In this paper, combined with the continuous, reliable and low-cost real-time detection requirements of respiratory dust concentration, a spectral application technology innovation is realized, and the detection system with respiratory dust based on photo-acoustic spectroscopy is proposed. The center wavelength of the spectrum of the low-power diode laser is 403.56 nm and the corresponding effective absorption cross-section of NO2 is 5.948 5×10-19 cm2·mole-1. In addition, the resonance frequency of 1.35 kHz was obtained by using frequency scanning fitting. The influence analysis of photo-acoustic cell structure is carried out. The conclusion is drawn that the length parameter of the photo-acoustic cell has little influence on the background noise but great influence on the laser signal and the inner diameter parameter has some influence on the background noise but little influence on the background noise. Considering the influence of quality factors, processing conditions, service occasions and properties of the objects to be tested, length parameters of 120 mm and inner diameter parameters of 8 mm are selected. Besides, based on the buffer cavity structure with a length of 60 mm and an inner diameter of 25 mm, the influence of buffer partition on system stability is analyzed. The background noise is reduced and the signal fluctuation isoptimized from (2.83±0.11) to (1.26±0.03) μv. The specific absorption coefficient of NO2 with 195.28 Mm-1·(mg·m-3)-1 is analyzed. The system is calibrated using NO2 gas absorption at 405 nm. The fitting slope is 0.043 68 μv/Mm-1, the correlation coefficient is 0.998, and the pool constant is 300.24 Pa·cm·w-1. At the same time, the lower limit of the detected concentration and the absorption coefficient are 2.30 μg·m-3 and 0.448 Mm-1. The influence of absorption coefficient of respirable dust on polystyrene based on standard microspheres as aerosol generator is analyzed. What’s more, the absorption coefficients of particles with different concentration and different diameters at the same concentration are measured. The absorption coefficient of respirable dust is directly proportional to the number concentration. The slope after linear fitting is 10.598±0.641 96, and the correlation coefficient is 0.993. The variance of the absorption coefficient curve is between 3～4 Mm-1, and the absorption coefficient is affected by particles of different particle sizes. At the same time, the absorption coefficient increases with the increase of particle size. The NO2 measurements have been carried out in the ambient atmosphere. The filter membrane with 0.2 μm has been used to remove dust interference. The experimental results show that the concentration of NO2 in the atmosphere is 16.4~61.6 μg·m-3, and the average concentration is 41.1 μg·m-3. In order to verify the accuracy of the measurement system, the long-path differential absorption spectrum system developed by our group is compared. The test results show that there is a good correlation between the concentration of NO2 measured by the photo-acoustic spectroscopy system and the LP-DOAS system. The slope after linear fitting is 1.011 78±0.040 13, and the correlation coefficient is 0.947 81. The respirable dust in the ambient atmosphere is measured. The filter membrane with 5 μm is selected to filter the ambient atmosphere. The “NO2+5 μm dust” and “NO2+0.2 μm dust” are measured. The change trend of respirable dust is obtained, which can satisfy the real-time measurement of respirable dust absorption coefficient under natural suspension state.

In this paper, a method to calculate the LOX/kerosene rocket engine plume infrared radiation characteristics is proposed. First of all, the simulation of engine internal flow field is carried out, and the nozzle throat section parameters are obtained as the inlet boundary condition. Then the calculation of single nozzle and multi-nozzle engine exhaust plume flow field are carried out, infrared spectral radiation characteristics and radiation imaging characteristics of the engine plume are calculated with the finite volume method (FVM) based on the field parameters. The accuracy of the method and the model are proved. On this basis, studies on the influence of chemical mechanisms and the reaction on the plume infrared radiation characteristics are carried out. It is found that the internal combustion field of the LOX/kerosene engine are simulated accurately with the multi-step chemical reaction model, the temperature is 3.34% higher than that from the thermodynamics calculation and the pressure is 2.89% greater than that from the engine test result. The reaction can enhance the infrared radiation of the plume, and the increase ratio in the 2~5 μm band of the two condition of the single-step chemical reaction and the multi-step chemical reaction achieve 50%~100% and 150%~170%, but it can’t affect the infrared spectral radiation characteristics and the variation trend of infrared total radiation intensity with detection angle. The clear infrared images are obtained with both the single-step chemical reaction and the multi-step chemical reaction, the infrared radiation intensity of the former is 90%~190% greater than that of the later, but the infrared spectral radiation characteristics and the variation trend of infrared total radiation intensity with detection angle vary widely with different chemical reactions.

In this paper, spaceborne atmospheric trace gas differential absorption spectrometer is introduced. This instrument is a new optical remote sensing instrument whose spectral resolution is better than 0.5 nm. With high resolution (spectral resolution 0.3～0.5 nm), wide wavelength range (240～720 nm), large field (114°field of view corresponds to the ground 2 600 km) features, the load is pushed and swept to achieve 1 day global coverage monitoring. This instrument acquires high accuracy UV/Vis radiation scattered or reflected by air or earth surface, and can monitor distribution and variation of trace gases (NO2, SO2, O3 and so on) based on differential optical absorption spectrum algorithm. Calibration is the premise when formally putting this instrument into operation. At the same time, in order to obtain the spectral characteristics of the load, on-ground spectral calibration is needed. According to the large field, wide wavelength range, high spatial resolution and high spectral resolution of this load, a set of spectral calibration system based on two dimensional turntables is set up. This system can finish the spectral calibration of full field of view. Spectral calibration was performed using standard spectral line method with mercury lamp as calibration source. The spectral response function is an important parameter to describe the spectral response characteristics of the spectrometer. The spectral resolution of the load can be obtained according to the spectral response function. It is also the key input parameter of inversion which is based on DOAS method. The accuracy of the spectral response function directly affects the inversion results of the atmospheric trace gas. According to the spectral response data of load tests, three function models of Gauss, Lorentz and Voigt are selected as the potential spectral response functions. In order to find the most suitable function model, two kinds of contrast tests are carried out. First, the Gauss function, Lorentz function and Voigt function are used to fit the monochromatic light response data of the load, and the sum of the squares of the three kinds of functions is used as the evaluation criterion, the fitting results show that the sum of the residual squares of the Gauss function as the slit function is 0.01, and the sum of the residual squares of the Lorentz and Voigt functions as the slit function is 0.033 and 0.021 respectively. From the analysis of the fitting results of monochromatic light response data, the Gauss function could be used as a spectral response function model of load. In order to further verify this conclusion, DOAS inversion of NO2 experiment was carried out, and the influence of three kinds of function models on inversion was investigated. The NO2 sample gas test was carried out in the laboratory. The atmospheric scattering light was incident through the 30 cm×40 cm quartz window to the load slit, and the NO2 sample pool was placed in the middle of the load slit and the quartz window. The data obtained were NO2 like gas absorption spectra, and then it was filled into the N2 gas to obtain the reference spectrum of the inversion. The experiment was carried out in sunny weather and can be completed in a short time, which can reduce the influence of weather conditions on the inversion results. In the experiment, the concentration of NO2 sample gas is 8.481 2×1016 molec·cm-2. During the inversion, Gauss function, Lorentz function, Voigt function were set as slit function respectively. The results of NO2 concentration corresponding to the different functional models of three groups are analyzed, and the function model is evaluated according to the relative deviation of the inversion results. The experimental results show that the relative deviation of the Gauss function as a slit function is 5.6%, and the relative deviation of the Lorentz and Voigt functions as the slit functions is 28% and 15.1%, respectively. The fitting results of spectral response data and gas sample inversion results show that the Gauss function can be used as a spectral response function model of load.

Spectrum is a kind of optical information that represents the properties of the material. By using spectral imager, the spectral image of the objects can be obtained in the field of view. At present, mature spectral imaging techniques require multiple snapshot to get a complete spectral image data cube. The time resolution of the corresponding system is relatively low, which is not suitable for the spectral acquisition of dynamic target, and the acquisition of dynamic target spectrum requires a snapshot imaging technique. Snapshots of spectral imaging has great advantage in dynamic target spectral imaging, the coded aperture snapshot spectral imaging (CASSI) technology is one method that integrates a compressive perceptual computation methodology into the spectral imaging procedure and the data cube reconstruction process. In the process of imaging, the data compression is completed, and CASSI has the advantage of high throughput. It is possible to reconstruct the target spectral data cube and realize the snapshot imaging by using the single exposure data, which makes it possible to monitor the dynamic target. But the targets information is difficult to prove the sparse hypothesis of this method, leading to a large reconstruction error, which is unfavorable to the monitoring of dynamic small targets. On the basis of CASSI, a new method of double dispersion channel coding aperture spectral imaging system is presented, which is used to obtain the spectral data of dynamic small targets in uniform background. The system consists of two channels, each containing a spectrometer whose dispersion directions are perpendicular to each other, and shares a front-end telescope system and coded aperture. Because the dispersion directions of the two channels are perpendicular to each other, the position of the small target and the corresponding coding can be separated from the background for spectral data reconstruction. So this new system can observe small dynamic targets in uniform background area in real time. Assuming that the radiation characteristic of the background changes little before and after the target appears in the field of view, the background spectrum can be calculated using the data before the target appears. And the target spectrum can be recovered through specific algorithm by utilizing the separated data and background spectrum. An imaging process mathematical model is established, and the reconstruction method has been tested. The target was made to randomly appear in a different position, and the PSNR probability distribution of the reconstructed spectrum was speculated. After adjusting the target size, the influence of target size on the reconstruction accuracy was analyzed. At last, the results were compared with the results of TwIST method reconstruction of coded aperture imaging system. Results show that this method improves the precision of target information recovery accuracy, and greatly reduces the computational complexity to realize real-time monitoring for moving targets when target with less than 5×5 pixels in homogeneous background.

Cryogenic radiometer is currently the world’s most accurate measurement system in optical radiation power metrology, and the uncertainty of measurement is up to on the order of 10 to the -5th. At present, there are only a few research institutes in the country which have imported cryogenic radiometers from abroad to carry out metrological study. A domestically-made cryogenic radiometer is urgently developed to replace imported products. Because the electrical substitution measurement principle in low-temperature superconducting state is used for optical radiation power measurements, one of the difficulties in the development of cryogenic radiometers lies in the development of a core light radiation receiving device-blackbody absorption cavity. The cavity requires a high spectral absorption over 0.999 9 at each wavelength. For the development of blackbody cavity with high absorption rate, the factors affecting the absorption rate of the blackbody cavity are given systematic analysis in this study. Based on the above, the influences of spectral wavelength, cavity length, diffuse reflectance of dark material, absorption rate of dark material and spatial position of incident light are mainly researched by Monte Carlo ray tracing method. The results showed that the absorption rate of blackbody cavity is positively associated with the absorption rate of dark material. The cavity has a high absorption rate over 0.9999 in the range of 300 nm-1000 nm, and gets the maximum value 0.999 941 5 at 700 nm. Above results indicate that the blackbody cavity meets the design requirements of cryogenic radiometer in the range of 300~1 000 nm. In the later design, the photo-electrical non-equivalent of low-temperature radiometer at different wavelengths should be corrected based on simulation and test results. If the structure and aperture are fixed, the absorption rate of blackbody cavity will gradually increase as the cavity length increases, which changes slowly at 40 mm and gradually tends to balance after 65 mm. Considering the limitation cryogenic radiometer’s cryogenic cabin, it is considered that the best ratio of the cavity length to the aperture is 6.5. The absorption rate of blackbody cavity is also affected by the diffuse reflectance of dark material. With the increase of the diffuse reflection of the black material, the absorption rate of cavity shows an approximately linear decrease. As a result, it is best to choose mirror absorption black material, when the absorption rate is the same. During the change of the absorption rate of black material from 0.8 to 1, the absorption rate of cavity increases by 0.05%. When the black material absorption rate is 0.92, the cavity absorption rate can reach over 0.999 9, which indicates that the black material should have a spectral absorbance over than 0.92 at any point within its effective operating wavelength range. The absorption rate of cavity is also influenced by the spatial position of incident light, which will get higher when the light is closer to the vertex of the inclined bottom cavity. However, the influence of light position on the absorption rate of the cavity is less than 0.004%, which is almost negligible. It is considered that the absorption rate of the inclined bottom cavity is uniform. The research results will have certain reference value for the development of cryogenic radiometer blackbody cavity.

The aggregation behavior of collagen molecules not only improves its physicochemical characteristics, but also provides theoretical guidance for its application in the fields of food, tissue engineering and biomedicine. In this paper, the aggregation behavior of collagen molecules at various concentrations and temperatures was analyzed by using the conventional wavelength, the synchronization fluorescence and the two-dimensional (2D) fluorescence spectroscopy technology based on the intrinsic fluorescence characteristic of the tyrosine (Tyr) and the phenylalanine (Phe). The results showed that: (1) At the excitation wavelength of 275 nm, the characteristic peak at 303 nm could be found, which was attributed to Tyr. Under the synchronous fluorescence scanning (Δλ=15 nm), two auto-peaks at 261 and 282 nm could be found, which were mainly assigned to Phe and Tyr, respectively. (2) It could find a good linear relationship between the fluorescence intensity and collagen concentration, indicating the feasibility of the quantitative analysis of collagen based on the conventional wavelength and the synchronous fluorescence spectroscopy. (3) With the increase of collagen concentrations, the amount of Tyr and Phe increased gradually, collagen molecular distance reduced and collagen molecules aggregated into fibrils, then Tyr and Phe were close to each other and participated in forming many hydrogen bonds, which led to the increase of the fluorescence intensity. When the temperature was increased from 10 to 70 ℃, the quenching opportunity between the fluorescence group and solvent increased and the fluorescence quantum yield of Tyr and Phe in collagen molecules decreased. Meanwhile, collagen molecular kinetic energy increased, then the collagen aggregate became loose and the trip-helix structure of collagen was destroyed gradually. Finally, the hydrogen bonds involved by Tyr and Phe were destroyed. Therefore, the fluorescence intensity of collagen decreased with the increase of temperatures. (4) The results of 2D conventional wavelength (275nm) fluorescence spectrum demonstrated that there were three relation peaks, which were located at 297, 303 and 310 nm. The peak at 303 nm was attributed to Tyr; the peak at 297 nm was recognized by Tyr, which participated in the formation of hydrogen bonds. Additionally, the peak at 310 nm might be assigned to an excimer-like species, which exhibited a blue shift to form stable Tyr with the aim of forming hydrogen bonds and then promoted the aggregation of collagen molecules. Finally, concentration-dependent and temperature-dependent 2D conventional wavelength correlation spectroscopy showed that the response order was 303 nm>297 nm>310 nm and 297 nm>310 nm>303 nm, respectively. (5) 2D synchronization fluorescence correlation spectroscopy demonstrated that Phe changed before Tyr. In a word, both the conventional wavelength and the synchronization fluorescence spectroscopy technology can investigate the aggregation behavior of collagen excellently at various concentrations and temperatures, and provide a new method for the quantitative analysis of collagen. Although the quantum yields of Phe is much lower than that of Tyr, the characteristic peak could be found by the synchronization fluorescence spectroscopy technology, demonstrating that the synchronization fluorescence spectroscopy technology has the advantages of narrowing the band and improving resolution. Combined with 2D fluorescence spectroscopy technology, the respond order of groups of collagen molecules can be illustrated further.

Iodine is one of essential trace elements. Deficiency of iodine and excess intake of iodine both can lead to thyroid diseases. Therefore, it is of great significance to develop a highly sensitive and selective method for the detection of iodine ions. Traditional analytical methods for iodine ions are usually involved in complex sample pretreatment and precision instruments, which are unfavorable for in-situ rapid detection. Fluorescent methods have been attracted great attentions due to their high sensitivity, high selectivity and easy operation. However, the present probes for iodine ions usually need complex organic syntheses and iodine ions are detected by means of coordination with heavy metal ions, which are unfavorable to promote the use of these methods. Fluorescent graphitic carbon nitride (g-C3N4) nanomaterial has attracted more attention due to the advatages of low-cost, easy preparation, high quantum yield, excellent photostability, and low toxicity. Furthermore, these nanomaterials can avoid complex synthesis for organic fluorophores or potential damage to environment for metal semiconductor quantum dots. These features make g-C3N4 nanomaterial an emerging fluorescent probe for the detection of metal ions. Recently, it was reported that Hg2+ ions could selectively and sensitively quench the fluorescence of g-C3N4 quantum dots (QDs). The addition of iodine ions could abstract the bound Hg2+ ions to form HgI2 complexes and restored the fluorescence of g-C3N4 QDs. Therefore, the fluorescent sensor for iodine ions could be developed. However, heavy metal ions (Hg2+) are also involved in this method, which limits its application. In this work, water-soluble g-C3N4 QDs with high fluorescence emission were prepared by using chemical oxidation of bulk g-C3N4 in nitric acid and hydro-thermal treatment. The maximal emission wavelength of g-C3N4 QDs located at 368 nm and did not change with the excitation wavelength, which indicates the size of g-C3N4 QDs is relatively uniform. There was a strong absorption peak at around 220 nm for iodine ions, which was overlapped with the fluorescent excitation spectrum of g-C3N4 QDs. On addition of iodine ions, the fluorescence of g-C3N4 QDs was quenched due to the inner filter effect. Therefore, a sensitive and selective fluorescent sensor for iodine ions was developed. Under the optimal conditions, there was a linear relationship between the fluorescence quenching (ΔF) of g-C3N4 QDs and the concentration (X, μmol·L-1) of iodine ions over the range of 10～400 μmol·L-1. The linear equation is ΔF=0.325 79X+6.039 05 (R2=0.999 5). The limit of detection is 5.0 μmol·L-1. The detection of iodine ions can be completed by “mixing and testing” without the need of coordination with heavy metal ions. Thus, this sensor is rapid, environment-friendly, simple and convenient.

Fluorescence is the main part of background within direct Raman spectrum, and a faithful and truthful method is needed to remove it from the Raman detection, and to provide one pure Raman spectrum. There are two paths for setting background of direct spectra, i.e., baseline fitting and fluorescence finding. The baseline fitting is a currently path whose significant advantage reflects in “visual experience” without additional hardware, nevertheless, “visual” means surface and not essential, so that its results probably may be wrong or unreal. The other way attends to find out the real fluorescence within spectra, but it needs additional design and cost, such as two and more wavelengths in laser sources. Furthermore, some handle methods, reagent for suppress fluorescence or long time photo-bleaching, are insufficient operation and low efficiency. This research focus on the time difference of Raman and fluorescence to find out fluorescence from the direct spectra of a stable system. For the stable system, and within a tiny time cell, a few milliseconds or so, the excitation light does not make the system to be changed significantly and a part of fluorescence has been out of the life cycle at the same time, that is, fluorescence is fading and Raman keep stable within the time cell. So that this difference of the time cell can be treated as a fluorescence infinitesimal and used to distinguish fluorescence and Raman from mixed signal. Based on this principle, the fluorescence photo-bleaching difference approach (FBDA) has been presented to eliminate the background under direct Raman spectra. The steps of FBDA is carried out as follows: Firstly, a series of Raman spectra between tiny time cell is directly measured, and then a series differences for each time cell is computed, these differences are de-noised by low pass filtering, to get the infinitesimals of fluorescence. Secondly, these infinitesimals are mean and normalized to be a fluorescence unit. Thirdly, the total fluorescence can be summed by inverse difference of the unit, in addition, the silence area of Raman, 2 000～2 500 cm-1, which usually do not appear Raman signal, can be used as a benchmark for deciding the gross of fluorescence. Finally, the fluorescence gross deducted from the original spectrum, and the background deduction or baseline correction is completed. The paper takes an example, the Raman spectrum measuring of metformin hydrochloride tablets, to illustrate the FBDA method and its validity. The FBDA is more objective and true than others baseline correction methods, which are considered to work well, such as, ALS and airPLS. Further advances of FDBA are more convenient and less costly than the current fluorescence-finding path, because all of the data for FDBA are collected by existing instruments without any change or addition. It should be noted that the tiny time cell is a key requirement for FDBA, and tiny time cell can ensure the real-time performance of spectrum, the difference of long time would affect the accuracy of the results. In addition, the applicability of FBDA needs further development under the complex background from photochemical reaction and other non-fluorescence.

In this paper, modulation characteristics of terahertz polarization transmission are studied based on the character of indium oxide nanofilm and metal wire-grid, using sample, indium oxide nanostructure with metal wire-grid as substrate, which is induced by ultraviolet laser. In the experiment, the indium oxide solution dissolved in ethanol is dripped onto the metal wire-grid, and the solution is just soaked in the gap of the metal wire-grid. At the same time, the temperature of the heating table is adjusted to 340 ℃ to thermal annealing on the indium oxide in the metal wire-grid. The results demonstrate that when the longitudinal direction of indium oxide-metal wire-grid is perpendicular to the polarization direction of terahertz electric field, the transmission intensity of the sample to terahertz is obviously attenuated induced by the low-intensity ultraviolet laser. When the UV power density is 7 mW·cm-2, the modulation depth of terahertz induced by sample with UV laser can reach 71%. When the longitudinal direction of indium oxide-metal wire grid is parallel to the polarization direction of terahertz electric field, the modulation effect of the sample excited by UV laser on terahertz is obviously weakened. When the UV power density is 7 mW·cm-2, the modulation depth of terahertz induced by sample with UV laser is about 20%. The presence of oxygen vacancies in indium oxide nanofilm makes the material particularly responsive to UV laser. With the absence of UV laser, oxygen molecules in the sample environment are adsorbed to the surface of indium oxide, and the O2- ion state is generated due to chemical reaction. When the sample is excited by UV laser with photon energy higher than the width of indium oxide band gap, electron hole pairs are excited on the surface of indium oxide, and the holes are bound by the O2- ion state and defect state on the surface of indium oxide, so as to release electrons into the conduction band and enhance the conductivity of the sample. Terahertz transmission intensity of indium oxide samples has a good correlation with the conductivity of indium oxide in the terahertz frequency range. Using existence of free electrons oscillating on the metal surface, metal wire-grid makes the electric field direction of terahertz polarized wave which parallel to the longitudinal direction of the metal wire-grid excite the electrons oscillation along the metal wire-grid direction. When electrons collide with atoms in a metal lattice, the polarized wave decay with radiating. However, the terahertz polarized wave whose electric field direction is perpendicular to the longitudinal direction of indium oxide-metal wire grid cannot stimulate free electron oscillation due to the limitation of the periodic structure, and it mainly shows transmission characteristics.In combination with the surface defect characteristics of indium oxide, UV laser can be used as an optically controlled polarization switch of indium oxide-metal wire grid structure, and indium oxide-metal wire-grid structure polarizer can be well applied to optically controlled polarization modulation interahertz frequency range.

The cavity ring down spectroscopy (CRDS) has been a proper detecting method for the trace gas with its ultrahigh sensitivity and super spectral resolution. However, the common CRDS is designed for a single gas or the measurement of multiple species with several laser sources. In this paper, a CRDS instrument has been developed for multicomponent greenhouse gas synchronous detection with a single laser. Considering the balance of the absorption losses, it utilizes the strong absorption peak of methane (CH4) and weak ones of vapor (H2O) and carbon dioxide (CO2) in the range of 1 653～1 654 nm simultaneously. The wavelength scan of that range is completed by a tunable distribution feedback laser diode. The corrected concentration of greenhouse gas has been determined by the CRDS instrument with a high finesse (F≈1×105) cavity and calculated with the spectral superposition inversion matrix. It demonstrates that the remove of data points, called the filter region, at the preliminary stage of the decay has an influence on the noise equivalent absorption coefficient, which has a interrelation with the measuring sensitivity. In most cases, the Levenberg-Marquardt (L-M) algorithm, which shows a good precision, is better than the discrete Fourier transform (DFT) algorithm on the measuring sensitivity as a fitting algorithm. But this conclusion will be dubious when the ring-down curve is deviated from the single exponential form. For studying this phenomenon, a CRDS instrument with a low finesse cavity (F≈6×103) is set up. Compared to the high finesse cavity, the low finesse cavity has a faster decay rate, and a bigger deviation from the single exponential form, which can be easily seen from the residual analyses. When the filter region is not long enough, the noise equivalent absorption coefficients calculated by L-M algorithm is larger than the ones calculated by DFT algorithm. Meanwhile, according to the definition of the fluctuation of the noise equivalent absorption coefficient, the influence of the DFT algorithm is less than that of the L-M algorithm affected by the length of the filter region in both high finesse cavity and low finesse cavity. The best length of the filter region in our CRDS instrument is 20 data points, which are basically the same at different averaging time. And according to Allan variance, the measuring sensitivity of the CRDS instrument can reach 2.4×10-10 cm-1 for an 8 s integration time. At 25 ℃ and 1 atm, the measuring sensitivities of CH4, H2O and CO2 are approximately 0.64 ppbv, 3.5 ppmv and 4.0 ppmv separately. Calculated with the spectral superposition inversion matrix, the atmospheric concentrations of CH4, H2O and CO2 in the lab are measured to 2.018 ppmv, 3 654 ppmv, and 526 ppmv separately with multiple wavelengths, in contrast to the results of 2.037 ppmv, 3 898 ppmv and 630 ppmv in the classical CRDS method. Using the temperature control of the DFB laser, an absorption spectrum of the greenhouse gas has been acquired with the wavelength scan. Compared to this measured results, the residuals of the complex fitting curve using the data from the multiple wavelength measurements are less than the ones of the simple fitting curve with data from the classical method.

In order to investigate the influence of magnesium on the phase transitions and Raman vibrations of calcite under highpressure conditions, and to explore the stable structure and physic-chemical properties of carbonates in the deep earth, experiments under high-pressure environment were carried out with natural calcite samples containing different magnesium concentrations by using diamond anvil cell and micro-Raman spectroscopy. Colorless transparent Iceland spar, pale yellow translucent calcite vein and white marble were selected as the research objects, results from ICP-AES analysis showed that the chemical compositions of contents of Iceland spar and calcite vein were CaCO3, whereas a Mg/(Mg+Ca) molar ratio of 0.03 and a chemical composition of (Mg0.03Ca0.97)CO3 were determined for the marble. The calcite samples were crushed and fragments of about 50~100×50×20 μm were loaded into the HDAC. In-situ observations and laser Raman measurements were made while the samples were under different pressures. The Raman vibrational frequencies of Iceland spar and calcite vein as measured under ambient pressure were 156.82, 283.55, 713.86 and 1 088.19 cm-1 for the T1, T2, ν4 and ν1 vibrations, respectively, whereasvalues of 158.15, 284.76, 715.07 and 1 089.20 cm-1 were obtained for the marble sample, indicating that the Raman peak positions shifted to higher frequencies by at least 1 cm-1 for the calcite containing 3 mol% MgCO3. Within the stability pressure range of calcite, no significant difference in the shifting rates of the Raman peak positions with pressure (ν/p) was observed among different samples. Both Iceland spar and calcite vein transformed to CaCO3-Ⅱ under 1.5 GPa, and further to CaCO3-Ⅲ and Ⅲb under 2.0 GPa. Whereas for the marble containing 3 mol% MgCO3, the phase transition pressures to CaCO3-Ⅱ and to CaCO3-Ⅲ were 2.4 and 3.7 GPa, respectively. Assuming that the influence of magnesium on the calcite phase transition pressures was linear, the shifting rates of the calcite to CaCO3-Ⅱ and CaCO3-Ⅱ to CaCO3-Ⅲ/Ⅲb phase transition pressures with MgCO3 concentration were determined to be 0.30 and 0.57 GPa·mol%-1, respectively. The shifting rates could be extrapolated to 16.5 and 30.5 GPa for samples containing 50 mol% MgCO3, which is in nice agreement with the transformation pressures from dolomite to dolomite-Ⅱ and dolomite-Ⅲ. By combining our results with those investigating the influence of MnCO3 on the phase transition pressures and Raman vibrations of calcite, it can be concluded that replacement of Ca2+ by smaller and lighter ions (e. g., Mg2+ or Mn2+) will result in changes in the M2+-CO2-3 and C—O chemical bond length and bond strength, and thus, leads to significant increase in the calcite phase transition pressures and shifts of the Raman peak positions. Therefore, it is highly necessary to ascertain the influence of Mg, Mn and Fe on the calcite structure stability and Raman vibrational frequencies while discussing the phase transitions and Raman vibrations of carbonates under high pressure and high temperature conditions.

Faustite is rare and rarely mentioned in existing research and reporting. Mexico is an important producer of American turquoise, and Mexican turquoiseis active in the market recently. In this paper, the authors studiedthe chemical composition, phase composition, and a series of spectrum of turquoise samples from Mexico by conventional gemmological tests, X-ray fluorescence spectroscopy, X-ray powder diffraction, fourier transform infrared spectroscopy and UV-Vis spectroscopy, andpreliminarily discussed the deposit genesis. The results indicated that the main colors of Mexican turquoise are light blue and bluish white, and the features of pyrite with high degree of idiomorphic in matrix and surrounding rock and rare radial growth of dravite in surrounding rock were obviously different from turquoise in other origins. Mexican turquoise was recognized as Cu-bearing faustite due to the ratio of zinc oxide to copper oxide was greater than 1, which belongs to turquoise-faustite isomorphism series close to the end member mineral of faustite. And because of paragenesis with copper deposit, the content of CuO and ZnO in Mexican turquoise is much higher. The results of XRD experiment showed that the main mineral phase of Mexican turquoise is faustite, which is in agreement with the testing results of EDXRF testing, and the common mineral assemblage was faustite, quartz, orthoclase and dravite. The infrared spectrum was determined by the vibrationof hydroxyl (located at 3 400~3 700 cm-1), hydrated ion (located at 3 000~3 300 cm-1) and phosphate group (located at 1 000~1 200 and 400~650 cm-1 in fingerprint region) in the structure. And all the samples from Mexico showed the infrared absorption peak at 3 732 cm-1 while rarely seen in other origins, which has the function of indicatingthe originin a sense. Meanwhile, the integral computation within a range of 3 500~3 600 cm-1which related to constitution water with strongest hydrogen bonding can be used to determine the water content in the samples. Besides, the UV-Vis spectrumshowed that there were two peaks caused by O2--Fe3+ and Fe3+ at 256 and 430 nm, respectively. The band associated with the forbidden transition of Cu2+ at 670 nm was enveloped by the band formed by the electronic transition of Fe2+ at the center of 852 nm, finally, it was shown with the band centered on 800 nm which was caused by the combined action of Cu-Fe ions. Inferred from associated mineral combination, mineral structure and geological characteristics, Mexican turquoise is a nonmetallic mineral associated with porphyry copper deposit and belongs to the typical hydrothermal alteration genesis of intermediate-acid volcanic rocks.

As main by-product of semi-dry desulfurization technology, desulfurization ash is very difficult in utilizing and cost consumable, which cannot be disposed in direct stacking and landfill, causing environmental pollution and the waste of potential resources. Carbon black (8 000 yuan·ton-1) and white carbon black (6 000 yuan·ton-1), as the commonly used rubber reinforcing filler, can only be prepared by using complicated process and always leads to large consumption of energy and resources, resulting in higher costs. Thus, the development of desulfurization ash into low-cost inorganic rubber reinforcing fillers has become one of main methods to achieve the sustainable development of resources and enhance economic performance by using high value-added utilization of solid wastes and reduce cost of fillers in rubber industry to a great extent, respectively. The desulfurization ash is organic while rubber is inorganic. Therefore, it is necessary to conduct chemical modification for desulfurization ash to weaken incompatibility of interface (organic/inorganic) between them. In this paper, the modified desulfurization ash was prepared by silane coupling agent Si69, silane coupling agent KH550 and desulfurization ash. Then, composite rubber was prepared by replacing part of carbon black with modified desulfurized ash. Next, mechanical properties of composite rubber, such as tensile strength, tear strength and shore hardness of composite rubber was tested by referring to national and industry standards. Specially, microstructure of composite rubber was characterized and analyzed by scanning electron microscope (SEM), composition structure of modified desulfurization ash was characterized and analyzed by using fourier transform infrared spectrometer (FTIR), and mineral composition of modified desulfurization ash was characterized and analyzed by using X-ray diffractometer (XRD), so as to reveal the modification mechanism of both agents as well as reinforcement mechanism of desulfurization ash for composite rubber. The results show that when desulfurization ash was modified by applying both silane coupling agent Si69 and silane coupling agent KH550, the best enhancement effect occurs with tensile strength, tear strength and shore A hardness as 20.36 MPa, 45.71 kN·m-1 and 66, separately. The desulfurization ash is modified by combined action of both silane coupling agent Si69 and silane coupling agent KH550 not only can still maintain good alkalinity, which can enhance reinforcement effect of composite rubber, but also can improve surface property and structure of desulfurization ash, so as to enhance the compatibility of organic/inorganic interface between modified desulfurization ash and styrene butadiene rubber.

In this work, gold nanocap-polystyrene particle (PSP) conjugates have been prepared using thermal deposition of gold flux onto PSP particle pre-coated on glass substrate, followed by the lift-up and separation into acetonitrile solvent under sonication. The hollow gold nanocaps were obtained after the PSPs were dissolved with dichloromethane. When the nanocaps were covalently modified with phenyl derivatives in diazonium salt form, the optical absorptions were found as red- or blue-shift, which was probably due to the changing of the electron density of the nanocap surface by the surface-bound electron donating or electron withdrawing functional groups.

Feldspar is the most important rock-forming mineral in surface rocks, accounting for up to 60% in the earth's crust. With the development of high-spectral technology, many scholars at home and abroad have been studying the response of the main building of rock and mineral content and the spectrum of the features, and it offers a variety of possibilities for remote sensing, mineralization, and mineralization. It is based on data of 18 igneous samples in the USGS spectral library to study the quantitative relation between the characteristic spectra and the content of the feldspar. Through the original spectral reflectivity and the transformation (including the three layers of the small wave to break down the high frequencies, the little wave layers, the spectrum of the wave to the back of the line, and then the little wave layer after the cable, and then the little wave layer after the cable, and then the small wave layer of the wave, and then the second layer of the cable) study the correlation between the high and the long rocks, and it turns out: (1) to analyze the transformation of six spectral reflectivity, the relation between the spectral reflectivity of the high frequency and the long rock content of the small wave three layers of the envelope, and the correlation coefficients are the best, and the correlation coefficients are constantly changing, and based on the relative coefficients of the relative coefficients, the high value of the long rock is 4, 31, 570, 972, 1 456, 1 856, 2 292.9, 2 481 nm; (2) the correlation between the original spectral reflectance and feldspar content curve trend is relatively flat, and after the wavelet decomposed high frequency component, through to the envelope and wavelet decomposition for the high frequency component, the change trend of correlation curve is becoming ever more obvious, therefore, the independent variable of the small changes will cause changes in the dependent variable, very hour when the content of the rock feldspar, wavelet decomposition processing can improve the accuracy of the model. The relation between the content of feldspar and the characteristic spectrum is quantified, using a multi-element stepwise linear regression analysis and a least-square method of modeling, establishing six linear regression models and six least-squares regression models, and the results show that: (1) the spectrum of the spectrum behind the envelope is more accurate than the original spectrum, and the low-frequency portion of the lower wave part of the wave is better than the one that's not done with the small wave decomposition, which is the best way to get to the back model of the low-frequency portion of the small wave after the envelope. (2) the multilinear regression model is better than the least-squares, and the variables that affect the variables that affect the larger variables, 972, 1 456, 1 856, 2 292.9 and 2 481 nm. In view of that relation between the content of feldspar and the spectral reflectance of the long stone and the influence factors on the content of feldspar in different absorption band, it is important to use the characteristic spectrum of feldspar to quantitatively invert the content of feldspar in a region.

Three compounds 4-naphthyltriphenylamine(a), methyl 13-［N,N-(4-naphthylphenyl)- phenyl］aminodeisopropyldehydroabietate(b) and methyl 13-［N,N-Bis(4-naphthylphenyl)］aminodeisopropyldehydroabietate(c) were synthesized by esterification, bromine reaction, nitration reaction, reduction reaction and C-N coupling reaction using triphenylamine and dehydroabietic acid respectively. Their structures were characterized by 1H MNR, 13C MNR, and MS. In order to study the relationship between structures and spectral properties of compounds, molecular configuration of compounds were optimized by DFT/B3LYP using Gaussian 09, then their bond lengths, bond angles, and dihedral angles were obtained and compared. The results show that the introduction of dehydroabietic acid skeletons and naphthalene rings affect the coplanarity and the introduction of naphthalene rings increase their conjugated degree. Their fluorescence emission spectra and UV absorption spectra in five solvents with decreasing polarity, such as methanol, dioxane, THF, dichloromethane and cyclohexane were also studied. The results show that the maximum fluorescence emission wavelengths of compounds differ greatly in different polar solvents. The fluorescence emission wavelength is the largest in methanol, and which is the smallest in cyclohexane, however, the fluorescence emission wavelengths are not increase with increasing polarity, the fluorescence emission wavelengths of compounds have large degree of blue-shift in dichloromethane, THF and dioxane with increasing polarity. In the same solvent, the maximum fluorescence emission wavelengths of compounds b and c were red-shift relative to compound a, the red-shift degree of compound c was similar to that of compound b. In UV absorption spectra, the maximum absorption wavelengths of compounds a, b and c in different polar solvents are also different. In 200～250 nm range, their maximum absorption wavelengths have big shift in dichloromethane from which is in other four solvents, but in 300～350 nm range, their maximum absorption wavelengths have big shift in methanol, and they have almost the same wavelengths in five solvents in 250～300 nm range. In the same solvent, their maximum absorption wavelengths in 300～350 nm range differ greatly, and compound c has a big red-shift (26 nm) from a. Combining with the structural optimization datas, it shows that the conjugated degree of compounds has a great effect on the fluorescence emission spectra and the UV absorption spectra, however, coplanarity affects the fluorescence emission spectra of compounds significantly. The differences in fluorescence emission spectra and UV absorption spectra of compounds a, b and c in different polar solvents indicate that three compounds have significant solvatochromism effect, which means they have potential to be used as fluorescent probes in different polar environment.

When crops are contaminated with heavy metals, their tissue structure and chlorophyll content will be destroyed, which will affect the metabolism and health of crops. People and animals will have fatal injuries if they eat the contaminated crops. Hyperspectral remote sensing is now widely used to monitor the extent of crops affected by heavy metals, and in the heavy metal pollution, the spectral crop leaves are still very similar to those of the traditional monitoring methods and spectral characteristic parameters of routine, so it is difficult to distinguish between different spectral weak information, and application of hyperspectral remote sensing is the focus and difficulty of the study. The maize leaf spectral data, chlorophyll content and relative content of heavy metals Cu2+ and Pb2+ were collected by setting different concentrations of Cu2+ and Pb2+ stress. A LD-CR-SIDSCAtan model combined with the continuum removal (CR), spectral correlation angle (SCA), spectral information divergence (SID) and tangent function (Tan) and Langmuir distance (LD) is proposed in this study, and the traditional measure, such as spectral correlation coefficient (SCC), SA (spectral angle), tangent spectrum (DSA), spectral information divergence and spectral correlation tangent (SIDSAMtan), spectral information divergence and spectral gradient tangent (SIDSGAtan) and conventional spectral characteristic parameters, such as the maximum value of red edge (MR), green peak height (GH) and red edge area surrounded by a first order differential (FAR), red edge derivative curve steepness (FCDR), blue (DB), red band depth (RD) compared to verify the feasibility and superiority of the model. The LD-CR-SIDSCAtan model was applied to measure the spectral difference information about the overall waveform and the subband of maize leaves under Cu2+ and Pb2+ stress at different concentrations. The results show that the LD-CR-SIDSCAtan model realized the qualitative analysis of heavy metal Cu2+ and Pb2+ pollution, could measure the spectral correlation coefficient of more than 0.99 of the difference information between the similar spectral information, and waveform difference information was significantly related to the leaf chlorophyll content and the relative content of heavy metals Cu2+ and Pb2+ that measured, and also found the spectra response wave band under the stress of heavy metals Cu2+ and Pb2+. When the whole spectral range of spectral data is measured, the spectral difference is more obvious when the value of the model is negative. When the value of the model is positive, the larger the value of the model is, the larger the spectral difference will be. Therefore, with the increase of heavy metals Cu2+ and Pb2+ concentration, the difference of spectra increased, which means that the heavy metal Cu2+ and Pb2+ pollution degree is more serious; maize plants suffer from heavy metal pollution in Cu2+ stress when measuring the local subband range of spectral data, in the “blue” and “red edge”, “near the Valley”, “at the peak of B” were specially sensitive to heavy metal Cu2+ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Cu2+; when the maize plants are under heavy metal pollution in Pb2+ stress, in the “Purple Valley”, “blue”, “yellow” and “red Valley”, “red edge”, “near at the peak of A” were specially sensitive to heavy metal Pb2+ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Pb2+. Finally, through the linear fitting analysis of the application results of LD-CR-SIDSCAtan model and the content of Cu2+ and Pb2+ in maize leaves, the pollution degree of heavy metals Cu2+ and Pb2+ to maize plants was inversed and predicted.

The biological content of crops is directly related to the optical properties of the crops, which are affected by the bidirectional reflectance distribution function (BRDF) of plants. The BRDF of plant leaves reflects the energy reflection ability of leaves in different directions and directly affects the spectral detection results of plant leaves, also one of the influencing factors of macroscopic optical characteristics of vegetation canopy. Studying the BRDF optical properties of plant leaves can effectively improve the stability and reliability of the plant NDT spectral model, thus improving the accuracy and reliability of using the crop spectral model to invert the physicochemical properties. In this paper, we first introduced the BRDF rapid acquisition method for plant leaves and the independently developed directional spectral detection instrument which can adjust the four dimensions of incident light azimuth and zenith angle, receiver probe azimuth and zenith angle to receive the reflectance data under multi-incidence and multi-reflectance angle. The leaf veins of monocotyledonous plants are longitudinally distributed, thus showing more significant anisotropy. Maize and wheat are two typical monocotyledonous crops. Then the self-developed instruments were used to obtain the reflectance spectra of corn and wheat under different wavebands, and their reflection distribution was analyzed and summarized. The spectral data and whiteboard correction data were calculated by using the BRDF calculation method described in this paper. In combination with the image mapping of spectral reflectance data from MATLAB program, the correlation of the reflectance results with two typical physicochemical parameters of leaves, chlorophyll content and water content was analyzed. After analysis, the method of using ANIX coefficient to quantitatively analyze the anisotropy of blade was discussed. The data of wheat in the visible light band and corn in the near infrared band were selected for final analysis. The results show that the fr distributions of wheat and corn in each band are symmetrical about the tiny space on both sides of the incident zenith angle, and the fr values under different incidence zenith angles are basically the same at the same band. Under the same incident zenith angle, wheat has the largest fr value at 800 nm, and the smallest fr at 680 nm. This is due to the strong absorption of chlorophyll near the 680 nm wavelength, and the strong reflectance near 800 nm. The increase of chlorophyll concentration will lead to the increase of fr value under the same band. At 1450 nm in the strong absorption band of water, the fr value of corn increases with the increase of water content. The analysis shows that the BRDF characteristics of crops can effectively reflect the changes of the main biological content of the leaves, and the calculated anisotropy index also shows a consistent change law, which provides a theoretical and practical basis for establishing a stable and reliable model for the quantitative analysis of crop spectra.

Persistent organic pollutants (POPs) have a wide variety of species, a wide range of distribution, and have caused widespread concern for the ecological environment and human health hazards. Monitoring POPs in the environment is of great significance for pollution assessment, contaminants remediation and production management. Traditional detection methods based on chromatographic separation technology have the advantages of low detection limitation, high sensitivity and good stability, but they also have the disadvantages, such as long cycle, high consumption, and complicated process. Fluorescence spectrum analysis develops rapidly with few samples used, simple pretreatment, and non-destructive advantages. Scholars have carried out a lot of studies and formed a relatively complete method system. In this paper, the control concentrations and detection methods of POPs in current environmental standards in China are described, the application and advance of using direct, indirect fluorescence, synchronous fluorescence scanning combined with other techniques, three-dimensional fluorescence coupled with chemical multiway calibration and laser-induced fluorescence in the detection of POPs, including polycyclic aromatic hydrocarbons, polychlorinated biphenyls and organochlorine pesticides in water and soil are reviewed, the latest research results are focused, the respective application of the technology is summarized, the shortcomings and areas to be improved are analyzed, and the research and application about detecting POPs directly by using fluorescence spectrometry are also discussed. This paper provides reference for the further development of rapid detection of POPs by using fluorescence spectrum.

Near-infrared (NIR) spectroscopy analytical technology has been applied more and more widely in polymer field, since it can provide rich information about structure and composition of samples, and has the prominent feature of fast analysis without dealing with the sample. This article reviewed the research progress of the application of NIR spectroscopy in all aspects in polymer’s entire life, which involves polymer synthesis, processing and recycling. NIR analytical technology can be used in quantitatively measurement of important material parameters during the polymer synthesis and processing, what’s more, due to the good chemical and thermal stability of the optical fiber, it can realize the in-line real-time measurement in environment with strong corrosion, high temperature and high pressure. As for polymer synthesis, the research progress in real-time measurement of polymerization conversion, polymer particle size and copolymer composition with NIR spectroscopy was discussed. With regard to polymer processing, the discussion on application of NIR spectroscopy for in-line measurement of important parameters which reflects the processing state of the material during the reactive extrusion process, such as the polymer molecular weight, the content of residual monomer and the polymer grafting degree, was mainly made. In the recycling of plastics, NIR can be applied for the qualitative classification and identification of waste plastic products, the article analyzed the research status of this application at home and abroad. In addition, the problems in the application of NIR analytical technology in polymer analysis and corresponding suggestions were pointed out. Finally, the development direction of this technology in polymer field was discussed. It is believed that, NIR spectroscopy will have a wider application prospect in polymer field with the deepening of research work and the development of in-line measurement instruments.

Raman spectroscopy is a powerful tool for providing information about material structure, but its application scope is limited due to its weak Raman scattering signal and low sensitivity. However, in resonant Raman spectroscopy(RRS), the absorption intensity of the incident light by the molecule is greatly increased due to the frequency of the excitation light source falls within one electron absorption band of the molecule and the transition to the electron excited state of molecule becomes a resonance absorption after absorbting photons. RRS can increase the signal intensity by a factor of 106 compared to conventional Raman spectroscopy. Therefore, it is more widely applied with its higher sensitivity and selectivity, especially in the fields of biology and medicine. For instance: (1) Analysis of pigments such as carotenoid and chlorophyll et al. in biological matrices; (2) Researches on organic substances such as cells, proteins, and DNA, as well as the diagnosis of some clinical diseases. RRS can obtain more important information of molecular structure which is hidden in normal Raman spectroscopy. RRS can be achieved at very low concentration, and the Raman lines with resonance Raman enhancement belong to the group that can generate electron absorption, which is crucial to coloured substance and biological samples. The active sites of many of these samples are close to the chromophore groups, and the object of research is often one part of biological macromolecules, so RRS plays an important role in researching the relationship of the structure and function of biological substances. In recent years, RRS has been innovated and extended such as the application of new technologies of Liquid-core optical fiber Resonance Raman spectroscopy and Transmission Resonance Raman spectroscopy with the development of spectroscopy. This view summarizes and analyzes the raw paper, data and main viewpoint of RRS technology applications in recent years. It introduced the historical background and research status of RRS, and carried out a detailed overview of the application of resonance Raman spectroscopy in the fields of pigment detection, biology detection and explosive detection as well as the development and application of relevant new technology. RRS will have an irreplaceable position in the field of scientific research field with the rapid development of spectroscopy technology.

Coolant liquid and brake fluid are important vehicle oils, and they are of great significance for the normal operation of vehicles. Adding water into the coolant liquid and brake fluid is the common adulteration method for coolant liquid and brake fluid. The active ingredients in adulterated coolant liquid and brake fluid will reduce, and function of coolant liquid and brake fluid will be influenced. This will result in the harm of vehicles, which will influence the normal operation of vehicles. Rapid and accurate detection of water content in coolant liquid and brake fluid is of importance for quality assurance. In this paper, Fourier transform near-infrared spectroscopy was used to determine water content in different brands of watered anhydrous coolant liquid and brake fluid. Three brands of anhydrous coolant liquid, and four brands of brake fluid were used. In addition, samples with water contents of 0%, 5%, 10%, 15%, 20%, 25%, 30% and 35% were prepared. Fourier transform near-infrared transmittance spectra of the samples were acquired, and the spectral range of 10 067~5 442 cm-1 were used for analysis. There were differences on near-infrared transmittance spectra among samples with different water contents. Based on each brand of anhydrous coolant liquid and brake fluid, principal component analysis (PCA) indicated the obvious differences among samples with different water contents. Besides, second derivative spectra were used to select optimal wavenumbers for each brand of anhydrous coolant liquid and brake fluid, as well as the combination of all brands. The selected optimal wavenumbers were similar among different brands of anhydrous coolant liquid as well as the combination of brands, and the selected optimal wavenumbers were similar among different brands of brake fluid as well as the combination of brands. The number of wavenumbers reduced at least 98.67% after selection. Based on the full spectra and the selected optimal wavenumbers, partial least squares (PLS) and least-squares support vector machine (LS-SVM) were built. All the models obtained quite good performances, with determination of coefficient (R2) over 0.9 and residual prediction deviation (RPD) over 3. These prediction models obtained good performances. The performances of models for single brands were similar to those for the combination of brands, indicating that it was feasible to build calibration models using the combination of brands which would benefit the practical application. The overall results indicated the feasibility of using Fourier transform near-infrared transmittance spectroscopy combined with chemometric methods could be used to determine water adulteration in different brands of anhydrous coolant liquid and brake fluid. The results would help to develop on-line detection systems for water adulteration in different brands of anhydrous coolant liquid and brake fluid, and would provide guidance for detecting water content other fluids for automobile.

For the first time, this study has established an attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) method for fast qualitative analysis of methamphetamine, ketamine, heroin, and cocaine. Characteristic peak method was chosen as the qualitative analysis criterion of this method. Due to the lack of proper qualitative identification criterion, FTIR method has long just been used for fast qualitative screening in drug analysis. In order to expand its application in forensic sciences, two qualitative identification criteria of characteristic peak method and soft independent modeling by class analogy (SIMCA) classification method were investigated and compared. A total of 516 calibration and 864 prediction samples were used for method development and validation. For characteristic peak method, five to eight peaks in the range of 2 500~650 cm-1 with relative high intensity and interference-free from common cutting agents were selected as the characteristic peaks. When characteristic peak method (all of the characteristic peaks should be detected) was used for the qualitative analysis of 646 validation samples, the positive detection rate was 98.1%. A SIMCA classification model was constructed based on 516 calibration samples, consisting of 7 PCA models for methamphetamine HCl, ketamine HCl, ketamine base, heroin HCl, heroin base, cocaine HCl, and cocaine base. The classification results of 646 validation samples showed a recognition rate of 95.4%, and a false rejection rate of 100%. Therefore, the results of both the characteristic peak method and the SIMCA method were reliable and accurate. It is easy for the characteristic peak method to be used by local police officers after simple training, thus suitable for popularizing. In comparison, as the building of SIMCA model needs the collection of large number of representative calibration samples and the use of professional mathematical software, the SIMCA method is not suitable for popularizing. Therefore, the characteristic peak method was chosen as the criterion method for fast qualitative identification analysis. The ATR-FTIR method that is based on characteristic peak method will greatly increase the efficiency and reduce the cost of drug qualitative identification analysis.

Near infrared spectral analysis technique can be used to detect samples quickly and nondestructively, which is playing an increasingly important role in people’s production and life. The support vector machine is a commonly used method for building qualitative analysis models. It separates two kinds of samples by finding the optimal classification hyperplane. In the case of small samples, the support vector machine method has its unique advantages. The principal component analysis is a commonly used method to reduce the dimension of data. After the dimension is reduced by this method, the data is used as input variables of the support vector machine method. The model can be simplified and the accuracy of discriminating by the model can be improved in this way. So the support vector machine based on the principal component analysis (PCA-SVM for short) is suitable for establishing the qualitative analysis model of near infrared spectroscopy. The multi-model method is a modeling method seldom used by people. The model established by this method usually has good stability. The multi-model method is successfully combined with the PCA-SVM method to form a new method in this paper. With cotton and nylon blended, cotton and polyester blended textiles being taken as an example, a qualitative analysis model of near infrared spectra of these two types of textile samples is established by the new method. In modeling, the spectral data are divided into 4 groups according to the wavelengths. A sub model is established with each group of spectral data. The final prediction results are obtained by weighted average of the output values of the sub models. The information contained in the spectral data can be used more fully in this way. In order to facilitate the comparison of different methods, the aforementioned calibration set and validation set are used. A qualitative analysis model of near infrared spectra of these two types of textile samples is also established by using the PCA-SVM method in the paper. The cross validation of the prediction results show that the mean value of the correct rate of discrimination by the model built with the new method is 85.49%, the standard deviation of the correct rate of it is 0.066 7, and the mean value of the correct rate of discrimination by the model built with the PCA-SVM method is 83.34%, the standard deviation of the correct rate of it is 0.109 6. Since the mean value 85.49% is higher than the mean value 83.34%, the classification effect of the model built by the new method is better than that built by the PCA-SVM method. Since the standard deviation 0.066 7 is much smaller than the standard deviation 0.109 6, the stability of the model built by the new method is obviously higher than that built by the PCA-SVM method. The prediction effect of the model built by the PCA-SVM method is greatly influenced by the composition of the calibration set. But the prediction effect of the model built by the new method is relatively stable. Sorting and recycling waste textiles can save a lot of textile raw materials. However, manual sorting is inefficient and costly. Classification of textiles by using the method of near infrared spectra analysis is proposed in this paper, which lays a certain foundation for large-scale fine sorting and grading of waste textiles. The new method put forward in the paper is also expected to be used for classification of some other types of samples.

In this paper, a rapid and quantitative detection method combing the laser Raman spectroscopy with Artificial Bee Colony-Support Vector Machine for Regression (ABC-SVR) is proposed for the determination of fatty acids content in three-component blend oil. This method establishes a mathematical model with higher prediction accuracy and higher modeling efficiency than similar comparison algorithms in solving the nonlinear and high-dimensional complex relationship between spectral data information and samples. And it can avoid complicated detection ways like gas chromatography and liquid chromatography, etc. The quality of fatty acids is obtained from the oil configuration volume according to the international standards for the content of three fatty acids in pure oils, which effectively reduces the cost and complexity of the experiment, and increases the practical value of the inspection work. Firstly, 66 groups of mixed oil test samples were arranged according to a certain gradient. The Raman spectroscopic information of the samples was collected from a portable Raman spectrometer and the background noise was subtracted at the time. Through the spectrum of the samples, we could see that the Raman spectra had the same characteristic peak shifts basically, but the intensities of the characteristic peaks were obviously different because of the difference in functional group concentration. Therefore, different components could be distinguished according to the characteristic peak information. Secondly, the spectra were pretreated by background subtraction, spectral smoothing and normalization to reduce the effect of uncontrollable external factors in the experiment. Then the mass of fatty acid was obtained from the oil volume by the international standard content of three kinds of fatty acids in pure oil in National Codex Alimentarius Commission Standard CODEX STAN210—1999.2/3 of sample data were randomly selected as the training set, and the remaining 1/3 of sample data were used as the prediction set. The characteristic peak intensity and the quality of fatty acid of train set were used as the input and output values of the regression model, and the quantitative analysis model of hybrid optimization algorithms of SVR, PSO-SVR and ABC-SVR were established to predict the content of fatty acids of test set. The accuracy of the model was tested by using mean squared error (MSE), the correlation coefficient (R2) and elapsed time. The experimental results showed that the ABC-SVR quantitative analysis model was effective: the MSE of the predicted and true values of three fatty acid contents were 0.88×10-4, 16×10-4 and 8×10-4, respectively. The R2 were 93.43%, 99.65% and 99.43%, respectively. The elapsed time were 1.26, 2.42 and 2.14 s, respectively. Therefore, the proposed method has higher accuracy, faster modeling time than other ways, and it can be applied to other sample detection work under theoretically similar conditions. This method can provide a viable theoretical basis for further study on the analysis of adulterated edible oils by vibration spectroscopy.

The “Beihong agate” from Heilongjiang province and the “Nanhong agate” from Liangshan (Sichuan province) and Baoshan (Yunnan province) are the most common gem quality red agate in the jewelry market of China. However, few researches have been performed to differentiate theirprovenance. In this paper, 73 red agate samples from three different areas were investigated by chromaticity, Raman spectrum and X-ray diffraction (XRD) to obtain their chromaticity, mineralogy and spectra characteristics. The results show that the “Beihong agate” is mainly composed of α-quartz and moganite, with minor amount of goethite and hematite responsible for the agate’s red color. The mineral phase composition of the “Nanhong agate” is α-quartz with hematite, goethite, calcite and moganite as auxiliary minerals. The domain wavelength of “Beihong agate” ranges from 574 to 605 nm and is concentrated at ［580, 590］ interval, which accounts for yellow to orange tune. In the CIE1976Lab color space, “Beihong agate” shows a≤6.2 and b≤6.3 characteristics. The domain wavelength of “Nanhong agate” from Sichuan province ranges from 589 to 624 nm, while “Nanhong agate” from Yunnan provinceranges from 589 to 599 nm, both are concentrated at ［590, 600］ interval with orange to orange-red tune, and the majority of samples are a>6.2 or b>6.3 in the CIE1976Lab color space. Generally, “Nanhong agate” exhibits deeper red tone, higher chromaand brightness appearance than “Beihong Agate”. In Raman spectra, the peak near 501 cm-1 of the Si—O—Si symmetry stretching and bending vibration in “Beihong agate” is stronger than those in “Nanhong agate”. The results of peak intensity ratio (I501/I463) consistent with the results of area ratio (A501/A463) . Thepeak intensity ratio (I501/I463) and area ratio (A501/A463) measured by powder method are located in the results measured by random points method. Compared with powder method, random points method does not require destruction of samples. Therefore, random points method is suitable for agate’s qualityanalysis. The peak area ratio (A501/A463) of “Beihongagate” is stable from 0.15 to 0.36, while “Nanghong agate” is stable from 0.00 to 0.08. We hypothesize that “Nanhong agate” have experienced a strong dehydration and recrystallization progress after the formation of primary agate, which caused the transformation of moganite intoα-quartz. The comprehensive utilization of chromaticity characteristics, Raman spectra and the peak intensity ratio (I501/I463) or area ratio (A501/A463) can be used to distinguish “Beihong Agate” from “Nanhong Agate”, which is also important to the identification ofagate’s origin and the tracing of unearthed relics.

The anaerobic-anoxic-oxic moving bed bio-film reactor(MBBR) process was used to treat campus sewage, and the removal efficiencies of main pollutants were studied in this paper. The composition and transformation of dissolved organic matter (DOM) in wastewater were analyzed with three-dimensional fluorescence spectra and ultraviolet spectroscopy. The results showed that the MBBR process had good purification effect on contaminants. The removal rate of COD, NH3-N, TN, TP and DOC had reached 83.0%, 70.9%, 52.4%, 59.2% and 62.2%, respectively. Moreover, there were three dominant peaks in the three-dimensional fluorescence spectrogram, and their central locations were near Ex/Em=230/325 nm(peak T), Ex/Em=280/350 nm(peak S) and Ex/Em=350/440 nm(peak R), respectively. The main fluorescent material in sewage included tryptophan (Trp), soluble microbial products (SMP) and humic acid (HA). The central position and fluorescence intensity of the peaks had changed along the MBBR process, indicating that the composition and concentration of DOM vary with the treatment process. The peaks of Trp and SMP almost disappeared after treatment, which revealed that MBBR process has remarkable effect on the removal of proteinoid. Meanwhile the fluorescence intensity of SMP in anaerobic tank, anoxic tank and oxic tank decreased to 37.1%, 20.3% and 13.1%, respectively, showing that the microorganism can well decompose SMP in each biochemical stage of the MBBR process. However, the fluorescence intensity of HA decreased slightly, and the process seemed to be ineffective on removing of humic acid. In addition, the vales of HIX, FI and BIX had gradually increased and the metabolism of microorganisms played an important role in the degradation of contaminants in the MBBR process. The humification and maturity of DOM had gradually enhanced with HIX during MBBR process. The FI values of wastewater from anoxic tank, oxic tank and effluent were about 1.9 (1.899, 1.881, 1.887, respectively), implying that the humus organic compounds in sewage were mainly derived from microbial metabolic activities. And the BIX values of DOM in anoxic pond, oxic pond and effluent were around 1.0 (0.985, 1.018, 0.979, respectively), indicating that other DOM in wastewater were also mainly caused by microbial metabolism. Besides, the value of E250/E365 had decreased gradually along the process but SUVA254 had risen continuously, which illuminated that the composition and content of DOM in sewage had changed significantly after being treated by the MBBR process. And the conjugate unsaturated double bonds of organic matter had increased, as well as the degree of polymerization, humification and molecular mass.

The gross alpha activity assay generally serves as one of screening approaches on gross radiation level to avoid the cumbersome radionuclide identification at low radioactivity level. Since the radioactivity investigation in China showed that the gross alpha activity in drinking water maintained a low level, the high-efficient quality control in laboratory should be provided in gross alpha activity assay to guarantee the accuracy. In this experiment, the low-background gross alpha/beta counter was employed with alpha scintillation as probe to detect the gross alpha activity in drinking water. The energy of alpha particles emitted from analyte sample was absorbed by alpha scintillation and transferred to organic scintillation with fluorescence emission on the probe, by which the nuclear radiation was converted into the flicker of fluorescence. The number of flickers is proportional to the number of kernel decay per unit time for counting the alpha particles numbers emitted from analyte sample layer. At first, the electroplating source with alpha particle emissivity of 2~20 particles numbers per second in 2π direction vector was used to determinate the alpha background counting rate (CPS). Then the alpha background count, the detection efficiency of work source (η), the common background beta to alpha ratio (Fα) were optimized in the experiment. Upon these optimized parameters, the standard source counting rate (ε) was calculated by fitting calibration curve. Finally, combining the value of CPS and ε, the gross alpha volume activity in controlled water samples was calculated as mathematical model. Based on these data statistics, the quality control on alpha background count, alpha count of standard source and the gross alpha volume activity were investigated for evaluating the influence of CPS and ε on the quality control of gross alpha volume activity assay. The results showed that while the spread sample with the density of 4 mg·cm-2 was placed for 24 h and measured with 239Pu as work source and 241Am as standard source for 60 min, the CPS could be obtained as 0.000 37 s-1 upon the η of 94.34%, the Fα of 0.41% and ε of 7.25% (Y=1.323X-5.285, R2=0.991 5). And the alpha backgrounds of 40 blank panel samples count over the range of -1.61~5.82. Among these samples, 33 of samples count in the controlled scope of upper auxiliary limit (UAL) and lower auxiliary limit (LAL). 2 of samples count in the controlled scope of upper warning limit (UWL) and UAL. 3 of samples count in the controlled scope of lower warning limit (LWL) and LAL. 2 of samples count in the controlled scope of UWL and upper control limit (UCL). The alpha background was well controlled. And the alpha particle numbers of 24 standard source samples count over the range of 523.7~644.3. Among these samples, 14 of samples count in the controlled scope of UAL and LAL. 5 of samples count in the controlled scope of UWL and UAL. 5 of samples count in the controlled scope of LWL and LAL. The alpha count of standard source was well controlled. Moreover, the gross alpha volume activity of 20 drinking-water samples distributed over the range of 0.007 91~0.057 86 Bq·L-1. Among these water samples, 11 of samples dispersed in the controlled scope of UAL and LAL. 5 of samples dispersed in the controlled scope of UWL and UAL. 3 of samples dispersed in the controlled scope of LWL and LAL. Only one sample dispersed in the scope of LWL and lower control limit (LCL). The gross alpha activity detection was well controlled in drinking-water samples. Therefore, while the gross alpha activity at low level was detected by using alpha scintillation probe, controlling two main uncertainty source of alpha background and standard source counting rate was an effective strategy in quality control of gross alpha activity assay in laboratory.

Raman spectroscopy equipment comes into use in the front line of public security gradually, which is mainly used for the detection of inflammable, explosive and easily-made drug chemicals. However, workers without professional knowledge may not be able to perform detection in full accordance with the best conditions. Frequent problems such as defocusing, offsetting and short sampling time may cause a great influence on the final comparison. In this article, five mainstream machine learning algorithms were used to train and classify the original data collected during the actual inspection and handling of the case. Also, the accuracy comparisons was given in this paper. According to the result, algorithm with the best accuracy will be used to improve the Raman spectroscopy in the future. The collected data were all from the EVA3000 Raman spectrometer developed by the Third Research Institute of the Ministry of Public Security. The spectrometer had been equipped in certain provinces, cities, prefectures and counties across the country. Front-line inspection personnel would periodically transmit the raw data back to the EVA3000’s back office management system. Through the management system, the raw data generated during the actual inspection was collected. A total of 160 cases including phenylacetic acid, methylene chloride, ephedrine and nitrobenzene, which had been qualitatively determined, were randomly extracted from the uploaded database. The 40-, 60-, 100-, 150-, 200-, 300-, 500-time trainings and predictions with decision trees, random forests, AdaBoost, support vector machines and artificial neural networks were executed to calculate average accuracy respectively. From the experimental results, we can see that among the five learning algorithms, the ranking of the prediction accuracy to actual samples is roughly random forest≈AdaBoost>decision tree>SVM>ANNs. The verification results are generally consistent with the experimental ones. The accuracy of random forest is similar to AdaBoost because both algorithms constantly build new training data sets from the original ones and improve the weight of the wrong samples in the next training. On the other hand, SVM and ANNs are perceptron-based algorithms. It can be seen that in the current mainstream algorithms, bootstrap aggregating method is more suitable for the sampling training of actual samples. In the next step, the research team will continue to optimize existing algorithms and implement them in the back office management system for on-line detection. The results of this paper are of great significance for further using machine learning algorithms to the practical applications in the field of the front line of public security.

The excessively high concentration of organic matter in water poses a great harm, which not only causes serious environmental pollution, but also harms human health. The traditional chemical method for detecting COD(Chemical oxygen denmand, COD) in water usually takes a long time, which is not conducive to rapid quantitative detection of COD in water. In order to solve these problems, a rapid and quantitative detection of COD using a combination of UV spectroscopy and combined weight models is proposed in this paper, the prediction model is based on the backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS) algorithm for screening the characteristic Intervals of UV spectra, and then based on the weights of the characteristic Intervals, a combination weight concentration prediction model is established. In this paper, 45 samples of COD standard solution are experimented; The first derivative and S-G screening of COD UV spect rum date are preprocessed to eliminate baseline drift and environmental noise; The SPXY algorithm is used to divide the experimental data sets into calibration sets and prediction sets. Then, the wavelength of the whole spectral range is screened based on the BiPLS algorithm. In the process of BiPLS screening, the selection of the number of target interval division will have a great influence on the model, so the number of Interval divisions is optimized, subintervals are divided into 15 to 25, and PLS modeling is performed under different interval numbers. The optimal subinterval number is selected by cross-validating root mean square error (RMSECV). When the number of intervals is 18, the effect of the model is the best. 6 characteristic wavelengths are selected from 18 wavelengths. The selected Intervals are 2, 1, 3, 11, 7, 6, and the corresponding wavelengths are 234~240, 262~268, 269~275, 290~296, 297~303, 304~310 nm, respectively. These 6 characteristic wavelength ranges cover a large amount of spectral information and contribute greatly to the final prediction model. Then, these 6 regions are further screened and combined through the SiPLS algorithm, PLS models with different characteristic intervals are constructed using different combinations under the same combination number, the optimal results of an interval combination number are screened out, and the error and correlation of the prediction models under different combinations are compared, the 6 interval are combined into 3 characteristic wavelength intervals, which are 234~240, 262~275 and 290~310 nm respectively. The optimal factor of the optimal PLS model for these three characteristic intervals is 4, 4 and 3, respectively. The characteristic interval combination method of the traditional SiPLS is improved, and the three characteristic intervals are linearly combined based on the weight value instead of the previous direct combination of characteristic intervals. The weights of these three characteristic intervals are calculated by the weight formula as 0.509, 0.318 and 0.173 respectively. Finally, a linear combination weight COD concentration prediction model is established. In order to verify the accuracy of the combined weight prediction model, a PLS prediction model over the full wavelength range, a PLS prediction model with a single characteristic wavelength interval, and a PLS model directly combining characteristic wavelength intervals are established, and the square of the correlation coefficient of the evaluation parameter (R2), the root mean square error of the predicted value and the true concentration value (RMSEC) as well as the Predicted recovery (T) are used to evaluate the model. Compared with other predictive models, the verification results show that the square of the correlation coefficient of the combined weight model reaches 0.999 7, which is obviously higher than the 0.968 0 of the direct combined characteristic interval model, the prediction root mean square error is 0.532, which is more than the prediction of the direct combination characteristic intervals. The model error is reduced by 29.3%, the predicted recovery rate is 96.4%~103.1%, which significantly improves the prediction accuracy. The method is simple and feasible without generating twice pollution, which can provide some technical support for on-line monitoring of COD concentration in water.

The fluorescence characteristic of small molecular organic compound in nature has potential application value for plant species identification. At present, the identification of two species about Dalbergia cochinchinensis Pierre and Dalbergia oliveri Prain based on the anatomical characteristics of wood has its limitation. So, we explored a new method for identification of Dalbergia cochinchinensis Pierre and Dalbergia oliveri Prain by fluorescence spectroscopy in the paper. The fluorescent substances were completely extracted in five different wood samples of Dalbergia cochinchinensis Pierre and Dalbergia oliveri Prain. Fluorescence photos, two-dimensional and three-dimensional fluorescence spectrum were used to confirm the species and quantity of fluorescent substances in wood samples of the two species. The results of fluorescence photograph shown that the extract solutions of Dalbergia cochinchinensis Pierre and Dalbergia oliveri Prain both present obvious fluorescence characteristics and the fluorescence substances are stable in two years of storage. Two-dimensional and three-dimensional fluorescence spectrum exhibit the fluorescence characteristics of the two species in more detail. The results indicate that there is a fluorescent substance in the extract solution of Dalbergia cochinchinensis Pierre, and the maximum excitation and emission wavelength of the fluorescence substance are 297 and 327 nm, respectively. However, the fluorescence spectrum of extract solution from Dalbergia oliveri Prain present two maximal excitation and emission wavelengths respectively, the maximum excitation is 317 and 333 nm, and the maximum emission is 342 and 358 nm, which indicates that there are two kinds of fluorescent substances or one kind of fluorescence substance containing two groups of fluorescence. The respective quantity and characteristics of fluorescent substances of the two species are obviously accordant, and there are obvious differences between the two species, which can be used to confirm their identities. Moreover, the extracting methods of fluorescent substances from the wood sample of the two species are built in this paper. Acetonitrile is an optimal reagent for the extraction of fluorescent substance in the wood sample of Dalbergia cochinchinensis Pierre, and the mixed reagent prepared according to the volume ratio of ethyl acetate and acetonitrile equals 10∶1 is more suitable for that of fluorescent substance in Dalbergia oliveri Prain. The extraction of fluorescent substance is beginning to level off within 12 hours for the wood samples of Dalbergia cochinchinensis Pierre and Dalbergia oliveri Prain. The results indicate that the fluorescence spectroscopy technique can be used to identify Dalbergia cochinchinensis Pierre and Dalbergia oliveri Prain. The method will be of very important value to the identification of rosewood species.

Oleanolic acid (OA) and ursolic acid(UA) are isomeric pentacyclic triterpenoid compounds, which share the similar physical and chemical properties but a little bit different pharmacological activity. At present, various chromatography and mass spectrometry methods have been used to discriminate OA and UA isomerically, but there seems no fluorescence spectroscopy-based method reported. This paper presents a method for the identification of OA and UA isomers using fluorescence quenching. Firstly, two common serum albumins, bovine serum albumin (BSA) and human serum albumin (HSA), interacting with OA and UA are examined, respectively. Experimental result shows that OA and UA can effectively quench fluorescence emission of BSA and HSA. The result of obtained fluorescence quenching data shows the bi-molecular quenching constants of BSA (or HSA) and OA (or UA) are much higher than the maximum scatter collision-quenching rate constant generally observed for any quencher against biopolymer (2.0×1010 L·(mol·s)-1), indicating the type of quenching is static quenching, namely, OA and UA can form stable complexes with BSA and HSA. Double logarithm equation was also applied to the fluorescence quenching data and binding site numbers of the complexes formed by OA or UA and BSA or HSA are obtained between 0.90 and 1.26, indicating all complexes are 1∶1 type. The apparent binding constants (KA) of BSA-OA and BSA-UA complexes are at the same order of magnitude, but those of HSA-OA and HSA-UA are very different. The KA of HSA-UA is 124.91 times higher than that of HSA-OA, indicating HSA-UA has much higher stability. Synchronous fluorescence experimental result shows that the spectrum at 60 nm wavelength difference (Δλ) can be quenched more effectively than that of 15 nm Δλ alongwith the addition of OA and UA, showing the binding sites of OA and UA at HSA may beclose toTrp residues. The results of molecular docking simulation show that both OA and UA are docked at a hydrophobic cavity in HSA, and there is strong hydrogen bonding and hydrophobic effect between the host and the guest. In addition, three hydrogen bonding interaction are found between OA and Arg218, His242 and Pro447 residues of HSA with bond lengths of 2.95, 2.97 and 3.17 , respectively. There are seven amino acid residues of HSA that has hydrophobic effect with OA including Lys195, Lys199, Trp214, Arg222, Leu238, Asp451 and Tyr452. Whereas UA forms three hydrogen bonds with Trp214, Arg218 and Lys444 residues of HSA with bond length of 3.01, 2.88 and 2.65 , respectively. Nine amino acid residues including Leu198, Gln221, Arg222, Asn295, Val343, Pro447, Cys448, Asp451 and Val455 have hydrophobic effect with UA. Since UA has stronger hydrogen bonding and more hydrophobic effect with HSA, it is more spatially fitted to the HSA’s hydrophobic cavity. Thus, according to the fluorescence spectral data and molecular docking results, the stability difference of HSA-OA and HSA-UA may be responsible for discrimination of OA and UA isomers.

Mine water inrush has been threatening the safety of underground construction personnel, so an accurate and rapid identification of mine water inrush source plays a key role in the safe production of the mine. Identification of mine water inrush source by laser induced fluorescence spectroscopy effectively avoids the shortcomings of conventional hydrochemical methods which need to determine a variety of chemical parameters and the identification time is too long. In this paper, a method of interval PLS (iPLS) and particle swarm optimization combined with support vector classification algorithm (PSO-SVC) is proposed. The iPLS algorithm is often used in spectral bands optimization and regression analysis of models, and the PSO-SVC is an important application in the field of machine learning. The laser induced fluorescence spectroscopy (LIF) technology has the characteristics of fast time response and high measurement accuracy, and the iPLS and PSO-SVC algorithms are applied to the analysis of spectral maps and spectral data, and then it can identify and classify water inrush sources. Firstly, The 210 sets of fluorescence spectrum data of 7 kinds (30 groups of each water sample) collected from Huainan mining area were used for experiment, and differences of laser-induced fluorescence spectra of mixed water samples with different volumetric ratios of old-kiln water, limestone water, limestone water and air water were analyzed. The classification accuracy of PSO-SVC model obtained by hold-out and Kennard-Stone partitioning was compared, and the training set water samples (140 groups) and test set water samples (70 groups) obtained by hold-out were used as experimental samples. Secondly, the full spectrum bands were divided into 10~25 bands by using the iPLS algorithm, and the band whose RMSECV(cross validation root mean square error) value is less than RMSECV value(threshold) of full spectrum bands was selected as the characteristic wave bands, and the results of modeling with 10 and 14 sub intervals were compared with spectrogram. It is found that there were errors in the characteristic bands selected by direct observation and the iPLS algorithm. Finally, under the condition of no pretreatment such as denoising and dimension reduction, the statistical data of evaluating indexes for dividing different interval numbers according to iPLS were obtained, and the data of 410.078~478.424 and 545.078~674.104 nm characteristic wave bands with 561 wavelength points selected from 11 regions were used as the input of PSO-SVC model. we compared with full spectrum bands and direct observation bands, and the classification accuracy of the training set and the test set was as high as 100%. The optimal penalty coefficient C of PSO is 1.367, and the kernel function parameter g is 0.576 2. It can be seen from the experimental results that it is feasible to select the characteristic wave bands of the fluorescence spectrum by using iPLS, and the extracted characteristic wave bands can fully reflect the effective information of the full spectrum bands, and it provides a theoretical basis for the application of laser induced fluorescence spectroscopy in the accurate on-line identification of mine water inrush source.

Heat value is one of the important parameters of coal quality, and it greatly affects the operation of coal-fired boilers. In order to overcome the shortcomings of traditional detection methods, laser induced breakdown spectroscopy (LIBS) was applied to quantitative analysis of heat value in coal. The structure of coal is complex. It contains many types of elements, including major, minor and trace elements, which would result the complexity of LIBS spectral information from coal. It is premise and foundation to effectively extract LIBS spectral information and achieve accurate quantitative measurement by using LIBS. In recent years, as the development of artificial intelligence technology, relevant analytical techniques have also been applied to the proximate analysis and heat value prediction of coal. In order to realize the effective extraction of LIBS spectral information in coal samples and overcome the problems such as transient fitting and poor convergence that are easily caused by conventional analytical methods, the K-fold cross validation (K-CV) parameter-optimized combined with Support Vector Machine (SVM) regression was proposed to quantitatively analyze the heat value in coal. The SVM method is an approximate realization of structural risk minimization, which can be used for pattern classification and nonlinear regression. 44 coal samples with different heat values commonly used in the power plant were selected as experimental objects, 33 of which were selected as training sets, and the remaining 11 were test sets. The correlation between the parameters of the SVM regression model-penalty factor C, the kernel function parameter g and the model accuracy were firstly analyzed based on the laser-induced coal spectrum and the best search scope for C and g were determined. Then the SVM regression model was established based on the LIBS full-spectrum and some typical elements (non-metallic elements and metal elements) feature spectra, respectively. The optimal parameters C, g of the heat value SVM regression model is obtained by using the training set spectral data, combined with the K-CV method. The spectral features of the prediction set as input are to test the reliability of the model. The calibration model established by the full spectrum, non-metallic element characteristic spectrum, as well as non-metal and metal element characteristic spectrum, respectively, could all reached 0.99, with the mean square error of 0.12 , 0.17 and 0.06 (MJ·kg-1)2, the forecasted average relative deviations were 1.2%, 1.23% and 0.69%. The results showed that the SVM regression method based on K-CV parameter-optimized could be used for quantitative analysis of coal heat value using LIBS technology, and could obtain higher analysis accuracy. At the same time, by comparing the quantitative analysis models using different spectral features, the quantification model of heat value by using the characteristic spectrum of non-metal plus metal elements, can effectively improve the accuracy of LIBS in the rapid detection of heat value in coal. This method can achieve accurate prediction of heat value in coal.

Pesticide residues on fruit surface are seriously harmful to human health. The conventional detection methods need sampling and processing, which are time-consuming and laborious. Laser induced breakdown spectroscopy has the ability of multi-element analysis and in situ measurement, and has great potential in the detection of pesticide residues on fruits. However, poor detection sensitivity limits the application of this technology to the detection of trace harmful elements on the surface of fruits. Improving the detection ability of laser induced breakdown spectroscopy is a hot research area. The enhancement effect of nanoparticle surface enhanced technology on the LIBS of chlorpyrifos residues on apple surface was studied in this paper. The metal nanoparticles were applied on the surface of the tested samples, and the induced atomic emission spectra were measured by laser induced breakdown spectroscopy. Through the study, it was found that the enhancement method of the metal nanoparticles can enhance the spectral peak intensity of the pesticide residue on the apple surface. The experimental results showed that the characteristic peak of phosphorus in the pesticide of chlorpyrifos increased by 5 times after the apple surface was applied metal nanoparticles. The application of this method is of great significance to the improvement of detection ability of trace harmful elements on the surface of fruits and vegetables. We then optimized the enhancement effect of metal nanoparticles. The enhancement ability of gold nanoparticles and silver nanoparticles, and the effect of particle size on the enhancement effect were studied. By comparing the enhancement effect of 20 nm gold nanoparticles, 20 nm silver nanoparticles and 80 nm silver nanoparticles, it was found that the enhanced effect of 80 nm silver nanoparticles on the pesticide spectrum of chlorpyrifos on the apple surface was the best. The effect of signal acquisition delay time on spectral signal-to-noise ratio (SNR) of laser induced breakdown spectroscopy system was studied, and it was found that the delay time of 0.2 μs can achieve an ideal signal-to-noise ratio. On the basis of the above study, using the optimal experimental parameters (80nm silver particles and 0.2 μs delay time), the quantitative analysis of chlorpyrifos residues on the surface of Apple was carried out by using the peak intensity of phosphorus in chlorpyrifos at 213.62, 214.91, 253.56 and 255.33 nm. The LIBS spectra of chlorpyrifos residues at concentrations of 30, 20, 15, 12, 10, 6 μg·cm-2 were collected respectively. Then, the four characteristic peaks of phosphorus were used to quantify the curve fitting. It was found that LIBS had good quantitative predictive ability for residual chlorpyrifos, and the R2 was above 0.89. According to the quantitative fitting curve, we discussed the detection limit of nanoparticle-enhanced LIBS. It was found that the detection limit of chlorpyrifos on the apple surface can be as low as 1.61 μg·cm-2. This study proved that metal nanoparticles can significantly improve the sensitivity of LIBS to pesticide residues on apple surface.

Because of great physiological changes during pregnancy, pregnant women can easily become a high-risk group with the lack or excess of trace elements, resulting in obstruction of normal body activities and abnormalities, especially in the second trimester, fetal function development will rapidly deplete maternal trace element reserves. Meanwhile, low-selenium regions are the high incidence areas of various endemic diseases. In this study, the contents levels of 12 trace elements (Al, Ca, Cd, Cu, Fe, Mg, Mn, Ni, Pb, Se, V and Zn) in hair samples, which were collected from 156 women of child-bearing age (non-pregnant and in the second trimester) living in the 5 townships in Ejin Horo Banner (Ordos City, Inner Mongolia), the influence factors and interaction between Se and other elements were studied by using the inductively coupled plasma-atomic emission spectrometry (ICP-AES) and atomic fluorescence spectrometry (AFS). The results showed that (1) there was a deficiency of Se in all the hair samples, which were related to the fact that the study area was located in China’s low-selenium region. In addition, 92%, 95%, 35% and 60% of women had an lack of Mn, Fe, Cu, and Mg, respectively. It was recommended to increase the production of Se-enriched foodstuff such as wheat, Brazil nuts, garlic, apples and persimmons, and the food of seafood, nuts, and lentils which were rich in Mn, Cu, Mg, and Fe. Whereas 80%, 52% and 34% of women had an excess of V, Cd and Pb, respectively. (2) The content levels of Al, Ca, Cd, Cu, Fe, Pb, V and Se in hair samples of the second trimester women were all lower than those in the non-pregnant group. This may be due to the fact that more nutrition is required for rapid growth and development of the fetus in the second trimester. (3) It is noteworthy that there were two significant positive correlations not only between Se content and age in non-pregnant women but also between Cu content and age in the second trimester. In addition, there was no significant correlation between Ca and Mg in all women in the study area, which may be related to a Ca/Mg ratio higher than 10. (4) We found that there was a significant correlation between Cd-Pb, Ni-Pb and Ni-Cd in the non-pregnant group and the second trimester group, indicating that Cd, Ni, and Pb might have the same source and there was a higher exposure risk in the study area.

X-ray fluorescence (XRF) core scanning has the advantages of simple pre-processing, non-destructive and high precision, and it has been widely used in the high-resolution environmental change studies of oceans, shallow lakes and loess. However, the hydrodynamic processes and the material translocation and transformation of deep lakes are apparently different from shallow lakes, and the research of XRF continuous scanning in deep lake is still relatively few. Based on the analysis of XRF elements continuous scanning of Fuxian Lake sediments, located in the central Yunnan plateau basin, the characteristics of geochemical elements and its environmental significance have been discussed, combined with comparative analysis of XRF core scanning and inductively coupled plasma optical emission spectrometry (ICP-OES) and mass spectrometry (ICP-MS) method, chronology and other environmental proxies. The results obtained are as follows: (1) There is a close correlation of geochemical elements between XRF core scanning and the ICP-OES/ICP-MS methods (p<0.01), except for the element P, Ge and Cr. The correlative coefficient of K, Ca, Ti, Fe, Rb and Zr is more than 0.85. (2) The result of principal factor analysis shows that the variance contribution rates of principal component 1 and 2 are 76.31% and 10.37%, respectively. The principal component 1 is much greater than component 2. The factor loading of K, Fe, Ti, Zn, Rb and Zr exceed 0.9. It indicates that the erosion intensity of Fuxian Lake catchment is the main control factor of the deposition process, and the exogenous detrital elements have a better indicative implications for regional environment change and human activity intensity. (3) Based on the geochemical element analysis by using XRF core scanning and other climate proxies of the lacustrine sediments, the environmental changes and human activities of the central Yunnan plateau basin have been reconstructed since 5 200 cal. a BP. The intensified human activity (e. g. agricultural farming, extensive deforestation) caused a heavy loss of soil of the lake catchment since 2 200 cal. a BP., and the significant increased element contents (e. g. Ti, Rb, Zr) indicated that a large amount of exogenous detritus had been carried into the lake basin. The results of this study can provide references and experience for the application of XRF technology in deep lake, and it will be helpful for the study of high-resolution environmental change and human activities in the Asian southwest monsoon.

Soil environmental safety is of great significance. When soil is contaminated by heavy metals, it will affect the safety of crops and foods and endanger human health. Therefore, it is particularly critical to look for ways to rapidly and efficiently measure heavy metal pollution in soil. Traditional chemical analysis methods have some disadvantages such as complicated process, time-consuming and labor-consuming. Hyperspectral remote sensing has obvious advantages in environmental monitoring and other applications because of its high spectral resolution, large amount of information, and rapid losslessness. Due to the complex reflection and radiation process of electromagnetic remote sensing signals, the soil hyperspectral data acquired by the instrument is difficult to directly interpret the information of heavy metal pollution. Therefore, it is very important to find out a method that can effectively excavate heavy metal pollution information in soils. The soil physicochemical properties will change because of different concentrations pollution of Cu, causing slight changes in the soil spectrum, the purpose of this study is to identify, extract and analyze the characteristics and weak difference information in the spectrum of Cu contaminated soil, and then tap the heavy metal pollution information in the spectra. In this paper, the continuum removal(CR) was used to preprocess the spectrum, the LH-PSD analysis model for analyzing soil spectra was constructed bydefining local maximum mean (LMM) and half wave height(HWH), combined with Short-time Fourier transform (STFT) of the time-frequency analysis method and power spectral density (PSD). The extremely similar soil spectrum was processed by the LH-PSD model, and the PSD distribution map visualized the faint differences between the spectra, and significantly distinguished the similar spectra, which verified the ability of the model to discriminate spectral features and weak differential information. At the same time, this model was used to extract and analyze heavy metal pollution information from experimental soil spectra with different Cu pollution gradients. The results of the study show that CR can plan the spectrum to the same background and highlight the differences between spectra, LMM and HWH of LH-PSD detection model can effectively extract the characteristics of the difference between the spectra and appear in a ladder. The visualized PSD map obtained after the model processing can directly and qualitatively discriminate whether the soil is contaminated by heavy metal Cu. Specifically, when the soil is contaminated by heavy metal Cu, at the same sampling frequency, the PSD distribution at frequencies of 100 and 600 Hz will be obviousvacant separation, with the increase of Cu pollution concentration, the distribution of PSD between 100~600 Hz is gradually sparse. The energy value E can be used to quantitatively monitor the degree of soil Cu pollution. That is, as the concentration of Cu in the soil increases, the E value decreases, and the correlation coefficient with the Cu content reaches -0.910 5, which is significantly correlated. In order to test the reliability of the model, the soil spectra of the planted corn crop was combined and analyzed by the LH-PSD detection model. The result of the visualized PSD map was basically similar to that in the experimental analysis, The correlation coefficient of the energy value E with Cu content in soil reaches -0.973 9, which has a significant correlation. The monitoring effect is ideal and the reliability of the model is verified. Therefore, through the LH-PSD analysis model, the dissection of soil spectrum from the spectral domain to the time-frequency domain provides a new idea for deepening the spectral features and weak information of heavy metal pollution spectra.

In the field of soil digital mapping, precision agriculture and soil resource investigation, the study of aerial hyperspectral data to provide scientific prediction results by aerial hyperspectral have become the focus of research, especially in the case of black soil rich in nutrients in Northeast China. Compared with the main components of soil in the black soil, selenium is a trace element, whose effect on the normal growth of crops is as important as a large number of elements, and it is also a necessary nutrient element for human. In this paper, an indirect extraction model based on the main component is created for the retrieval of selenium content. This model can significantly increase the regression coefficient of selenium content and reduce the error between the measured value and the predicted value. The data source is CASI-1500 aerial hyperspectral imaging system with a spectral range of 380~1 050 nm, and a spatial resolution of 1.5 m. 60 soil samples were collected from the Jiansanjiang area of Heilongjiang. The data of selenium, organic matter, total iron, pH and calcium oxide content were obtained. The BP neural network was selected to establish the inversion model of spectrum and content. In addition, the law of spectral change in the visible and near infrared range of different content of black soil composition was analyzed, and the rule that the spectral reflectance would increase gradually as the content of selenium increased. However, when the selenium content was low, the law would gradually weaken until the other components are disturbed. The spectral characteristics of organic matter were opposite to that of selenium. In general, the reflectance decreases as the content increases, which is closely related to the spectral properties of organic matter. The spectra of the total iron showed similar laws with the organic matter spectrum, indicating that the two have high correlation. The spectral characteristics and detection values of different pH values and calcium oxide contents did not show obvious characteristics, and the law of reflection was not obvious. The correlation coefficients of nutrient contents in different nutrient contents of 60 sampling points were obtained by bands. The results show that the correlation coefficient of each band of pH is the highest, the mean value is 0.63, the second is the correlation coefficient of total iron, 0.54, the correlation coefficient of organic matter and calcium oxide is close to 0.42 and 0.47, while the average correlation coefficient of selenium element content and bands is the lowest, which is 0.38. The first 5 bands with higher correlation coefficients are selected as modeling bands. The characteristics of selenium are 447, 437, 456, 466 and 475 nm; the characteristic bands of organic matter are 447, 456, 466, 437 and 475 nm; the characteristic bands of the whole iron are 752, 695, 800, 762 and 733 nm, and the characteristics of pH are 905, 752 and 695 nm. By calculating the correlation coefficient of sample point selenium content and other components, selenium has a positive correlation with organic matter, and the correlation coefficient is 0.79. The correlation coefficient is negatively correlated with total iron, pH and calcium oxide, and the correlation coefficients are -0.80, -0.94 and -0.69, respectively. In view of the high precision of the inversion of organic matter, total iron, pH and calcium oxide, while the content of selenium is low and the accuracy of direct inversion is insufficient, a method of extracting the functional relationship of selenium elements by extracting the content of four components is designed, and the content of selenium elements is indirectly retrieved. First, the five components and characteristic spectra are analyzed by using neural network, and the regression coefficients R2 and RMSE of each component are calculated. It is concluded that total iron and pH have higher inversion accuracy, while organic matter and calcium oxide coefficient are lower than 0.8, but they are also significantly higher than those of selenium. A regression model for the content of selenium and other four components was obtained, and Se=0.522 9+0.041 8 Som-0.016 6 Fe2O3-0.035 6 pH-0.005 CaO. The selenium element was extracted indirectly, the regression coefficient increased from 0.516 to 0.724, the root mean square error was reduced from 0.182 to 0.136 based on this model, which improved the accuracy of the selenium content inversion, and provided a new technique for the precise mapping of selenium elements in a large scale.

Salmon is expensivebut popular among consumers because of its good-taste, sweet flavor and high nutritional values. The import volume of salmon in 2017 reaches 350 million dollars. The problems of selling shoddy salmon for quality salmon by unscrupulous businessmen, who are pursuing high profit only, become more and more serious. The problems can be mainly manifested by the following steps: (1) Using fresh water rainbow trout with low price and similar appearance like Amur salmon, Pacific salmon to masquerade Norwegian salmon that of high price and high consumer acceptance; (2) Replacing high cost and high quality fresh salmon (stored in 0～4 ℃, with short shelf life, on ice crystal produced and longest maintaining flavor and taste) with low cost and low quality frozen-thawed substitute (stored in -18 ℃, with long shelf life, destroyed organizational structure by ice crystal and destroyed flavor); (3) Selling stale salmon as the fresh ones. Therefore, considering the disadvantages of big error in sensory detection of salmon quality as well as the time consumption in physical and chemical testing, the article intends to research a fast identification method for genuine and counterfeit salmon, fresh and frozen-thawed salmon as well as fresh and sub-fresh salmon based on near infrared spectral characteristics. Firstly, genuine and counterfeit salmon samples were taken from Norwegian salmon and fresh water rainbow trout, Amur salmon, Pacific salmon; fresh and frozen-thawed salmon samples were taken from fresh salmon with chilling for 1, 3 and 5 d and frozen-thawed salmon with frozen for 15, 30 and 45 d; fresh and sub-fresh salmon samples were taken from fresh salmon with 0, 2, 4, 6 and 8 d storage. Secondly, NIRs information was collected, meanwhile, the salmon with different storage days were analyzed by national standard method for determination of the TVB-N. Thirdly, the different pre-processing methods (Standard normal variate transformation, Vector normalization, Multiplicative scatter correction, Savitzky-Golay, First derivative, Second derivative) were employed, then Principal component analysis (PCA) and Genetic algorithms (GA) were used to reduce the spectral and the excess spectral bands. Finally, K-nearest neighbors (KNN) and Least-squares support vector machine (LS-SVM) models were used for the construction of identification model of genuine and counterfeit salmon as well as fresh and frozen-thawed salmon; the prediction spectra were constructed associated with their corresponding TVB-N using Synergy Interval Partial Least Square Method (Si-PLS). Modeling results show that for genuine and counterfeit salmon, the spectral information were treated with SNV and PCA, the LS-SVM model recognition rate of the testing set is 97.50%; for fresh and frozen-thawed salmon, the spectral information were treated with SNV and PCA, the LS-SVM model recognition rate of the testing set is 98.89%; for fresh and sub-fresh salmon, the spectral information were treated with SNV, the feature spectra were associated with their corresponding TVB-N using Si-PLS, the Si-PLS model correlation coefficient of the validation set is 0.864 1, the Si-PLS model recognition rate of the testing set is 90.00%. According to research results, using combination of NIR spectroscopy and chemometrics, genuine and counterfeit salmon, fresh and frozen-thawed salmon, as well as fresh and sub-fresh salmon, can be detected quickly and non-destructively, thus realizing the rapid and multi-index detection of salmon quality.

In this paper, we used cucumber leaves disease as the research object, and identified cucumber leaves disease based on the difference of visible spectral reflectance. The support vector machine recognition is an efficient recognition method, which is always used as identification model. For cucumber leaves diseases, if we constructed the support vector machine based on digital image process, we can get accurate and efficient recognition. Consequently, this paper studied the cucumber leaves disease recognition method based on support vector machine. Firstly, the method of wavelet domain denoising was applied to image denoising. The segmentation results were compared with K mean clustering, OTSU and edge segmentation. The results showed that K-means clustering method was more accurate. We extracted texture, color and shape feature parameters, 15 feature parameters. Then, the optimal parameters of c and g were selected by cross-validation, and the parameters of the kernel function were optimized and using RBF kernel to construct SVM classifier. By comparing the linearity kernel, polynomial kernel and RBF kernel of the SVM recognition’s correct rate, we got that the RBF kernel is most accurate for the recognition of the cucumber leaf disease. Therefore, we used RBF kernel to construct SVM classifier. Finally, there was an identification model of cucumber leaf disease which was based on SVM classifier, and two other efficient identification models, back Propagation neural network, fuzzy clustering identification model. We constructed three kinds of identification models through comparing the correct recognition rate and running time. The results of the test showed that the cucumber downy mildew's correct recognition rate based on SVM classifier was 95%. The correct recognition rate of cucumber powdery mildew and brown spot was 90%, and the average diagnosis accuracy was 92%. In addition, the method running time was the shortest. In summary, the results show that, among the three recognition methods, cucumber leaves disease recognition based on the SVM classifier is the most suitable, demonstrating that the method can be used to rapidly identify cucumber leaves diagnosis based on visual spectrum.

Remote sensing is the main approach to carry out non-invasive detection of plant chlorophyll information on the ground/near ground, airborne and spaceborne levels. At present, spectral index for multi-band calculation has been widely used in empirical/semi-empirical estimation of canopy chlorophyll content. Taking the difference of leaf inclination angle distribution (LAD) between different crops and different varieties of homogeneous crop into consideration, this study analyzed the influence of LAD on retrieving chlorophyll content, and selected chlorophyll-related spectral indices that are insensitive to the variation of LAD and researched canopy chlorophyll retrieval model. PROSAIL radiative transfer model was used for simulating the canopy reflectance corresponding to different leaf chlorophyll content (LCC), leaf area index (LAI) and LAD. The simulation results showed that under the same LAI and LCC conditions, the canopy reflectance corresponding to different LAD was significantly different, and the canopy reflectance decreased with the increase of the average LAD. By calculating the correlation coefficient of 12 common chlorophyll-related spectral indices with CCC, the sensitivity of spectral indices in retrieval of chlorophyll content under different LAD was evaluated, consequently, four spectral indices that are insensitive to variation of LAD were selected: MTCI, MNDVI1, CIred-edge and MNDVI8.80 measured corn samples were utilized to model and validate the estimation models of CCC. Model establishment and verification results showed MNDVI8 was the most insensitive to variation of LAD, thus it was the best spectral index for estimating chlorophyll content with the coefficient of determination (R2) of 0.70 and the root mean square error (RMSE) of 22.47 μg·cm-2. The precision of CIred-edge (R2=0.63, RMSE=24.06 μg·cm-2), MNDVI (R2=0.66, RMSE=24.07 μg·cm-2) and MTCI (R2=0.65, RMSE=26.76 μg·cm-2) retrieval model was relatively close but was weaker than that of MNDVI8. Through analyzing the retrieval results, it was concluded that different spectral indices had different sensitivities to variation of LAD. The preferred spectral indices generally had the best correlation and highest sensitivity to chlorophyll content, among which MNDVI8 was least affected by LAD and can be used to retrieve CCC in corn under different LAD types. Although retrieval capabilities of MTCI, CIred-edge and MNDVI1 were slightly weaker than MNDVI8, they were less affected by variation of LAD and also had good retrieval capabilities. This paper researched the influence of LAD on retrieving chlorophyll content based on spectral indices, and the results from measured corn data were in accordance with those from simulated data. Based on the sensitivity analysis and validation results of canopy chlorophyll content retrieval models under different LAD types, this paper has a certain reference significance for remote sensing application of estimation of chlorophyll content in crop without prior knowledge of LAD in large scale.

The analysis of canopy spectral response mechanism of crop lodging stress is an important basis for remote sensing monitoring of large-scale crop lodging disasters. Lodging stress directly change the ratio of visual stem, leaf and panicle in remote sensing spectrum detection field of view. By analyzing the relationship between canopy spectra and the ratio of visual stem, leaf and panicle, this paper explores the change regulation of visual stem, leaf and panicle components and spectral response of rice canopy under different intensities of lodging stress, and provides theoretical support for remote sensing monitoring of large-scale crop lodging disaster. Taking the real lodging rice in Xinghua City and Dafeng District of Jiangsu Province in 2017 as the research object, with the support of field observation experiment , the rule of canopy spectral variation of lodging rice with different lodging intensities was analyzed, and the correlation between the ratio of canopy visual stem, leaf and panicle and lodging angle under different lodging intensity was analyzed, and parameters of sensitive agronomy that can effectively represent the lodging intensity was screened. A response model between rice canopy spectral indices and sensitive agronomic parameters was constructed by grey relational analysis to realize the spectrum diagnosis of rice lodging disaster, and field-measured samples were used to evaluate the diagnostic accuracy. The results showed that with the increase of lodging strength, the canopy spectra showed regular changes, red-band and near-infrared band response was more obvious, “Red edge” position is obviously “blue shift”, and “red edge” amplitude and “red edge” area increase, it shows that the red-band and near-infrared band on rice lodging stress intensity is more sensitive. The correlation of the canopy visual leaf-stalk ratio and lodging strength decreased with the increase of lodging strength, which was more than 0.715, indicating that the visible leaf stem ratio of canopy was better in characterizing the lodging strength. Through correlation analysis between visual leaf-stem ratio and hyperspectral reflectance, 698 and 1 132 nm in the red and near-infrared bands were respectively selected as the sensitive bands, and then the characteristic vegetation index was calculated. The spectral response model of rice visual leaf-stem ratio based on characteristic vegetation index was constructed by using grey correlation analysis, and the determining factor for the test sample was 0.635, and the precision of the classification of the disaster level with the visual leaf-stem ratio inversion result reached 82%. Therefore, the contribution proportion of stem, leaf and panicle in the canopy of rice in the field of spectral detectors was changed regularly after lodging. The difference of spectral reflectance and the ratio of apparent field in the Miho of stem, leaf and panicle is directly reflected in the spectral difference of lodging rice canopy. While visual leaf-stem ratio can effectively characterize the population structure change of rice under lodging stress, which has a good response relationship with the lodging intensity. The response law of visual leaf-stem ratio and rice canopy spectrum of different lodging intensity can effectively distinguish the lodging intensity, which will help provide a prior knowledge for remote sensing monitoring of rice lodging at the regional scale.

Hyperspectral imaging technology has been widely used in the detection of agricultural products. This paper studies the non-destructive identification of millet samples from different regions based on hyperspectral imaging and machine learning algorithms. The millet samples from seven provinces were divided into five categories according to geographical regions. They were Dongbei, Hebei, Shaanxi, Shandong, and Shanxi, respectively. A total of 23 samples were collected in these areas, including 6 samples in Dongbei, 5 samples in Shanxi, and respective 4 samples in Hebei, Shaanxi, and Shandong. Each sample was equally divided into 10 equal parts and the hyperspectral data of millet in the wavelength band from 900 to 1 700 nm was collected using a hyperspectral imager. In order to reduce the influence of uneven illumination and dark current on the experiment, the collected hyperspectral data was corrected in black and white. The ENVI software was used to select the region of interest (ROI) of millet hyperspectral image, and 9 ROIs were selected for each sample of millet. The average spectral value in the ROI was calculated, which was used as a spectrum record of the sample. Finally, a total of 2 070 spectral curves were collected, of which 540 from Dongbei, 450 from Shanxi, and several 360 from Hebei, Shandong, and Shaanxi respectively. In order to reduce the scattering phenomenon caused by the unevenness of the sample surface, which would affect the true spectral information of millet, the multivariate scatter correction (MSC) pretreatment was performed on the original spectrum. In addition, randomized division method was used to divide the corrected spectral data into training set and test set. The ratio of test set was 0.3. Linear Discriminant Analysis (LDA) was used to visualize spectral data of millet from different origins. Substituting the test set into a well-trained LDA model, and finally a confusion matrix of prediction results was created. The results showed that LDA had a prediction accuracy of 0.84 and 0.99 for Shaanxi and Shanxi, and only 0.68, 0.68, and 0.40 for Dongbei, Hebei, and Shandong. Therefore, the recursive feature elimination (RFE) was used to select useful spectral information, remove redundant information, and improve the prediction accuracy. The RFE combined with support vector machine (SVM) and Logistic Regression (LR) were used to compare and analyze the discriminant of millet from different regions. Substituting training set of millet spectral data into SVM-RFE and LR-RFE models, and the corresponding feature subsets were selected optimally by the micro-averaging of the model F-values and 3-fold cross validation technology. The results showed that the number of wavelengths selected by the LR-RFE was 74 and the Micro_F of the model was 0.59; Meanwhile the number of wavelengths selected by the SVM-RFE was 220 and the Micro_F of the model was 0.66. The selected feature subset was applied to the test set. Substituting the test set into SVM and LR models respectively, and confusion matrix of model prediction results and the receiver operating characteristic curve (ROC) of the model were used as the evaluation method. The results showed that the accuracy of SVM-RFE prediction was 1, 0.37, 0.72, 0, and 1 for Dongbei, Hebei, Shaanxi, Shandong, and Shanxi, and the area under ROC curve (AUC) was 0.82, 0.92, 0.93, 0.70, and 0.99 respectively. The accuracy of LR-RFE prediction was 0.92, 0, 0.97, 0, and 0.80, and the AUC was 0.72, 0.74, 0.94, 0.66, and 0.88 respectively. It can be seen from the prediction results that the overall classification performance of SVM-RFE model was better than that of LR-RFE, while the discrimination of Shaanxi class LR-RFE was better than that of SVM-RFE. For the Hebei and Shandong categories, neither model could effectively discriminate it. Compared with LDA, the prediction accuracy of these two models had been improved.

Zinc element and lead element were gauged to reveal their occurrence forms and distribution characteristics inraw Calamine and calcined Calamine in order to get ready for the deducting of lead. X-ray diffraction (XRD) mothed was used to analyze the occurrence form, main carrier mineral of zinc and lead. Electron probe microanalysis (EPMA) methodwas usedto performregion-wide scanning of elements, qualitative and quantitative analysis of micro-area components of the raw and calcined Calamine. Stood on the XRD data of 21 batches samples, hydrozincite (Zn5(CO3)2(OH)6) was provedto be the main mineral species in raw Calamine, while smithsonite (ZnCO3) was occasionally. As impurity minerals, hemimorphite (Zn4(OH)2(H2O)(Si2O7), calcite (CaCO3), dolomite (CaMg(CO3)2), quartz (SiO2) were detected. For comparison with the raw Calamine, themain mineral of calcined Calaminewas zinc oxide (ZnO). The impurity minerals were zinc silicate (Zn2SiO4) and calcium Calcite (CaCO3), while zinc ferrite (ZnFe2O4) was occasionally. Zinc is the subject element of Calamine. According to 21 batches of raw Calamine, Zn existed generally as the main mineral species as hydrozincite (Zn5(CO3)2(OH)6), and the impurity mineral speciesas hemimorphite (Zn4(OH)2(H2O)(Si2O7), occasionally as smithsonite (ZnCO3). The backscatter electron images and element distribution data from EPMA of raw Calamine samples illustrated that: Zn and Pb were simultaneously existent in hydrozincite area; Zn and Si were simultaneously existent in the hemimorphite area; Ca and Mg were simultaneously existent in the dolomite area; Ca was individually existent in the calcite area. In raw Calamine samples, Pb was mainly existent in hydrozincite area, closely associated with Zn element. Quantitative inspection of electron probes in a large number of hydrozincite sites showed that, the ratio of ZnO/PbO content in hydrozincite tended to be fixed, and Pb was statistically evenly distributed in hydrozincite, verifying that Pb occurs mainly in the lattice of hydrozincitein isomorphism. But Pb content was much lower in the area of hemimorphite, calcitenor dolomite of raw Calamine, even can’t be detected. Zn and Pb in the lattice of hydrozincite were respectively transformed into ZnO and PbO after calcination. Zinc distributed continuously in calcined Calamine, mainly existing in the form of zinc oxide and less of zinc silicate (Zn2SiO4). Lead distributedin isolation in calcined Calamine, mainly existing in the form of lead oxide, which no longer has correlation with zinc element. Calcination destroyed the lattice structure of hydrozincite, broke the symbiotic state of zinc and lead in Calamine, changed the distribution characteristics of the elements and enhanced the feasibility of deducting lead.

In this paper, a novel highly sensitive fluorescent aptasensor was constructed and used to detect ochratoxin A based on gold nanoparticles/aptamer/amino-functioned carbon quantum dots by using self-assembly. Gold nanoparticles/aptamer/amino-functioned carbon quantum dots were prepared as following. First, thiol-modified aptamer was attached to the surface of the gold nanoparticles in pH 3.0 tartaric acid-HCl buffer to form gold nanoparticles/aptamer by assembling. Second, amino- functioned carbon quantum dots were added to the gold nanoparticles/aptamer dispersion to form gold nanoparticles/aptamer/amino-functioned carbon quantum dots under electrostatic interactions in phosphate buffer solution (pH 7.0), by removing the excessive amino-functioned carbon quantum dots with centrifugation. The fluorescence of the amino-functioned carbon quantum dots was efficiently quenched by the gold nanoparticles, which are excellent quencher for fluorescence sensing as they have very high molar extinction coefficients and broad energy bandwidth. The fluorescent intensity of the quenched system was background fluorescence (F0). When ochratoxin A was addition to the fluorescence quenched system, the specific reaction between aptamer in the nanocompostes and ochratoxin A took place, simultaneously, amino-functioned carbon quantum dots were released, and a turn on amino-functioned carbon quantum dots fluorescence signal (F) was detected. The emission intensity increase (F-F0) could be used for the quantification of the amount of ochratoxin A in samples. The influence facts on the sensor performance were investigated including the molar ratio of gold nanoparticles and Apt, pH and incubation time. The optimum conditions were gold nanoparticles∶aptamer=1∶190 in molar ratio, pH 7.0 and incubation time was 6 minutes. Under the optimum conditions, a linear fluorescence signal response to ochratoxin A concentration was over a wide ochratoxin A concentration range of 0.005～1.00 ng·mL-1. The linear regression equation is: F-F0=6.499+211.6 ｃ(ng·mL-1), linear correlation coefficient is: r=0.995 5 with a diction limit of 3 pg·mL-1 according 3σ/k (σ: relative standard deviations, k: slope of the working curve). The recovery was between 93.3%～108.9% in real samples, and the relative standard deviation was less than 5%. The proposed method was employed to detect ochratoxin A in beer samples, the results showed that ochratoxin A was found in 6 of 13 beer samples, with a positive rate of 46.15%. The concentration of ochratoxin A was in the range of 0.008～0.63 ng·mL-1. The fluorescent apasensor method used to detect ochratoxin A has the advantages of highly sensitive, highly specific, without interference of common mycotoxins, simple, very fast, convenient for popularization and application.

Located at the top of the red giants in Hertzsprung-Russell diagram, M giants are the brightest stars that evolved from the sun-like main sequence stars. The study of M giants is crucial to understand the Milky Way, especially the Galactic haloes. The spectrum of an M giants in medium and low resolution is often mixed with spectra of M dwarfs because of insignificant features, noise effects, and other factors. Previous studies often used the molecular index of CaH2+CaH3 vs. TiO5 to search for M giant candidates, then checked them with human eyes. However, this method only used three important molecular band indices associated with giants, without using other spectral features to identify the M giants, which may cause misclassification due to noise pollution of the index. Moreover, relying on human eyes to check a large number of spectra is time-consuming, and the quality of the inspection dependings on people’s experience and its reliability is not guaranteed. Since 2011, LAMOST has released more than 9 million celestial spectra. The latest spectral data product data release 5(DR5) contains 520 000 M-type spectral data, which needs an automatic, accurate and effective method to distinguish the M sub-samples of different luminosity levels. This study uses four ensemble tree models: Random Forest, GBDT, XGBoost, and LightGBM to construct classifiers that distinguish between M giants and M dwarfs. The accuracy of four classifiers is 97.23%, 98%, 98.05%, and 98.32%, respectively. Experiments showed that LightGBM has higher accuracy and less training time when compared to the other threemodels. The analysis of important features obtained by the classifier models showed that ensemble tree model can efficiently extract and express the structural features that distinguish M giants and M dwarfs. These features include not only the atomic lines, molecular bands, but also their adjacent pseudo-continuum spectrum, which is consistent with the features and pseudo-continuum spectra that we traditionally need to calculate the indices. Compared to the traditional classification methods, ensemble tree can use the combination of tens or hundreds important features in the spectrum rather than only several features to avoid misclassification affected by noises. The results of this study showed that the ensemble tree algorithm has significant advantages in the process of M giant recognition, and it can completely replace the traditional M giant spectral discrimination method using only CaH and TiO indices. In this study an effective method has been provided for LAMOST to efficiently and effectively process the massive celestial spectra. As the LAMOST survey continues, more and more M spectra will be accumulated, which provides massive data for the studies of structure and evolution of the Milky Way.

Interesting optical properties of metal surface plasmon, especially the behavior on luminescence enhancement field, have become a hot research topic globally. The surface plasmon is just a kind of collective oscillation made of free electron and light electromagnetic fields because their resonant frequencies are similar when they are interacting with each other. In present paper, the erbium luminescence resonant enhanced by surface plasmon of Ag nanoparticles (NPs) in telluride glass is studied. The absorption, excitation, luminescence spectra, and lifetime are measured. First, we select the 365.5 and 379.0 nm excitation peaks as excitation wavelength to measure the visible luminescence spectra in the wave range of 385～780 nm. We find 4 luminescence peaks positioned at 408.0, 525.0, 546.0, and 658.5 nm. They are, respectively, the fluorescence transitions of 2H9/2→4I15/2, 2H11/2→4I15/2, 4S3/2→4I15/2, and 4F9/2→4I15/2 of Er3+ ions. It is easy to calculate the peak intensities of the above 4 visible luminescence of (A) Er3+(0.5%)Ag(0.2%): Telluride glass with the average diameter of 80 nm for Ag NPs are about 1.44～2.52 times larger than that of the (C) Er3+(0.5%): Telluride glass. Moreover, the peak intensities of the above 4 visible luminescence spectra of (B) Er3+(0.5%)Ag(0.2%): Telluride glass with the average diameter of 50 nm for Ag NPs are about 1.08～1.55 times larger than that of the sample (C). Then, we select the 365.5 and 379.0 nm excitation peaks as excitation wavelength to measure the near infrared luminescence spectra in the wave range of 928～1 680 nm. It is found that near infrared luminescence peaks are positioned at 979.0 and 1 530.0 nm. They are, respectively, the fluorescence transitions of 4I11/2→4I15/2 and 4I13/2→4I15/2 of Er3+ ions. The peak intensities of the above 2 near infrared luminescence spectra of sample (A) are about 1.43～2.14 times larger than that of the sample (C). Similarly, the peak intensities of the above 2 near infrared luminescence spectra of sample (B) are about 1.28～1.82 times larger than that of the sample (C). Therefore, the largest enhancement is about 2.52 times larger. From the experiments of fluorescence lifetime dynamics, we find that the 550 nm fluorescence lifetime of sample (A) is about τA(550)=43.5 μs, that of sample (B) is about τB(550)=43.2 μs, and that of sample (C) is about τC(550)=48.6 μs. The experimental results illustrate that τA≈τB＜τC. This means that the luminescence enhancement of sample (B) relative to sample (C) results from the spontaneous radiation enhancement effect. However, this also means that the luminescence enhancement of sample (A) relative to sample (B) results from the Size r effect, in which the increased of the scattering cross section Cs is much larger than that of the absorption cross section Ca when r is enlarged, because the scattering cross-section Cs are proportional positive ratio to r6 and the absorption cross-section Ca are proportional positive ratio to r3. As we know that the scattering cross-section Cs is the reason for the increase of fluorescence, meanwhile the absorption cross-section Ca is the reason for the decease of fluorescence. When r is increase, scattering cross-section Cs is the main part. When luminescent material couples with the metallic surface Plasmon, the energy is transferred quickly to metallic surface Plasmon, and then scattered to the far field. It’s beneficial for the enhancement of fluorescence. As a comprehensive result, the fluorescence is enhanced when r is enlarged. It has excellent application prospects in the optogalvanic electricity generation of solar cell and biophysical application other as well as fields.

Compared with gas chromatography-mass spectroscopy (GC-MS) and high performance liquid chromatography (HPLC), attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is an “analysis-without-separation” technique so that the analytical procedures can be simplified and the consumption of the art samples can be minimized in the analysis of color layers in paintings. This study examined the feasibility of the ATR-FTIR spectroscopy for identifying the protein binders in color layers. The fresh and accelerated UV light aged reference samples with single protein binders and the mixtures of pigment-protein binders were prepared, by using milk, animal glue, egg white as protein binders, and chalk, azurite as pigments. Second derivative infrared (SD-IR) spectral pattern recognition models were obtained by using principal component analysis (PCA). The protein binders in artificial art samples can be discriminated by the pattern recognition models, which were established based on the analysis of fresh samples. Therefore, the ATR-FTIR spectroscopy has great potential in micro-destructive and rapid chemical identification of protein binders in the field of cultural heritage.

The main task of this article was to prepared of new pigment model in situ solar cells accordance to charge-transfer complexes of rhodamine C (RhC) donor as dye laser gain media with iodine (σ-acceptor) and chloranilic acid, CLA (π-acceptor). The synthesis stoichiometry of these complexes were of 1∶2 (donor∶acceptor) with general formulas ［(RhC)］I·I3 and ［(RhC)(CLA)2］. The discussed data of elemental analysis, conductivity measurements, FT-IR, UV-Vis spectroscopy and photometric titration data visualized the stoichiometry, formula and complexity of the complexes. The physicochemical and spectroscopic analyses obtained suggested that the electron transfer occurred through nitrogen atom in a tertiary amine —N(C2H5)2 of RhC donor with acceptor. The synthesized solid complexes were under go to thermogravimetric analyses to investigate their thermal stability and decomposition steps. The molar conductance measurements revealed that RhC complexes have an electrolytic statement. The thermal stability of rhodamine C complexes was enhanced in comparable with RhC itself. The polymer membranes of poly-methyl methacrylate) (PMMA) combined with the RhC charge(transfer complexes in chloroform solvent have been prepared and characterized by (infrared & electronic) spectroscopy and scanning electron microscopy (SEM) morphological examination. The photo-stability properties of the RhC complexes have been investigated.

A new six intraperitoneal injection insulin-mimetic vanadyl(Ⅱ) compounds ［(VD-13)(VO+2)(AA-1n)］ (where (n=1～6); AA1=isoleucine, AA2=threonine, AA3=proline, AA4=phenylalanine, AA5=lysine and AA6=glutamine) were synthesized by the chemical reactions between vitamin D3 (VD3), VOSO4 and amino acids (AAn) with equal molar ratio 1∶1∶1 in neutralized media. The structures of these complexes were elucidated by spectroscopic methods like, infrared and solid reflectance spectroscopes. Magnetic moments and electronic spectra reveal square pyramid geometrical structure of the complexes. The infrared spectra assignments of these complexes revealed that the chelation towards vanadyl(Ⅳ) ions existed via deprotonation of the hydroxyl group of VD3 drug ligand and so amino acids act as bidentate ligand via N-amino and O-carboxylate groups. The anti-diabetic efficiency of these complexes were evaluated against streptozotocin induced diabetic male albino rats.