in 1986, the generalized two-dimensional correlation algorithm had been extended to more two-dimensional correlation algorithms, such as projection two-dimensional correlation, tandem two-dimensional correlation, model-based two-dimensional correlation, hetero-mass spectrum two-dimensional correlation and moving window two-dimensional correlation, have been proposed and widely applied. With the rapid development of biotechnology in recent years, the structure (especially advanced structure) analysis of peptides, proteins, enzymes and other protein substances is the key factor inthe quality and efficacy of protein substances due to their participation in important physiological and chemical reaction processes of the human body. 2D-COS provides a fast, non-destructive qualitative and quantitative analysis method for studying protein structure in biomedicine. It can be used to analyze further the microstructures, such as protein secondary structure, which provides strong support for the mechanism research of biological macromolecule drugs. This article reviews both the technology and application development of 2D-COS and systematically summarizes the basic method principles, algorithm implementation and application progress in protein analysis, which provides a ready reference and overview for the applications of 2D-COS.

The safety and health of organisms have always been a concern. Metal ions exist in organisms and have an important impact on the health and disease of organisms. However, the human body environment is complex, and the specific mechanism of metal ions in the human body is still unclear. Therefore, finding a way to detect metal ions in the human body is of great significance for exploring their role in the human body. Molecular fluorescent probes are generally composed of three parts: recognition group, fluorescent group and linking group. It mainly uses the interaction between the probe recognition group and metal ions to change the structure of the fluorescent probe, thereby causing changes in fluorescence properties to detect metals ion. The changes in these fluorescence properties involve different fluorescence mechanisms, such as the photo-induced electron transfer (PET) mechanism. Fluorescence will appear fluorescence quenching phenomenon due to the PET mechanism, and on-off or off-on fluorescent probes can be designed according to this mechanism; The intramolecular charge transfer (ICT) mechanism is suitable for the design of ratiometric fluorescent probes due to the red-shift or blue-shift caused by the reaction between the probe and the detector. Fluorescence imaging technology has developed rapidly due to its specific and high-sensitivity identification ability and the advantages of real-time monitoring in vivo. It has been widely used in detecting active substances in vivo, and many metals ion probes have also been reported. This paper is mainly based on detecting different types of common metal ions such as copper ions, iron ions, zinc ions, mercury ions, etc., to study their content in the organism. Cholesterol probes and novel open-type near-infrared fluorescent probes for the detection of copper ions are reviewed, based on redox properties and the mechanism of linking N-oxide groups with unique Fe2+ deoxygenation to fluorophores to specifically recognize Fe2+. Design a fluorescent probe for detecting iron ions, construct a fluorescent probe for detecting mercury ions based on the deprotection reaction of thiocarboxaldehyde, fluorescent probes for zinc ions based on ICT and ESIPT effects, quinoline fluorescent probes for detecting magnesium ions, and fluorescent probes for detecting cadmium ions The advantages and disadvantages, design mechanism, mechanism of action, research progress and biological properties of different types of fluorescent probes for detecting metal ions in the past three years are reviewed. Application and prospect of fluorescent probes for unmonitored metal ions.

Foodborne pathogens are widely present in water, air, food, dust, and excrement, the infectious diseases caused by these seriously endanger human health. Therefore, it is essential to develop rapid detection methods for pathogens. Since pathogens in actual samples often co-exist, the simultaneous and sensitive detection of multiple foodborne pathogens is problematic in microbial detection. Molecular biology and immunohistochemical analysis techniques have made some attempts in this detection field. Still, due to the limitations of primer design and antibodies, the effects of these two techniques in practical applications are not very satisfactory. Surface-enhanced Raman spectroscopy (SERS) technology has gained essential applications in the simultaneous detection of multiple pathogens due to its rapid, non-destructive, high-resolution, no interference from water, and in-situ detection. This review systematically summarizes the application strategies of SERS technology in the simultaneous detection of multiple pathogenic bacteria, including the application principle, application characteristics, and application effects. Firstly, a brief overview of the combination between the SERS substrate materials and the pathogens is introduced. Then the direct and indirect methods used in detecting multiple pathogens are presented separately. The direct method is simple and fast, and the spectral information of the pathogen itself is obtained directly through the signal amplification of the substrate material, which helps to study the information of the pathogen itself. It is widely used in multiple pathogenic bacteria discriminant, quantitative, and point-of-care testing (POCT). However, a large amount of spectral information, often needs to be used in conjunction with multivariate statistical analysis methods, imaging techniques, and microfluidic devices. Raman reporter molecules and recognition elements such as aptamers and antibodies are needed in the indirect method, converting the detection of pathogenic bacteria into the analysis of signal molecules. The significantly improves the sensitivity and specificity of the detection assay. Simultaneous analysis of multiple pathogenic bacteria can be achieved at the gene, protein, and cellular levels. Besides, a more comprehensive detection system that integrates bacteria’s separation, identification, and inactivation can be built by combining with other identification elements and functional molecules, which has essential prospects in analyzing multiple pathogens in actual samples such as clinical blood. Finally, the existing problems and following efforts of SERS technology are pointed out, which can be a reference for designing and applications of SERS technology in the rapid and sensitive detection of multiple pathogenic bacteria.

At present, the research of hyperspectral inversion methods for heavy metals is mostly focused on single areas or not consider the influence of soil type on the inversion results. However, the differences of soil type and soil forming factors may have a certain degree in influence on the applicability of hyperspectral data-based inversion model of soil parameters. Hyperspectral remote sensing data proposed a method for inverting soil Zn metal content and considering soil types. It extracted the characteristic spectrum of soil spectrally active constituents with strong sorption and retention for the heavy metal from laboratory spectra of soil samples to enhance the inversion mechanism. For each soil type, the improved genetic algorithm (IGA) was employed on the characteristic spectrum to select the effective bands furtherly, and these bands were used to construct model by the partial least squares regression (PLSR). Finally, different modeling methods are evaluated using the coefficient of determination (R2), relative deviation (RPD) and root mean square error of prediction (RMSEP). The proposed method was validated by 38 yellow soil samples and 35 red soil samples collected in the Dong River lead-zinc mining area in Chenzhou City, Hunan province, and then the soil Zn content inversion model of each of the two soil types was constructed by the characteristic spectrum of organic matter and clay minerals extracted from the laboratory spectra and finally both were modeled by the IGA+PLSR. The results showed, when modeling with all soil samples regardless of soil types, comparing with inversion using the entire spectral range, the R2 and RPD were improved from 0.624 and 1.668 to 0.755 and 2.069 and the RMSEP decreased by 40.591 by using the characteristic spectrum associated with soil organic matter and clay minerals. When considering soil types and modeling respectively, comparing with the inversion without considering soil types, for yellow soil, the R2 was improved from 0.761 to 0.879, RPD was improved from 2.137 to 3.001, and the RMSEP decreased by 74.737; for red soil, the R2 was improved from 0.866 to 0.939, RPD was improved from 2.848 to 4.212, and the RMSEP decreased by 89.358. Inversion models for yellow and red soil samples met the criteria for an excellent model. Therefore, the proposed hyperspectral remote sensing inversion method of soil heavy metals content, which extracts the characteristic spectrum of soil spectrally active constituents and takes into account the soil type, is beneficial to improve the accuracy of heavy metal inversion and lays the foundation for monitoring of soil heavy metal pollution on a large scale by using hyperspectral remote sensing images.

Panax notoginseng is a traditional Chinese medical herb with high medicinal value. Nowadays, adulteration is common in the Chinese medicine market, and many unscrupulous traders sell rootlet or rhizome powder as the main root powder, which seriously damages the interests of consumers. Therefore, this study aims to rapidly and non-destructively identify Panax notoginseng powder of different parts by applying a hyperspectral imaging techniques combined with multivariate analysis methods. The hyperspectral images of Panax notoginseng rhizome, fibrous root and main root powder were collected by the hyperspectral imaging system in the spectral range of 400~1 000 nm （a total of 300 samples）. Savitzky-Golay（SG）smoothing combined with Standard Normalized Variate （SNV） was applied to eliminate the noise in spectral data and reduce the spectral difference caused by scattering. In order to remove the overlapping and redundant information in spectral variables, a Binary Competitive Adaptive Reweighted Sampling （BCARS） algorithm that considers the interaction effect among variables proposed in this paper was used to select the feature wavelengths. At the same time, the Competitive Adaptive Reweighted Sampling （CARS） algorithm was also used. Based on the full spectrum, CARS and BCARS feature wavelengths, Support Vector Machine （SVM） and eXtreme Gradient Boosting （XGBoost） classification models were established, respectively. The results showed that the BCARS-XGBoost model had the best performance, with classification accuracies of 100% and 99.33% for the training and prediction sets, respectively. In addition, fewer feature wavelengths were selected by BCARS, which is conducive to developing a multi-spectral system and portable detector. Therefore, it is feasible to identify Panax notoginseng powder of different parts by applying a hyperspectral imaging technique combined with the BCARS-XGBoost model.

Laser-induced breakdown spectroscopy (LIBS) has become a good material identification technique. A hot research direction of laser-induced breakdown spectroscopy (LIBS) is to increase its accuracy and detection sensitivity. In improving LIBS detection sensitivity, the most important thing is how to increase the spectral intensity of laser-ablated plasma (LAP), such as spark discharge-assisted LIBS, magnetic field enhanced LIBS, spatial confinement LIBS, flame-enhanced LIBS, resonance-enhanced LIBS, double-pulse LIBS. In addition, increasing the target temperature is an effective and straightforward technique to enhance the LIBS spectral intensity and detection sensitivity. Mainly because the target temperature is rose, its reflectivity will decrease, which can enhance the coupling of the laser-target. Moreover, the target with an increased temperature will couple more pulse energy, improving the plasma intensity. Additionally, heating the material also heats the gas on its surface, resulting in a decrease in gas density. The decrease in the gas density can reduce the collision between the LAP and the gas, and the pressure decreases during the LAP expansion, which indirectly increases the spectral intensity of the LAP. The preheated target can significantly improve the spectral emission from the previously published reports, but these reports only provided spatially integrated spectra without spatially resolved spectral analysis. For this reason, it is necessary to investigate the influence of increasing the target temperature on the spatially resolved optical emission. In this paper, the copper target was heated to a higher temperature, and a Q-switched Nd∶YAG laser was used to ablate copper to generate laser-induced plasmas. By measuring the plasma emission, it was found that the preheated copper’s emission intensity was higher than that at room temperature. For spatially resolved plasma emission, the emission intensity first increased and then decreased with increasing the distance from the copper target. Furthermore, the distribution of the copper plasma was influenced by the target temperature; the spatially resolved emission region for the preheated target moved to a longer distance from the target surface than the unheated target. The study also investigated electron temperature distribution and density with the distance from the copper target. The spatially resolved electron temperature and density had a distribution similar to the emission intensity. The plasma with high temperature and high density expands further with the increase of target temperature.

Periodic nanostructure arrays have attracted great interest because of their unique optical effects with great potential for applications in novel sensing technologies. Their optical properties depend on morphology and structural parameters, and their optical properties can generally be regulated by tuning these parameters, while the adjustment of their optical properties by an applied magnetic field has rarely been reported. We prepared colloidal crystal templates by gas-liquid self-assembly method and realized the regulation of the structure size of colloidal crystal templates by a plasma etching technique. Combined with magnetron sputtering technology, subwavelength-sized magnetic Co nanosphere array films with hexagonal periodic arrangement were synthesized, and its optical properties under the action of structural parameters and external magnetic field were investigated. The UV-Vis-NIR light reflection spectrum showed that the peak position of the light reflection peak shifted from 512 to 430 nm in the visible band with an increase in etching time from 0 min to 4.5 min, a blue shift of 82 nm. The peak intensity of light reflection decreased from 10.69% to 7.69%, weakening by 2.73%. In the NIR band, the peak position of the light reflection peak was blue-shifted by 237 nm f rom 1 929 to 1 692 nm, and the peak was reduced by 3.01% from 10.92% to 7.91%. By controlling the etching time, effective tuning of the peak position and peak intensity of the light reflection peak of the Co nanosphere array films can be achieved. A perpendicular external magnetic field was applied to both the unetched and etched Co nanosphere array films, and both exhibited different degrees of enhancement in the peak intensity of light reflection under the effect of the external magnetic field. The peak intensity of the optical reflection of the unetched Co nanosphere array films in the NIR band (1 938 nm) increased from 10.81% (0 Oe) to 16.56% (1 100 Oe) with the increase of the applied magnetic field, with an enhancement of 5.8%. Nevertheless, the NIR reflection peak (1 921 nm) of the Co nanosphere array films after plasma etching, its peak intensity increased from 8.45% (0 Oe) to 16.74% (1 000 Oe), which is enhanced by 8.29%. The results show that the reflection spectra of the magnetic Co nanosphere array films after plasma etching exhibit a more sensitive external magnetic field response. Based on the relationship between the maximum value of the NIR light reflection peak and the strength of the external magnetic field, the effect of the external magnetic field on the light reflection performance of the magnetic Co nanosphere array films was qualitatively explained. For unetched samples, the external magnetic field mainly changes the magnetic order of the sample, thereby affecting its complex refractive index and thus its light reflection properties. For the etched sample, in addition to the influence of the external magnetic field on the magnetic order of the sample, which affects its light reflectivity, there are other competing physical mechanisms such as scattering, diffraction and so on. This study provides a method for dynamically modulating the light reflection properties of materials by magnetic fields and a model for the study of novel optical devices.

The spectral bidirectional reflectance distribution function(BRDF) of metallic coatings is related to its composition. It is of great significance to reconstruct the BRDF data of metallic coatings with similar composition quickly and accurately, which can guide the modeling of BRDF and reduce the workforce and material cost of BRDF data acquisition for metallic coatings. In order to solve this problem, this paper innovatively proposes a method to reconstruct the BRDF data of metallic coatings with similar composition by using that of a few metallic coatings based on the additivity of the scattering spectrum. In this method, the BRDF of metallic coatings is regarded as the linear combination of the BRDF of each component, which can be obtained by solving the linear equations. Then, the weighted summation method is used to reconstruct the BRDF data of the metallic coatings with similar components by giving different weights to the BRDF of each component. In the experiment, we selected six different proportions of bright yellow color cards from Pantone metallic color as samples, and the BRDF values of each sample were measured at an incident angle of 45° and a hemispherical reflection space with the wavelength range of 380～760 nm by the comparative measurement method. Then two color cards were selected to obtain the BRDF data of the basic components, and the BRDF data of the other four color cards with similar components were reconstructed by giving different weights to the BRDF data of the basic components. The experimental results show that the BRDF data of the reconstructed four color cards agree with the measured BRDF data under the geometric conditions of 45° incident angle and hemispherical reflection space. The RMSE of prediction results of BRDF were all less than 0.057%, and the GFC were up to 99.998%. The experimental results show that the proposed method has the characteristics of high accuracy and simple calculation, and can be used for BRDF data reconstruction of metallic coatings.

Leaf nitrogen Concentration(LNC) is an important criterion for determining the nutritional status of rubber trees. Rapid and accurate detection of rubber trees’ leaf nitrogen content is necessary to ensure the growth of rubber trees. In this paper, the leaf nitrogen content of 119 rubber leaves was quantitatively analyzed by near-infrared spectroscopy, a high-precision prediction model was established, and the rapid and accurate detection of nitrogen content in rubber leaves was realized. The experimental objects of rubber leaf crops in Hainan were collected. First, the GaiaField-F-N17E spectrometer was used to measure rubber leaves’ near-infrared spectral reflectance data, with a wavelength range of 942nm to 1680nm. Then, abnormal samples in the measured spectral data were eliminated, and three different preprocessing methods were used to transform the data and compare their effects on improving model accuracy. Due to the massive redundant information and highly collinear spectral feature bands in the near-infrared spectral data of rubber leaves, a hybrid variable selection algorithm consisting of machine learning and evolutionary algorithms was proposed. it can effectively eliminate the redundancy and collinearity of spectral features and use the proposed method to extract the 28 bands from all 224 spectral bands effectively. Finally, using partial least squares regression (PLSR) and the selected spectral bands were used to establish the LNC estimation model of the rubber leaves. The results show that the spectral curve after multivariate anti-scattering effect (MSC) processing and the estimation model established by the CARS-NNS algorithm performed on the prediction set as follows: the RMSEp reaches 0.116, and the coefficient of determination is 0.116. R2p was 0.951. Both evaluation metrics were better than other models. In conclusion, the prediction model based on NIR spectroscopy and the hybrid learning IMF framework can well reveal the relationship between the spectral data and the nitrogen content of rubber leaves, providing a necessary technology for the nutrient diagnosis of rubber forests. Ensure the good growth of rubber trees to improve the yield and quality of natural rubber.

This study compared the stability and accuracy of the portable near-infrared spectrograph (901~1 650 nm) and visible spectrograph (400~900 nm) in nondestructive detection of moisture content in rice. 100 different varieties of rice were selected and their spectral information was collected, including japonica rice, indica rice and glutinous rice. The number of varieties were 52, 34, 14. Firstly, the direct drying method in GB 5009.3—2016 was used to determine the water content of each rice sample. Then, the outliers in rice samples were eliminated by Monte-Carlo partial least squares method, 8 and 4 outliers were eliminated from the dataset based on near-infrared and visible spectra. Moreover, the sample set partitioning based on joint x-y distance (SPXY) was used to divide the samples according to 3∶1. The near-infrared and visible data sets obtained 69, 72 calibration sets and 23, 24 prediction sets, respectively. In addition, nine algorithms including orthogonal signal correction (OSC), multivariate scattering correction (MSC), de-trend, standard normal variate (SNV), baseline, Savitzky-Golay convolution derivative (S-G derivative), normalization, moving average smoothing, and Savitzky-Golay convolution smoothing (S-G smoothing) were used to preprocess the original spectral data, OSC and SNV based on near-infrared and OSC and moving average based visible spectra had good effects, and subsequent model processing is carried out. Finally, feature wavelengths were selected to reduce spectral information redundancy and improve the model detection effect. The best wavelength selection methods based on near-infrared and visible spectra were successive projections algorithm (SPA) and competitive adaptive reweighting sampling (CARS), respectively, with 15 and 39 feature wavelengths reserved. And then, partial least squares regression (PLSR) and principal component regression (PCR) models were established. The results showed that the best combination of the models for near-infrared and visible spectra were SPA-PLSR and OSC-CARS-PCR, respectively. The correlation coefficient (R2P), root mean square error (RMSEP) and normalized root mean square error(NRMSEP) of the prediction set were 0.810 3, 0.802 1, 0.412, 0.388 and 3.62%, 3.34%, respectively. The SPA-PLSR model based on the near-infrared spectrum had a better prediction effect, and better robustness than other models. The prediction effect of the near-infrared spectrum was better than that of the visible spectrum. This study verified the feasibility of portable near-infrared spectrograph and visible spectrograph for rapid and nondestructive detection of moisture content in rice, provided technical support for determining moisture content in rice harvesting, storage and other processes, and provided a reference for the development of subsequent portable spectrographs.

Plasma is the spectral source of laser-induced breakdown spectroscopy (LIBS), and the distribution of its internal species will directly affect the signal-to-noise ratio of the collected emission lines. Therefore, research on the species distribution of vapor plasma is of great importance for improving the quantitative performance of LIBS. In this paper, the laser-induced plasmas on the surface of binary Al-Sn alloy are analyzed using spectrally, spatially and temporally resolved dual-wavelength differential imaging to obtain the emissivity images of species and explore the species distribution and evolutionary mechanism of plasmas with different laser supported absorption wave (LSAW) regime. The laser-supported combustion wave (LSCW) and laser-supported detonation wave (LSDW) dominated plasmas are induced using low and high-irradiance laser pulses, respectively. The interactions between laser, alloy and plasma are analyzed by observing the morphology, species distribution, species lifetime and internal structure of the plasma, and combining with the physical properties of elements and spectral transition structure, the space-time evolutionary mechanisms of the binary laser plasma are formed. From our observation of the emissivity images of species, we can conclude that: (1) laser irradiance can change the species distribution of plasma; (2) the LSCW-dominated plasma has an obvious layer structure, and the absorption zone of laser energy mainly located in vapor plasma. The species’ lifetime is relatively short, and the species distribution mainly depends on the melting points of constituted elements in the sample. The element with a lower melting point will melt faster and distribute in the top of vapor plasma; (3) The propagation model of plasma induced by high irradiance laser is LSDW. A large mixing region between the vapor plasma and the shocked gas layer can be observed, and the main absorption zone of laser energy is the shocked gas layer. The lifetime of species in plasma is prolonged, and the species distribution mainly depends on the atomic masses of elements. At this point, the species in the ablation zone of high laser irradiance on the surface of immiscible alloy will vaporize at the same time, and the velocity of the species is inversely proportional to the square root of the relative atomic mass. The species with smaller atomic mass move faster and distribute at the top of the vapor plasma. The above behaviors on species distribution in binary plasma are expected to be suitable for other elements or multi-element plasmas.

Coal ash is one of the most important alternative rare earth elements (REEs) sources to conventional REEs ores. Standard methods for determining REEs in coal ash have still not been established. Compared with other traditional ores, the chemistry and mineralogy of coal ash are more complicated. The reported analytical methods have severe problems in the pretreatment process (low efficiency) and are difficult to reduce the mass interferences when using inductively coupled plasma mass spectrometry (ICP-MS) as an analytical tool to detect REEs in the liquid digested sample. This study proposed a new efficient and accurate REEs determination method based on graphite digestion-(dynamic reaction cell) inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). By comparing the effects of different digestion reagents (nitric acid, nitric acid-hydrochloric acid, nitric acid-hydrofluoric acid, nitric acid-hydrochloric acid-hydrofluoric acid) and different digestion temperature (100, 140, and 180 ℃) on the test results, it was found that the HNO3-HCl-HF digestion at 140 ℃ was the best pretreatment method. The addition of HFcould significantly improve the extraction efficiency of REEs, and the secondary addition of water to eliminate HF avoided the formation of insoluble fluoride and hydrofluoride, which decreased adverse effects of F-1 on mass spectrometry. ICP-MS/MS measurement process, Rh and Re elements were added online as internal standards to compensate for the matrix effect, respectively. By comparing the gas-free mass in situ mode (M-SQ-N/A), helium collision and kinetic energy discrimination mode (M-SQ-KED), oxygen mass transfer mode (M-TQ-O2), ammonia mass transfer mode (M-TQ-NH3), it was found that the detected value in M-SQ-KED mode is closer to the standard value with minimum mass spectrum interference. The recovery rate of REEs (compared to values reported in standard coal ash) is 92.49%~112.88%, the detection limit is lower than 0.005 2 μg·g-1, and the relative standard deviation is less than 1.71%. Tracing Eu in a high-Ba solution revealed that the M-SQ-KED mode can greatly reduce the ion interference because the He was likely to collide with interfering polyatomic ions with a larger collision probability than that of the target analytes. Finally, the established method was applied analyse fly ash samples from different coal-fired power plants. The results showed that the distribution of the normalized REE curves (relative to UCC) was uniform and smooth, indicating that the results were stable and reliable. Compared with microwave digestion, the proposed method has the advantages of low cost, simple operation, and high efficiency and can be used for vast amounts of ash sample determination. The dynamic reaction cell technology can eliminate the interference of mass spectrometry online, which greatly improves the determination efficiency.

Obtaining more attribute information about the target is the goal the optical sensor constantly pursues. Polarization spectral imaging technology, which combines polarization attribute detection and traditional spectral imaging technology, can distinguish “different objects with the same spectrum”, and achieve “target highlighting” and “dynamic adjustment”. The shortcomings of the current polarization spectral imaging system include complex structure, large volume, channel crosstalk, and cumbersome multi-dimensional information extraction. In this paper, a compact polarization spectral imaging method based on Linear Variable Filter (LVF) and pixelated polarization modulation is proposed to solve the above problems. The work includes: under the constraints of high spectral resolution and short focal length, the double Gaussian structure is used as the initial optical structure, and the simulation and implementation of the optical system are carried out at the same time; The polarization modulation detector is coupled on the image plane to achieve simultaneous acquisition of spectral information and polarization information. In the laboratory darkroom, the optical index test of the system prototype developed based on the above technical route is carried out. The final index is as follows: working band: 430~880 nm, spatial resolution: 0.22 mrad, spectral resolution: 10 nm, synchronous acquisition of four polarization states, System transfer function: 0.547, polarization detection accuracy: 89.4%, total size of optomechanical system: 45 mm×45 mm×80 mm. Experiments were carried out outdoors, and the conclusion was that the monochromatic images of different polarization states of the central wavelength have obvious intensity changes; the multi-dimensional information extraction and fusion of the global image show that the characteristic spectral curves of different objects have obvious spectral differences. This method breaks through the shortcomings of the traditional polarization spectral imaging technology route and provides a new and important application method for the multi-dimensional information acquisition of polarization spectral imaging.

Arsenic pollution has aroused widespread concern worldwide, and the removal of arsenic has immediately become a problem to be solved. The toxicity of trivalent arsenic is far higher than that of pentavalent arsenic. Arsenic mainly exists in groundwater with trivalent arsenic. The removal of arsenic in water is closely related to its hydration characteristics. However, there are few studies on the hydration characteristics around different protonated arsenite ［HmAsO3］m-3(m=2, 3), let alone the infrared spectral characteristics of ［HmAsO3］m-3(m=2, 3) hydration layer. In this paper, the B3LYP/6-311G(d, p) method was used to optimize and calculate the hydration energy of ［HmAsO3(H2O)12］m-3(m=3, 2). The type, location and intensity of interaction between water molecules and ［HmAsO3］m-3(m=3, 2) species were analyzed by using a reduced density gradient function colorimetric isosurface. The IR characteristics of ［HmAsO3(H2O)12］m-3(m=3, 2) hydrated clusters were analyzed in detail. The results show that HmAsO3 tends to be distributed on the surface of ［HmAsO3(H2O)12］m-3(m=3, 2) hydrated clusters. H3AsO3 has a lower hydration capacity than H2AsO-3. Interestingly, the first hydration layer of H3AsO3 forms a deformed six-membered ring through a hydrogen bonds, with an average bond length of 1.79 . However, the first hydration layer of H2AsO-3 forms a deformed five-membered ring through hydrogen bond, and the average bond length of hydrogen bond is also 1.79 . In the infrared spectrum, the As—OP (proton-O) stretching vibration peaks of ［H3AsO3(H2O)12］0 are 701 and 637 cm-1, which are consistent with the FTIR spectra, while the As—OP stretching vibration peaks of ［H2AsO3(H2O)12］- are 573, 562 and 449 cm-1. The As—ON (unprotonated O) stretching vibration peak is 798 cm-1. In ［H3AsO3(H2O)12］0, the independent OP—H stretching vibration peaks are 3 696 cm-1, and the OP—H stretching vibration peaks in OP—H…OW are 3 598 and 3 105 cm-1. The independent OP—H stretching vibration peak in ［H2AsO3(H2O)12］- is 3 678 cm-1, and the OP—H…OW stretching vibration peak is 3 576 cm-1. The OW—HW characteristic stretching vibration peaks of OW—HW…OW in the six-membered ring composed of the first hydration layer of H3AsO3 are 3 233 and 2 911 cm-1, and the bending vibration peak is 1 606 cm-1. When the water in the first hydration layer forms a hydrogen bond with H or OP in H3AsO3, both OW—HW stretching vibration peak and bending vibration peak shift blue. The OW—HW characteristic stretching vibration peak of OW—HW…OW in the five-member ring composed of the first hydration layer is 3 383 cm-1, and the OW—HW bending vibration peaks are 1 680, 1 674, and 1 660 cm-1. When the water in the first hydration layer of H2AsO-3 forms HW—OW…H with H of H2AsO-3, the OW—HW stretching vibration peak shifts blue, and the bending vibration peak shifts red. When the water in the first hydration layer forms a hydrogen bond with OP or ON of H2AsO-3, the OW—HW stretching vibration peak shifts red and the bending vibration peak shifts blue. Compared with the infrared characteristics of the first hydration layer of H3AsO3, the OW—HW stretching and bending vibration peaks for the first hydration layer of H2AsO-3 have a blue shift.

For the pulse width of a femtosecond laser is shorter than the electron-lattice thermal relaxation time of the medium, and the processes of femtosecond laser ablation and the dynamic process of plasma expansion are different from that of a nanosecond pulse laser, it is very important to study the emission spectrum characteristics of femtosecond laser-induced plasma for studying the femtosecond laser ablation mechanism and the femtosecond laser-induced plasma expansion dynamics. Ge material is commonly used for mid-far infrared detectors and optical components. In this paper, the processes of the temporal and spatial evolution of plasma emission spectroscopy intensity produced by femtosecond pulsed laser ablated Ge material with center wavelength of 800 nm and pulse width of 50 fs are studied in air, and the effect of laser pulse energy on plasma emission spectral intensity is discussed. The experimental results show thatthe Ge plasma emission spectrum induced by the femtosecond pulsed laser is mainly composed of line spectrum and continuous spectrum at the early stage of plasma plume expansion, and the continuous spectrum weakens gradually. In contrast,the line spectrum becomes dominating within the delay time of 200ns. With the rapid expansion of the plasma plume, the plasma emission spectral intensity increases first and then decreases with the delay time increasing, and plasma emission spectral intensity reaches the maximum at a delay time of 335 ns. With the distance increasingto the Ge target surface at the delay time of 335 ns, the plasma emission spectral intensity increases first and then decreases and reaches the maximum at a distance of 0.8 mm to the Ge target surface. In the process of plume expansion, the existence time of the ion spectrum line is shorter than that of the atom spectrum line. Due to the existence of the self-absorption mechanism of the plasma plume, the plasma emission spectral intensity increases with the increasing of femtosecond pulse energy. When the pulse energy is 0.627 mJ, femtosecond laser-induced Ge plasma has a self-absorption phenomenon, which decreasesthe plasma emission spectral intensity.

As an oral vitamin K antagonist, warfarin has been widely used in treating thrombotic diseases for decades. Therefore, the research on the kinetic process of warfarin has become people’s focus. Time-dependent density functional theory (TD-DFT) simulated the excitation and emission spectra and charge transfer processes of several states during the warfarin molecules binding to human serum proteins in aqueous solution. Study the transition mode and charge transfer during the excitation process, the differences in the spectra. Explore the exciting state change mechanism of the entire kinetic process. The results show that the UV-Vis absorption spectrum of warfarin in aqueous solution exhibits double absorption, mainly caused by different excited state transitions. Before deprotonation, the main absorption peak wavelength is 291 nm. After deprotonation, the absorption intensity decreases and the wavelength is red-shifted. When warfarin binds to serum protein, charge transfer occurs, resulting in an absorption gain of 307 nm and the absorption peak intensity is the highest. By calculating the structure and excitation energy of the first excited state (S1) to simulate the fluorescence spectra of different states of warfarin, the fluorescence peak in the initial state is 360 nm. After deprotonation, the fluorescence intensity decreases and the wavelength red shifts. The structural changes after the combination result in fluorescence gain. According to the changes in the fluorescence spectrum before and after the combination of warfarin and protein, warfarin has different fluorescence emission processes in the whole dynamic process. The charge transfer of the entire dynamic process of warfarin molecules is analyzed by molecular frontier orbital and electron-hole methods. The results show that the fluorescence emission process of warfarin monomer is local excitation, and the fluorescence emission process after binding with protein is charge transfer excitation. The combined fluorescence gain feature makes warfarin a fluorescent probe. This paper reveals the mechanism of spectral changes in the warfarin and protein binding process, and provides new research methods and theoretical support for future exploration of molecular binding dynamics.

Metal Organic Frameworks (MOFs) are a kind of inorganic coordination porous framework materials with high specific surface area, and diverse and flexible structures. It has great application prospects in volatile organic compounds adsorption, sensing, etc. The evaluation of freshness is of great importance to the development of the meat industry. When meat deteriorates, the volatile substances will be produced, which is sensitive to changes in the freshness of meat. Therefore, this study prepared a kind of MOFs composite film based on fluorescence spectra to indicate thefreshness of chilled pork. Then a PLS quantitative analysis model was established according to the response information of the membrane to deteriorating volatile compounds and the freshness index of corresponding pork samples (TVB-N value). It is proved to be a new technology for monitoring the quality deterioration of chilled meat. The main conclusions are as follows: with the ratio of zinc nitrate hexahydrate∶terephthalic acid=2∶1, and a concentration of 2×10-3 mol·L-1 Rhodamine B is added, RhB@MOF-5 is prepared and then characterized. According to the infrared spectra change of RhB@MOF-5 before and after adsorbing the stale smell of chilled pork, amines might be absorbed into it during meat deterioration. After wards, MOF composite film was prepared with the ratio of MOF∶PVDF(w/w)=4∶5 according to the mixed matrix method. The storage experiment showed that MOFs composite films could stabilise and maintain the fluorescence intensity for at least 60 days in a 4 ℃ dark environment. In addition, the MOFs composite membrane combined with fluorescence sensing technology was used to adsorb and respond to volatile compounds during the entire deterioration period of chilled pork, and we observed the changes in its fluorescence properties. At the same time, three-dimensional fluorescence technology was used to observe that the intensity of MOF-5 and Rhodamine B correlation peaks were weakened after adsorption. It is because the Rhodamine B electron cloud changes caused by amines, resulting in fluorescence quenching. Collecting the surface fluorescence spectra at the excitation wavelength of 340 nm, the response information of two characteristic peaks with emission wavelengths of 435 and 550 nm and TVB-N value can be obtained simultaneously. A freshness index indication model of TVB-N was established by PLS combined with surface fluorescence spectraat the excitation wavelength of 340 nm. R2C, R2P, RMSEC and RMSEP were 0.908, 0.821, 3.435 and 3.647 mg·(100 g)-1 respectively, showing the model’s good prediction accuracy. The results showed that the functional MOFs composite films could be used to predict the freshness of chilled pork.

Nickel base single crystal superalloy is a complex alloy containing 10~15 elements, with excellent high-temperature strength and corrosion resistance. Almost all turbine parts of advanced gas turbine engines use single crystal blades with hollow structures. During their service, they must bear high temperatures hundreds of degrees Celsius above their metal melting temperature and huge centrifugal tensile stress. They are aviation parts with the worst working conditions and are known as the “pearl on the crown”. The development of more high-temperature resistant blade materials and the improvement of blade cooling technology are key to improving the turbine gas temperature. Many dense refractory elements such as Ta, W and re are added to the new generation of single crystal blades to improve the temperature resistance. These elements have serious dendrite segregation during solidification, resulting in uneven distribution of components in the microstructure. Complex step-by-step heat treatment is usually used to dissolve the non-equilibrium structure and reduce segregation. The detailed characterization of dendrite composition is of great significance in optimizing heat treatment and blade design. Microbeam X-ray fluorescence spectroscopy is a nondestructive testing technology. It is simple to prepare samples without plating conductive film and can provide information on the distribution of deep components of samples. It can detect multiple elements simultaneously. It is mostly used in biological and archaeological fields. It is not easy to characterize metal materials with complex components quantitatively, and there are few application cases. Single crystal superalloy has a special cross-dendrite structure with a size of hundreds of microns. Microbeam X-ray fluorescence spectroscopy can meet the needs of detailed characterization of single crystal blade dendrite composition and quantitative statistics of composition distribution in a large area. The quantitative statistical distribution characterization method of dendrite composition of nickel base single-crystal superalloy was established based on microbeam X-ray fluorescence spectroscopy. This method is applied to the quantitative distribution characterization of the global dendritic structure of the new single-crystal turbine blade, the evolution law of the composition from the crown to the tenon of the single-crystal turbine blade is discussed, and the primary segregation ratio and secondary segregation ratio of the key alloy elements in different parts are obtained. The results show that for the elements Re, W and Ta with serious segregation, the segregation degree of each element from blade crown to tenon gradually decreases with the increase of blade section size and the distance from the blade to cooling copper disk, and the reduction degree of secondary segregation ratio is greater than that of primary segregation ratio. The segregation ratio of Cr, Co and Mo elements is close to 1, the segregation change is not obvious, and the distribution is uniform.

As a bridge between the synthesis and decomposition of a biological organisms, plant litter impacts the structure, function and key ecological processes of terrestrial ecosystems through material, energy and information flow. Litters decompose as species mixtures in natural systems, especially in species-rich subtropical evergreen forests. It is difficult to accurately identify leaf litter for non-professionals due to complex tree species in the field. Besides, misidentifications cause many problems for thesubsequent litter decomposition research. As a fast and nondestructive analysis method, near-infrared spectroscopy has been successfully applied to identify boletus, citrus and rice. The technique mentioned above systems provided a new way to solve problems of leaf litter identification. In this study, 540 leaf litter samples of 6 dominant tree species of typical mid-mountain moist evergreen broad-leaved forests in the Mts. Ailaoshan were collected. The diffuse reflectance spectra were recorded on individual samples using an Antaris Ⅱ FT-NIR analyzer and the average spectral characteristics of different litter species were analyzed. During each modeling, 540 sample data were divided in to the training set and test set at a ratio of 2∶1 by using the Kennard-Stone algorithm. 360 sample data were used to develop discriminant models and 180 sample data were used to test the models. Single and combined spectral pretreatment methods (SNV, SG, MSC, and Derivative) were applied to improve the performance of discrimination models. Two qualitative pattern recognition methods (i. e., principal component analysis, PCA and orthogonal partial least-squares discrimination analysis, OPLS-DA) were conducted to identify the species of leaf litter. The results showed that: (1) the spectra data of different litter groups intertwined in the PCA score plot. Using SNV+SG as the pretreatment of spectra could improve the model parameter. PCA method cannot identify the leaf litter of six tree species, though Castanopsis wattii and Hartia sinensis can be separated from the rest litter species using the improved discriminant model. (2) SNV+SD pretreatment method combined with the OPLS-DA algorithm was used to develop discriminant models and showed excellent prediction ability (training set=100%; validation set=100%). Key statistical parameters of this model including R2Ycum and Q2Cum were 0.922 and 0.894, respectively. The permutation test indicated that the discriminant model was not overfitted. Our study indicated that NIR calibration models built with OPLS-DA algorithm have a good discriminative ability for different leaf litter species, and thus provide definite technological support for further plant litter research.

Laser-induced breakdown spectroscopy was widely used for quantitative elemental analysis of the field for its rapid, efficient, harmless, full spectrum advantage of direct reading and almost do not need sample preparation. In order to establish a simple method, developed by using polypropylene (PP) plastic reference material(RM), combined with partial least squares (PLS), using laser-induced breakdown spectroscopy (LIBS), the PRM-PLS-LIBS model of Pb and Cr was established. PP reference materials were developed by the requirements of international standards. The content gradient of Pb and Cr were set in the range of 0~1 000 mg·kg-1 according to the limitation requirements of various countries and regions. The certification results combined with uncertainty gave the specific values, and the certified reference material had good homogeneity and stability. The correlation coefficients of the standard curves of Pb and Cr were 0.999 2 and 0.998 9, respectively, and the detection limits were 35 and 28 mg·kg-1, respectively, which had reached the quantitative analysis ability. In order to improve the accuracy of the quantitative analysis model, it was necessary to optimize the data of standard curve further. Partial least squares (PLS) and classical least squares (CLS) were compared by multiple optimizations of baseline types for small data volume. Experimental results showed that the root mean square error of calibration (RMSEC) and correction coefficient (Corr. Coeff) of PLS were better than that of CLS. By optimizing the analytical wavelength range and baseline type of Pb and Cr, the correction coefficient between the given value and the predicted value of the correction curve reached 1.000 0, which further improved the model’s accuracy. A set of PP-certified reference materials were then selected to verify the calibration curve. High content samples (PP-306) and low-content samples (PP-302) were taken for determination, and Pb and Cr determination data were substituted into the PRM-PLS model. The Pb and Cr determination values in PP-306 were 998 and 96 mg·kg-1, respectively. The determination values of Pb and Cr in PP-302 are 980 and 95 mg·kg-1, respectively, within the given range. The method was effective and reliable.

Atomic emission spectrometry (AES) was widely used for the determination of silver (Ag), boron (B), molybdenum (Mo) and tin (Sn) in multi-target geochemical surveys, ecological geochemical assessment and International Geochemical Mapping Program (IGCP259/360). The carrier distillation technique based on Alternating Current (AC) arc powder method can effectively reduce the matrix effect and improve the fractionation process of the elements to be measured. By establishing the carrier distillation technique with aluminum oxide (Al2O3), barium carbonate (BaCO3), potassium pyrosulfate (K2S2O7), sodium fluoride (NaF), sulfur (S), ferric oxide (Fe2O3), teflon (［C2F4］n) as the main components, it was experimentally confirmed that the carrier buffer could well induce the reaction of oxidation, fluoridation and sulfurization. The evaporation process of elements such as Ag, B, germanium (Ge), Mo, and Sn was improved by adjusting a series of physical and chemical reactions to increase the volatilization of the elements to be measured and reduce the volatilization of the sample matrix elements. Scanning electron microscopy (SEM) showed that the carrier buffer formed a complex salt solid melt with the primary elements of the sample in the high-temperature arc, which can absorb CaO, SiO and other matrix oxides and suppress their interference with the components to measured, and the mutual synergy between the components of the carrier in the buffer promotes the reaction of each element in the cupro-graphite electrode. The evaporation curve showed that the carrier buffer could effectively control the evaporation process of each element, and the whole arc flame region is in thermodynamic equilibrium. The evaporation of each component to be measured was basically completed within 30 seconds, and the controlled excitation current can improve the signal-to-noise ratio and reduce the detection limits. On this basis, a new single electrode carrier distillation method for rapid determination of Ag, B, Ge, Mo, Sn and other elements in geochemical samples by AES-7200 direct reading emission spectrometer is established. The working curves of the elements to be measured had good linearity, the correlation coefficient was 0.997 21～0.999 37, and the detection limit of the method for Ag, B, Ge, Mo and Sn was 0.008, 0.646, 0.160 and 0.129 μg·g-1 respectively with the precision (RSD%) was in the range of 2.27~10.0, and the accuracy (Δ|logC|) was less than <0.1 respectively. Through a large number of analytical verification tests on aqueous sediments, soil and rock samples, the single electrode carrier distillation method can improve the sensitivity of the element to be measured and the accuracy of analysis results, and was suitable for the analysis of regional geochemical exploration samples of complex carbonate, silicate containing iron oxide and high bound water, which can satisfy the needs of different geochemical survey and eco-geochemical assessment in different regions.

More than 26 million tons of waste textiles are produced in China every year, and with the development of the economy, it is showing a trend of sharp increase year by year, but its recycling rate is less than 10%. The diversity of waste textile components and the complexity of the structure are the biggest obstacles that affect their accurate classification, rapid recycling and high value-added reuse. Manual identification and sorting are time-consuming, labor-intensive and inaccurate, while near infrared spectroscopy analysis technology can quickly, non-destructively and efficiently identify and sort waste textiles. According to the optimized test conditions explored in the early study, the online raw near-infrared spectra for polyester, cotton, wool, nylon, silk, viscose, acrylic, polyester/wool, polyester/cotton, polyester/nylon, silk/cotton blended fabrics and “special type”, a total of 1060 waste textiles samples of 12 types were collected with the self-developed “online near-infrared high-efficiency identification and automatic sorting device for the fiber products”. Based on the online original NIR spectra of the collected samples, the convolutional neural network method was used to conduct network training according to the input sample spectral data and corresponding classification labels, and an online NIR qualitative identification model for 12 types of waste textiles was established. The two-dimensional model was better compared to the one-dimensional and two-dimensional convolutional neural network models-. The one-dimensional array of 901~2 500 nm was normalized and converted into a two-dimensional grayscale image of 40×40 pixels and then alternately performed multiple convolutions and pooling for spectral feature extraction, compression and data dimensionality reduction. The class probability value of each kind of waste textile sample was obtained through model calculation, and its maximum value was taken as the final classification of this kind of fabric. In the model’s training process, epoch number was set to 500, batch size was set to 32 and the learning rate was 0.001. After training, the preset 12 types of fabric labels were output, and the internal training accuracy of the model can reach 96.2%. To verify the applicability of the model, the prediction test of the model was carried out with 232 fabric samples that did not participate in the modeling, and the recognition accuracy was 96.6%. After the model was imported into the “Textile Online Master Control Program”, the 12 types of fabrics included in the modeling samples could be identified and automatically sorted. The identification and sorting time of each sample is less than 2 s. The establishment of the model and the application of the device provide a new sorting technology and equipment for the recycling of waste textiles in China.

Detecting table grapes’s soluble solids content(SSC) is a crucial issue since berry quality and flavor are directly related to it. In recent years, as the technology of chip-level spectral sensors is becoming more and more advanced, on-chip spectral sensors with high accuracy and stability have blazed a new trail for spectral detection. In this work, a small, user-friendly, and cost-effective optical device that can detect the SSC of table grapes nondestructively has been designed, built, and tested. New generation Vis/NIR spectral sensor AS7263(sensor 1, 2) with the capacity of chip level spectral analysis, does the key work for the system. Each sensor has six digital spectral channels with an integrated Gaussian filter and anLED with the programmable current (1~100 mA). The central wavelength of the spectral channel increases uniformly from 610 to 860 nm. Moreover, LEDs can emit light at 730 or 850 nm with fullwidth half max (FWHM) of 50nm. Firstly, this optical prototype collected a spectrum from 276 grape berries in a dark room. PAL-1 was used to detect SSC, and then we calculated the SSCt based on t-distribution: SSCt0.9 and SSCt0.95. Secondly, for the original spectral data, PCA was used to extract the principal components, and 16 abnormal samples located outside the confidence interval were excluded according to the distribution of the score factors. Besides, First Derivative (FD), Normalization (0, 1) and Standardization (0, 1) were used to preprocess the data. After that, we calculated the absorbance or KubelkaMunk function value F(R) for the samples at 12 channels. According to the multiple correlations between the independent variables of the Vis/NIR spectrum and the nonlinear correlations between the spectrum and SSCt, a PLS-BP neural network prediction model was developed for the grape SSC detection. The results showed that when β was 0.95, the preprocessing method was Standardization (0, 1), the Parameter of the spectrum was absorbance(A), and the prediction model worked best: R2p=0.93, RMSEP=0.181, Bias=-0.01, and RPD=3.78, which can be considered that the model has high accuracy and better adaptability to predict the SSC of table grapes. Finally, on the one hand, referring to the experimental results, a very interesting molecular scale principle analysis is obtained for the grape absorption spectrum: among numerous molecular vibration types, the 3x, 4x frequency of O—H bond (3x means stretching vibration at a triple fundamental frequency), the 3x+C, 4x+C frequency of O—H bond (3x+C means the combination of scissoring vibration at a fundamental frequency and stretching vibration at a triple fundamental frequency), the 8x, 9x of CO bond are the effective vibration frequencies for Vis/NIR spectral detection. On the other hand, the prototype provides a technical reference for future online quality inspection equipment that is high-precision, portable and low-cost.

N-butanol is considered an ideal diesel additive because of its good solubility, low volatility, low price and corrosiveness. The accurate quantitative analysis of n-butanol in diesel has important scientific significance and practical value for its quality evaluation and market supervision. This paper proposes a rapid quantitative analysis method for n-butanol in diesel based on Raman spectroscopy combined with partial least squares (PLS). Firstly, Raman spectra of 40 diesel samples were collected, and the effects of different pretreatment methods (first derivative, second derivative, multivariate scattering correction, standard normal transform, Normalization and wavelet transform) on the prediction performance of the PLS calibration model were investigated. Secondly, variable importance in projection (VIP) is used to extract characteristic variables from the spectral data preprocessed by the Normalization method, and the threshold of VIP is optimized by five-fold cross-validation. Finally, based on the optimal spectral pretreatment method, input variables and model parameters, a PLS calibration model was built to analyze the content of n-butanol in diesel quantitatively. The prediction performance was compared with the RAW-PLS and Normalization-PLS models. The results show that the Normalization-VIP-PLS calibration model has excellent predictive performance (R2CV and RMSECV are 0.998 4 and 0.236 2%, R2P and RMSEP are 0.998 7 and 0.208 4%; RSD 0.035 5). Therefore, this paper successfully established a rapid quantitative analysis method of n-butanol in a diesel by Raman spectroscopy combined with the PLS algorithm. This method has the advantages of being fast, accurate and convenient and it can provide new ideas and methods for the detection and quality analysis of diesel and other fuel additives.

Large-scale production and wide application of industrial dyes have considerably impacted the Earth’s ecology. Water pollution is very serious. Traditional chromatographic and spectral tools cannot detect weak spectral and chemical information. Therefore, the development of portable and rapid detection technology is essential. Surface-enhanced Raman spectroscopy (SERS) is a new analytical technique combined with nanotechnology, which can realize the detection of single molecular weight chemical substances. However, the potential is easily limited by the universality of SERS substrate enhancement ability and stability. In this study, a simple and general strategy was proposed to prepare a hydrophobic organic semiconductor bis (dicyanomethylene)-end-capped-dithieno ［2,3-d; 2’,3’-d］ benzo ［2,1-b; 3,4-b’］-dithiophene (4CN-DTmBDT) film is a new SERS substrate. Firstly, the organic semiconductor substrate was prepared by the spin-coating method. The π-conjugated organic semiconductor has the advantages of controllable molecular structure, biocompatibility, fine-tuning of photoelectric properties and controllable film-forming morphological parameters. The substrate surface has hydrophobicity, which makes silver nanoparticles (AgNPs) form a tight coffee ring on its surface. The organic semiconductor-nano silver SERS composite substrate was prepared to explore the enhancement effect of the Raman signal of the substrate. This study proposed a possible synergistic enhancement mechanism between the organic semiconductor and silver nanoparticles, and the enhancement ability and mechanism were studied. The results showed that the formation of tight coffee rings reduced the space between silver nanoparticles, and the hot spot effect was enhanced by concentrating the analyte. The detection limit of rhodamine 6G (R6G) with organic dye as the probe molecule was as low as 1×10-8 mol·L-1, and the SERS enhancement factor (EF) was as high as 1.30×106, while the detection limit of PDMS and silver nanoparticles composite substrate with better hydrophobicity was 1×10-5 mol·L-1. At the same time, it was proved that the substrate Raman signal was further enhanced by the synergistic effect between the organic semiconductor and silver nanoparticles in this study, the sensitivity was high, and the repeatability was good. The relative standard deviations (RSD) for detecting 1×10-4 and 1×10-8 mol·L-1 R6G dyes were 8.3% and 4.7%, respectively. Experiments show that the organic semiconductor-nano silver composite substrate has good application potential in trace analysis of dyes in wastewater.

Iron artefacts are important part of the cultural heritage in China. Due to the high activity of iron, iron artefacts are prone to corrosion and deterioration. Corrosion products greatly influence the stability of iron cultural relics. Therefore, determining the composition of iron corrosion products is significant for evaluating iron artefacts’ stability. In this study, pure chemical reagents were used to simulate three types of corrosion products commonly found on iron artefacts, including hematite (α-Fe2O3), magnetite (Fe3O4), and akaganeite (β-FeOOH). Raman spectroscopic imaging, combined with Principal Components Regression (PCR), Partial Least Squares (PLS) and multiple spectral pretreatment methods, were applied to establish quantitative models for two sets of a binary mixture of standard corrosion samples (α-Fe2O3+ Fe3O4, α-Fe2O3+β-FeOOH). The results indicate that, for α-Fe2O3+Fe3O4 mixed standard samples, the model effects of PCR and PLS algorithms are not much different. The quantitative model results show that the best spectral processing method of PCR modeling is first derivative +Savitsky-Golay (S-G) smoothing (9). For α-Fe2O3+β-FeOOH mixed standard samples, the model constructed by the PLS method is superior to the PCR method. The best PLS modelling spectral processing method is MSC+S-G smoothing (5). The research results provide an effective method for quantitatively evaluating the chemical stability of corrosion products of iron artefacts.

As one of the important material basis and natural resources for human survival, the content of nutrient elements in soil is not only the basis of agricultural production but also an important indicator for evaluating soil quality. The traditional method for determining soil nutrient elements is mainly based on liquid injection, which is cumbersome to operate and has certain environmental pollution. In this paper, the ultra-high pressure sample preparation technology is combined with LIBS technology, which integrates the green and pollution-free sample pretreatment technology and the determination technology that is simple to operate and can carry out multi-element synchronous and rapid detection. The analytical method for determining Silico, Aluminum, Iron and Potassium in soil by ultra-high pressure sample preparation and laser-induced breakdown spectroscopy technology is established. The comparative study on the sample preparation pressure found that when the sample preparation pressure is 2 000 kN, the surface of the prepared sample is smooth and flat, with better compactness and the best measurement precision. By optimizing the measurement conditions of the LIBS instrument, we found that the use of multiple location sampling, the laser energy, gate delay, and spot size were 0.80 mJ, 0.5 μs, and 60 μm, respectively, could reduce the influence of thermal effect caused by the ablation of the sample and sample surface unevenness on the measurement results. It increases the signal-to-background ratio of the measurement, thereby increasing the accuracy and precision of the measurement results. Under the optimized conditions of this method, we used the Certified Reference Materials for the Chemical Composition of Soils to do the calibration curve of linear regression with multiple variables. We obtained a good linear relationship, which reduced the influence of the matrix effect on the measurement results. According to the verification of the Certified Reference Materials for the Chemical Composition of Soils, except for individual elements, the method’s precision for determining nutrient elements is between 0.31%~4.21%, and the determination results are basically consistent with the certified values. This method is not only simple to operate and can avoid the environmental pollution caused by traditional methods, but it also can realize the simultaneous determination of multiple elements, which promotes the further development of LIBS technology in quantitative analysis.

Water-soluble acrylate emulsions are commonly used in cultural heritage conservation, mostly for the reinforcement and bonding of murals, colored paintings, bone relics and lacquerware. The most widely used acrylate emulsions for heritage protection are the Primal, formerly produced by Rohm and Haas. Commercial materials such as Primal AC33, SF016, B60A, WS24 and MC76 have been widely used in the conservation and restoration practice of cultural relics at home and abroad. However, most of the literature reports are restoration cases of direct purchase and use, and their is a lack of systematic scientific performance evaluation research. In this study, we investigated the hygrothermal and UV ageing performance of five commonly used Primal acrylate emulsions, comparing their ageing resistance properties through microscopic morphology, colour change and gloss, etc. Infrared spectroscopy was used to trace the molecular structure changes and absorption peak intensities during UV ageing to explain their degradation mechanisms. The results show that WS24 and B60A have better heat and humidity aging resistance, while SF016 has the worst performance during the heat and humidity aging test. AC33 showed the most obvious color and glossiness changes. UV ageing test revealed that all five materials underwent chain scission reactions during UV irradiation, and AC33 and MC76 had lactones formation. After 4 200 h UV aging, the carbonyl index (CI) of AC33, SF016, B60A and MC76 increased to different degrees, and carbonyl absorption peak (Dt) intensity decreased by 10%~15%. The carbonyl index of WS24 decreased rapidly, and the carbonyl absorption peak intensity decreased by 53%, which was the worst among the five materials in the UV aging test. According to the comprehensive evaluation, B60A has excellent resistance to UV and hygrothermal aging, and is the best Primal acrylic emulsion for cultural relic protection.

With the rapid development of low-altitude unmanned aerial vehicle technology, small consumer-grade UAVs equipped with optical sensors can quickly and flexibly acquire high-resolution image data of target objects, which presents a broad prospect for expanding applications in various fields of geology. UAV-SfM is the latest technological method for imaging 3D stereo construction that is a core technology for deepening research in the field of low-altitude UAV technology geology, but at present the lack of research on the comprehensive accuracy of data results obtained by using the UAV-SfM method has affected the further promotion and application of this technical method. This paper addresses whether the DJI Phantom 4 RTK consumer-grade UAV has the technical potential to be used for detecting shallow surface changes in the mountainous areas of the central Yunnan plateau, and selects a typical sloping field in the red land of Dongchuan as the test area. The DSM and DOM data from the same area were obtained using the key SfM-MVS technique. In order to evaluate the accuracy of the repeated UAV observations on typical sloping land, 3D point accuracy evaluation was carried out for the bare sloping land I and the growing sloping land Ⅱ in the experimental area, using 3D discrete point sampling based on profile lines and 3D point set sampling based on window surfaces, respectively. The point accuracy analysis showed that: (1) In 3D discrete point sampling and precision analysis based on profile line, the mean precision error of slope farmland Ⅰ plane point position is ±0.029 m, and the accuracy of 3D point position error is ±0.072 m. The mean accuracy of slope farmland Ⅱ plane point position is ±0.032 m, and the 3D point position error is ±0.075 m. (2) Based on the 3D point set sampling and precision analysis of the window surface, the mean precision error of slope farmland I plane point position is ±0.013 m, and the 3D point position error accuracy is ±0.066 m. The mean precision error of the slope farmland Ⅱ plane point position is ±0.038 m, and that of the D point position is ±0.076 m. The comprehensive analysis shows that the evaluation accuracy of single point sampling based on profile line is better than that of 3D point set sampling based on the window, but the plane accuracy and vertical accuracy can reach centimeter level on the whole. According to the experimental comparative analysis, different surface roughness impacts on the UAV repeated observation accuracy, and the 3D point position error with large surface roughness is larger than that with small surface roughness. The research results of this paper can provide a quantitative reference for the precision control and acquisition scheme setting of geomorphic data acquisition and 3D reconstruction based on UAV and SfM methods.

In this paper, the coloration mechanism of green tourmaline was determined by conducting a series of tests on the light green tourmaline mined from Afghanistan, including routine gemological tests, XRD analysis, FTIR Analysis, UV-Vis spectral analysis, XPS analysis, and electron probe microanalysis. The results showed that the tourmaline has a relative density of 3.04, an ordinary refractive index (No) of 1.639, an extraordinary refractive index (Ne) of 1.620, and weak pleochroism. XRD analysis indicated that it is lithium tourmaline. FTIR spectra showed absorption peaks at 456, 500, 605, 645, 715, 780, 980, 1 030, 1 110, 1 290, 1 350, 3 460, 3 580 and 3 640 cm-1, etc. Among them, the peaks at 605, 645, 715, 780, and 1 110 cm-1 were caused by the symmetric and asymmetric stretching vibrations of Si—O—Si; the peaks at 980 and 1 030 cm-1 were caused by the symmetric and asymmetric stretching vibrations of O—Si—O; the peak at 500 cm-1 was caused by the bending vibrations of Si—O; the peak at 1 290 cm-1 was caused by the stretching vibrations of ［BO3］; the peak at 1 350 cm-1 was caused by the bending vibrations of OH; the peaks at 3 460 and 3 580 cm-1 were caused by O3H vibrations; O1H vibrations caused the peak at 3 640 cm-1; and the strong peak at 456 cm-1 was caused by ［AlO6］ vibrations. The differences between the absorption peaks at 605 and 645 cm-1 in the measured FTIR spectrum and those in the standard FTIR spectrum may indicate that the presence of color-producing ions has some impact on ［Si6O18］ vibrations. XRD and FTIR analyses revealed the underlying crystal structure resulting in the light green color. In the visible light range, the absorption peaks of the tourmaline in E∥c and E⊥c directions are roughly at the same position, and only differ slightly in absorption intensity, which results in weak pleochroism of the tourmaline. Absorption was found at both 718 nm in the red region and 420 nm in the blue-violet region, whereas good transmission was detected in the yellow-green region, which produced the tourmaline’s unique color of bright light green. UV-Vis spectral analysis revealed the color structure of the light green. XPS analysis showed that the tourmaline mainly contains Li, Na, Al, Si, O, F, B, and other elements. It also contains traces of transition metal ions such as Fe2+, Fe3+, Mn2+, and Ni2+, of which Fe2+, Fe3+, and Ni2+ occupy the Y site and Mn2+ occupies the Z site. XPS analysis revealed the types, valence states, occupancy, and other chemical states of the transition metal ions that produce the light green color. In combination with the results of electron probe microanalysis, the crystal chemical formula of the sample can be estimated as X(Na0.612Ca0.063K0.008)Y(Li0.989Fe2+0.070Fe3+0.117Al1.824)Z(Mn2+0.035Al5.762Si0.203)［Si6.000O18］［BO3］3V(OH2.134O0.866)W(OH0.542F0.458). Electron probe microanalysis revealed the chemical composition of the crystal responsible for the production of light green. Comprehensive analysis of the UV-Vis spectrum and chemical components of the sample as well as the chemical states of the transition metal ions contained in the sample suggested that the absorption at 718 nm may be caused by charge transfer from Fe2+ to Fe3+, where the absorption at 420 nm may be caused by the d—d electron transition of Ni2+. These study results may provide a reliable basis for color change optimization based on the chemical state of color-producing ions and the place of origin identification based on crystal chemistry and spectroscopic characteristics.

Changbai jade is a high-quality seal stone produced in Changbai County, Jilin Province. It has rich reserves and high economic value. X-ray diffractometer, infrared spectrum, Raman spectrum, scanning electron microscope and EDS were used to study the varieties of Changbai coloredjade which is one variety of Changbai jade. XRD test results show that in addition to dickite type, Changbaicolord jade also has kaolinite, nacrite-dickite, forsterite-serpentine, brucite and talc type. The impurity components include pyrite, hematite, brucite, serpentine, calcite and dolomite; Through the full peak fitting (WPF) and Rietveld fine fitting calculation, it is found that forsterite accounts for 49%, serpentine accounts for 23%, dolomite accounts for 15%, brucite accounts for 13% in sample CB9, and the main mineral composition in other samples accounts for more than 90%. Combined with the analysis of gemological characteristics: the color of the kaolinite group is mainly gray, dark gray, red, light yellow and brown, with a hardness of 2~3, fine texture and good knife feeling; Forsterite serpentine is green, and brucite is yellow green and light gray; Except for the Changbai coloredjade of kaolinite group, the hardness of other types is low, ranging from 1 to 2, with poor toughness and poor carving knife feeling. Infrared spectrum analysis verifies the XRD test results, and distinguishes the polytypics of kaolinite in Changbai colord jade. It is considered that CB1 is disordered kaolinite, CB15 is nacrite, CB6, CB11 and CB14 are orderly dickite, and the degree of order is CB14>CB6>CB11. Combined with the characteristics of Raman spectroscopy and EDS element analysis, it is inferred that the red of Changbai jade is related to cryptocrystalline hematite, the black is related to large-scale crystalline hematite, and the gray, gray black and black are also related to the existence of amorphous carbon. The serpentine leads to the green and yellow green of Changbai colord jade; The existence of brucite increases the transparency of Changbai jade, and fine and dense pyrite reduces the transparency of Changbai jade. The analysis of Microscopic morphology is considered that the dickitetype is a flake, heteromorphic, semi automorphic, and locally visible, the flakes are closely stacked, and the crystalline particles are large, ranging from a few microns to more than ten microns; the Kaolinite type is flaky, uneven in size and disorderly distributed in three-dimensional space; Brucite type is in the form of large scale lamination, sharp edges, flake crystals up to tens of microns, and the lamella is very thin; Serpentine is fibrous and combined with flake brucite; Forsterites are in dense, massive structure.

Pre-production estimation of wheat production is related to the formulation of agricultural production plans, food security, national economy and macro-decision-making, and the application of drones can estimate wheat production in a non-destructive, fast, accurate, timely and efficient manner. The machine learning method is used to fully tap the potential of multi-source remote sensing data to estimate the grain yield of multiple wheat varieties and to clarify the effect of multi-source data fusion on improving the yield estimation accuracy of cultivars. It is significant for crop field management and ensuring a high and stable yield in wheat. In this study, field trials of winter wheat were carried out with 140 main wheat varieties in the Huanghuai wheat region as materials. The drone platform equipped with red green blue (RGB) and multispectral sensors were used to collect the canopy information of 140 winter wheat varieties during the grain filling period. Six machine learning algorithms were used, namely Ridge Regression (RR), support vector regression (SVR), Random Forest Regression (RFR), Gaussian Process (GP), k-Nearest Neighbor (k-NN) and Cubist, to build yield estimation models from single sensor data and multi-source data fusion. Coefficient of determination (R2), root mean square error (RMSE) and relative root mean square error (RRMSE) were used to evaluate the estimation model. The results showed that the selected 10 visible vegetation indices and 13 multispectral covered indices were significantly correlated with the measured yield (pmulti-spectral sensor yield estimation accuracy (R2=0.53～0.69)>RGB sensor yield estimation accuracy (R2=0.35～0.51). Compared with RGB data, the R2 of multi-source data fusion increases by 0.17～0.23, and the mean root mean square error (RMSE) decreases by 0.06～0.09 t·hm-2; compared with multi-spectral data, the R2 increases by 0.01～0.06, and the RMSE decreases by 0.01～0.03 t·hm-2. Compared with the other five algorithms, the multi-source data fusion model established by the Cubist algorithm has the highest yield estimation accuracy, with an R2 of 0.71 and an RMSE of 0.29 t·hm-2. It shows that compared with the yield estimation model of single sensor data, multi-source data fusion can effectively improve the yield estimation accuracy of winter wheat varieties, and the Cubist algorithm can better process multi-mode data to improve the yield prediction accuracy, providing theoretical guidance for predicting the yield of different wheat varieties.

The sclerotinia stem rot on oilseed rapeis soil-borne disease. There are no visible symptoms in the leaves in the early onset stage, so it is not easy to monitor from the plant surface. It cannot be recognized by ordinary spectral images or RGB images of oilseed rape leaves. In this study, hyperspectral imaging is used as monitoring technology, combined with deep learning to build an early identification model of sclerotinia stem rot on oilseed rape to solve the problem of early identification of sclerotinia stem rot on oilseed rape. In this study, the stem rot on oilseed rape was used as the research object, and the mycelium inoculation method was used to induce the disease in the root of oilseed rape. The hyperspectral images of diseased rape plants and healthy plants were collected on the 2nd, 5th, 7th and 9th day after onset. After removing the background, S-G smoothing of the spectral curve, cutting and segmentation, the model training and testing dataset was constructed. Based on the resnet50, the number of feature images was improved, and the first layer’s convolution kernelsize was reduced to improve the model’s recognition ability. The model’s recognition performance and generalization ability were verified based on cross validation. The accuracy of the three models with different structures was 66.79%, 83.78% and 88.66% respectively. The accuracy of the improved model was increased by 16.99% and 4.88% respectively, and the precision and recall rate were improved too. The average accuracy of the improved resnet50 model was 88.66%, the precision and recall rate was more than 83%, and only the recall rate on the seventh day of onset was 79.04%. If the model is binary whether the rape is under disease stress, the accuracy of the model is 97.97%, the precision is 99.19%, and the recall rate is 98.02%. At the same time, the accuracy of the model for the test dataset reached 91.25%.The results of cross-validation showed that the improved model had a good recognition ability for sclerotinia stem rot on oilseed rape within one week and could be used to identify the different stages of sclerotinia stem rot on oilseed rape. The improved model has a stronger ability to identify whether rape was stressed by sclerotinia stem rot on oilseed rape, and the accuracy, precision and recall rate all reached more than 97.97%. At the same time, the model’s accuracy for the test dataset(Day 9 of onset) reached 91.25%, indicating that the model had a good generalization ability for the early recognition of sclerotinia stem rot on oilseed rape. This study solved the problem that asymptomatic disease recognition cannot be carried out based on GRB images and provided are ference for the development of crop diseases early recognition.

Soil nutrient status is directly related to crop yield and quality. However, traditional chemical methods have problems such as large consumption of chemical reagents, being time-consuming and labor-intensive, and cannot meet the needs of precision agriculture. Quickly obtaining soil nutrient information is the key to developing precision and green agriculture. To understand soil fertility, one must first understand the content of organic matter and total nitrogen. Many studies have shown that near-infrared spectroscopy is widely used in soil detection, but visible/near-infrared spectroscopy is very rare in the study of soil organic matter and total nitrogen. Taking four villages in Anfu County, Ji’an City, Jiangxi Province, and Xinjian District, Nanchang City as the study areas, the three most typical soil samples, brown soil, red soil and paddy soil, with a depth of 10~30 cm were collected according to the 2×2 grid method180 share. After grinding, air-drying, etc., the samples were divided into two parts by the method of quartering, which was used to determine the samples’ spectral and physicochemical information. The soil samples were divided into modeling set and a prediction set according to 2∶1 (120∶60). Considering the large noise in the first-end band, the 325~349 nm and 1 051~1 075 nm bands were removed the remaining 350~1 050 nm band was used for spectral analysis. 12 wavelength points of OM and 11 wavelength points of TN were screened out by successive projections algorithm. Considering the possible nonlinear relationship between soil spectral information and soil physical and chemical properties, a full-band, the linear partial least squares regression (PLSR) model of characteristic wavelengths and the nonlinear least squares support vector machine (LS-SVM) model were used to study soil organic matter and total nitrogen. The LS-SVM model was optimized by a two-step grid search method. Two hyperparametersγ and σ2. The results show that: (1) The spectral reflectance of soil increases with the increase of wavelength, and the reflectance curve has obvious absorption characteristics at 460, 550, 580, 740 and 900 nm. (2) From the analysis of the results of the PLSR model and the LS-SVM model, it can be seen that the nonlinear model LS-SVM has better prediction accuracy, which may be due to the nonlinear relationship between soil spectral information and soil physical and chemical properties. (3) The characteristic wavelength screened by the continuous projection algorithm improves the model accuracy and optimizes the model operation efficiency. The SPA-LS-SVM model was the best predictive model among all the models, among which the R2pre of the organic matter model was 0.884 7, the RMSEp was 0.104 8, and the RPD was 2.945 0. The R2pre of the total nitrogen model was 0.901 8, the RMSEp was 0.010 4, and the RPD was 3.191 1. (4) This study shows that visible/near-infrared spectroscopy can measure different types of soil organic matter and total nitrogen content, achieving better prediction results. Visible/NIR spectroscopy has great potential in the field of soil detection.

Soil organic carbon content was a major determinant of soil fertility and soil quality and was closely related to soil productivity. The estimation of soil organic carbon content using hyperspectral models has become an important method of understanding soil fertility. Using hyperspectral analysis combined with machine algorithms to achieve rapid and highly accurate estimation of soil organic carbon contents was essential for the sustainable use of soil fertility. Using the measured soil organic carbon content and its hyperspectral reflectance data as the research object, we applied the Savitzky Golay method to smooth and demise the spectral bands, used successive projection algorithm (SPA) and genetic algorithm (GA) to screen the original spectra and its five different mathematical transformed spectra respectively for the characteristic bands, and constructed the random forest (RF) method based on the soil organic carbon content. The hyperspectral estimation model of soil organic carbon content was constructed using the random forest (RF) method. The SPA algorithm was combined with the GA algorithm to find the optimal feature parameters to improve the recognition rate and confidence in the SOC feature bands. The results showed that in the original spectrum, the hyperspectral response bands based on the GA algorithm to screen SOC content were mainly concentrated on 350~410, 827~928, 997~1 064, 1 201~1 234, 1 541~1 574, 1 667~1 710, 2 153~2 186, 2 357~2 707 nm. When the RMSE was 6.09, 11 characteristic variables were screened by the SPA algorithm. The dimension of the original spectrum, standard normal variables (SNV), multiple scattering corrections (MSC), first-order differential (FD), logarithmic reciprocal (RL) and continuum removal (CR) were reduced to 407, 697, 668, 667, 493 and 784 dimensions respectively, accounting for 18.93%~36.47% of the full spectral band when filtering the characteristic bands based of the GA algorithm. After screening based on the GA-SPA algorithm, the dimensions of the six spectral variables ranged from 8 to 17 dimensions, and the RMSE ranged from 4.53 to 6.30. In the first-order differential spectral form, the RF model constructed based on 12 feature variables selected by the GA-SPA algorithm predicted the best results from a modeling set R2c of 0.78 and RMSEc of 5.48, a validation set R2p of 0.82, RMSEp of 4.50, and RPD of 2.18. It was shown that the first-order spectral differentiation could enhance the spectral information about soil, the GA algorithm combined with the SPA algorithm to find the spectral feature variables simplifies the complexity. It improves the accuracy of the estimation model, and the hyper spectral model based on the genetic algorithm-continuous projection algorithm has a high estimation capability.

To improve the effectiveness of identifying the origin of Chinese Medicinal Materials based on infrared spectroscopic data with high dimensions, appropriate data preprocessing(DP) should be firstly used, and advanced algorithms can be considered secondly if necessary. Faced with the dataset consists of 658 samples with wavelengths from 551 to 3 998 nm, with the help of support vector machine (SVM) algorithm, ten sample-based DP methods (namelynon-DP, maximum and minimum normalization, standardization, centralization, moving average smoothing, SG smoothing filtering, multivariate scattering correction, regularization, first order derivative followed by second order derivative calculation), five spectral feature based methods (i. e., non-DP, centralization, maximum and minimum normalization, standardization and regularization) and their combinations (50 kinds in total) were investigated accord to the prediction effectiveness and stability. Numerical results show that the right DP is conducive to improving the model accuracy. Moreover the standard variate and Max-Min average DP methods achieve higher scores (the coefficient R2 is approximately 85%) among 10 sample based methods. Feature based only methods get little model improvement. The sample based only and feature-based only methods get the approximately equal average ratio of 64%. The combined methods of standard normal variate or normalization processing followed by second order derivative DP achieve the relatively highest prediction score with R2 of nearly 94%. However, the DP approach of data regularization added to centralization performs most poorly. The suggestions are also given. The research is valuable for further analysis of medicinal efficacy and chemical composition. Furthermore, it can be a reference to infrared spectral data analysis. Moreover, the research also provides references for modeling data with high dimensional small samples.

Wavelength modulation-direct absorption spectroscopy (WM-DAS) combines the advantages of DAS and WMS, which can directly measure the absorbance and improve the measurement signal-to-noise ratio(SNR). It can be used to measure the spectroscopic parameters of gas molecular spectral lines. Firstly, the WM-DAS method is used to measure the absorbance of CO molecule 4 300.700 cm-1 spectral lines under the condition of 24.151 μmol·L-1 CO concentration, room temperature and pressure, combined with Herriott cell with an effective optical path length of about 45 m. The absorbance is optimally fitted by Voigt profile (VP)and the results show that the standard deviation of the absorbance fitting residual from WM-DAS is reduced by more than half compared with that from the traditional DAS method, which proves that the anti-interference ability of the WM-DAS method is stronger than that of the DAS method. Then, this method combined with an absorption cell with an optical path of about 50 cm was used to measurethe absorbance of 8 weak absorption spectral lines of CO at 4 278～4 304 cm-1 under different pressures. The CO standard gas with a concentration of 0.411 μmol·L-1 was used in the experiment. The measured absorbance was fitted by VP, Rautionprofile (RP) and quadratic-speed-dependent-Voigt profile (qSDVP) to obtain the collision broadening coefficient γ0(T0), the Dicke narrowing coefficient β0(T0) and the speed-dependent collision broadening coefficient γ2(T0) in qSDVP, respectively, and the uncertainty of the measurement results was analyzed. The measured γ0(T0) obtained by Voigt profile fitting agree well with those from the HITRAN database, with a relative error of less than 1%. The measurement results of β0(T0) and γ2(T0) provide an important data for further perfecting molecular spectral database and high-precision measurement of gas parameters.

To realize the rapid and non-destructive identification of jadeite origins and enrich the diversity of methods for the identification of precious jadeite origins, a support vector machine SVM recognition model was established to analyze jadeite of three origins based on the data obtained from infrared spectral analysis. The experiments collected a total of 106 infrared spectral data of three jadeite species from Myanmar, Russia and Guatemala in order to achieve better model identification, the original infrared spectral data were transformed from reflectance to absorbance before modeling, and then the spectra were pre-processed differently. The purpose of preprocessing is to reduce the effects of noise, baseline drift and scattering phenomena on the model recognition effect. The methods used for preprocessing in this experiment are SG smoothing, mean centering, normalization, trend correction, multivariate scattering correction, maximum-minimum normalization, standard normal transformation and standard normal transformation followed by trend correction. The experimental results show that the recognition accuracy of the models obtained by preprocessing the infrared spectra is higher than that of the original spectra by 73%; the recognition accuracy of the models obtained by multivariate scattering correction and maximum-minimum normalization of the infrared spectra of the three emerald origins separately is higher than that of the results obtained by mixing preprocessing; some preprocessing methods used in combination also improve the recognition accuracy of the models, such as standard normal transform and trend correction. The recognition accuracy obtained after maximum-minimum normalization of the infrared spectra of the three origins of jadeite separately reached the highest 95%, indicating that this support vector machine SVM recognition model built using infrared spectroscopy can achieve rapid recognition of jadeite origins.

The cooperative observation of atmospheric ozone and aerosol is realized by using the self-developed four-wavelength solid-state lidar system, which is also the first three-dimensional observation application of the system in China. Based on the radar system, the spatial vertical distribution characteristics of pollutants before, during and after a dust episodein Mid-April 2021 were studied. We found that the vertical distribution of ozone was mainly concentrated within 1.5 km from the ground, and the ozone concentration before the dust was significantly higher than during and after dust. The vertical distribution of aerosol could reach the height of 2.5 km before and in the dust. Due to the sudden entry of dust, the local extinction coefficient will increase abruptly by more than 2.5 km-1. After the passage of sand dust, the aerosol was mainly suppressed within 500 m near the ground. Through the continuous observations and the vertical-profile analysis of lidar, it is found that before the dust, a region of low ozone concentration with a minimum concentration of 13 μg·m-3 near the height of 300 m around 7 a. m. was displayed, which is about 1/4 of the nearby region. This low ozone region may be caused by the “titration effect” before sunrise in a stable atmospheric environment. However, the sudden input of dust not only eliminated the stable “titration effect” but also erasesd the daily variation of ozone by reducing the peak-to-valley value of ozone concentration on the ground to 55 μg·m-3, which was 0.44 and 0.46 times that before and after dust respectively. Meanwhile, the input of dust, reduced the proportion of fine particle mass concentration to less than 20% and transported more primary pollutants from upstream, further inhibiting the generation and transformation of ozone.

Low-temperature freezing injury is one of the most serious natural disasters in tea plantations. Quantitatively detecting tea leaves under low-temperature stress is of great significance for evaluating the degree of freezing injury in tea plantations and taking timely measures. The traditional detection of tea plants under low-temperature stress is mainly through observing gardeners and determining physicochemical parameters. However, this method has some problems, such as low accuracy, low efficiency and strong subjectivity, which seriously affects the management of tea plants in the later stage of disasters. A method for quantitatively judging the freezing degree of tea plants based on hyperspectral imaging was proposed. First, the hyperspectral imaging equipment was used to collect spectral data on tea leaves in the early and later stages of the non-freezing periods. Moreover,the average reflectance of tea leaves was extracted. The physicochemical parameters such as relative electrical conductivity (REC), chlorophyll (SPAD) and malondialdehyde (MDA) in the corresponding leaves were determined. Secondly, the collected original hyperspectral data were preprocessed by using multivariate scattering correction (MSC), first derivative (1-D) and Savitzky-Golay (S-G) algorithms, and the characteristic bands of the preprocessed hyperspectral data were screened by using the uninformative variable elimination (UVE) and successive projections algorithm (SPA) algorithms. Finally, the quantitative prediction models of SPAD, REC and MDA content were established by using a convolutional neural network (CNN), support vector machine (SVM) and partial least squares (PLS).The results showed that: (1) The spectral curve preprocessed by the MSC+1-D+S-G algorithm had more prominent peaks and troughs than the original spectral curve, which improved the resolution and sensitivity of the spectrum and helped to improve the accuracy of the regression model established later. (2) The number of feature bands screened by the UVE algorithm was the largest, and the later modeling effect was good. The number of feature bands screened by the SPA algorithm was the least, and it was more suitable for building regression models with traditional machine learning methods. (3) The best prediction models of SPAD, REC and MDA were SPAD-UVE-CNN (R2P=0.730, RMSEP=3.923), REC-UVE-SVM (R2P=0.802, RMSEP=0.037) and MDA-UVE-CNN (R2P=0.812, RMSEP=0.008). In this study, the combination of hyperspectral imaging technology and a variety of algorithms can non-destructively, accurately and quantitatively monitor the degree of low temperature stress in tea leaves, which is of great significance for quickly predicting the occurrence of freezing damage in tea plantations and taking necessary measures.

To detect minor mechanical damage to apples without damage, the most common Fuji apple in China was used as the research object. The spectral information of intact, just damaged and 1, 3, 6 and 24 h after damage were collected using the hyperspectral imaging. Competitive adaptive reweighted sampling and continuous projection algorithms were used to extract the feature wavelengths of apple hyperspectral data. The extracted feature wavelength image data were compressed using the minimum noise fraction transform to study damage detection of Fuji apples. Taking random forest, Support Vector Machine, and Spectral Angle Mapper Classifier algorithm as primary learners and logistic regression as secondary learners, a new Stacking model, is established to extract the slight damage area of the apple. Its performance is evaluated by establishing a training set and prediction set and comparing it with three single algorithms in primary learners. The results show that: (1) for the classification detection of damaged fruits, the detection accuracy of the stacking model for damaged samples is 100%, for intact samples, the detection accuracy is 96.67%, and the overall detection accuracy is 99.4%, indicating that the model can be effectively applied to the classification detection of Apple damage in different damage periods. (2) The stacking model is compared with the other three single algorithms for detecting damaged areas. It is found that for the newly damaged fruits, the classification accuracy of the support vector machine algorithm and the triangular algorithm is poor, both of which are less than 60%, and the classification accuracy of the random forest algorithm is relatively good, reaching more than 75%, The classification accuracy of stacking model for damaged and undamaged fruit areas reached 90.2% and 92.3% respectively. For the fruits damaged for 1~6 hours, the classification accuracy of the stacking model for the two fruit regions reached more than 92%, which was significantly better than other classification models. For the fruits damaged for 24 hours, there is little difference among the four models, all of which have a good classification effect, and all of them have a classification accuracy of more than 97%, indicating that the stacking model can extract the slightly damaged area of Apple relatively accurately. It has a high reference value for the follow-up study of fruit damage based on Hyperspectral.

There are few spaceborne hyperspectral sensors, and the estimation of soil salt content based on hyperspectral images is still under exploration. GF-5 is the satellite with the highest spectral resolution in China. This paper aims to study the feasibility of estimating salt content of saline soil in arid areas on a large area using GF-5 hyperspectral image. In this paper, 198 soil samples were collected from the experimental field at Yanqi, Xinjiang. Firstly, the soil salt contents (total salt content, Na+, Ca2+, SO2-4 and Cl-) were determined, and the spectra of the soil samples were measured with an ASD Fieldspec3 field spectrometer in the laboratory. Then, the laboratory soil spectra were subjected to SG (Savitzky-Golay) smoothing pretreatment, and the competitive adaptive reweighted sampling method was used to select the characteristic bands of soil salt. Partial least squares, ridge regression and support vector machine established the regression model of soil salt content. It is found that the soil salt retrieval model established by laboratory spectra has high accuracy. The determination coefficients of the correction set and prediction set of the five soil salt retrieval models are greater than 0.97 and 0.90 respectively. Next, the GF-5 hyperspectral image data at the same time as soil sampling are obtained and preprocessed. The spectra of 198 soil samples were extracted from the image based on the location of the sampling points. Soil salt retrieval models based on GF-5 hyperspectral image spectra were established using the same retrieval method of laboratory spectra. The best prediction set determination coefficients of the five soil salt (total salt content, Na+, Ca2+, SO2-4 and Cl-) retrieval models were 0.76, 0.66, 0.76, 0.63 and 0.77 respectively. Finally, according to the retrieval results of soil salt based on the GF-5 image spectra, the characteristic band combination and modeling method with the best accuracy were selected estimate soil salt content in the whole study area. The estimation results have been divided according to the salinization grade. The saline soil in the study area accounts for 76%, and the land can not be cultivated. Non saline soil accounts for 16%, and crops can be planted. The distribution area of weak, medium and strong saline soil is small, accounting for 8% in total. The spatial distribution trend of the five soil salt estimation maps is consistent with the total salt content interpolation map. This paper shows that the results of estimating soil salt content in this study are based on GF-5 hyperspectral image are highly reliable.

Metallo-β-lactamases (MβLs) could hydrolyze almost all β-lactam antibiotics, the primary mechanism resulting in drug resistance against bacterial infections. This has become a substantial concern due to the lack of clinically approved inhibitors. SMB-1 from Serratia marcescents is a novel B3 subclass MβL that inactivates almost all β-lactam-containing antibiotics. The interaction mechanism between carbapenem antibiotic imipenem (IMIP) and Metallo-β-lactamase SMB-1 was ascertained in this paper using endogenous fluorescence spectroscopy, synchronous fluorescence spectroscopy, three-dimensional fluorescence spectroscopy and molecular docking methods. The quenching spectrum results demonstrated that IMIP quenched endogenous fluorescence of SMB-1, and the quenching mechanism was a combination of dynamic and static quenching, of which static quenching is the core one; the binding constant Ka was 16.11×103 L·mol-1 (277 K), indicating a strong binding force between them; the thermodynamic parameters in the binding process obtained from the Van’t Hoff equation ΔG<0, ΔH=-79.65 kJ·mol-1, ΔS=-238.69 J·mol-1, illustrating that the binding was driven by both enthalpy and entropy changes and hydrogen bonding and van der Waals forces were the main forces; Moreover, the maximum emission wavelength of SMB-1 in synchronous fluorescence results was blue shifted by 4.4 and 2.9 nm with increasing IMIP concentration, revealing that Tyr and Trp residues were involved in both. The significant decrease of Peak B and Peak C intensity of SMB-1 with IMIP introduced in the three-dimensional fluorescence spectra indicated that the microenvironment and conformation of SMB-1 changed after the interaction with IMIP, which is consistent with the synchronous fluorescence results. Furthermore, the β-lactam ring of IMIP entered the binding pocket of SMB-1 in the molecular docking results, while the side chain was located outside the active pocket due to the spatial site block effect, inferring that SMB-1 mainly recognized the core structure of IMIP and interacted weakly with its R2 side chain; the amino acid residues involved in the interaction with IMIP including Ser175, Thr177, Gln157, His215 and Glu217, implying that these amino acid residues with the two zinc ions in the active site are key factors in the design of SMB-1 inhibitors with strong affinity; the binding free energy was also negative, suggesting that the binding of both was a spontaneous exothermic process, which is consistent with the fluorescence results. Therefore, the present study provides insights into the recognition and binding of SMB-1 to IMIP, which may help design new substrates for β-lactamases and develop new antibiotics with resistance to superbugs.

The compactness of roadbeds is an important factor affecting highway construction’s quality and service. Therefore, it is of great practical demand and significance to grasp the compactness of highway subgrade quickly, non-destructive and accurately. However, the traditional detection of the compactness of highway roadbeds is mainly based on the accurate detection of a small number of discrete points, which can not meet the need for comprehensive and accurate detection of roadbed construction quality. Hyperspectral technology is a high and new technology that can realize real-time, fast, non-destructive and accurate monitoring of surface information, providing a new solution for detecting compactness of highway roadbeds. In order to explore the feasibility of hyperspectral technology in detecting the soil compaction degree of highway roadbed, soil compaction degree and corresponding spectral data were obtained through the soil compaction experiment and soil spectral measurement experiment, and the soil compactness coefficient was constructed with the help of soil spectral response mechanism analysis. Then the soil compactness coefficient is constructed by soil spectrum before and after compaction, and the soil compactness coefficient is processed and analyzed by discrete wavelet algorithm. The correlation between low frequency and high-frequency information and soil maximum dry density is analyzed quantitatively by correlation algorithm. The characteristic bands are extracted and screened, and then the soil maximum dry density estimation model is constructed based on the partial least square algorithm. The results show that: (1) after compaction, the soil spectrum decreases with the increase of soil water content, and the decrease range increases with the increase of soil water content, and the relationship between the variation range of soil spectral reflectance and the difference of soil water content is non-linear. Compared with the soil spectrum before compaction, except for 20% soil water content, the soil spectral reflectance increases or decreases in different degrees in the whole band range after compaction, and this change is easy to have a certain impact on the detection of soil composition. (2) the soil compactness coefficient generated by the soil spectrum before and after compaction can improve the sensitivity of the spectrum to the maximum soil dry density after compaction, and the correlation coefficient R is up to 0.811, which is highly correlated. (3) under the discrete wavelet algorithm, high-frequency information can improve the ability of soil compactness to estimate the maximum dry density of soil, among which the model based on D1 has the highest accuracy and is the best model, and its R2=0.957 and RMSE=0.023. The resolution of the soil compactness coefficient greatly influences the accuracy of the estimation model. The research results of this paper can provide basic theory and method support for the application of hyperspectral technology to the monitoring of highway roadbed compaction, other engineering foundation compaction and topsoil compactness.

Straw is an important organic resource. To study the effects of different calcium salts on the straw decomposition process and decomposition products, which can provide a theoretical basis and technical reference for the efficient utilization and high-quality decomposition of organic materials. The straw decomposition experiment was carried out by adding different types of calcium salts (without adding (CK), CaC2O4, Ca(OH)2, CaCO3, CaCl2, CaSO4 and Ca(H2PO4)2) in the laboratory. Then the decomposition rates and chemical properties of the decomposition products were measured at different decomposition stages (30, 60 and 180 d). Three-dimensional excitation-emission matrices (3DEEM) and parallel factor analysis (PARAFAC) were used to explore the chemical composition characteristics of the dissolved organic matter (DOM) of the decomposition products of straw. The results showed that : (1) compared with the control, the carbon conversion rate of straw in CaC2O4, Ca(OH)2, CaCO3 and CaSO4 treatments increased by 25.6%, 44.1%, 33.6% and 29.7%, respectively, and decreased by 76.8% and 17.5% in CaCl2 and Ca(H2PO4)2 treatments, respectively. CaC2O4 and Ca(OH)2 treatments significantly increased the pH of the decomposed products. CaCl2 and Ca(H2PO4)2 significantly increased the EC of the decomposed products. The relative humus content of CaC2O4 and Ca(OH)2 treatments was 3.4%～20.9% and 2.3%～25.3% higher than that of the control, respectively. (2) The composition of DOM was analyzed by 3DEEM-PARAFAC method and three fluorescent components were identified, including tryptophane-like (C1), fulvic-like (C2) and humic-like (C3). Ca(OH)2, CaCO3 and CaC2O4 treatments had a higher humic-like/fulvic-like ratio (H/F) than CK treatment, which increased the complexity of the decomposed products. The humification (HIX) index of DOM in CaC2O4, Ca(OH)2 and CaCO3 treatment was slightly higher than that in CK treatment, and the HIX index of Ca(H2PO4)2, CaSO4 and CaCl2 treatment was significantly lower than that in CK treatment. (3) Correlation analysis showed that pH and EC were the main factors affecting straw decomposition after adding calcium salt. The humification degree of straw decomposition products was positively correlated with pH and negatively correlated with EC. In conclusion, CaC2O4, Ca(OH)2 and CaCO3 treatment can promote the process of straw humification and improve the quality of decomposed products, while Ca(H2PO4)2 and CaCl2 have opposite effects. In addition, pH and EC are the main factors affecting straw humification. This study can provide a scientific reference for selecting suitable calcium salt additives to promote straw decomposition and improve the quality of straw decomposition products.

When chlorine leaks, measures should be taken immediately. Also the recovery and purification of chlorine-containing exhaust gas should be strengthened. At present, activated carbon with a developed porous structure and rich specific surface area is widely used to adsorb harmful gases. However the current production of activated carbon generally requires the consumption of natural resources such as wood and bamboo, resulting in high costs and is not conducive to sustainable development. Therefore, the preparation of activated carbon using biomass waste materials, and the modification of activated carbon by using metallurgical solid waste to reduce production and environmental costs further and improve adsorption properties, have become a hot spot in the field of activated carbon production. This study used special steel slag and discarded walnut shells as research objects to prepare steel slag-based biomass-activated carbon. The property of chlorine absorption was tested by a P-C-T adsorption device, and the inductively coupled plasma mass spectrometer (ICP-MS), X-ray fluorescence spectrometer (XRF), laser particle size meter (LPSA) and field emission scanning electron microscope (SEM) was used to test the concentration of the leached heavy metals, chemical composition, particle size distribution and microscopic morphology, respectively. Moreover, the mechanism of preparing steel slag-based biomass-activated carbon from special steel slag-discard walnut shells was elaborated from the microscopic level. The results show that the special steel slag ultrafine powder solution contains heavy metals such as Cd, Cu, Pb, Zn, Ni, Cr, As, etc., and the leaching toxicity content of Pb, Ni and Cr is higher than the leaching toxicity limit in the “Leaching Toxicity Identification Standard” (GB 5085.3—2007). Phosphoric acid has destructive structural characteristics, and anhydrous ethanol promotes dispersion, which is conducive to eliminating the gravitational force between the micronized particles and improving the dispersion of the discard walnut shell ultrafine powder and the special steel slag ultrafine powder. The magnetic Fe2O3 contained in the special steel slag ultra-fine powder and the catalytic CuO and MnO form a synergistic effect, which is conducive to the formation and enrichment of chlorine gas on the surface of the steel slag-based biomass-activated carbon, and improves the adsorption capacity of chlorine gas. The adsorption capacity of steel slag-based biomass-activated carbon to chlorine gas shows a tendency to decrease slightly and then decrease greatly with the rise of the ambient adsorption temperature. The excessively high adsorption ambient temperature will enhance the activity of chlorine molecules, resulting in the analytical phenomenon of chlorine adsorbed by steel slag-based biomass activated carbon. The activated carbon formed during the activation treatment and roasting process of discarding walnut shell ultrafine powder not only wraps the special steel slag ultrafine powder but also solidifies the heavy metals in the special steel slag ultrafine powder.

Fourier Transform Infrared (FTIR) spectroscopy enables simultaneous measurement of the concentrations of various greenhouse gas components. However, instrument noise and spectral line overlap can affect the quality of spectroscopic data, thereby influencing the accuracy of component concentration inversion. In this study, we address this issue by reconstructing the time-resolved measurement spectroscopic matrix using different numbers of principal components. The Euclidean and cosine distances between the reconstructed and original spectroscopic matrices are employed as criteria for dynamically selecting the number of principal components, thus enhancing the quality of time-resolved spectroscopic data. The proposed method is applied to numerical simulation spectra, standard gas measurement spectra, and field experiment spectra. The results show that the structure characteristics of the simulated spectra, with an additional 0.001 RMS noise, remain essentially unchanged after spectral reconstruction. The residual standard deviation between the reconstructed and original spectra is 4.191×10-4, effectively reducing the impact of noise in the measurement spectra. The method is further utilized to reconstruct the average measurement spectra of standard gases, and the accuracy of component concentration inversion between the reconstructed and average spectra is compared. The precision of component concentration inversion for 1-minute average measurement spectra is as follows: CO2: 0.24 μmol·mol-1, CH4: 5.24 nmol·mol-1, N2O: 2.92 nmol·mol-1, and CO: 4.72 nmol·mol-1. Similarly, for 5-minute average measurement spectra, the precision is: CO2: 0.24 μmol·mol-1, CH4: 5.24 nmol·mol-1, N2O: 2.92 nmol·mol-1, and CO: 4.72 nmol·mol-1. The precision of component concentration inversion for 1-minute reconstructed spectra is: CO2: 0.17 μmol·mol-1, CH4: 2.97 nmol·mol-1, N2O: 0.72 nmol·mol-1, and CO: 1.40 μmol·mol-1. For 5-minute reconstructed spectra, the precision is: CO2: 0.15 μmol·mol-1, CH4: 1.74 nmol·mol-1, N2O: 0.29 nmol·mol-1, and CO: 0.97 nmol·mol-1. Utilizing reconstructed spectra improves the accuracy of gas concentration inversion, with the precision of component concentration inversion for 5-minute reconstructed spectra meeting the requirements of the World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) for extended measurement accuracy. In field experiments, the correlation coefficient between the concentration of CO2 obtained from 1-minute reconstructed spectra and that from 1-minute average spectra reaches 89.40%. The comprehensive analysis demonstrates that the dynamic selection of principal components for FTIR time-resolved spectroscopic data reduces the influence of noise and effectively preserves the characteristic variation information of time-resolved measurement spectra.

There are advantages of quick response and simple measurement in the water biological toxicity detection method based on the photosynthetic inhibition effect of algae. However, more than ten photosynthetic fluorescence parameters are derived from the PSⅡ photosynthetic reaction center, showing poor sensitivity or no response to typical PSⅠ photosynthetic inhibitors. In this paper, based on the characteristic that toxic stress will cause deformation of algae fluorescence kinetics curve, and the change degree is proportional to the toxicity intensity, the morphology of the multiphase chlorophyll fluorescence kinetics curve is directly taken as the analysis object. Based on the accurate location of the characteristic sites, a comprehensive characterization parameter CPI based on the segmentation inhibition of the curve is constructed. The toxicity response time, response sensitivity and stability were compared with the commonly used photosynthetic fluorescence parameters Fv/Fm and the comprehensive fluorescence parameter PIabs. The results showed that for paraquat, a typical PSⅠ inhibitor, the comprehensive parameter CPI showed significant inhibition at 15 min (1 mg·L-1 and above), while Fv/Fm had no response. Compared with PIabs and Fv/Fm, the minimum detection limit and relative standard deviation decreased by 70.3% and 24.1% and 75.6% and 30.0%, respectively, under 8h stable inhibition. Atrazine, a PSⅡ inhibitor, showed significant inhibition in the whole concentration range of samples within 15 min, and the minimum detection limit and relative standard deviation decreased by 52.4% and 51.6% and 75.6% and 30.0%, compared with PIabs and Fv/Fm. These conclusions indicate that the comprehensive parameter PICTE, as a response index, can be used to detect the biotoxicity of PSⅠ and PSⅡ inhibitors, and it shows good response sensitivity and stability. The results of this study provide an important parameter for highly sensitive and high-precision detection of biotoxicity in water based on algae photosynthetic inhibition.