Three-dimensional fluorescence spectrum (3D-EEMs) and principal component analysis (PCA) were used. The three-dimensional fluorescence spectrum of urban sewage was divided into four spectral regions: aromatic proteins, microbial metabolites, humic acids and fulvic acids. Determine the regional principal component contribution rate of the lambda λ. Calculate the value of the first principal component area of each area, to establish it with water chemical oxygen demand (COD) and total nitrogen (TN), studies the urban sewage treatment effect rapid analysis and evaluation method.The results show that the urban sewage fluorescent material is mainly composed of aromaticity protein material, microbial metabolites, humic acid and fulvic acid material, regional fluorescent distribution is different, the material in the process of sewage treatment aromaticity protein material area spectral changes obviously, and the microorganism metabolites humic acid and fulvic acid material area spectral changes smaller, Spectral regions the value of the first principal component area and water body has a good correlation between COD and TN, aromaticity of protein material spectrum coefficient of the value of the first principal component areas related to the COD reached 97.63%, aromaticity protein material and the sum of the value of the first principal component area microbial metabolites and humic acid and fulvic acid material ratio of the sum of the value of the first principal component area (Yp/Yf) and TN correlation coefficient reached 94.02%.By combining the three-dimensional fluorescence spectrum of water with the principal component analysis method, the dimensionless extraction of fluorescence spectrum information of each process of sewage treatment is realized, the overlapping of fluorescence peaks and redundancy of spectral information of each substance are avoided. Through the spectral characteristics of each substance in the water, the spectrum is divided into different material regions, the first principal component region value of each region is obtained, which improves the accuracy of substance identification and effectively solves the problem of spectral information identification of each substance. By using the correlation analysis of the first principal component area value of aromatic protein spectrum and Yp/Yf and the conventional water quality indexes COD and TN, it provides a real-time and effective method for monitoring the quality of domestic sewage and solves the problem that the sewage treatment process is difficult to accurately monitor in real-time. Therefore, the three-dimensional fluorescence spectrum combined with principal component analysis method can be used for fast discrimination of urban domestic sewage treatment process, providing a new fast on-line monitoring and analysis method for water quality monitoring, process optimization and treatment effect evaluation in the sewage treatment process.

N-butanol is a promising alternative fuel for diesel. The study on the self-luminosity spectra of combustion intermediates and flame development in diesel engine cylinder was helpful to understand the influence law of diesel blended n-butanol deeply on the combustion process in diesel engine cylinder. Therefore, this paper used the high-speed flame imaging technology and self-luminosity spectroscopy analysis to study the effects of pure diesel and diesel blending n-butanol on the flame development and self-luminosity spectrum of the engine cylinder on an optical engine. During the test, the optical engine speed was 1 200 r·min-1, with an injection pressure of 600 bar and an intake air heating to 398 K, bringing the temperature around the top dead center to approximately 900 K. Pure diesel, diesel blended with 20% n-butanol fuel and diesel blended with 40% n-butanol fuel were represented by D100, DB20 and DB40 respectively. The injection masses of D100, DB20 and DB40 were respectively 17.5, 18.7 and 19.2 mg per fired cycle to ensure the same engine output. The experiment results show that when the cooling water temperature remains unchanged, with a delayed start of fuel injection (SOI), the ignition delay is shortened, the initial fire nucleus formation time is delayed, the blue premixed flame proportion is reduced; when the SOI remains unchanged, with the increase of cooling water temperature, the ignition delay is shortened, the initial nucleus formation time is advanced, the proportion of blue premixed flame decreases. With the increase of n-butanol blending ratio, the characteristic of the local mixture is first ignited, the ignition time is delayed, the proportion of blue premixed flame increases, and the flame luminosity of fuel decreases. The luminosity of the flame is from D100>DB20>DB40. For D100 fuel, with delayed injection, the peak of the whole spectrum shifts to a larger wavelength direction, soot radiation is enhanced, the peak light intensity of the OH band first increases and then decreases, and the occurrence time of the OH band and the CH2O band is delayed, indicating the high temperature and the low temperature reaction delayed. When the SOI remains unchanged, with the increase of cooling water temperature, the light intensity of whole spectrum increases and the occurrence time of OH and CH2O bands is ahead of schedule, indicating the high temperature and the low temperature reaction advanced. With the SOI delay, the whole light intensity of the spectrum of DB40 fuel after the diesel blended with n-butanol, increases, the peak light intensity of the OH band and the CH2O band increases, which means that delaying injection to DB40 fuel also helps to promote high temperature and low temperature reactions. The whole intensity of the DB40 fuel spectrum is lower than that of D100 fuel, and the occurrence time of OH and CH2O bands appear later than D100 fuel, indicating that both the high temperature and the low temperature reaction of the fuel after the addition of n-butanol are delayed relative to the D100 fuel. Under the condition of SOI-15 and cooling water temperature of 95 ℃, the spectrum of D100 fuel exhibits similar characteristics of the soot blackbody radiation spectrum after 2 ℃A, while DB40 fuel first exhibits the characteristics of CO oxidation continuous spectrum, then the characteristics of the soot blackbody radiation spectrum are exhibited after 15 ℃A.

Two-dimensional correlation spectroscopy (2D-COS) can extend the dynamic spectrum to two dimensions, which contains synchronous and asynchronous maps. It not only can separate overlapping peaks to enhance resolution, but also discriminate variation orders of different components. 2D-COS heterospectrum can combine two different spectra into one spectrum, which is applied to trace the relationship between different bands, and identify complementarityof component variations. Synchrotron fluorescence spectroscopy (SFS), Fourier transform infrared (FTIR) and two-dimensional correlation spectroscopy (2D-COS) were applied to analyze the structural composition of dissolved organic matter (DOM) in Wuliangsuhai Lake, trace the covariance between DOM components and functional groups, and reveal their spatial variations. According to the FTIR spectroscopy, DOM was mainly composed of CO, C—H, N—H, C—O functional groups. DOM contained protein-like (PLF), microbial humic-like (MHLF), fulvic-like (FLF) and humic-like (HLF) components by the SFS, among which the MHLF was the dominated component. The decreasing order of the contents of fluorescent materials, PLF and MHLF abundance was north>south>central. The decreasing of the relative FLF abundance was south>north>central, but the relative abundance of HLF kept stable. According to the SFS 2D-COS, the PLF variation was larger than the MHLF in the northern region, whose order was PLF→MHLF; the MHLF showed the negative correlation with the PLF in the central region, whose variation order was PLF→MHLF; the FLF variation was larger than the PLF and MHLF in the southern region, whose order was PLF→FLF→MHLF. According to the FTIR 2D-COS, the C—O was positively correlated to the C—H and CO in the northern region, whose variation order was C—O→C—H→CO; the C—O exhibited the positive correlation with the N—H and CO in the central region, whose variation order was C—O→N—H→C—H→CO; the C—O was the positive relationship with the N—H and CO in the southern region, whose variation order was C—O→C—H→CO. According to the 2D-COS between SFS and FTIR, the C—O showed the positive correlation with the PLF in the northern region; the C—O, N—H and CO had the positive correlations with the MHLF in the central region, but had the negative correlations with the PLF; the C—O, C—H, N—H and CO exhibited thepositive relationship with the FLF. In summary, SFS and FTIR combined with 2D-COS can be used as an effective method to analyze the covariance of DOM components and functional groups in water and reveal their spatial variation.

Micro X-ray fluorescence (μXRF) imaging is powerful non-destructive technique for imaging distributions of nonradioactive elements within the body, including scanning X-ray fluorescence and X-ray fluorescence computed tomography. The spatial resolution is determined by the size of the X-ray focused beam spot. The μXRF instrumentation, which uses a simple pinhole aperture to restrict the incident beam size on the sample surface, has been established and opened to users at SSRF X-ray imaging beamline (BL13W). It has a typical spatial resolution ranging in diameter from 200 micrometers up toseveral millimeters. Ellipsoidal shaped single-bounce glass capillaries have been used as achromatic X-ray focusing optics for various applications at synchrotron beamlines, which can provide efficient and high demagnification focusing with large numerical apertures, large Working distance, the wide energy range of x-rays, small volume and so on. The support a wide range of applications, including Micro X-ray fluorescence, full-field transmission X-ray microscopy (TXM), etc. But the challenge is to make an accurate profile with small slope errors. In order to meet the requirements of users for μXRF imaging, the single bounce ellipsoidal glass mono-capillary was designed and fabricated and its performance was measured by an X-ray test. The focus spot and the gain of this mono-capillary were 14μm and 255 at 8 keV, respectively. The images of focal spot by detector showed that this fabricated mono-capillary had high quality and satisfied the requirement of the designed data for μXRF. The μXRF instrumentation has been established, based on ellipsoidal mono-capillary designed and fabricated by ourselves, andcarried out scanning X-ray fluorescence and X-ray fluorescence computed tomography experimental research at SSRF X-ray imaging beamline (BL13W). Firstly, the fluorescence spectrum of the trace elements copper, iron, calcium and zinc in the stroke rat brain by scanning X-ray fluorescence. Secondly, the Arsenic of standard solution and the rat brain were subjected to microX-ray fluorescence computed tomography. Two-dimensional slices of Arsenic element in solution and copper element in the rat brain by OSEM algorithm to reconstructed. This study demonstrates that the μXRF instrumentation is a powerful X-ray analytical microscope with the high resolution and high sensitivity μXRF capabilities available.

Spectral analysis has been increasingly applied to estimate plant species diversity through the world, especially for biodiversity field. Although spectral variability hypothesis (SVH) has been widely proved in estimating plant alpha diversity for tropical, temperate, and sub-tropical forests, meadow, steppe and grasslands, however, the performance on beta diversity is still lack. In this study, we measured the hyperspectral reflectances and plant species diversity indices of 270 plots at a fine scale (0.8 meter) in central Hunshandak sandy grasslands of Inner Mongolia, China. 195 plots were used as training data and 75 plots as validating data. Bray-Curtis dissimilarity index (BC), Srensen index (S) and Jaccard index (J) were calculated to indicate actual beta diversity. Based on spectral biological features of different plant species, 164 hyperspectral indices were developed and used to assess plant species beta diversity. Pearson’s correlation analysis and multiple linear stepwise regression were conducted based on sensitive wavebands to produce hyperspectral models. The hyperspectral indices which high Pearson’s correlation coefficients will be remained for further tested. Communities with different coverages and richness were also used to test the robustness of proposed models. By comparing the stability of hyperspectral indices under different communities, the indices with high stability is remained for validation by 75 plots. Results demonstrated that BC, Euclidean distances of first-order derivation values between 400～1 000 nm, and BC of 760～800 nm could accurately estimate species beta diversity. BC can be accurately estimated by hyperspectral indices, since they were both calculated as parameters of the distance between plots. The Jaccard and Srensen indices were hardly estimated, it is hard to find the suitable wavebands or other parameters in spectral data to replace the “common reflectance” between pairwise plots. This study promotes the development of methods in assessing plant species beta-diversity using hyperspectral data.

In order to accurately measure the trace methane (CH4) concentration in nitrogen, a continuous wave-Cavity Ring-Down Spectrometer (CW-CRDS) measuring device based on the triangular annular cavity was designed and built. The ring-down cavity is designed for autonomous processing. It consists of a concave mirror with a radius of curvature of 1 m and a diameter of 25 mm and two plane mirrors with a diameter of 12.7 mm. The total length of the optical path in the cavity is 410 mm, and the cavity material is Invar. First, we measured the baseline loss of the system, and then used nitrogen as the mixture to configure a mixture of five different concentrations of CH4 and N2. The gas was detected by the absorption peak of CH4 at 1 653.7 nm (CH4ν3 with R5). According to the absorption line characteristics, the attenuation time constant τ is calculated by the least square method using the Lorentz linear function and the CH4 concentration is calculated. The detection sensitivity of the built device to the methane volume concentration can reach 54×10-9 (540 million). Finally, the absorption spectrum of CH4 with a volume concentration of 510×10-9 in the range of 6 046.7~6 047.2 cm-1 was measured, and the measured data was determined according to the cavity free spectrum. The range (Free Spectral Range, FSR) is fitted to the τ and the absorption coefficient respectively. The obtained CH4 absorption coefficient is compared with the data in the database. The maximum error is less than 1.2×10-9 cm-1, and the highest precision is 8.8×10 -11 cm-1.

Osteoarthritis (OA) is a chronic degenerative joint disease caused by many factors, which affects the limb function and daily life of patientsseriously, and is one of the most common joint diseases affecting human health.When OA is severe, it will be irreversible. Therefore, it is critical for the timely detection and diagnosis of OA. Raman spectroscopy shows the potential of minimally invasive, label-free and objective diagnosis at the molecular level. So it is used increasingly in the study of OA. The creative findings and progress of the researches on articular cartilage and OA are reviewed in detail, as well as the limitations and prospects of Raman spectroscopy at home and abroad in future. This review mainly introduces that changes in protein, lipids and nucleic acid components which exit in the extracellular matrix, pericellular matrix, and chondrocytes as well as changes in main components of subchondral bone and synovial fluid.Their corresponding OA feature or physiological functions have been detected by three different Raman spectroscopy techniques that include macro-Raman, micro-Raman and optical fiber Raman modes. This review provides a reference for the coming study of OA and suggests the effectiveness and feasibility of Raman spectroscopy in detecting OA. On the other hand, Raman spectroscopy has many advantages indetection and diagnosis, especially not being affected by water. It is promising to be powerful molecular spectroscopy techniques and clinical tools for early clinical diagnosis and rehabilitation monitoring in this field.

Watermelon and muskmelon are sweet, juicy and rich in nutrients.There is great significance in manufacture and circulation for its internal quality detection. The traditional detection methods for internal quality of watermelon and muskmelon are inefficient, long time, high cost and destructive, which can not meet the needs of modern production. With the rapid development of spectral analysis techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) for the internal quality non-destructive detectionof watermelon and muskmelon has become a research hotspot. In order to track national and international progress of research, this paper presents the technical characteristics and system composition of NIRS and HIS. The spectral information analysis methods are concluded, including spectral information preprocessing, variable selection, model establishment and evaluation. Afterwards, the recent progress of NIRS and HSI in the non-destructive detection for the internal quality (soluble solids content, firmness, total acid content, maturity and moisture, etc.) of watermelon and muskmelon is summarized. Finally, the future trends of spectral analysis techniques in the internal qualitynon-destructive detection of watermelon and muskmelon are discussed from the technical difficulties and practical applications.This review indicates thatthe following aspects are identified as the direction of future research, using deep learning methods to analyze spectral information, establishing comprehensive evaluation model of multi-feature information fusion, and developing the rapid non-destructive detection system based on the deep integration of artificial intelligence and mobile terminal.

Soil is the basis of agricultural production. The determination of soil nutrient content is determined by the determination of soil nutrient content, and it has a certain influence on the growth of crops. Therefore, the detection of soil component content has gradually become a research hotspot at home and abroad. Multi-spectral technology utilizes the difference in the physical structure and chemical composition of an object. Under the same conditions, the object is irradiated with different light reflections to obtain different reflectances on the corresponding spectral bands, and then the acquired spectral data is analyzed to identify the target. In recent years, the application of multi-spectral technology has provided a new idea for the detection of soil component content which is helpful for the accurate detection of soil component content, and contributes to the realization of non-destructive real-time online detection technology and precision agriculture. In this paper, the related pieces of literature on the application of multi-spectral techniques in the soil composition index of soil water, organic matter, NPK, heavy metals and soil salinity in the past six years are reviewed. The characteristics of multi-spectral imaging technology are analyzed, and the multi-spectral is briefly described. The detection process of soil composition content by technology focuses on the research progress of multi-spectral technology in soil component content detection, and prospects the future development trend of multi-spectral technology in soil component content detection, and proposes the future technology development direction: machine learning. The unsupervised and supervised model of the algorithm can analyze the data in different actual measurement environments, reduce the influence of spectral data with uneven distribution of soil components on the modeling results; multi-spectral images combined with panchromatic images to obtain multi-spectral panchromatic bands, In the multi-spectral soil component content detection, the accuracy and accuracy of the prediction model are improved. In the multi-spectral data preprocessing process, two or more algorithms are combined to process the spectral data more effectively.

There are two types of hemoglobin in the cerebral blood stream: oxygenated hemoglobin (HbO2) and reduced hemoglobin (HbR). The changes in the concentration of these two hemoglobins in the cerebral blood flow can reflect the neural activity in the brain. Extracting the signals of concentration changes can provide basis and reference for the diagnosis and treatment of related diseases such as epilepsy focus localization and depression. At present, algorithms for extracting cerebral blood flow signals using near-infrared spectroscopy include the EEMD-ICA method principal component analysis (PCA) , independent component analysis (ICA), the coherent averaging method, Adaptive filtering, etc. The above algorithms have their own characteristics and advantages in the extraction of near-infrared brain neural activity signals. However, the above methods all pay attention to various physiological interferences such as respiration and eye movement and ignore measurement interferences that conform to Gaussian distribution during measurements, such as instrument precision and crosstalk in signal transmission. In order to extract signals of changes in the concentration of oxygenated hemoglobin (HbO2) and reduced hemoglobin (HbR) in cerebral blood flow, a functional near infrared spectroscopy (fNIRS) cerebral blood flow parameter acq uisition device is designed in this article. In the device, a light source Diode near-infrared light sources with wavelengths of 750 and 830 nm were selected to collect brain blood flow changes. The extended Klaman Filter (EKF) algorithm was used to establish a corresponding mathematical model of physiological interference and measurement interference. Perform recursive calculation with the minimum principle, and combine the initial state estimation of the system at the next moment with the measured feedback to obtain a state estimate of infinitely close to the real value at that moment.), The change of the optical density signal is converted into a signal of change in oxygenated hemoglobin (HbO2) and reduced hemoglobin (HbR) concentration. The results show that the method proposed in this paper can effectively remove the measurement interference that conforms to the Gaussian distribution. In the Valsava experiment and the visual evoked experiment, the curve of changes in the concentration of oxygenated hemoglobin (HbO2) and reduced hemoglobin (HbR) in the cerebral blood flow can be extracted. Compared with the mainstream EEMD algorithm for extracting brain signals, its RMSE value is increased by 0.96%, and r value is increased by 0.6%, which indicates that the proposed method has certain advantages. The method proposed in this paper provides an effective method for detecting neural activity in related brain diseases.

Compared with infrared, ultraviolet and Raman spectra, terahertz spectra have low energy and no harmful photoionization phenomena in the substances to be measured. With the maturation of terahertz technology, the terahertz wave has become a common wave for non-destructive testing. Many biological macromolecules have fingerprints under high-frequency light detection. THz time-domain spectroscopy is the best method for nondestructive detection of biological macromolecules. At the same time, different biological molecules show different absorption peaks in Terahertz Absorption spectra. After obtaining Terahertz Absorption Spectra of the substance to be measured, compared with standard spectra, qualitative identification of the substance to be measured can be made. On this basis, combined with data processing techniques such as least squares method and support vector machine, the quantitative analysis of measured substances based on terahertz time-domain spectroscopy can also be realized. The basic principles and methods of quantum mechanics are applied in the quantum chemical analysis method. From the electronic point of view, the approximation error of the electronic analysis theory in the analysis of systems with large molecules or atoms is small, and the density functional theory does not depend on the support of experimental data and prior knowledge. The Terahertz Absorption Spectra of amino acids can be calculated by a quantum chemistry calculation method, which can match the molecular vibration mode of terahertz absorption peaks of amino acids, provide certain reference and directivity for qualitative analysis of amino acids, and provide theoretical support for terahertz time domain spectra of samples obtained from experiments. Quantum chemistry calculation is carried out on the basis of the terahertz absorption spectra obtained from experiments. It can further verify the accuracy of the experimental results. In this paper, the Terahertz Absorption Spectra of imported threonine samples were obtained by the terahertz time domain spectroscopy system. Then, three configurations of threonine samples in the form of zwitterionic ions were constructed, and the structure optimization of each configuration was completed by quantum chemical calculation method. Finally, the Terahertz Absorption Spectra of three threonine molecular configurations were calculated. The results show that the terahertz calculation spectra of the monomer and dimer configurations are quite different from the experimental spectra, but in the high frequency band, the absorption peaks of the calculated spectra are basically in agreement with the experimeotal spectra, while the lattice configuration calculation spectra of the more comprehensive reaction of intermolecular hydrogen bond and van der Waals force are in good agreement with the experimental spectra. At the same time, the smallest structure, which is consistent with the sample structure and keeps the physical properties of threonine is the cell.

Remifentanil is a new, ideal, narcotic analgesic commonly used in surgery, which has the advantages of rapid effect, short half-life, easy to control, quick recovery after the operation etc. After entering the human body, remifentanil is mainly metabolized by the kidneys. Then remifentanil acid is formed after de-esterification metabolism. Therefore, it is necessary to study the structure of remifentanil and its metabolite remifentanic acid. At present, only the structure of the prototype drug has been studied, but the structure of its metabolite remifentanic acid has not been reported. Methods For the first time, this study established ultraviolet spectra, infrared spectra, nuclear magnetic resonance (NMR) spectra (i. e., 1H NMR, 13C NMR, 1H-1H COSY, 1H-13C HSQC, 1H-13C HMBC and DEPT) and mass spectra for the chemical structure identification of remifentanil acid. The ultraviolet spectrum showed the aromatic structure and the information of the conjugated system of remifentanil acid. The maximum absorption in the vicinity of the ultraviolet end and 254.0 nm respectively corresponded to the E2 and B bands of the substance, which showed the presence of the benzene ring characteristic structure in the molecule. The infrared spectrum test was performed by preparing the remifentanil acid samples by the potassium bromide tablet method. The infrared spectrum was used to analyze the peaks of the functional group vibrations of remifentanil acid. All the 1H NMR and 13C NMR chemical shift signals of the compound were assigned rationally by NMR spectroscopy (including 1H NMR, 13C NMR, 1H-1H COSY, 1H-13C HSQC, 1H-13C HMBC and DEPT). Mass spectra were performed by positive electrospray ionization, and the mass-to-charge ratio (m/z) was 362.98, 331.02, 303.10, 259.09 and so on, which was consistent with the molecular weight of remifentanil acid and corresponding to the structural characteristics of remifentanil acid. The results showed that the above analytical data of a variety of spectral techniques coincided with the structure of remifentanil acid. Spectral analytical data is shown to be consistent with the structure of remifentanil acid, which can be used in the study of the structure of remifentanil acid and provide a reference for the study of its quality and purity.

With the rapid development of spectroscopy and fluorescence detection technology, monochrome fluorescence labeling is unable to analyze cell samples accurately and has been gradually replaced by two-color or multi-color fluorescence labeling. In the multicolor fluorescence analysis, since the cells were labeled with a variety of fluorescein usually, partial spectral overlap will occur in the emission spectrum, which need to be decomposed into an independent spectral peak to analyze accurately. Aiming at it, optimized BP neural network based on genetic algorithm (GA_BP) were used for overlapping spectral peak analysis. Firstly, the concrete structure of BP neural network was determined, and the overlapping peak was pre-processed by quadratic differential to find out the number and positions of single peaks as the characteristic value of overlapping peaks to be the input layer of BP neural network; in addition, weights and thresholds of BP neural network were Initialized, and optimal parameters like initial population and population size of the genetic algorithm were selected by using the advantage of global search; after a series of genetic evolution operations like selecting, crossing and mutating, the individuals containing the optimal weights and thresholds of BP neural network were obtained; and then the optimal parameters of the network were selected to carry out network training, which the width and intensity of the independent peak can be calculated from the output node of the optimized BP neural network; finally, combined with the eigenvalues of overlapping peak identified by quadratic differential, independent spectral peak can be separated. The randomly generated Gaussian overlapping peaks model was used as experimental simulation data, and the decomposition experiments showed high precision of the peak intensity and peak width. Wherein, the maximum relative error of decomposition of two overlapping peaks was 0.30% and 3.57%, and which of the three overlapping peaks was 0.64% and 3.83%. It can also be decomposed when the four overlapping peaks. Moreover, compared the GA_BP network model with the unoptimized BP neural network model, the results showed that the GA_BP network could reach the preset error value after five steps, while the unoptimized network model takes 19 steps. This further proves that the GA_BP network model converged faster with a fairly high precision that can be widely used for the decomposition of spectral and other overlapping peaks, which has a certain practical value compared with traditional methods.

Midazolam in aqueous solution, urine and serum were rapidly detected on site, based on surface-enhanced Raman spectroscopy (SERS) technology in this paper. The Raman spectra were recorded by a portable laser Raman spectrometer BWS415-785H with a wavelength of 785 nm. The spectrometer provided that the spectral measurement range would be 68～2 700 cm-1 and spectral resolution would be better than 3 cm-1. The output power was maintained 80 mW in the experiment with 5 s of integration time. First, the Raman spectra of Midazolam were calculated by density functional theory and compared with the experimental values. The possible characteristic peaks were identified. Then, using silver nanoparticles as active substrate, MgSO4 aqueous solution as neutral salt coagulants, the Raman peak at 689 and 827 cm-1 was selected as the characteristic peak on detection, the SERS detection of Midazolam was conducted. The limit of detection of Midazolam in aqueous solution samples is 6 μg·mL-1. In the range of concentrations of 5～40 μg·mL-1, the relationship of Raman peak intensity and concentration of Midazolam in aqueous solution can be expressed by a linear equation that was y=188.18x+743.05. The correlation coefficient was r=0.972, the recovery was 98.2%～107.2%, and the relative standard deviation (RSD) was 2.08%～3.25%. The limit of detection of Midazolam in urine samples is 20 μg·mL-1, In the range of concentrations of 15～125 μg·mL-1, the relationship of Raman peak intensity and concentration of Midazolam in urine can be expressed by a linear equation: y=59.78x-640.71. The correlation coefficient was r=0.958, the recovery was 96.9%～107.9%, and the RSD was 4.45%～5.75%. The limit of detection of Midazolam in serum samples is 20 μg·mL-1, In the range of concentrations of 15～125 μg·mL-1, the relationship of Raman peak intensity and concentration of Midazolam in serum can be expressed by a linear equation: y=30.81x+176.66. The correlation coefficient was r=0.963, the recovery was 94.2%～105.7%, and the RSD was 3.60%～4.41%. This method is rapid, accurate, non-destructive and easy to operate, and it lays a good foundation for Midazolam detection in the field.

Carbon nanotubes-polydimethylsiloxane (CNT-PDMS) is a new type of laser ultrasonic transducer (LIU-T)composite material with high frequency, wide width and high amplitude. The composite film can be used as an efficient and robust ultrasonic emitter for diagnosis and treatment. The intrinsic structure of nanocomposites provides unique thermal, optical and mechanical properties, which are not only conducive to energy conversion but also robust to pulsed laser ablation. PDMS polymers have high thermoelastic coefficients that allow materials to stretch and produce ultrasound. In this paper, the characteristics of photoacoustic signals produced by several kinds of composite films are studied, the photoacoustic signal characteristics under the different substrate and water boundary conditions were tested. Photoacoustic transducers made of carbon nanomaterials with high light absorption and PDMS polymers with high expansibility not only reduce the thickness of materials but also are expected to generate high frequency and high intensity ultrasonic signals. The thickness of the hard glass substrate realized in this paper is about 1mm, the thickness of the soft film substrate is at the micron level, and the thickness of water boundary conditions are 3 mm. Under pulsed laser excitation, the ultrasonic pressure at the end surfaces of water boundary conditions and hard glass substrates and soft film substrates was 2.0, 3.9 and 5.2 MPa, respectively. Through a series of studies, it is concluded that: (1) soft film substrate (3×3) has better negative pulse than hard glass substrate (3×3), which is more suitable for photoacoustic cavitation treatment; (2) water boundary conditions are not conducive to the generation of high-intensity photoacoustic signals. In a word, compared with piezoelectric transducers, laser-induced ultrasonic transducers have more potential to produce high- amplitude ultrasonic signals with a wide width and provide a new method of ultrasonic excitation without interference structures such as electronics, which is expected to be a new generation of laser ultrasonic transducers to replace piezoelectric transducers. The application of this new method in magneto-acoustic imaging can greatly reduce the interference of ultrasonic excitation sources. At the same time, compared with mixing CNT into PDMS, the method adopted in this experiment is more simple, convenient and material saving. For traditional hard glass substrate, the implementation of soft film substrate can produce high sound pressure 5.2 MPa, and center frequency in 5 MHZ, and -6 dB ultrasound is relatively close to 5 MHz wide bandwidth, compared with the 4.5 MPa pressure produced by the early implementation of CNT-PDMS Photoacoustic transducer in 2014, this paper implementation has more clinical application prospect, applied in Magneto-acoustic electric imaging etc. to avoid electromagnetic interference has the very good effect.

In this paper, a measurement system based on the FWHM (Full Width Half Maximum) method for junction temperature of LED is designed, which uses ordinary spectrometer. First, the relative spectral distribution of various color LEDs under different ambient temperatures and driving currents are measured by a general spectrometer. Since the spectral data collected by the spectrometer are discrete, in order to obtain a more accurate FWHM, it is necessary to fit the discrete spectral data into a continuous peak near the half of the strongest peak value Imax, i. e. 0.5Imax. So, the more accurate FWHM at different temperatures can be calculated and then the functional relationship between junction temperature Tj and FWHM can be obtained by fitting a certain function. Experiments show that the linear relationship between Tj-FWHM function of white and blue LED is higher than that of other color LED, and its linear index R2 is very close to 1. It shows that the two parameters of Tj and FWHM have strong linear function relationship. Using the functional relationship between Tj and FWHM, the junction temperature corresponding to any measured value FWHM can be calculated. Because this method uses the normal driving current, the self-heating effect can not be neglected. In order to reduce the junction temperature rise of LED devices caused by self-heating effect in fixed reaction time and the measurement error caused by the temperature deviation introduced by temperature control system, Tj and FWHM in a certain state are selected as the reference state, and the corresponding Tj and FWHM are obtained by point-by-point difference method, and then the corresponding ΔTj and ΔFWHM are obtained. ΔTj and ΔFWHM are fitted into corresponding linear functions to obtain the calibration function, which can greatly reduce the deviation caused by the self-heating effect and temperature control system. Finally, the results obtained by this method are compared with those obtained by T3Ster instrument of Mentor Graphics. It is found that the deviation is 2.5%, which is within the acceptable error range. The results show that the proposed method of measuring the junction temperature of LED by the FWHM method is feasible. This method overcomes the shortcomings of small peak wavelength drift of spectroscopy, which brings great errors to the test results, and has the advantages of not destroying the original packaging structure and not requiring expensive instruments.

The 3D ray tracing method is used to simulate the transmission of oxygen in the atmosphere from ground to 110 km in the stellar occultation technique. The carrier frequency is 3.53×1015 Hz, the Earth is ellipsoid, and the model is the neutral atmosphere. It is known that the three-dimensional position coordinates of target stellar and low-orbit satellite orbit data in the earth-solid system. And then the high resolution of oxygen molecular absorption line parameters in the HITRAN database are used, including the absorption line intensity, low-level energy, etc., to calculate the transmittance of oxygen molecules in the near-infrared absorption band A. In addition, taking Sirius’ infrared spectrum as the original receiving spectrum, that is, removing the absorption and scattering of the Earth’s atmosphere, the spectral energy decreases as the wavelength increases. The characteristic absorption lines of oxygen are selected at 760 and 762 nm, and the atmospheric transmittance of the line position is calculated as a function of height. The signal-to-noise ratio of the received spectrum is calculated by transmittance to guide the instrument design. In addition, due to atmospheric refraction, the resulting transmittance must be corrected for refraction. According to the simulation calculation, the atmospheric transmittance of three heights of 80, 100 and 110 km is calculated by using the near-infrared band of 755~774 nm, approaching 1 as the height increases gradually. Compared with 0.2 nm resolution, the atmospheric transmittance obtained under 0.1 nm resolution range is larger, is 0.28~1, the transmittance at 110 km is 0.987, and the accuracy of the detection can be a small one. The transmittance caused by atmospheric refraction above 60 km is equal to 1. Therefore, the influence of atmospheric refraction on atmospheric transmittance can be neglected above 60 km, so no refraction correction is required above 60 km. The signal-to-noise ratio is greater than 100 on the characteristic absorption lines at 760 and 762 nm. When the resolution is 0.1 nm, the value of the spectral intensity signal-to-noise ratio is smaller, indicating that the absorption of oxygen by the spectrum is strong. The amount of change in the number of photons obtained under the two resolution conditions is not much different and is greater than one. Finally, based on the above results, parameters such as the telescope, CCD, grating resolution, and integration time canconfirm. The inversion algorithm used to study and test the stellar occultation to form a miniaturized instrument that detects the change in the density of oxygen from the ground to the height of 110 km, and can also analyze the detection error in advance.

Underwater wet welding technology has been widely used in recent years. Improving the quality of underwater wet welding is the focus of many pieces of researches. The components of arc plasma of underwater wet welding directly affect welding stability and welding quality, but there has been little research on the composition of underwater wet welding arc plasma, there is a lack of diagnostic research on the composition of arc plasma from underwater wet welding by spectral spectroscopy. Through the research on the underwater wet welding process, the underwater wet welding experiment platform was built, through the arc spectroscopy diagnostic system, the obtained arc spectrum was diagnosed and analyzed, and the main elements considered in calculating the arc plasma composition were determined. Based on the spectral diagnosis results, the dissociation and ionization process of the arc bubble component in underwater wet welding were further analyzed, and 18 kinds of particles considered for the calculation of the underwater wet welding arc plasma component were determined. Based on the calculation of the partition function, the Newton iterative method is used to solve the equations consisting of the Saha equation, the charge quasi-neutral and the equation atom conservation equation, the number density of each particle is obtained, and the curve of the number density of each particle as a function of temperature is plotted. The calculation results show that the reaction occurs in the underwater wet welding arc plasma in different temperature ranges, and the main particles are different. At low temperatures, underwater wet welding arc plasma is mainly composed of unionized molecules, atoms and low-value ions with low ionization energy, as the temperature increases, the dissociation reaction and ionization reaction continue, and the ions of high valence state are continuously ionized. The tendency of different particles to change with temperature is also different. The calculated results show that the trend of each particle changing in different temperature ranges is consistent with the spectral diagnosis result. The determination of the plasma composition lays a foundation for the study of the arc of the underwater wet method from the mechanism layer, and provides a theoretical basis for further research on the thermodynamic properties and radiation properties of the underwater wet welding arc.

Polyoxometalates (POMs) are an important class of metal-oxygen clusters, which are composed of cluster anions bridged by pre-transition metals (VⅤ, NbⅤ, TaⅤ, MoⅥ, MoⅤ and WⅥ) and oxygen. On the basis of previous studies (H2en)2{SiW11O39Sm(H2O)2}·(H3O)·6H2O, we changed rare earth salts and successfully synthesized three isomorphic crystals (H2en)2{SiW11O39Ln(H2O)2}·(H3O)·6H2O［Ln=Ce(1), Pr(2), Nd(3)］, X-ray single crystal diffraction experiment measured that the four belong to the triclinic system, the P1 space group, and the unit cell parameters are consistent, indicating that they have the same crystal structure. XRD experiments show that they have the same peaks, indicating that the substances are identical. Due to the same cluster anions and only the substituted rare earth ions are different, these isomorphic crystals exhibit similar phenomena in many characterization methods, for example the similar absorption curves in 1D infrared spectroscopy: the vibrational absorption of anion skeleton belonging to Keggin cluster appears at 1 039, 949, 889 and 787 cm-1, and the absorption peaks of νas(O—H) and δ(O—H) occur in the vicinity of 3 600～3 300 and 1 600～1 630 cm-1. The stretching vibration peaks of N—H and C—H ligands of ethylenediamine were observed in 3 277, 2 927 and 2 855 cm-1. However, the two-dimensional infrared correlation spectra under magnetic perturbation are sensitive to the magnetic field response. The two-dimensional infrared correlation spectra under thermal perturbation are easy to capture the subtle changes of hydrogen bond vibration modes. Therefore, two-dimensional infrared spectroscopy can be used for fine determination of molecular structure, and the comparative analysis of two-dimensional infrared spectroscopy of such isomorphic polyoxotungstate has not been reported. Two-dimensional infrared correlation spectra under magnetic perturbation show that compound 1 has response peaks at 468, 560 and 810 cm-1, which belong to νas(Ln—O), skeleton ν(W—O) and νas(W—Ob), respectively. Compound 2 exhibits as (Ln—O) at 450, 464, 475 cm-1, and the response peak at 570, 675 cm-1 belongs to skeleton ν(W—O). The response peaks of compound 3 at 452, 468, 472 cm-1 belong to νas (Ln—O), and 518, 533, 545, 565, 695 cm-1 belong to skeleton ν(W—O). The number of response peaks of compound 1, 2, 3 belongs to skeleton ν(W—O) increases. This is due to the valence electron configurations of Ce3+, Pr3+, Nd3+ are 4f1, 4f2, 4f3. The valence electron number increases, so the influence of magnetic particle Ln3+ on adjacent W—O bonds increases. Two-dimensional infrared correlation spectra under thermal perturbation show that compounds 1, 2 and 3 have as (Ln—O) response peaks at about 400 cm-1, and the response peaks of νas (W—Ob) and νas (W—Od) appear at 810, 860 and 940 cm-1, which are due to the same cluster skeleton and the same hydrogen bond. However, the most strong peak positions of νas (W—O) belong to compounds 1, 2, 3 appear at 810, 850, 855 cm-1, which may because the different polarities of Ln ions substituted on cluster skeletons have different effects on the dipole moments of adjacent W—O bonds. The similarities and differences of these isomorphic rare earth substituted polyoxotungstate can be well analyzed by two-dimensional infrared spectroscopy.

The spatial heterodyne spectroscopy technology has been widely used in the detection of weak spectral signals such as interstellar dark matter and atmospheric trace gas components by virtue of its ultra-high spectral resolution, high through put, transient detection, and no moving parts. In order to study the feasibility of real-time Raman spectroscopy (RS) based on spatial heterodyne spectroscopy, and the integratedspatial heterodyne spectroscopy system HEP-765-S is used as the Raman characteristic spectrum detector. Firstly, Gaussview6.0 was used to construct the molecular structure of the main pigments in clover: chlorophyll a, chlorophyll b, α-carotene and β-carotene. Then Gaussian16 was used to obtain the optimized simulated RS, analyze the band range of the strongest Raman spectrum peaks of the four pigments, and determine the strong signal characteristic bands of the four components were 1 537~1 800 cm-1. According to the theoretical relationship between the excitation light source and the Raman displacement, combined with the detection band range of the detection system of 759~769 nm. It is calculated that the laser with the wavelength of 680 nm can be used as the light source to stimulate the Raman signal, which can ensure that the Raman signals with the strong characteristic of four pigments fall within the detection range, and avoid the influence of Rayleigh scattering light and fluorescence interference of the light source. Finally, a laser with a central wavelength of 680.28 nm and space heterodyne spectrum system HEP-765-S were purchased to conduct the direct detection experiment of clover strong peak Raman signal. The results show that the system can directly measure the RS of clover, but the measured Raman signal strength is weak, which is mainly caused by two reasons: one is that the peak power of the laser used is relatively small. Second, the space heterodyne spectrum system HEP-765-S is an integrated design instrument. The software and hardware system and parameters cannot be adjusted after curing, and the maximum integral time of instrument data collection is 832 ms. The acquisition signal is weak due to the insufficient power of the light source and the small instrument integral. Compared with the simulated spectrum, the measured spectrum in the detection band of the spatial heterodyne system is basically consistent with the envelope superimposed by Raman signals of four main pigments in clover leaves. The main peak ends with time are in line with the good, the measured spectra and simulation spectrum has a good consistency, using the spatial heterodyne system for material Raman signal is quick, direct detection is feasible.

It is very important to prepare high sensitive surface enhanced Raman scattering (SERS) substrates in the SERS detection process. The preparation of metal nanoparticle aggregates by light manipulation technology is a hot topic in the field of SERS. In this paper, femtosecond laser wet etching technology was used to etch slot array with the cross-sectional area (width×depth) of 10 μm×7 μm, 30 μm×12 μm, 60 μm×15 μm, 70 μm×19 μm, 90 μm×21 μm in the range of 5 mm×5 mm on the surface of silicon wafer, a Silicon-based micro-nano structure substrate (SiMS) with the different cross-sectional area was prepared. SERS enhancement of analytes on the substrates was achieved using optical manipulation techniques combined with SERS technique. The laser was focused on the substrate slot, due to the action of the light radiation pressure, the gold nanoparticles move along the direction of the beam propagation and accumulate in the slots on the surface of the structure to form gold nanoparticle aggregates, which promote “hot spots” effect. The sensitivity of the SERS detection was improved, and the SERS enhancement of the probe on the substrates was achieved. Experiments show that the metal nanoparticles can effectively accumulate in the slot on the surface of the SiMS when the optical radiation pressure is greater than the optical gradient force, forming more “hot spots”, which can greatly improve the SERS enhancement effect. The SERS signal of pyrene is gradually enhanced with the increase of the cross-sectional area of the slot, the enhancement effect of the slot with width and depth of 70×19 μm2 was the best, SERS intensity of pyrene was increased by about two magnitudes, and the minimum detection concentration was 5.0×10-9 mol·L-1. Beyond this cross-sectional area, the SERS intensity begins to decrease. The lowest detection concentration of pyrene is 5.0×10-9 mol·L-1. In the low concentration range (5.0×10-9～1.0×10-7 mol·L-1). It demonstrated a good linear correlation between the SERS intensity of characteristic peaks at 588 and 1 234 cm-1 and concentration, and the fitting equation and linear correlation coefficient were 0.992 and 0.971, respectively. The SiMS with a cross-sectional area of 70×19 μm2 was used for repetitive experiments, and eight different positions on the substrate were selected. After each position was measured, the laser was switched off and the action of the laser was disappeared, the gold nanoparticles were re-dispersed in the solution. Three measures were repeated for each position. The relative standard deviation (RSD) of the two peaks at 588 and 1 234 cm-1 at different positions of the substrate were 9.9% and 2.0%, respectively, which showed good repeatability. The study showed that the SERS effect could be greatly improved by the optical manipulation-SERS method based on the SiMS and this method has potential for application in the detection and analysis of materials in fields such as chemistry and biology.

Multi-spectral radiation thermometry is a non-contact temperature measurement method which can retrieve the true temperature of the radiator. The method collects the brightness and temperature information of the target under different wavelengths and retrieves the true temperature of the target using related algorithms. Multi-spectral pyrometer is one of the most important measuring tools to retrieve the true temperature of the target by this method. After nearly half a century of unremitting efforts and exploration, many scholars at home and abroad have made considerable progress. Because the spectral emissivity is less than 1, the true temperature of the target can not be measured directly by using a radiation pyrometer. Only by processing the wavelength and brightness temperatures of multiple spectral channels and using the processing technology of multi-spectral radiation temperature measurement data can the true temperature of the target be obtained. In the process of true temperature inversion, it is generally necessary to find the functional relationship between spectral emissivity and wavelength or temperature variables, and replace spectral emissivity with expressions containing wavelength or temperature variables. This method lacks sufficient theoretical support for model selection. For non-professionals, it is difficult to select a suitable spectral emissivity model. The solution to the equation is realized. Because of the instantaneous variability of spectral emissivity, there are always some differences between the assumed spectral emissivity model and the actual spectral emissivity, which may lead to large errors in a true temperature inversion. In addition, the mathematical model between spectral emissivity and wavelength or temperature variables needs a lot of experiments and experience to determine. This mathematical model has poor generality, especially when the radiator to be measured changes; this mathematical model loses its significance. Therefore, it is an urgent need to find a universal multi-spectral true temperature inversion method without assuming a mathematical model between spectral emissivity and wavelength or temperature. For each spectral channel of a multi-wavelength pyrometer, the measured data of each spectral channel satisfies a mathematical equation, and for each spectral channel, an undetermined system of equations can be formed. In order to solve this system of equations, the idea of optimization is introduced into the process of multi-spectral solution. A multi-spectral true temperature inversion method based on multi-objective minimum optimization principle of reference temperature is proposed. The problem of solving multi-spectral true temperature is transformed into a multi-objective extreme value optimization problem, and the inversion of true temperature and spectral emissivity without assuming the spectral emissivity model is realized. Compared with the traditional quadratic measurement method, the new method has the same inversion accuracy as the quadratic measurement method, but inversion speed has been greatly improved. With the help of the true measurement data measured by previous scholars, the inversion of true temperature and spectral emissivity is realized by using the multi-spectral true temperature inversion method based on the multi-objective minimum optimization principle of reference temperature. The new method has greatly improved the inversion speed. With the help of the true measurement data of plume temperature of solid rocket motor in the past, the inversion of true temperature is realized by using a multi-objective minimum optimization method based on reference temperature.

Based on support vector machine and Mahalanobis distance, the ability of mid-infrared spectrum analysis to identify imported rosewood, windmill wood, micro ebony, fuel rosewood and east African rosewood was explored. Five hundred group of test samples were collected and analyzed by the mid-infrared spectrometer, and the test data were preprocessed. Firstly, in order to ensure the validity of the samples, the abnormal spectra were diagnosed. Based on Wright’s test, two groups of abnormalities were found in rosewood and micro ebony, one group of abnormalities was found in windmill wood, fuel rosewood and east African rosewood respectively. In order to unify the sample size, five species of trees were excluded from the five sets of data, including the abnormal spectrum. Secondly, the research of tree species recognition in near-infrared spectroscopy was analyzed. The results showed that the first derivative processing of spectral data could improve the recognition accuracy. Therefore, the mid-infrared spectroscopy data were smoothed and first derivative processing. The eigenvalues of the spectral data were extracted by principal component analysis. The scatter plots of the first and second principal component scores of the test set showed that the clustering of the smoothed plus first derivative processed test set was smooth. Based on the scores of principal components, the recognition research was based on support vector machine and Mahalanobis distance. Considering the selection of the number of principal components in the recognition method would directly affect the accuracy of recognition, and usually, the selection of principal components only referred to the cumulative contribution rate. In order to make the selection of principal components more scientific, in the support vector machine identification method, the particle swarm optimization algorithm was used for parameter optimization, the relationship between the number of principal components (range ［5, 30］) and the best discrimination accuracy under the 50-fold test was tested. The results showed that the optimal discriminating accuracy of the number of principal components in the range of ［7, 11］ of smoothing processing and smoothing plus first-order derivative processing was relatively high, and the optimal number of principal components was determined as 8 based on the corresponding discriminating accuracy. The first eight principal components were used as input variables, and the test set was tested based on support vector machine and Mahalanobis distance. The results showed that the correct recognition rates of the two recognition methods were higher, and the recognition rate of support vector machines was slightly higher than that of Mahalanobis distance. The recognition rate of smooth distance plus first-order derivative processing was better than that of smoothing processing. The correct recognition rate of support vector machine with smooth plus first-order derivative processing reached 98%, and the recognition effect was the best. Therefore, the mid-infrared spectrum can be used as an effective means to identify timber species.

Water-based paints are environmentally friendly and resource-saving and are increasingly favored by consumers, with broad market potential and development prospects. Due to the poor compatibility between water and organic resin, the hardness, wear resistance, and aging resistance of water-based paint film are poor. In order to improve the properties of water-based paints, the bonding mechanism between Scotch pine and -water-based acrylic paint was studied using Fourier transform infrared (FTIR) technique; the -water-based acrylic paint was modified by nano-SiO2 and nano-TiO2, and the effects of the modified water-based paint on the hardness, wear resistance and aging resistance of the paint film were studied. The results showed that the intensity of 3 349 cm-1 (O—H stretching vibration) of Scotch pine decreased after coating with water-based paint, indicating that more stable hydrogen bonds were formed between pine and water-based paint. The intensity of the peak at 1 727 cm-1 (the stretching vibration of CO in the carboxyl group) of coated Scotch pine was lower than that of pure water-based paint, and the intensity of 1 239 cm-1 (stretching vibration of C—O in ester group) of Scotch pine increased after coating with water-based paint. It characterized the esterification of the carboxyl group in water-based paint with the hydroxyl group in Scotch pine. A new peak of coated Scotch pine appeared at 1 109 cm-1, assigned to the asymmetric vibration of C—O in C—O—C, indicating the etherification between the hydroxyl groups in Scotch pine and water-based paint. This study revealed that in addition to the physical combination of acrylic acid and Scotch pine, chemical reactions occurred to make the film and wood bond more firmly. At the same time, the performance analysis of the nano-modified acrylic water-based paint film showed that it had the same adhesion and water resistance as the commercial one. The nano-SiO2 modified acrylic water-based paint was superior in the film hardness and abrasion resistance, while the nano-TiO2 modification had greater effects on anti-aging properties. It was believed that adding 3% nano-SiO2 to the top and bottom paint respectively was most suitable for indoor wood products. After this modification, the hardness of the paint film reached 3H, and the adhesion and water resistance were up to Grade 1. Besides, adding 1% nano-TiO2 to the top paint was most suitable for outdoor wood products, and the hardness and water resistance of the paint film reached H and Grade 1, respectively, and the aging resistance was optimal. This study can provide a theoretical data for the research of nano-modified water-based paint, and has guiding significance for promoting the optimization and modification, broadening the application range and increasing the added value of water-based paints.

The infrared absorption peak at 3 309 cm-1 caused by the vibration of OH often appears in natural sapphire. This peak is significant for the identification of heat treatment sapphire. The color of sapphire produced in Changle County, Shandong Province is often dark blue, and the color zones are usually well developed. The absorption peak at 3 309 cm-1 is often found in FTIR spectrum of Changle Sapphire. At present, the intensity distribution of this peak in color zones of sapphire is still not studied, and the assignment of this peak is still controversial. In this paper, the intensity distribution of peak at 3 309 cm-1 in Changle sapphire color zones and the relationship between this peak and trace elements are studied, and the assignment of this peak is further speculated. In spectroscopic technology, FTIR area scanning was innovatively used to analyze the intensity distribution of peak at 3 309 cm-1 in the color zones. In the spectroscopic analysis, the assignment of the peak at 3 309 cm-1 was creatively predicted based on the charge compensation theory in sapphire and the distribution of trace elements in the color zones. We found that the intensity distribution of this peak showed a trend of increasing from the lower left corner to the upper right corner in the scanning area. Along the direction of this peak increasing, we measured the trace elements contents at five points by Laser Ablation Inductively Coupled Plasma Mass Spectrometer. Based on the charge compensation theory, Ti4+ prefers to compensate with Mg2+ in sapphire. If the content of Ti4+ is higher than Mg2+, almost all Mg2+ will compensate with Ti4+ and remaining Ti4+ will compensate with Fe2+ to form Fe2+-Ti4+ pairs which produce the blue color. The content of Ti in the colorless region is low, and all Ti4+ will compensate with Mg2+. So there is almost no Fe2+-Ti4+ pair in colorless region and the peak at 3 309 cm-1 is very weak. The contents of Fe2+-Ti4+ pairs in the blue region determine the depth of blue. The intensity of 3 309 cm-1 peaks in the blue region is obviously higher than that in colorless region, but the intensity of this peak in the dark blue region is not necessarily stronger than that in the blue region. The intensity of this peak has no certain relationship with the contents of Fe2+-Ti4+ pairs in sapphire. The intensity distribution of this peak shows a positive correlation with the contents of Ti. The more the Ti contents are, the stronger the is peak. We found this peak was positively correlated with the content of Ti, and we further speculated that the defect cluster containing Ti and OH produced the peak at 3 309 cm-1. The Fe2+ is not necessary to produce a peak at 3 309 cm-1 but to compensate with the Ti4+ to produce the blue color.

Because of the rapid development of Raman spectroscopy, especially the popularization of portable Raman spectrometer, Raman spectroscopy is particularly important in the rapid analysis of food safety. However, Raman spectroscopy is still less used to analyze the quality of liquor. Because the microstructures of aqueous ethanol solution are different at different concentrations, the Raman spectra are different at different concentrations. Here we proposed an absolute Raman difference spectra to quantify these spectral differences and the concentration of aqueous ethanol. The absolute Raman difference spectra are obtained through subtracting the normalized spectra of pure ethanol from the normalized spectra of solution. The intensity of the absolute Raman difference spectra related to the concentration of ethanol. Using the relationship, we measured the ethanol concentration of a series of bottled Chinese liquors. The measured values agree well with those marked on the bottle, which confirms the reliability of the absolute Raman difference spectra. Based on this method, we also measured the spectra of a series of bulk liquors in Xi’an. The results show that the ethanol concentration of bulk liquor is generally a few degrees lower than its identification value, which highlights the need for supervision of bulk liquors. Our results show significantly that the absolute Raman difference spectrum is a good method to detect the ethanol concentration of liquors rapidly.

Color-change garnet is a special variety in the garnet group, usually belongs to pyrope-spessartine solid-solution series and contains a trace of Cr and Fe. Few people have studied it in China, and no color-change garnets with obvious red fluorescence under ultraviolet radiation have been reported at home and abroad. A color-change garnet with red fluorescence from the Umba region of Tanzania was studied by chemical and spectral tests. Laser ablation inductively coupled plasma mass spectrometry was used to study sample’s chemical properties. The result shows that this color-change garnet belongs to the pyrope-spessartine solid-solution series and contains a certain amount of grossular end-member, with some trace elements of Cr, V, Fe. The average composition of it is Prp46.28Sps38.40Grs13.57Alm2.33Uvt0.35. In ultraviolet-visible absorption spectrum, sharp absorption peaks at 409, 422, 430 and 486 nm are assigned to the spin-forbidden transition of Mn2+, and additional peaks at 459, 503 nm is caused by Fe2+, the broad absorption band peaked at 571 nm in the yellow region which is responsible for the color-change effect is attributed to the spin-allowed transition of Cr3+ and V3+ together. Two absorption maxima in violet-blue and yellow-orange regions and comparably equal transmittance of green and red light cause the color change effect, garnet appears greenish-yellow in day light and becomes purplish-pink under incandescent light. The emission peak at 690 nm in the fluorescence spectrum is ascribed to the 2Eg→4A2g multiplicity-transition of Cr3+. And red fluorescence is most excited by violet light (400～440 nm) and yellow light (500～560 nm), that correspond to absorption bands caused by two spin-allowed transitions of Cr3+, 4A2g→4T1g and 4A2g→4T2g respectively. Compared with other color-changing garnets in previous studies, this sample contains lower Fe2+. It is speculated that high Cr and low Fe makes the color-change garnet can be excited to red fluorescence under ultraviolet radiation. The detailed spectra study of color-change garnet with red fluorescence and discussionon of its color-change effect and fluorescence mechanism provide spectral data and theoretical basis for further research on special garnets’ fluorescence in the future.

The precise determination of mineral chemical composition is significance to the exploitation and utilization of mineral resources, and inversion of SiO2 content in iron ore by thermal infrared spectrum makes up for the shortcomings of the traditional methods in terms of time-consuming and so on. The thermal infrared spectrum of iron ore, however, is affected by surface roughness and other factors, which results in the decrease of the inversion accuracy of SiO2 content. The recent study doesn’t consider the influence of ore surface roughness on the inversion of ore composition and quantitatively inverted SiO2 content in iron ore by thermal infrared spectrum. The inversion result can’t provide any effective help for precise delineation of ore body range and ore blending. Therefore, this paper aims roughness on the factor to influence the inversion of SiO2 content in iron ore. Taking the “Anshan-type” iron ore in Liaoning Province as the research object, the samples are made into a total of 14 blocks of cylindrical blocks with a diameter of 6 cm and a thickness of 1 cm, which formed a sequence according to their SiO2 content. Two levels of roughnesses are made on both sides of each sample, and the surface roughness is observed by using Surtronic S128 roughness meter. The infrared spectroradiometer Turbo FT is used to observe the thermal infrared spectroscopy emissivities of samples. The correlation indexes between the spectral index and SiO2 content are analyzed by the normalized index (NDI) to determine the sensitive bands of SiO2 content of two grade roughness samples. Located at 8.12~8.13, 8.02~8.03 μm, the correlation coefficients are 0.947 and 0.972, respectively. A quantitative inversion model of the sensitive band and SiO2 content is established to analyze the effect of roughness on the inversion of SiO2 content. The results show that: (1) The increase of roughness Rq has a significant effect on the spectral emissivity of RF(Reststrahlen Features) characteristic regions. The average roughness Rq is increased from 1.05 to 2.47 μm, so that the maximum difference between the rough surface and the smooth surface emissivity of the same sample is 0.17 (relative difference 42.9%). (2) When the same grade roughnesses are used for content inversion, the inversion error is small, and the average absolute error is 1.88%. The inversion accuracy of most samples can meet the error requirements of the geological and mineral industry standards. (3) The experimental results of inversion SiO2 content accuracy are great higher than the inversion accuracy of 3.57% without considerating the iron ore surface morphology, and the relative improvement accuracy is 47.3%. Therefore, considering the influence of roughness is of great significance for improving the inversion accuracy of SiO2 content, then it is of great significance to realize the precise division of iron ore and mine iron ore resources reasonably and efficiently.

Nitrogen-defective g-C3N4 catalytic materials were synthesized by nitric acid-assisted high-temperature polycondensation of melamine. The microstructure and spectroscopic characteristics were analyzed by scanning electron microscope (SEM), Brunauer Emmett Teller (BET), X-ray diffraction (XRD), Ultraviolet-visible spectroscopy (UV-Vis), X-ray photoelectron spectroscopy (XPS) and Fourier Transform infrared spectroscopy(FTIR). The modified material of sem images exhibits a smaller pore size and a rougher surface resembling a “flower” shape, indicating that the addition of nitric acid significantly changes the structure of the material. The BET spectrum clearly shows that the nitric acid-assisted synthetic material exhibits a large specific surface area and pore size. The XRD pattern shows that the modified material not only maintains the general structural characteristics of the carbon nitride material, but also changes the peak width and angle at both peaks, indicating that assistance of the acid can change the structure of the raw material. It is seen from the UV-Vis spectrum that the modified material has a significant red shift phenomenon, indicating that the material has a certain enhancement to the visible light compared to the original carbon nitride material. The FTIR spectrum shows that the modified material has a change in the carbon-nitrogen single bond and an increase in the amino group on the basis of maintaining the original material groups. From the image of the XPS spectrum, it was found that the binding energy of the modified material and the peak area changed, and the N element content was significantly improved. It is speculated that the conversion of the melamine caused by the action of nitric acid caused the increase of nitrogen. Finally, the Catalytic performance of the materials was tested under visible light and sunlight. The results show that the method is not only simple, the consumption of nitric acid is also low, and the synthesized g-C3N4 material has better micro-structure advantages such as porous structure, smaller particle size and higher specific surface area, and more importantly, The C/N ratio of the materials synthesized by this method has a significant downward trend, and the amino groups also have an increased performance, which may be caused by the chemical reaction between nitric acid and melamine during high-temperature sintering. The results of catalytic degradation of RhB by visible light and sunlight showed that the catalytic effect of g-C3N4 was the best when the volume of nitric acid was 2 mL, and the degradation rate reached 99%, which was 2.8 times and 2.5 times than non-nitric acid materials, respectively. The cyclic degradation test of the material indicates that the material has high recyclability. This materials which include highly efficient, easy-to-industrial and recyclable provides an excellent reference for future practical applications.

Recently, one kind of turquoise with special appearance appears in the market. Their color is mostly pale blue and bluish green. Most of these materials are variegated with white or light blue-green plaques, whose boundaries are blurred. Some samples have similar flow structure on the surface, and their appearance is very similar to that of pressed turquoise. The raw material of the turquoise is mainly from Mongolia. The conventional gemological method, X-ray Fluorescence Spectrometer, Laser Raman Spectrometer and X-ray Power Diffraction were applied to the Mongolia turquoises in order to clarify their gemological properties, chemical and mineral composition in detail. The results show that the overall appearance of Mongolia turquoise is blue-green to deep blue-green with uneven distribution of color and uneven plaques on the surface. It often contains impurities such as quartz, pyrite, illite, feldspar and limonite etc. The refractive index of the turquoise in this area ranges from 1.60 to 1.62 (spot method), the relative density ranges from 2.43 to 2.76, and the relative density of the Mongolia turquoise is lower than that occurred in Hubei and Anhui. The samples fluoresce a weak ultraviolet to blue light glow under LWUV, with an inert reaction to SWUV. Weak ultraviolet light to blue fluorescence was observed in most samples under long-wavelength ultraviolet light, and fluorescence was inert under short-wavelength ultraviolet light. The main chemical composition of the Mongolia turquoise sample deviates from the theoretical chemical composition of turquoise, w (Al2O3) ranges from 26.75% to 30.30%, w (P2O5) ranges from 32.54% to 36.40%, w (CuO) ranges from 6.99% to 10.73%, w (FeO) ranges from 1.73% to 4.39%, w (ZnO) ranges from 0.35% to 2.93%. There is a certain amount of SiO2 in Mongolia turquoise samples, and the mass fraction can reach 2.38%~8.87%. This characteristic is different from that of other domestic turquoise areas, which contain nearly no or very trace SiO2. X-ray powder diffraction and infrared absorption spectra show that the main components of the uneven color plaques of Mongolia turquoise are turquoise, and they are natural and not optimised. Infrared absorption spectra show the vibrational spectra of crystal water, hydroxyl water and phosphate groups, which are consistent with those of natural turquoise. The laser Raman spectroscopy analysis of minerals with different transparency and color in Mongolia Turquoise show that the white opaque impurity minerals in the turquoise are sodium feldspar, white translucent impurity minerals are quartz, and brass with metallic lustre minerals are pyrite.

Quick and accurate identification of mine water inflow has important research significance for preventing coal mine flood accidents, the laser-induced fluorescence (LIF) spectroscopyis used to integrate withthe intelligent classificationalgorithm to identify the mine water inflow, it breaks the shortcomings of traditional water chemistry methods, such as long time consuming, etc., and has the characteristics of high sensitivity and fast response. However, these currently used algorithms can only rely on the classification accuracy to qualitatively discriminate the types of water samples from different mine water inflow. This paper combines the random forest algorithm with the competitive adaptive weighting algorithm (RF-CARS), the partial least squares regression (PLSR) model based on fluorescencespectrum data from the laser-induced fluorescence was used to predict the water inflow in different mines and to achieve quantitative assessment of water samples. Firstly, 300 sets of mine water inflow samples mixed with different sandstone waters based on goaf water were collected, and the collected water samples were randomly divided into the calibration set and the prediction setaccording to the ratio of 4∶1, a total of 240 sets of calibration sets were used to establish a regression model, a total of 60 sets of prediction sets were used to predict different water samples, and a laser-induced fluorescence inflow spectroscopy system was built to complete the acquisition of spectral data and generated a fluorescence spectrum. Then the original fluorescence spectrum was denoised by S-G convolution smoothing method and Lowess smoothing method, and it was found that the processed fluorescence spectrum was more dispersed than the original spectrum, which was suitable for spectral analysis, the prediction accuracy of two denoising methods were compared, the Lowess was chosen as the final denoising method. Then, the RF algorithm was used to reduce the spectral attributes with low attribute importance after denoising, according to the performance of the optimal regression model, the 223 reduced attributes were selected and then it was used for the secondary attribute reduction of the CARS algorithm. The PLSR model was established based on 77 spectral attribute data selected according to the principle of minimum cross validation root mean square error in the sampling process of CARS algorithm. Finally, we compared with the full spectrum, other variable selection methods, and different regression models, the RF-CARS algorithm had the best streamlining effect, and the total spectral modeling attribute was reduced from 2 048 to 77, the model prediction set determination coefficient R2pre increased from 0.991 4 to 0.996 7, the predicted root mean square error RMSEP decreased from 0.029 4 to 0.018 3, the prediction accuracy was improved, and the remaining evaluation indicators were relatively good. The experimental results show that the RF-CARS combined with laser induced fluorescence technology can quickly and accurately predict mine water inflow, the simplified spectral attributes are used to establish regression model, which provides a theoretical guarantee for real-time quantitative evaluation of mine water inflow.

The rapid and accurate identification of coal mine aquifer water source is of great significance for coal mine water inrush warning and post-disaster rescue. It takes a long time for water source identification with the traditional method, and it is not suitable to construct an online early warning system. A method of using laser induced fluorescence technology to identify the type of coal mine water source is proposed. The laser is used to excite the water sample. Then the fluorescence spectrum is obtained, with pattern recognition the water source can be rapidly identified. Two kinds of water samples-goaf water and sandstone water of Xieqiao Coal Mine in Huainan Mining Area were collected, and five mixed water samples were prepared according to different mixing ratios. Firstly, according to the various noise and interference information that may exist in the obtained water source fluorescence spectrum, the spectral data were pretreated by SG, Normalize, Gapsegment derivation, Detrend and MSC. Secondly, PCA was used to reduce the dimension of fluorescence spectral data due to a large amount of data. As a comparison of the six pretreatment methods (including the original spectrum), the number of principal components was taken by 3, and the results showed that the cumulative contribution of SG pretreatment is the largest, which was 97.26%. The second was the original spectrum, which was 92.38%. The cumulative contribution of Normalize and Detrend were not much different, which were 88.04% and 87.59%, MSC was 66.41%, and Gapsegment was the worst with 22.65%. Finally, the linear model of LDA and nonlinear model of RBF-SVM were used to identified and compared with the data of reduced dimension by PCA. Using LDA for modeling, SG-PCA-LDA had the highest accuracy rate, which reached 98.86%. According to the LDA model established, the verification set data were identified, and the accuracy rate of SG-PCA-LDA was still the highest with 100%. Using RBF-SVM for modeling, Original-PCA-RBF-SVM, SG-PCA-RBF-SVM, and Normalize-PCA-RBF-SVM had the highest accuracy rate, both of which was 97.14%. Based on the RBF-SVM model established, verification set data were identified, and the accuracy rate of Original-PCA-RBF-SVM and SG-PCA-RBF-SVM was still the highest, which is 97.14%. Tt can be found that the accuracy rate of the LDA verification set was improved which compared with the modeling set, and the accuracy rate of the RBF-SVM verification set was slightly lower than the modeling set, which showed that LDA model had better generalization ability and higher accuracy rate for fluorescence spectral data of this coal mine water. The results showed that the SG-PCA-LDA model combined with laser induced fluorescence technology is a better method for local coal mine water source identification, and it verified the possibility of identification for goaf water, sandstone water and mixed water, which can be extended to identify other mixed water sources of coal mines.

Natural soil samples are the primary data source of heavy metal hyperspectral predicting models. However, their spectra are often confounded by complex components, resulting in poor performance model and unclear explanation for heavy metal spectral response mechanism. Near standard soil samples provide a promising method for the mechanism research. In this paper, 86 natural soils samples and relatively clean background soil were collected in a lead-zinc mine in Hunan province, and 40 near standard samples were made by artificially adding heavy metals in background soil using control variable method. Feature bands for soil Pb spectra were first selected based on near standard soil samples using partial least squares regressions(PLSR). The feature bands were used to calibrate the prediction model with PLSR for natural soil samples. The existence of Pb absorption features was confirmed by the overall consistent and change trend of near standard samples reflectance spectra. Near standard samples provided acceptable estimation accuracies of Pb concentrations, with the determination coefficient (R2p), and the ratio of prediction to deviation (RPD) values of 0.85 and 2.30. When compared with the entire-band PLSR model, feature-band model for natural soil samples increased the R2p and the RPD from 0.32 and 0.20 to 1.55 and 1.44 by removing uninformative spectral variables. The mechanism investigation strategy we proposed could effectively solve the problem of complex sample composition and weak heavy metal spectral signal in previous research, and be applied in further soil heavy metal remote sensing monitoring.

In terms of the application of spectroscopy in-situ for soil quality monitoring from grassland, this paper takes the soil spectrum of Hulunbeier’s typical grassland as the research object. Verification by indoor simulated spectroscopy experiment and field spectrum measurement, and reveal the influence of plant litter on soil spectrum by analyzing the characteristics of mixed spectra. The blind source separation (BSS) independent component analysis (ICA) algorithm is used to separate the mixed spectra. Furthermore, spectral similarity value (SSV) is calculated to optimize BSS- ICA for unmixing soil spectra. The accuracy of the SOC prediction model before and after unmixing is compared to valid applicability of BSS-ICA algorithm. The results show that, (1) the cellulose absorption index (CAI) based on the characteristics of mixed spectra could effectively detect the extent of plant litter cover in the mixed spectra. CAI index would increase with the increasing of plant litter cover in quadratic regression; (2) It is found that a steep slope occurs at the transition band of 700 nm and weak lignin absorption characteristics in 1 680 and 1 754 nm, strong cellulose absorption occurs at 2 100 nm from mixed spectra; The SOC would be overestimated by about 11.94% using SVM prediction model once soil surface covered by only 5% plant litter. (3) The unmixing method of BSS-ICA can reduce the spectral characteristic from plant litter effect, and using partial least squares (PLSR), support vector machine (SVM) and random forest (RF) to model the prediction of organic carbon before and after unmixing. SVM has the highest accuracy among the three methods. The accuracy of SOC prediction was improved from R2 of 0.71 before unmixing to 0.75 after unmixing, RMSE of 4.82 g·kg-1 before unmixing to 4.50 g·kg-1 after unmixing. The optimized BSS-ICA algorithm can effectively separate soil from mixed spectra with litter and might improve the accuracy of SOC estimation by field spectra. This experimental study of reducing the external factors on soil spectra provides a theoretical basis for SOC prediction based on in-situ measurement of soil spectra.

The importance and complexity of soil organic matter have been a hot topic both at home and abroad. As the main body of soil organic matter, soil humus is an important material foundation of soil fertility and plays an important role in soil nutrient cycling and soil structure. Humin is an important component of soil humus, which accounts for the majority of soil organic C and organic N. It is also a relatively stable humus component. Humin plays an important role in the fixation and availability of nutrient elements (C, N, S, etc.), soil fertility and ecological environment. Based on the long-term localization test of black soil (which began in 1979), the variation characteristics of Hu molecular structure were analyzed by differential thermal analysis, infrared spectroscopy and nuclear magnetic resonance spectroscopy. The results showed that all fertilization treatments could significantly increase the content of soil organic carbon, and organic and inorganic fertilizers had the most significant effect. There were differences in Hu content among different fertilization treatments, but not significant. The thermal properties of soil Hu showed that the decomposable organic matter and aliphatic structure were higher when organic manure was applied, while the decomposable organic matter and aromatic structure were higher when chemical fertilizer was applied alone. The infrared spectra of Hu also showed that the ratio of Hu in soil increased by 2 920/1 620 with the application of organic fertilizer and organic-inorganic fertilizer alone, and the aromaticity of Hu was weakened. The effect of applying organic fertilizer alone on increasing the ratio of aliphatic chain hydrocarbons in Hu in soil was higher than that of other treatments.13C NMR spectroscopy analysis showed that compared with CK, organic and inorganic fertilizers could improve the stability of soil organic carbon, while the decomposition degree of organic matter increased and the stability decreased under the single chemical fertilizer treatment.

This paper points out KICA-NFCM algorithm to identify 4 alpine grassland types using HSI hyper-spectral images, by the comparative study of three spectra and two algorithms. Spectral reflectance data for stipa purpurea, kobresia tibetica, little kobresia and kobresia pygmaea was collected from HSI images, based on field investigation and inspection on the spot. Logarithm transformation and derivative transformation were used in the original spectra of 4 alpine grassland types. Sensitivity bands were determined for original spectra data, first-derivative spectra and logarithmic transform spectra, after the application of waveform analysis, one-way ANOV and correlation analysis. Then, sensitivity bands were imported into KICA-NFCM algorithm to identify 4 alpine grassland types mentioned above. For the sake of contrast, ICA-FCM algorithm was tested too. For original spectra data, first-derivative spectra, and logarithmic transform spectra, sensitivity bands were as follows: 788~925, 711~742, 669~682 and 788~925 nm respectively. Based on original spectra data, first-derivative spectra, and logarithmic transform spectra using KICA-NFCM algorithm, overall classification accuracy and KAPPA coefficients were as follows: 75.38%, 0.685; 81.26%, 0.752; 87.65%, 0.823. In contrast, overall classification accuracy and KAPPA coefficients were as follows: 64.39%, 0.569; 67.74%, 0.604; 73.14%, 0.662, based on three types of spectra using ICA-FCM algorithm. Results show that comparing with original spectra data and first-derivative spectra using ICA-FCM algorithm, logarithmic transform spectra using KICA-NFCM algorithm can make a more accurate and efficient identification of 4 alpine grassland types mentioned above, as well as the “salt and pepper noise” was suppressed in classed images. In contrast, ICA-FCM algorithm decreased boundary precision of patch in classed images and region consistency. Using “logarithmic transform spectra / ICA-FCM algorithm” proposed in this paper, the above 4 alpine grassland types in Naqu prefecture can be identified more accuracy. This method provides technical foundations for the development of hyper-spectral imaging observation for alpine grassland.

Beef meatball is a deep-processed meat product with a unique taste. In the market, some unscrupulous traders cashed in on mixing beef with cheap meat such as pork and chicken to make meatballs. The traditional methods of meat adulteration detection are time-consuming and costly. Hyperspectral imaging technique has the advantages of fast, non-destructive and low cost on meat test. Therefore, the detection of beef meatballs adulterated with pork and chicken was carried out by hyperspectral imaging technique in this study. Adulterated meat was added to the beef meatballs at a level of 0, 5%, 10%, 15%, 20% and 25% of the quality of raw meat respectively. All meatballs hyperspectral data were collected while their spectral data were extracted. The spectral data were pretreated by six methods, first derivative (1st Der), second derivative (2nd Der), mean centering (MC), multiplicative scatter correction (MSC), Savitzky-Golay (SG), standard normal variate transformation (SNVT), which established the Partial least squares model of adulteration content at the full-wave band and obtained the optimum pretreatment method by comparison. After the optimum pre-processing method, the characteristic wavelengths were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), synergy interval partial least squares (siPLS), synergy interval partial least squares-competitive adaptive reweighted sampling (siPLS-CARS), for the purpose of comparing, the prediction effects of models were evaluated on different screening wavelengths methods. The results suggested that the best pre-processing methods of PLS prediction model for beef meatballs adulterated with pork and chicken were MSC and 1st Der. 13, 51 and 32 characteristic wavelengths of adulterated pork spectra were screened by SPA, CARS and siPLS-CARS, respectively. The characteristic subinterval combinations were screened by siPLS: the full-wave band was divided into 14 subintervals, which was then combined with the 1st, 3rd, 7th, and 13th subintervals to establish PLS prediction models. The prediction model of adulterated pork content by CARS wavelength screening method had the best effect, with the RC and RP at 0.981 4 and 0.972 1 respectively, while RMSECV and RMSEP at 0.016 3 and 0.020 3 respectively. 15, 61 and 28 characteristic wavelengths of adulterated chicken spectra were screened by SPA, CARS and siPLS-CARS, respectively. The full spectrum was divided into 15 subintervals by siPLS, combined with the 7th, 8th, 11th, and 12th subintervals to establish PLS prediction models. Analogously, the prediction model of adulterated chicken content by CARS wavelength screening method had the best effect as well, with RC and RP at 0.990 2 and 0.987 8 respectively, and RMSECV and RMSEP at 0.012 3 and 0.012 6 respectively. In this study, compared with siPLS, siPLS-CARS not only reduced the number of characteristic wavelengths but also improved the accuracy of the model prediction. Compared with CARS, it screened for fewer wavelengths, but with slightly lower accuracy. Compared with adulterated pork, the prediction model of adulterated chicken was better on the whole. The research results suggested that hyperspectral imaging technique can realize the content prediction of adulterated pork and chicken in beef meatballs, which provides a theoretical basis for rapid detection of beef meatball adulteration.

Red and blue light are the main active spectra of plants for photosynthesis and photomorphogenesis. Also red and blue light-emitting diodes (LEDs) have been the dominant light source for plant factory. Therefore, the response characteristics and mechanism of plants to continuous light of red and blue spectrum need to be explored. In order to realize the application of continuous lighting in plant factories, effects of LED red and continuous blue light (CL) on lettuce growth and nutrient absorption before harvesting were studied using ICP-AES technology in an environmentally controllable plant. Under the light intensity of 150 μmol·m-2·s-1, three kinds of red and blue light were set up: 2R∶1B (Q2∶1), 3R∶1B (Q3∶1) and 4R∶1B (Q4∶1), and two nitrogen forms: 80% nitrate nitrogen (N80%) and 100% nitrate nitrogen (N100%). The results showed that: The interaction between LED light quality and nutrient liquid nitrogen form had a significant effect on the dry shoot weight of hydroponic lettuce and had no significant effect on the fresh weight of the ground and the dry weight of the root before CL. There was no significant effect on the content and accumulation of N, C, P, K, Ca, Mg, Fe, Mn, Cu and Zn before CL. After CL, the interaction between light quality and nutrient liquid nitrogen form had significant effects on fresh root weight and dry root weight of hydroponic lettuce, and there was no significant difference in the effect of the fresh shoot and dry weight, only the content of N and P, N. The accumulation of P, Fe and Zn had a significant effect. CL had a significant effect on the biomass, nutrient elements content and accumulation of hydroponic lettuce. Comparing with pre-CL, the fresh shoot weight, fresh root weight, dry shoot weight and dry root weight increased significantly. The content of each nutrient element decreased in different degrees, and CL significantly reduced the contents of N, P, Fe and Zn, the content has no significant effect on the contents of C, K, Ca, Mg, Mn and Cu. CL significantly increased the accumulation of N, C, P, K, Ca, Mg, Fe, Mn, Cu, and Zn. In summary, LED light quality and nutrient solution nitrogen form treatment had a significant effect on the dry weight of the ground and had no significant effect on the content and accumulation of various nutrients before CL. The light quality of LED and nitrogen form of the nutrient solution had significant effects on the fresh and dry weight of root and had a significant effect on the content of N and P, the accumulation of N, P, Fe and Zn after CL. Compared with pre-CL, the fresh shoot weight, fresh root weight, dry shoot weight and dry root weight of hydroponic lettuce increased significantly after CL treatment. CL significantly reduced the contents of N, P, Fe and Zn, but N and C contents were significantly increased. The accumulation of P, K, Ca, Mg, Fe, Mn, Cu, and Zn were increased. In conclusion, the combination of cultivation light quality & nitrogen form (i. e. N80%Q4∶1) with pre-harvest continuous lighting by LED red and blue light is an optimal strategy for improving contents and accumulation of some kinds of nutrient elements in lettuce.

The spectral characteristics of vegetation can be used to monitor their growth and development. Additionally, exploring the changes in spectral characteristics of maize throughout its developmental period following water stress during the seedling stage provides theoretical data for building vegetation spectral databases, as well as a basis for the hyperspectral identification of vegetation water stress. Using the large-scale farmland soil moisture control field of Jinzhou Ecological and Agricultural Meteorological Station in western Liaoning as the research area, an ASD FieldSpec Pro spectrometer was used to spectrally observe maize at the seedling, jointing, tasseling and milk stages in the seedling water stress and water-suitability control areas. The differences in the spectral characteristics of the water stressed seedlings and those of the controls were identified based on the original spectrum, the first derivative spectrum and multiple spectral parameters. The results were as follows: (1) The characteristics of the original spectrum of maize subjected to drought stress in the seedling stage are significantly different from those of maize that receives suitable water in the same stage. Specifically, the reflectance in the visible or short-wave infrared band was higher than that of control maize at the same stage, while that of the near-infrared band was significantly lower than that of control maize, especially at the jointing stage, the difference was about 5%; however, these differences gradually decreased with crop growth. (2) The first derivative spectra of maize at the seedling, jointing, tasseling and milk stages revealed double peaks in the visible bands, and the peak of the red light position reached the highest level at the tasseling stage. The peaks of the red light position for the first derivative spectrum of maize from seedlings subjected to water stress were lower than those of control maize, and the differences were significant, especially in the jointing stage, when the difference was about 0.003. In the milk stage, the peaks of red were significantly reduced relative to the concurrent control maize and the distinguishability was weakened. (3) Comparison of the spectral parameters of maize under seedling water stress with those of control maize revealed that the red edge position undergoes a blue shift-red shift-blue shift from the seedling stage to the milk stage, while the green peak position undergoes a shift in the long-wave direction. However, the difference between the blue edge position and blue edge amplitude and the yellow edge position and yellow edge amplitude was not significant at the tasseling and milk stages, the red edge area is lower than that of the control, and the yellow edge area is higher than that of the control. (4) Among the eight water-sensitive vegetation indices, the difference index of NDWI and NDW-2 reached more than 50% in the four Critical developmental stages of maize, and the distinguishability was enhanced. Overall, this study provides basic data that can be useful for plant water stress spectral libraries and a basis for selecting spectral bands and setting hyperspectral bands for identification of crop drought.

The study, targeting at 10 Maize varieties with different storage time and the same origin and harvest time, aims to study the effects of storage time on the results of the near infrared spectrum analysis technology applied in the near-infrared spectrum authenticity identification of maize single-kernel varieties. The authenticity model (monthly modeling) of breeds was established by using spectral data from January to identify the same samples which spectral data from February to December. The original spectrum was pre-processed by smoothing, first order difference and vector normalization. PLS-DA was used to establish the model for analysis and comparison, the results showed that the correct identification rate was decreasing month by month. The average correct identification rate of the model is reduced by 26.27% when the storage time is increasing from 1 month to 11 months, Which indicates that the longer the storage time of maize seeds is, the lower the accuracy of the near-infrared spectrum authenticity identification will be. This research also indicated that with the increase of the storage time of maize seeds, the spatial distribution of the spectral data of the same species but at different storage time is different. The discretization of spectral data becomes obvious, and the repeatability and consistency are reduced, which makes the accuracy of authenticity identification results of maize seeds is reduced. We endeavor to expand the models to centralize the range of the information that is easily interfered, that is, expand the spectral data collected under different environmental factors, instrumental factors and seed samples in different periods of time in 1 year to the modeling spectrum data to increase the inclusiveness of the prediction model of the near infrared spectrum based on the expanded data. Then, the inclusive model (joint modeling) has established by jointing the January and February modeling sets, after that, identifies the test set samples from March to December respectively, and then increases the model set spectrum data month by month, and the identifies the months that non-modeling set is located month by month. It taking JK968 as an example, the results showed that the accuracy of the model for the adjacent months of the modeling set is high, and then decreases month by month. When the feature spectrum of the model set is added from January to June, the average correct identification rate of the inclusive model can be more than 92%. In the above way, 10 maize varieties were tested, which can be seen that the correct identification rate of the inclusive model for maize seed authenticity is significantly higher than that of the single month model. The average correct identification rate of J92 and XY211 is increased by 11.58% and 7.71%, respectively. At the same time, in order to further improve the correct identification rate of the model, this study added the spectral data of the year 2016 to the modeling concentration of the inclusive model, so that the average accuracy identification rate of maize hybrids in 2017 reached 94.68%, and the inbred line reached 95.03%, providing the basis for further developing special models and practical equipment.

In order to screen the dominant species of hybrid corn F1 generation effectively, a fast seed screening method was proposed based on near infrared spectroscopy and two-dimensional (2D) correlation spectroscopy. The jingke 958 (JK958) which is a variety of Naiman banner in Inner Mongolia local promotion as a control sample, the near-infrared (NIR) spectra of 26 maize lines were collected and clustering analysis was carried out. When the class spacing was equal to 10, 26 samples were clustered into 3 groups in terms of the results of cluster analysis. The first group is close to the control sample: JK958, 26, 14, 489, 263, 320 etc. The second group is 9, 542, 16, 121 and 57; the third group is far from the control sample: 317, 582, 284, 264 and 157. In general, if the difference between the two samples is small, the autocorrelation intensity is small. If the two samples are identical, synchronous 2D correlation spectrum cannot represent characteristic information under ideal conditions. Therefore, the similarity between the two samples can be judged by the autocorrelation intensity. According to the results of cluster analysis, lines 14, 26, 9 and 157 were selected for 2D correlation analysis. Among them, lines 14 and 26 are close to JK958 (contrast sample) in NIR cluster analysis, while the 9 and 157 with JK958 are far apart. The autocorrelation intensity is in the range of 0.000 0～0.000 2 a.u. for sample 14, 0.000 00～0.000 10 a.u. for sample 26, 0.000 0～0.001 6 a.u. for sample 9, 0.000 4～0.002 0 a.u. for sample 157. As a whole, the order of autocorrelation intensity between four samples and JK958 sample is 157>9>14>26, which shows that sample 26 is the most similar to that of control sample JK958. In order to verify the validity of the above screening methods, cluster analysis was carried out based on 16 Main Agronomic Traits of maize measured in the field, and compared with the results of rapid screening by near infrared spectroscopy and 2D correlation spectroscopy. It can be found that there is a cross between the agronomic characters of clustering analysis and the near infrared spectral clustering (line 320, 26, 24, 147, 109 and 263). Samples 26 with the 10-5 autocorrelation intensity was close to JK958, and were clustered into one group, while samples 14, 9 and 157 with the 10-4 autocorrelation intensity were clustered into one group. The clustering results of agronomic traits confirmed the validity of NIR spectroscopy for rapid screening of maize lines. The results showed that NIR spectroscopy combined with 2D correlation spectroscopy was feasible and effective for rapid screening of maize lines.

Kiwifruit is one of the fruits with strong development momentum and economic benefits in China, its pulp color has become an important indicator for evaluating the quality of kiwifruit. Here, near-infrared spectroscopy was employed to study the changes in pulp color in different depths of kiwifruit during different storage periods. In this study, the “Mute” kiwifruit’s pulp color features (L*, a*, b*) in depths of 0, 5, and 10 mm under the skin wereviewed as the research object, the near-infrared spectroscopy (830～2 500 nm) was used as a technical tool, and chemometric methods were combined to analyze the pulp color features of kiwifruit. By establishing a partial least-square regression (PLSR) model based on the full-wavelengths, it found that the established model offered good results by using color features (L*5, a*5, b*5) at a depth of 5 mm, which indicated that the pulp color features and the spectrum data had a relatively high correlation. Then, the competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) algorithms were used to select the characteristic wavelengths related to color features from the high-dimensional full-wavelengths. And the PLSR and multiple linear regression (MLR) prediction models were respectively established based on the color features (L*5, a*5, b*5) and spectra at characteristic wavelengths. Results revealed that the CARS-PLSR model with the RC=0.942 7, RMSEC=1.699 7, RP=0.885 0, and RMSEP=1.642 4 has the best predictive effect for the pulp color feature L*5; the UVE-MLR model with the RC=0.946 3, RMSEC=0.342 4, RP=0.854 9, and RMSEP of 1.354 3 exhibited the best predictive results for pulp color feature a*5, the CARS-MLR model with the RC=0.944 3, RMSEC=1.010 1, RP=0.839 8, and RMSEP=1.354 3 performed best predictive results for pulp color feature b*5. The results demonstrated that the near-infrared spectroscopy technique would be employed to detect the color features at different depths of kiwifruit, which provided technical support for the quality evaluation of kiwifruit.

To improve the development and utilization of blueberry pomace, the test measured the feasibility of near-infrared spectroscopy for the determination of anthocyanins in blueberry pomace of the three species which includes Northland, Bluebeauty No.1 and Brightwell. We gathered the near-infrared spectroscopy data of three blueberry pomaces through DA7200 and eliminated 1, 4 and 8 abnormal samples of Northland, Bluebeauty No. 1 and Brightwell respectively by principal component analysis-Mahalanobis distance. The K-S was used to divide the sample set into correction set (686 samples) and verification set (171 samples). Normalization, standardized normal variate (SNV), multivariate scattering correction (MSC), Norris first derivative (NFD), Norris second derivative (NSD), SG convolution first derivative (SGCFD), SG convolution second derivative (SGCSD), Savitzky-Golay (SG) convolution smoothing and orthogonal signal correction preprocess were performed on the sample set respectively, and the full spectrum PLS model was built accordingly. Preprocess methods with sequential combinations of MSC, SGCSD, SG convolution smoothing and orthogonal signal correction were compared. The results showed that the optimal preprocess method in the full spectrum PLS model was orthogonal signal correction+SGCSD+SG convolution smoothing, with R2c as 0.940 0, R2p as 0.886 7, RMSEC as 0.722 5, RMSECV as 0.246 2, RMSEP as 1.005, RPD as 2.970 8. Wavelength filtering algorithms SPA and CARS were used to screen the pre-processed spectral data. Then PLS regression model was established and the ability to predict anthocyanins in blueberry pomace was quantitatively analyzed. In the screening of wavelength variables for all pretreatment methods, both SPA and CARS algorithms can effectively screen out the wavelength variables, but the wavelength variables screened by SPA algorithm cannot be used to build PLS regression model, while the wavelength variables screened by CARS algorithm can. The data showed that the optimal combination of CARS-PLS was orthogonal signal correction+MSC+SG convolution smoothing+ SGCSD, with several selected 25 wavelengths. Compared with the original spectrum, its R2c increased from 0.900 8 to 0.940 3, R2p rose from 0.881 8 to 0.885 7, RMSEC decreased from 0.929 1 to 0.720 9, RMSECV dropped from 0.317 6 to 0.245 6, RMSEP changed from 1.021 8 to 1.004 9, and RPD was raised from 2.908 8 to 2.957 5. In the measurement of anthocyanin content in blueberry pomace by near infrared spectroscopy, the orthogonal signal correction has strong denoising effect, while CARS algorithm has the advantages of the simplified model, good applicability and high prediction accuracy. The result indicated that near-infrared spectroscopy could be used to determine anthocyanin content in blueberry pomace of three different varieties, and it can provide a fast and large sample size detection method for blueberry pomace quality classification.

In order to quickly analyze the growth of the crop in the field, the spectral imaging sensor was used to detect the chlorophyll content of the maize canopy. The imagesof 47 maize plants were photographed using an IMEC 5×5 imaging unit multispectral camera. The camera was designed based on the coating principle to obtain spectral images of 25 wavelengths in the range of 673～951 nm. At the same time, the chlorophyll content was measured by SPAD-520 device. There were 2～3 sampling points in each leaf, and they were measured 3 times at each point so that 242 sample data were collected. A linear inversion formula was established based on the relationship between the gray value of multi-spectral images and the gray plate standard reflectance. The gray plate standard was made up of 4 gray level standard plates. In order to separate the plant from flowerpots and soil background, a combination method was studied. Although the canopy was segmented using OTSU method, it was not useful. After analyzing the spectral reflectance characteristics of different objects, a plant extraction algorithm was proposed based on normalization difference vegetation index (NDVI) image and region marker calculation. Firstly, the initial segmentation was conducted based on NDVI calculation on each pixel. Secondly, the noise points were eliminated by the edge-preserved median filtering algorithm. Thirdly, the region algorithm was used to obtain a mask and finally segment the multi-spectral images of theplant canopy. The characteristic wavelengths were selected based on CA (Correlation Analysis, CA) and RF (random Frog, RF) algorithm, which was used to construct the Near-Infrared (NIR) and Red (R) data set. The vegetationindices were calculated by the traversing NIR and R sets including the Ratio Vegetation Index (RVI), the Normalized Difference Vegetation Index (NDVI), the Difference Vegetation Index (DVI), and the SPAD Transfer Index (TSPAD). According to the proportion of 7∶3, the total samples were divided into calibration and validation setby SPXY (Sample set partitioning based on joint X-Y distance, SPXY) algorithm. After screening the vegetation indices by CA and RF algorithm again, the model of chlorophyll content was established by CA+RF-PLSR (Partial least squares regression, PLSR). The results showed thatthe calibration accuracy of CA+RF-PLSR model was 0.573 9, the RMSEC was 3.84%, and the validation accuracy was 0.420 2, the RMSEV was 2.3%. The chlorophyll contentdistribution of crop was analyzed visually using the pseudo color image. The study could provide technical and application support for chlorophyll distribution of field maize plants and visual monitoring of corn growth dynamics.

The paper was aimed to explore the chlorophyll spectral absorption characteristics of potato crops, fully analyze the spectral characteristic wavelength variables, and establish a high--precision chlorophyll content detection model. The 314 reflectance samples were collected using an ASD portable spectrometer at the seedling stage (M1), tuber formation stage (M2), tuber expansion stage (M3) and starch accumulation stage (M4). The chlorophyll content was determined by the simultaneous collection of leaves. After spectral data pre--treatment, the spectral reflectance changes of different growth stages of potato were analyzed. The algorithms based on model population analysis were used to select chlorophyll characteristiccharacteristic chlorophyll wavelengths, including Monte Carlo uninformative variables elimination (MC--UVE), random frog (RF) and competitive adaptive reweighted sampling (CARS) algorithm. The partial least square regression (PLSR) was used to establish the chlorophyll content detection model. The sample set was divided by a ratio of 3∶1 in each growth stage using the sample set partitioning based on joint X-Y distance algorithm (SPXY) with the 240 calibration samples and 74 validation samples. The different algorithms (MC-UVE, RF, CARS) were used to select chlorophyll characteristic wavelengths. The influence of the number of iteration (N) and the number of the latent variables (LV) on the results of characteristic wavelength selection of MC-UVE and RF algorithms were discussed, and the influences of N on that of CARS algorithm were discussed. Six gradients were set for the number of iterations (N), which were N=50, 100, 500, 1 000, 5 000 and 10 000, respectively. Four gradients were set for the number of latent variables (LV), which were LV=15, 20, 25 and 30 respectively. Taking the validation set result of PLS model as the evaluation index, the optimal parameter combination of N and LV was analyzed. Based on the optimal characteristic wavelengths selected by the three algorithms, the chlorophyll detection PLSR models were established and denoted as RF-PLSR, MC-UVE-PLSR, and CARS-PLSR, respectively. The research results showed that the chlorophyll characteristic wavelengths selection results were optimal when N=50 and LV=30 of MC-UVE, N=500 and LV=30 of RF, N=100 of CARS. By comparing the RF-PLSR, MC-UVE-PLSR, and CARS-PLSR models, it was indicated that the performance of the RF-PLSR model was best, the determination coefficient of validation (R2v) was 0.786, the root means square error of validation (RMSEV) was 3.415 mg·L-1; MC-UVE-PLSR was second, the R2v was 0.696, the RMSEV was 4.072; and the CARS-PLSR was the worst, the R2v was 0.689, the RMSEV was 4.183. Above results showed that the RF algorithm was superior to MC-UVE and CARS in selecting the characteristic chlorophyll wavelength of potato.

Quickly and effectively identifying the water contaminants is vital for reducing the impact of sudden drinking water pollution incidents. PCA is mostly used to extract the feature of different contaminants in drinking water with UV-Vis spectra. However, for the organic contaminants with high similarity in UV-Vis spectra, the identification result is ineffective when only extracting the feature of the largest variance direction from the data-driven point of view. This paper studies the classification of organic contaminants in water distribution systems developed by SPA and multi-classification SVM using UV-Vis spectroscopy. Firstly, the original spectral data of phenol, hydroquinone, resorcinol and m-phenylenediamine are measured by UV spectrometer and pretreated. The correlation between wavelength and concentration of four contaminants was compared. The peaks between phenol and resorcinol, hydroquinone and m-phenylenediamine are overlapped seriously, the classification results can interfere easily. In feature extraction, the SPA is introduced to select the organic contaminants’ characteristic wavelengths of UV-Vis spectra. Then, multiple linear regression analysis is carried out to choose the optimal parameter combination, which corresponds to the minimum prediction standard deviation. Based on this, the multi-classification support vector machine is used to form an identification model for drinking water organic contaminants. Finally, the classification results of spectral data based on full spectrum, PCA and SPA under different classification methods and different concentrations are compared and analyzed, and the applicability and stability of SPA are further explained. Experimental results demonstrate that SPA-based feature extraction method eliminates the interference of multi-collinearity and amplifies the difference among the UV-Vis spectra of different organic contaminants, thereby improving the accuracy of the classification model. This method has certain reference value for solving the problem of identifying the types of pollutants with overlapped peaks in the drinking water.

Traditional beer freshness detection methods usually need very expensive analytical instruments and chemical reagents, which consume a lot of time and cost a lot. In this paper, spectral analysis technology is used to explore the Beer Fresh Index (BFI), which can detect the agree of beer freshness rapidly and non-destructively. Specifically, the spectrum of beer samples was collected by PSR-3500 spectrometer every 24 hours. Then, the spectral data were processed by band selection and continuum removal. The enhanced spectra showed that the depth at 842.0 nm was decreased with the increase of storage time. Therefore, the characteristic spectral index (BFI) of beer freshness was constructed based on the depth at 842.0 nm. The experimental results show that BFI value decreases gradually with the increase of storage time, which can indicate the freshness of beer well. In addition, the sensitivity of BFI to the spectral equipment was evaluated by simulating different spectral resolution and signal-to-noise ratio levels. Specifically, the data with a spectral resolution of 5～40 nm and signal-to-noise ratio of 10～60 dB are generated by using the Gauss function distribution function and the average distribution function respectively and the BFI values are calculated and analyzed. Experiments show that when the spectral resolution is less than 15 nm and the signal-to-noise ratio is less than 10 dB, the absorption feature of 842.0 nm in the spectrum are gradually concealed, and BFI is difficult to indicate the freshness of beer. However, as long as the spectral resolution is better than 10 nm and the signal-to-noise ratio is not less than 35 dB in 798～872 nm, BFI can accurately indicate the freshness of beer. The requirements of BFI for the spectrometer are not strict. To sum up, the BFI proposed in this study can accurately indicate beer freshness, serve the design of portable beer freshness equipment, and promote the application of spectral analysis technology in non-destructive detection of beer quality.

Delineating abnormity zones of elements in mobile forms is one of the effective methods of deep-penetrating geochemistry for finding concealed orebodies, which can obtain deep mineralization information and predict the supply of metallogenic materials. However, samples have to be transported to the laboratory for analysis under conventional experimental techniques. Transportation process and time may affect the concentration of the elements of mobile forms. Moreover, the large amount of samples required for conventional analysis limits the study of extraction time of mobile forms. Total reflection X-ray fluorescence spectrometry (TXRF) has the advantages of high sensitivity, simple quantification and less dosage. With characteristics of the compact and portable instrument as well as no need for carrier gas and cooling water, TXRF is suitable for on-site analysis. In this paper, TXRF was used to establish a method for the analysis of mobile forms of Ti, V, Mn, Fe, Ni, Cu, Zn, Rb, Sr, Y, Ba, Ce and Pb in soils, by selecting internal standard and controlling quality. Because of the high content of organic matter in the extract, the concentration of internal standard Se was set higher (10 μg·mL-1 Se in the analytical solution), so as to improve the analytical accuracy of Se under high background. In order to control the pollution and reduce the error, blank reflectors were analyzed by TXRF for 100 s. The reflectors without impurity peak were used for the experiment. The main steps list as follows: (1) 5 g soil samples were mixed with 50 mL polymetallic extractant (0.09 mol·L-1 ammonium oxalate, 0.1 mol·L-1 ammonium citrate, 0.001 mol·L-1 EDTA, 0.001 mol·L-1 DTPA, 0.001 mol·L-1 NTA, 0.005 mol·L-1 TEA). The mixture was oscillated for 72 h at room temperature and then filtered with 0.45 μm filter membrane. (2) The 100 μL of filtrate (extract) was spiked with 10 μL internal standard of 100 μg·mL-1 Se. (3) After vortexing, 10 μL drop of aqueous solution was deposited onto a siliconized quartz glass reflector and dried at 50 ℃ for TXRF analysis (monochromatic excitation of Mo-Kα, measure time 1 000 s). The results showed that the detection limit of elements ranged from several to dozens of μg·L-1. The RSD of most elements wa less than 10%. The average relative error was 18% compared with ICP-MS/ICP-OES. This method is suitable for rapid on-site analysis of elements mobile forms. 10 μL drop of 100 μL prepared samples to make it suitable for time experiment of mobile forms by using small volume continuous sampling, with high efficiency, relatively strong continuity and minor error.

Vanadium is an important scarce resource and strategic metal, and often coexists with a variety of complex metals in the form of secondary mineral phases in nature. The effect of co-exist elements on the selection of spectral lines in the determination of vanadium with Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES) was studied in this article. The selected PE Optima 7300V instrument was operated under the conditions of incident power 1 300 W, observation height 15 mm and atomization gas flow rate 0.65 L·min-1. The results show that Al, Mo, Ti, Cr and Ni have significant effects on the determination of vanadium under six recommended spectral lines, and the relationship between the relative error of measurement results and the mass ratio of the corresponding element to V waslinear. The presence of trace Al leads to drastic changes when V 309.31 nm spectral lines was adopted. The relative error of V 290.88 and V 292.402 nm spectral lines were increased to more than ±5% as the Mo/V mass ratio increased to 0.89 and 5.98, separately. The Mo measurement results were unstable when V 270.093 nm spectral lines was adopted, but nothing regular. The relative error of V 311.07, 290.88, 270.093 and 310.23 nm spectral lines were increased to more than ±5% as the Ti/V mass ratio increased to 5.98, Cr/V mass ratio increased to 10.33, 13.6, and Ni/V mass ratio increased to 13.56, respectively. Considering the above effects and spectral stability, V 311.07 nm spectral lines can be adopted when there has no titanium in vanadium-containing raw materials, and V 310.23 nm can be used when there has titanium contained. Under the optimum analytical conditions of the spectrometer, the method was used for the determination of vanadium in typical vanadium-containing raw materials such as vanadium-titanium magnetite, stone coal and vanadium-containing catalysts with the detection limit of 0.054 mg·L-1 at 310.23 nm, 0.194 mg·L-1 at 311.07 nm, recoveries between 93.4% and 103.1%, and relative standard deviation of 0.59%. By comparing with the results of ammonium ferrous sulfate titration method, the results were basically consistent with the relative error of less than ±4.34%. In conclusion, the method was simple and efficient, with high precision and accuracy, which can be used for research and routine production of vanadium determination in raw materials containing vanadium.

Additive manufacturing technology is often used to prepare complex metal parts due to its high processing speed, high precision, and there is no need for molding for shaping. The preparation of component gradient samples is more popular in the manufacture of metal additives, Since the technology is not yet mature. There are often many defects in the workpiece. It is very important for the quality monitoring of additive manufacturing products to make the study of component distribution characterization method, which is suitable for additive manufacturing samples. The macroscopic component distribution characterization methods are mainly composed of Laser-induced breakdown spectroscopy combined with original position statistical distribution analysis (LIBS-OPA) and Spark source atomic emission spectroscopy combined with original position statistical distribution analysis (Spark-OPA), due to the large excitation spot, Spark- OPA is not suitable for layer-by-layer analysis of additive manufacturing samples. LIBS-OPA has gradually been used to characterize the element distribution of metal block samples with the advantages of multi-element synchronous positioning analysis, high spatial resolution, large optional analysis area, small sample damage, This method can achieve high-precision composition distribution characterization of metal workpieces. In this paper, the composition distribution of gradient stainless steel samples prepared by additive manufacturing technology was studied by laser induced breakdown spectroscopy. By optimizing the instrument parameters and analysis conditions, the analytical sensitivity and signal stability was ensured. The optimum test conditions were selected as follows: laser lamp voltage 1.32 kV, Q-switching delay 280 μs, sample chamber argon pressure 6 300 Pa, spot diameter 200 μm, 0 pre-denudation, integration of 15 denudations. Under this condition, the calibration curves of Cr with spectrum line of 298.9 nm, Ni with spectrum line of 218.5 nm, Mo with spectrum line of 203.8 nm, Si with spectrum line of 212.4 nm, P with spectrum line of 178.3 nm, C with spectrum line of 193.1 nm, Co with spectrum line of 384.5 nm, and Mn with a spectrum line of 293.3 nm was plotted. Most element determination coefficients exceed 0.99, Two gradient stainless steel samples prepared with different multi-pass powder feeding processes were scanned by LIBS-OPA. The distribution information of eight elements in the deposition surface of the samples was obtained. The quantitative distribution results had good agreement with Spark-OPA, and the quantitative accuracy has been verified by spark direct reading spectrometer. The study achieved a layer-by-layer analysis of additive manufacturing samples and selected the sample preparation process by composition distribution results. At the same time, the causes of cracks in the samples were also discussed through the characterization results of composition distribution. This research can play a guiding role in the improvement and perfection of the manufacturing process.

The excavation of bizili cemetery is an important archaeological discovery in the southern margin of the Tarim Basin in recent years. Which further enriches the archaeological materials at the northern foot of Kunlun Mountain and the south of the silk road. This paper presents the study of the colored coffin from bizili cemetery in Luopu County of Xinjiang, China. The techniques applied include Carbon-14 accelerator mass spectrometry (AMS-14C), Scanning electron microscope and energy spectrometer (SEM-EDS), Raman spectroscopy and Thermally assisted hydrolysis and methylation (THM) -Pyrolysis Gas Chromatography and Mass Spectrometry (Py-GC/MS). The results show that the colored coffin dates back to the northern and southern dynasties of ancient china ［(1 520±25) B. P］; Characteristic markers of indigo 2-Amino-benzoic acid, methyl ester and 2-(methylamino)-benzoic acid, methyl ester were detected by Thermally assisted hydrolysis and methylation (THM) in the presence of tetramethylammonium hydroxide (TMAH), which shows blue colour was made with plant dye indigo C16H10O2N2. In addition to the plant dye indigo, the other pigments used in colored coffin are some common mineral pigments in Xinjiang, mainly includes: Gypsum (CaSO4, white), iron oxide red (Fe2O3, red), carbon black (C, black); Drying oil, protein-egg white and Gum Benzoin were determined as binding media in the colored painting. The detection of this information not only further improves the basic information of the colored coffin, but also plays an important supporting role in the further research, repair and protection of the colored coffin in the future. In addition, the analysis of blue pigment and binding medium by THM-Py-GC/MS showed that Py-GC/MS has the advantages of high sensitivity, structure information can be obtained from very small amounts of samples without any preliminary processing, and it is also easy to operate compared with gas chromatography-mass spectrometry (GC-MS). Moreover, carboxyl group and hydroxyl group were methylated by THM-Py-GC/MS with TMAH. The higher polar molecules were translated into low polarity molecules, which increased the volatility of pyrolysis products and further improved the chromatographic separation effect. Therefore, THM-Py-GC/MS has great potential in rapid identification of natural organic materials in cultural relics.

X-ray fluorescence analysis of adjacent peaks overlapping decomposition problem is very common, spectrum peaks overlapping spectrum for further qualitative analysis and quantitative analysis are brought difficulties, and by means of hardware to reduce the spectral peaks overlapping often occurs the restriction of the capital and the working conditions, will often go on the overlapping spectrum is obtained by mathematical means of relevant information to complete the overlap of each peak spectral decomposition. This paper proposes a model GMM parameters of the independent model and GMM parameters correlation model based on the gaussian mixture (GMM), based on these two models and differential evolution algorithm of the overlapped peaks decomposition method. GMM model parameters constitute the individual genes differential evolution algorithm, presents a fast algorithm for target function, through the randomly generated initial population, In the fitness value of each individual in a population and the constraint conditions of each individual parameter as selection criteria, avoids the local convergence of the problems of the improper initial value, and all the measurement of random data involved in the operation of individual fitness value, avoid the loss of the original spectral data. Respectively for independent parameter model and the parameters associated with the model to understand the spectrum analysis, two cases through the decomposition of three kinds of overlapping spectra show that the model based on two kinds of differential evolution algorithm for the overlapping peaks decomposition is effective. First of all, the three peak simulation analysis and four peaks overlap decomposition results show that the spectral accuracy based on GMM parameters associated model spectrum GMM parameters are independent of the model solution precision is high. Three peaks overlap, parameters independent model and correlation model respectively to get the weight of maximum error is 8.15% and 2%, a maximum error of 0.30% and 0.06%, the standard deviation of the maximum error is 7.5% and 1.35%. Four overlapping peaks, parameters independent model and correlation model respectively to get the weight of maximum error is 8.3% and 4.3%, a maximum error of 0.12% and 0.13%, the standard deviation of the maximum error is 5.04% and 0.45%. Then through measured three peaks overlapping spectra of the solutions of spectrum analysis shows that with this two kinds of model of overlapping spectrum decomposition, decomposition results relative error and the measuring element content is about, with the loss of the element under test content, the decomposition results in accuracy is reduced. Simulation and measurement show that. Using differential evolution algorithm based on gaussian mixture model and overlapping spectra for solution spectrum, if you can get ahead of the small overlapping peak weight, mean, standard deviation, the relationship between the GMM parameters correlation model is set up, and decrease the number of optimization of individual parameters to improve the accuracy of the breakdown of the complex peak is very important.

Stimulated Brillouin Scattering(SBS) is an important nonlinear effect which is observed in optical fiber, the properties of SBS gain spectrum lineshape play a key role, for use in some applications such as spectrum analysis, microwave photonic filtering and optical fiber sensors based on Brillouin amplification. High signal to noise ratio (SNR) measurement data is impossible with current methods, limits analyzing the lineshape of SBS gain spectrum. A method for brillouin gain spectrum measurements in standard single-mode fiber by two narrow-linewidth laser is presented, using one as the probe signal to measure, and the other as the pump signal, sweeping a wide spectral span of around the probe signal. Benefit from narrow-linewidth laser and polarization pulling of SBS, high SNR measurement of brillouin gain spectrum is achieved. The brillouin gain spectrum is measured for different power levels of pump signal, the relation of power levels and relevant SBS parameters such as linewidth and gain profile is studied, particularly, the evolution of the SBS gain profile from Lorentzian to Gaussian as predicted by current theory is also experimental verification and analysis, show the SBS spectrum lineshape affected by the gain level involving different optical power levels of the stimulating signal. Experimental results show that with pump power up from 15dBm, the saturation is preset, so if directly measured the FWHM of SBS gain spectrum, its broadens as the pump power increases. This gaussian functional form of SBS gain spectrum could not be directly measured experimentally at high pump powers, linearizing the response is proposed, in order to get valid information of the convolution between the SBS spectrum and the measured signal spectrum, so the lineshape is confirmed to be gaussian. In the intermediate gain region, the SBS spectral shape does not fit well with current functions, lack of proper lineshape evolution model, so a mathematical model with Lorentzian function to the power of a variable value k was proposed, to resolve the difficult fitting problems of lineshape evolution from Lorentzian to Gaussian. Experimental results with excellent accuracy, proven an effective mathematical model of SBS gain spectrum lineshape, to describe the profile of SBS spectrum with different gain level. The use of SBS response for spectral analysis is proposed. Accordingly, the quality of the SBS based spectrum analysis is highly dependent on the optical power of the pumping signal, the SBS gain as filtering with Gaussian profile is used. The optical spectrum of 6314CA stabilized optical source with 0.2 pm resolution is obtained, show a great potential candidate for ultra-high resolution spectral analysis, will be a useful diagnostic tool for new generation optical network research and development (R&D).

The establishment of a near-infrared spectroscopy multivariate calibration model relies on calibration samples. However, changes in the near-infrared spectroscopy measurement environment can cause differences between the spectra easily. In order to reduce the consumption of rebuilding calibration model on offset spectrum, this paper proposes a nonlinear spectral transfer method based on deep autoencoder (DAE), which can realize the spectral transfer in an end-to-end way. This method compensates for the poor performance of existing linear spectral transfer methods in the face of nonlinear offset spectra. Moreover, this method can realize the transfer between raw spectra without data process and feature extraction operations. In the paper, we propose an error function penalty term based on the conditional probability distribution and parameter maximum likelihood method, and combine it to the gradient back-propagation algorithm to optimize the parameters of DAE. In order to verify the effectiveness of the method, we perform the proposed method on tablet dataset and corn dataset, which are both public near-infrared spectral datasets. First, we divide the two datasets into the calibration set, validation set, and prediction set using Kennard-Stone (KS) method respectively. Then, we design a network structure that conforms to the spectral sample dimension of the dataset. Finally, samples in calibration set are input to the DAE, and the network parameters are iteratively optimized by the proposed error function and back-propagation algorithm. After the transfer model is established, we compare it with the spatial transformation (SST) and piecewise direct standardization (PDS), both of them are classical linear transfer algorithm. The transferred spectrum obtained by these three algorithms are respectively inputted into the established multivariate calibration model, and we can find that the root means square error of prediction (RMSEP) of the proposed method averagely improves 5.7% and 10.1% than SST and PDS respectively, which can be demonstrated that the spectral samples transferred by the nonlinear deep autoencoder are highly efficient and useful.

Polycyclic aromatic hydrocarbons (PAHs) are a group of persistent organic pollutants (POPs) which are mutagenic, carcinogenic and teratogenic. They are widely distributed in air, water and soil. Once PAHs enter the soil, they remain in the soil for a long time. PAHs are concentrated in the soil. They can enter the human body in many ways and pose a threat to human health. Therefore, it is necessary to monitor PAHs in soil. Now, traditional detection methods are cumbersome and time-consuming, which is not conducive to the widely rapid detection of PAHs in the contaminated sites. The method based on laser-induced fluorescence spectroscopy can quickly identify and detect organic pollutants in the soil. However, PAHs are volatile and can be degraded by ultraviolet light, so the selection of UV laser energy is very important. In this work,a 266nm laser-induced fluorescence system is established in the laboratory. Anthracene, pyrene, and phenanthrene are used to investigate the decomposition and fluorescence spectra of PAHs under different laser energies. The results showed that when the energy density of the laser changed, the peak positions of the fluorescence center did not shift, but the relative standard deviations of the maximum intensity at the fluorescence peaks of three PAHs decreased firstly and increased then. When the energy density was 8.54 mJ·cm-2, the relative standard deviations of the three PAHs in 10 spectral measurements were the largest, and the relative standard deviations of the fluorescence peak intensities of anthracene, pyrene and phenanthrene reach the minimum value at 1.72, 1.00 and 1.47 mJ·cm-2. The decomposition rates were 59.3%, 69.8% and 63.6% for anthracene, pyrene and phenanthrene at 100 s, respectively. At higher energies, three PAHs decompose rapidly. Compared with the other two PAHs, pyrene was more prone to photodegradation and thermal decomposition, and the relative standard deviation of fluorescence peak intensity was also higher than that of anthracene and phenanthrene. For anthracene, when the laser energy density was 1.72 mJ·cm-2, the decomposition rate was close to 0 at 10 s and 12.8% at 100 s, and the relative standard deviation of the fluorescence peak intensity was the lowest. When the laser energy density was reduced to 0.88 mJ·cm-2, the decomposition of anthracene in 100s was almost negligible. For pyrene, when the laser energy density dropped below 1.00 mJ·cm-2, the decomposition tended to be consistent, and the decomposition rate was 47.3%～47.4% at 100 s. For phenanthrene, when the energy density of the laser was lower than 1.47 mJ·cm-2, the decomposition rates no longer decreased, and the decomposition rates were 36.8%～38.6%. Pyrene and phenanthrene still decompose in low energy density.