Formaldehyde (HCHO) plays an important role in atmospheric photochemical reactions. Besides, it’s an important aerosol precursor and photochemical oxidation indicator. The sources of HCHO in the atmosphere are mainly from primary direct emissions and photochemical reactions. Atmospheric photochemical reaction is closely related to the intensity of solar radiation. In general, the stronger the intensity of solar radiation is, the more active atmospheric photochemistry reaction will be so that the secondary sources of HCHO are higher. Thus, the research on HCHO has become an important topic in today’s atmospheric environment research. This paper introduces a method for retrieving tropospheric vertical column density (VCD) and vertical profile in HCHO based on multi-axis differential absorption spectroscopy (MAX-DOAS). This method is a two-step inversion method based on nonlinear optimal estimation method. Firstly, the vertical profile of aerosol is retrieved. Secondly, the vertical distribution of HCHO is retrieved based on the retrieved aerosol profile. In the second step of the gas profile inversion, the aerosol information affects the inversion accuracy of the gas vertical profile by influencing the weight function. Therefore, the effects of three different aerosol profile types (exponential, Gaussian and Boltzmann) on HCHO vertical profile inversion were studied. The results show that the total gas inversion error, the envelope curve of the average kernel, the limit of sensitivity height, the degree of freedom, and the vertical profile of HCHO retrieved in the three aerosol types are similar, which means that the aerosol profile type has little effect on the HCHO vertical profile inversion. For the near-surface below 200 m (including 200 m), the differences between the real HCHO volume mixing ratios (VMRs) with the HCHO VMRs obtained by the aerosol profile in exponential, Gaussian and Boltzmann shapes are 36.89%, -0.04%, and 23.3%, respectively. It is shown that the retrieved HCHO vertical profiles using the priori aerosol types in exponential and Boltzmann overestimates near-surface HCHO VMRs, while it is just the opposite in Gaussian shape. Furthermore, the vertical profile of HCHO in one polluted episode in the University of Chinese Academy of Science in Huairou District (UCAS) of Beijing is studied to obtain the vertical distribution in HCHO. The results indicate that the high value in HCHO mainly concentrates below 1.0 km from the vertical profile in HCHO and the high value in HCHO diurnal variations appears in the early afternoon, which indicates that the HCHO is mainly from local photochemical reaction. In a word, the southwest wind brings the polluted VOCs gas mass to the UCAS, and then HCHO generates and accumulates in local photolysis by VOC, which causes this HCHO polluted episode. Combined with backward trajectories determined (HYSPLIT) model, the transport from the southwest of the UCAS is an important cause of this polluted episode in HCHO. Therefore, HCHO at the observation site is mainly affected by pollution transport and secondary oxidation. Finally, the influence of different aerosol conditions on the HCHO profile inversion during this pollution episode was compared. The results show that when the aerosol optical depth increases, the retrieved limit of sensitivity height, the degree of freedom and the height resolution decreases, and the retrieved total error increases.

Because of the importance of formaldehyde in atmospheric photochemical reaction and its harm to environment, climate and human health, it is urgent to control and monitor the concentration of formaldehyde. At present, traditional monitoring is mostly limited to indoor monitoring based on chemical methods and chromatographic methods or outdoor monitoring in a small range, while outdoor formaldehyde monitoring in a wide range of atmosphere is often ignored by people. In order to effectively monitor the concentration of formaldehyde (HCHO) in the atmosphere, the ground-based MAX - DOAS observation system was established. Compared with the active DOAS, the MAX - DOAS observation system was not limited by the light source and reflex attachment, with simple platform construction, wide measurement range. Based on this, continuous observation experiments were carried out based on MAX - DOAS observation system in the summer of 2018 at Hefei site, combined with the new generation of spectral processing software QDOAS, the nonlinear least squares fitting of DOAS algorithm was used to retrieve the differential slant column densities of formaldehyde (HCHO). Then the differential slant column densities of formaldehyde were converted to the vertical column densities by use of the atmospheric quality factor (AMF), and the observed data in July were analyzed, the result showed that the slant column densities of formaldehyde under low elevation angle are higher, therefore, tropospheric formaldehyde is mainly concentrated in the position near the earth’s surface. It can also be seen from the experimental data that the change trend of nitrogen dioxide and formaldehyde is basically consistent, indicating that the atmospheric formaldehyde is correlated with the nitrogen oxides (NO2, etc.) discharged by motor vehicles or industry in the process of atmospheric source and sink. It was found that the change trend of the two kinds of measurement data had a good consistency by the comparison between the ground-based MAX - DOAS measurement data and the OMI observation data, and the correlation coefficient was 0.518 9, and the reason why the OMI observation value was low was analyzed. The results showed that the ground-based MAX - DOAS system can not only study the evolution of regional pollution, but also provide a real-time and rapid monitoring method for formaldehyde measurement, a new analytical method for analyzing the source of atmospheric formaldehyde, and an effective method for verifying satellite observation data.

Para-xylene(PX) is an important raw material in chemical industry. In order to study the change of ultraviolet-visible(UV-Vis) spectra of PX under external electrical field, the density functional theory (DFT) has been employed to calculate geometrical parameters of the ground state of PX molecule under different external electric fields ( from 0 to 0.025 a.u.) in this paper. On this basis, the UV-Vis absorption spectra of PX molecule were calculated by employing the time-dependent density functional theory (TDDFT). At last, by employing the TDDFT in the same fundamental group, we studied wavelength and the molar absorption coefficient of the first twenty-six excited states of PX molecule under different external electric fields. The results show that the most absorption of UV-Vis absorption spectra appears in the E1 belt of benzene electronic transition from π to π*; the ultraviolet absorption peaks of excited states of PX are proved to appear observably red shift and the molar absorption coefficient sharply decreases with the increase of the field intensity.

Helicon plasma sources have gradually been widely adopted in various research fields due to their high ionization efficiency and high density. Lacking in understanding of the mechanism for the high ionization efficiency and the power coupling mode of helicon discharge has been a great challenge for scholars to deal with in this field. To diagnose the discharge process and the characteristics is an important way to reveal its physical mechanism. Spectral diagnosis can avoid the interference of contact measurements on plasma and is free from plasma ablation. It responds quickly and is flexible to operate. In order to study the discharge characteristics of helicon plasma and the influence of gas pressure, researches on emission spectral diagnosis of argon discharge and numerical simulation of the Helic code for these experiments were conducted. The radial profiles of the line intensity were obtained by changing the focal length of the fiber optic probe to adjust the radial diagnostic position. Atomic emission lines of argon are mainly concentrated in the 740～920 nm region, which are generated by the transition of argon atoms between the 4p-4s energy levels and stronger than the relative intensity of ion lines. It can be found that at lower pressure ranges (0.2 Pa<PAr<1.0 Pa), the discharge intensity increases rapidly with pressure, and tend to be nearly saturated when the pressure reaches 1.0 Pa or larger. Langmuir probe measurement shows a similar trend in ion density. A “bump-on-boundary” of the line intensity profile was observed near the radial boundary (r≈4 cm), which was more obvious as the pressure increased. By calculating the electron temperature, it was found that the discharge uniformity will be influenced when the pressure is increased to a certain extent. The simulation results show that the radial profile of power absorption gradually increases toward the radial boundary, which is consistent with the experimentally observed “bump-on-boundary” of the line intensity. And the coupling efficiency of the helicon-TG waves increases. As the gas pressure increases, the radial boundary peak of Er decreases because the TG wave is more damped and effectively confined to the narrower radial boundary. The current density Jz shows a significant decrease in the peaks inside and at the boundary of the plasma. It can be seen that although the pressure increase improves the plasma density to some extent, the ionization rate is correspondingly reduced, resulting in limited axial current density. However, the radial current density Jr firstly decreases and then increases, and the growth rate is obvious. Overall, the discharge efficiency has improved. Appropriately raising the gas pressure helps to improve power coupling efficiency and strength of the discharge, as well as the plasma density. The light intensity ratio method is a typical method for the calculation of linear spectral line parameters. The Helic code is also a highly recognized tool in the professional field. Therefore, the results are reliable and the analytical methods have the value of reference. The experimental and simulation results provide a certain reference value for improving the helicon discharge intensity under argon working fluid.

As the vibration and rotational energy levels of many biological molecules fall in the terahertz band, which has the characteristics of low electron energy (about 4meV) and not destroying the samples, terahertz wave can be used to detect biological samples. Many biological molecules need to keep their bioactivity in the liquid environment, to explore the influences of acid and alkali environment in the salt solution, and to study their biological characteristics in the salt buffer solution. However, water as a polar liquid has strong absorption of terahertz wave, so it is necessary to explore how to reduce the absorption of terahertz wave by water, which is mainly caused by hydrogen bonds between water molecules. At present, the most common method is to reduce the distance between water and the terahertz wave and to destroy the hydrogen bonds between the water molecules. In this paper, by the aid of the terahertz time-domain spectroscopy system, the influences of different kinds and different concentrations of electrolyte on the hydrogen bond between water molecules are investigated by observing the change of spectral intensity in the range of 0.1～10 THz by using a sandwich microfluidic chip, which can not only reduce the distance between water and the terahertz wave, but also explore the influences of electrolyte on hydrogen bonds between water molecules. The specific performances are that some electrolytes can promote the association of hydrogen bonds, while the other destroy the formation of hydrogen bonds between water molecules, and this can be shown by the intensity of the spectra. If the association of hydrogen bonds between water molecules is promoted, terahertz absorption will increase and spectral intensity will decrease, and if the association is destroyed, terahertz absorption will decrease and spectral intensity will increase. The research results show that the strength of the terahertz spectrum increases when potassium chloride and potassium bromide are injected into the water, which indicates that they can destroy the hydrogen bonds and make the spectral intensity become larger. However, when magnesium chloride and calcium chloride are injected, the intensity of the terahertz spectrum is weakened. This indicates that they have an associative effect on hydrogen bonds, which makes the spectral intensity smaller. By using terahertz technology in the range of 0.1 to 1.0 THz to study the characteristics of potassium chloride, potassium bromide, magnesium chloride and calcium chlorideelectrolyte solutions with different concentration, it is found that these electrolytes can only affect the spectral intensity, and do not introduce new characteristic absorption peaks and cause interference to the sample. This has some practical value for the study of biological molecules such as Escherichia coli, Bacillus subtilis etc., which have characteristic absorption spectra in the range of 0.1～1.0 THz. With the help of microfluidic chip, injecting the required electrolyte in the solution can not only identify the samples to be measured, study the spectral information of the samples, and explore its biological characteristics, but also provide a prerequisite for further promoting the application of the terahertz technology in biochemistry.

Traditional laser linewidth characterization is usually done by introducing a self-heterodyne technique. This technique is an optical fiber delay based on laser beat note, which generates a Lorentzian spectrum related to the linewidth of a laser. In order to obtain the complete characteristics of a laser linewidth and frequency noise spectrum in its frequency domain, a new method based on β algorithm is proposed. The basic principles of the β algorithm have been analyzed and explained at first. The relationship between frequency noise and laser linewidth in different frequency ranges are analyzed based on the Wiener-Khintchine theorem. When the cut-off frequency tends to be zero or infinity, the laser line shape evolves from Gaussian type to Lorentzian type. Meanwhile, the cut-off frequency of the conversion laser line shape has been deduced, which is represented by the frequency noise function, namely β separation line. Secondly, the frequency noise spectral density has been measured with OE4000 test system. The frequency noise and the line shape of diode lasers are numerically simulated. In the low frequency region, where the noise level is much larger than its frequency, it produces a slower frequency modulation than that in the high frequency region. The linewidth of the laser is an integration of the frequency noise in the Gaussian line shape region. The error in linewidth calculation is smaller over the entire cut-off frequency range. Finally, a set of measured frequency noise power spectral density of a RIO’s laser was used to calculate the linewidth using the β algorithm. When the frequency noise spectral density is greater than the β separation line, the laser appears as a Gaussian line shape and the linewidth decrease with frequency banwidth; on the contrary, the laser shows a Lorentzian line shape, and the linewidth is fixed. Meanwhile, a delayed self-heterodyne measurement system with delay fiber of 50 km and frequency shift of 60 MHz is constructed. The measured linewidth of a RIO’s laser working under 110 mA inject current is about 1.8 kHz, which is consistent with the calculated result of the β algorithm, of which the frequency bandwidth is ~2.8 kHz. In conclusion, β algorithm is able to characterize the linewidth of any type of narrow linewidth laser, which is of great significance to the research on narrow linewidth lasers.

With the development of social economy, environmental air quality has become a hot issue in the research field of indoor environmental comfort. TiO2 was an n-type semiconductor material with high chemical stability, strong corrosion resistance and non-toxic to human body. Using TiO2 photocatalysis performance to improve indoor air quality has become the research focus, but because TiO2 has high photocatalytic efficiency only under ultraviolet light source, and under visible light source the efficiency was low, which greatly limits the development of TiO2 in the field of indoor environment. Therefore, it is imperative to develop a TiO2 composite with good photocatalytic performance under visible light source. Using the method of element doping modification technology and improving the specific surface area can improve the photocatalytic reaction in the process of quantum efficiency and utilization of energy, in order to speed up the migration rate of electrons and holes to the surface and reduce the light composite probability of carrier. In this paper, with silicon dioxide SiO2 as template, polyvinylpyrrolidone as film-former, cerium nitrate Ce(NO3)3·6H2O and copper nitrate Cu(NO3)2·3H2O as modifier by sol-gel method to make Ce-Cu/TiO2 hollow microspheres with uniform particle size distribution, and its preparation process is divided into four stages, such as preparation stage of nano SiO2 ball template, preparation stage of Ce-Cu/TiO2-SiO2 composite microsphere gel, preparation stage of Ce-Cu/TiO2-SiO2 composite microsphere and preparation stage of Ce-Cu/TiO2 hollow microspheres. First, the products in each stage of Ce-Cu/TiO2 hollow microspheres were tested and analyzed by fourier transform infrared spectrometer (FTIR) and X-ray diffractometer (XRD), such as construction process of nano-SiO2 ball template was studied from a microscopic point of view in preparation stage of nano SiO2 ball template, attachment of TiO2 to nano-SiO2 ball template was studied in preparation stage of Ce-Cu/TiO2-SiO2 composite microsphere gel, effect of calcining process on crystal phase and structure of Ce-Cu/TiO2-SiO2 composite microsphere was studied in Ce-Cu/TiO2-SiO2 composite microsphere and effect of sodium hydroxide solution on the washing effect of nano-SiO2 ball template in Ce-Cu/TiO2-SiO2 composite microsphere in Ce-Cu/TiO2 hollow microspheres. Secondly, photoresponse performance of Ce-Cu/TiO2 hollow microspheres were tested and analyzed by ultraviolet-visible spectrophotometer (UV-Vis), in order to study the utilization efficiency of Ce-Cu/TiO2 hollow microspheres to visible light source. Finally, particle size distribution and microstructure of Ce-Cu/TiO2 hollow microspheres were tested and analyzed in order to uniform particle size distribution of Ce-Cu/TiO2 hollow microspheres. The results show that: the construction of amorphous nano-SiO2 ball template with amorphous structure using Si—O—Si group is conducive to the adhesion of polyvinylpyrrolidone on the surface of nano-SiO2 ball template, so as to control the cavity structure of Ce-Cu/TiO2 hollow microspheres. Ce-Cu doping basically enters TiO2 crystal, and rarely enters nano-SiO2 ball template crystal, thus inhibiting the transformation of TiO2 from anatase phase to rutile phase in Ce-Cu/TiO2-SiO2 composite microsphere. Ce-Cu-doped TiO2 can promote the formation of new energy levels within TiO2 and realize the capture of e- and h+ by photons with less energy, thus improving the utilization efficiency of Ce-Cu/TiO2 hollow microspheres to visible light source. The surface of Ce-Cu/TiO2 hollow microspheres is smooth and there is no obvious defect. Its morphology is good and the particle size distribution is even, that is, d90 is 219.54 nm, d50 is 151.60 nm, d10 is 103.84 nm, and d90-d10 is 115.7 nm. The above study provides a theoretical basis and research foundation for further obtaining uniform size distribution Ce-Cu/TiO2 hollow microspheres with good photocatalytic performance under visible light sources.

As one of the most important indicators for studying marine primary productivity, chlorophyll concentration in seawater can be quickly measured by laser-induced fluorescence (LIF) technology. In the traditional theory for obtaining chlorophyll concentration by LIF, the chlorophyll concentration nchl=CIＦ／Ｒ, where ＩＦ and Ｒ are fluorescence intensity of chlorophyll a (Chl-a) at 685 nm and Raman scattering intensity of water respectively, and Ｃ is a system constant. Withoutconsidering induced fluorescence saturation, this theory is based on an assumption that both the fluorescence intensity at 685 nm and water Raman intensity are linear with the intensity of incident laser. However, experimentsconfirmed the existence of non-linear relationships between the induced fluorescence energy at 685 nm and laser energy. While the linear relationships between water Raman intensity and pulse intensity have always existed without saturation excitation. In order to explore the effect of non-linear fluorescence change under saturation excitation, two series of measurement were done in the experiments. Fluorescence of the solution with constant Chl-a concentration was measured by varied laser powers, and a constant laser power was used to obtain the solution fluorescence of varied Chl-a concentrations. The third harmonic of Nd∶YAG laser at 355 nm was the excitation source. Thus, Raman scattering at 404 nm and fluorescence at 685 nm of Chl-a solutions were the key part of emission spectra. The experiment results were discussed in section 3. In the first part, the emission spectra of Chl-a solutions were measured by LIF with excitation light intensity variation. It shows a linear relationship between Raman scattering and excitation intensity, while fluorescence intensity at 685 nm appeared nonlinear change under saturated excitation. Moreover, fluorescence intensity of Chl-a solution with higher concentration increased to plateaus earlier, and the ratio of Raman scattering intensity to excitation intensity in the linear relationship decreased with Chl-a concentration. The data analysis shows that a polynomial of degree 4 fitting the changes of fluorescence intensity and the value of the Raman scattering efficiency can qualitatively characterize the saturation of fluorescence at 685 nm. Secondly, for the purpose of analyzing the effect of fluorescence nonlinearity on the applicability of traditional theory in chlorophyll concentration inversion, with considering the phenomenon of fluorescence saturation existing in the application of ocean Lidar for detecting chlorophyll concentration, the emission spectra of samples with different Chl-a concentrations were measured with a constant excitation intensity. The relationship between IＦ/R and Chl-a concentration was obtained under the excitation power at 52.00, 80.70, 132.10 and 197.30 mW·cm-2. Experiments show that IＦ／Ｒ is still in linear relationship with Chl-a concentration under the condition that the exciting radiation is not changed. But, the concentration from the traditional inversion theory by LIF is less than the real Chl-a concentration measured by a high excitation intensity which leads to fluorescence saturation effect. Therefore, the inversion module is necessary to be corrected with ＣＦ which is related to fluorescence nonlinearity under saturation excitation. A more accurate inversion is based on IＦ／Ｒ＝ｎchl/C+CＦ. And, it is worth mentioning that the system constant C in this correction module increases with the exciting intensity. Consequently, saturation excitation causesfluorescence nonlinearity and affects the measurement of Chl-a concentration by LIF technology. It is regrettable that the polynomial obtained by the fluorescence data fitting cannot quantify the impact of fluorescence saturation effect, due to the complexity of the nonlinear factors. However, when the excitation power is constant, a corrected inversion can be experimentally obtained and used to measure Chl-a concentration by LIF in field surveys.

CaAl2Si2O8∶xTb was prepared by the high temperature solid state reaction method. The fluorescence intensity is the strongest when the sintering temperature is 1 350 ℃. The X-ray diffraction patterns show that the base material in the system is CaAl2Si2O8, and the Tb element exists in Ca2Tb8(SiO4)6O2 phase. Raman spectrum shows that the vibration peak at 870 cm-1 is related to the stretching vibration between Tb atom and silicon tetrahedron in Ca2Tb8(SiO4)6O2, and the bending vibration between Tb atom and silicon tetrahedron produces 408 cm-1 vibration peak. With the increase of Tb doping content, the intensity of Raman vibration peak, the intensity of fluorescence spectrum measured by fluorescence spectrophotometer and Raman spectrophotometer increased first and then decreased. The amount of Tb matching with silicon-oxygen tetrahedron in the system increases gradually. When Tb doping exceeds a certain limit, concentration quenching occurs in the system, resulting in a decrease in fluorescence performance. Using 325 nm laser as excitation source, the fluorescence peak shape produced by Raman spectrometer Photoluminescence spectrum mode is consistent with the spectrum curve of traditional fluorescence spectrophotometer, but its spectral resolution is obviously stronger than that obtained by traditional fluorescence spectrophotometer, which is helpful to distinguish fine level transition phenomena.

The increasing amount of petroleum products has increased the risk of the pollution accidents, which could pose an acute threat to ecological safety. The leakage accidents often have the characteristics of suddenness, contingency and large pollution. The traditional chemical sampling detection method is not suitable for on-site emergency monitoring and quantitative warning. The development of satellite remote sensing and airborne imaging technology provides effective methods for the detection of accidents. However, the color characteristics of the floating transparent oil product are not obvious, and it seriously affects the imaging monitoring effect and poses a great threat to ecological security. According to the difference of the spectral reflectance characteristics of the water surface and the oil product, selecting a suitable operating band to improve the imaging effect is a common method for oil spill monitoring. For the spectral measurement of floating oil products, the existing researches were often carried out in the experimental containers, which have different optical characteristics in natural water. It is difficult to provide corresponding data support for the monitoring of on-site leakage accidents. In order to simulate the actual leakage scene, UV-Visible spectroscopic characteristics of transparent oils such as gasoline and xylene were investigated on the surface of artificial lake in this paper. Spectral measurement results show that the spectral angle cosine of the sample and water in each band interval is close to 1, but the spectral reflectance difference is significantly larger than the other band in the ultraviolet band, indicating the spectral characteristics of transparent oil were similar to the lake water in shape, but there is the largest difference in amplitude in the ultraviolet wavelength. To further demonstrate the results of spectral feature analysis, four ultraviolet-visible band filters of 365, 436, 546 and 700 nm were selected for imaging verification. The results show that the difference between the spectral reflectance curve of transparent oils such as floating gasoline and xylene and the amplitude of lake water is the largest at the ultraviolet wavelength, and the overall grayscale contrast of the two floating transparent oils and lake water at the ultraviolet wavelength is made. The difference between value and texture features is significantly higher than that in other visible wavelengths. Therefore, the use of the ultraviolet wavelength for imaging monitoring of floating oil products can effectively improve the imaging contrast between oil and natural water surface. The experiments were carried out under the conditions of natural light and natural water on the lake surface, which greatly simulated the actual transparent oil spill pollution scenarios and provided the most representative theoretical and data support for transparent oil imaging band optimization for airborne remote sensing monitoring.

The Grade Ⅰ water specified in national standards is the water commonly used in analytical laboratory. In order to analyze the quality of pure water quickly and accurately to provide scientific basis for the automation and intelligence of the equipment in analytical laboratory, this paper designed a national standard Grade Ⅰ water purification system (NSGI-WPS) based on ultraviolet-visible spectroscopy (UV-VIS) by full-band (200~900 nm) UV-VIS analysis technology. The system collects modules with Raspberry Pi as the core controller and spectral detector and conductivity sensor as the sensors. As a method for the determination of soluble silicon, the improved silicon-molybdenum blue spectrophotometry realized the simultaneous on-line detection of the 254 nm absorbency, conductivity and the content of soluble silicon of the water used in analytical laboratory. In order to eliminate the impacts of noise interference on the determination test on the content of trace silicon, the system adopted Savitzky-Golay smoothing de-noising method to pretreat the spectrum. 80 groups of spectral data after smoothing were obtained via the different combinations of window width and the degree of polynomial and received unitary linear recursive analysis with the concentration of silicon standard solution 0.004, 0.006, 0.008, 0.010 and 0.012 mg·L-1 respectively, after which the correlation spectrum between single wavelength absorbency and concentration of silicon standard solution was obtained. The results showed that when the window width is 17 and the degree of polynomial is 2, the characteristic peak of the correlation spectrum is the widest; the interval of the characteristic peak is 796~824 nm; and the wavelength of the characteristic peak is consistent with that of the peak of absorption spectra of chromogenic solutions after smoothing. It was found by comparing the distribution of the absorption spectrum of chromogenic solution of different standard silicon solutions that the concentration of silicon standard solution is positively correlated to absorption spectra. Therefore, 812.638 nm was selected as the optimal characteristic wavelength in the test. In order to establish the relationship model between the content of soluble silicon and the absorbency of chromogenic solution, the working curve of the model is drawn with the concentration of the silicon standard solution added as the x-coordinate and the absorbency of the chromogenic solution at 812.638 nm as the y-coordinate. The determination coefficient of the curve R2=0.999 6, indicating that the model has strong fitting ability. In addition, the system management software written for NSGI-WPS realized the real-time processing and automatic control of parameters. The pure water not up to the specifications on National Standard Grade Ⅰ Water used in analytical laboratory was strictly controlled by means of reverse osmosis (RO), continuous electrode ionization (EDI), mixed bed ion exchange resin and UV photooxidation and other techniques. By comparing and analyzing the changes of various parameters of tap water, grade Ⅲ water and grade Ⅱ water before and after purification, it was found that the values of all parameters decreased significantly after purification, among which, the maximum electrical conductivity decreased by 99.94%, and the relative average deviation of all parameters was less than 2%. The test results showed that NSGI-WPS based on UV-Vis has such advantages as strong purification capacity, high accuracy and good robustness, and the purified water after detection, analysis and purification meets the requirements on National Standard Grade I Water used in analytical laboratory. This paper made an exploratory study on the application of UV-Vis analysis in pure water purification system.

In recent years, carbon fiber reinforced polymer (CFRP) has received extensive attention in the industrial field, due to its excellent performance. Laser cleaning technology for pretreatment of carbon fiber reinforced polymer is beneficial to the improvement of the surface properties and the bonding strength of the interface. The technology of detection assessment is the key to ensuring the quality of laser cleaning, and the core of the automation and the integration of laser cleaning device. Laser induced plasma spectroscopy can quickly analyze the changes of surface elements and realize the on-line detection of surface state of laser cleaning. It has a wide application prospect in the field of laser cleaning. In this paper, laser pulse which was produced by Nd:YAG pulse laser, with a wavelength of 1 064 nm,was used to induce plasma under the condition of room temperature and atmospheric pressure. Modified ME5000 grating spectrometer was used to collect the plasma spectrum. Laser induced plasma spectroscopy was used to detect the process of laser cleaning of carbon fiber reinforced polymer online. The plasma spectrum of air was obtained, and the influence of plasma spectrum was investigated. It was found that the spectrum of 350~700 nm can be used to analyze the surface composition of CFRP. The surface morphology observed by electron scanning microscope and the elements measured by X-ray electron spectroscopy were used to characterize the detection results of plasma spectrum. In order to obtain the threshold of removing the surface material completely of carbon fiber reinforced polymer, the plasma spectrograms under different laser energy and different cleaning times were collected. The relationship between laser cleaning quality and the main elements and their intensity changes of plasma spectrum were studied. The results showed that the 393.3 nm S (Ⅱ) and 589.5 nm S (Ⅱ) lines can effectively characterize the surface cleaning quality of carbon fiber reinforced polymer in the plasma spectrogram. The cleaning threshold of single laser to remove the surface material completely is 10.68 mJ. Low laser energy can remove epoxy resin more than once. High laser energy can remove the surface resin with single laser, but multiple cleaning will damage the carbon fiber. These results provide basis and technical support for the intelligent integrated application of laser cleaning carbon fiber composites.

In order to investigate the molecular structure and spectrum of Hesperetin (HES), a kind of dihydroflavone drug. Density Functional theory (DFT) and basis set 6-311G(d,p) are combined to optimize the ground state’s geometry of HES molecules under an external electric field (EEF) ranging from -0.005 to 0.010 a. u. in C2—C1 direction. Total molecular energy, infrared spectrum (IR), dipole moment (DM), and HOMO-LUMO Energy Gap are investigated at the same time. Based on the optimized configuration, time-dependent density functional (TDDFT) is applied to study the influence of different EEF on excited states and UV-Vis spectrum of HES. The results show that when there is no external EEF, the single bonds between C1—O18 and C2—C26 are optimized as double bonds, and the enol structure is converted to a more conjugated system, forming the most stable structure. With the increase of EEF, the total energy of molecule rises at first and then falls, DM decreases at first and then increases. The change of bond length is complicated. When the negative EEF increases, due to the IR absorption peak (AP) generated by the vibration of different chemical bonds in HES molecule, different spectrum shifts occur, and the intensity of each AP also varies according to its relative chemical bond. In the absence of EEF, there are two APs at 223.6 and 262 nm in UV-Vis, related to in the E2 band and the K band respectively. AP at 223.6 nm appears blue-shift (BS) with the increase of EEF. When EEF is greater than 0.002 5 a. u., peak 223.6 nm disappear. AP at 262 nm shows a red-shift (RS), and the absorption intensity shows a declining trend with the increase of EEF. When EEF is 0.01 a. u., peak 262 nm shifts to 283 nm, and it’s intensity reaches 5 898.64 L·mol-1·cm-1. BS occurs under negative EEF and the absorption intensity increases. When EEF is -0.002 5 a. u., peak 262 nm shifts to 261 nm and the intensity increases to 12 500.36 L·mol-1·cm-1. When positive EEF increases, both energy gap and excited energy (EE) show a decreasing trend, indicating that the HES molecule is easily excited and is in an active state. In the absence of EEF, oscillator strength (OS) is greater than zero, indicating that it can be stimulated. When EEF is continuously enhanced toward the positive direction, OS of ES increases at first and then decreases; OS has complex changes under negative EEF. Investigating the molecular structure and spectrum of HES under EEF will provide a theoretical reference for its electric field dissociation.

Artemisinin is a kind of sesquiterpene lactone drug containing peroxide groups, which is extracted from Artemisia annua L. Now it has become an antimalaria drug recommended by the world health organization. The compound preparation of Chinese material medica containing artemisinin has strong anti-malarial, antibacterial and immunomodulatory effects. However, there is no measurable and generalized standard for controlling and evaluating the quality of the compound preparations. Currently, the main researches of artemisinin compound preparations focus on the qualitative analysis of the active ingredients, and only less work is aimed at how to quantitatively analyse the artemisinin content. Therefore, it is urgent to develop a convenient, rapid and nondestructive method to monitor the production and use of artemisinin. Laser Raman spectroscopy is a kind of molecule scattering spectroscopy, which is characterized by frequency excursion that is caused by interactions of molecules and photons, to obtain information on molecules. Laser Raman spectrometry is a potential method for quantitatively analyzing artemisinin content. In this study, we demonstrate that Laser Raman spectroscopy is a suitable and non-destructive technique to qualitatively analyze artemisinin. The spectral characteristics of artemisinin will provide standard Raman spectra for identification and analysis of artemisinin in Chinese traditional compound medicine. It is found that the vibrational mode at 724 cm-1 is related to the vibration of peroxide groups, and also it is the key to verifying its anti-malarial function, in Artemisinin. In addition, the phonon mode at 1 736 cm-1 is not affected by the surrounding vibrational modes and its intensity is strong. The vibrational mode at 1 736 cm-1 corresponds to the vibrational mode of lactone bond, so it could be used for detecting artemisinin. Hence, this paper attempts to adopt the characteristic vibrational modes at 724 and 1 736 cm-1 simultaneously to analyze Artemisinin qualitatively and quantitatively. A series of artemisinin/flour binary mixture with different artemisinin mass percentage were synthesized with the aim of obtaining Raman spectral parameters at 724 and 1 736 cm-1. The average of Raman peak area ratios in accordance with the abscissa, the ordinate artemisinin content for result analysis. Afterwards the results are analyzed through nonlinear regression in order to get the function between the artemisinin content and Raman peak area ratios. The quadratic function is y=0.907 22x2+0.465 93x(0<x<0.9), and its correlation coefficients is 0.992 65. In addition, measurements of artemisinin content in Chinese traditional compound medicine by Laser Raman analysis were carried out on artemisinin-piperaquine tablets. The values of artemisinin content obtained by Laser Raman spectroscopy technology and true values (14.29%) are in good agreement with each other (relative errors <10%). This research provides a fundamental analysis tool for determining artemisinin content quantitatively by using Laser Raman spectroscopy. Experiment results demonstrate that this method has the potential for obtaining artemisinin content in Chinese traditional compound medicine.

In response to the recent negative news of several expired drugs, in order to further improve the speed of drug testing, this paper proposes a new method for nondestructive testing of drug quality using terahertz time-domain spectroscopy (THz-TDS). Firstly, the terahertz time-domain spectra of expired amoxicillin capsules, compound paracetamol and amantadine hydrochloride tablets, Banlangen and compound Banlangen granules were tested by the Zomega THz-TDS system (Z3), and the femtosecond laser produced by Mai Tai. Secondly, through the Fast Fourier Transform (FFT), the frequency spectrum of sample was obtained. Thirdly, according to the THz-TDS extraction optical parameter model proposed by Dorney and Duvillaret et al., the four cold medicines’ spectrum of terahertz characteristic absorption peak, absorption coefficient and the refractive index were obtained. Finally, the experimental results were compared with the terahertz characteristic absorption peaks, absorption coefficients and refractive indexes reported in the related literatures. Experiments show that the average refractive indexes of expired amoxicillin capsules and compound paracetamol and amantadine hydrochloride tablets in the effective frequency range of 0.2~0.9 THz are 1.90 and 1.85, respectively, which is smaller than the minimum refractive index reported in the related literature of 1.92 and 2.05, respectively. The average refractive index of expired Banlangen granules and compound Banlangen granules in the effective frequency range of 0.2~1.4 THz was 1.84, which was slightly larger than the maximum refractive index reported in the related literature 1.797. The five THz characteristic absorption peaks of expired amoxicillin capsules all appeared, but the amplitudes decreased. The three THz characteristic absorption peaks of compound paracetamol and amantadine hydrochloride tablets still exist, but the amplitudes are reduced, and a new terahertz characteristic absorption peak appears near 1.50 THz. The expired Banlangen granules have a new terahertz characteristic absorption peak at 1.43 THz and the absorption coefficient is reduced. The expired compound Banlangen granules appear three new terahertz characteristic absorption peaks at 1.43, 1.48 and 1.54 THz, and the absorption coefficient is also reduced. The results showed that the effective components of expired amoxicillin capsules, compound paracetamol and amantadine hydrochloride tablets, Banlangen granules and compound Banlangen granules were reduced, and some chemical components had been changed. The refraction index of western medicines such as expired amoxicillin capsules and compound aminophenol tablets will increase, while that of expired Banlangen granules and compound Banlangen granules will decrease. The above four kinds of expired drugs can be distinguished by the change of the refractive index and their terahertz characteristic absorption peaks, which could provide a reference for distinguishing expired drugs with terahertz spectroscopy.

The methane (CH4) gas released by hydrothermal enters into the ocean and atmosphere successively by diffusing and causes inestimable effect on earth in physics, chemistry and biology. The principle and environment effect of abyssal hydrothermal still require further study because limited information is available about dissolved methane. In our previous work, we propose an optical passive imaging interference system (OPIIS) for the real-time detection and long-term observation of hydrothermal methane’s concentration, temperature, and pressure. To accurately, stably, and rapidly obtain the information of hydrothermal methane from OPIIS’s interferogram, this paper processes OPIIS’s data by combining interference spectra and partial least squares (PLS) algorithm. We built three single-dependent variable models between methane radiance spectra and gas concentration, temperature and pressure, respectively. Then we can establish the PLS prediction model between interference fringes indirectly on the basis of relationship between interference fringes and radiance spectra, which can improve the capacity of resisting disturbance and stability of prediction models in practical application. On the basis of Lorentz profile, we build the deep ocean gas emission model different from atmosphere emission and obtain the synthetic methane radiance spectrum database at any concentration, temperature and pressure by using the methane spectral parameters from HITRAN2016 molecular spectroscopy database. The six spectral lines of methane in the range of 1.64～1.66 μm are selected for the PLS regression model between methane radiance spectra and gas concentration, temperature and pressure. Furthermore, this paper analyzes the contribution of number of training samples, interval of training samples and number of principal components to the improvement of the comprehensive performance of regression model. The 96 groups of concentration, temperature and pressure regression model are built by using different groups, intervals and principal components, and those regression models are cross-validated using 25 groups of prediction samples. The comparison results of those regression models’ root mean square error of prediction (RMSEP) and coefficient of determination (R2) indicate that the change of single factors such as the number of training samples, the interval of training samples and the number of principal components can not improve the prediction model’s comprehensive performance about prediction accuracy, stability, application scope and computation. Finally, the optimized model with balanced performance is determined with concentration, temperature and pressure application ranges at 5～375 mmol·L-1, 580～678 K, 10～34.5 MPa, training samples of concentration, temperature and pressure are 50 groups, 25 groups, 25 groups, intervals at 5 mmol·L-1, 2 K, 0.5 MPa, principal components are 2, 2, 5. The RMSEPs of concentration, temperature and pressure are 3.082×10-6, 0.977 0, 5.052×10-3, and R2s are 0.999 9, 0.998 9, 0.999 9, respectively. The prediction errors of concentration, temperature and pressure are ±1.21×10-7, ±3.63×10-3, ±9.49×10-4, and the corresponding precisions are ±45.4 nmol·L-1, ±2.5 K, ±3.3×10-2 MPa. The results indicate that this retrieval algorithm can accurately, stably, and rapidly obtain concentration, temperature and pressure of hydrothermal methane.

The olive oil, known as “liquid gold”, has become a synonym for healthy edible oil. It not only has a steep increase in its price, but also has become a best-selling oil in the non producing market. Spectral method has many advantages compared with other technologies, such as fast, nondestructive and non sample processing. Different spectral detection methods have a particular emphasis on the material components, for example, infrared spectroscopy focuses on the detection of fatty acid content, Raman spectroscopy focuses on the detection of molecules, fluorescence spectroscopy focuses on the detection of photosensitive substances, and absorption spectroscopy focuses on the detection of unsaturated fatty acids. Fluorescence and absorption spectra are very sensitive to photosensitive substances, and olive oil is rich in chlorophyll and other photosensitive substances. Therefore, fluorescence and absorption spectra have become an effective technique for identifying olive oil. Chlorophyll is an organic molecule containing the structure of cycloporphyrin. The molecular structure of this kind of molecular structure has the characteristics of absorption of light, and the absorption spectra of different kinds of chlorophyll are unique, among which the content of chlorophyll a in green plants is the most. In order to study the application of the absorption spectrum of chlorophyll and the fluorescence characteristics of the extra virgin olive oil, the content of chlorophyll in olive oil was indirectly regulated by mixing different proportion of corn oil in the special primary olive oil. The fluorescence and absorption spectra of different adulterated olive oil were measured and the phase of chlorophyll concentration was studied. The effecus of chlorophyll concentration and adulteration amount on the absorption spectra and fluorescence characteristics of olive oil were studied. 10 samples of the same batch of extra virgin olive oil were taken, 9 of them were diluted in equal proportion and 10 samples were sequentially ordered according to adulteration. The fluorescence and absorption spectra of the 10 samples were collected in turn, and the correlation between the concentration of chlorophyll and adulteration were compared and the effects of the two spectral techniques on the identification of olive oil were compared. With the increase of chlorophyll concentration, the fluorescence intensity becomes stronger and weakens sharply after a certain time, that is, the aggregation fluorescence quenching. This phenomenon is mainly due to the intermolecular π—π action caused by the molecular structure of the phyphyrphyrin, which makes the non excited low energy molecules and high energy molecules stacked together. The radiation transition of energy (Fluorescence) is also transformed into the energy transfer (heat exchange) between the molecules. As for the absorption spectrum, the intensity of absorption spectrum increases with the increase of chlorophyll concentration. The main energy of the absorption of chlorophyll in olive oil consists of two parts, including the emission of magnesium electron emission and the intermolecular heat exchange, while the absorption spectrum of olive oil does not appear like aggregation fluorescence quenching, and there is an approximate linear correlation between the absorption spectrum intensity and the adulteration concentration. The results show that when the fluorescence quenching occurs, the energy of the absorption of chlorophyll is still linearly related to the concentration, and the efficiency of heat exchange caused by the stacking of high and low energy molecules increases.

As deep mining goes, the water inrush threat is from the roof goaf water and the bottom pressure karst water. Coal mines water inrush water types on-line discrimination, serving as an effective monitoring method to predict mine water hazards, is an important step in Mine water disaster prevention and control work to ensure coal mine safety production. Representative ion method, as a traditional method to discriminate mine water inrush sources, must collect and seal water samples on-site, test samples in laboratory using 7 typical inorganic ion concentrations, and calculate water bursting evaluation factor. The method has disadvantages of too long detection time,easy contamination for samples, delayed warning response and misjudgment. Due to above reasons, the paper proposes a mine water inrush sources discrimination model based on Laser Induced Fluorescence (LIF) and Convolutional Neural Network (CNN). First, based on 4 types of water sources, 161 samples were collected from Xinji Second Mine of Huainan mining group during June 2016 to June 2017, including oaf water 46 items, Sandstone water 59 items, Limestone water 42 items and Ordovician limestone water 14 items. In the experiment, samples were stimulated by 405 nm laser using LIFS-405 Laser Induced Fluorescence System, and the fluorescence spectra of four kinds of 161 groups of water inrush samples were obtained. During principal component analysis, the cumulative contribution rate of the top ten components was less than 85%, making 4 types of water samples almost indistinguishable. Second, considering the random high frequency fluctuations in water fluorescence spectra, first-order lags filtering method should be used to reduce periodic high frequency fluctuations. Considering data update rate, recursive averaging method should be adopted. The paper proposes an improved recursive average first-order lag smoothing filtering method further to calculate autocorrelation processing to get enhanced two-dimensional autocorrelation characteristic fluorescence spectra. The experimental results show that calculated autocorrelation characteristic fluorescence spectra have excellent performance on interference elimination and discrimination. Finally, based on autocorrelation characteristic fluorescence spectra, mine water inrush sources discrimination model using CNN was constructed to discriminate water inrush types. The method adopts deep learning framework using autocorrelation characteristic fluorescence spectra to avoid selecting features in subjective ways. Theoretical analysis and experimental results show that the correct recognition rate of water source type can reach 98%. It is an effective way to discriminate the source of water inrush from mines and provides a new idea to discriminate the types of mine water inrush sources.

As ultraviolet light will be scattered by rainfall particles, the changes of scattered light characteristics can reflect the physical properties (such as particle size parameters, density and shape ) of rainfall particles. Therefore, it is of great significance to study the influence of the physical parameters of the particles on the characteristics of the scattered light that can effectively improve the accuracy of the detection of precipitation by spectroscopy. Due to the representation of raindrops in non-spherical precipitation particles, in this paper, the raindrop particles are taken as an example. Using the UV line-of-sight and non-line-of-sight scattering models, we analyzed the relationship between the scattered light intensity and a series of object parameters, including wavelength of incident light, the morphology of raindrop particles, rainfall intensity and particle size. For non-spherical raindrop particles, we also simulated and analyzed the relationship between scattering angle and scattered light intensity at different particle size, rainfall intensity and the influence of wind shear in rainfall on ultraviolet light scattering properties using Monte Carlo method. Through theoretical and simulation analysis, the path loss under different groups of raindrop particle shapes, the scattered light intensity distribution under different rainfall intensity, wind shear rate and particle size were obtained. The simulation results show that the communication quality in the rainfall environment is worse than that in the sunny day, which means greater path loss under UV LOS and NLOS communication. When the particle size distribution is known with the increase of rainfall intensity, attenuation coefficient increase and the path loss increase, the attenuation of LOS communication can be less than about 7 dB for NLOS communication. With the increase of rainfall intensity, wind shear rate and particle size, scattered light intensity curve shows a downward trend, among which, the change in rainfall intensity has the greatest effect on the scattered light intensity. When the communication distance is the same, the intensity distribution of UV light scattering under different rainfall intensity decreases with the increase of scattering angle, when the scattering angle increases by more than 90 degrees, the effective scattering volume decreases, the received photon energy decreases, so the scattering intensity in rainstorm attenuation is larger. Under the same rainfall intensity, when the wind shear is taken into account, the scattering intensity decreases, and the path loss increases about 5 dB when compared with that without wind. In addition, the effects of ellipsoid and Chebyshev particles on the intensity of UV light scattering have also been studied in this paper, which show that when the particle size is the same, the attenuation of ellipsoid particles is larger than that of Chebyshev particles. The scattered light intensity distribution and path loss of the scattering particles can be used to distinguish whether the raindrop particles are composed of the same particle size and morphology, providing a theoretical basis for particle measurement. And we analyzed the characteristics of light scattering of raindrop particles in rainfall, which provides a theoretical basis for improving the numerical simulation of rainfall attenuation assessment by spectroscopy and also provides a design reference for the wide application of optical technology in the detection and identification of rainfall phenomena and other meteorological fields.

Quick and accurate identification of water inrush types and sources of water inrush is of great significance for safe mining of coal mines. Laser-induced fluorescence (LIF) technology is rapid and sensitive in detection, which applies LIF to the detection of water inrush in coal mines and uses pattern recognition algorithm to quickly identify the source of water inrush. The current algorithms for identifying water samples are too dependent on pre-established water sample spectral databases When the water source is not in the library, it is easy to cause misidentification. The unsupervised learning algorithm DBSCAN does not require the label and category information of the sample set when clustering, which can reduce the misidentification of unknown categories. Therefore, the DBSCAN algorithm is used to identify the laser-induced fluorescence spectra in water inrush, and MVO is used for the parameter optimization of DBSCAN, which can eliminate the cumbersome manual parameter optimization process. In the experiment, four water samples were taken from the water intake point of Xieqiao Coal Mine, and 30 sets of spectral data were collected for each water sample. The fluorescence spectra of the water samples were collected using a USB2000+ spectrometer with a pixel of 2 048. First, the unsupervised learning algorithm automatic encoder (AE) reduces the dimension of the original spectral data to reduce the influence of redundant information in the spectral data on the clustering. The structure of the AE designed in this paper is a multi-layer network model between shallow and deep layers, which can reduce the original spectral data to 2 dimensions. In order to make the dimensionality reduction model sparse, the author adds a Dropout layer to the traditional AE algorithm. It can be seen from the experiment that the dimensionality reduction model after adding the Dropout layer has a faster convergence speed. Then, using the multivariate optimization (MVO) algorithm to optimize the DBSCAN parameters. In the parameter optimization process, the spectral recognition rate of the water sample after DBSCAN is up to 97.5%, and the corresponding range of the parameter Eps is ［0.023 66 0.040 65］. The normalized unscaled spectral data is used for DBSCAN cluster identification to verify the effectiveness of AE on the dimensionality reduction of water sample spectral data. The recognition rate of the original water sample spectrum by DBSCAN is up to 95%, which is 2.5% lower than that of the post-dimensional water sample. The results show that using AE dimensionality reduction data can improve the recognition rate of DBSCAN for different spectra. Finally, the supervised learning algorithm K nearest neighbor (KNN) is used to identify the water sample spectrum after dimension reduction, and the recognition result and the unsupervised learning algorithm DBSCAN are compared. The training set uses three water samples, and the test set uses four water samples. For the test set data, the supervised learning algorithm can only accurately identify the water sample categories contained in the training set, but all the categories that are not in the training set are identified incorrectly. On the contrary, DBSCAN can accurately identify the water sample spectrum not in the training set. The nonlinear dimensionality reduction algorithm AE can achieve dimensionality reduction on high-dimensional water spectral data. The use of MVO-DBSCAN for LIF spectral identification of coal mine water inrush can effectively reduce the misidentification caused by the incompleteness of the mine water source spectrum database.

The usage of semiconductor photocatalysts for removal of contaminants is one of the greenest and most effective methods under sunlight, whose core is obtaining high-efficient photocatalysts. The most widely studied photocatalysts are TiO2, ZnO, etc., but they cannot fully utilize sunlight due to their large band gapenergy, thus limiting their practical use. In addition to modifying TiO2 to improve its visible light catalytic activity, the development of other materials as photocatalysts is also an important solution. Bismuth based compound semiconductors have become important research objects for their abundant raw materials, various types, good solar response and excellent photocatalytic activity. Bismuth oxyhalide compounds ［BiOX, X=Cl, Br, I］ exhibit excellent photocatalytic activity owning to layered structure, but they still have low photocatalytic efficiency when used alone. However, their photocatalytic degradation efficiency could be improved by preparing solid solutions (a mixture of solids that are molecularly dispersed with each other). In this work, a low-temperature wet chemical method can be used to obtain solid solutions BiOCl1-xIx and BiOBr1-xIx with sheet like structures, which is prepared by the reaction of a certain proportion of KI/KBr or KI/KCl aqueous solution with Bi2O3/HAc solution for half an hour at room temperature. X-ray diffraction (XRD) patterns showed that the synthesized BiOCl1-xIx and BiOBr1-xIx samples have good crystallinity and can form a solid solution in the range of x=0～1. The prepared solid solution was found to have an irregular sheetlike shape by a transmission electron microscope (TEM). X-ray photoelectron spectroscopy (XPS) tests further demonstrate their surface element composition and chemical state. Ultraviolet-visible diffuse reflectance spectroscopy (DRS) analysis indicates the red-shifted absorption edge of the solid solution and decreased band gap energy as the iodine content increased, so the visible light absorption capacity is enhanced and the number of generated carriers is agumented. The photocatalytic tests of MO degradation under visible light excitation manifest that BiOCl0.25I0.75 and BiOBr0.25I0.75 exhibit the highest photocatalytic activity. Cyclic experiments show that BiOCl0.25I0.75 and BiOBr0.25I0.75 have high stability. Photocatalytic mechanism studies show that the active species in the photocatalytic degradation of MO in these bismuth oxyhalide samples were holes and superoxide ion radicals. Combined with their energy band structures, it is believed that the formation of solid solution not only increases the visible light absorption capacity, but also modulates its energy band structure. Compared with BiOI, the formation of solid solution lowers the valence band position and raises the conduction band position. Therefore, the reducing ability of the photogenerated electrons and the oxidizing ability of the holes are enhanced, so that the catalytic performance is improved. The novelty of this work is low-temperature solid solution preparation, which avoids hydrothermal method or the addition of surfactants. Furthermore, the prepared BiOCl1-xIx and BiOBr1-xIx solid solutions, especially BiOCl0.25I0.75 and BiOBr0.25I0.75, have excellent photocatalytic degradation ability for MO under visible light excitation. Moreover, the catalysts have good stability, so it is expected to be applied in environmental management.

Ⅲ-Ⅴ nitride GaN light emitting diodes (LEDs) have been widely studied and applied due to their long lifetime, small size, high efficiency and energy saving. With the further development in the fields of optical communication and interconnection, it is necessary to develop high quality micro-nano photonic sources and waveguides. Nano-column GaN-LED is an important micro-nano light source which has a broad application prospect. On the other hand, the silicon semiconductor material which is the most widely used is not a direct semiconductor itself, and its luminous efficiency is low and cannot be used as a light source. Therefore, it is very important to study nano-column GaN-LED micro/nano light sources based on silicon substrates. In this paper, a GaN buffer layer, a Si-doped n-GaN layer, a 4-period InGaN/GaN quantum wells layer and a Mg-doped p-GaN layer were deposited and grown on Si substrate by radio frequency molecular beam epitaxy technology(rf-MBE). The surface and side morphology of the nanostructures were observed by scanning electron microscope (SEM). Nano-columns which were grown on the surface of the substrate at a certain oblique angle and arranged closely and neatly can be observed. Nano-column GaN-LED was prepared by micro-nano processing technology. SOG filling and FAB etching were performed on the obtained nano-pillar epitaxial wafer, and electrodes were vapor-deposited on the p-GaN layer and S substrate side. DC voltage was applied to both electrodes of the LED. Photoelectric properties such as I-V curves and electroluminescence (EL) spectra were tested. The results show that the threshold voltage of nano-column GaN-LED is 1.5 V and the peak wavelength of nano-column GaN-LED is 433 nm at 290 K. The nano-column structure effectively reduces the LED threshold with smaller voltage. At the same voltage, the nano-column LED has a higher brightness and exhibits better light emission characteristics. Compared with bulk materials, the existence of stress relaxation in the nanostructures can effectively reduce the dislocation density. The size of the nanostructure is smaller than the diffusion length of photo-generated carriers or excitons, reducing the localization in the active layer of the optoelectronic device. By the TCAD simulation, the luminescence spectra of the nano-column LED can be obtained by applying voltages of 5, 6 and 7 V to the two electrodes of the nano-column GaN-LED respectively. The wavelength of nano-column GaN-LED is 414～478 nm. The luminous color covers from sky blue to blue-purple and the peak wavelength is 442 nm, which is close to the 433 nm of the EL. As voltage increases, the peak wavelength of the emission spectrum decreases, with a slight blue shift of the peak wavelength. In the nano-pillar structure, the InGaN/GaN region produces a strong polarization effect, and the nano-column structure increases the carrier concentration in the quantum wells region, which weakens the quantum-confined Stark effect, thereby shifting the peak wavelength of the LED to a high frequency which is called blue shift. Moreover, the nanopillar structure can cause stress release and also cause a blue shift in peak wavelength.

In order to test the variability in color discrimination among observers with normal color vision and study the spectral responses of cone cells in R, G, B three channels, the spectral pseudoisochromatic images consisting of background layer, lightness layer and number layer, which have different spectral reflectance were designed and created. By interacting with the light source and the spectral response of the human cone cells, different observers perceive various color differences. The obtained visual results were used to further study the difference of human perception on the metameric color pairs. The experiments were based on different output equipments with different primary inks producing different spectral reflectances, and the metameric color pairs were created under different light sources, which can amplify the observer’s variability. For the CIEDE2000 color difference calculated by different color matching functions, some were within the threshold of color discrimination, and some could be clearly recognized. By optimizing calculation, the nearly metameric color pairs, which can magnify the color discrimination of observers, were applied to design the number and background layers of the spectral pseudochromatic image. The Epson inkjet printer and OKI laser printer were used to print the number layer and background layer of the pseudochromatic spectral image correspondingly. 72 color-normal observers, including 55 young observers aged from 18 to 25 and 17 old observers aged from 62 to 74, were organized to view and test the images under D65 and LED-5000K light sources respectively. The visual results showed that the spectral pseudochromatic images can be successfully used to classify whether the spectral response of retinal cone cells is aging or not. Under the same light source, young observers can read the numbers in images (②/④) that the old observers can’t read, while the old observers can read the numbers in images (①/③) that most young observers can’t read, and 4 of the 55 young observers have the same visual results as the elderly. In addition, the spectral responses of young observers were consistent with the CIE1964 and CIE2006 (age=25 y) Color Matching Functions(CMFs), while the spectral response of old observers were more consistent with the CIE1931 and CIE2006 (age=75 y) CMFs. It was found that the cone cell spectral responses of old observers were shifted to the long wavelength, and the spectral responses decreased due to the increase of the optical density of the ocular system.

One task of coral reef remote sensing is to obtain the composition and distributionof benthic categories. However, there is still a great deal of uncertainty anddifficulty to discriminate reef benthos by means of remote sensing owing to thespatial heterogeneity and complicated spectrum of coral reef. Spectral characteristics of different coral reef benthos are the basic prior knowledge for remote sensing of coral reefs. Based on in situ spectral data and simulated satellite data, this paper analyzed the spectral characteristics of different coral reef benthos, especially the spectral properties of different coral types. The influence of coral pigments on coral spectra was also analysed. Finally, four kinds of commonly used satellite data(Landsat 8, IKONOS, Quickbird and SPOT 5) were simulated to investigate the spectral separability of different reef benthos from space. Results showed that sand and bleached corals could be easily identified by the reflectance curves in the visible bands. The first-order spectral derivation in visible bands was a good way to distinguish algae, seagrass and healthy corals. The differences of families, genera, species, coral shapes and coral colors would have obvious impact on the spectral characters of corals. In addition, Chlorophyll contents (including Chlorophyll-a, Chlorophyll-b and Chlorophyll-c) had high correlativity with reflectance of corals, which would exert notable influence on coral spectral features. Zooxanthella had the similar influence, but not as obviously as that of chlorophyll. Its density would affect the peak features of coral reflectance. Among the commonly used multi-spectral satellite data, Landsat 8 had the ability to distinguish sand, bleached corals, algae, healthy corals and seagrass owing to its coastal band, while IKONOS and Quickbird could identify sand, bleached corals and seagrass. Comparatively, SPOT5 had a poor performance, which could only identify sand and bleached corals. However, in the identification of different types of corals, multi-spectral satellite data failed to capture the elaborated spectral features and hyperspectral data with high spatial resolution was needed for effective identification. In the future work, we will further expand more coral reef benthos samples and establish the spectral database of coral reef to provide the data support for the establishment of coral reef monitoring system in China.

Many colorful corundum crystals were found in Umba, Tanzania. The sample of this study is a sapphire with special color-changed effect from Umba. It shows slight yellow under D65 light source (colour temperature is 6 500 K) and slight purplish red under A light source (colour temperature is 2 856 K). In order to study the UV-Vis spectroscopy and the origin of the color-changed effect of this sapphire, the charge compensation theory was innovatively used to analyze the assignment of absorption characteristic in UV-Vis spectrum. UV-Vis spectrophotometer and laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS) were used to test the sample. The result showed that there are three absorption peaks at 377, 388 and 450 nm and a absorption band centered at 560 nm in UV-Vis spectrum of the sample. The color of the sample is mainly affected by the peak at 450 nm and the band centered at 560 nm, which leads to the color-changed effect. According to the results of LA-ICP-MS, the trace elements in this sample are Fe, Ti, Cr, V, Mg, etc. The absorption peaks at 377, 388 and 450 nm in the UV-Vis spectrum of samples are caused by Fe3+. The absorption features near 560 nm may be caused by Cr3+, V3+, Fe2+-Ti4+ pairs in sapphire. Combining with charge compensation theory, Mg2+ will preferentially compensate with Ti4+ in corundum. The content of Mg in sample is slightly higher than that of Ti. So almost all Ti4+ will compensate with Mg2+ and almost no Fe2+-Ti4+ pair exists in the sample. The absorption characteristic of charge transfers between Fe2+ and Ti4+ has strong polarization. The absorption characteristic caused by Fe2+-Ti4+ pairs, especially after 580 nm, will change obviously with the change of polarization direction. Polarized UV-Vis Spectrum of this sapphire shows that there is no obvious polarization feature on the absorption band centered at 560 nm, which could further confirm that there is almost no Fe2+-Ti4+ pair in the sample. Thus the absorption band centered at 560 nm is mainly caused by Cr3+ and V3+. The color of sample is mainly caused by Fe3+, Cr3+, V3+ and the color-changed effect is mainly caused by Cr3+ and V3+. This research innovatively combines charge compensation mechanism and polarized UV-Vis spectroscopy to explain the assignment of the absorption band centered at 560 nm in the UV-Vis spectrum of this color-changed sapphire. It provides a new method to study the assignment of the absorption band around 560 nm which is a common absorption characteristic in UV-Vis spectrum of corundum.

Red light and blue light are the main spectra of photosynthesis and photomorphogenesis in plants, and also is a developing tendency of light source in plant factory. Therefore, to realize the application of continuous light in plant factory, the response characteristics and mechanism of plants to continuous light in red and blue spectra need to be explored. The effect of continuous light and its intensity on the growth and mineral elements absorption of lettuce was investigated by ICP-AES technology in an environmental-controlled plant factory. Five lettuce cultivars were cultivated under normal light(12 h/12 h) and continuous light (24 h/0 h) in experiment 1, and grown under continuous light with five light intensity(80, 120, 160, 200 and 240 μmol·m-2·s-1) in experiment 2. The results showed that dry weights and dry weight ratios of lettuce significantly increased during 30 days’ continuous light, and increased with light intensity. However, continuous light only enhanced fresh weight in first 15 days, which also increased with light intensity. 30 days’ continuous light had no positive effect on fresh weight, even significantly reduced the fresh weight of Yidali cultivar. Compared with normal light, 30 days’ continuous light significantly reduced the Ca, Mg, Fe, Mn, Cu, and Zn contents, but slightly or significantly enhanced the accumulation of these mineral elements. Under different light intensity, the contents of Ca, Fe, Cu, and Zn decreased with light intensity, the accumulation of Ca and Fe increased with light intensity, while that of Cu and Zn were not affected by light intensity. Content and accumulation of Mg increased first and then decreased with the increase of light intensity. The content of Mn element was not significantly affected by light intensity, but the accumulation increased with the increase of light intensity. In addition, 30 days’ continuous light induced severe leaf chlorosis and necrosis, and this negative effect aggravated with light intensity, which indicated that the decrease of mineral element content aggravated the injury of continuous light to some extent. In conclusion, 15 days’ continuous light could enhance lettuce yield remarkably. However, although 30 days’ continuous light improved the dry weight, it had no positive effect on yield and induced the decrease of mineral elements contents and leaf injury. Even though lettuce plants grown under continuous light with low light intensity (80 and 120 μmol·m-2·s-1) had no leaf injury and relative higher mineral elements contents, it had no positive effect on yield. This study indicated that short-term continuous light is more suitable for the cultivation of lettuce in the plant factory, and can obtain higher yield than energy input. The decrease of mineral element content may be one of the mechanisms of long-term continuous light injury.

The soil reflectance spectrum curve reflects the physical and chemical properties and internal structure of the soil. Hyperspectral remote sensing technology has been used to classify soil based on the soil reflectance spectrum characteristics. The first order differential principal component of soil reflectance spectrum is generally used to construct the spectral classification model, but the principal component data is lack of physical significance, contrast and limited scope of application. Compared with the first-order differential reflectivity data, the extraction of the characteristic parameters based on the de-enveloping line can improve the accuracy of soil classification and find a high-precision soil classification model. In this study, four typical soils (wind-sand soil, meadow soil, calcareous soil) were selected in Nong’an County, Jilin Province. The collected soil samples were dried, ground and treated by 2mm sieve. ASD FiledSpec3 portable spectrometer was used to measure the visible near infrared spectrum of the treated soil samples, and the spectral data of the soil samples were obtained. The spectral data were smoothed by nine points, the noise was reduced by 10nm resampling, and the processed data were processed by the first order differential principal component and the de-enveloping line respectively. The spectral characteristic parameters were extracted by using the continuum removed line of soil samples. The first order differential principal component data and spectral characteristic parameters were input into Logistic clustering model, artificial neural network clustering model and K-means clustering model respectively. In this paper, the reflectance spectra of different soils, the difference of the envelope, the reflectivity curve of the same soil, and the advantages and disadvantages of the soil classification are determined. And the spectral characteristic parameters which can distinguish different soil types are extracted on the basis of de-enveloping line. Secondly, when the first order differential principal component is compared with the spectral characteristic parameter as input, the accuracy differences of the three spectral classification models are compared and the reasons for the difference in the accuracy of different models are analyzed. The results showed that: (1) The difference of the reflectance spectra of the four soils was small, and the spectral difference between the four soils could be greatly enhanced by the continuum removedline. The spectral characteristic parameters with clear physical meaning are constructed on the basis of the de-enveloping line. (2) The first order differential principal component and spectral characteristic parameters are introduced into the three clustering models respectively. The soil spectral classification model with spectral characteristic parameters as input is more accurate than that of the first order differential principal component model, because the spectral characteristic parameters retain the physical meaning of the original data. More accurately reflects the differences between different soil types, and due to the fact that the first order differential principal component data have a certain degree of fuzziness and are lack of contrast between different ranges, it is more advantageous to use spectral characteristic parameters as input in soil classification. (3) Among the three soil classification models, the Logistic clustering model has the highest classification accuracy of 76.67% kappa coefficient of 0.56; the average classification accuracy of the artificial neural network model is 72.50% and the Kappa coefficient is 0.48 K-mean clustering model has the lowest classification accuracy, only 65.00% . And Kappa coefficient is 0.33. The research results can provide technical support for fine mapping of soil and the development of soil classification instrument.

Spectroscopy is regarded as a quick and nondestructive method to classify and analyze quantitatively many of elements of the soil. Visible and near-infrared re?ectance spectroscopy offers a conductive tool for investigating soil heavymetal pollution. In this work, 51 soil samples with depths of 0～10 cm were collected, which were in the Eastern Junggar coal-field mining area, Xinjiang. The soil organic matter (SOM) content, Arsenic (As) content and indoor hyperspectra were measured in the laboratory. The significant relationship between As content and hyperspectral data was conductive analysis of NPDIs, which were calculated from Vis-NIR region. For calculating the indices, on the basis of the raw spectral reflectance (R), its three mathematical transformations were calculated, i. e., the reciprocal (1/R), logarithm (lgR) and root mean square method (sqrt-R/R), respectively. The two band combination of optimized indices software V1.0 (No: 2018R11S177501, independently developed based on the JAVA) was used during the calculation of the indices. NPDIs were calculated using all possible combinations of available bands (i nm and j nm) in the full spectral region (400～2 400 nm). In the optimal spectral indices (|r|≥0.73 and p=0.001), an index of VIP≥1 was further selected as a model independent variable by the Variable importance in projection (VIP) selection method. The main goal of this work is to obtain optimized spectral index (NPDI) related to soil heavy metal As, to estimate As concentration in soil based on geographically weighted regression (GWR) model, and to investigate the plausibility of using optimized spectral index for hyperspectral detection of heavy metal Arsenic in soil of coal mining areas. To assess the performance of the soil heavy metal contents prediction models, four cross-validation metrics were used; Residual Prediction Deviation (RPD), the Coefficient of Determination (R2), the Root Mean Square Error (RMSE) and Akaike Information Criterion (ACI). The results of this study are as follows: (1) As has the largest dispersion in the study area, SOM contents in all samples are less than 2%, and the As concentration has no significant correlation with the SOM content at a significance level of 0.01 (|r|=0.113). (2) Single-bandreflectance shows low correlation with As contents, lower than 0.228. However, the highest correlation coefficient and lowest p-values (｜ｒ｜≥0.73 and p=0.001) between As and NPDIs calculated by original and transformed reflectance (R, 1/R, lgR, R) are found in theNear-infrared (NIR, 780～1 100 nm) and Shortwave-infrared (SWIR, 1 100～1 935 nm) long wavelength infrared. The original spectral region formed with long wave length near-infrared (LW-NIR) regions show highest correlation with As contents (｜ｒ｜=0.74). (3) VIP value of NPDIR(1 417/1 246), NPDI1/R(799/953, 825/947), NPDIsqrt-R(1 023/1 257, 1 008/1 249, 1 021/1 250, 1 020/1 247) and NPDIlgR(801/953, 811/953, 817/951, 825/947, 828/945) higher than 1, thus these NPDIs are chosen as independent variables. (4) From the four prediction model (GWR) performances it can be seen, the Model-a (R) showed superior performance to other three models (Model-b (1/R), Model-c (R) and Model-d (lgR)), and it has the highest validation coefficients (R2=0.831, RMSE=4.912 μg·g-1, RPD=2.321) and lowest AIC value (AIC=179.96). The hyperspectral optimized index NPDIR(1 417/1 246) may help to quickly and accurately evaluate Arsenic contents in soil, furthermore, the results provide theoretical and data support to accesse the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.

Currently the application of fractional differential (FD) algorithm for field spectra and the optimal band mechanism of spectra inversion salt in saline soil has not been seen. In view of the problem that traditional integer-order differential algorithm causes fractional-order spectral information lose and model accuracy decrease the salinity and field spectra of saline soils in Fukang City of Xinjiang, China, were taken as data sources and original spectrum and common four transforms were subjected to a total of 21-order FDbetween 0～2 orders (interval is 0.1), in order to explore the mechanism improvement of pretreatment precision for saline soil spectra. Results showed that: (1) FD could accurately display the details of spectral transformation in the derivation process due to continuous orders, and improve the resolution between peaks of the spectra. Italso gradually changed peak shape and removed peaking operationdue to the increase of orders, which resulted in the gradual change of FD curve of saline soil to the slope of the curve, namely, a detailed description of subtle differences from 0-order to slope and between slope and curvature. (2) Correlation coefficient (CC) between FD value and salt content of five spectral transforms was tested by 0.01 significance level, and max CC absolute larger than integer order (1-order, 2-order) was mainly concentrated at 1.3, 1.4, 1.5 orders. Among them, CC of 1.4 order 1/lgR and 1.3 order 1/R had the largest increase percentage, which were 12.78% and 13.03%, respectively. (3) Regardless of the spectral transformation, the corresponding bands for max CC between salt content and all FD value appeared at 598 nm (1/R) and 618 nm (R, R, 1/lgR and lgR), and they were all in 1.3 or 1.4 order. (4) Na+ accounted for 65.74% of total cations, and its correlation with total salt was 0.738. The 589.3 nm spectrum was the main reason why the bands with the best correlation between various spectral transforms and soil salinity were located at 598 and 618 nm.

Above ground biomass (AGB) is one of the most commonly used traits indicating the growth of winter wheat at early growth stages. It is of great practical significance to monitor the growth and to estimate the yield. The conventional methods involving destructive sampling and manual calculating of the dry weight to measure AGB are prohibitively time consuming and laborious. The non-destructive approach to estimate AGB of winter wheat is through the estimation of vegetation indices (VIs) and regression analysis, which heavily depends on tools such as Remote Sensing and LiDAR. Therefore, the method is subject to specialized knowledge and high-cost. An estimating method for above ground biomass of wheat winter at early growth stages was proposed by using the digital images of winter wheat canopy. The canopy images were captured by a digital camera. The image segmentation of vegetation was achieved by using Canopeo. Based on the segmented images, eight images features, i. e., CC (Canopy Cover), ExG (Excessgreen), ExR (Excess red), ExGR (ExG-ExR), NGRDI (Normalized Green-Red Difference Index), GLI (Green Leaf Index), RGRI (Red-Green Ratio Index) and RGBVI (RGB Vegetation Index), were extracted. Correlation analysis was conducted between pairs of the images features and the biomass measurements. The feature that were highly correlated to the biomass measurements were used to build the estimation model. Results showed that ExR, GLIand RGBVI were not correlated to biomass, resulting in the elimination in the following experiments. The left five features were highly correlated to the biomass measurements. Among the five selected features, CC, ExG and ExGR were positively correlated to biomass measurements while NGRDI and RGRI were negatively correlated to biomass measurements. Based on the selected features, four model, i. e., Partial least squares regression (PLSR), BP neural network (BPNN), Support vector machine regression (SVR) and Random forest (RF), were built to estimate the above ground biomass. The influences of the number of features and the sowing density on the estimating accuracy were analyzed quantitatively. Results showed PLSR achieved the best accuracy based on the selected five features, the R2 value was 0.801 5, and the RMSE was 0.078 8 kg·m-2, indicating that the PLSE was able to accurately estimate the above ground biomass of winter wheat at early growth stages. Thenumber of feature was proved to be an influence of factor. The accuracy of the model decreased with the reduction of the number of features. Experiments on the models by using different sowing density datasets were conducted as well. The results showed that PLSR outperformed the other three models over all the density datasets, the R2 values were 0.897, 0.827 9 and 0.788 6, and the RMSE values were 0.062, 0.072 and 0.079 1 kg·m-2, indicating that the PLSR the estimated above ground biomass had a goof agreement with the ground truth. With the increase of the sowing density, the accuracy of all the models decreased while the PLSR achieved the minimum reduction. In Summary, the above ground biomass can be estimated by using the digital images, which can provide support to the field management of winter wheat at early growth stages.

In order to study the influencing mechanism of carrageenan oligosaccharide on the muscle quality and protein in Litopenaeus vannamei during F-T cycles, the myofibrillar protein of samples during F-T cycles with different pretreatment methods (sterile distilled water, tripolyphosphate and carrageenan oligosaccharide) were measured respectively by Fourier Transform Infrared (FT-IR) and Micro-Raman (M-Raman). The data of FT-IR and M-Raman for myofibrillar protein in Litopenaeus vannamei with different pretreatments after 0, 2, 4, 6 F-T cycles were collected respectively. The results showed that the destructive effect of F-T cycles on the protein structure in Litopenaeus vannamei could be demonstrated by first-order spectrogram, and the structural changes of protein under different pretreatments could be qualitatively and quantitatively analyzed by second order derivation and gaussian curve fitting. The intensity changes of each characteristic peak at first-order spectrogram in FT-IR and M-Raman were indicated that F-T cycles could aggravate the loss and structural damage of muscle protein in shrimp. The primary chain conformation of secondary structure in the shrimp muscle protein was mainly characterized by amide band Ⅰ (1 600～1 700 cm-1). The results of FT-IR showed that the secondary structure of fresh shrimp protein was mainly β-turn, followed by β-sheet. It could compensate for the insensitivity of β-turn and β-sheet by M-Raman. The amide band Ⅰ of FT-IR and M-Raman spectrum after gaussian fitting qualitatively and quantitatively showed the secondary structure changes of protein during F-T cycles mainly made the decrease of α-helix and the increase of random coil, and carrageenan oligosaccharide pretreatment could significantly inhibit the loss of α-helix in shrimp protein during F-T cycles. FT-IR was not sensitive to the changes in the compositions of amino acid exposed to protein surface, while M-Raman spectra could compensate it to show the changes in protein conformation on side chain. The bands represented that tyrosine residues appeared at 850 and 830 cm-1, and the peak intensity ratio indicated the exposure of tyrosine, which showed an increasing trend during F-T cycles. The C—H bending and stretching vibration of aliphatic side chain amino acid residues were in 1 440～1 465 and 1 465～2 800 cm-1 respectively, and the peak strength changes of 1 448 and 2 935 cm-1 were on behalf of hydrophobic interaction strength of amino acid on side chain, which increased during F-T cycles. The changes in characteristic spectral bands of protein on side chains by M-Raman showed that F-T cycles made the hydrogen bond rupture of protein intramolecular or intermolecular, the exposure of tyrosine or amino acid residues on aliphatic side chain, and carrageenan oligosaccharides pretreatment could slow down the change significantly. Therefore, carrageenan oligosaccharides could delay the rupture of hydrogen bond and exposure of side chain hydrophobic group in muscle protein during F-T cycles, the secondary structure of protein could be further stabilized, the function of protein could be maintained and the quality of shrimp could be improved under F-T cycles. In addition, the innovative combination of FT-IR and M-Raman spectra was applied to the research on the mechanism of carrageenan oligosaccharide for improving the muscle quality under F-T cycles. It was found that FT-IR was more sensitive to the characterization of secondary structure in muscle protein, while M-Raman spectra could provide the theoretical references for the conformation changes of protein on side chains. The combination of them could provide the compatible and complementary information to better analyze the structural changes of protein in samples after different pretreatments.

In order to realize accurate prediction of tea disease and avoid secondary damage in the process of disease feature extraction, the fluorescence transmission technology was used to study the spectrum characteristics of tea red leaf disease. The total of 45 samples of healthy tea leaves, 60 samples of early stage of red leaf disease and 60 samples of intermediate stage of red leaf disease were collected in the experiment, and were divided to training set and prediction set according to the proportion of 2∶1 for each kind. The original fluorescence transmission spectra of these leaves were collected using hyperspectral instrument by fluorescence transmission. Through the analysis of average spectral intensity curves of the three groups of leaves, the feasibility of using fluorescence transmission spectral information to classify the three types of leaves was confirmed. Then the polynomial smoothing (Savitzky-Golay, S-G) method was carried out for smoothing and noise reduction on the original spectral. Finally, competitive adaptive reweighted sampling (CARS) algorithm was used to select the characteristic wavelengths of the preprocessed spectral data. After 50 weighted samples, 4 characteristic wavelengths were selected finally, which were 463, 512, 586 and 613 nm respectively. In order to maximize the disease feature information of the samples and strengthen the typification of the classifier input value of disease feature, hyperspectral images were collected on 4 characteristic wavelengths respectively. Gray level co-occurrence matrix (GLCM) algorithm was used to extract image texture information, and 0°, 45°, 90°and 135° direction of the four gray level co-occurrence matrix were calculated. Then, the mean value and square error of the five symbiotic matrices were calculated, and the average value of the four image texture information was taken as the texture feature value of the leaf in order to enhance the recklessness. Finally, 10 feature values were obtained. The LBP (Local binary patterns) algorithm was used to extract the texture information from spectral image, and the uniform mode was used to reduce the dimension of LBP mode. Eventually, 944 dimension characteristic values of LBP were got from each image, similarly, the average value of the four images was taken as the characteristic value of LBP texture. Finally, the LBP eigenvalues of 944 dimensions were obtained for each image, and the average value of 4 images was also taken as the LBP texture feature value of the leaf. Finally, the prediction model was established under characteristic spectrum associated with the gray level co-occurrence matrix and the LBP operator respectively by using the extreme learning machine (ELM). As the input eigenvalues of the model were not in the same dimension, the input eigenvalues were normalized firstly, and then the output labels of the model were defined, that is, the output of the prediction model of healthy leaves was 1, the early stage of red leaf disease was 2, and the intermediate stage of red leaf disease was 3. The prediction accuracy based on CARS-GLCM-ELM model was 81.82%, and the prediction accuracy of CARS-LBP-ELM model was 85.45%. It showed that the effect of combining fluorescence transmission spectrum with LBP operator texture information was better. Due to the undesired results, the hidden layer activation function in ELM was optimized by using Softplus function instead of Sigmod function. The prediction accuracy of the optimized model was 92.73%. In this study, the fluorescence spectrum information of diseased leaves and texture information of hyperspectral images at corresponding characteristic wavelengths were fused, and the results can provide some reference for rapid and accurate prediction of tea diseases.

The class-identification and grade-determination of tea have practical importance. Hyperspectral imaging possesses conspicuous advantages in data form of the combination of image and spectra, as well as in fast and undamaged checking in food safety, compared with traditional methods. In this study, hyperspectral images of four kinds of tea which have similar appearance were obtained at the spectral range from 1 000 to 2 500 nm. MNF (Minimum Noise Fraction) and NWFE (Nonparametric Weighted Feature Extraction) were used to rotate and project the hyperspectral data from high dimension to lower subspaces. Then, ANOVA (Analysis of Variance) was used to estimate and select the projected subspaces which have better separability, and they are MNF1, MNF2, MNF4, MNF6, MNF8, NWFE1, NWFE2. Then selected subspaces together with the sum of all original bands were fed to SVM classifier. On the other hand, Finally, IID (image intrinsic decomposition) was applied to decompose the original spectra into material reflectance spectra R and shadow spectra S. Next, gradient image was obtained from R, and watershed algorithm was adapted to segment image in spatial dimension. Finally, results of both pixel-classification and spatial segmentation were fused to have better tea identification. The proposed method was proved to have a satisfying result with an overall accuracy of 94.3% and Kappa coefficient of 0.92 given the only 1% training pixels of all the tea pixels. The proposed model well avoids the phenomenon of same material but different spectra, and significance of reference in practical production is expected.

Chlorophyll is an important pigment in vegetation photosynthesis, and the traditional laboratory method needs destructive sampling and complex operation. By constructing a high-precision SPAD spectral estimation model, the real-time non-destructive monitoring of chlorophyll content in rice leaves can be realized. In this paper, the data of five key stages of transplanting, tillering stage, jointing stage, booting stage and heading stage were obtained from rice under different nitrogen levels in Heilongjiang Province. The reflectance spectrum data of rice leaves were measured by SVC HR768i spectral radiometer with a spectral detection range of 350~2 500 nm. The spectrum of the blade was measured directly by the handheld blade spectrum detector with its own light source, which was built-in halogen lamp. The SPAD value of rice leaves was measured synchronously by SPAD-502 hand-held chlorophyll meter. Leaf water is the basic raw material of plant photosynthesis, and the decrease of leaf water content will affect the normal photosynthesis of plant, resulting in the decrease of chlorophyll content and the indirect effect of water content on chlorophyll content. Therefore, the chlorophyll sensitive band and the range of water absorption are combined as the input of SPAD. The Random Forest model is an algorithm based on multiple classification trees. In the process of sampling, the algorithm includes two completely random processes, of which one is that the sampling process is carried out with a return sampling, and the other is that the sample may be repeated, and the other is random when we select the independent variables. In this paper, the spectral reflectance of rice leaves is extracted by continuum removal (CR), and the characteristic parameters of reflectance spectrum and vegetation index of rice leaves are extracted by taking into account the visible and near infrared bands. The correlation between spectral indices and SPAD was analyzed and the SPAD hyperspectral estimation model with different inputs was constructed by the Random Forests. Results are: (1) The correlation coefficient between SPAD and spectral reflectance of rice leaves was above 0.75 in the range of chlorophyll sensitive band (600～690 nm), red edge region (720~760 nm) and water absorption band (1 400~1 490, 1 900～1 980 nm). (2) In the correlation analysis between spectral parameters and SPAD, the correlation between, NDVI, DP2 and SPAD value of rice leaves was the best, and the correlation coefficients were 0.811 and 0.808; (3) The Random Forests model with CR(V1, V2, V3, V4) combined with water spectral information had the highest accuracy and R2 was 0.715, RMSE was 2.646, which could be used as a chlorophyll prediction model for rice leaves. The results revealed the spectral response mechanism of different varieties of rice, provided a high precision inversion method of SPAD values of rice leaves, and provided technical support for monitoring and regulating the normal growth process of rice in Northeast China.

The effects of pH values on the characteristics of photochemical degradation of dissolved organic matter (DOM) from Dianchi Lake by using UV-Visible absorption and three-dimensional excitation-emission matrix fluorescence spectroscopy (EEMs) combined with parallel factors analysis (PARAFAC) were investigated in the present study. The photochemical degradation characteristics and differences of DOM under different pH values can provide useful support for the basic data of biogeochemical cycle of DOM, and have important enlightenment for the improvement and effective control of water quality in eutrophic lakes. Three fluorescent components were identified during the process of photo-degradation of DOM (30 days), based on the split-half validation procedure. The three components identified from the fluorescence spectra were a fulvic-like fluorescence component C1 (325, 425 nm), a protein-like component (C2) (295, 390 nm) and a humic-like component (C3) (260/350, 360/450 nm)with high aromaticity. The change of pH values had important influences on the characteristics of absorption and EEM spectra of DOM in the process of photo-degradation. The results showed that the absorption coefficients of DOM increased and total fluorescence intensity decreased gradually with the increase of pH value from 4.0 to 9.0. During the 8～30 days incubation period, the intensity of fluorescent component C2 showed a trend of gradually decrease with the shift of pH value from 4.0 to 9.0, which suggested that high pH value can potentially promote photodegradation of DOM. The influences of pH values on the photo-degradation, absorption and fluorescence spectra emphasize the need for pH to be monitored and accurately controlled. It is also highly recommended that when we contrast the absorption and fluorescence data or constructing PARAFAC models among samples with different origins, the pH should be held constant to remove any potential interference of data.

Pine wilt disease is a devastating disease of pine tree species, thus, early diagnosis of forest pests and diseases in small forest lands even single wood level is particularly important for forest resources protection and sustainable development. This study used black pine as the research object and the multi-angle hyperspectral data from specific black pines canopy were collected through different infected periods, then, we analyzed the spectral characteristics by directional reflectance. The main results were as follows: (1) the reflectance of the backward scattering direction was greater than that of forward scattering direction in theprincipal plane when viewed from the top, in addition, in the backward scattering direction, during the four periods of infection, the four bands had hotspot effect at about 40° of zenith angle. Both in the principal plane and theorthogonal principal plane, the reflectance of pine canopy in the blue wavelengths (450 nm) and the near infrared wavelengths (810 nm) showed a change rule at the azimuth angle of 0°, that were, the early>the health>the metaphase>the end, the red light band (680 nm) and the green light band (560 nm) were the early≈the health>the metaphase≈the end. At all azimuth angles; the canopy reflectivity increased with the increase of observed zenith angle. (2) on upward observation, the reflectance of the forward scattering direction was greater thanthat of the backward scattering direction in the principal plane, in other words, the reflectance was bigger when the azimuth angle was 0°; Both in the principal plane and theorthogonal principal plane, the blue light band (450 nm) and the red light band (680 nm) and the green light band of pine canopy reflectance in azimuth angle were 0°, presenting the early>the health>the end>the metaphase, and the near infrared wavelengths (810 nm) is the early>he health>the metaphase>the end; For all azimuth angles, canopy reflectance decreased with the increase of observed zenith angle. (3) the anisotropy of the bidirectional reflectance of each feature band was the strongest in the principal plane and was the weakest in the main vertical plane, and the forward and backward reflectance of the main vertical surface presented symmetry, namely “mirror reflection”; the reflectance of the canopy of black pine changed significantly with the observed zenith angle at the end of infection period, while it did not change significantly with the observed zenith Angle in other periods. The reflection characteristics of the canopy at different bands and angles can promote the accuracy and reliability of UAV remote sensing to monitorforest diseases at different scales, also promote the construction of portable and real-time diagnosis system for forest diseases, and achieve the rapid acquisition of hyperspectral data at single wood level.

Three dimensional fluorescence spectroscopy combined with multivariate calibration analysis for petroleum pollutants multicomponent determination method has problems of complex spectra aliasing, and is susceptible to blank fluorescence and interference fluorescence reducing the accuracy of result. Temperature information as one dimension added to the traditional three dimensional fluorescence spectrum to construct excitation wavelength-emission wavelength-temperature-samples four dimensional fluorescence spectrum data array (excitation-emission-temperature-sample data array, EEM-temperature data array) was proposed, and four linearity component model combined with high dimensional fluorescence spectrum qualitative and quantitative analysis was applied. Experiments demonstrate that fluorescence spectral shape of mineral oil does not change with the change of temperature in 15 to 25 ℃ range, but the intensity changes linearily, satisfying the requirement of four linearity, providing possibility for developing four dimensional fluorescence spectra with third-order correction to extract more useful information from the high dimensional data. The third order correction not only has “second order advantage”, namely to quantitatively determine interesting constituents in the presence of interferences, but also has higher selectivity and sensitivity, higher resolution ability for colinearity and background interference of the overlapping spectra, namely, “third order advantage”. The complex petroleum pollution system composed of 0# diesel, 97# gasoline and engine oil as components to be determined and humic acid as water interference component are experimental samples. The parallel factor (PARAFAC) algorithm and the alternating penalty trilinear decomposition (the alternating penalty trilinear decomposition, APTLD) algorithm are applied to the three dimensional fluorescence spectra for the second-order calibration analysis; the four dimensional fluorescence spectra data array containing temperature information is constructed by stacking three dimensional fluorescence spectra along temperature direction dimension, and is analyzed by four dimensional parallel factor algorithm (4-PARAFAC) and alternating penalty quadrilinear decomposition (alternating penalty quadrilinear decomposition, APQLD) for third-order correction analysis. The prediction results of 0# diesel, 97# gasoline and engine oil are compared and show that the four way fluorescence spectrum with adding the affecting factor of temperature increases the extraction ability for effective information and the four dimensional fluorescence spectroscopy combined with high-order correction algorithm can improve the oil spectrum recognition and concentration detection precision and improve the recovery rate and the root mean square prediction error (root mean square error of prediction, RMSEP) compared with the traditional three dimensional fluorescence spectrum analysis, advantageous to the effective, accurate, real-time, green environmental detection for petroleum pollutants. At the same time, the characteristics of 4-parafac and APQLD algorithms and their different applicable environments are pointed out, which can provide a basis for the algorithm selection for the detection of oil pollutants. The four-dimensional fluorescence spectra with introduction of the temperature parameters combined with third-order correction algorithm detection technology, no matter in qualitative resolution of constituent spectra or quantitative concentration determination of the complex system of oil pollution compared with three-dimensional fluorescence spectrum technology, is capable of realizing fast, effective, green and pollution-free detection, thus “mathematical separation” can replace “chemical separation” more effectively.

A fluorescence resonance energy transfer (FRET) system between fluorescence carbon dots (CDs, donor) and Eosin B (EB, acceptor) was constructed, and a new method for sensitive and selective determination of pefloxacin (PEFL) was proposed. Fluorescent carbon dots (CDs) were synthesized by pyrolysis treatment using Setcreasea purpurea boom as carbon source. The carbon dots exhibited preferable dispersibility in water, high stability and a quantum yield of 3.7%. The as-prepared CDs were characterized using high-resolution transmission electron microscopy (HRTEM), powder X-ray diffraction patterns (XRD), and Fourier transform infrared spectroscopy (FTIR). Theresults showed that the CDs demonstrated an amorphous structure with the presence of —OH and —COOH groups on their surface. Using Frster’s theory of resonance energy transfer, the FRET between CDs and EB was determined, and the FRET system was constructed between CDs and EB. Some important factors were optimized, such as reaction medium and acidity, reaction time, the concentration of donor and acceptor, salt effect. According to the result, in PBS buffer solution at pH=3.0, the energy transfer from CDs to EB occurred with λex=340 nm, which resulted in an enhancement of the fluorescence intensity of EB. Upon the addition of pefloxacin, the fluorescence intensity of CDs was significantly increased because of the interaction between pefloxacin and CDs. Under the optimized experimental conditions, the change of CDs fluorescence intensity (ΔF) with the PEFL concentration exhibited a linear relationship in range of 0.016 8~6.71 μg·mL-1. The detection limit was 0.072 5 ng·mL-1 based on the formula 3s/k (n=11). Most of common substances such as cations (Fe3+, Al3+, Ca2+, Zn2+, Cr3+, Co2+, Cu2+, Mn2+, etc. ), anions Cl-, NO-3, I-, S2-, SCN-, SO2-4, Br-, NO-2, IO-3, F-, ClO-3, SO2-3, etc.), medicines (isoniazide, ascorbic acid and heparin sodium) and melamine did not interfere with the detection PEFL. The proposed method was applied to determine PEFL in drugs. The recoveries were 100.4%~105.1%, and the relative standard deviations (RSD, n=5) were not more than 2.5%. This method has the advantages such as high sensitivity and good selectivity.

A rapid method for the detection of histamine in fish products was developed in this study based on Surface-enhanced Raman spectroscopy (SERS). Silver nanoparticles (Ag NPs) were used as the active substrate and sodium chloride solution as an aggregating agent to obtain histamine SERS peaks. The histamine content in fish extracts was determined by linear regression algorithm. In this research, the normal Raman spectra of histamine powder, the SERS characteristic peaks and vibrational assignment of histamine standard solutions as well as histamine in fish extracts were analyzed. The SERS reaction conditions for the concentration of silver nanoparticles and sodium chloride solution were optimized. Lastly, the optimization conditions were used to quantitatively analyze the histamine in fish extracts. The results showed that the silver nanoparticles had the maximum absorbance at 400 nm. The shape of the nanoparticles was mainly spherical and the average size was about 30 nm. Then using 4-MBA as a probe molecule to detect the stability and sensitivity of Ag NPs and obtained Raman peak has good repeatability and high Raman intensity. Therefore, not only is the synthesis method of the active substrate less time-consuming and easy to operate, but the synthesized Ag NPs can be used as a reliable reinforcing substrate in the SERS test. In addition, it was proved by UV-Vis spectrophotometer that the NaCl solution can arouse the agglomeration of Ag NPs to form hot spots, which can enhance SERS signal. The Raman spectrum of solid histamine reflects that the characteristic peak appeared at 1 167 cm-1 due to the in-plane bending of N-H, the in-plane bending of imidazole in C—H plane and ring breathing (1 236 cm-1), the ring stretching (1 291 cm-1), the in-plane bending vibration of imidazole N—H and ring stretching (1 474 cm-1). Then the optimized reaction conditions showed the highest enhancement effect when the concentration multiple of silver nanoparticle was 15× and the concentration of sodium chloride solution was 1 mol·L-1, and the optimal condition was detected at the of 5～250 mg·L-1 histamine aqueous solution, of which the lowest concentration was detected at 5 mg·L-1. At the same time, the SERS spectra of histamine in fish extracts ranging from 10 to 100 mg·L-1 were detected under the optimized conditions. It was revealed that 3 calibration curves established at the characteristic peaks at 1 180, 1 258, 1 425 cm-1 corresponding Raman peak intensities had a good linear relationship when the concentration of histamine in fish extracts ranged from 10 to 100 mg·L-1 with R2= 0.918 1～0.947 3, of which the R2 value of the prominent peak at 1 258 cm-1 was the largest. The lowest concentration detected in fish was 10 mg·L-1, much lower than maximum limit of detection of histamine in the national standard. The calibration curve had superior accuracy with the recovery between 100%～111%. And the applicability of the method was verified by high performance liquid chromatography (HPLC). The experimental results showed that surface-enhanced Raman spectroscopy using silver nanoparticles as an active substrate and sodium chloride solution as an aggregator coupled with linear regression to establish a standard curve for rapid detection of histamine in fish is feasible and accurate, which provides a reference basis for rapid quantitative analysis of histamine content in fish products.

Milk powder is rich in human body’s five major nutrients, and is one of the main sources of nutrition for infants and young children also. So the nutritional components in milk powder have an important impact on the growth and development of infants. However, excessive sugar content may have adverse effects on the health of infants, except lactose. The chromatography and near infrared spectroscopy detection techniques are difficult to meet the requirements of the rapid and nondestructive testing of milk powder, due to the complex composition of milk powder. Therefore, it is necessary to explore a fast and nondestructive testing method for the content of glucose and sucrose in milk powder. Terahertz wave has fingerprint characteristics for the absorption peaks of different macromolecular substances, which can be used to identify different macromolecular substances. In this paper, the terahertz time domain spectroscopy (THz-TDS) combined with chemometrics method is used to discuss the qualitative and quantitative methods for the determination of glucose and sucrose in milk powder. The experimental device adopts the TAS7500TS terahertz spectroscopy system, and the experimental samples are free-sugar infant milk powder and glucose and sucrose crystals whose purity is greater than 99%, and different concentration of milk powder-glucose, milk powder-sucrose mixture. The terahertz time domain signals of the pressed-slices of pure milk powder, glucose, sucrose and 15 different concentrations of milk powder-glucose, milk powder-sucrose mixture sample, each sample was collected three times and the mean value was taken as the time domain spectral signal. Then the terahertz frequency domain signals of each sample are obtained by Fast Fourier Transform (FFT). The absorption coefficient spectrum and refractive index spectrum of all samples are calculated according to the Dorney optical parameter extraction formula. The partial least squares (PLS) method is used to establish the corresponding quantitative analysis model, based on the absorption coefficient spectra and refractive index spectra of the two mixture samples, respectively, and the ratio of calibration set and prediction set is 2∶1. The experimental results show that there is no obvious absorption peak in the terahertz band, while glucose and sucrose have strong characteristic absorption peaks at the frequencies of 1.45, 1.8, 1.98, 2.7 THz and 1.5, 1.9 and 2.6 THz, respectively. So the glucose and sucrose can be identified according to the THz “fingerprint” characteristic peaks. Meanwhile, the absorption peak of the two mixtures with the concentration varied basically are same as the location of terahertz absorption peak of pure glucose and sucrose, and have stable absorption characteristics. Quantitative analysis of glucose and sucrose in milk powder can be achieved by partial least square (PLS) method based on absorption coefficient spectrum and refractive index spectrum data, and the mixture regression models of glucose and sucrose PLS based on the sample refractive index spectrum are better than the models established based on absorption coefficient spectrum. The former modeling result shows that, the correction set correlation coefficient Rc and root mean square error RMSEC of the PLS regression model of milk powder-glucose mixture are 0.99 and 0.18%, and the prediction set RP and RMSEP are 0.96 and 0.66%. The correlation coefficient Rc and root mean square error RMSEC of the PLS model of sucrose content in the milk powder-sucrose mixture are 0.96 and 0.55%, and the predictive set RP and RMSEP are 0.99 and 0.25%. So the prediction results of glucose and sucrose quantitative analysis models are ideal. The results show that terahertz time-domain spectroscopy can be used in the qualitative and quantitative analysis of glucose and sucrose in milk powder, which provides a reference for the study of adulteration and rapid quality detection of milk powder by using THz-TDS technology.

Haploid identification plays a key role in the field of maize-haploid breeding. To achieve mass and automated identification, Near-infrared Spectroscopy (NIRS) Analysis Technology is widely used. Its advantages include online monitoring, rapid analysis, easy operation, lossless process, cost-effectiveness, etc. At the beginning of the experiment, NIRS data of haploid and polyploidy maize seeds are cross collected via JDSU’s near-infrared spectrometer. To enhance validity, this experiment encompasses a testing set of data besides a training set. After pre-processing, experiment data is subsequently mapped in a higher-dimensional space to enhance its divisibility, and haploid feature is extracted. Then the experiment establishes identification models to predict whether maize seeds are haploid. It needs to point out that the experiment applies different feature extraction algorithms, thus different identification models are established accordingly. The experiment results show that the feature extraction algorithm of Kernel Locality Preserving Projection (KLPP) guarantees accurate recognition in a more stable way. Recognition rate of testing set and training set reaches up to 95.71% and 96.43%. The above experiment proves that NIRS data of maize seeds can be classified more effectively and accurately through non-linear transformation (Gaussian kernel transform in this experiment) and high-dimensional spatial mapping. The above process also maintains partial characteristics of NIRS data. Therefore, this paper may provide some new idea and method for Maize Haploid Identification technology.

Fruit freshness is an important quality index reflecting whether the fruit is fresh and full. In order to explore the prediction and discrimination methods of different shelf life of fruits, this paper takes the pear as the research object, and uses hyperspectral imaging technology combined with partial least squares discrimination (PLS). DA and partial least squares support vector machine (LS-SVM) algorithm to distinguish the shelf life of pears. The spectrum of the sample is collected by a high-spectrum imaging device consisting of a light source, an imaging spectrometer, an electronically controlled displacement platform, and a computer. The device light source is designed with a ladder power of 200 W four bromine tungsten bulbs, and the spectral range is 1 000～2 500 nm. 10 nm. The material was selected from 30 high-quality pears, and the shelf life was set to 1 day, 5 days and 10 days. Three spectral images were acquired for 30 samples and the original image was corrected. The experimental results show that the image-based analysis of the shelf life of the pears is carried out by PCA compression of the original images of different shelf life samples, and the weight coefficient data of three different shelf periods are obtained. The wavelength points of PC1 image extraction are 1 280, 1 390 and 1 800 nm. 1 880 and 2 300 nm, with the average gray value of the feature image as the independent variable and the shelf life as the dependent variable to establish a qualitative discriminant model, 68 modeling sets and 22 prediction sets. When the least squares support vector machine uses RBF as the kernel function, the number of misjudgments in the predicted concentrated samples is 1, and the false positive rate is 4.5%. When the lin kernel function is used, the number of misjudgments of the sample is 0, and the false positive rate is 0. The RMSEC for PLS-DA qualitative analysis was 1.24, which was 0.93. The RMSEP is 1, which is 0.96, and the prediction set false positive rate is zero. The characteristic image has better model for the lin kernel function in the LS-SVM of the shelf life of the pear, which is better than the modeling effect of the RBF kernel function and better than the PLS-DA discriminant model. The LS-SVM and PLS-DA discriminant models were established by ENVI software to extract the spectra of the experimental samples. The false positive rates of RB-SVM using RBF and lin kernel functions were 4.5% and 0, respectively. Compared with the RBF kernel function, the model established by the lin kernel function predicts the shelf life of the pears better. The PLS-DA method has a principal component factor of 12, RMSEC and RMSEP of 0.48 and 0.78, respectively, and 0.99 and 0.97, respectively. The false positive rate of the modeling set and the prediction set are both zero. The model established by the lin kernel function in LS-SVM is better than the detection model established by PLS. The spectral information of the pears combined with LS-SVM can realize the detection and discrimination of the shelf life of the pears. Compared with the spectrum, the shelf life prediction model based on the image was used to distinguish the shelf life of the pear, while the feature image method, the selected area was less lost part of the information, the calculation amount was small, and the modeling result was relatively poor. The research on the hyperspectral imaging detection model of the shelf life of the pear provides theoretical guidance for consumers to correctly evaluate the freshness of the fruit, and also provides technical support for the development of the fruit shelf detection instrument in the later stage.

Green tea is the most popular type of tea in China. The differences of green tea leaves from different categories are very small, and it is hard to distinguish them for non-experts by appearances. Traditional chemical methods are complicated in operation and are destructive to samples and it is difficult to achieve fast and nondestructive analysis. Near infrared spectroscopy (NIR) is a new technology, which is simple, fast, non-destructive, good in reproducibility and can be used for on-line analysis. The differences in the composition and content of the organic components in tea samples would be formed due to different growing environments and panting patterns, which can be measured by the NIR spectra. With the help of NIR spectra, the characteristic information of hydrogen groups can be obtained. The difference information of green tea leaves from different categories can be obtained, and the identification of green tea samples can be achieved. In this study, NIR was applied for nondestructive analysis of green tea leaves from different categories with the aid of chemometric methods. The dataset consists of eight brands of green tea samples. A relation has been established between the spectra and the tea varieties. The data was analyzed with principal component analysis. Furthermore, baseline elimination by continuous wavelet transform was used for improving the accuracy of the method. The wavenumber selection based on standard deviation and relative standard deviation was used to further improve the accuracy. The results show that the total variance explained by the first two principal components in principal component analysis was over 90% and they were enough for further analysis. The result of classification analysis using the original data was poor and cannot be used for the real application. The baseline interference can be eliminated with continuous wavelet transform method and the classification results were improved. The wavenumber selection method based on standard deviation and relative standard deviation consists of two steps. At first, the wavenumbers with standard deviation below 0.005 and the average below 0.01 were removed. Then, the wavenumbers that have large value of relative standard deviation were selected as informative ones, because the larger value of the relative standard deviation, the more variation between the samples. It was found that acceptable classification results can be obtained when several or several tens informative wavenumbers are used. It was found that, the main differences between varieties of tea are polyphenols, amides and amino acids. The results show the classification of different brands of green tea samples can be achieved by the proposed method, which provides a new idea for the rapid analysis of tea samples.

Near-infrared spectroscopy(NIRS) was used to quantify moisture content about refined-honey, and the practicability to classify the samples degree through refining was investigated. Process Analysis Technology is vital to affecting the quality stability of traditional Chinese medicines, with the lagging nature of detection methods, accurate real-time control of them cannot be achieved, ultimately affecting the homogeneity and stability of quality about traditional Chinese medicines. Sweetpill has a wide application market. The honey refining process is a critical process to affect the quality of it. However, the transition from tender to medium honey takes only a few to several tens of seconds. The traditional detection method cannot monitor it, leading to the fact that the uniformity of honey quality cannot be achieved. In order to identify and predict moisture content for tender, medium and old honey at the same time, the 88 different honeies were refined, and the qualitative and quantitative models were established based on Near Infrared Spectroscopy (NIRS). Using refractometer to measure the moisture content, and near infrared spectra of samples were also measured. Combined with partial least squares(PLS) algorithm were conducted on the calibration of regression model; the qualitative prediction model was used to establish the level of the refined honey by the factorization method. For the quantitative calibration model, the R2, RMSEE, RMSECV were 99.43, 0.299, 0.34, respectively. For the prediction model, the R2, RMSEP were 98.19, 0.347, respectively. Through the discriminant analysis, the qualitative model showed obvious clustering phenomenon. NIRS has good application prospects to be applied to real-time monitoring and moisture determination of refined honey.

Polycyclic aromatic hydrocarbons (PAHs) have teratogenic, carcinogenic and mutagenic properties, which seriously pollute the ecological environment and threaten the health of human beings and the growth of animals and plants. PAHs enter into the water environment through various ways, such as sewage discharge, atmospheric settlement and surface runoff. Because of their large variety and similar chemical properties, it is difficult to achieve rapid and accurate determination of the conventional methods, such as chemical titration and electrochemical methods. In order to realize the qualitative and quantitative analysis of the PAHs in complex system, this article is based on the three-dimensional fluorescence spectrum analysis, and combined with the ensemble empirical mode decomposition(EEMD) which is used to de-noise, and the self-weighted alternating trilinear decomposition(SWATLD) which is used to do two order correction, the ANA and NAP in water and pond water environment were analyzed and measured. The sample is prepared by a reasonable concentration, and the three-dimensional fluorescence spectrum of the sample is measured by the FS920 fluorescence spectrometer. The real spectral data can be obtained by eliminating the scattering of the spectral data by the blank deduction method. Then the EEMD is carried out to remove the noisy information and improve the signal to noise ratio. This method has the advantages of strong self-adaptive and simple parameter setting. The denoising parameters were compared with fast Fourier transform, wavelet filtering and empirical mode decomposition. Finally, using “mathematical separation” instead of “chemical separation”, the SWATLD algorithm is used to identify and predict the ANA and NAP in ultra pure water and pond water environment. The algorithm is not sensitive to the selection of the group fraction, and can be used to detect the multi component object simultaneously under the coexistence of unknown interferon. It has the “two-order advantage”, and the prediction results are compared with parallel factor analysis. The results show that the EEMD method makes the spectrum of ANA and NAP more regular and smooth, and the effective information is more prominent. The signal to noise ratio of the de-noised data is 16.845 2, the root mean square error is 11.136 6, the waveform similarity coefficient is 0.990 9, and this three indexes are better than the other de-noising methods, such as fast Fourier transform and empirical mode decomposition. It can achieve the denoising effect of wavelet filtering without setting a priori parameter. Using the SWATLD two order correction method, the decomposition spectra of ANA and NAP in the verified samples are basically consistent with the actual spectra, and the average predicted recovery rates are 96.4% and 104.2% respectively. The predicted mean square root errors are 0.105 and 0.092 μg·L-1 respectively. In the pool water samples with unknown interferon, the decomposition spectrum is still consistent with the actual spectrum. The average prediction recovery of ANA and NAP is 94.8% and 105.5%, respectively, and the root mean square root error is 0.067 and 0.169 μg·L-1 respectively. Compared with the parallel factor analysis, this two indexes have the advantages.

The current qualitative classification models of soil heavy metal content based hyperspectral remote sensing technology mostly use indoor measured spectral data from the same area for model training and testing. However, the indoor spectrum measurement requires a complicated processing process with high cost and low efficiency, and thus cannot obtain the spatially continuous spectral information in the target area quickly. Moreover, whether this kind of model can be transferred to the outdoor measured spectral data in different test areas is still unclear. In order to answer this question, two lead-zinc mining areas in Chenzhou City and Hengyang City of Hunan Province were selected as research areas. Support Vector Machine was used as classifier. Then 83 sample data from indoor sampling in Zhangzhou experimental area and 46 sample data from indoor sampling in Hengyang experimental area were used for classifier training, and 46 sample data from field sampling in Hengyang area were used for classification testing. The difference of spectral distribution between the indoor and outdoor measured spectral data was reduced by the transfer learning method based on joint distribution adaptation (JDA), and then the domain adaption model for two research areas was constructed. The experimental results show that:(1) The spectral data measured by outdoor samples may be affected by factors such as solar radiation and differences in extracted soil components, leading to the significantly spectral difference for indoor and outdoor samples. As a result, it is difficult to directly transfer the qualitative classification model of soil heavy metal pollution trained by indoor samples to the outdoor samples from the same area. However, after the reduction of indoor and outdoor distribution differences by JDA transformation, the transfer ability of the model has been significantly improved, and the classification accuracy of three heavy metals As, Pb and Zn has reached over 84%. The accuracy of classification of Zn elements exceeding the standard even reached 89%. (2) Due to seasonal influences, regional component interference, and spectral noise, there are even more significant differences in the distribution of spectral data in different areas. This further increases the difficulty of soil heavy metal pollution monitoring in different areas, and it is difficult to directly transfer the qualitative classification model of soil heavy metals based on indoor sampling spectral data to field sampling data in other areas (with an average classification accuracy of about 50%). After the indoor and outdoor spectral transformation processing by JDA, the transfer ability of the model has been greatly improved. Therefore, the outdoor spectral sampled can be directly used to investigate the pollution situation of heavy metals (As, Pb and Zn) in different test areas.

The vigor of seeds plays a vital role to the agricultural development. But the low vigor and storage-tolerance seeds are common problems for sweet corn. Therefore, it has a certain practical significance to detect the sweet corn seed vigor accurately and timely. Electrical conductivity test is a traditional method of determining the vigor ofseeds. However, it is a labor-intensive, time-consuming, and destructive process, which is subject to human error. Given that, this study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging (HSI) technique to detect the electrical conductivity of sweet corn seeds. Sweet cornseeds treated by high temperature and high humidity aging 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 sweet corn seeds. After HSI spectra collection, electrical conductivity tests were conducted in sweet corn seeds. The average reflectance data of the region of interest were extracted for spectral characteristics analysis. Then different pre-processing algorithms including standard normal variate (SNV), first derivative (FD), second derivative(SD), multiplicative scatter correction (MSC) were conducted to build partial least squares regression (PLSR) models of the conductivity. Lastly, the hyperspectral effective wavelengths related to conductivity of sweet corn seeds were extracted by SPA and GA for PLSR models. The results showed that the best pre-processing algorithm was MSC method. The SPA was not performing as well as GA which selected only 25 characteristic wavebands from the all 853spectral wavebands. The PLSR model built by using MSC and GA exhibited the optimal performance with correlation coefficient of 0.976 and 0.973 for calibration set and prediction set, respectively, and root mean squared error for calibration and prediction were 0.194 and 0.212. The results indicated that combining the visible and near-infrared hyperspectral imaging technique with MSC-GA-PLSR can be used as a feasible and reliable method for the determination of conductivity in sweet corn seeds. The result can provide a theoretical foundation for rapid detection of seed conductivity using spectral information.

Study and establish a method of separation and enrichment of different from the traditional fire assay——small fire assay preconcentration of gold in ores, and the detection method for gold content Flame atomic absorption spectrometer. The optimal small fire assay conditions were determined: flux ratio, borax-soda-yellow lead-starch 5∶5∶10∶1, melting temperature 900 ℃, dosage of protectant 10mg. After the separation and enrichment with best small fire assay condition using 3 g sample, the lead button of the mineral samples got the alloy particle of silver, gold by cupellation. After dissolution in dilute nitric acid, gold was separated from silver in hydrochloric acid solution and determined by AAS. In this paper, the factors influencing the detection of gold by AAS were discussed, which include the setting of parameters of the instrument, the liner range and the interfering ions. The interference test indicated that other precious metals had no influence for gold determination. Under the selected instrument conditions, the gold in the standard samples was tested, and the result was in agreement with the standard value. The RSD (n=11) of gold was between 0.72%～5.49%, the recovery was between 98.82%～99.20% by detecting standard and certain mine gold ore sample. This method is stable, reliable and accurate which is suitable for the gold content from 0.X to XX.0 mg·g-1 in ore and extends the analysiscontent range for Au by FAAS. More importantly, it reduces the traditional fire assay on human and environmental damage and pollution. AAS has the advantages of fast response, high sensitivity and good accuracy which provides a technical basis for further research on the detection of other precious metals by this method.

Knots affect the mechanical properties of solid wood plates.The accurate description of knots in wood plates and the calculation of wood board mechanical properties have been issues with great practical value. Nowadays, machine vision method is used to detect the defects on wood surface, ultrasonic testing is used to determine the existence of defects, and X-ray method can give a full description of solid wood, but the cost is high. The near infrared spectroscopy analysis technology has the characteristics of rich structure, convenient testing and being nondestructive, but the redundancy and nonlinear information in the spectrum affect the precision of the modeling. In this paper, a method of identifying the knots based on the fusion of Isomap and wavelet neural networks is proposed, and the nonlinear dimensionality reduction is completed by Isomap. The nonlinear relation is modelled by the wavelet neural network between the material and the angle of the knot edge, and the shape structure of the knot inside the wood plate is performed by the multi-point angle of the edge. First, the method uses the cone model to express the knot structure proposed by Pablo. Knots are extracted by machine vision method from the image, and their center positions are obtained by calculation. Then, the information of multi-point position about the edges of knots is extracted and processed by the baseline drift and denoising methods. After that, abnormal spectrums are eliminated by combining PCA and mahalanobis distance, the calibration sample sets are divided by K-S, and effective spectral information are extracted through Isomap, which set the dimensionality reduction and adjacent number, and the fast modeling of different spectral dimensions is completed through PLS, and then the ideal spectral feathers are iterated. Finally, wavelet network is used to establish the relationship between the edge spectrum and their inclination angel of the knots, and the 3D status of these knots is realized by Solidworks software. In this experiment, 160 sets of spectral data of 40 knots were collected from Larix gmelinii plates. After measuring the relative spatial position of the upper and lower surfaces of knots, true inclination angles of every point were obtained. The result of the experiment reveals that S-G smoothing and first order derivative can give clear outline in spectral pre-processing and the absorption peak is more obvious. When Isomap method is for dimension reduction, with non-linear dimension reduction number d=12, the nearest neighbor number k=19, the SECV is the minimum and the redundant data of spectrum is eliminated. When wavelet neural network is used to build the model of nonlinear inclination angles of knots, the correlation coefficient is 0.88, the prediction standard deviation is 7.65, and the relative analysis error is 2.14. This method can realize the inversion of knot structure in wood plates, and can provide quantitative analysis means for the prediction of mechanical properties.

Line index is widely used in describing the features of spectral lines for astronomical objects because it retains the main physical characteristic information of these objects. Based on line index, a multi-parameter model for regression analysis could be used to uncover co-variation relationship of data and the inherent laws of spectral lines. The observed spectra released by LAMOST, which has the highest spectra acquisition capability, provide us with real data for establishing a robust regression model. The multivariate linear regression was applied to get the co-linearity of the dependent variables, however, it resulted in large variance. It is unstable to obtain the least squares regression coefficient sometimes. Especially, it’s difficult for the multivariate linear regression to obtain the evaluation coefficient of independent predictor from the regression equation. In this paper, we use the A-type stellar Lick line index in the LAMOST survey data as the data source. Selecting the spectra with effective temperature (Teff) from 7 000 to 8 500 K, and the signal-to-noise ratio higher than 50 to realize the regression analysis. After a set of linear biased estimation experiment for A-type stars, the method of ridge regression training was employed. In the catalogue of LAMOST data release 5 (DR5), 86 097 A-type spectra have provided the Teff value. After statistical analysis of the eigenvalues of 26 line indices, the kp12, halpha12 and hgamma12 with similar distribution and bandwidth of 12  were selected to reduce the data redundance. The number of variety was optimized for the redundant variable variance expansion factor (VIF) coefficient. Two regression experiments selected the same observation dataset to locally fit the regression scatter, using the overall contour of the scatter plot to generate a high-density scatter plot, highlighting the data-intensive region with the color difference transparency. The results show that both the multiple linear regression and the ridge regression algorithm can determine the effective temperature (Teff) of the A-type star through the low-resolution spectrum, but the co-linearity data analysis has some biased estimation. The ridge regression model can more accurately predict the effective temperature of A type stars from the low resolution spectra.

White OLED microdisplays have an important application in the field of information display. In this paper, The vacuum coating system is used to sequentially deposit an Ag/ITO composite film as the anode structure and Mg: Ag composite film is co-distilled to form a translucent cathode structure. NPB is used to hole transport material and yellow light host material, rubrene as yellow light dopant, AND as blue host material, DSA-Ph as blue light dopant and Alq3 as electron transport material. The white OLED microdisplay structure with blue and yellow complementary colors prepared by co-evaporation is Ag/ITO/NPB/NPB∶rubrene (1.5%)/ADN∶DSA-Ph(x%/x=2, 5, 8)/Alq3/Mg∶Ag. The photoelectric performance of the device was characterized by a spectral testing system consisting of Photo Research PR655 spectrometer and Keithley 2400 program-controlled power supply. The effect of blue light doping ratio on the performance of white OLED microdisplay was studied. The result shows that with the increase of blue doping ratio, the brightness of white OLED microdisplay increases first and then decreases, the blue and yellow peaks shift to a certain extent, the color coordinates have a certain drift aod the blue color purity increases. The performances of white OLED microdisplays can be controlled by doping material ratio. The optimized emitting structure of white OLED microdisplay is NPB: rubrene (1.5%) /ADN: DSA-Ph (5%), The device brightness is 3 679 cd·m-2 and the CIE coordinates is (0.263, 0.355) with the driving voltage being 5.0 V.

Terahertz spectroscopy, as the main method to obtain the information of substances in terahertz frequency band, has been widely used in the determination of substance composition, and it has a broader application prospect in the imaging of composition distribution, such as the detection of effective components of tablets and drugs, the detection of dangerous goods in baggage security inspection and so on. The current terahertz spectral detection methods, time-domain spectroscopy (THz-TDS) and frequency-domain spectroscopy (THz-FDS), can’t take account of both spectral resolution and scanning time well, and it often takes several seconds or even minutes (depending on the structure of the spectrometer) to obtain spectral data, which makes the multi-pixel imaging system appear to be overdone. The application of terahertz spectroscopic imaging is seriously restricted because of the delay and the inability to meet the speed requirement of video imaging. At present, terahertz imaging is mostly full-band intensity imaging, which can only reflect the spatial distribution information of the sample, but can’t reflect the spectral information of the sample. Therefore, it is very urgent to improve the detection rate of terahertz spectroscopy. The realization of high-speed detection can not only greatly reduce the time-consuming of spectroscopy experiment, but also provide the possibility to realize the terahertz spectroscopic composition distribution imaging. In this paper, a high-speed detection method of terahertz spectrum based on Michelson interferometer is proposed. On the basis of designing the structure of the device, the working process is analyzed theoretically, and the terahertz spectrum is calculated. Then, the aspects of data sampling, data processing and parameter selection are analyzed, and the results show that the method can significantly speed up the acquisition rate of THz spectra. Finally, the method is modeled and simulated, and the whole detection process is simulated. Taking the spectrum distribution of terahertz radiator as an example, the simulation results of this method are compared with the test results of time domain spectroscopy (THz-TDS). It is found that the spectrum curve measured by time domain spectroscopy (THz-TDS) can be approximately regarded as the envelope of the spectrum curve obtained by this high-speed spectral detection method. The results have strong consistency. This shows that the proposed method can detect the terahertz spectra of samples, and it can significantly accelerate the spectral formation rate compared with the time-domain spectroscopy (THz-TDS) on the premise of the same resolution. It provides a possibility for practical and high-throughput terahertz spectroscopy imaging.

Aiming at the problem of lacking national measurement ability of short UV spectral irradiance, based on high temperature blackbody source, national primary standard apparatus of spectral irradiance in the spectral wavelength from 200 to 400 nm was developed independently at NIM in 2017. A group of stable deuterium lamps were used to maintain and disseminate the scale of spectral irradiance in short UV wavelength. New traceability system based on deuterium lamps was setup to provide the highest standard for the application fields. As to temperature measurement, spectral bandwidth, signal to noise ratio, fluorescence in the system, series new methods and key measurement technology were adopted to cut down the main error sources. The temperature measurement of blackbody was traced to Pt-C and Re-C fixed point blackbody, and checked against the WC-C fixed point blackbody. The deviation between NIM and VNIIOFI (All Russian Research Institute for Optical and Physical Measurements) at 3 021 K was 0.07 K, and measurement uncertainty at 200 nm was cut down by 0.2%. Owing to the great difference of spectrum between blackbody and deuterium lamp, the bandwidth effect of the monochromator should be considered. A seven point bandwidth novel correction method based on differential quadrature formula was put forward to correct bandwidth error 0.86% at 200 nm. Absolute and relative measurement principle was adopted to reduce the repeatability uncertainty about 20 times at 200 nm. A selective filter method was used to remove fluorescence in the primary standard apparatus. The standard measurement uncertainty of temperature was 0.64 K when BB3500 M blackbody was operating at 3 021 K, and the non-uniformity was less than 0.17 K over the effective diameter. The maximum shift of the temperature of the blackbody was less than 0.2 K during measurement period. Wavelength error of the double grating monochromator was less than ±0.01 nm. The standard measurement uncertainty of the secondary primary standard of spectral irradiance were 4.0% at 200 nm, 1.3% at 250 nm, 1.2% at 330 nm, 1.9% at 400 nm respectively. The establishment of new primary standard apparatus is based on deuterium lamps from 200 to 400 nm, which is possible for NIM to participate the international key comparison CCPR-K1. b sponsored by the International Bureau of Weights and Measures (BIPM). Wavelength range of the new primary standard is linked up with the original primary standard of spectral irradiance based on tungsten halogen lamps. In the overlap wavelength from 250 to 400 nm, the average deviation between two kinds transfer standard lamps, deuterium lamp and tungsten halogen lamp, was verified to be less than 0.39%, which was consistent with the associated measurement uncertainty.

In this work, the in-situ wavelength calibration method for the newly installed fast-time-response Extreme Ultraviolet (EUV) Spectrometers on EAST is introduced and the result and its application are described. Both of the spectrometers are grazing incidence flat-field spectrometers with temporal resolution of 5 ms·frame-1. The two spectrometer works at 20～500 and 10～130  respectively, and wavelength scanning is done by moving the detector along the focal plane. The impurity behavior is monitored for EAST operation with observed EUV spectrum and the calculated time evolution of impurity line intensity. High spectral resolution and capability of accurate wavelength measurement is required for line identification of EUV spectra from high-Z impurities especially tungsten, due to the complexity of the spectrum composition. Exact wavelength calibration is therefore one of the key techniques for the tungsten spectroscopy diagnosis and tungsten behavior study. Emission lines from hydrogen-, helium-, lithium- and beryllium-like low- and medium-Z impurities, e. g., O Ⅷ 18.97 , O Ⅶ 21.60 , CⅥ 33.73 , CⅥ 40.27 , LiⅢ 113.9 , LiⅢ 135.0 , LiⅡ 199.28 , ArⅩⅤ 221.15 , HeⅡ 256.317 , HeⅡ 303.78 , ArⅩⅥ 353.853 , CⅣ 384.174 , and their 2nd even 3rd order emission lines are used to perform the in-situ wavelength calibration for the two EUV spectrometers in the whole wavelength range. Line identification is then carried out with the result of wavelength calibration. It is found that for most of the emission lines the difference between observed wavelength and their standard value is very small, e. g., ≤0.08  and ≤0.03  for the spectrometer working at 20～500 and 10～130 , respectively. A module for the in-situ wavelength calibration is developed and is inserted into the interactive software developed for real-time data upload, which realizes a real-time upload of the calibrated EUV spectra and calculated time-evolution of line intensity to the EAST data server. Meanwhile, the interactive software for spectrum analysis and visualization is also developed, combining with the use of the routine EAST data visualization tools, the quasi real-time analysis, reading and visualization of EUV spectrometer data are realized during the EAST operation.

In this paper, we used the S450 near-infrared high-density grating spectrometer with technology of high-speed acquisition developed by Shanghai Lengguang Technology Co., Ltd. and China Agricultural University, took wheat and tobacco as the experimental object, and aimed at the high-density spectra (wavelength range is 900~ 2500nm, interval of wavelength is 0.1 nm, contains 16 001 data points). By adapting processing methods such as S.G. (Savitzky-Golay) smooth, FCMWS (Fixed window combine moved window smoothing) and the First Derivative, Partial Least Squares (PLS) was also used to model and predict the content of crude protein in wheat, nicotine and total sugar in tobacco, evaluate performance of the spectrometer, and optimize the parameters of processing methods. The results show that: (1) The performance of the models was greatly improved after the high density spectrum was processed by S.G. and the first derivative. Optimizing the parameter M (fitting order) and N(number of smoothing point) , if M is a fixed number, N can be selected from a wider range, and when M=2, N is in the interval of 201~801, the performance of models is ideal and stable; (2) The FCMWS was designed for smoothing layers of two, fixed window size of the first layer K1 and second layer K2 , and it was concluded that the performance of models is better and superior when the multiplication of K1 and K2 is about 150~310, moreover the FCMWS algorithm is speedy in modeling. (3) In order to analyze instrument differences, only took wheat as the object, which was measured by two S450 spectrometers, experimentally, whether the spectrum is processed by S.G. or FCMWS, the relative deviation of the predicted data from different models between instruments is less than 2.00%, which is far lower than the relative deviation between the predicted and reference values. It indicates that the above two methods can reduce the instrument differences and models can transfer stably among instruments. For wheat, tobacco and other agricultural products, the results of this study reflect that the domestic high-density grating spectrometer S450 combined with de-noising methods, can meet the actual requirements of quality detection and model transfer, and the grating instrument is relatively low-cost, which is significant for popularizing application of the rapid detection technology of near infrared in the agricultural field.