As an indispensable part of the analytical technique, atomic spectrometry is showing great importance for promoting scientific and technological progress, especially in environmental science, energy technology, food science, biotechnology, and materials science. With our country’s increasing emphasis on high-tech, domestic analytical and detection technologies are advancing rapidly, and the development of atomic spectroscopy has become extremely important. In this review, a brief summary on the researches and applications of atomic spectrometry in China for year 2015—2018 has been given. The main contents include: Atomic Emission Spectrometry (AES), consisting of Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), Glow Discharge Optical Emission Spectrometry(GD-OES), Dielectric Barrier Discharge Optical Emission Spectrometry (DBD-OES) and Laser Induced Breakdown Spectrometry (LIBS); Atomic Absorption Spectrometry (AAS), consisting of Flame Atomic Absorption Spectrometry (FAAS), Graphite Furnace Atomic Absorption Spectrometry (GFAAS) and Hydride Generation Atomic Absorption Spectrometry (HGAAS); Atomic Fluorescence Spectrometry (AFS); X-ray Fluorescence Spectrometry (XRF); Elemental Mass Spectrometry (EMS), consisting of Inductively Coupled Plasma Mass Spectrometry(ICP-MS), Glow Discharge Mass Spectrometry (GDMS), Laser Ionization Mass Spectrometry (LIMS) and Atom Probe Tomography (APT); hyphenated techniques of atomic spectroscopy. We focus on the breakthroughs and innovations in technology, instrumentation, detection methods, and performance with various technologies and various combinations. Related applications in electronics, metallurgy, geology, environment, pharmaceuticals, food, life sciences and other fields are introduced briefly.

Atomic spectrometry and elemental mass spectrometry are very powerful techniques for elemental analysis, which have been widely applied in bio-analytical chemistry. In single cell analysis field, research work focuses on element distribution and bioavailability in individual cells with the aid of inductively coupled plasma mass spatrometry (ICP-MS); In the field of elemental label strategy, high sensitive methods are explored for the measurement of small molecules, nuclear acids, proteins and other biological targets; For metal drug analysis, ICP-MS is applied for the investigation on uptake, distribution, metabolism and excretion of metal drugs in organisms, which provides important information for further biomedical research and metal drug design; Considering bio-imaging, laser ablation （LA） ICP-MS is employed for in-situ analysis and micro-analysis of biological samples, providing information for molecular mechanism of relevant biological process; elemental speciation analysis in plant/animal tissues is realized by combining atomic spectrometry or ICP-MS with separation techniques. In this paper, these applications are reviewed including single-cell elemental analysis, elemental label strategy, metal drugs transport and metabolism, and elemental distribution analysis in biological tissues. The development and the prospective of atomic spectrometry and inductively coupled plasma mass spectrometry in biological chemistry are discussed.

Highly sensitive techniques for biomolecules detection play important roles in the elucidation of molecular mechanisms and early diagnosisof many diseases, because many specific functions of cells and tissues are determined by biomoleculescontent under different physiological conditions, while even a few molecules may be sufficient to trigger pathophysiological processes and affect the biological functions of cells. Metal stable isotopes are similar with radioactive isotopes in chemical properties. After labeled with metal stable isotopes, multiple biomolecules can be detected simultaneously by elemental mass spectrometry with high sensitivity. As an accurate detector for inorganic elements, the advantages of ICPMS include low detection limit, low matrix effect, wide dynamic range, and high spectral resolution for isotopes, which is applicable for metal stable isotope tagging-based bioassay. Metal stable isotope tagging has been successfully applied for the detection of proteins, nucleic acids, enzyme activity, small biomolecules, andeven single cells, demonstrating great potential in bioassay. Despite a series of excellent reviews about metal stable isotope tagging have been presented recently, most of them were not in Chinese. Herein, to promote the related research, we briefly introduce the progress of metal stable isotope tagging-based bioassay in this review. The main contents include: metal stable isotope detection tool-elemental mass spectrometry, sensitive bioassay, multiple biomolecules simultaneous analysis, accurate bioassay, and single cell analysis based on metal stable isotope tagging. Metal stable isotope tagging has three distinct characteristics: high sensitivity-most metal stable isotopes possess high tagging sensitivity, and signal amplification can be achieved by metal nanomaterial tagging etc.; simultaneous multiplex analysis- high resolution isotope line of mass spectrometer provides multiplex analysis capability; high accuracy-isotope dilution method provides traceability to International System of Units.

Laser-based ionization time-of-flight mass spectrometry techniques, as an emerging mass spectrometry imaging technique, has been widely used in material, geology, environment, pharmacology, and especially life science. However, it is difficult to achieve sub-micrometer-scale imaging resolution due to the limits of diffraction limit of light, focusing distance and numerical aperture of focusing lens. The introduction of the near-field optics technique has overcome this limitation. By combining the near-field optics technique and laser ionization mass spectrometry, nanoscale crater on the solid surface could be achieved. In addition, traditional mass spectrometry imaging techniques usually neglect the topographical information of the irregular sample surface and cause unreal imaging. So it is important for multifunctional in-situ characterization to get the chemical and topographical information simultaneously. In this paper, a near-field nanometer aperture tip desorption postionization time-of-flight mass spectrometer was developed for sub-micrometer-scale chemical and topographical analysis. 532 and 355 nm laser were used as the desorption and postionizationlaser respectively. A tuning fork based AFM system was used to control the distance between the tip and sample. Copper phthalocyanine molecular layers was ablated to produce a series of nanoscale craters with the size from 550 to 850 nm, which indicated that the technique could achieve sub-micrometer-scale lateral resolution. Furthermore, a mass spectrometry imaging with high lateral resolution was carried out on a 7.5 μm×7.5 μm copper phthalocyanine grid pattern sample. As the results showed, the chemical imaging of the sample surface was achieved simultaneously with the topographical information, expanding the in-situ characterization ability of the mass spectrometry imaging techniques.

In this paper, the trace amounts of selenium, arsenic and mercury in seawater were quantitatively detected by coupling hydride generation (HG) device with solution cathode glow discharge optical emission spectrometry (SCGD-OES). Instrument conditions were optimized, and the optimal instrument conditions for quantitative analysis of selenium, arsenic and mercury were determined: 5% HCl as the carrier acid for subsequent experiments, 1.5% NaBH4 as the reducing agent, the SCGD parameters of the electrolyte, discharge voltage, and flow rate were maintained at pH 1 HCl, 1 060 V, and 2.2 mL·min-1, respectively. Element wavelengths of 203.4, 228.8 and 253.7 nm were selected as the analytical lines of selenium, arsenic and mercury, and the mixed standard solutions of selenium, arsenic and mercury were determined under the optimal working conditions simultaneously. The mass concentration of selenium, arsenic and mercury is linear with the emission intensity in the range of 2~100 μg·L-1, and the linear correlation coefficients are 0.999 2, 0.999 4 and 0.998 5, respectively. The detection limits were 0.54, 0.92 and 1.91 μg·L-1. The relative standard deviations of the signal values of selenium, arsenic and mercury are less than 3% at a concentration of 0.1 mg·L-1. Compared with SCGD-OES, the detection limits of selenium, arsenic and mercury are reduced by three, four and two orders of magnitude, respectively. Soil reference material GBW07405 was selected to verify the accuracy of the results of the coupling instrument, and the measured value was consistent with the reference value. The method has been applied to the analysis of trace selenium, arsenic and mercury in seawater samples from the Yellow Sea coast of China and the analysis results are in agreement with those of ICP-MS. The recoveries of standard addition are between 94.9% and 105.3%. HG-SCGD-OES enables highly sensitive on-line quantitative detection of trace amounts of selenium, arsenic and mercury in seawater.

A simple, novel atomic emission spectrometer (AES) based method for the determination of trace iron ion in water sample was proposed by atmospheric pressure glow discharge (APGD) coupled with photochemical vapor generation (PVG). The Fe solution mixed with formic acid was going through an ultraviolet (UV) lamp to generate volatile specie of iron and then entering the APGD excitation source for excitation and detection with microspectrometer. Several working conditions were optimized to acquire best analytical performance such as argon flow rate, sample flow rate, concentration of formic acid, pH value, and discharge current. The increase of argon flow rate, sample flow rate, and pH value along with the Fe signal intensity was increasing to a maximum value and then decreasing with similar trend. The optimal values of the argon flow rate, the sample flow rate, and the pH value were 300 mL·min-1, 2.6 mL·min-1, and 3.5, respectively. The Fe signal intensity increased with the increase of formic acid concentration from 10% to 50% (V/V) but the formic acid concentration with 40% (V/V) was selected when taking the stability of discharge into consideration. The Fe signal intensity decreased with the increase of discharge current from 10 to 35 mA. When the discharge current was below 10 mA, the plasma was unstable and easy to extinguish and the discharge current at 12 mA was selected. Under the optimal operating conditions, the detection limit (DL) for Fe (249.8 nm) was 2.1 μg·L-1 and the relative standard deviation (RSD) was 2.5% (n=9) with the proposed PVG-APGD-AES. The interferences caused by a series of metal elements including Cd2+, Mg2+, Ca2+, Au+, Zn2+, Mn2+, K+, As5+, Al3+, Cr3+, Ni2+, and Cu2+ in determining Fe using PVG-APGD-AES method were examined separately and the recoveries were all in the range of 87.6%～107.2%. The accuracy of the proposed method was validated by the determination of certified reference material (GSB 07-1188-2000) and the results agreed well with the certified value. The results suggested that the developed simple, robust, and cost-effective PVG-APGD-AES is promising for the determination of trace Fe in field.

The stability of three Ionic liquids (［Bmim］HSO4, ［BPy］HSO4 and ［Bmim］BF4) in DBD (Dielectric Barrier Discharge) Plasma under atmospheric pressure was investigated by using the OES (Optical Emission Spectrometer), UV-Vis (Ultraviolet and Visible Spectrophotometer), FTIR(Fourier Transform Infrared Spectroscopy) and NMR (Nuclear Magnetic Resonance) techniques. The influences of the stability of three Ionic liquids on the crystal phase structure of TiO2 were also studied by preparation of TiO2 with DBD plasma under atmospheric pressure using three kinds of Ionic liquids as assistant, respectively. The results showed that the position and quantity of argon argon plasma emission spectra peak did not change when three kinds of ionic liquid were introduced in DBD plasma. This indicated that the three ionic liquids of above did not evaporate in the plasma area and form excited species. However, the intensity argon emission peak decreased obviously. Both FTIR and UV-Vis spectrospecy of ［Bmim］HSO4 and ［BPy］HSO4 showed no difference before and after plasma discharge. This indicated that the ［Bmim］HSO4 and ［BPy］HSO4 were stable in plasma. There was no significant difference in the infrared spectra of ［Bmim］BF4 before and after plasma treatment. However, the position of the absorption peak in the UV-Vis spectra of ［Bmim］BF4 before and after plasma treatment had a large shift, and the analysis of 1H NMR showed that all the peaks shifted to right 0.2 units approximately. That indicated that the structure of some ［Bmim］BF4 changed in plasma. The XRD spectra of ［BPy］HSO4-TiO2 and ［Bmim］HSO4-TiO2, which were prepared by DBD plasma under atmospheric pressure using ［Bmim］HSO4 and ［BPy］HSO4 as assistant respectively, showed that all the diffraction peaks were the same as the standard spectra of anatase TiO2, and this indicated that ［BPy］HSO4-TiO2 and ［Bmim］HSO4-TiO2 were pure anatase. However, The XRD spectra of［Bmim］BF4-TiO2, which were prepared by DBD plasma under atmospheric pressure using ［Bmim］BF4 as assistant, showed that the diffraction peaks at round 24.1° shifted to lower 2θ values, while the diffraction peaks at around 48.0° shifted toward higher 2θ values. The shift of the diffraction peak for ［Bmim］BF4-TiO2 samples indicated that lattice imperfection was formed due to F doping. The fluorine atoms entered into the lattice of TiO2, therefore breaking the equilibrium of original TiO2 atoms and varying the inter planar crystal spacing of anatase TiO2. This revealed that same of ［Bmim］BF4 were broken down in plasma. The formation of F doped TiO2 photocatalyst also indirectly proved the instability of ionic liquid ［Bmim］BF4 in atmospheric pressure plasma, and this results were the same with the analysis of UV-Vis and 1H NMR. The formation of F-doped TiO2 photocatalytic materials also indirectly proved the instability of ionic liquid ［Bmim］BF4 in atmospheric pressure plasma, which was consistent with the results of NMR and UV-Vis. It was also proved that ionic liquids play an important role in the formation of pure anatase crystals and the promotion of highly reactive photocatalysis materials under the action of plasma. It provides an important experimental and theoretical basis for the preparation of high performance nano-materials by plasma-assisted ionic liquids.

Terahertz time-domain spectroscopy (THz-TDS) is widely used in materials, biomedicine, chemistry, pharmacy, security and other fields. Traditional scanning THz-TDS technologies need to scan point by point by changing the time delay between the probe pulse and the THz pulse so as to reconstruct the time domain signals, only suitable for sample detection in THz radiation source with high repetition rate and high stability. However, in the cases of THz radiation source with low repetition rate, large fluctuation, or in irreversible processes, scanning THz-TDS detection technique is not applicable. In these cases, single-shot THz-TDS techniques are desirable. In principle, single-shot THz-TDS technologies require only one shot probe laser pulse to obtain a complete THz temporal waveform. In this article, the main detection techniques in single-shot THz-TDS are introduced. These techniques utilize the Pockel effect of the electro-optic crystal to retrieve the terahertz signal by measuring a physical quantity change of the probe pulse. According to the different single-shot methods, these techniques may be classified into the spectral-encoding technique, spatial-encoding technique and cross-correlation technique. In spectral-encoding technique, different frequency components of probe pulse are separated in time, and different temporal components are modulated by electric fields at different times of THz pulse. The THz waveform can be extracted from the difference between the spectral distributions of the probe pulse with and without THz pulse modulation. This technique has shown its advantages with simple optical path, visual measurement results and high signal-to-noise ratio (SNR), but also shown its disadvantages with low time resolution and distortion of the measured THz signals. In order to improve the time resolution, the spatial-encoding technique was proposed. In this technique, different positions of probe pulse are modulated by electric fields at different times of THz pulse. The THz waveform can be retrieved by measuring the difference between intensity of the probe pulse with and without THz pulse modulation. There are two methods of this technique: one-dimensional spatial-encoding and two-dimensional spatial-encoding technique. Although the technique has shown high time resolution, the SNR of detected signal is relatively low because of the dispersive energy of the probe beam. Another technique to improve the time resolution is cross-correlation technique, which can be classified into the collinear cross-correlation and non-collinear cross-correlation technique. In the non-collinear cross-correlation technique, the second-harmonic generation from the cross-correlation between the short readout probe pulse and the chirped probe pulse is modulated by the terahertz pulse. The THz waveform can be extracted from the difference between the second-harmonic distribution with and without THz pulse modulation. In the collinear cross-correlation technique, the chirped probe pulse is modulated by the THz pulse and a short readout probe pulse with collinear incidence to the spectrometer. The THz waveform can be extracted from the difference between the interference fringes with and without THz pulse modulation. The method has shown high time resolution and SNR, but the optical path is complex, and the signal cannot be real-time monitored. In this article, the development of the above mentioned main single-shot THz-TDS detection techniques are introduced. The principles, the application and some measurement results of these techniques are reviewed in detail. The advantages and disadvantages of them are also discussed.

Nicotinamide, also known as vitamin PP, is component of coenzyme Ⅰ and Ⅱ, and it turns into a coenzyme for most of dehydrogenases. Pimelic acid has also been utilized previously as a cocrystal former. A cocrystal of nicotinamide and pimelic acid exists in two different polymorphic form-Ⅰ and form-Ⅱ. It is important to find a suitable technical means in the pharmaceutical field since the polymorphs have different physio-chemical properties which influence their solubility, stability and also other performance characteristics. The absorption spectra of nicotinamide-pimelic acid cocrystal were measured using terahertz time-domain spectroscopy ranging from 0.2 to 2.2 THz at room temperature, and the absorption peaks have obvious difference. Form-Ⅰ and form-Ⅱ have five characteristic absorption peaks at 1.51, 1.73, 1.94, 2.01, 2.17 THz and 1.66, 1.74, 1.88, 2.02, 2.16 THz, respectively. Form-Ⅰ has three strong absorption peaks at 1.94, 2.01 and 2.17 THz, while form-Ⅱ has two strong absorption peaks at 2.02 and 2.16 THz. Density functional theory was used to simulate the polymorphs of the nicotinamide and pimelic cocrystal. The calculated characteristic peaks were in accord with those in the experiments. The results showed characteristic absorption bands come from skeletal and hydrogen bond vibrations. This research suggested that terahertz time-domain spectroscopy technique is important for applications in solid-state analytical tools to distinguish polymorphs of cocrystal in pharmaceutical fields.

The granularity detection of pulverized coal in pneumatic conveying system is of great significance to the optimum control of coal mill. The traditional approach to detect the granularity of the coal granule is sampling the pulverized coal in the pipeline and applying sieving process. These steps are time-consuming and complex. Some rapid detection methods for the granularity of coal have already been released home and abroad, however, some drawbacks still exist, such as limiting the concentration of the pulverized coal to a low level during measurement and the instability of the testing equipment. Terahertz time-domain spectroscopy system (THz-TDS) is a newly developed non-destructive analytical technique. Compared with other spectrometry, THz-TDS has the superiority of low-energy photon, perm-selectivity and coherency. The previous research on the interaction between THz wave and granular medium indicated that the particle in granular medium has a strong influence on THz wave, which provides the technical feasibility of granularity detection of coal particle adopting THz-TDS. The propagation of the THz wave in granular medium could be regarded as a non-linear dynamic process involving complicated dynamic effect, leading to a THz signal combined with some certain chaos features. In this paper, the concept of non-linear chaos dynamic system was applied to the terahertz spectral analysis for the first time. Following this point of view, the detected THz signal was considered as a time series generated by a complex non-linear dynamic system and the interaction between THz wave and granular medium could be described by some chaos features. In the experiment, the coal was grounded and sieved into <38.5, 55~74, 74~88, 88~105 and 105~200 μm firstly. Then these pulverized coal samples were mixed with HDPE powder and compressed into tablets. The power spectral entropy, wavelet energy entropy, Box dimension, correlation dimension, skewness and kurtosis were extracted from the THz time domain signals of the six coal-HDPE tablets. Visually, the extracted chaos feature vectors showed a dependency with the granularity of the measured coal samples, so that the range of granularity could be roughly distinguished. However, the exact diameters of the measured coal samples remain unknown. Support vector machine (SVM) is a powerful tool for solving the small sample and non-linear classification problem. Appropriate parameters should be selected firstly, so that an accurate prediction model can be established by SVM. Particle swarm optimization (PSO) was used to optimize the parameters selection of SVM. The extracted chaos features were selected as inputs of the PSO-SVM to establish a regression model for predicting the grain size of the investigated granulated coal samples. The experimental result showed that the regression model trained by the chaos feature vectors had a worse performance of predicting the grain size of samples containing <38.5 and 38.5~55 μm coal granule than the model trained by the frequency depended extinction spectrum. This might be because of a relatively weak interaction between the THz wave and small grains that the chaos features of these samples are not significant. For the rest samples containing 55~74, 74~88, 88~105 and 105~200 μm coal grains, a better performance was achieved. Specifically, compared with the model trained by the extinction spectrum, for samples containing 74~88 and 105~200 μm coal grains, the prediction model trained by the chaos features obtained a lower RMSEC that declined by 29.48% and 26.14%, respectively and the RMSEP of this two samples declined by 88.62% and 56.86%, respectively. Overall, for the prediction model trained by the chaos features, the correlation coefficient between the predicted and actual particle diameter is 0.961 8, however, for the prediction model trained by the extinction spectrum, the correlation coefficient between the predicted and actual particle diameter is only 0.780 7. The RMSEP obtained by the model trained by the chaos features is 9.52, while the RMSEP obtained by the model trained by the extinction spectrum is up to 24.48. Furthermore, the elapsed time for modeling when adopting the chaos features declined by 43.19%. The research provides scientific basis and references for the application of granularitydetection of pulverized coal in pneumatic conveying system.

Aiming at the problem that existing passive DOAS methods using natural light sourcecannot detect the vertical distribution of NO2 and other trace gases at night, this paper proposes a method of DOAS measurement of NO2 based on the blue LED technology of narrowband light source, and uses this method to build the instrument system. Successfully realized the concentration measurement of NO2 gas by using the instrument system. This system is mainly composed of two parts: a light source emitting system and a telescope receiving system. The LED with the dominant wavelength of 450 nm is used as a light source to collect the scattered light of the light beam through the telescope. The scattered light received by the telescope is imported into the spectrometer through fiber coupling, DOAS principle using a computer for processing. The theoretical basis for DOAS is Lambert-Beer’s law, which describes the attenuation of the electromagnetic radiation energy as it penetrates the material. Based on this principle, the data processing can be summarized as follows: Firstly, a relatively clean spectrum is taken as the background reference spectrum, and the actual measured atmospheric spectrum is divided by the reference spectrum, and the digital high-pass filtering is used to remove the slow changes and then take the logarithm to obtain the optical thickness. Secondly, the instrument function is convolved with the high-resolution cross-section of NO2 to get the low-resolution absorption cross-section matched with the instrument used. Finally, the differential absorption cross-section is combined with the processed differential optical thickness, and the least square method is used to fit out the NO2 concentration value with the light path L. At the same time, by adjusting the angle of light emission and the receiving angle of the telescope, the NO2 concentration at different positions can be measured, and then the three-dimensional distribution of NO2 gas concentration can be obtained. Under the condition ofthe algorithm determined, the quality of the LED light spectrum is particularly important for the reliability of the instrument system. As the temperature and drive current have a greater impact on the LED spectrum, in order to ensure that the LEDs are in the best working condition, carried out the LED spectral temperature and drive current sensitivity experiments. The test results show that to make sure the acquired spectrum is stable and of high quality, the LEDs should operate at a temperature lower than 20 ℃, and the drive current needs to be controlled within 1. 5A, and both of them should have a small fluctuation range. In the experiments, the LEDs are working with the temperature of 10~15 ℃, the driving current of 1.4 A and the accuracy of driving current of ±1 mA, and all the conditions can meet the experimental requirements. In order to improve the LED array density, to get a more focused beam of light, LED base block with a regular hexagonal structure is chose, and all the blocks have 7 LEDs connected in series, and the blocks are connected in parallel. Compared with using rectangular structure, the space utilization increased by 8% with using regular hexagon structure. At the same time, it is easier to expand and more convenient to maintenance with the working drive of 1.4 A and the maximum voltage of 23.8 V. In order to verify the feasibility of the program and the reliability of system, laboratory tests and outdoor experiments were conducted. The concentration of the sample gas of NO2 used in the laboratory was 1 642.86 mg·m-3 and the uncertainty was 5%. The system measurement was 1 607.54 mg·m-3 with an error of 2.15% from the nominal value, within the uncertainty range of the calibration. The calculated system test line was 0.014 3 mg·m-3(6.942 ppb), therefore, the measurement result can be considered as accurate. The results of outdoor experiments were compared with the data of NO2 given by the national control station over the same period. The results showed that the deviations of the results were within 10% in the corresponding time periods. The linearity of the data fit well with the correlation coefficient of 0.967, indicating that the system NO2 measured results were accurate. The results of this paper show that the DOAS method based on the blue LED with narrowband light source can measure the vertical distribution of NO2 gas at night, while ensuring the stability of LED light source. It provides a new idea for measuring the vertical distribution of trace gases in atmosphere, especially for measuring the distribution of trace gases in nighttime conditions.

In order to investigate the effect of pump power on the luminescence properties of upconversion materials doped with different concentrations of sensitizer ions, in this study, NaYF4∶Yb3+, Er3+ upconversion nanoparticles doped with different concentration of sensitizer Yb3+ were successfully synthesized by solvent-thermal method. The morphology and structure of prepared sample were charactered by XRD and TEM measurements. The results suggested that these samples were all hexagonal nanocrystals with good crystallinity. As the concentration of Yb3+ increased, the particle size increased slightly. At the same time, the photoluminescence properties of these prepared nanoparticle excited by 980 nm were studied in detail by collecting the pump power-dependence fluorescence spectrum. For all samples, the intensity of upconversion fluorescence increases with the enchancement of excitation irradiance which can be attributed to the fact that high pump power induced the higher absorption efficiency of nanoparticles. Besides, the red green ratio (RGR) can be tuned by adjusting the excitation irradiance too. And it’s worth noted that the tuning range of RGR depends on the doping ratio of sensitizer Yb3+ in NaYF4∶Yb3+, Er3+. In order to deeply understand the mechanism of upconversion luminescence, the possible electron energy transfer process was proposed. We assumed that the tuning range of RGR is related to the different average distance between rare earth ions and the comprehensive effect of the process of multi-phonon relaxation, cross-relaxation, and back energy transfer. The sample with low Yb3+doping concentration has a negligibly back energy transfer probability due to the fact that average distance between Yb3+ and Er3+ is long. The multiphonone relaxation and corss-relaxation are the main processes that convert a part of green emission into red emission. Following the enchancement of excitation irradiation, the benefit of the high excitation irradiance can relief this insufficient, and the red green ratio increases slightly. In heavily doping samples, the back energy transfer process between neighboring Yb3+ and Er3+ happened more probably and became the main factor for the nonradiative process. High-lying levels show a decreasing contribution, which leads to a increasing red green ratio followed the enhancement of pump power. The red green ratio increases with the increasing pump power due to different emphases of nonradiative processes in NaYF4∶Yb3+, Er3+nanoparticles doped with different concentrations of Yb3+. The luminescence properties of the prepared UCNPs not only allow us obtaining upconversion nanoparticles with better red emission performance, but also determine the doping ratio by measuring the red-green ratio of the material. All results indicated that the material is potentially to be a multifunctional photodynamic therapy nanoplatform used in bio-detection filed through further design and modification. The possible electron energy transfer process is proposed which is helpful in designing and optimizing the doping of rare earth ion-pair, and understand the mechanism of upconversion luminescence.

Dy3+, Tb3+ doped and Dy3+/Tb3+ co-doped silicate oxyfluoride scintillating glass were prepared by high temperature melting method. The Fourier transform infrared spectra, transmission spectra, photoluminescence excitation and emission spectra, X-ray excited luminescence spectra and luminescence decay curves were analyzed. The influence of the energy transfer between Dy3+ and Tb3+ions and Dy3+doping onluminescence properties of Tb3+ activated silicate oxyfluoride scintillating glass was studied. The results indicated that Dy3+/Tb3+ co-doped silicate oxyfluoride scintillating glass has relatively high density and good transmittance in visible region. The networkstructure of glass isconstituted of tetrahedral ［SiO4］ and ［AlO4］. Under the irradiation of ultraviolet light, the luminescence of Dy3+-doped glass originates from 4F9/2→6H15/2 (483 nm) and 6H13/2 (576 nm) transition emission of Dy3+ ions, while the luminescence of Tb3+-doped glass originates from 5D4→7F6 (489 nm), 7F5 (544 nm), 7F4 (586 nm) and 7F6 (623 nm) transition emission of Tb3+ ions. As for Dy3+/Tb3+ co-doped silicate oxyfluoride scintillating glasses, the emission spectra aremainly due to fluorescence emission of Tb3+ ions. The emission spectra under ultraviolet excitation with different wavelengths revealed that Dy3+/Tb3+ co-doped scintillating glass includes manifold energy transfers. When Tb3+-doped glass is excited by the characteristic excitation wavelength (452 nm) of Dy3+ ions, the luminous intensity of Tb3+-doped glass is very weak. With the introduction of Dy3+ ions, Tb3+ ions emission are sensitized and enhanced by the energy transfer of 4F9/2 (Dy3+)→5D4 (Tb3+). The luminous intensity ofDy3+/Tb3+ co-doped glassesis improved with the increase of Dy2O3. The luminous intensity ofDy3+/Tb3+ co-doped glasses reaches the maximum when the content of Dy2O3 is 1mol%. However, when the content of Dy2O3 is further increased, the concentration of Dy3+ ions is quenched, which results in the decrease of the energy transfer to Tb3+ions and the reduction of the luminous intensity. When the excitation wavelength is decreased to 350 nm, Dy3+ and Tb3+ ions are excited to higher energy levels of 6P7/2 (Dy3+) and 5L9 (Tb3+). At this point, the energy transfer of both 4F9/2 (Dy3+)→5D4 (Tb3+) and 5D4(Tb3+)→4F9/2 (Dy3+) occurs. When the doping concentration of Dy3+ ions is relatively low, the energy transfer of Dy3+→Tb3+ is stronger than that of Tb3+→Dy3+, which enhancesthe luminescence of Tb3+ ions by sensitization. With the increase of Dy2O3 content, the energy transfer of Tb3+→Dy3+ is enhanced. When the content of Dy2O3 is above 0.4 mol%, the energy transfer of Tb3+→Dy3+ is strongerthan that of Dy3+→Tb3+, which reduces the transition luminescence of Tb3+ ions and thus decreases theluminous intensity of Dy3+/Tb3+ co-doped glass. Due to the efficient energy transfer from Gd3+ to Dy3+ or Tb3+, the competition of energy transferfrom Gd3+ to Dy3+ and Tb3+ion soccurs under characteristic excitation wavelength of Gd3+ ion at 274 nm. With the increase of content of Dy2O3, the captured energy of Tb3+ions decreases constantly. At the same time, the energy backtransfer of Tb3+→Dy3+ and the nonradiative cross relaxation between Dy3+ ions appear, which leads to the reduction of theluminous intensity of Dy3+/Tb3+ co-doped glass. The 5D4→7F5 luminescence decay curves of Tb3+ions for Dy3+/Tb3+co-doped scintillating glass showed that with the increase of Dy2O3 content, the lifetime of 5D4 (Tb3+) reduces from 2.24 to 1.15 ms and the curve changes from single-exponential to double-exponential form, indicating the possibility of the energy back transfer of 5D4 (Tb3+)→4F9/2 (Dy3+) in the glass. The X-ray excited luminescence emission spectra showed that the introduction of Dy3+ ions has a negative effect on the luminescence of Tb3+ activated scintillating glass. Becausethat negative effect is not enough to make up for the Dy3+→Tb3+ energy transfer, the radiation luminescence intensity of Dy3+/Tb3+ co-doped glass decreases with the increase of the Dy2O3 content. Therefore, Dy3+ ions should not be used as sensitizers to enhance the luminescence intensity of Tb3+ ions in Tb3+ activated silicate oxyfluoride scintillating glass.

In order to defog the image under hazy weather, multiple images defogging algorithm is one of the commonly used methods. Multiple images defogging algorithm also takes many forms, some of which are usually confronted with the problems of difficult hardware implementation, limited data source achievement approaches, or poor implementation et al. Meanwhile, multiple images defogging algorithm usually needs image registration in the comparison process, causing poor real-time performance and high computation cost. For the above problems, this study supplies a new idea for multiple images defogging algorithm, the near-infrared sensor images are used as new data source. The near-infrared sensor could penetrate haze to some extent, capturing the image details that the visible light sensor could not get. Meanwhile, the hardware of dual sensor system is simple. In the dual sensor system, the visible light image has abundant color information and the near-infrared sensor image can better describe the scene details at close range. The captured images could be completely registered with little rectification. The fusion of the infrared image and visible light image could extract the image details of the near-infrared image to the color visible light image to get the defogging image with abundant edge and contour information. Therefore, this study proposed a defogging algorithm using near-infrared and visible light image fusion method based on the edge details descriptive ability of the near-infrared sensor and the expressive ability of color information. Firstly, the color visible image was transformed from RGB space to HIS color space to get the hue channel image, saturation channel image and intensity channel image. The intensity channel image and original near-infrared image were decomposed by the Non-subsampled Shearlet Transform (NSST) method to get the high frequency coefficients and the low frequency coefficients. The high frequency component was treated by the double-exponential edge smoothing filter and the low frequency component was treated by the Unsharp Masking method, then the fusion rules and the inverse NSST were adopted to get the new intensity channel image. For the color information treatment of the visible light image, the degeneration model of the saturation channel image was established and the dark channel prior was used to evaluate its parameters to get the new saturation channel image. Finally, the new intensity channel image, the new saturation channel image and the original hue channel image were inversely mapped to the RGB space to get the defogging image. In order to verify the algorithm, we adopted 4 groups of foggy near-infrared images and visible light images as the experimental data. The processed images were compared with the defogging images observed by other two defogging algorithms. The experiment results showed that the proposed algorithm has better effect in improving the edge contrast and visual clarity. This study put forward the near-infrared image as new data source and the binary channels image fusion algorithm as the defogging method, and it was verified that the new algorithm for image defogging is feasible. This algorithm has four main advantages. The first one is that we combined the image fusion method with the defogging algorithm to get a novel idea for defogging algorithm. We transformed the color visible image to HSI color space. The obtained intensity channel image and original near-infrared image were fused by the NSST method, and the image details in the near-infrared image were simultaneously extracted to the color visible image in the defogging processes. The defogged image has abundant detailed information of edge and rough. The second advantage is that this algorithm adopted near-infrared sensor image as new data source. From the perspective of image processing, the near-infrared sensor could penetrate haze to some extent, capturing the image details that the visible light sensor could not get. Meanwhile, the hardware of dual sensor system is simple. The third advantage is that we adopted multiple images defogging algorithm, which captured images by binocular sensor system, and the visible light sensor got the image with abundant color information and the near-infrared sensor got the image with good detail description ability of close shot. The fusion of the two kinds of images has better effect than the single image defogging algorithm. The fourth advantage is that the visible light image was transformed to the HIS color space, and the images of the three channels can be targetedly processed according to their data characteristics. The process of intensity channel image of visible light image and the near-infrared image adopted image fusion and enhancement methods. The process of saturation channel image of visible light image adopted image restoration method. These processing enhances the effect of defogged image on the whole. This study supplies a new technological approach and way for image defogging algorithm.

Variable selection plays an important role in the quantitative analysis of near infrared spectra. The accuracy of near infrared spectroscopy can be improved by eliminating the redundant variables and selecting the characteristic variables. Competitive adaptive reweighted sampling (CARS) method is a newly developed strategy for wavelength selection by employing the principle “survival of the fittest” on which Darwin’s Evolution Theory is based. The number of selected wavelengths by CARS is much smaller than those of other methods with fast calculating speed and high accuracy. However, it is easy to get inconsistent results between the calibration and validation set due to the excessive attention on the cross validation results. In order to develop a robust variable selection method, by combining the advantages of CARS and “window”, a new tactic called window competitive adaptive reweighted sampling (WCARS) is employed to select characteristic variables and applied to the analysis of the near infrared spectra of the complex plant samples and the contents of the chemical components. Compared with the results of CARS method, accurate quantitative results can be obtained by the WCARS method. Furthermore, the results of correction set are consistent with those of the prediction set, and the problem of overfitting can be avoided. The results show that WCARS tactic can efficiently improve the accuracy and stability of variables selection and optimize the precision of prediction model, which has a certain application value.

Traffic accidents, falling from high altitude, and bruises caused by heavy objects may cause Spinal Cord Injury (SCI). SCI interrupted the transmission channel of human nerve signal. Most patients had paraplegia, lost their physical function, and had incontinence resulting in permanent disability . According to the statistics of the World Health Organization in 2016, the prevalence of SCI in the world is (258～785) per million people, and the annual incidence rate is (13.8～86) per million people. At present, there are about 6 million patients in the world, and about 600 000 people are newly added every year. SCI has been a difficult research topic in the medical community. After long-term’s research and exploration, doctors and researchers have not found an effective treatment method to repair the micro-environment and promote the regeneration of injured nerve after SCI. The method proposed in this paper focuses on the following aspects: (1) Conventional detection instruments such as X-ray, MRI, CT and others only in imaging and morphological observations, which can not locate the nerve cells in the SCI area and can not analyze the activity in real-time. As a result, doctors and researchers can’t grasp the condition of the patient accurately and it is likely to delay the patient’s condition and even endanger the patient’s life. (2) In December 2013, the quantum communication team of Xi’an University of Posts &Telecommunications proposed the quantum relay “bridge” method, which uses quantum entanglement to establish a “connection” between the upper and the lower break point of SCI to achieve human neural signal relay. And the University together with the Xi’an Jiaotong University completed the experiments on rats and rabbits which have made breakthroughs; on January 16, 2015, the Chinese Academy of Sciences regenerative medicine research team and the Chinese Armed Police Brain Hospital combined with mesenchymal stem cells and used nerves. The Cell Regeneration Scaffold completed the “first bypass” surgery for the first SCI in the world and achieved a good therapeutic effect. How to ensure that the “bridge” is built on the alive nerve cells, not on the died nerve cells? How to find a suitable “bridge pier” position so that the “bridge” distance is as short as possible to ensure that the fidelity of the neural signal transmission can be higher? By the method proposed in this paper, we can find the accurate position of nerve cells after SCI. Spectroscopic analysis techniques are widely used in the biomedical field and play a crucial role in the early detection and have a highly efficient detection in certain diseases. Neuron-specific nuclear protein can be used to specifically identify nerve cells. Since the surviving nerve cells can produce neurotransmitters, this article based on animal experiments, simulates photon transmission in biological tissues through the Monte Carlo model. Establishing a polar coordinate transformation in the plane Oxy, calculating the attenuation coefficient matrix of light in biological tissues, detecting near-infrared light at the same position before and after SCI, the clustering algorithm was used to process the neuron-specific nuclear protein and neurotransmitter detection data. Through matlab simulation, a two-dimensional distribution map of the attenuation coefficient before and after SCI was obtained. The coordinates of the voxels in the spinal cord injury area in Oxy are determined, in the anomalous part of the plane Oxy, the abnormal point W and the Z axis are selected to establish the plane Ozw, and the position coordinates of the nerve cells at the site of the SCI are finally determined. The method proposed in this paper can provide theoretical basis for doctors and researchers in the study of limb function reconstruction in patients with SCI.

For the first time, this study established an attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) method for fast qualitative analysis of 13 precursor chemicals. Due to the lack of qualitative identificationcriterion, FTIR has long just been used as a fast qualitative screening method. In order to expand its application in forensic sciences, two qualitative identification criteria of similarity coefficient method and characteristic peak method were investigated and compared based on the FTIR data of 152 seized ephedrine type samples. The range of the similarity coefficient values for ephedrine samples was 0.437～0.981. Generally speaking, sample with higher purity resulted in higher similarity coefficient value, but there is no linear relationship between the similarity coefficient value and the sample purity. Therefore, it is hard to select a threshold value. For characteristic peak method, 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 the detection of all characteristic peaks was selected as the positive identification criteria, the positive detection rate for 152 ephedrine samples was 98.7%. Therefore, the characteristic peak method showed stronger specificity and wider application scope, and the results were reliable and accurate. This study established the characteristic peaks of 1-phenylpropan-2-one, 3,4-methylenedioxyphenylpropan-2-one, piperonal, N-acetylanthranilic acid, anthranilic acid, ephedrine, pseudoephedrine, 3-oxo-2-phenylbutanenitrile, 2-bromo-1-phenylpropan-1-one, N-phenethyl-4-piperidone, 4-anilino-N-phenethylpiperidine, 1-phenylpropan-1-one, and 1-chloro-N-methyl-1-phenylpropan-2-amine. The developed FTIR method that based on characteristic peak for precursor chemical identification provided a useful alternative to mass spectrometric method. It could significantly reduce the analysis time and cost, and greatly improve the identification efficiency.

Wheat is one of the main raw materials for making steamed bread, and the water, protein and starch in wheat vary depending on the place of production and the degree of drying, which in turn affects the quality of processed steamed bread. Therefore, it is particularly important to quickly identify the place of origin and degree of drying of wheat. Sensory evaluation is a common method used to identify the origin and degree of drying in wheat, in contrast to sensory evaluation, spectral analysis techniques can identify information such as molecular structure in a sample. Based on this, this paper attempts to use the near-infrared and mid-infrared spectral fusion technology to achieve the simultaneous identification of wheat from different producing areas and different degrees of drying. In this study, wheat from two different origins were selected and microwave drying was used to pretreat the wheat from the two different origin so that the moisture content of the dried wheat decreased to 12%±0.5%, while the moisture content of the undried wheat was 18%±0.5%. They were marked as undried wheat A, dried A, undried wheat B, dried B and then ground into powder, sieved with a 100 mesh screen and placed in a sealed bag for use. Subsequently, the near infrared and mid-infrared spectral information of four wheat samples were collected, and then the raw spectral data collected were pre-processed using the standard normal variate transformation (SNVT) using Matlab 7.10 version. The principal component analysis was used to reduce the dimension of the preprocessed data, and then the linear and short-infrared (NIR) and mid-infrared (MIR) spectral data were identified using linear discriminant analysis (LDA) and support vector machine (SVM), respectively to create a recognition model. In addition, using the synergy interval partial least square (SiPLS) method, the characteristic spectral ranges of the near-infrared and mid-infrared spectral data of the wheat pretreated with the standard normal variable transformation (SNVT) were screened out. After the fusion of the characteristic spectral ranges of the near-infrared and mid-infrared spectral data, a linear discriminant analysis (LDA) and support vector machine (SVM) were used to establish the identification model of the fusion spectral information of wheat. The recognition rate of wheat identification model established by linear discriminant analysis (LDA) and support vector machine (SVM) under the same spectral data were compared and the near-infrared and mid-infrared spectral data of the same modeling method established the wheat identification model. Recognition rate, comparison of spectral data fusion under the same modeling method and single spectral data were used to establish the recognition rate of the wheat identification model. The results showed that using the same kind of spectral analysis method, the recognition rates of the four wheat identification models established using SVM were higher than those of wheat identification models established using LDA. The recognition rate of wheat identification model established by near-infrared spectral data using the same modeling method was better than that of wheat identification model established by mid-infrared spectral data. Under the same modeling method, the identification rate of the wheat identification model established by the fusion of the characteristic spectral interval data of the near-infrared and mid-infrared spectral data filtered by SiPLS was the highest. After the fusion of spectral data, the wheat identification model established with LDA was integrated. The recognition rate of the correction set was 98.75%, and the recognition rate of the prediction set was 97.50%. The recognition rate of the correction set and the prediction set of the wheat identification model established by combining this selected variable with the SVM reached 100.0%. Comparing the recognition rate of wheat identification model established by using single spectral data, the recognition rate of wheat identification model established after fusion of spectral data was significantly improved. This study compared the wheat model’s recognition rate established by spectral data from both vertical and horizontal directions. The results can provide a reference for the more accurate use of spectral fusion technology to establish wheat production areas and drying degree identification model.

The analysis of genetic basis of breeding materials is the precondition for the improvement programs on populations and interesting traits in eucalypt. However, the traditional ways for that have high professional requirements and are time-consuming and laborsome. The aim of present study was to study the relationship between NIRs and genetic information of eucalypt, and discuss the practicability and the accuracy of the discriminant model for the classification of eucalypt hybrids by NIRs data. The NIRs of seven eucalypt hybrids and four parental pure species were scanned with healthy leaves using handheld portable near infrared spectrometer Phazir Rx (1624). 10 individuals were selected for a genotypic species, and 10 healthy current-year leaves were chosen per individual tree. Specially five scans for NIRs from each side of the middle part of the frontal vein of the leaves were taken, and estimated the average of that as the NIRs information of a leaf. In total, 100 NIRs were gained per genotypic species, 70 of which constitute the calibration set, and the validation set consists of the rest 30 NIRs. The transformation of S.G 2nd derivative were performed for the raw NIRs data in present study so as to eliminate the effects of baseline and other factors on the NIRs information, and to strengthen the characteristic peaks of NIRs. The later analysis were conducted after the pretreatment. Firstly, the relationship between NIRs and genetic information of eucalypt hybrids was studied by the scores plot of principal components (PCs) in principal component analysis (PCA), and on this basis, the NIRs discriminant model was developed. The soft independent modeling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA) pattern recognition were used to classify eucalypt hybrids with the NIRs model calibrated. The coefficient variation curves of NIRs transformation showed that all phenotypic species studied had rich characteristic peaks, and big differences among them after the wavelength of 2000 nm. The scores plot of PC1 and PC2 in PCA demonstrated clear groups among parental species, hybrids, as well as between hybrids and their parents, suggesting NIRs was a direct response to the genetic information of different genotypes. The discriminant accuracy of SIMCA pattern recognition between some cross combinations, which shared close genetic relation of cross parents, were relatively low using NIRs model. In contrast, the discriminant accuracy of SIMCA pattern recognition among most of eucalypt combinations changed between 73% and 100%. The discriminant accuracy of PLS-DA pattern recognition using single and combined NIRs model of hybrids all were 100%, and the combined model of hybrids based on PLS-DA pattern can discriminate seven hybrids clearly. Studies showed that, the discriminant accuracy of PLS-DA pattern was much higher than that of SIMCA pattern recognition. The current study indicated that NIRs information is the correct response of different genotypic eucalypt species, and the NIRs calibrated model can classify different species of eucalypt accurately, so the NIRs would be used in the qualitative discrimination analysis of eucalypt hybrids and pure species in field, providing an alternative way for the analysis of genetic basis of breeding materials in eucalypt.

Liquid water is the chemical backbone of most biochemical processes on earth and is therefore essential for the metabolism of living beings. As a result it is a key topic across a wide range of scientific disciplines. The physicochemical properties of water are considered to be the result of hydrogen bond derived structures. However, it is still difficult to quantitatively assign the physicochemical properties of water molecules with the hydrogen bond structure in order to form a complete theory of liquid water molecular structure. Raman spectroscopy is one of the main methods to characterize the molecular structure of liquid water because of its fast and non-destructive advantages. At present, Raman spectroscopy of water molecule is mainly concerned with its high frequency vibration modes. Wide low-frequency Raman modes in liquid water are the result of hydrogen bonds and their local structural effects, which contain characteristic information that high-frequency peaks cannot be characterized. The ultra-low-frequency Raman spectroscopy can reveal many key details of water molecules (super-) structure at high temperature. Therefore, this article provides novel precision temperature-dependent ultra-low frequency Raman spectra of water molecules for the first time. All four translational characteristic modes predicted by theory have been experimentally detected, including the bending mode (51.7 cm-1), torsional mode (81.4 cm-1), symmetrical (154.0 cm-1) and asymmetrical stretching mode (188.6 cm-1) , an additional translational-rotational coupled characteristic mode is found at 225.2 cm-1. All feature modules are accurately assigned. From the spectroscopy results, first of all, when the temperature rises from 0 to 400 ℃, due to hydrogen bond breaking and the rapid average structure correlation length (SLG) decreasing, the frequencies of all four ultra-low frequency characteristic modes shift significantly blue with the increase of temperature. Secondly, the strength of the stretching modes drops obviously between 100 and 200 ℃. While The strength of bending mode increases in turn from high frequency to low frequency with the decrease of stretching modes, which has never been involved in theoretical research. Finally, the the Stokes/Anti-Stokes Ratio (RS/AS) increases rapidly from 1.1 to 1.3 at the temperature range 150～170 ℃ (about 2 kbar) and display a linear temperature dependence when the temperature is above 170 ℃. In general, the effects of temperature on the structure of water molecule, especially on hydrogen bond derivation, are obtained by studying the blue shift of resonance modes, the change of intensity and the Stokes/anti-Stokes ratio. This study provides a deep insight into the analysis of hydrogen-bond derived properties and a new experimental spectroscopy method for understanding the structure of water molecule in depth and comprehensively.

As a kind of Benzourea, Forchlorfenuronis widely used to regulate the growth of fruits and vegetables. However, excessive use of Forchlorfenuron extremely hinders the growth of agricultural products. The residual Forchlorfenuron taken by humans also leads to damage of health. Various technologies were limited to fulfil both high accuracy and low cost in detecting Forchlorfenuron, so simple and time-saving technologies need to be developed. In this paper, highly sensitive and efficient Raman spectroscopy based on two-dimensional correlation technique were applied to detect the concentration change of Forchlorfenuron in ethyl acetate solution. The development of this technology provided theoretical basis of relevant analysis method for detecting Forchlorfenuron, which has a great influence on food safety. Raman spectrogram of Forchlorfenuron powders and its formula were used to analyze the Raman peaks. Different concentration of 2.5, 5.0, 7.5, 10.0, 12.5, 15.0, 17.5, 20.0 g·L-1 Forchlorfenuron in ethyl acetate solution were also tested to analyze the relationship of Raman peaks with the concentration change by using the synchronous and asynchronous spectroscopy of Forchlorfenuron. The result of synchronous showed that a synergetic effect of the peaks occurred at 842, 992, 1 044, 1 442, 1 604 cm-1 and the intensity of peaks increased with the rise of Forchlorfenuron concentration. Meanwhile, The results of asynchronous spectroscopy showed that the sensitivity of peaks had the following relationship: 1 044 cm-1>992 cm-1>842 cm-1, 1 735 cm-1>1 604 cm-1>1 442 cm-1, 842 cm-1>1 735 cm-1. Raman characteristic absorption peaks of Forchlorfenuron in ethyl acetatesolution were determined to be 842, 992, 1 044, 1 442, 1 604 and 1 735 cm-1, respectively, among which the peaks at 1 044 cm-1 (stretching vibration of benzene), 992 cm-1 (breathing vibration of pyridine), 842 (C—N—O asymmetrical stretching vibration) and 1 735 cm-1 (CO stretching vibration)were more sensitive to the concentration of Forchlorfenuron and the sensitive order were stretching vibration of benzene, breathing vibration of pyridine, C—N—O asymmetrical stretching vibration, CO stretching vibration, CC stretching vibration of various coupling peaks and C—H deformation vibration. Such a combination of Raman spectroscopy based on two-dimensional correlation technique could exactly analyze the concentration change of Forchlorfenuron in ethyl acetatesolution, which will provide a new idea and method for the detection of Forchlorfenuroncontent in fruits and vegetables.

A prediction discrimination model of fresh and stale rice was established based on Raman Spectroscopy and Chemometrics. The pretreatment processes have to be employed before the experiment. A total of 60 samples were put in the special box. The samples were measured by 785nm Raman spectrometer, which can collect spectral range of 200~2 400 cm-1. Smoothing, baseline correction were conducted to process the raw spectra. Principal Component Analysis (PCA) was employed to reduce dimension analysis of full-wave band of fresh and stale rice, and it could classify the samples preliminarily. The discrimination model was developed with Partal Least Squares (PLS). The correct classification rates in the training set and prediction set were 100% and 95%, respectively. The results in this research indicated it is a quickly useful method to discriminate between fresh and stale rice.

The identification of counterfeit medicines has been a serious problem confronted by the contemporary world, particularly for the developing countries. Thus, anti-counterfeit innovations can help find solutions on avoiding the hazards to health and lives, as well as for the harmful effects on social morality and commercial culture. In this work, a modular Inverse Spatially Offset Raman Spectroscopy (Inverse SORS) system was built for overcoming the limitations of traditional Raman spectroscopy whose detection depth is confined to a few hundred micrometers. Besides, it can also effectively avoid the background of the container for realizing the detection and analysis of deep chemical components in a non-destructive and non-intrusive way by using different spatially offset values (Δs), which will lay the foundation for a simple and efficient method for direct detection of drugs. A 785 nm diode laser and WITec UHTS300 Raman spectrometer were employed to construct Inverse SORS system. During the experiments, a collimated beam passed through an axicon lens to form a ring beam, and its radius was adjusted by controlling the distance between the cone lens and sample. The Raman spectra of paracetamol and metronidazole in polyethylene（PE）bottles (with a 1.5 mm thickness) and polytetrafluoroelene （PTFE） centrifuge tubes (with a 4 mm thickness) were respectively measured by using a built-in spectral detection device. One of the container’s Raman peaks was selected as the reference peak for normalizing and processing acquired results. The featured Raman spectra of drugs were obtained by a scaled subtraction of the two spectra (Ring-Spot). The experimental results showed that the Inverse SORS not only avoided the optical background from the opaque container, but also truly presented the molecular fingerprint information of the sample inside. When the radius of the ring beam doubled its size, the intensity of acquired paracetamol spectra increased by six times, whereas the characteristics of Raman peaks of metronidazole in the PTFE tube increased by 100%. The above results showed that the inverse SORS can accurately detect the fingerprint spectrum of the chemical components inside the opaque/translucent container. Moreover, by optimizing system performance and adopting a variety of data processing methods, inverse SORS technology, which is expected to be a fast, accurate and convenient detection method, will be an indispensable complement to the existing pharmaceutical analysis technologies.

Dissolved Organic Matter (DOM) is a component that is sensitive to climate change in soil and plays an important role in environmental chemical behaviors such as transportation and transformation of heavy metals and carbon release. Meanwhile, the Qinghai-Tibet Plateau is one of the most sensitive region to climate change all over the world. However, few researches are focusing on the application of fluorescence spectroscopy to ascertain the impact of climate change on soil DOM and to reveal the response of environmental chemical behaviors of DOM to climate change. In this study, fluorescence spectral characteristics of DOM in meadow soils in Daban Mountain, Qinghai, under different altitudes (2 800, 3 000, 3 300, 3 600 and 3 900 m) was determined by using three-dimensional fluorescence spectroscopy and parallel factor analysis to reveal the response of the sources, compositions and properties of DOM to climatic conditions at different altitudes. The results showed that altitude had an important influence on the physical and chemical properties of the soil. With the increase of altitude, the soil pH decreased significantly, while the average organic matter increased from 6.32% to 13.75%. However, there was no significant change in the content of dissolved organic carbon at different altitudes. Additionally, altitude also had an impact on the origin and nature of DOM. The BIX index of DOM increased with the rise of altitude, indicating a more contribution to DOM in high-altitude soils by microbial sources, which may be due to a lower decomposition of plant residues and the mineralization of organic matter induced by low temperature at high altitudes. The FI index (1.332~1.621) was found to be lower than the eigenvalue indicating an autogenous source (FI=1.9) and the one indicating a terrestrial source (FI=1.4), showing that the DOM not only derived from the autogenous microbial activity, but also terrestrial input, such as plant residue and root exudates. However, the HIX index had no significant difference in soil DOM at different altitudes, indicating that elevation of altitude did not significantly change the degree of DOM humification. The results of parallel factor analysis showed that there were six organic components (C1—C6) in the DOM of meadow soil in Qinghai : which are two humic acid components (C2 and C4), two fulvic acid components (C1 and C3), and one water-soluble microbial by-product (5) and one protein-like component (6). Among them, fulvic acid-like and protein-like were the components with the highest proportion (54.69%~59.78%) and the lowest proportion (5.42%~8.47%) of DOM, respectively, while humic acids accounted for an average of 25.08% of DOM. The principal component analysis of the organic components in DOM at different altitudes showed that the samples of DOM at different altitudes were basically dispersed, indicating that the composition of DOM was responsive to altitude. The fulvic acid-like component (C3), the humic acid-like component (C4) and the protein-like component (C6) contributed the most to the differences in DOM compositions. With the increasing altitude, the relative proportions of C3 and C6 increased significantly, while the C4 decreased significantly. This indicated that the climate conditions at high altitude enhanced the production of fluvic acid and protein, but limited the production of humic acids. It could be concluded that the source, nature and composition of DOM in meadow soils of Qinghai have significant differences under different altitude conditions. And the results may provide theoretical basis for the assessment of soil carbon pool in Qinghai-Tibet Plateau and for the prediction of the transportation and transformation of heavy metals and carbon cycle under global climate change.

A contactless self-calibration temperature sensor based on the rare-earth fluorescence was developed. The new temperature sensing film Yb@PSMM was prepared by dispersed K［Yb(Az)4］ in poly (styrene-block-methyl methacrylate) and then attached to a clean quartz plate, and the optical properties of Yb3+ in this system under different temperature were investigated. The shape of the fluorescence emission spectrum of Yb3+ changed regularly with temperature, and the distribution of extra-nuclear electrons in the Stark cleavage sublevels of Yb3+ at different temperatures still obeyed Boltzmann distribution law. The natural logarithm (ln) of the ratio of the two characteristic emission peak areas at 900~990 and 990~1 150 nm in the fluorescence spectrum linearly varied with the reciprocal of temperature (1/T) from -195 to 105 ℃. Upon using this linear relation as the standard curve, this temperature sensing method exhibited a temperature resolution of 0.1 ℃ around 0 ℃. Compared with the reported luminescence temperature sensors, the new temperature sensor proposed in this paper had advantages as follows. Firstly, the Stokes shift of the selected luminescent material was larger than 500 nm, which effectively avoided the interference of environmental backgrounds. Secondly, due to the use of fluorescence integrated peak areas instead of fluorescence intensities, the influence of random errors introduced by the instrument or measurement was greatly reduced. Thirdly, by taking advantage of the radiometric relationship between the intensities of different fluorescence peaks in one compound, a reliable self-calibration was introduced in this system equality, which effectively reduced the influence of external factors such as the variation of fluorescent material concentration, geometric configuration, or light source intensity. Fourthly, as a rare-earth luminescence material, the sensing method could utilize the characteristics of long fluorescence lifetime, good fluorescence monochromaticity, and high fluorescence intensities. Fifthly, the temperature sensing film was almost insoluble and indiffusible in water, which was convenient for direct measurement of the in-situ temperature changes. Lastly, Yb3+emission was from 900 to 1 150 nm, due to the deep penetration of near infrared light, this temperature sensor would have a wide potential use in temperature-sensing and imaging of complex system. Further ensuring method for the measurement results of the temperature sensor was adopted in our measurement device: the irradiated spot size on the sample could be adjusted to be about 1 mm2, and the angle between the placement direction of Yb@PSMM film and the excitation light was set to be 225°. Thus, the influence of the reflected light was circumvented, but the fluorescent emission light was hardly affected.

High concentration of nitrates in water will not only cause water environment pollution but also pose a great threat to human health. The traditional methods for detecting nitrates have a long detection time and are complex to operate. In view of the difficulty in rapid on-line detection of nitrate nitrogen in water, a method combined a mixed prediction model with spectral integration was proposed to rapidly detect nitrate concentration in water based on ultraviolet absorption spectroscopy. The mixed prediction model is a model after data fusion of the dual wavelength prediction model established by low-concentration samples and the partial least-squares support vector machine (LS-SVM) prediction model based on the high concentration samples. According to the appropriate concentration gradient, 19 sets of nitrate nitrogen standard solution were equipped, and the spectral data of nitrate nitrogen samples of different concentrations were measured by experiment. First, Regression analysis was performed on all samples based on the dual wavelength method. Absorbance A was calculated for different experimental samples according to A=A220-2A275, where A220 and A275 were the absorbance of the samples at 220 and 275 nm. The values were linearly regressed to fit the predicted values of the sample concentrations. The results showed that when the sample concentration is small, the correlation is very good, and r is 0.997 4. The two-wavelength method is only suitable for the establishment of low-concentration samples prediction model with a serious nonlinear drift in the rising curve of the experimental samples concentration. For high-concentration samples, spectral overlap is severe and it is suitable for establishing nonlinear prediction models. Both support vector machine (SVM) and partial LS-SVM are suitable for nonlinear data modeling of small samples. The LS-SVM has a slightly higher prediction accuracy and a slightly faster operating speed. By performing full-wavelength spectral integration on all experimental samples and comparing the rate of change of the spectral integrals of adjacent samples, the critical concentration of the sample can be selected. The 4 mg·L-1 nitrate sample has the largest change rate before and after the integrated value, so it is appropriate to select 4 mg·L-1 as the the critical concentration value. The LS-SVM prediction model was established for experimental samples with concentrations higher than 4 mg·L-1. Cross-validation methods were used to select the appropriate parameters. The regularization parameter was γ=50, and the Gaussian kernel function width was σ2=0.36. The other samples were used to establish the dual-wavelength prediction model, and finally performed the data fusion of the two models, which formed the detection of nitrate from low concentration to high concentration. In order to verify the prediction accuracy of the mixed prediction model, the model of SVM, LS-SVM and PLS was established, and evaluated the model with mean absolute error (MAE), correlation coefficient (r), and root mean squared error (RMSE). The verification results showed that compared with other models, the correlation coefficient of the proposed mixed model regression is 0.999 86, which is increased by 1.8%, 1.6%, and 0.45% respectively, and the average absolute error between the predicted value and the true concentration is 2.55%, which decreased by 6.27%, 4.49%, and 1.01% respectively, and the root-mean-square error is 0.303, which is the smallest of the four prediction models. The relative error of SVM and LS-SVM is relatively high, and PLS model fluctuates up and down relatively. The relative error of mixed forecasting model is the most stable and remains at a low level, and the forecasting effect of mixed forecasting model is obviously better than that of other models. Compared with the measurement method in ［5-7］, this hybrid prediction method can simply and quickly measure the nitrate nitrogen concentration in water without reagents and no secondary pollution, andthe prediction accuracy is significantly improved compared with the model in [9]. Therefore, the proposed mixed model can correctly and quickly predict the concentration of nitrate in water, and provide an effective technical reference for on-line monitoring of nitrate concentration in water.

In this study, we selected Egeria najas as the sample plant, which was exposed to different concentrations of Zinc oxide nanoparticles (ZnO NPs) suspensions for six days. The effects of different concentrations of ZnO NPs on photosynthetic processes of Egeria najas were explored respectively, by analyzing the O-J-I-P fluorescence induction dynamics curve and the pulse transient fluorescence induction dynamics curve. ZnO NPs strengthened the connectivity between photosystem Ⅱ (PSⅡ) units, promoted the efficiency of the electron transport at the acceptor side of PSⅡ and the utilization of the absorbed light energy, indicated by the significant decrease (p<0.05) in the net rate of PSⅡ closure （MＯ）, the relative variable fluorescence intensity at phase J （VJ） and the effective dissipation of an active RC（DI0/RC）, and the significant increase (p<0.05) in the maximum quantum yield of primary photochemistry （ΦＰ０）, the efficiency with which a trapped exciton can move an electron into the electron transport chain further than Q－Ａ（Ψ０）, the quantum yield of electron transport （ΦＥ０） and the effective quantum yield of electron transport at PSⅡ （′PSⅡ） after exposure to ZnO NPs suspensions. These results suggested that ZnO NPs improved the photosynthetic performance to some degree. Corresponding concentrations of Zn2+ solution was also used to cultivate Egeria najas. Zn2+ lowered the connectivity between PSⅡ units, inhibited the electron transport at the acceptor side of PSⅡ and the utilization of absorbed light energy and damaged the PSⅡ reaction centers, as indicated by the significant increase (p<0.05) in the net rate of PSⅡ closure, the relative variable fluorescence intensity at phase J, the effective dissipation of an active RC, the effective antenna size of an active RC （ABS/RC）, the energy trapping capacity per active PSⅡ RC (TR0/RC), and the quantum yield of dissipation through fluorescence and basal thermal processes （′ＮＯ） and the significant decrease (p<0.05) in the maximum quantum yield of primary photochemistry （ΦP0）, the efficiency with which a trapped exciton can move an electron into the electron transport chain further than Ｑ－Ａ（Ψ０）, the quantum yield of electron transport （ΦＥ０） and the effective quantum yield of electron transport at PSⅡ （′ＰＳⅡ） after exposure to Zn2+ solution. These results suggested that Zn2+ inhibited photosynthetic processes of Egeria najas. When the sample plant was exposed to ZnO NPs suspensions, the effect of the Zn2+ released from ZnO NPs suspensions on the sample plant was not obvious, which meant that the enhancement was stronger than the inhibition.

Excessive nitrate in water may influence some aquatic organisms’ survival and cause harm to humans, especially infants. Therefore, nitrate concentration becomes an important indicator in water quality monitoring. Due to the complexity of operation and slow response of conventional methods for measuring nitrate concentration, many researchers have begun to use ultraviolet/visible (UV-Vis) spectroscopy combined with artificial neural network (ANN) methods to measure nitrate content in water. This paper proposes a modeling method combining locally linear embedding (LLE) in manifold learning with back propagation neural network (BPNN). The relationship between the spectral curve of nitrate and the concentration was obtained, so that a rapid and accurate quantitative analysis of the nitrate concentration in the wheat island of Laoshan District, Qingdao was achieved. In the experiment, we selected 59 groups of spiked solutions with different concentrations of filtered wheat island seawater, and collected spectral measurements of these samples using a laboratory-developed spectrum analyzer, with standard normal variate (SNV) method calibrating spectral data of measured nitrate solution to reduce the noise caused by the instrument itself or the environment. First 1 500-dimensional of the pre-processed spectral data was used to avoid insufficient memory when using the entire 2 048-dimensional data to build BPNN model, and a control experiment was performed. Then the number of neighboring points k and the embedding dimension d in the LLE were optimized by the grid search combined with the ten-fold cross validation method, obtaining the optimal k=15, d=3. Then the dimension of the experimental data was reduced. The spectral information of the reduced-dimensional training set and its corresponding concentration information were modeled by the BPNN to achieve a quantitative analysis of the nitrate concentration in the prediction set. Coefficient of determination (R2) and root mean square error of prediction (RMSEP) were introduced to evaluate modeling effects. And compared with the predicted results obtained by only using BPNN modeling, R2 of our improved method increased from 0.926 3 to 0.992 8, and RMSEP decreased from 0.442 5 to 0.280 4, and prediction modeling program run time decreased from 327 s to about 0.5 s. In addition, we used all 2 048 dimensions of the 59 data sets for LLE-BPNN modeling, with R2=0.995 7 and RMSEP=0.136 5, which was improved compared to the modeling accuracy when only using the first 1 500 dimensions, while elapsed time was similar. The analysis results above showed that using the LLE-BPNN method can achieve a rapid prediction of nitrate concentration in seawater, while significantly improving prediction accuracy and reducing prediction time.

As an very important part of the Qinghai-Tibet Plateau ecosystem, it is of great significance to study alpine shrubs. But for a long time, due to the remote location and underdeveloped transportation, as well as the harsh growing conditions, the alpine shrub on the Qinghai-Tibet Plateau has been less studied. Remote sensing detection technology can overcome the difficulties caused by geography and environment and can be used to detect large areas and non-destructive. Therefore, remote sensing detection technology can be used to study alpine shrubs in Qinghai-Tibet Plateau. As the traditional high-resolution remote sensing detection technology is often adopted with three bands of RGB, the discrimination accuracy of different plants is low, and the difference of NDVI′ index and RVI′ index of corresponding plants is small, which cannot effectively distinguish various types of vegetation. At the same time, hyperspectral reflectance curve and irradiance curve contain spectral information of thousands of bands. If a single band is selected for plants detection, the loss of spectral information is very large, and the characteristics of thickets reflected are not obvious, resulting in low confidence. In order to distinguish the alpine shrub vegetation, this paper uses hyperspectral technology to carry out spectral characteristic analysis of the shrub, providing theoretical support for remote sensing detection of the shrub on the Qinghai-TibetPlateau. The research draws support from FieldSpec4 high resolution spectrometer of the America. It was used to identify 6 shrubs (Rhododendron capitatum, Caraganajubata, Potentillafruticosa, Salix cupularis, Daphne tangutica and Berberisdiaphana) grown in the eastern Qilian Mountains through measuring the reflectance rate and absorption rate, calculating the first order differential of absorption rate (GREF and GABS) to enlarge the resolution of spectral curve, screening the sensitive wavelength, and then identifying different shrubs by calculating their values with NDVI and RVI. The result indicated that (1) the absorption spectral curves of shrubs were similar with most plants, but their first absorption valley shifted to left; (2) the shrubs performed unique spectral features in some sensitive wavelengths, and these features could be used to improve the resolution by REF, ABS, GREF and GABS transformation to identify the shrublands; (3) The spectral values of the 6 shrubs are different, and the relatively stable wavelengths are 550～680, 860～1 075, 1 375～1 600 and 1 900～2 400 nm. Therefore, these 4 wavelengths can be selected as sensitive areas to identify shrub plants; (4) NDVI and RVIcalculated with the REF average value of sensitive wavelengths of 575～673 and 874～920 nm and/or the area value of sensitive wavelengths of 685～765, 556～590, 635～671 and 1 117～1 164 nm could effectively identify 6 shrubs.

It is a significant technique for choosing proper solvents during liquefaction of biomass process to produce fuel additives and valuable chemicals. In this study, the mixtures of new low-cost diethylene glycol (DEG) with 1,2-Propanediol (PG) and the traditional ethylene glycol (EG) with PG (6∶1 ω/ω) were adopted as liquefying agents. And analyses were conducted to throw light on the effects of these two different liquefying agents on the liquefaction yield and the properties of the biomass liquefaction oil products. The properties of the corn stalk, bio-oil and residue were studied with gas chromatography and mass spectrometry (GC-MS), Fourier transform infrared spectroscopy (FTIR), pyrolysis-gas chromatrography/mass spectrometry (Py-GC/MS) and X-rays diffraction (XRD). The results indicated that when the DEG and PG were cooperatively used as liquefying agent, the liquefaction yield was 98.57%. And there was a liquefaction yield of 96.08%, with the mixture of EG and PG as liquefying agent. GC-MS analysis results showed that the main components of bio oils were alcohols and organic acids, with a total content of more than 97%. However, the bio oil obtained by EG mixed PG liquefaction contained nearly 60% of organic acids, which was the main cause of the acidity and corrosiveness of the bio oil, and was not conducive to the liquefaction. The characteristic absorption peaks of the corresponding functional groups of some oligomers in the bio oil were detected by FTIR to compensate for the limitation of the GC-MS characterization. It turned out that many active chemical bonds were generated in the liquefaction system, leading to improving the activity of the reaction system, and the bio oil contained a large number of C—O and CO functional groups, which strongly supported the results of the conclusions of GC-MS. Furthermore, Py-GC/MS, FTIR and XRD were applied for the characterization of the liquefaction residues. Py-GC/MS explained that the liquefaction residue composition produced in this way was complicated and contained a certain amount of large molecular substances which were very difficult to degrade. The liquefaction residues were mainly originated from the polycondensates or derivatives of interactions between small molecules of lignin or hemicellulose degradation or unreacted cellulose, and the macromolecular substances generated by the reaction of the degradation products of three components and liquefying agents. The signals of FTIR reported that the functional groups of cellulose, hemicellulose and lignin were disappeared and the liquefaction degree of lignin was the largest. Results from XRD presented that because of the destruction of crystalline structure of carbohydrates, the cellulose molecules was cracked, indicating that the cellulose was degraded and the degree of liquefaction was high. Consequently, all the results successfully confirmed that the liquefaction effect of DEG mixed PG was better than the mixture of EG and PG. Even it provided an efficient and environmental process for generating bio-oil from lignocellulosic mass at a low cost in liquefaction of corn stalk.

The quality and freshness of vegetables not only affect the taste, but the nutrient content. The research on detection of chlorophyll and water content that are important reference indexes of vegetables quality and freshness has become more attention to the researchers both at home and abroad. With the quickness, high efficiency, non-destruction and non-contact features, the novel visible/near-infrared spectral analysis technology is more suitable for real-time detection of vegetables, comparing wtih traditional estimating methods by naked eyes. The relevant research is primarily focus on retrieval of growing vegetation chlorophyll and water content at present. There is little research aiming at ripe vegetables in market, or lacking of universality because of single species. Moreover, collecting of spectral data requires professional Field Spectrometer, wasting time and energy. There is a distance between research of physiological and biochemical index and practical application. In order to combining the research with the real life, this paper builds quickly, precise, universal models that can retrieve chlorophyll and water content in vegetables, based on Smart Cellphone Spectral System(SCSS). Simultaneously, SVC are used to validate the reliability of SCSS. Five kinds of common vegetables (spinach, rape, romaine, lettuce and baby cabbage) are selected as samples in experiment, and the ways of cold storage and normal temperature preservation are used to simulate the market and supermarket environment. Datas are collected per 24 hours. Then Band-Selecting and Wavelet-Transform preprocessing are adopted to improve the quality of spectral data. This paper constructs Vegetable Chlorophyll Retrieval Index (VCRI) and Vegetable Water Retrieval Index (VWRI), and extracts the correlation coefficients between the two indexes and measured values of chlorophyll and water content as weight coefficients. Finally, the chlorophyll and water content retrieval models are built. The result shows, SVC and SCSS have the same sensitive bands to chlorophyll and water content. The sensitive wavelength for chlorophyll retrieval is from 730 to 980 nm. The precision R2 are 0.863 and 0.8081, and standard deviation are 8.679 5 and 8.892 5 respectively. The sensitive wavelength for water content retrieval is from 950 to 1 000 nm. The precision R2 are 0.742 9 and 0.712 9, and standard deviation are 8.789 9% and 8.861 4% respectively. The result of SVC and SCSS is similar enough to prove the validation of new-style Smart Cellphone Spectral System. Furthermore, SCSS has the advantage of small size and low price. It can smartly detect the quality and freshness index of vegetables, with the features of internet cloud services and data feedback in real-time. This makes the spectral analysis technology applying to the people’s daily life.

Annual ring parameter is an important indicator of tree growth. The current annual detection methods mainly include manual measurement, scanner method and X-ray method. These methods are time-consuming, labor-intensive, expensive to detect, and difficult to operate. For this reason, this paper proposed the method of using the visible spectrum to detect annual ring parameters. A special core analysis device was designed. The device is consist of a wide-spectrum symmetrical light source, a closed dark box, and a color CCD which is assembled external. Taking pine wood core as an example, The polished wood core is fixed horizontally on the stage, and capture images of the sample. Based on spectral analysis and extracting RGB grayscale intensity images, we can identify boundaries of early-wood and late-wood, and then a series of characteristic parameters are gained. Quick acquirement of tree-ring parameters by tree-ring picture processing can be realized. First, converts the RGB image acquired by the CCD to the NTSC color space to expand the color domain. Then, sets the filter window to filter out the background and cutting out the wood core image, by extracting the R, G, and B grayscale component images of the wood core image, it is found that the wood core B gray image has the most distinct difference in the early and late material regions. Based on this feature, the positional information of the boundary line of the early and late materials can be extracted. Differentiate the grayscale component map of the wood core B to obtain the spatial gradient of the gray level in the horizontal direction. The points corresponding to the maximal value of the gray change rate are determined. In view of the growth characteristics of the wood core, the maximum value of the gray change rate corresponding to the spatial position is first taken as a narrow pixel region, and then an intermediate value is taken in a narrow pixel region. Among them, the center point of the early material corresponds to the maximum value of the spectral curve, and the center point of the late material corresponds to the minimum value of the spectral curve. Combined with expert experience, establish the gray relationship between the center point of the early and late materials and the boundary line, and the position of each boundary line can be obtained. The indicators of annual rings can be further derived from the relationship between the boundaries of the wood and the annual rings. Comparing with the results of artificial identification of three forest tree breeding experts, the results of this method have extremely high accuracy, except for the position of the wood core near the end point. Using the data acquisition and analysis method of the visible spectrum channel to detect tree annual ring parameters, the detection process can be fully automated, highly efficient, and non-destructive. The accuracy can reach 0.1 mm and the result is accurate. Compared with the manual measurement method and the scanner method, the detection efficiency is higher; compared with the X-ray method, the detection process is safer, lower in cost, and more convenient to operate. It is a method with strong application.

The chlorophyll within crop leaves and crop canopy produces energy and participates in photosynthesis process by absorbing sunlight. Therefore, it is important to estimate the crop canopy chlorophyll content timely and accurately for crop growth monitoring, nutrient content monitoring and crop quality evaluation. Sentinel-2 has a wide-swath sensor with 5-days revisit period, so the Sentinel-2 image is produced with high spatial resolution (10 m) and 13 spectral bands. Specially, there are three red edge bands in Sentinel-2 image, which are sensitive to crop canopy chlorophyll content and its change. So the Sentinel-2 image is an ideal remote sensing data source for chlorophyll content estimation. Vegetation indexes depict the difference for the crop between different growth conditions and different chlorophyll contents, through the band combinations based on the reflection characteristics of crops at different spectral bands. So the vegetation indexes from Sentinel-2 image can be used to estimate the corn canopy chlorophyll content timely and accurately in a regional area. Therefore, this study is focusing on estimating the corn canopy chlorophyll content using 10 kinds of vegetation indexes computing from Sentinel-2A remote sensing images. And the study area is located in three counties of Baoding City, Hebei Province, ranging from 115°29′E to 116°14′E, 39°5′N to 39°35′N. We measured the corn plant chlorophyll content in 24 sampling areas distributed randomly in the whole study area from 6 August to 11 August, 2016. And each sampling area was located using Huace i80 real-time kinematic (RTK) GPS receiver (Huace Ltd., Shanghai, China). The Sentinel-2A image was preprocessed including geometric correction, radiometric calibration and atmospheric correction, and Sen2Cor model and SNAP were used to do atmospheric correction. 10 vegetation indexes were computed including CIgreen(Green Chlorophyll Index), CIred-edge(Red-edge Chlorophyll Index), DVI(Difference Vegetation Index), LCI(Leaf Chlorophyll Index), MTCI(MERIS Terrestrial Chlorophyll Index), NAVI(Normalized Area Vegetation Index), NDRE(Normalized Difference Red-Edge), NDVI(Normalized Difference Vegetation Index), RVI(Ratio Vegetation Index), SIPI(Structure Insensitive Pigment Index). Secondly, the statistical correlativity was analyzed between these 10 vegetation indexes and measured chlorophyll content value for every sampling area. So the corn canopy chlorophyll content estimating was developed using this correlation analysis results. Lastly, the optimal chlorophyll content estimation model was selected to estimate the chlorophyll content in the whole study area. This study was focusing on (1) developing the estimation model for corn canopy chlorophyll content in the study area, and the accuracy was assessed using R2, RMSE and RE; (2) deciding the optimal band combination; (3)deciding the optimal amount of red edge band participating in vegetation indexes calculation. The accuracy assessment results indicated that (1) there was polynomial correlation between measured chlorophyll content and the selected 10 vegetation indexes in this study, and the accuracy of estimated chlorophyll content using the vegetation indexes considering the red edge bands is better than the ones without red edge bands. The CIgreen(560, 705)and DVI which were all considering red edge bands improved the chlorophyll content estimation accuracy, and the R2 improved 0.516 for CIgreen(560, 705). The statistical relationship between the measured chlorophyll content and the vegetation index in the field work was established, and the relationship was extended to the whole study area. This study was about the estimation of corn canopy LAI and chlorophyll content using these ten vegetation indexes, which was focusing on the following four parts. Firstly, we compared if the vegetation with or without red-edge band could get accurate LAI and chlorophyll content estimated result. Secondly, we added two red-edge bands to the vegetation indexes without red-edge band originally. Thirdly, we added two red-edge bands to the vegetation indexes with one red-edge band originally only. Fourthly, we set up the vegetation index with two red-edge bands. The results showed that there are polynomial regression between the selection of multi-VI and the field survey of canopy chlorophyll content. Because the introductions of the red edge band, the fitting accuracy improved more than 0.3 between the vegetation index and corn canopy chlorophyll content, and the CIgreen (560, 705) (Green Chlorophyll Index) improved 0.516 that is the highest. The index calculating between the visible light band and the first red edge band (705 nm), the near infrared band with the second red edge band (740 nm), both of which established the regression model with the field survey of corn canopy chlorophyll content, and promoted the best fitting precision. The MTCI (MERIS Terrestrial Chlorophyll Index) has the highest fitting precision in which the R2 is 0.803, RMSE is 3.185, RE is 4.819%. It is shown that adding the red edge band will improve the fitting precision and it is suitable for crop growth monitoring.

The problem of soil heavy metal pollution has always attracted attention. Therefore, many results have been achieved in this field by the research on the use of hyperspectral remote sensing, mainly focusing on predicting heavy metal content in soil using conventional methods such as derivative of soil spectra and continuous continuum removal. The soil spectral data showed tremendous similarity with non-linear non-stationary mechatronic signals, medical signals, etc. In this study, HHT was used to analyze the soil’s lead (Pb) pollution experimental spectra in the frequency domain. The purpose of the HHT analysis was to achieve the HHT identification of soil’s Pb pollution spectra, and establish the model for predicting Pb content in soil. Firstly, the soil Pb pollution experiment was conducted to collect the spectrum, water content, and organic matter content of soil Pb-contaminated samples. Secondly, the HHT time-frequency analysis and the second derivative of the instantaneous frequency of the second intrinsic mode function (IMF2) component of the Pb pollution spectra of soil samples were used to identify the characteristic bands of soil Pb contamination. Finally, the prediction model of soil Pb content that the parameters were appropriate frequency domain parameters, soil first-order derivative, soil organic matter content, and soil water content was established using boxplot, cluster analysis, and partial least squares. The results showed that HHT time-frequency analysis charts of soil Pb-contaminated could identify soil Pb contamination spectra. There was no abnormal signal in the band sequence between 250 and 430 from HHT time-frequency analysis plots of uncontaminated soil spectrum. There were many abnormal signals in the band between 250 and 430 from soil spectral HHT time-frequency analysis plots under Pb contamination, and with the increase of the concentration, the abnormal signal distribution range became wider and wider. When the pollution concentration reached 800 μg·g-1, a strong abnormal signal was obtained in the band sequence of 270 and the frequency before 0.3 Hz. The mutation of second derivative of IMF2 instantaneous frequency of uncontaminated soil spectrum was very weak after the EMD, while there were obvious mutation points of Pb-contaminated soil spectrum. The characteristic wavelength band of soil Pb-contaminated soil spectrum was 2 150~2 300 nm according to the mutation points and second derivative of IMF2 instantaneous frequency of Pb-contaminated soil spectrum. Six groups of abnormal samples were removed from boxplot based on Hilbert energy spectrum peaks, EMD energy entropy, first derivative, organic matter and water content under different Pb concentrations. Then the Pb-contaminated soil samplings were divided into two categories by cluster analysis. Finally, Hilbert energy spectrum peak, EMD energy entropy, spectral first derivative of 2 134, 790, 1 276 and 2 482 band, organic matter and water content were used as parameters. The BC-PLSR (Boxplot Cluster-Partial Least Squares Regression) models for the data of two categories were established to predict Pb content in soil. The accuracy of the validated model was high, and the correlation coefficients were 0.88 and 0.99, respectively.

Self-modeling curve resolution can resolve the bilinear spectral datasets as the profiles of pure signal and their contributions which can be explained easily in physical and chemical meaning. With the advantage that their results can provide the pure spectra and the corresponding relative concentrations in the analyzed complex systems, MCR methods have been widely applied in the analysis of hyperspectral imaging. However, when the constraints applied are not strong enough, multivariate curve resolution models for bilinear data always suffer the problem of order ambiguity, scale ambiguity and rotation ambiguity which induce non-unique solution. Rotation ambiguity is the most difficult to be removed. To investigate the level of rotation ambiguity and provide the range of feasible solutions, in the published works, the researcher used grid search or Monte Carlo random sampling to display some feasible solutions fulfill the bilinear model under certain constraints. In this way, the concentrations and pure spectra results resolved by MCR can be better explained for their application by providing a range of feasible solutions. Polygons projected by feasible solutions based on geometry were also employed to illustrate the feasible solutions and the extension of rotation ambiguity. These methods normally are time consuming and cannot be used in the system with more than four components. More importantly, they cannot apply different constraints based on the properties of the analyzed samples, except non-negativity. In this work, we applied MCR-BANDS to evaluate the level of rotation ambiguity for resolved by MCR-ALS on the remote sensing hyperspectral imaging. In the first part, the mineral spectra selected from United States Geological Survey Committee were used for simulating a hyperspectral imaging dataset. In the simulated dataset, the noise level can be controlled and the differences of the spectral features between different components were easily identified. The concentrations of different components were simulated the real conditions, with gradual changes in the space. The rotation ambiguity was evaluated in the simulated data by using MCR-BANDS. To better explain the application of MCR-BANDS, this method was used to analyze a remote sensing dataset collected by Airborne Visible Infrared Imaging Spectrometer (AVIRIS), and the affection of rotation ambiguity on different components was visually displayed as concentration distributions in maps. The results show that MCR-BANDS can provide the level of feasible solutions of MCR-ALS by using maximum and minimum signal contribution functions (SCCF). This method can be applied to the system with any number of components, and can use almost all of the constraints which are chosen in MCR-ALS, like non-negativity, unimodality, closure, selectivity/local rank etc. The concentration distribution results from maximum and minimum SCCF are helpful to locate the specific targets in the remote sensing hyperspectral imaging.

Seeds are the basis of the agricultural industry. The viability of seedsis a very important index of seed quality, which is closely related to resistance to biotic and abiotic stress, germination percentage, plant performance, and which decreases with increasing storage period.Increased understanding of wheat seed viability would be beneficial to the wheat industry by ensuring a higher yield for farmers and reducing crop variability. Seed companies would also benefit from enhanced viability by being able to ensure a higher quality product. As the viability of seeds was gradually brought to the public attention, the rapid detection of seed viability without destroying has been a research hot spot. This study aimed at investigating the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging (HSI) technique to discriminate viable and nonviable wheat seeds. Firstly, 190 wheat seeds treated by high temperature and high humidity aging (128 germination samples and 62 non-germination samples) were prepared as experimental materials. The visible and near-infrared hyperspectral imaging acquisition system （400~1 000 nm） was constructed to acquire the hyperspectral images of the wheat seeds. After HSI spectra collection of the wheat seeds, a germination test was implemented to check for seed viability. We recorded a seed as germinated (yes=1) if the plumule and radicle were both over 2 mm long, and non-germinated (no=2) if not. The average reflectance data of the region of interest were extracted for spectral characteristics analysis. Secondly, different pre-processing algorithms including the first derivative (FD), orthogonal signal correction (OSC), multiplicative scatter correction (MSC), mean centering (MC) were conducted to build partial least squares discriminant analysis (PLS-DA) model of the viability of wheat seeds. Lastly, three variable selection methods including the uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to screen the characteristic wavelengths related to seed viability. PLS-DA models were established by these characteristic wavelengths. The results showed that, the classification accuracies of different pre-processing algorithms were diverse. Among them, the MC method was the best pre-processing algorithm, from which the overall classification accuracy were 82.5% and 83.0%, and the viability classification accuracy were 94.8% and 90.6%, in calibration and prediction sets, respectively. Among the single variable selection methods, UVE method was superior to other two variable selection methods while maintaining an excellent performance of the model for overall classification accuracy (84.6%, 83.0%) and viability classification accuracy (86.5%, 78.1%) in the calibration and prediction sets. This model could promote the germination percentage of the seed lot from 67.4% to 96.2%. Comparing all variable selection methods comprehensively, the UVE-CARS-SPA method selected only 8 variables (473, 492, 811, 829, 875, 880, 947 and 969 nm) from the all 688 spectral variables. The PLS-DA model built by using UVE-CARS-SPA method exhibited the optimal performance with overall accuracy of 86.7% and 85.1% for calibration and prediction, respectively, and accuracy for viable seed was 93.8% and 84.4%. After screening by this model, the germination percentage of the seed lot enhanced from 67.4% to 93.1%. The results indicated that appropriate variable selection could improve the performance of a model, simplify the classification models, and increase the classification accuracy of viable and nonviable wheat seeds. In the future, combining the visible and near-infrared hyperspectral imaging technique with MC-UVE-CARS-SPA-PLS-DA can be used as a feasible and reliable method for the determination of seed viability during the storage. The result can provide the theoretical reference for rapid detection of seed viability during grain storage using spectral information.

How to estimate the coverage rate of oil spills is an important part of the sea oil spills detection. To be limited by spatial resolution of airborne hyperspectral remote sensing image, it is difficult to detect the coverage of oil spills accurately. Under the action of ocean waves and broken waves, the oil spill tends to be banded distribution. Because there are a lot of oil and water mixed pixels in the hyperspectral data, the traditional image segmentation method which is used to calculate the oil spill area was mistaken in many ways. It is difficult to find the pure spectral spectrum because the traditional extraction method is influenced by the angle and height of the sensor and the spectral variation. In this paper, we proposed a second extraction method of endmembers. Firstly, N-FINDR algorithm is used for the endmembers’ preselection. Secondly, the Independent Component Analysis (ICA) is used for the ultimate refinement of endmembers’ selection and the candidate endmembers are obtained according to the maximum output of negative entropy. Thirdly, the ground synchronous reference spectra are used as constraints to identify the similar oil spill terminals. Finally, the end members’ abundances are obtained based on the nonnegative matrix decomposition method (NMF) and the real oil spill coverage are obtained through correction of solar flare region. The extraction of partitioned mixed endmembers is a good solution to the problem of global endmembers mutation and poor environment adaptability, so that the selected endmembers have better environment robustness. In order to evaluate the accuracy of this algorithm, the simulation data and the real hyperspectral image data are combined to verify the experiment. In the simulation experiment, the difference between the estimated abundance and the set abundance are evaluated by using the mean square error (RMSE) and the abundance estimation error, the algorithm adaptability and anti-noise experiment are designed. The result indicated that, under two hyper-spectral compression case by MNF and PCA, estimation error of abundance is less than 3%. The minimum RMSE of reconstructed image is up to 0.030 6 and has good anti-noise ability. The accuracy evaluation results verify the effectiveness of the proposed algorithm. In the real experiment, 8 hyper-spectral remote sensing image collected by airborne of Shandong Changdao in 2011 are used for real test data. Because the real remote sensing data often lacks the ground synchronization abundance data, it is difficult to evaluate the accuracy of the algorithm. The combination of simulation data with verification and visual interpretation data are used to evaluate the accuracy. The total oil spill coverage area of airborne hyperspectral imaging estimated by the flare area is 1.17 km2, the oil spill coverage is 22.85%, and the field artificial estimation area deviation is 2.15%. Obviously the method is superior to the traditional method. It is difficult to analyze the abundance of single pixel in ocean oil spill remote sensing because it is influenced by the ocean breaking wave, spectral variability and the limitation of ground resolution of aerospace remote sensor. Based on the idea of the abundance decomposition of the image, this paper discusses the problem of the coverage of ocean oil spill, and puts forward a comparatively perfect method for calculating the covering degree of ocean oil spill. The method is validated through the simulation data and the actual hyperspectral oil spill data. The method is an objective automatic oil spill coverage (abundance) detection method and could realize the automatic monitoring of oil spill coverage rate. It is meaning for fine detection of oil spills area.

Spectral absorption characteristics are used in the analysis of soil, mineral and plant material composition with spectral measurement technology, which is a hyperspectral remote sensing technology developed in recent years. It has many advantages, such as fast speed, high efficiency, low cost, low loss and so on. Rare earth is a strategic element with rich and unique physical and chemical properties, such as magnetic, optical, electrical, etc., which is widely used in aerospace, electronics, petrochemical, metallurgy, machinery, energy, agriculture and other fields. It is indispensable strategic material for the development of high-tech and cutting-edge national defense technology and the transformation of traditional industries in the world today. In recent years, As a result of the increasing demand and the enhancing value of rare earth resources, it has become an important research area to discover how to detect rare earth resources rapidly in large area and implement rare earth mining properly. Through spectral collection and analysis of rare earth minerals, this study carried out a series of researches on the correlation between rare earth elements and their chemical features. During the study, 12 rare earth mineral samples were collected from Liutang rare earth ore area of Chongzuo City, Guangxi, and the corresponding reflectance spectrum data were measured by using SVC HR1024I portable ground object wave spectrometer in laboratory. Continuous dispatch is implemented for the measured spectral features of the samples, and relative absorption analysis is carried out for prominent diagnostic absorption wavelength. Thus the linear relationship between the spectrum and the total content of rare earth elements and the contents of rare earth elements in ore samples was established according to its spectral characteristics. The results reveal that the five characteristic absorption bands of rare earth elements are 370, 950, 1 400, 1 900 and 2 200 nm in visible light and near infrared, respectively. The intensity of the five absorption band is related to the total rare earth content linearly, with R2 reaching 0.69, also discovered that the correlation between rare earth content and the visible light band is larger, and the correlation analysis between the visible light wave band and the total rare earth content of the sample was carried out. The 10 bands which have the strongest correlation with the total content of rare earth are 340, 350, 360, 370, 390, 400, 420, 480, 550 and 760 nm, respectively. The linear regression method is used to get the prediction model of the reflectance value and the total sample content of visible bands with high accuracy with R2 greater than 0.95. Also linear modeling is established by using the visible light wave band and 15 rare earth elements content values, with the correlation coefficient may reach above 0.9, which also shows that each single rare earth element has a strong correlation with the visible light wave region. By studying the spectral characteristics and chemical analysis of rare earth mineral samples, a linear regression analysis was carried out for 5 diagnostic absorption wavelengths and the visible band and the total rare earth element content of the samples, and for 15 kinds of rare earth element contents. The quantitative evaluation model of rare earth content in ore samples is established, which has certain reference value for rapid quantification-semi-quantitative evaluation of rare earth ores, and lays a theoretical foundation for extracting mineral information from hyperspectral remote sensing of rare earth ores and elements. It provides a scientific and effective theoretical basis for the ultimate realization of efficient exploitation of rare earth resources, reducing consumption and production costs at the source, reducing environmental damage and pollution, and promoting the strategic development and utilization of medium-heavy rare earth resources.

Variable rate fertilization in precision agriculture depends on the understanding of the distribution of soil nutrients in the farmland. The rapid acquisition of soil information is the basis for the application of precision agriculture. Available potassium is an important parameter of soil fertility, and it is a necessary nutrient element for plant growth. The measurement of the content of available potassium in soil is an important way to understand the soil fertility, and it is a precondition of the realization of precision agriculture. In this paper, a total of 169 farmland plough cinnamon soil samples were collected in Shanxi province. All samples were air dried, with the larger soil particles crumbled and impurities removed manually and directly used for measuring soil near infrared hyperspectral without grinding and sieving. According to the measuring results of the available potassium content in the laboratory, all soil samples were divided into two parts. There were 144 samples of available potassium content less than 100 mg·kg-1, and 108 samples were randomly selected as the low content modeling sets (Lc), and the remaining 36 samples as the low content validation sets (Lp). There were 25 samples of available potassium content more than 100 mg·kg-1, and 19 samples were randomly selected as the high content modeling sets (Hc), and the remaining 6 samples as the high content validation sets (Hp). Lc and Hc were collectively known as all modeling sets (Tc), and Lp and Hp as all validation sets (Tp). Near infrared hyperspectral imaging technology was used to obtain near infrared hyperspectral images in the range of 950~1 650 nm of all soil samples. There were five different spectral data preprocessing methods used in this paper: the average spectral curve (R), the first derivative of the average spectral curve (FD), the average spectral curve and the first derivative co-modeled (R&FD), the product of the average spectral curve and the first derivative (R*FD) and the quotient of average spectral curve and first derivative (R/FD). Combined with partial least squares (PLS) method, the models were built using the modeling set Tc, Lc and Hc respectively. The validation sets Tp, Lp and Hp were verified respectively. The results showed that: along with the increase of available potassium content, the average spectral reflectance of soil increased first and then decreased. When the content of available potassium was less than 100 mg·kg-1, the spectral reflectance of all bands increased with the increase of available potassium content. When the content of available potassium was between 100~200 mg·kg-1, the spectral reflectance of all bands reached the maximum. When the available potassium content was more than 200 mg·kg-1, the spectral reflectance of 950~1 400 nm decreased sharply, but the overall slope of the curve increased significantly. The higher the available potassium content was, the larger the overall slope of the curve was. When the content of available potassium was higher than 100 mg·kg-1, the first derivative of the average spectral curve increased significantly, and increased with the increase of available potassium content. The PLS models proposed in this paper could predict the whole (all available potassium content) and high content (≥100 mg·kg-1) of available potassium effectively; but could not predict the low content (≤100 mg·kg-1) of available potassium. The best spectral preprocessing method was: R*FD, followed by FD and R. The predict results of R&FD and R/FD were relatively poor. The optimal modeling method was R*FD combined with Tc. The number of PLS principal factors was 2, RMSEc=29.293, RPDc=4.669, R2c=0.956; RMSEp=29.438, RPDp=4.740, R2p=0.958 for the validation sets of Tp; RMSEp=23.033, RPDp=3.199, R2p=0.915 for the validation sets of Hp. This model could classify soil according to the content of available potassium. When the predicted value was less than 100 mg·kg-1, it indicated that the content of available potassium in soil was less than 100 mg·kg-1, and the specific content was uncertain; while when the predicted value was higher than 100 mg·kg-1, the predicted value could reflect the real content of soil available potassium well. Because the soil samples selected in this paper were used without ground or sifted, the time of sample preparation could be greatly shortened and the prediction efficiency could be improved greatly. The results in this study can provide a reference for the rapid prediction of nutrients including available potassium content in cinnamon soil using near infrared hyperspectral imaging technology.

Phosphorus (P) is the major controlling factor for the eutrophication in the water. After the external P pollution is gradually controlled, the influence of surface sediments as the main source of endogenous phosphorus on the water quality of the Yellow River can not be ignored. It is of great significance to master the accumulated degree of various P fractions in surface sediments and exchange ability of phosphorus at the water-sediment interface for administering the water environment and regulating the P load. In this study, surface sediments of high-water period (2011. 07), low-water period (2014. 05) and normal-water period (2014. 10) from Gansu, Ningxia and Inner Mongolia sections of the Yellow River were collected, respectively. The contents of various phosphorus fractions were determined by using standards measurements and testing (SMT) method and molybdenum antimony spectrophotometry, and the isothermal adsorption and adsorption kinetics processes of P in surface sediments were simulated in the laboratory. This study found: (1) compared with the characteristics of phosphorus fractions of surface sediments in major rivers of China, the content of OP was as low as that of NaOH-P, and the content of HCl-P was higher in surface sediments. The average phosphorus content of all fractions was the highest at high-water period, indicating that the accumulated degree of phosphorus in surface sediments was the highest at high-water period and the water environment of the study area was greatly impacted by the development of agriculture along the route. So, reasonable use of phosphorus fertilizers and optimizing irrigation return water quality will be the developing direction to reduce the risk of phosphorus pollution in the Yellow River in the future. (2) comparing the equilibrium phosphorus concentration (EPC0) of surface sediments at all sampling sites from isothermal adsorption for low phosphorus concentrations with the criterion about the phosphorus concentration threshold of the eutrophication in the water, we found that surface sediments at most sampling sites played a role as “phosphorus source”, and there was a trend of phosphorus release from sediments to overlying water, especially for most sampling sites with high values of EPC0 in low-water period, the release trend was more obvious. Based on the fitting parameters of L model and F model from isothermal adsorption at high phosphorus concentrations, the retention capacity of surface sediments to phosphorus in high-water period was the strongest, followed by low-water period and the minimum in normal-water period, the adsorption processes of surface sediments on phosphorus in all sampling sites were easy to occur. Based on the changed trend of kinetics curves of phosphorus adsorption, we found phosphorus adsorbent contents of all selective sampling sites increased rapidly in the first 12 h, increased gradually and tended to be stable during 12 h to 48 h. According to fitting results of the pseudo-second-order kinetics for adsorption kinetics processes, the reaction rate of phosphorus adsorption on surface sediments was mainly controlled by chemisorption. According to results that different sampling sites at the same water period had different rate-limiting steps and the pore diffusion was the rate-limiting step in nearby sampling sites at different water periods, we inferred differences of composition and physicochemical properties of surface sediments on phosphorus adsorption rate had a greater influence than variances of flow rates and flow fluxes of overlying water among different water periods.

In 2017—2018, the concentrations and sources of ambient fine particulate matter (PM2.5) and its 15 elements (Al, As, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, Zn) in three sampling points (Jiefang North Road Jiancaoping District, Xinjian South Road and Shuangtasi Street Yingze District) in Taiyuan city were investigated during the non-heating season, the light pollution period in heating season and the heavy pollution period in heating season. The samples were collected continuously by medium flow particle samplers in 6 days with more than 20 hours each day, and the concentrations of the 15 elements were determined by ICP-MS after pretreatment of microwave digestion. The spatial and temporal distribution characteristics were analyzed by descriptive statistical method. The main sources of PM2.5 were analyzed by enrichment factor method (EF) and the principal component analysis method (PCA). The concentrations of PM2.5 and its 15 elements showed the tendency of the heavy pollution period in heating season > the light pollution period in heating season > the non-heating season. There were no significant differences in concentrations of PM2.5 and most of the 15 elements between the 3 sampling points (p<0.01). The main sources of the 15 elements in the non-heating season was soil, construction and metallurgical industry with the contribution of 32.03%, 30.52% and 18.26%. The main sources in the light pollution period in heating season was the mixed source of construction, soil and metallurgical industry, the mixed source of construction and biomass combustion and soil with the contribution of 37.98%, 37.05% and 16.55%. The main sources in the heavy pollution period in heating season was the mixed source of construction and biomass combustion, the mixed source of construction, soil and metallurgical industry and soil with the contribution of 40.62%, 35.52% and 13.96%. Compared with previous studies, the results of this research showed that although the control measures of PM2.5 in Taiyuan had been effective in recent years, the management and control of pollution sources such as metallurgical industry, motor vehicles and coal burning should be further strengthened.

In order to enhance the intensity of emission spectra of laser-induced plasma, a method of combining magnetic field enhanced laser induced breakdown spectroscopy(LIBS) with nanoparticle enhanced LIBS (NELIBS) was proposed. 20 nm in diameter Au-nanoparticles(Au NPs) was deposited on the surface of the sample by thermal evaporation. Copper and brass were induced to breakdown by a pulsed Nd∶YAG laser (1 064 nm, maximum energy 200 mJ) at room temperature and under standard atmospheric pressure. Laser-induced breakdown of copper was performed respectively using conventional LIBS, magnetic field-enhanced LIBS, NELIBS, and combining of the magnetic field enhanced LIBS and NELIBS with changing laser energy of 30~110 mJ. The enhancement factor and SNR for Cu Ⅰ 521.8 nm were obtained and the enhancement mechanism was analyzed. Brass and copper were induced to breakdown under four different constrains in the same environment to detect trace elements in the sample. When Au NPs were precipitated on the surface of the sample or the sample precipitated with the Au NPs was put in a magnetic field, the characteristic line of Mg Ⅱ 279.569 nm was found in the spectrum of the copper sample and the characteristic line of Si 251.611 nm was found in the spectrum of the brass sample. The experimental results showed that applying a magnetic field alone or add the Au NPs on the sample surface can effectively enhance the spectral intensity of the plasma, but the enhancement effect is weaker than the combination of the two methods. Magnetic field confinement enhancement of the spectrum is weaker than that of NELIBS. When the NELIBS is combined with magnetic field enhanced LIBS, the highest enhancement factor is up to 14.3 (CuⅠ 521.8 nm) and increased by 28% and 59% compared to magnetic field-enhanced LIBS and NELIBS, respectively. In the four cases, when the laser pulse energy was gradually increased, the Lorentz force that was generated by the magnetic field to restrain the plasma reduced relatively due to the increased expansion intensity of plasma, at the same time, the enhancement effect of the Au NPs on the emission spectrum of the plasma was weakened, the line intensity decreased, and the enhancement factor of plasma gradually decreased and tended to be stable. The combination of NELLBS and magnetic field enhanced LIBS can not only effectively increase the emission line intensity of the plasma and improve SNR of spectral , but also trace elements that cannot be detected in the conventional LIBS due to the low intensity of the spectral line and large background noise can be detected, and the ability of LIBS to detect trace elements is significantly improved, and the limit of detection of trace elements becomes lower. The method of combining NELIBS with magnetic field enhanced LIBS has higher sensitivity and accuracy, providing a new idea for the enhancement method of laser induced breakdown spectroscopy. It has broad application prospects in this field.

Apostichopus japonicus, an important part of the mariculture, is a fishery resources with extremely high economic value. Therefore, it is of great practical significance for the mariculture to study a flexible, stable and efficient method to identify the origin information of apostichopus japonicas. There are three main aquaculture methods for the apostichopus japonicas, including bottom sowing culture, captive culture and raft culture. Different aquaculture methods are used in apostichopus japonicus of different producing areas, and there also exist great differences in the nutritional value, medicinal value and economical value from different producing areas. The compositions of primary producers vary from one provenance to another, and the characteristics of fatty acids in different algae and plankton as primary producers are also different. Through the transmission of the food chain, apostichopus japonicus from different producing areas have different fatty acid characteristics. Gas chromatographic fingerprint is a fast and accurate traceability technology for food origin. Carbon stable isotope ratio mass spectrometry can not only identify origin but also distinguish the nutritional value of food. Samples of apostichopus japonicus were collected from nine representative producing areas, and total lipid data were extracted by using the Folch method. Then determined the data of fatty acid kinds and relative content through gas chromatography. Finally, stable isotope ratio mass spectrometer was used to determine the data of fatty acids carbon stable isotope compositions. One-factor analysis of variance (ANOVA) was used to test the significance of the data of fatty acid relative content and fatty acids carbon stable isotope compositions, and then selected 17 kinds of fatty acid data as inputs for the two models. The principal component analysis(PCA) method can reduce the dimensions of the data, and aggregate the origin characteristics of different fatty acids to improve the accuracy of origin identification model. Support vector machine (SVM) is a classification algorithm that aims to minimize structural risk and has good ability of generalization. The results indicated that there were significant differences in the fatty acids relative content and fatty acids carbon stable isotope compositions data of apostichopus japonicus from different producing areas. After the principal component analysis, the clustering characteristics of the fatty acids data were more obvious. With the cross-validation method, the first six principal components were determined as inputs of the two support vector machine classifiers. Applied the particle swarm optimization improved based on genetic crossover factor(GPSO), and the average accuracy of 100 cross-validation results of different K values of the particle was used as the fitness to find optimal parameter combinations of the SVM classifier model. Finally, the optimal parameter combinations of fatty acids relative content model were σ=6.247 599 and C=14.313 042, and average accuracy was 79.49%; the optimal parameter combinations of fatty acids carbon stable isotope compositions model were σ=7.626 194 and C=2.193 410, and average accuracy was 98.33%. Compared with the results of cross-validation, the fatty acids carbon stable isotope compositions origin identification model has higher accuracy and stronger ability of generalization. When origin identification results of two models were inconsistent, the results of fatty acids carbon stable isotope compositions model were used. The laboratory detection was integrated with Internet technology to build an apostichopus japonicus origin identification online system. The Integrated model of “Internet+origin identification” has achieved to provide a scientific basis and technical support for the further studies on origin identification of food.

Light irradiation of display lighting can cause fading, aging and other radiation damage to photosensitive cultural relics, especially to calligraphy and painting, dyed silk, painted pottery and other cultural relics that are responsive to light. In order to reduce the radiation damage to cultural relics, the level of illumination is strictly controlled by standards at home and abroad. For example, the illumination level of cultural relics responsive to light is only 50 lx. This is very detrimental for the audience to appreciate those precious works of art. With the development of semiconductor solid-state light source Light emitting diode (LED) technology, the spectrum of semiconductor solid-state light source does not contain ultraviolet and infrared bands, which are the most harmful to cultural relics. So it has a natural advantage over traditional light source to achieve less damage to cultural relics under the same illumination condition, making it possible to improve the brightness of the lighting environment and thereby improving the level of the lighting environment without increasing the damage to the cultural relics. However, even if there is only visible light spectrum, the visible light photon energy will still cause irreversible damage to the material of culture relics. Nevertheless, the spectrum of the LED light source is diverse, leading to the fact that the damage will even be great different. When LED light source enters the field of exhibition lighting, how to scientifically guide the research, development and application of museum heritage lighting source is the key to improving the lighting environment of cultural relic exhibition. In this paper, the changes of surface color properties of common photosensitive cultural relic materials under continuous visible light irradiation were measured and studied. Through the samples preparation of traditional Chinese painting pigments and plant dyestuffs (Chinese painting pigments are mainly cinnabar, ochre, azurite, gallocyanine, rouge, charcoal black, eosine, phthalo blue; plant dyes are mainly madder, amur corktree, cape jasmine, indigo blue, Sophora japonica, sappanwood, puccoon), using monochromatic lights with different wavelengths and LED with different color temperature as light sources, a large dose continuous irradiation experiment was carried out on the samples. In the course of irradiation, the colorimetric parameters L*, a*, b* of the surface color for the materials were measured periodically, and the color differences of samples were calculated with CIE 1976 L*, a*, b* uniform color space chromatic difference calculation method, after irradiation by LED light sources with different spectra. The effects of long-term irradiation caused by LED light sources with different spectra for traditional Chinese painting pigments and plant dyes were analyzed respectively from radiometry and photometry. The experimental results showed that after the same irradiation or exposure, short wavelength blue light causes the largest color difference on the samples, the green light is the second, and the red light is the least, whether from the perspective of radiometry or photometry. In composite light experiments, because of the larger proportion of blue light, the effect of high color temperature LED light source for irradiation impact is obviously higher than that of low color temperature LED light source. At present, when the illumination degree is used to evaluate the illumination environment of the museum, compared with the radiation illumination evaluation, the effect of blue light irradiation on the cultural relics is further underestimated because of the low value of the human visual acuity function corresponding to the blue light. Under the same illumination condition, the aging degree of plant dyestuffs is higher than that of traditional Chinese painting pigments, and the yellow plant dyestuffs (amur corktree, sophora japonica) as well as red color traditional Chinese painting pigments (cinnabar, eosine) are more apt to aging in the process of irradiation. Therefore, the LED light source for museum display lighting should strictly control the composition of blue light, and the use of low color temperature light source is more conductive to the protection of cultural relics. In the future, when formulating the lighting standards for cultural relics exhibition and display, the proportion of blue light should be restricted. In addition, for the yellow, red and other cultural relics responsive to light, the corresponding display lighting standards should be more stringent. This study is of great significance to the development and application of LED light source in museum lighting, as well as the improvement of lighting standards and lighting conditions in the future.

With the continuous accumulation of astronomical data, Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) has completed six years of large-scale sky surveys. The DR5 dataset has obtained more than 9 million spectrum data, including early-type stellar spectra with the lower observation proportion. The correct stellar classification template library can improve the classification accuracy. The currently used classification templates in LAMOST pipeline which don’t completely cover all the subtypes such as B-type stars, because they were constructed using DR1 data without enough early type stars. In this paper, the B-type stellar spectra in LAMOST DR5 have been collected as our research object, and the reference B-type spectra are from the library of ELODIE. Firstly, we complete the correlation analysis of 37 spectra of ELODIE B-type stars. After removing three weakly correlated spectra, 34 spectra of ELODIE B-type stars spectra were selected as the cluster center. The majority of the published LAMOST DR5 labels were marked as B6 (7662) and B9 (3969) and spectra were measured by Mahalanobis distances, with the supervised clustering, 34 LAMOST early-type stellar spectral data were marked as 13 subtypes according to ELODIE labels covering from B2 to B9 subclasses. The intra-class distance of each spectral subtype is determined by linear analysis to ensure that the wavelength coverage and resolution are completely consistent with the LAMOST data. The average spectral line of the corresponding subclass is calculated removing the outliers, thus 13 subtype spectral classification templates of B-type stars provide a good reference for later template completion.

The first Mars Global remote sensing and regional survey mission of China has been approved for the first time, and the first Mars probe is going to Mars. In order to meet the needs of Mars material composition analysis, different types of instruments on Mars rover have been developed in China, including Mars Surface composition detection Package (MarsCoDe) using laser-induced breakdown spectroscopy (LIBS) technology. Because the surface of Mars is covered with dust, we must get rid of the dust layer or destroy rock surface if we want to detect the material composition under the Mars dust accurately. LIBS can be used to ablate the surface of the object by its laser, and obtain the spectral information of deep rock. In addition, LIBS is almost suitable for detecting every element in Mars exploration, including light elements H, Li, Be, B, C, N, O, etc., which helps to find evidence for organic matter and water-bearing geological process. Due to Mars environment, the physical properties of the plasma are completely different from those on earth. In order to ensure the quality of the returned LIBS spectral data, it is necessary to carry out onboard calibration after landing, meanwhile carrying calibration board with specific standard samples on Mars rover, for data correction, ensuring the reliability of the returned data and more accurately interpreting the Martian surface material. The selection of calibration samples is a very important work. There are various factors such as the limitation of equipment engineering conditions, the representativeness of the calibration sample types, the distribution range of the elements and the stability of the samples. In this paper, we summarized the research progress of onboard calibration of Mars onboard LIBS and focused on analyzing the selection criteria of LIBS calibration samples and the advantages and disadvantages of foreign sample selection. After summarizing experiences, some suggestions were put forward to provide reference for our onboard calibration work. This article is of scientific significance to the correct interpretation of Mars exploration data and future research on the origin of Mars and the long-term geological evolution of Mars.

Drilling is one of the important means of geological exploration. In recent years, with the development of China’s geology, the storage and sharing of a large number of cores has become an urgent problem to be solved. The problem has been solved through research and development of a core spectral scanner to realize the digitalization of cores. However, the massive production of core spectral data and image data puts forward new requirements for data processing. According to the principles of spectroscopy and spectral analysis methods, spectrum analysis and altered mineral mapping of the spectral data of the core scanner can provide the basis for geological scientific research, deposit analysis, and prospecting. This paper proposes a classification database retrieval method, which not only improves the accuracy of retrieval, but also greatly accelerates the retrieval speed. The core image is also an indispensable part of the core information. Because of the limitation of the core scanner detector, the illumination conditions, and the influence of the cylindrical core, the collected core image will have uneven illumination and radiation distortion. Utilizing the nonlinear bilateral filtering method to sharpen the image, and applying the black and white plate calibration method to correct the core image make the core images closer to the real condition. The automatic image mosaic is completed by detecting feature points using the corner detection method. Core images are spliced into core columns and core trays one by one according to the drilling sequence, making the core images display more direct-viewing. Spectral analysis of minerals is the key to core scanning technology. Different minerals have different peak positions of characteristic absorption. Normally utilize the peak absorption matching method to search minerals, which is suitable for mixed mineral spectral retrieval. The matching of peak positions is based on a standard database. This paper proposes a classification database search method: based on the types of minerals, the standard database is divided into sub-databases of argillization alteration database, porphyritic alteration database, sericite alteration database and so on. According to the images of the samples and the geological environment in which they are located, select the appropriate subdatabase for search analysis. Two experiments were conducted in this paper. The same batch of samples were analyzed using the standard database and the classification database respectively. The results showed that the accuracy of the latter was higher; 141 samples were processed taking 233 seconds and 44 seconds with each method. Experiments prove that the classification database method is an effective method for retrieving large amounts of data accurately and quickly, which can both improve the accuracy of the retrieval, and greatly speed up the retrieval. This method is a novel, unique, and effective means in spectral retrieval. Solving efficiency problems for batch mineral data retrieval is the innovation of this paper. Mineral spectrums contain rich information, whose peak intensity, peak-to-peak- ratio, peak shift, FWHM, and reflectance are related to the relative content of minerals, temperature, cation exchange, crystallinity and color respectively. The metallogenic model can be obtained by comparing and analyzing the information from the same batch of minerals, which reveals the regularity of mineralization as well. This paper takes a drilling in Xuancheng, Anhui Province as an example to process automatically image stitching, spectral analysis and altered mineral mapping of the core spectral data. According to the analysis of information extracted from altered minerals, the area is an acidic, low-temperature geological environment, with darker rocks in the low-temperature areas and kaolinite and montmorillonite in the middle of the low-temperature areas, indicating a good oil storage environment. The experimental results show that this method can not only save a lot of manual workload, but also obtain high-quality core tray and columnar core splices and altered mineral information extraction graphs, which is a practical and reliable method for geological workers to process core data.

The petrochemical pipeline can usually be divided into the normal temperature region and the high temperature region. The existence of high temperature region affects the safe operation of the whole system, and the loss of heat will cause a series of problems, such as the waste of resources and the pollution of the environment. For the realization of quickly and accurately separating the high temperature region from the infrared image, based on the basic one-dimensional Otsu algorithm we propose an improved two-dimensional multi threshold method. First, the algorithm divides the infrared image of pipeline into two parts: background and pipeline through classical single threshold segmentation. Then, based on the image region of the pipeline, the two thresholds of the target image are divided by the two dimensional image of the pipeline gray image and the average value image, and the larger threshold is finally taken as the segmentation point of the normal temperature region and the high temperature region. We analyze the different pipeline for several tests. The results show that the improved two-dimensional Otsu threshold algorithm can extract the pipeline from complex background more clearly, and on the basis of the step segment the high temperature region more accurately.

Diamond color grading has been firstly designed to form a set of mature grading system by Gemological Institute of America. Traditionally, diamond color grading method is to observe the sample visually and compare it with a set of standard colour master stones. The classification results are mainly affected by the testing environment, size of diamond, inspectors and so on. Then, grading deviations will always exist for the same sample, even if it is tested by the same person in the same environment.To avoid the above problems, it has been a hot research topic in diamond field to use an excellent instrument to evaluate the color of diamond. However by now, there have not yet been any instruments which can completely meet the requirement for the grading of diamond. This paper gave an interesting method for quantitative evaluation of diamond color, and the micro zone measurement was applied and proposed to avoid the effect of size variation results in color grading of the diamonds. The principle of spectrophotometer was used in the method, which consisted of a sample system, a micro positioning display system, an automatic acquisition and calculation system. D65 light source was employed and its color temperature was (6 500±200) K. The color signal acquisition was realized by CCD optical fiber spectrometer with high speed, high resolution and large pixels. Several sets of master diamonds with the national(China) standard certificate and with different sizes and different colors were employed to test. The results showed that the method could obtain the index of the color of the diamond accurately and could transform to the degree of diamond grading color system. By using the micro area accurate positioning method, selected the common features of the diamond pavilion measurement area, which could eliminate the errors of the same color grade diamond due to the size. The method is a potentially useful technology for diamond color grading.

Measurements of the particle-to-gas partitioning of semi-volatile atmospheric aerosols are crucial for providing more accurate descriptions of the compositional and size distributions of atmospheric aerosol. As a major component of semi-volatile aerosol species, ammonium nitrate (NH4NO3) is ubiquitous in the sub-micron particulate matter, particularly in high pollution episodes. In order to further understand gas-particle partitioning of NH4NO3, determination of the saturated vapor pressure of NH4NO3 is needed. Here, we investigate the volatility of NH4NO3 at different relative humidities (RHs) using aerosol optical tweezers coupled with Raman spectroscopy as an instrument for sampling and detecting. According to the Maxwell equation, the vapor pressures at different RHs are calculated, and the values are (1.67±0.24)×10-3, (1.82±0.19)×10-3, (2.91±0.13)×10-3, (3.5±0.28)×10-3, (4.59±0.22)×10-3 and (6.64±0.3)×10-3 Pa, when the RH is 80%, 73%, 68%, 57.3%, 55.4%, 44.8% respectively. Obviously, the vapor pressures of NH4NO3 increase with RH decreasing, i.e. low RH promotes the evaporation of ammonium nitrate. Additionally, we also calculate the volatilizing flux of NH4NO3 at different RHs, and the values are in the range of (4.01±0.79)×10-7~(3.32±0.77)×10-8 mol·(s·m2)-1. The results obtained herein are of important significance in understanding the partitioning processes of semi-volatile aerosols.

Pure cotton and mercerized cotton products are widely used in daily life. It is difficult to classify the pure cotton and mercerized cotton products with simple methods because they are similar in chemical and physical structures. In this work, a new method of rapid identification of pure cotton and mercerized cotton products with two-dimension correlation spectra analysis was proposed. In this work, 200 textile samples including 100 pure cotton fiber products and 100 mercerized cotton fiber products were collected. For each sample, the water content was changed 4 times and one-dimension spectra was collected, among them, the water content of 4 times was 20.20%, 14.52%, 7.77% and 0% respectively. Then their simultaneous two-dimension correlation spectra were calculated based on correlational analysis. Three kinds of classification features were extracted from the synchronous two-dimension correlation spectra. Support Vector Machine (SVM) was combined with different kind of the classification features to construct different classifiers. In this work, an information fusion method was proposed to make the multi-classifier decision. To verify the feasibility and effectiveness of the proposed method, the comparative experiments have been done. The accuracy of identification with the classifier based on extracted one-dimensional spectra features with PCA was only 76%. The accuracy of identification with the three classifiers based on extracted features from two-dimensional correlation spectra were 88%, 90% and 88% respectively. The accuracy of identification with the proposed method was 92%. Compared with one-dimension spectra based feature extraction, the two-dimension correlation spectra based feature extraction achieved feature enhancement and the multi-classifier fusion decision could improve the accuracy of classification obviously. Two-dimensional correlation spectroscopy extended spectral information to higher dimensions, unfolded hidden fold peaks in one-dimensional spectra, and had higher classification accuracy. The proposed method provided a new way for rapid identification of pure cotton and mercerized cotton products.