With the restriction of detector resolution in EDXRF, the peaks will be widened, and the overlapping of several peaks cannot be neglected especially in multi elements sample. The process of achieving the intensity of pure peaks, named spectrum unfolding is difficult. There are three unfolding methods traditionally, ROI integration, pure element peeling, and the coefficient method. Both of the methods cannot unfold the complex EDXRF spectrum perfect. In this work, we introduce an optimized algorithm to EDXRF spectrum unfolding process. Pure peaks, processed by applying the smooth and debackground operation to raw spectrum, can be considered as the sum of several Gaussian peaks, which can be formulated by Gaussian functions with 3 parameters each, height, width and position. By alternating the values of the 3N parameters within certain boundary conditions, the error between theory function and raw spectrum can be smaller and smaller. In mathematics, this alternating process can be finished by Optimize algorithm. When we developed the algorithm by 3N parameters Conjugate Gradient Method and coding the program, an EDXRF spectrum of Pr/Nd solution was studied. Sum of 11 Gaussian peaks with 33 parameters was very well closed to the raw spectrum composed of 11 L-series peaks of Pr and Nd, and the error calculated changed from 37.645 to 1.699 4. The innovative of this paper is the application of optimized method in complex EDXRF spectrum unfolding process.

The total organic carbon content (TOC), soluble hydrocarbon content (S1) and pyrolysis hydrocarbon content (S2) of source rocks are important indicators for the study and evaluation of source rocks. In the past, they were mainly obtained through laboratory analysis, and the timeliness could not be guaranteed. In order to explore the feasibility of rapid measurement of these indicators in the field, this study collected 10 hydrocarbon source rock samples from the qilakum block in uzbekistan, and measured the visible light-near infrared-short wave infrared (vis-nir-swir) reflectance spectrum and content parameters of TOC, S1 and S2 of the samples respectively by using the ground ASD spectrometer and carbon and sulfur analyzer. The first derivative and second derivative of the reflectance spectrum of the sample were obtained by spectral fitting differential. The correlation between these data and the contents of TOC, S1 and S2 was studied by using linear regression equation fitting and statistical analysis of fitting results. The results show that S1 content of source rocks was correlated with the original spectrum, the first derivative of the spectrum and the second derivative of the spectrum in 1 973, 511 and 239 bands, respectively. The absolute value of the correlation coefficient was 0.612, 0.823 and 0.889, respectively. The correlation degree was weak, strong and strong, respectively. S2 content in source rocks was correlated with 2, 144 and 205 bands of the original spectrum, the first derivative of the spectrum and the second derivative, respectively. The absolute value of the correlation coefficient was 0.561, 0.867 and 0.926, respectively. The correlation degree was weak, strong and extremely strong, respectively. There was no correlation between the TOC content of source rocks and the original spectrum, and it was correlated with 18 and 180 bands of the first and second derivatives of the spectrum, respectively. The absolute value of the correlation coefficient was 0.882 and 0.879, respectively. The correlations were extremely strong. In addition, the best fitting degree of S1 content and the second derivative of spectrum at the band of 2 012 nm was 0.790. The best fitting degree of S2 content and the second derivative of spectrum at the band of 363 nm was 0.858. The best fitting degree of TOC content and the first derivative of spectrum at the band of 2 480 nm was 0.777. This study lays an important foundation for in-situ detection of source rocks in the field.

The re-sample frequency modulation technology is a kind of ranging method. The optical structure of this method has boarder frequency modulated bandwidth, higher spatial resolution and accuracy. However, in the processing of practice measurement, the re-sample measurement device and the object to be measured are easily disturbed by the vibration from the outside environment. This paper analyses the influence of the Doppler error and the re-sample frequency error from vibration. In order to compensate these errors from the two kinds of vibrations, we present a vibration compensation method based on three light paths structure, this method brings two partial reflectors on the measurement light path to produce two compensatory lights, and the Doppler error and the auxiliary fiber error can be reduced by the differential value of the measurement signal and the compensation signals. The experiment verifies that the ranging accuracy of the typical re-sample method is 23.6 μm, and the ranging accuracy of the three-light-paths ranging method can achieve to 11 μm. It is proved that the three-light-paths ranging method can effectively compensate the vibration error of the measurement system.

Laser-induced breakdown spectroscopy (LIBS) is a new material identification and quantitative analysis technology developed in the past twenty years, which has unique features including the simplicity of method, being rapid, simultaneous multi-element detection and being non-destructive to sample. The traditional LIBS technology has a weak emission line spectrum, resulting in poor detection accuracy. Applying cavity confinement or depositing nanoparticles on the surface of the sample can significantly enhance the intensity of plasma emission spectrum, and the accuracy of detection and quantitative analysis can be effectively improved. However, the survival time of plasma is very short, usually between 1 and 10 μs. The acquisition time delay is too short and will be collected together with the background noise, while the acquisition delay time is too long, the acquired spectral intensity may be low, so it is important to choose the appropriate acquisition delay time for obtaining spectral data. Focusing on the time evolution of LIBS under the action of cavity confinement and nanoparticles, the alloy samples were used to generate plasma, and the time-resolved spectra of plasma at the acquisition delay time from 0.5 to 5 μs were collected. Ni Ⅱ 221.65 nm and CⅠ 193.09 nm were selected as the target lines, and the changes of spectral line intensity, enhancement factor and signal-to-noise ratio (SNR) were analyzed. Experimental results showed that under the unconfinement, cavity-confinement laser-induced breakdown spectroscopy (CC-LIBS), nanoparticle-enhanced laser-induced spectroscopy (NELIBS) and the above two cases worked together, as the acquisition delay time increased, the spectral intensity decreased in turn. When the cavity confinement was applied, the intensity of spectral line became very low after the acquisition delay time was greater than 2 μs. When nanoparticles were deposited on the surface, a considerable amount of plasma could still be collected even if the acquisition delay time was greater than 3 μs. When the acquisition delay time was 1us, the enhancement factor under dual action was the highest, reaching 2.1. When cavity confinement was involved, the spectral intensity was lower than that without confinement after the acquisition delay time was greater than 3 μs. When only the nanoparticles were deposited, the SNR was optimal, reaching 9.52. Under the condition of dual constraints, the trend of SNR was basically the same as that with only cavity confinement. Nanoparticles are helpful for the detection of trace elements in samples in the whole acquisition delay time range, while cavity confinement inhibits the detection of trace elements when the acquisition delay time is large.

Chemiluminescence is an inartificial indicator of flame structure and combustion processes, but the study on the measurement and diagnosis of turbulent flames using chemiluminescence is relatively rare. A piloted jet burner was designed to investigate the chemiluminescence of OH* and CH* in laminar and turbulent premixed flames, in order to study and develop the turbulent combustion theory further. Chemiluminescence images of OH* and CH* at varying velocities (u), and equivalent ratio (φ) were captured by employing ICCD cameras with filters and characterized by the height (h), the peak position (yp), the ratio of the intense reaction zone (s), and the peak value (P). Results show that the distribution of OH* is different from that of CH* in laminar flames, while the effect of turbulence leads to a similar distribution of the two. With the increase of φ, the h of OH* and CH* rises monotonically at different speeds, but the increasing trend of turbulence is relatively gentle. It should be mentioned that the variation trend of yp is consistent with the h, which indicates the dominant formation reaction of the radicals remains unchanged. The performance of s in the laminar and turbulent state is visibly opposite. From fuel-deficient to fuel-enriched state, the sof laminar flow decreases from 0.1 to 0.05, while that of turbulence increases from 0.05 to 0.1, suggesting that turbulence acts as an inhibitor and an accelerator under fuel-deficient and fuel-enriched state respectively. In addition, it is found that the P of OH* and CH* can be used to judge the flow state of flame, and that of CH* is particularly evident. As φ increases, if the P rises first and then decreases, the flames can be considered as laminar; if it increases monotonically, the flames are turbulent. Taking the u and φ as independent variables and the peak ratio of OH* and CH* as dependent variables, a unified formula for quantifying the φ by chemiluminescence under different u is proposed. It solves the problem that it needs to be fitted separately at different u, which is of considerable significance to the subsequent research on combustion diagnosis based on chemiluminescence.

AlO radicals have attracted widespread attention in the fields of organometallic chemistry, catalytic materials, combustion chemistry and astrophysics. The study of the effects of external radiation on the physical and spectral properties of AlO radicals will help to further understand and enhance their applications in related fields. The density functional theory B3PW91 method is used to optimize the ground state configuration of AlO radicals under different radiation fields (-0.04~0.04 a.u.) at the 6-311+G (3df, 2p) basis level. On this basis, the molecular structure, total energy, energy gap, infrared spectrum, Raman spectrum and ultraviolet-visible absorption spectrum of AlO radicals were calculated by the same method and level. The results show that under the action of external radiation field, the molecular structure changes obviously, and it has a strong dependence on the radiation field. The total energy of the molecule increases slightly and then monotonically decreases with the radiation field (the maximum is -0.03 a.u.). The bond length of molecule hardly changes significantly under the radiation field (-0.04~0 a.u.), but increases monotonously under the radiation field (0~0.04 a.u.). The dipole moment decreases first and then increases (the minimum is -0.03 a.u.). The energy gap increases first, and then stabilizes in the radiation field (-0.02~0.03 a.u.), and then decreases monotonously. The infrared spectra of AlO radicals under 0.04 a.u. radiation field are red-shifted by 74 cm-1, and the corresponding spectral intensity is 80 times that of the non-radiation field. The strong Raman activity of AlO radicals under -0.03 a.u. radiation field is 688 times that of the non-radiation field. The UV-Vis absorption spectra of AlO radicals in the positive radiation field (0~0.04 a.u.) show that the maximum absorption wavelength of 170 nm in the non-radiation field is blue-shifted by 22 nm and the absorption intensity decreases to half, which is consistent with the trend of the absorption wavelength in the negative radiation field (0~-0.04 a.u.). The UV-Vis absorption spectra of AlO radicals in the directional radiation field (0~0.04 a.u.) show that the second maximum absorption wavelength (282 nm) is blue-shifted by 13 nm and the absorption intensity increases by 2.2 times compared with that in the non-radiation field, but in the negative radiation field (0~-0.04 a.u.), the absorption wavelength is red-shifted by 10 nm and the absorption intensity increases by 6.2 times, which exceeds the maximum absorption wavelength intensity in the non-radiation field.

In the study of the solution-processed organic light-emitting devices (OLEDs), PEDOT∶PSS is often used as a hole injection layer (HIL) in OLEDs due to its good film-forming property and high light transmittance. However, related studies have shown that PEDOT∶PSS itself has defects, such as poor stability and low work function, which may result in poor and unstable device performance. At the same time, blue light is one of the three primary colors of the display, and the preparation of high efficient blue light OLEDs is indispensable for realizing high-quality white light and solid-state lighting devices. At present, most solution-processed blue OLEDs suffer from poor device performance, hence the research on the highly efficient solution-processedblue OLEDs will be of great significance. In this paper, we applied an efficient blue thermally activated delayed fluorescent (TADF) emitter DMAC-DPS to fabricate solution-processed blue TADF OLEDs, and the mixed hole injection layers (mix-HILs) were prepared by doping PEDOT∶PSS with PSS-Naand its effect on device performance of blue-light TADF OLEDs was investigated. First, we mixed different volumes of PSS-Na solution in PEDOT∶PSS aqueous solution and the mix-HILs were spin-casted under the same conditions to fabricate blue OLEDs. The electroluminescence (EL) spectrum is blue-shifted after the incorporation of PSS-Na, which can be attributed to the decrease in the thickness of the mix-HIL layer. The reduction in the thickness of the mix-HIL layer results in a blue shift in the EL spectrum under the effect of the microcavity. By comparing the current density-voltage-luminance (J-V-L) curves and its calculated current efficiency of each device, the results show that with the incorporation of PSS-Na, the brightness and current of the device increase, and the current efficiency of the device is also improved. The increase is the highest when the doping ratio is 0.5∶0.5 (PEDOT∶PSS/PSS-Na) of which the device brightness is increased by 86.7% and the current efficiency is increased by 34.3%. Finally, the behavior of internal carriers of blue OLEDs based on mix HILs with different doping ratios was observed by means of transient electroluminescence (EL) test system. Forward bias of 10 V was applied to the blue OLEDs. When the electroluminescence of the device reached a steady-state, the bias was removed, and the intensity of the delayed EL peak was observed. After a time delay of 50 μs, a reverse bias of 7 V was applied to observe the intensity of transient EL peak. It shows that after the removal of the forward bias, the EL spike of the device decreases as the PSS-Na incorporation ratio increases, indicating that the accumulated charge at the internal interface of the device is reduced. And the increase in the EL spike of the device after application of the reverse bias indicates that the injection of holes is more efficient after the incorporation of PSS-Na, and the injection barrier at the mix-HIL/EML interface is decreased. With the help of transient EL test system, the change of EL intensity of the device was observed by applying or removing the driving voltage, and the charge accumulation at the mix-HIL/light-emitting layer (mix-HIL/EML) interface was analyzed. The incorporation of PSS-Na increases the work function of the hole injection layer, and the accumulation of charge at the interface is reduced. By preparing the mix-HIL with doping PSS-Na into PEOT∶PSS, the device performance of blue TADF OLEDs is improved, which is a feasible method for obtaining high efficient solution-processed OLEDs.

In the study of Quantum-Dot Light-Emitting Diodes (QLEDs), theambient air, especially water and oxygen, has been a major factor affecting device performance. However, some phenomena have shown that water is one of the causes of the increase of luminance under the certain current of QLEDs. Therefore, this effect can be improved by water vapor treatment to optimize device performance greatly. This work aims to study the effect of water vapor treatment to the performance of Quantum-Dot Light-Emitting Diodes (QLED) based on the passivation of the surface states of the quantum dots by water and explore the best processing time. Firstly, The QLEDs were fabricated through solution processes, and after the fabrication of the emission layer, the QLEDs were placed in the argon atmosphere with a certain humility. In this progress, the degree of water vapor treatment was depended by the different processing time, and the effect of air was isolated by the argon atmosphere. Secondly, the electroluminescence spectra of each device were characterized. The results show that the spectrum of treated QLEDs has a slight blue shift and the shift increases with the increase processing time. The reason for the blue shift is that the particle sizes of QDs decrease due to the oxidation of the surface states of the quantum dots by water. In addition, the current density-voltage-luminance (J-V-L) curves were characterized. The luminance and current intensity of each device was compared, and the current efficiency-current intensity (CE-J) curves were obtained. The results show that the luminance and CE of QLEDs with water vapor treatment were greatly improved with the extension of the processing time. (The luminance is increased by 10%, and the CE is increased by 50% when the processing time is 3 mins) In the end, the luminance of all QLEDs in the certain current were tested and the luminance-time curves were obtained. The aging curves show that the luminance of each QLEDsincreasedbefore the deterioration of each device. The initial luminance of devices was improved by 50% to 70% and the time taken to increase to the maximum value decreased with the extension of processing time. The results indicate that the water vapor treatment is effective to optimize the performance of QLEDs. The lifetime of all devices wascalculated by the life formula, and the comparison of each deviceshows that the treated devices have a longer life than others. (The lifetime is about 1.7 times of the untreated device) As the processing time increases,the decrease of the maximum luminance and the intensifieddeteriorationare attributed to the negative effects on the organic materials of water and the new defect states generated during the oxidation of the quantum dots by the water molecules.In summary, the water vapor treatment optimizes the performance of QLEDs, which is beneficial for the research of the aging mechanism of the QLEDs and the simplification of the packaging process.

Rare earth doped upconversion material is a luminescent material that converts two or more long-wavelength, low-energy near-infrared photons into one short-wavelength, high-energy visible or ultraviolet photon. Due to its sharp line emission, long lifetime and no background luminescence characteristics, it has great application value in many fields such as bio-imaging, detection and treatment, solar cell, drug delivery and photocatalysis. Among them, NaYF4∶Yb3+/Er3+ micro/nanocrystal is one of the most important upconversion materials, and there is a lot of research in upconversion luminescence mechanism, preparation method, spectral modulation and practical application. However, low luminescence efficiency is still a bottleneck that restricts various practical applications. In addition, the transport and coupling properties of a single NaYF4∶Yb3+/Er3+ micro/nanocrystal are unclear. One-dimensional micro/nanomaterial is a restricted system in two dimensions. Therefore, it provides an ideal model for studying the transport properties of electrons and photons. In this study, one-dimensional NaYF4∶Yb3+/Er3+ microcrystals and NaYF4∶Yb/Er@NaYF4∶Yb/Tm core-shell structure microcrystals with controllable length to diameter ratio are synthesized by the hydrothermal method assisted by sodium citrate. The laser confocal excitation system is used to study the generation of luminescence and synchronous luminescent pattern in a single one-dimensional NaYF4∶Yb3+/Er3+ microrod by controlling the length to diameter ratio of the rod, the excitation mode and the preparation of special core-shell structures. It reveals that in one-dimensional rod structure, the transport mode of luminescence is: luminescence that is perpendicular to the direction of the rod axis skin-spreads along the ring-shaped cavity of the cross section of a rod, luminescence approximately along the axis of the rod targetedly transports to a rod end with total reflection waveguiding approach. In the one-dimensional NaYF4∶Yb3+/Er3+@NaYF4∶Yb3+/Tm3+ core-shell rod structure, a single-particle functional material that is excited by single wavelength with local multicolor light emitting is constructed. It also provides a way for the detection of local doping of traced rare earth luminescence centers. We have achieved the controllable emission colors in the modulation of luminescence generation and transport in single-particle one-dimensional NaYF4∶Yb3+/Er3+ and NaYF4∶Yb3+/Er3+@NaYF4∶Yb3+/Tm3+ core-shell microrods in waveguide-excitation mode and point-excitation mode. Moreover, we also reveal that luminescence spreading along the rod length mode is easier to couple than that spreading along the radial mode. The properties of luminescence transport and coupling in one-dimensional microrods suggest their potential applications in photonic coupling devices, upconversion waveguide lasers and luminescence imagings.

With the development of China’s economy, the development of highway tunnel construction is also rapidly. In the early stage of engineering design to ensure the safety of traffic in the tunnel, the power and location of the tunnel lighting sources all depend on the maximum outside luminance value and vehicle speed value through out the year. Although such a design considers safety, but it blindly increases the luminance of the tunnel illumination and it can not alleviate the problem of visual adaptation. However, with the application of LED in tunnels, the impact of LED sources spectra gradually attracted people’s attention. Moreover, the investigation found that the traffic accident rate of the highway tunnel in the exit section is relatively high. The main reason is that the luminance difference between the inside and outside of the tunnel is large, and the light adaptation time is long when the driver leavesthe tunnel exit. In order to study the impact of LED sources spectra on the light adaptation in tunnel exit, and to provide a basis for the selection of light source in tunnel exit, the LED source was characterized by a wide spectrum double-peak pattern. In the long wavelength range, the spectral content is different. The light adaptation is mainly related to two factors: the change of pupil area and photochemical reaction of the photopigment. LED light sources with different color temperatures have different spectral characteristics. The spectrum affects the time of light adaptation by affecting the synthesis of photopigment. The experiment selected 7 color temperature LED light sources used in the field of highway tunnel. The color temperatures are: 3 000, 3 500, 4 000, 4 500, 5 000, 5 700 and 6 500 K. Thirty observers were chosen for the experiment. All observers had normal visual corrected visual acuity. None of the observers had night blindness, color blindness or other diseases affecting night vision functions. The experiment was carried out in a simulated tunnel with a height of 2.8 m, length of 9 m and width of 5 m. Three photopic luminance levels for the experimental LEDs (4, 8 and 12 cd·m-2), three illumination angles (15°, 20° and 25°) and two mounting heights (2.0, 2.4 m) were applied. In total, 126 lighting conditions were evaluated in the experiment. The results show that the higher luminance of the tunnel exit section, the shorter light adaptation times. When the luminance is the same, as the correlated color temperature increases, the light adaptation times decrease. The installation angle and height of the lamps have little effect on the sources spectra. Changing the installation height and angle can not effectively reduce the light adaptation time. This paper provides data and theoretical support for the selection of the color temperature in the exit section by analyzing the spectrum of LED sources with different color temperatures.

Fourier transform infrared (FTIR) spectroscopy has great potential for on-line analysis of cement raw meal components. As the air humidity on site is not stable due to the complex environment, it will cause interference to the on-line FTIR quantitative analysis of the four key components of Fe2O3, SiO2, CaO, Al2O3 in the raw material samples. In this paper, the on-line FTIR analyzer for raw meals was used to collect near-infrared spectra of raw meal cement samples under different humidity conditions. The influences of different humidity conditions on near-infrared quantitative analysis were analyzed, and a method of eliminating the background moisture interference was proposed. The specific researches were as follows: (1) Spectra of each 50 samples at two different humidity levels were analyzed. The results were that sample spectra at high humidity level compared to that at low humidity level were similar in shape, while the absorbance intensities were deceased overall and baselines were inclined. These demonstrated that background moisture affected the near-infrared spectra of the samples. (2) Two FTIR quantitative analysis models for samples under high humidity and low humidity conditions were established respectively, and the four component contents of 8 samples in prediction set under another humidity condition were predicted. The results were that the values of the correlation coefficient (r) between the content values of the four components predicted by model under high humidity condition and the standard values in the prediction set were 83.74%～92.74%, and the values of the root mean square error (RMSE) were 0.12～0.83. The values of R obtained by model under low humidity condition were 67.32%～82.41%, and the values of RMSE were 0.12～0.84. These indicated that background moisture had affected the FTIR quantitative analysis of raw meal cement components. (3) In order to eliminate the influence of water absorption, the characteristic absorption of background moisture from the measured spectrum were removed refer to the mid-infrared spectroscopy technique. The FTIR quantitative analysis models under high humidity and low humidity conditions were established respectively, and the four components contents of samples in prediction set were predicted by these models. The results were as follows: ① Under high humidity condition, the prediction accuracy of the model with eliminating moisture absorption was improved compared with model without eliminating moisture absorption, the predicted values of r were 90.73%～97.76%, and the values of RMSE were 0.12～0.82, ② Under low humidity condition, the prediction accuracy of model with eliminating moisture absorption was higher than that of model without eliminating moisture absorption, and the predicted values of r were 94.07%～98.69%, the values of RMSE were 0.12～0.82, ③ The values of r obtained by models under high and low humidity conditions were above 90%. The experimental results showed that the method could effectively eliminate the influence of moisture absorption on the quantitative analysis model of raw material cement compositions. It provided the theoretical basis and technical support for the online analysis of raw material cement compositions based on FTIR technology.

Spectral analysis is an important application of chemometrics and has been widely used in various fields. Spectral variable selection is a key part of spectral analysis. Therefore, it is critical to study different variable selection methods to objectively identify useful information variables or eliminate irrelevant and interfering variables. In our study, a new variable selection method of the selectivity ratio competitive population analysis (SRCMPA) is proposed. This algorithm adopts the idea of selection ratio, adaptive weighted sampling and model population analysis, and combines the method of variable arrangement and exponential decline function. The key wavelength is defined as the wavelength with a high score value in the regression model. In this paper, the score value of the selection ratio under the PLS model is used as an index to evaluate the importance of each wavelength. Then, according to the importance of each wavelength, SRCMPA sequentially selects N wavelength subsets from Monte Carlo sampling, and runs in an iterative and competitive manner. In each sampling operation, the PLS model is built with a fixed ratio samples and the selection ratio value of each variable is calculated. Based on the score value of the ranking selection ratio and the normalized SR (selection ratio) score value as the weight, the key variables are selected by two steps: the compulsory selection of exponential decline function and the competitive selection of adaptive weighted sampling. Finally, cross validation (CV) method is applied to select the optimal subset with the lowest cross validation mean square root (RMSECV). The algorithm has been tested on wheat protein data set and beer data set, and compared with three efficient algorithms. Through the experimental results to evaluate the superiority of the algorithm, this algorithm can find the best combination of the key wavelength variables of the data set, and can be used to explain the chemical characteristics of interest, the evaluation results after modeling are also the best. Compared with the PLS model of full-spectrum beer data set, the number of variables in this algorithm has been reduced from 567 to about 42. And the RMSECV of model decreased from 0.622 to 0.115, RMSEP decreased from 0.823 to 0.363, and the prediction accuracy increased by 81.5% and 55.9%, respectively. Q2_CV and Q2_test also increased from 0.940, 0.852 to 0.994 and 0.995. For wheat protein data sets, Compared with the PLS model of full-spectrum wheat protein spectral data set, the number of variables has been reduced from 175 to about 18. And the RMSECV of the model decreased from 0.607 to 0.292, the RMSEP decreased from 0.519 to 0.234, and the prediction accuracy increased by 51.9% and 54.9%, respectively. Q2_CV and Q2_test also increased from 0.748, 0.774 to 0.931 and 0.839.

High temperature vulcanized (HTV) silicone rubber composite insulators are widely used in UHV transmission lines, and more and more attention has been paid to their ultraviolet aging resistance. In this paper, the samples of high temperature vulcanized silicone rubber come from two manufacturers, and the testing instrument is a self-designed adjustable ultraviolet aging test chamber, with 320~750 nm ultraviolet radiation as experiment condition. Then aging test by ultraviolet radiation (0, 500, 1 000 h) was carried out. Fourier transform attenuated total reflection infrared spectroscopy (ATR-FTIR) was used to analyze the relationship between the change of spectral peak and the functional group of sample (surface) before and after radiation, which was supplemented by SEM, hydrophobicity and volume resistivity measurement. Thus the aging mechanism of HTV silicone rubber was studied. The results showed that with the increase of ultraviolet radiation time, the reflection peak intensity of HTV silicone rubber side chain Si—CH3 and Si— (CH3)2 decreases, and the main chain Si—O—Si reflection peak intensity increases, the hydrophobicity decreases clearly, and the volume resistivity decreases slightly. Scanning electron microscope (SEM) showed that large particles and potholes appear on the surface of the irradiated, and the weight percentage of Al, O, Si increases. The results showed that ultraviolet light cut off the part of Si—C bond of HTV silicone rubber and removed the nonpolar methyl group from the main chain of Si—O—Si, which weakened the shielding effect on the strong polarity of the main chain. The exposed free radical —Si· was further oxidized and crosslinked, which enhanced the binding of Si—O—Si, enhanced the polarity of molecular chains, and decreased the hydrophobicity. The decrease of hydrophobicity made HTV silicone rubber easy to absorb water in the air, and the weak ionization of water itself increased the carrier concentration, resulting in a slight decrease in volume resistivity. According to the infrared spectrum analysis, the following conclusions were drawn: ultraviolet ray accelerated the aging of high temperature vulcanized silicone rubber by cutting off the functional groups on the surface of high temperature vulcanized silicone rubber; The application of infrared spectroscopy is of great significance to the study of aging of silicone rubber composite insulators at high temperature and its application in UHV transmission lines.

According to the anatomical structure of the skin tissue, we established a six-layer model, and the characteristic parameters of each layer of skin tissue were given. We considered the absorption characteristics of oxidized hemoglobin and reduced hemoglobin, and gave the spectral absorption coefficients of each layer of skin tissue according to the contents of water, blood, fat and oxygen saturation in each layer of skin tissue, as well as the size of blood vessels. The scattering coefficients at different wavelengths were simplified properly, and then the scattering coefficient spectra of each layer of skin tissue were obtained. In this paper, we used Monte Carlo method to simulate the transmission process of 400～1 000 nm wavelength light in the multi-layer model of skin tissue under the conditions of contraction and relaxation. The spectral reflectance of the skin tissue was obtained by counting the distribution characteristics of a large number of photons. The amplitude spectrum of volume pulse wave was obtained by calculating the reflection coefficient of the two states obtained from the simulation. The simulation results showed that the volume pulse wave amplitude of green light is better than that of red light and blue light when the incident light intensity is constant. The penetration depth spectrum of skin tissue was obtained by calculating the corresponding skin tissue depth when the light flux of different wavelengths decreased to 1/e along the direction of skin tissue depth. The results showed that the penetration depth of blue light and green light is small, the blue light can only reach the surface layer, the green light can reach the micro-circulation layer, and the penetration depth of red light is the largest, which can reach the dermis directly. Considering when the light travels through the skin, it involves a dynamic process from contraction to relaxation, so we define the depth of pulse signal generation based on penetration depth, and the spectral generation depth is calculated by using the penetration depth of vasodilation and contraction in two different states. The results showed that the depth of light generation at different wavelengths is greater than the penetration depth, the depth of blue light is shallowand the blood absorption modulation is small, so the pulse signal obtained is more easily interfered by noise. The volume pulse wave of red light is deeper than that of green light, but compared with green light, its absorption and modulation by blood is smaller, and the depth of green light generation is enough to reach the dermis vascular layer, so the amplitude of red light volume pulse wave is smaller than that of green light. Our simulation results confirm some spectral characteristics of skin tissue, which provides a theoretical basis for the accurate acquisition of multispectral volume pulse waves and other related studies.

Because the optical signal has the characteristics of chaotic signal obviously in laser welding, such as large number of mixed information, complex changing in the time domain and the frequency domain, fluctuation violently of random signals, and so on, it is difficult to separate the characteristic information of penetration by conventional means in laser welding. So, according to the distribution characteristics of complex mixed signals extracted from spectral perspective imaging, a new chaotic signal analytical method for high power solid state laser welding is proposed in this paper, which bases on the synergistic extraction method for coaxial penetration characteristic signal. The method includes extracting each transient probability density function of the chaotic signal, dynamically forming a series of simplified feature models with trend recognition feature, and establishing compound correlation recognition between the multi-morphological characteristics of the feature models and penetration state. In this paper, three kinds of characteristic parameters and their definitions and mathematical models were introduced in detail, and there are basic value, dispersion degree and distortion rate, which not only can accurately describe the different characteristics of the feature model, but also have much relationship with different penetration states of laser welding, by experiment verifying. For example, the base value characteristic parameter has obvious recognition effect when the weld penetration degree is bigger, but has much fluctuation amplitude when the penetration degree is smaller. The dispersion degree characteristic parameter is only sensitive to the small penetration state test, which can effectively supplement the basic value, but there are some shortcomings in the larger penetration state. The distortion rate characteristic parameter although fluctuates violently near the moderate penetration rate, when the penetration degree of weld is smaller or larger, the characteristic parameter can get better recognition, which the characteristic parameters of the basic value and the dispersion degree can't do. Therefore, in the characteristic parameters of basis value, dispersion degree and distortion rate, there are relationship of complementary and mutual verification. By the composite identification of their characteristic curves, the laser welding penetration state can be detected qualitatively and quantitatively .The experimental results showed that, after the chaotic signal analytical method combined with the coaxial synergistic extraction method, it can realize the multidimensional compound recognition effect of spectroscopy, optics, statistics, abstract analysis, etc, which can enhance the penetration detection ability of high power solid laser welding effectively, and realize reliable on-line identification of penetration.

Visible-near infrared spectroscopy has been shown to be a fast and effective tool for organic carbon (TOC) content prediction. However, the research target of using spectral prediction of TOC content is mainly soil or lake sediment, and there is little research on marine sediments in intertidal zone. In order to predict the content of TOC in intertidal sediments quickly and accurately, this study constructed TOC prediction model by combining abnormal sample elimination, spectral feature transformation and feature wavelength extraction, that is, collecting sediment spectra of samples in intertidal zone, using Markov distance, standard lever value and student residuals combined analysis method to remove abnormal samples, using multivariate scattering correction (MSC), smoothing + differential for spectral transformation, using genetic algorithm (GA) to extract characteristic wavelengths, using partial least squares method (PLS), least squares support vector machine (LSSVM) and BP Neural Network (BPNN) to model and predict sediment TOC content, using the decision coefficient (R2) and residual estimation deviation (PRD) to evaluate model accuracy. The results showed that the elimination of abnormal samples improved model accuracy, and the test R2 and PRD of the BPNN model increased by 28% and 39% respectively. The effect of MSC was better than that of smoothing+differential, and the test R2and PRD of PLS, LSSVM and BPNN models based on MSC were 0.81, 0.86, 0.78 and 2.25, 2.59, 2.07, respectively, which enhanced 9%~20% (R2) and 11%~22% (PRD) than that based on smoothing+differential, suggesting that MSC has a strong ability to extract TOC information. GA is not conducive to increasing model accuracy, the test R2 and PRD of models based on GA reducedby 9%~36% and 18%~33%, respectively. This may be related to the low number of characteristic wavelengths extracted by GA. The BPNN model has the lowest predictive accuracy and may be related to its vulnerability to local minimums. PLS model has high accuracy and can predict TOC content in intertidal zone. Basing on abnormal sample elimination and MSC, the modeling set R2 of PLS model was 0.98, and the prediction set R2 and RPD were 0.81 and 2.25 respectively. The accuracy of LSSVM model was better than that of PLS, the modeling set R2 was 0.99, the test set R2 and RPD were 0.86 and 2.59 respectively, implying excellent TOC quantitative prediction ability of LSSVM. In a word, for the prediction of TOC content in intertidal sediments, the combination of abnormal sample elimination, MSC spectral transformation and LSSVM modeling can obtain a reliable and stable prediction model.

Acrylamide (AAm) is classified as a potential carcinogen and neurotoxin because of its neurotoxicity, reproductive toxicity, genotoxicity and immunotoxicity. The cooking of some carbohydrates-rich food at high temperature increases the high risk for exposure to AAm. It is of great significance and practical value to establish a rapid analytical method for acrylamide. Surface-enhanced Raman scattering (SERS) technique has been developed rapidly in recent years. Based on the highly active SERS substrate, the identification of the fingerprint of compound can be realized. To achieve the rapid SERS analysis of the target substance in the complex substrate, a highly active enhanced substrate and an efficient sample pretreatment technique were required. A rapid, reliable, and quantitative method to determine acrylamide content in fried food based on Ag nanorod/Au nanoparticle composites (AgNR@AuNPs) SERS has been proposed. Based on the enhanced effect of double “hot spots” between AgNR nanorods and AuNPs nanoparticles, the substrate has a high SERS enhanced activity for acrylamide. Besides, AgNR was a solid phase substrate. Before each operation, the oxide on the surface was removed by dilute nitric acid, which greatly improved the stability of SERS analysis. The size of AuNPs and the testing sequence which influence the sensitivity of SERS detection have also been investigated. The fried food was complicated with lots of interferences. The dispersive solid-phase extraction was used for decreasing the serious interference of complex matrix. The defatting and extraction solvent, the kind and ratio of cleanup material have been optimized. The results have shown that the optimized defatting solvent, the extraction solvent and the cleanup materials were hexane, the mixture of water and acetonitrile (1∶1, V/V), and MgSO4+NaClrespectively. The total detection time approximately costed was within 5 min with the limits of detection as low as 1 μg·kg-1 based on the quick pretreatment and SERS enhancement. The peak at Δν=1 482 cm-1 was selected as the characteristic peak of acrylamide for quantitation in the concentration range of 5～100 μg·kg-1 (r=0.985). The recovery rates for acrylamide were 77.1%～93.6% with coefficients of variation less than 4.0% at five different fortified concentrations. The developed method can potentially be used for acrylamide detection in the field.

As a new type of fluorescent carbon nano-functional materials, carbon quantum dots (CQDs) have extensively captivated attention due to their excellent biocompatibility and outstanding optical properties. In this work, a one-step method was developed for the preparation of green non-polluting CQDs with strong fluorescence by using water and peanut rich in protein, fat and carbohydrate as precursors in a hydrothermal reactor at 190 ℃ for 20 h. Transmission electron microscopy (TEM) showed that the particle size distribution of the peanut CQDs (PN-CQDs) was relatively uniform. X-ray powder diffraction (XRD) showed that the crystalline form of PN-CQDs was amorphous carbon, which was attributed to highly disordered carbon particles. The infrared transform spectrum (FTIR) indicated that the surface of the PN-CQDs was rich in hydrophilic groups such as hydroxyl, carboxyl and nitrogen-containing functional groups, so it had good water solubility. Then the ultraviolet-visible spectra (UV-Vis) of the PN-CQDs was measured. There was an obvious absorption peak at 275 nm, which was the characteristic ultraviolet-visible absorption peak of CQDs. The fluorescence spectra results showed that CQDs prepared by peanut had a characteristic of excitation wavelength dependence, and their emission peaks significantly changed with the excitation wavelengths. When excited at the optimal excitation wavelength of 326 nm, the maximum emission wavelength was 408 nm, and PN-CQDs could emit blue fluorescence with highest fluorescence intensity. The fluorescence quantum yield of PN-CQDs measured by the reference method was 5.0%. Based on its luminescent properties, a “off-on” fluorescent method was constructed for high sensitivity detection of dopamine by using the PN-CQDs as a probe. The experiments indicated that in pH 3.80 HAc-NaAc buffer solution, when Ce(Ⅳ)was added in PN-CQDs solution, the fluorescence of PN-CQDs was quenched, and the fluorescence signal of the system was in the “off” state. It was found that both electron transfer from PN-CQDs to Ce(Ⅳ) and aggregation of PN-CQDs were responsible for the PN-CQDs fluorescence quenching at λex/λem=326 nm/408 nm. In the presence of dopamine, the fluorescence of PN-CQDs was recovered because Ce(Ⅳ) preferred to react with dopamine, which resulted in the departure of Ce(Ⅳ) from the surface of the PN-CQDs, and the fluorescence signal of the system was “open”. Under the optimal conditions, the recovered fluorescence value ΔF of PN-CQDs at λex/λem=326 nm/408 nm were linearly related with the dopamine concentration in the range 2.5×10-7 to 1.0×10-5 mol·L-1, the detection limit was 9.0×10-8 mol·L-1 and the coefficient of determination R2 was 0.997 6. The fluorescence quenching-recovery mechanism of the system was discussed. The fluorescence lifetimes of PN-CQDs and PN-CQDs-Ce(Ⅳ) systems were 6.02 and 5.15 ns, respectively. The fluorescence quenching type of Ce(IV)on PN-CQDs was dynamic quenching. The fluorescence of Ce(Ⅲ) generated in the reaction at λex/λem=251 nm/350 nm had no effect on the determination of dopamine. The method was sensitive, selective, simple and rapid. It has been applied to the determination of dopamine in practical samples with satisfactory results. The recovery (mean±SD) was between 97.5%±1.3%～103%±1.5%. The study can expand the application of CQDs in the field of analytical chemistry and provide new ideas for pharmaceuticals fluorescence analysis.

In view of the fact that shallow artificial neural networks (ANNs) rely on prior knowledge for artificial extraction of features, while shallower network structures limit the ability of neural networks to learn complex nonlinear relationships, this paper applies deep neural networks (DNN) to the study of inversion of multi-component volatile organic compounds (VOCs) by leaf-transformed infrared spectroscopy (FTIR), and the effectiveness of the algorithm was verified by simulation experiments. Eight VOCs including benzene, toluene, 1,3-butadiene, ethylbenzene, styrene, o-xylene, m-xylene, and p-xylene were selected from the US Environmental Protection Agency (EPA) database. In the wavelength range of 8~12 μm, each gas has four different concentration lines, and the absorbance spectrum at one concentration is selected from each VOCs gas according to Beer-Lambert's law to obtain 65 536 different kinds. Samples of VOCs mixed gas absorbance spectra. The absorbance spectra of 5 000 groups of mixed gases were randomly selected, of which 4 000 were used as training samples and 1000 were used as prediction samples. The dimensional reduction of the spectral matrix was performed by integral extraction and principal component extraction, and the spectral dimension was reduced from 3457 to 30 dimensions. The new matrix obtained by preprocessing the spectral matrix was used as the network input, and the concentration matrix of the eight VOCs was used as the output. A deep neural network regression prediction model of 30-25-15-10-8 was established, and multiple groups were realized by using spectral data. Inversion of VOCs concentration, the root mean square error of the sample obtained by inversion was 0.002 7×10-6, which was obvious compared with the accuracy of previous methods using nonlinear partial least squares fitting and artificial neural network. improve. The root mean square error of each VOCs gas does not exceed 0.005×10-6, and the root mean square error of each sample does not exceed 0.006×10-6, which proves that the deep neural network prediction model has good nonlinear fitting ability. And good stability. When the training sample is insufficient (typical value: less than 500), the deep neural network cannot fully learn, the network error is larger, and the accuracy is lower than that of the single hidden layer artificial neural network, but as the number of training samples increases, the deep neural network accuracy is continuously improved. When the number of training samples is sufficient, the deep neural network has stronger nonlinear relation learning ability than the shallow artificial neural network, and the prediction accuracy is higher and the model is more stable. At the same time, due to the dimensionality reduction of the spectral matrix before training, the complexity of the algorithm is greatly reduced, and the inversion efficiency is effectively improved. The analysis shows that the deep neural network prediction model has good nonlinear fitting ability and good stability. It can fully learn the data features without manual extraction of features, and at the same time, the concentration inversion of multi-component VOCs can achieve higher precision.

UV-Vis absorption spectroscopy can be applied not only to the analysis of phycobiliprotein species and purity, but also to the analysis and guidance of its extraction and purification processes. In this paper, the fresh cyanobacteria of Chaohu Lake were used as experimental material, and the phycocyanin and allophycocyanin were refined by column chromatography using Cellufine A-500 and hydroxyapatite as fillers. According to the characteristics of the elution peaks corresponding to the two kinds of fillers on the elution curve, making full use of the difference in the UV-visible spectral characteristics of phycoerythrin, phycocyanin, allophycocyanin and nucleic acid, carotenoids, general proteins, so the dynamic change of the elution peak composition and content can be judged. UV-Visible absorption spectroscopy was used to study the spectral characteristics and variation of phycobiliprotein elution peaks by two kinds of packed column chromatography, the change of composition and content of each elution peak can be qualitatively and quantitatively determined; combining the characteristics of the two kinds of fillers, the charge characteristics and coordination ability of phycocyanin, allophycocyanin, phycoerythrin, etc. can be analyzed, revealing the intrinsic mechanism and essence of the fractional elution of the two column chromatography materials. During the purification of phycobiliprotein by Cellufine A-500, four elution peaks appeared on the elution curve with the replacement of the eluate. After scanning the ultraviolet-visible absorption spectrum of the sampling point, it was found that: The main component of peak I is positively charged or electrically neutral hetero protein and carotenoid; the main component of peak Ⅱ is phycoerythrin, heteroprotein and nucleic acid with a small amount of negative charge; the main component of the peak Ⅲ is high-purity phycocyanin with a large negative charge and a small amount of allophycocyanin, and the further improvement of the purity of phycocyanin is restricted due to the incomplete separation of phycocyanin and allophycocyanin; The main component of the peak Ⅳ is a hetero protein and a low-purity phycocyanin with a large amount of negative charge. In the process of purifying phycobiliprotein by hydroxyapatite, three elution peaks appeared on the elution curve with the replacement of the eluate. After scanning the ultraviolet-visible absorption spectrum of the sampling point, it was found that: The main component of the peak I iscationic or basic protein, such as hetero protein, nucleic acid, carotenoid, or the like; The main component of the peak Ⅱ is high-purity phycocyanin which combines with calcium ions to form a weak coordination bond, and the phycocyanin and the allophycocyanin can be completely separated, which is beneficial to further improvement of the purity of the phycocyanin; The main component of the peak Ⅲ is a high-purity allophycocyanin which combines with calcium ions to form a strong coordinate bond.

Quantum dots are widely used in the field of luminescence because of their unique and excellent optical properties. The most prominent feature is that the spectral tuning is convenient, and only the size of the material needs to be changed to realize the tuning of the luminescence spectrum. In combination with the needs of practical applications, CdSe materials were selected as the main research object. By improving the process, introducing Schlenk line to isolate water and oxygen, using high temperature thermal injection method, adjusting the ratio of cadmium source and zinc source, selenium source and sulfur source in raw materials, the core-shell structure CdSe/ZnS red and green quantum dot materials with high color purity and high efficiency and stability were obtained. The synthetic quantum dots have a size of about 6.0 and 4.2 nm, luminescence peaks of 625 nm and 525 nm, the full width at half maximum (FWHM) in PL spectra of 30 and 28 nm, and photoluminescence quantum yield (PLQY) of 82% and 61%, respectively. And then the application of quantum dot LED in backlight display was studied. The composite red and green quantum dot materials were used to replace the phosphor materials in the traditional process. By improving the packaging method, the quantum dot light conversion layer was protected by a double-layer epoxy resin AB glue, at the same time, a PMMA lens coating was introduced to fundamentally isolate water and oxygen. Finally, the packed quantum dot white light-emitting diodes(wLEDs) have red, green and blue emission peaks of 630, 535 and 453 nm, respectively. The FWHM of PL spectra are 20, 28 and 30 nm. The three-segment spectral emission peaks have good symmetry on both sides, effectively solving the problem that the LED is missing in the red spectral band of the traditional phosphorwLEDs. And it achieves the advantages of good monochromaticity, high color purity, and high color saturation. The quantum dot wLEDs with CIE color coordinate (0.329, 0.324) was obtained under the condition of 20 mA current in the LED integrating sphere photochromic test system. The color coordinate was very closed to the standard white light. Its color temperature was 5 094 K, the luminous efficiency reached 94.72 lm·w-1, and the color rendering index Ra could approach 78.6, and the lifetime was more than 400 h. Finally, the quantum dot LED strip was packaged to complete the backlight. According to the quantum dot wLEDs emission spectrum gained from the test, we could obtain the sRGB color triangle, ie the color gamut. By comparing the NTSC1931 standard color gamut, we discovered a high color gamut quantum dot LED backlight with a colorgamut coverage of 109.7%. The LED backlight developed was made up of 240 quantum dot wLEDs and successfully demonstrated the 29-inch LCD TV panel for the first time. This result will further develop tailor-made quantum dots, especially in high-performance display applications.

For effectively overcoming the influence of rough background including point-clutter, strip wave and highlighted area in single-spectral forward-looking infrared (FLIR) image, a smoothing method for sea surface rough background based on multi-spectral FLIR images fusion is proposed. The method makes full use of the complementarity and difference existing in multi-spectral FLIR images. It aims at combining multiple images into a quality image in which the sea surface rough background is smoothed and the feature information of the ship targets is maintained good. Firstly, the multi-spectral source images were decomposed into low frequency sub-bands and high frequency sub-bands by discrete wavelet transform (DWT). The high frequency sub-band mainly contains the detailed information of the background and the ship target while the low frequency sub-band mainly contains the grayscale information. After obtaining the high frequency fusion image based on the maximum value of the high frequency coefficient, the regional energy of each pixel was calculated to modulate the high frequency fusion image in order to suppress the details of the background and maintain the details of the ship targets simultaneously. Then the low frequency fusion image was obtained by the average strategy and smoothed by the guided filter. Finally, the fusion image was reconstructed based on the high frequency fusion image and the low frequency fusion images by inverse wavelet transform. When the simulation experiment was carried out on the actually collected multi-spectral FLIR images to prove the effectiveness of the proposed method, the proposed method was compared with the other 6 smoothing methods including bilateral filter, guided filter, gradient minimization, relative total variation, bilateral texture filtering and rolling guidance filtering. A large number of experimental results show that the smoothing performance of the proposed method is better than the other 6 methods. The proposed method can effectively smooth the sea surface rough background and maintain the structure, grayscale, contrast of the ship targets, which can greatly enhance the separability of the ship targets. In the future work, the proposed method needs to be optimized to further improve the timeliness.

With the rapid development of China’s economy, surface water pollution has become increasingly serious. Therefore, it is of vital importance to realize the continuous monitoring of surface water quality to ensure human health and protect the environment. Nitrite nitrogen concentration is an important index in water quality assessment. Polluted water poses a great threat to human, livestock and aquatic products. The detection of organic pollutants by UV-Visible absorption spectrum has become an important method for water quality detection. A few papers about the detection of nitrite nitrogen concentration in water by UV-Vis spectroscopy in China can be found. Most methods require chemical pretreatment of water samples and then use UV spectrophotometer to predict the nitrite nitrogen concentration. These methods are tedious, time-consuming and labor-consuming so thatthey can’t realize real-time continuous detection. Besides, they will cause further environmentpollution. The detection of nitrite nitrogen concentration without chemical pretreatment based on UV-Vis absorption spectrometry is rarely mentioned in the literature. Therefore, UV-Vis spectroscopy was adopted in this paper to carry out basic research on unattended automatic continuous monitoring of surface water quality. Nitrite nitrogen solution samples were preparedand a three-day experiment was designed to measure the ultraviolet and visible spectra of all samples (group D1, group D2, and group D3) respectively every day. Firstly, samples from group D1 and group D2 were modeled respectively by partial least squares regression method (PLSR). The mean absolute percentage error (MAPE) obtained by full cross validation was 1.19% and 1.85% respectively. The result shows that the PLSR model has good prediction accuracy. Secondly, in order to verify the adaptability of PLSR model under different measurement conditions, the experimental data of groupD1 and group D2 were used for mutual prediction analysis. The MAPE was respectively 3.36% and 4.51%, less than 5%, indicating that PLSR model has good robustness. Finally, all samples from groupD1 and group D2 were used for PLSR modeling, and samples from group D3 were used as the test set. The MAPE of the test set was 2.19%. The results show that the MAPE of the PLSR algorithm based on UV-Vis spectral analysis technique for detecting the nitrite nitrogen concentration in solution is controlled under 5%, better than similar reports. In addition, the modeling process of PLSR model is simple and the operation time is short. The model is simple in structure and easier to be transplanted and solidified into embedded systems, bringing convenience to the later development and design of portable devices. As the basic research of the detection of nitrite nitrogencon centration in surface water, this paper can provide guidance for the accurate and rapid detection of surface water quality in the future.

The steady-state absorption and fluorescence spectra and time-resolved fluorescence spectra of riboflavin in water, DMSO and chloroform were measured by British Edinburgh FLS920P spectrometer. It was found that the absorption peaks hardly change in different solvents, and the fluorescence peaks show a significant red shift with the increase of the polarity of solvents, because the charge distribution of the excited state leads to the difference between the absorption and fluorescence spectra. Besides, the fluorescence lifetime of riboflavin in water is also longer than that in the other two solvents, which can be attributed to the hydrogen bond interaction between riboflavin and water. Furthermore, the ground and excited states of riboflavin molecules in different solvents were calculated by using Gaussian 09 software. Through frontier molecular orbital analysis, the stimulated transition of riboflavin is the transition of π electrons on benzene ring and nitrogen heterocycle ring to the antibonding orbital π* of benzene ring and CN, CO conjugated double bond. The results show that the charge distribution of riboflavin increases the dipole moment and the solute-solvent intermolecular interaction, which leads to spectral variation and a red shift in fluorescence spectra. Finally, the effect of H-bond on the spectra of riboflavin was considered by the molecular electrostatic potential and noncovalent interaction analysis. The theoretical absorption and emission peaks of the polymer structure are closer to the experimental results, which shows that the results of the polymer are reasonable. The ring structure formed by water dimer and riboflavin can increase the rigidity of riboflavin molecule, which is beneficial to the fluorescence emission, thus decreasing the probability of non-radiative transition and prolonging the fluorescence lifetime.

Two-trace two-dimensional (2T2D) asynchronous correlation spectroscopy is a new method of generating two-dimensional asynchronous correlation spectroscopy based on a pair of one-dimensional (1D) spectra. Compared with the conventional two-dimensional asynchronous correlation spectroscopy where at least three 1D spectra are needed, 2T2D asynchronous correlation spectroscopy makes the experiment easier, and is a better method for the expensive samples. In the present paper, the feasibility of using 2T2D asynchronous correlation spectroscopy to characterize intermolecular interactions was explored. Firstly, a model system containing two solutes P and Q were set up. P possessed a characteristic peak, and Q had no characteristic peaks. The mathematical analysis demonstrated that the intensity of 2T2D asynchronous correlation spectra was always zero when the initial concentrations of P and Q were set incorrectly. Furthermore, the variations of the absorptivity induced by intermolecular interactions could not be discerned by 2T2D-asynchronous correlation spectra. Therefore, incorrect results may be obtained when 2T2D asynchronous correlation spectroscopy is adopted to characterize intermolecular interactions. In order to develop 2T2D asynchronous correlation spectroscopy into a reliable method for characterizing intermolecular interactions, the setting method of the initial concentrations of P and Q in the 2T2D asynchronous correlation spectroscopy was first studied. The case that the intensity of 2T2D asynchronous correlation spectra was always zero induced by incorrectly setting the initial concentrations of P and Q could be avoided when the initial concentrations of P and Q satisfied the requirement of Eq.(6) in the text. On this basis, the 2T2D-asynchronous correlation spectroscopy with auxiliary cross peaks (ASAP-2T2D-asynchronous correlation spectroscopy) was developed by introducing a virtual substance S with an isolated peak and proper concentration into the research system to solve the problem that 2T2D-asynchronous correlation spectra could not reflect the variations of the absorptivity induced by intermolecular interactions. The results of computer simulation experiments demonstrated that the ASAP-2T2D-asynchronous correlation spectra could correctly reflect the variation of the peak position, bandwidth and absorptivity indicating that the ASAP-2T2D-asynchronous correlation spectroscopy was a reliable method for characterizing intermolecular interactions. Finally, the ASAP-2T2D-asynchronous correlation spectroscopy was adopted to characterize the intermolecular interaction between Li+ and benzo-15-crown-5 (BC). The results demonstrated that the variation of the peak position and absorptivity of the characteristic peak of BC could be reflected by the ASAP-2T2D-asynchronous correlation spectrum. These results further confirmed that the ASAP-2T2D-asynchronous correlation spectroscopy could correctly characterize intermolecular interactions.

The detection of parameters for water quality with spectral technique is a research hotspot at present. This paper proposes a method for the determination of chemical oxygen demand (COD) based on the fluorescence emission spectrum. Two groups of experimental samples are provided in the experiment, among which the fundamental group is 20 COD standard solutions, and the remaining 63 are actual water samples as the other group. Rapid digestion spectrophotometry is utilized to detect the COD of experimental samples. Three dimensional fluorescence spectrophotometer is used to collect the fluorescence emission spectra of the water samples at EX=275 nm (all the range of fluorescence emission spectra are EM=325~450 nm), then the data of fluorescence emission spectra of two kinds of water samples are processed and modeled. Principal component regression (PCR) and partial least squares regression (PLSR) are utilized to establish the prediction models based on fluorescence emission data respectively, and the effects of the models are compared. In order to verify the feasibility of the proposed method and the prediction ability of the model, the results of the PLSR mode are compared with the standard method. The comparison results show that, for the COD standard solution, when the number of principal component of PLSR and PCR is 5 and 8 respectively, the optimal results are obtained for both models, of which the determination coefficients of the correction model are R2PLS=0.999 9 and R2PCR=0.989 7, respectively. The prediction error of validation set data in the calibration model is less than 10%, and the PLSR model is better than the PCR model. While for the actual water samples, when the number of principal component of PLSR and PCR is 6 and 7 respectively, the cross-validation effect of the correction model is the best. Among them, root mean square error of cross-validation of the PLSR method is RMSECVPLS=0.932 2 mg·L-1, while for the PCR algorithm, RMSECVPCR=0.976 4 mg·L-1. For the validation set, the determination coefficient of PLSR is 0.940 2, while for PCR method, it is 0.919 0. It shows that PLSR method has better prediction effect. Consequently, the PLSR model based on fluorescence emission spectrum data has high prediction ability and strong adaptability, which can detect water COD quickly and accurately. Through the comparison of the method proposed in this paper and the traditional detection, we can see that proposed method can be used to detect the water with low concentration of organic pollutants, however, the detection error will increase if the concentration of organic pollutants in the detected water is high. This paper providesa new design idea for the research and development of water quality detection optical sensor.

As an aromatic compound, Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in human production and life. They have strong carcinogenicity and threaten human lives and health. Therefore, it is necessary to implement a simple, efficient, universal and accurate method to detect polycyclic aromatic hydrocarbons. According to the common types of polycyclic aromatic hydrocarbons, solid powdery substances of polycyclic aromatic hydrocarbon naphthalene (NAP), fluorene (FLU) and acenaptene (ANA) were selected as experimental samples. Of all the samples NAP, FLU, ANA powder were carried out by 1 g and dissolved in a small amount of methanol (spectral grade) solution, then transferred them to 100 mL of deionized water solution, getting a configure PAHs standard solution. The experiment was carried out by the FS920 fluorescence spectrometer. In order to avoid the Rayleigh scattering effect generated by the fluorescence spectrometer itself, the initial emission wavelength was set to lag the excitation wavelength by 10 nm. It could obtain the fluorescence spectrum of the aqueous solution of ANA, NAP and FLU, on the basis of the standard solution, a 0.1 μg·mL-1 aqueous solution of a simple substance was placed. Then, different volumes of ANA, NAP and FLU were mixed to form two mixed solutions, each of them formed a mixed solution of 16 different concentration ratios, and then took different volumes. The three solutions were mixed with each other, they were shaken and finally a total of 48 mixed solutions of different volume ratios were formed. Finally, the experimental data were input into Matlab to obtain the fluorescence spectrum of the mixed solution of naphthalene, anthracene and anthracene naphthalene. It was found that the excitation wavelength of the mixed solution was in the wavelength range of 260～320 nm and the emission wavelength was 300～380 nm, and the position of the optimal emission wavelength was similar. Most of the excitation wavelengths corresponding to the fluorescence peaks overlap. Support vector machine (SVM) based on genetic algorithm (GA) optimization was applied to the species detection of PAHs mixture, because the shortage of species in which the fluorescence spectrum cannot directly react with the mixture solution. The data were randomly scrambled, and the genetic evolutionary algorithm havd a termination evolution algebra of 200. Training data and prediction data are 36 and 12, respectively. Under the optimal conditions, the average accuracy of the training result was 95.42%. The experimental results were evaluated by comparing with traditional support vector machine and BP neural network. The results showed that SVM based on genetic algorithm optimization has potential for the smaller classification error and can distinguish the mixture more accurately.

The identification of the type and the writing time of black signature pen handwriting has always been a hot topic in the field of forensic science at home and abroad. Based on the advantages of Raman spectroscopy, such as high resolution, good stability, high efficiency and nondestructive testing, 16 brands of black signature pen handwriting samples were tested. The samples of the same brand at the same time with different storage conditions (darkroom and lighting) and different paper background (copy paper and notebook) handwriting samples were completed. The handwriting samples were clipped and fixed on the glass slide with the double-sided adhesive, and stored in the storage box to protect them from light, and then the samples were tested at intervals. The test lasted for one year to form a Raman spectrum database. The optimum conditions were selected based on the investigation of the influence of experimental conditions, such as measuring point, laser power and confocal type. Based on the optimum conditions, the handwriting samples of black signature pen were determined under different writing time, storage environment and paper background. The experimental results indicated that: (1) Under different conditions of storage conditions (darkroom and lighting) and paper background (copy paper and notebook), the same kind of black signature pen handwriting had the same Raman shift, and the repeatability was good, indicating the less disturbance by the background of water and paper. (2) According to the Raman shift, 16 types of black signature pen could be classified into five categories. (3) Taking 2# sample as example, a characteristic peak of Raman spectrum at 1 140 cm-1 was assigned to the C—O stretching vibration ν(CO) of the ester compound, the content of which decreasing over time. Therefore, the relative intensity of the characteristic peak was increased with the writing time (8→1). The relative area value I was calculated by the fitting method, and it indicated that the I value was increased with the writing time of the sample. Our strategy can be applied in quick and nondestructive determination of the types and relative writing times of the black signature pen handwriting.

The multiferric BiFeO3 (BFO) has attracted much attention due to its magnetoelectric coupling effects. However, the phase transformation involved in the reaction sintering is still not clarified, which consequently restricts the synthesis of pure-phase BiFeO3 ceramics. In-situ High Temperature Raman Spectroscopy (in-situ HT-Raman)is a powerful means to characterize complex solid phase transitions and reactions. In this paper, the phase transformation in the reaction sintering of BiFeO3 ceramics with different molar ratio (1∶1, 1.03∶1 or 1.05∶1) of Bi2O3-Fe2O3 and heating/cooling rates (10, 100 ℃·min-1) was studied by In-situ HT-Raman for the first time. We also studied the thermal contraction of the sintering product in cooling process. Results showed that two intermediate transition phases Bi2Fe4O9 and Bi25FeO39 will be produced in the reaction sintering process of BFO ceramics. The content of impurity phases mainly depends on the molar ratio of Bi2O3-Fe2O3 and heating/cooling rates, and 1.03∶1 with higher rate is optimum. There is a good linear relation ship between BFO A1-1 peak position and temperature in the cooling process, which indicates that the temperature reduction only brings about the lattice contraction of BFO ceramic and no structural change. Moreover, the phase composition and morphologies of sintering products were measured by Two Dimensional X-ray Diffraction (2D-XRD) and Electron Backscattered Diffraction (EBSD). XRD results showed that the content of impurity phases is relatively low when Bi is excessive, which is consistent with Raman results. Combined 2D-XRD and EBSD results, rapid heating/cooling rate is beneficial to the grains nucleation and growth, on account of the larger and more uniform grains when Bi exceeds 3%. This work provides useful experimental guidance for the preparation of pure-phase BiFeO3 ceramics by further elucidating its mechanism of phase transformation in the reaction sintering.

Surface-enhanced Raman scattering(SERS) devices with advantages of being non-destructive, ultra-sensitive, and real-time are of significance. For now, most SERS devices are constructed on non-transparent substrates. When such non-transparent SERS devices are used to detect analytes with high concentrations, laser can only be incident from their front-sides. It means that incident laser needs to penetrate analyte molecules to reach metallic nanostructures at the bottom, so laser energy used to excite surface plasmon resonance (SPR) of metallic nanostructures is attenuated, and accordingly, SERS spectral signals are also attenuated; besides, SERS spectral signals cannot be efficiently returned to the charge-coupled device(CCD) due to the blocking of analyte molecules, so that the signals are greatly reduced and in some cases, they cannot be detected at all. In contrast, when a transparent SERS device is adopted, the analyte molecules are placed on front-side of the device and Raman laser is incident from the back-side. In this way, analyte molecules with high concentration have minimal influence on incident laser and SERS spectral signals, so better spectral signals can be obtained. In this work, a polyimide(PI) layer was spin-coated on a quartz substrate, and then the substrate was bombarded by oxygen plasma without masks. After that, nanofiber masks were formed on the quartz substrate. Later on, quartz nanocone forests were formed by a reactive-ion-etching (RIE) step. Subsequently, metallic nanoparticles were sputtered on the nanocones, thus, a transparent SERS device was obtained. For this SERS transparent device, Raman laser can be incident from front-side and back-side of the device during the test. The preliminary experimental results showed that for Rhodamine 6G (R6G), in a concentration range of 10-3～10-6 mol·L-1, the SERS spectra from the back-side were with higher intensities than those from the front-side. In addition, repeatability of the device detected from the back-side was further studied. These results demonstrated the feasibility of the device in practical biochemical detection applications. This work is expected to extend the applications of SERS technique in the field of analyte detections.

In recent years, the special color change effect and “light tarry effect” Amber from Burma have been found, and because of precious and rare, those special optical effect Burma Amber have high commercial value and research value. The formation mechanism of color change effect, “light tarry effect” and common Burma amber were studied by using standard gemological methods, modern testing method as: X-ray fluorescence spectrometer (XRF),X-ray powder diffraction (XRD), Fourier infrared absorption spectroscopy (FTIR), Ultraviolet-Visible spectrometer (UV-VIS) to obtain the gemology and spectra characterization of samples. (1) XRF result showed that “light tarry effect” Burma Amber contain high element S which is over 4 times common Burma Amber’s, and contain high element Ca which is 6 times common Burma Amber’s; (2) The UV-VIS result showed that the color change effect Burma Amber have 210 and 447 nm absorption in the UV area; (3) XRD result showed the color change effect Burma Amber have the 150 sharp absorption peaks; (4) Because the five samples are from the same origin which is the Burma,the infrared absorption spectra are similar from all of the samples that have no obvious difference. Conclusion: The above special optical effect Burma amber maybe relate to the temperature and pressure of the stratum which formed the Burma Amber, also relate to the geological timescale. Because of high degree oxidation and the fact that the Burma Amber were founded deeper in the ground, the internal micro structure and trace substance composition have changed, so that special optical effects of some Burma amber arised.

This paper studied main characteristics of Dougeng stone, by means of several Spectra methods like X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and Raman spectra, as well as mineralogical methods like hydrostatic weighing method, scanning electron microscope (SEM) and liquid chemistry analysis. XRD result showed that the main mineral component of Dougeng stone was dickite, and the disintegration degree of the diffraction peaks and the strength of weak spectroscopie k≠3 represented the difference of the order degree of the sample dickite. Phase and polytype analyses showed that apart from dickite with 2M1 structure, there were also pyrite, alunite, pyrophyllite with a transition structure of 2M and 1Tc, and 2M1 illite. The analysis of the spectroscopies of the infrared spectrum showed that although there were differences in the order degree of samples, they all conformed to the infrared spectral characteristics of disordered dickite, according to peak position change and disintegration degree, which was consistent with the XRD test results. Since the microscopic characteristics of the sample section showed that the contents of the infected dark veins and opaque minerals in the sample had a significant influence on the appearance color of the sample, in-situ Raman spectroscopy was used to test the dark veins and opaque minerals. The results showed that the spectrum position, Raman shift and Raman scattering peak turned out to be typical spectral characteristics of FeS2, which meant that it was pyrite in the sample as the dark component played an important role in the color of Dougeng stones. The scanning electron microscope result revealed that the dickite particles were in microscopic cryptocrystalline-microcrystalline structure, appearing as pseudohexagonal platy, with local pyrite found to be microgranular in automorphic-semiheteromorphic structure in some samples. According to the results of chemical analysis and spectral analysis, the physical and mineralogical properties of Dengeng stones were correlated with the density, ordered measurement and the degree of uniform densification of internal microstructure; and there was a distinct origin of color in black Tianhuang stone or Kengtou stone in Shoushan and sample Dougeng stone, which had main chromogenic factor as impurity mineral pyrite with microcrystalline distribution (particle size ranging from 3 to 20 μm). Ferric ions in matrix dickite made less contribution to color rendering.

At present, spectroscopy analysis has become an important technical means to support the development of archaeometry, and has played an important role in origin research, coloring mechanism, dating analysis, etc. The Xipo site in Lingbao city, Henan Province, with its rich Yangshao culture remains, has been identified as one of the first-choice sites for “the exploration of Chinese civilization”. Lingbao area is the central area of Miaodigou type of Yangshao culture, which may correspond to the archaeological culture sites in the Yellow Emperor era. Therefore, many archaeological experts believe that the exploration and excavation of Xipo site will help discover the mystery of the origin of China’s 5 000-year-old civilization. Xipo site is also rich in unearthed relics, including pottery, jade, stone tools, and some special green frits, which predate the appearance of proto-porcelain glazes by more than 1 000 years. This study uses ICP-MS/AES, EPMA, Raman spectroscopy analysis techniques to analyze 50 pieces of pottery and 1 piece of frit unearthed from Xipo site in Lingbao city, Henan Province. The pottery unearthed at Xipo Site is mainly red pottery and brown pottery. At the same time, there were painted pottery, red leather pottery (pottery with red coating), yellow pottery, black ash pottery, etc. Previous studies mainly attributed different colors of pottery to different firing atmospheres, and did not discuss the source of raw materials for different types of pottery. Based on the measurement of 16 trace elements in argillaceous pottery and multivariate statistical analysis, the source characteristics of different kinds of pottery are discussed. At the same time, the microstructure of some painted pottery and the phase compositions of black color were studied, and the structural characteristics of green frit unearthed from the site were analyzed. The results show that the Ba content in the raw materials of the clay used in most pottery with red coating is relatively high, which is different from other potteries and may have a special source of raw materials. Magnetite is the coloring mineral raw material for the black color on the painted pottery surface. The chemical composition of the green frit is close to that of the proto-porcelain except for the low manganese and phosphorus content. These results provide a scientific basis for understanding pottery making techniques and social production in Yangshao period. The study shows that the combination of various spectroscopic analysis techniques based on elemental analysis and structural analysis has achieved remarkable results in the research of archaeological materials.

Steel slag tailings are the main solid waste in metallurgical industry, with the production of 15%~20% of crude steel. The utilization ratio is quite low and only reaches 10% of steel slag tailings production due to limited technology. Meanwhile, steel slag tailings are disposed in direct stacking and landfill in general since the management system is not perfect, which pollutes land source, underground water source and air quality. Recycling of solid waste is one important method to achieve sustainable development of resources. The main chemical compositions are CaO, SiO2, Al2O3, MgO, Fe2O3, MnO, f-CaO, etc. In the face of the above problems, the development of low price and superior performance of activated carbon mixed with steel slag composite material has become not only one of main methods to achieve the high value-added utilization of metallurgical solid waste and the sustainable development of resources, but also one of main methods to achieve the great reduction of the production cost of modified activated carbon and improve economic benefits. In this paper, activated carbon and steel slag were studied firstly, and activated carbon mixed with steel slag composite material for sintering flue gas of desulfurization and denitration was prepared by metal oxides contained in the steel slag were modified to treat the activated carbon, and desulfurization and denitration performance of activated carbon mixed with steel slag composite material was tested by setting up the experimental reaction device. Chemical component of steel slag was characterized and analyzed by X-ray fluorescence spectrometer (XRF), pore structure of activated carbon mixed with steel slag composite material was characterized and analyzed by specific surface area and pore size distribution analyzer (BET), composition structure of steel slag was characterized and analyzed by Fourier transform infrared spectrometer (FTIR) and microstructure of activated carbon mixed with steel slag composite material was characterized and analyzed by scanning electron microscope (SEM), so as to reveal the mechanism of preparing activated carbon mixed with steel slag composite material from activated carbon and steel slag, and the degradation mechanism of desulfurization and denitration in sintering flue gas by activated carbon mixed with steel slag composite material. The results show that the activated carbon mixed with steel slag composite material (steel slag is layer pouring slag from electric furnace, mass ratio of steel slag to activated carbon is 2:4 and fineness of steel slag and activated carbon is 400 mesh) has good properties of desulfurization and denitration and reasonable economy with desulfurization efficiency of 100% and denitration efficiency of 58%. The porous structure of activated carbon mixed with steel slag composite material adsorbed SO2 and NO, and Fe2O3 and MnO2 in steel slag promote the catalytic reduction reaction of activated carbon functional groups to improve the properties of desulfurization and denitration, where adsorption is the leading and prerequisite, and catalytic reduction reaction is auxiliary and synergistic. It aims to provide a new way for the utilization of high value-added steel slag, and achieve the target of waste management waste and waste to increase efficiency in iron and steel enterprise.

Indium is a silver-white rare and scattered metal with an average mass fraction of 0.000 01% in the earth’s crust. In order to accurately determine the low content of indium in the flue ash sample, we preliminarily analyzed the composition of the sample to ascertain the dissolved acid and its proportion in our work. The hydrochloric, nitric, hydrofluoric and perchloric acids (Ｖ∶Ｖ∶Ｖ∶Ｖ=15∶5∶2∶2) were successively and gradually added to completely dissolve the sample, and then cooled to room temperature and transferred into a shunt funnel. Following, the above-obtained mix was diluted with ammonium bromide as salt precipitator (the volume of solution was controlled to about 25 mL), and ethyl acetate as an extractant and diluents. The extraction solution was directly introduced into the inductively coupled plasma atomic emission spectrometry (ICP-AES) equipped with an organic injection system to determine the indium with the analytical spectral line 230.606 nm in the flue ash sample. Thus the method for determination of indium in the flue ash sample by ICP-AES was established. In this work, an extraction separation method was to eliminate the interference of matrix elements and coexisting elements after the sample was dissolved. The optimal experimental conditions were obtained through a series of conditional experiments on extraction acidity, extractant, extraction method, salt analyzer and analytical spectral line. The mass concentration of indium was linear with its emission intensity in the range of 0.25 to 4.00 mg·L-1 with the linear correlation coefficient 0.999 3 and the detection limit 0.03 mg·L-1. The relative standard deviation of the determination results (n=11) was less than 5%, and the recovery rate was 92%~102%. The determination results of indium in five flue ash samples were relatively consistent compared with inductively coupled plasma mass spectrometry (ICP-MS) method according to the above experimental steps. Moreover, this method would be used for batch detection of flue ash samples with indium content between 0.000 8% and 0.10% because of its simple, fast, sensitive, and higher accuracy advantages compared with existing analytical methods (EDTA titration, spectrophotometry, atomic absorption spectroscopy, X-ray fluorescence spectroscopy, inductively coupled plasma atomic emission spectroscopy, mass spectrometry, etc.).

Laser Induced Breakdown Spectroscopy (LIBS) analysis with solid target is greatly affected by the physical morphology and chemical properties of the sample surface. Therefore, the analysis of matrix effect is greatly significant for the study of LIBS online detection. In view of the fact that the current research objects of LIBS are mostly flat samples, in this work, LIBS was used for the quantitative analysis of iron filings with different granularity. The nine kinds of iron filings used in the experiment were loose powder, granules or strips. In order to avoid splashing during the laser ablation of iron filings, the samples were stuck to the double-sided tape for fixing before the experiment. The 1 064 nm laser with the energy of 35 mJ was used as the ablation source. And the delay and gate width of detector were 1 and 10 μs. In order to evaluate the influence of the matrix effect caused by the different particle size of the samples on the LIBS spectrum, firstly, the series of samples were classified by principal component analysis (PCA). The results showed that the four samples in powder form were separated, that is, the matrix effect caused by the difference in particle size was the main reason for the difference in the spectral signals of the sample. Secondly, the elemental characteristic line of Fe Ⅰ 330.635 nm in the C5 sample before and after grinding was taken as the research object. It was found by comparing the intensity and relative standard deviation (RSD) that the smaller the particle size, the stronger the line intensity and the better the stability. In order to correct the interference of the matrix effect on LIBS spectrum, two methods of sample grinding pretreatment and spectral data pretreatment were used. For the sample grinding pretreatment, the slender strip samples of C3 and C5 were grinded in the experiment, and the intensity and stability of the lines were greatly improved after grinding. For the spectral data pretreatment, the intensity normalization, multiple scattering correction (MSC) and the combination of them were studied respectively. The three spectral pretreatments all significantly improved the stability of the line. The quantitative results of Cu element were evaluated and compared by support vector machine (SVM) method. It was found that the calibration results obtained by the grinding samples combined with intensity normalization and MSC pretreatment were the best. Finally, the predicted relative error (RE) of the Cu element in S1 and S2 samples was reduced to 1.745% and 1.857%, respectively, and the root mean square error of prediction (RMSEP) was 0.020. This study can provide a certain method basis and reference for LIBS detection of the samples with irregular surface.

Lithium is an important strategic metal and new energy material, and its development and utilization has attracted extensive attention worldwide. There are huge amounts of lithium resources in high salinity brines especially in salt lake brines and underground brines. It is necessary to accurately determine lithium content during the development of these resources. However, high concentrations of Na+, K+, Ca2+ and Mg2+ in brines will result in serious interference with the determination of trace lithium. Inductively coupled plasma optical emission spectrometry (ICP-OES) has the ability of multi-element simultaneous analysis with wide linear range. In order to realize swift and accurate determination of lithium in brines, the analysis of lithium in high salinity samples by ICP-OES was carried out in detail in this work. Results showed that lithium has a higher signal to noise ratio at 610.364 nm, where no obvious spectral line interferences of Na+, K+, Ca2+, Mg2+ and Ar were presented for the determination of lithium. However, large amount of Na+, K+ and Mg2+ coexisting in the samples would lead to positive matrix interferences, while the Ca2+ caused negative interference. The internal standard method widely used to eliminate matrix interferences could not effectively solve this problem using whether yttrium or scandium as an internal standard element. Because the standard addition method involves complex operations and is not suitable for batch sample analysis, while the matrix matching method must match these interfering components with corresponding ions and thus is not suitable for the analysis of batch samples with different matrix compositions, the feasibility to match complex matrix with a single component was investigated in this work. Since NaCl widely exists in brines and has obviously sensitizing effect on lithium determination, we found after a series of studies that when the total amounts of NaCl, KCl, MgCl2 in samples were no more than 40 g·L-1, the interferences of these coexisting ions could be successfully solved by adding 10 g·L-1 NaCl into both the samples and the standard solutions. Although the negative interference of Ca2+ could not be effectively solved by this method or pre-separation by precipitation, it did not lead to obvious impact on the determination when its concentration was no more than 1.8 g·L-1. When this method was used for the determination of three kinds of spiked samples with different matrices, the recoveries were within the range of 96.60%～104.20%. Meanwhile, the accuracy and reliability of this developed method were also demonstrated by the comparison with these obtained by the inductively coupled plasma mass spectrometry (ICP-MS) method (The relative errors varied within ±3.66%). Only single NaCl was used in this proposed method to match different coexisting ions, by which it not only simplified the operation, but also realized the batch analysis of samples with different matrix compositions. Therefore, it has important significance for the swift and accurate determination of lithium in brines as well as for the development and utilization of lithium resources in brines.

Since the element’s own characteristic X-rays with low atomic number and the characteristic X-rays between different elements interfere with each other, the measured spectra will be seriously overlapped due to the limitation of the energy resolution of the instrument. In this paper, chromatographic resolution Rs is used to calculate the separation degree of spectral peaks. Taking the overlapping peaks with Rs below 0.5 as the research object, a new method of decomposing low-resolution overlapping peak by using a peak sharpening method combined with the double-tree complex wavelet transform method is proposed, which is verified by decomposing the simulated X-ray fluorescence spectra and the measured X-ray fluorescence spectra. First, based on the principle introduction of the peak sharpening method and the double-tree complex wavelet transform to decompose overlapping peak in detail, the simulation results showed that when Rs=0.38, both methods can not decompose the overlapping peaks with low-resolution separately. However, the spectrum processed by the peak sharpening method not only retains the peaks position characteristics of the original spectrum, but also has the phenomenon that the resolution becomes larger. Therefore, it can realize the initial sharpening of the low-resolution overlapping peak by adjusting the weight of the peak sharpening method, then, using the double-tree complex wavelet transform is performed on the sharpened spectrum. The results showed that the simulated overlapping peak is decomposed. It is proved that the new method is superior to the decomposition of low-resolution overlapping peak. Wherein, the decomposing level of the double-tree complex wavelet is set to 2~6, the first level selects the near_sym_b filter, the higher level selects the qshift_d filter, and when the detail coefficient magnification is set to 1~10, the decomposing results of the overlapping peak is more accurate. Second, the overlapping spectrum of the Kα energy peak and the Kβ energy peak of K (Rs=0.44) and the overlapping spectrum of the Kβ energy peak of Fe and the Kα energy peak of Co (Rs=0.34) are simulated. The new method is used to decompose the spectra, and the two overlapping spectra are decomposed. The relative error of peak position and peak area of the decomposed spectra is within 1% and 6% respectively, which verified the feasibility of the new method to decompose the low resolution overlapping peaks in the spectrum. Finally, the actual measurement Ca X-ray fluorescence spectrum is decomposed by a new method, and the errors of the peak position are 0.8% and 0.7% respectively. The results showed that the peak sharpening method combined with the double-tree complex wavelet transform can effectively decompose the low-resolution overlapping peaks, and it has practicality in solving the problem of severe overlapping of X-ray fluorescence spectra.

Soil is composed of particles of different sizes and shapes, among which clay mineral is one of the important parts of soil. It not only contains the formation track, genetic characteristics and changes of soil environment, but also plays a role in retaining and storing nutrients and water in soil. At the same time, due to the fact that different types of clay mineral compositions of the soil are not the same, different ways of using and long-term locating fertilization will have an effect on the evolution of soil clay minerals, so the research on soil clay mineral composition and evolution law can contribute to a more comprehensive understanding of the genetic regularity between different types of soil, which is of great significance to explore the sustainability of soil fertility. At present the study of soil clay minerals has become one of the focus in the study of green agriculture, X-ray Diffraction analysis (X-Ray Diffraction, XRD) as the research of soil clay minerals, the most effective means with the advantages of being convenient, being fast, high accuracy, and being pollution-free, such as on the sample, has received extensive attention from scholars both at home and abroad in recent years. This paper introduces the basic principle and analytical method of soil clay minerals by X-ray diffraction analysis, summarizes the research progress of this technology in soil clay minerals determination at home and abroad, and prospects the application prospect of X-ray diffraction analysis in soil clay minerals determination. Apply XRD to study soil clay minerals and combine with traditional analysis methods to determine the basic properties of soil, which can reveal the composition and differences of clay minerals in different types of soils, as well as the composition and evolution of soil clay minerals under different utilization methods. So as to provide theoretical basis and technical support for the sustainable use of soil fertility and agricultural green development. At present, researches on the evolution of soil clay minerals are mostly focused on long-term fertilization, but there are very few studies on the evolution of soil clay minerals caused by long-term straw return to the field and different farming methods. I believe that in the future XRD can be applied to more aspects of soil research and contribute to the green development of agriculture.

To study the effect of straw returning on composition of soil water-soluble carbon, the fluorescence spectral characteristics of WSOCwith depth were researched onland meadows with continuous no-tillage, shallow turning, deep turning and no-till cover, shallow and deep straw returning methods, and the results showed that the WSOC was all resolved two types of three fluorescent components in 0～50 cm soil layer with 3 kinds of tillage and corresponding straw returning, namely humus-like components C1 (240/400 nm) and C2 (245, 400/465 nm) and protein-like component C3 (220, 275/325 nm). Regardless of shallow or deep returning, the sequence was C1>C2>C3 in 0～20 cm topsoil, butthe change of C3 composition in 20～30 cm soil layer was negatively correlated with C1 and C2 components. It was also founded that the content of C3 component in 30～50 cm soil layer was increased with continuous straw returning. Fluorescence index analysis showed that the WSOC composition is a mixture of non-biological sources and biological sources, which indicated that the heterogeneous characteristics of the soil layer was obvious in 10～20 cm soil. For the reason of no disturbance to the soil in whole growing season with no-tillage and no-tillage straw cover, the self-generated characteristics in 20～50 cm soil layer was much strong. The ratio of C2/C1 in the 40～50 cm soil layer of straw deep returning was higher than that of shallow tillage and straw cover, indicating that thetenth of humus production and accumulation would be increased.

In this paper, we studied the correlation between multispectral soil reflectance and soil moisture content. In this paper, the Tongzhou experimental station of China Agricultural University was selected as the research area. In the experimental wheat field of this experimental station,wecollected100 groups of soil samples on the spot. According to a certain gradient, we prepared the soil moisture content between 10% and 50%. The real value of soil content was determined by drying method. Multispectral cameras are convenient. Red Edged-M multispectral camera was mounted on innovative Phantom 3 UAV. The multispectral images of soil samples were collected in a sunny environment. The multispectral images of soil samples were processed. The spectral reflectance of soil samples was extracted, and the model between multispectral soilinformation and water content was established. The multispectral information of soil samples was extracted by ENVI5.3 software.We, asthe reflectance of the standard whiteboard is 100%. The spectral reflectance of soil samples in five bands of blue, green, red and near-infrared was calculated. In order to explore the correlation between spectral reflectance and moisture content of soil samples, we established BP neural network algorithm, support vector machine algorithm, andpartial least squares algorithm prediction models of soil moisture content based on UAV multispectral images. Based on 80 sets of soil sample data as a training set, a prediction model of soil moisture content based on the multi-spectral image was established. The BP neural network algorithm is improved bythe Levenberg-Marquardt method, which improves the training speed of its BP neural network model. When the network structure is 5-10-1, the number of iterations is the least, and MSE is the least. This paper chooses the network structure. The support vector machine algorithm adopts the Gauss kernel function, and when the parameter is 0.56, the model has the best effect. In this study, normalized root means square error (NRMSE) and decision coefficient were used to compare the three prediction models of soil moisture content quantitatively. We used 20 sets of soil sample data as the test set, the results showed that the normalized root mean square error of soil moisture content prediction model based on BP neural network was 0.268, and the decision coefficient was 0.872; the normalized root mean square error of soil moisture content prediction model based on support vector machine was 0.298, and the decision coefficient was 0.821; The normalized root mean square error (NRMSE) of the prediction model multiplied by soil moisture content is 0.316, and the decision coefficient is 0.789. According to the analysis of the three models, the prediction model of soil moisture content based on BP neural network has a good effect. Through the research, we can know that itis a close correlation between soil spectral reflectance and water content. The multi-spectral camera can effectively monitor soil moisture content in real-time on the UAV. In this study, multi-spectral technology is applied in the field of soil moisture content detection to provide technical support and theoretical support for monitoring soil moisture.

In the process of fry breeding, the same breeding pond will appear that the individual large fry attacks small individual fry, and the individual small fry will suffer injury or even death, resulting in economic loss, and the fry pond and the selling price are mainly related to their body length parameters, so separation of different sizes of fry is required. The classification of fry is mainly dependent on mesh screens of different sizes, which is time consuming and laborious, and is easy to cause damage to fry. Aiming at the low efficiency and lack of scientific guidance of traditional artificial separation methods, this paper proposes a study on the estimation method of fry body length based on visible spectrum, which can calculate the length and classify the fry according to the fry image. In order to obtain the body length of the fry accurately and without loss, a method for estimating the length of the fry based on the migration learning ResNet50 model was proposed. Firstly, images of different lengths of fry taken under the same height conditions were collected. At the same time, the actual length of the fry was manually measured as the label of the data set. The four migration learning models AlexNet, VGG16, GoogLeNet, ResNet50 were used to estimate the body length of the fry, and the verification set was passed. Accuracy, test set accuracy, and running time of different methods were analyzed. The accuracy rate of AlexNet model verification set was 90.04%, the test set accuracy rate was 89.82%, and the running time was 52 minutes and 3 seconds; the VGG16 model verification set accuracy rate was 91.01%, the test set accuracy rate was 91.17%, and the running time was 131 minutes and 37 seconds; the accuracy rate of GoogLeNet model verification set was 88.02%, the test set accuracy rate is 88.39%, and the running time was 45 minutes and 2 seconds; the ResNet50 model verification set accuracy rate was 91.92%, the test set accuracy rate was 91.09%, and the running time was 99 minutes and 17 seconds; then determined ResNet50. The model had a 50-layer Residual Network architecture. The migration learning method was used to transfer the parameters of the convolution layer trained on ImageNet to the model used in this training, and the softmax layer was adjusted to adapt to this problem. The experimental results on the fry datasets of 6677 samples from 10 different lengths showed that the method can be effectively used for fry classification, and the model was optimized by stratifying the number of layers, the number of iterations, the learning rate of the model ResNet50 and the Mini Batch Size. The experimental results showed that when the migration learning model had 30 migration layers, the number of iterations was 6, the learning rate was 0.001, and the Mini Batch Size was 10, so the method effect was optimal. The accuracy of the model verification set was 94.31%, and the accuracy of the test set was 93.93%. The algorithm in this paper estimates the accuracy of fry body length by about 2% compared with the traditional image processing method. In the future, in the actual production scenario, the method can be nested into the fry body length separation device, the scientific research can be put into actual production, the fry damage can be reduced, and the foundation for the future unmanned fishery can be laid.

As a kind of cold pressed vegetable oil, extra virgin olive oil is abundant in unsaturated fatty acids and polyphenols. Therefore, the problem of olive oil adulteration is also one of the most serious problems in the industry. China’s consumption of olive oil is increasing day by day, the domestic olive oil market is more chaotic, and the phenomenon of adulteration and counterfeiting is emerging in an endless stream. From the overseas import of olive oil to the domestic secondary packaging, there may be human interference and quality counterfeiting. If it is not effectively monitored and stopped, the national health and property will be affected. If the traditional chemical analysis method is used to obtain all the information of ingredients, it will increase the detection cycle, which is not conducive to the rapid circulation of goods, and it is a loss for manufacturers and consumers. In order to deal with the complex and changeable adulteration technology of olive oil and the shortage of qualified olive oil testing institutions in China, this paper proposes a rapid detection method based on supercontinuum Spectra of extra virgin olive oil, which provides the possibility for rapid identification. It studies and selects extra virgin olive oil, rapeseed oil, tea oil, sesame oil, rice oil, sunflower oil, corn oil and soybean oil as the research object, the supercontinuum spectra of each kind of vegetable oil were collected and the preliminary spectral data were preprocessed. Finally, the Pearson correlation coefficients of supercontinuum spectra between different samples were calculated and used as the main basis for the discrimination of extra virgin olive oil. The experimental results show that the Pearson correlation coefficients of supercontinuum spectra of different samples are more than 0.901 1, while those of supercontinuum spectra of extra virgin olive oil and other kinds of vegetable oil are between 0.172 2 and 0.899 0. The results show that the Pearson correlation coefficient of 0.901 1 is used as the detection threshold to distinguish the extra virgin olive oil and other vegetable oil, which can achieve fast and real-time accurate detection and recognition. Compared with the absorption and transmission spectrum of spectrophotometer, the biggest advantage of this technology lies in the short collection period and rich spectrum fingerprint features, which are manifested by the collection time of spectrum exposure being only 100 ms, and the rich spectrum fingerprint features as the unique fluorescence spectrum of various fluorescent active substances. In addition, the application of supercontinuum light source is extended to the field of food safety detection technology. The device is simple and easy to be popularized, which has certain research significance for the detection and market specification of olive oil in China.

Water is part of the essential elements for the normal growth and development of plants. The ability to detect and obtain plant leaf moisture quickly is of great importance to the study of crop irrigation production management and the physiological water demand characteristics of crops in the field. Using RedEdge-M multispectral camera, 55 groups of maize leaves at different growth stages were selected as the test objects, and the test maize leaf samples were photographed in a mellow light environment without shading. During the photographing process, the influence of solar elevation angle on spectral reflection was eliminated by directly connecting down light sensors, and TIFF images in 5 bands of blue, green, red, near-infrared and red edges were obtained by photographing each group of maize leaf samples. With the help of image processing software ENVI 5.3, the region of interest (ROI) of maize leaf samples was constructed, and the average reflection spectrum of maize leaf samples within the ROI range was used as the reflection spectrum of the samples to reduce the error caused by lens edge dimming phenomenon. According to the calibration reflectivity of the standard white board, the average reflection spectrum in the ROI range of the white board and the average reflection spectrum in the ROI range of the maize leaf sample white board, the ratio was converted to obtain the spectral reflectivity of each group of maize leaves at five bands. At the same time, using YLS-D chlorophyll meter, using five-point sampling method. The average water thickness of maize leaf samples was measured in five areas of maize leaf as the measurement index of leaf water content. Randomly selected spectral reflectance of 43 sets of maize leaf samples as training samples, using BP neural network to build an inversion model of maize leaf water content, which was based on multi-spectral image, and the Levenberg-Marquardt method was introduced to improve the existing shortcomings of classical neural network. The number of input neurons was 5, that is, the reflectance corresponding to the five and images of blue, green, red, near-infrared and red-edged, and the output neurons were one, that is, the moisture content of the maize leaves. The remaining 12 sets of samples were invoked as verification samples for correlation verification analysis of model inversion data. The results showed that the multispectral image spectral information combined with the improved BP neural network based on the Levenberg-Marquardt method can be utilized to retrieve the water content of the maize leaf. The fitting correlation coefficient of the model inversion can reach 0.896 37. As a verification of the 12 groups of maize leaves moisture reference value and the inversion value of the correlation coefficient R2 reaches 0.894 8, the inversion result is ideal. It can realize the rapid and accurate detection of the moisture content of maize leaves, and provides a method and reference for the promotion and application of precision agriculture.

The proportion of Metmyoglobin (MetMb) in meat directly affects the color of the meat. This paper combined the visible near-infrared spectroscopy (ViS-NIR) data of Tan sheep meat with the chemometric method to explore the feasibility of rapid non-destructive detection of MetMb content in Tan sheep by hyperspectral imaging technology and develop a quantitative function of MetMb content. The MetMb content of the sample was measured by a spectrophotometer, and the interest region of 200 sample spectral images during storage were extracted by ENVI4.8 software. The relationships between MetMb content and spectral date of samples were established to quantitatively analyze. In this study, according to the ratio of 3∶1, the whole dataset (n=200) was divided into a calibration set (n=50) for performing independent validation (external validation) of the developed calibration models using the sample set partitioning based on joint x-y distance method. Several spectral preprocessing techniques such as multiplicative scatter correction (MSC), first derivative (1st derivative) and De-trending were applied to eliminate noise. Competitive Adaptive Reweighted Sampling (CARS), Interval variable iterative space shrinkage approach (iVISSA), Interval Random Frog (IRF), Variables combination population analysis (VCPA) and Successie Projection Algorithm (SPA) were used to select and optimize variables. Partial least squares regression (PLSR), which was one classical linear calibration method, were used for developing prediction models based on full-band and feature bands. The results showed that the original spectral model was best, and its R2C=0.852, R2P=0.788, RMSEC=4.604, RMSEP=5.729. The CARS, IRF, SPA, iVISSA, VCPA, IRF+SPA and iVISSA+SPA methods were applied to select 16, 13, 48, 14, 45, 13, 10 and 11 feature wavelengths from the original spectra, accounting for 38.4%, 10.4%, 11.2%, 36%, 10.4%, 8%, 8.8% and 12.8% of the full wavelength, respectively. The IRF+SPA-PLSR model was the best among the models developed, and its R2C, R2P, RMSEC and RMSEP values were 0.808, 0.826, 5.253 and 5.149, respectively. The IRF+SPA algorithm greatly reduced calculating time and generated more accurate and more robust prediction model compared with full band. Finally, the quantitative linear relationship between spectral data and MetMb parameters was established based on the IRF+SPA algorithm, and the quantitative function was: (MetMb)=3.249 7+1.9002λ468-4.791 2λ482+5.913 5λ512-1.856 2λ530-5.879 3λ545+2.246 3λ560+5.066 1λ580-2.320 1λ588+1.214 9λ790-1.348 8λ814. It is shows that Vis-NIR is feasible for the rapid non-destructive detection of MetMb content in Tan sheep. Simultaneously, the quantitative function developed provides a reference for the rapid determination of MetMb content in Tan sheep.

Lycium barbarum produced in Ningxia belongs to the genuine regional drugs contained in the Pharmacopoeia of the People's Republic of China. Due to the small planting area, low yield, high medicinal value and high consumer preference, the market is filled with chaos, and the phenomenon of passing others origins off as Ningxia happens occasionally. Therefore, it is of considerable significance to establish a rapid and effective geographical origin identification model of Lycium barbarum to supervise the market. In the process of market transactions, discriminating origin of Lycium barbarum is often based on experience, which has much error and low credibility. The traditional physical and chemical experiment has a long identification cycle and can't be operated by non-professionals. In recent years, some scholars have found that the content of Lycium barbarum in different producing areas is different. However, because of the small sample size, irregular shape and uneven distribution of components, the near-infrared spectroscopy identification technique often needed to smash Lycium barbarum to collect spectral information. Near-infrared hyperspectral image technology combined with near-infrared spectroscopy and image technology, which contains rich spatial information and spectral information, can achieve non-destructive acquisition of spectral information. In this research, near-infrared hyperspectral image technology was used to discriminate the geographical origin of Lycium barbarum samples, which were gathered from Gansu, Qinghai, Xinjiang, Ningxia and Inner Mongolia in China. After collecting the hyperspectral information of 1 650 samples by hyperspectral image system, the region of interest (ROI) was effectively extracted by threshold image segmentation and denoising. During the pretreatment process, the comparison between zero-phase component analysis (ZCA) whitening results and normalization results indicated that ZCA whitening was an effective spectral preprocessing method to remove correlation between features and improve the accuracy of the model. Partial least squares based dimension reduction (PLSDR) was used to reduce the complexity of the model for the preprocessed data. The experimental results indicated that the data after ZCA whitening pretreatment could be reduced from 288-dimensional features to 4 principal components, which made the correlation-removed features can be represented by fewer hidden features. Finally, the dimensionality-reduced features were fed to different classifiers to train model, including support vector machine (SVM), linear discriminant analysis (LDA) and softmax regression. Among those models, the average recognition accuracy based on ZCA whitening, PLSDR and softmax regression was 94.06% on the test set. The results demonstrated that the proposed method could effectively discriminate the origin of Lycium barbarum samples.

In recent years, food safety problems happened frequently, and consumers pay more and more attention to the environmental safety of food origin, which leads to an increase in demand for geographical indication products. As a healthy food, the quality of Boletus edulis is greatly affected by the environment of its origin. In order to protect consumers’ health and prevent fake and inferior products from entering the market, it is urgent to develop an efficient and low-cost identification technology of the origin of delicious Boletus edulis. Data fusion strategy and partial least squares discrimination (PLS-DA) model were used to identify the origin of Boletus edulis. In this paper, Fourier transform near infrared and Fourier transform middle infrared spectra of 141 samples from 8 Origin (Kunming, Chuxiong, Yuxi, Diqing, Dali, Baoshan, Wenshan and Qujing) were scanned. Kennard-stone algorithm was used to divide all samples into 2/3 training set and 1/3 prediction set. Three fusion strategies (low-level, mid-level, high-level) were used to analyze four single spectral matrices spectra: near-infrared average spectra of stipes (N-b), near-infrared average spectra of caps (N-g), mid-infrared average spectra of stipes (M-b), mid-infrared average spectra of caps (M-g) and to establish a partial least squares discriminant (PLS-DA) model. In which root mean square error of cross validation (RMSECV) and the root mean square prediction error (RMSEP) are used to evaluate model stability. The purpose of the non-error ratio (NER), training set classification accuracy and forecast set classification accuracy evaluation model classification performance. It contributes to find out the best way to geographic origin identification of Boletus edulis. The results showed that: (1) near infrared and middle infrared spectra can identify the origin of Boletus edulis; (2) the model established by middle infrared spectrum is better than that in near infrared spectrum; (3) all the three fusion strategies can improve the identification effect of origin of Boletus edulis, and the identification results of producing area from good to bad are in order of mid fusion, high fusion, low fusion and single spectral model. By using PLS-DA intermediate fusion strategy to fuse in near infrared and Mid-infrared spectrum, different origin Boletus edulis identification models are established, with the least number of variables (49), the highest accuracy of training set in producing area (100%), the highest accuracy of prediction set of origin (100%), the lowest RMSEP (0.133). As a reliable method, it can identify the geographical origin of Boletus edulis fast and accurately.

In order to study the effects of root zone temperature on the main metabolites and chlorophyll content of maize seedlings, the spectral characteristics and chlorophyll content of two maize cultivars under three root zone temperature levels were determined by Fourier transform infrared spectroscopy and SPAD-502 chlorophyll meter. The results showed that different root zone temperature treatment had significant effects on the organic matter content of various organs of maize seedlings, but the degree of influence was different, and the general realization was root>stem>leaf. The transmission of wave number of maize seedlings in 3 330, 2 927, 1 639, 1 515, 1 350, 1 250 and 1 055 cm-1 was affected by the temperature of the root zone. The peak value was lower under medium temperature conditions and higher under the high temperature condition of the root zone, and the difference was most obvious at 1 055 cm-1. The FTIR infrared spectral characteristics of different varieties in the root zone temperature range are quite different, and SD902 has a large difference, while SD609 has a small difference. The temperature of root zone had significant effects on the root activity and chlorophyll content of different maize varieties, and both showed medium temperature (30 ℃)>control (24 ℃)>high temperature (36 ℃). In summary, appropriate increase of root zone temperature can significantly increase the content of chlorophyll, root activity, carbohydrates, especially polysaccharides in corn seedlings, as well as protein and nucleic acid content in stems and leaves, but excessive temperature will rapidly decrease the content of polysaccharides, root activity, lipids and chlorophyll. The increase in root zone temperature has a significant effect on the seedling metabolites of SD902 and SD609 corn varieties, and the SD902 is more sensitive to the temperature response in the root zone, and the SD609 has better heat resistance. The root zone temperature had the greatest effect on the roots of maize seedlings, and by effecting the roots absorption, material synthesis and material transport functions, and then affect the stalk, leaf organic matter and chlorophyll content.

SO2 emissions from industrial smokestacks and ship exhausts is an important factor causing air pollution. SO2 is easy to be oxidized into sulfuric acid fog or sulfate aerosol, which produces acid rain and seriously endangers the balance of atmospheric, ecological environment and harms human health. The existing SO2 optical remote sensing measurement techniques, such as Raman scattering lidar, Differential absorption lidar (DIAL), Fourier transform infrared (FTIR) spectroscopy, Differential absorption spectrometer (DOAS), high-resolution spectral imaging techniques, etc., are all difficult to satisfy with the application requirements in consideration of a high temporal resolution, high spatial resolution and portable mobile, etc. In recent years, the ultraviolet SO2 camera imaging detection technology gets rapid development for its high precision and strong practicability. With features of high temporal resolution and strong spatial resolution, this technology can directly get pollution gas concentration distribution in the two-dimensional space and the emission rates over time by analyzing image's spatial information and the correlation between SO2 concentration, which makes it play an important role in monitoring environmental pollution. This paper carries out researches based on the ultraviolet SO2 camera imaging detection technology, which focuses on the measurement principle and influencing factors, instrument design and experimental methods, inversion algorithm and result comparison. The main achievements are as follows: (1) combining the narrow-band filter's narrow-wave window, measuring ultraviolet absorption of SO2 near 310 nm by using ultraviolet cameras, the ultraviolet imaging remote sensing theory model is established, and the measurement principle that the ultraviolet imaging remote sensing image detection technology acquires SO2 concentration is introduced; (2) the influence of different incident angle for the filter center wavelength and transmittance curve is discussed by placing the filter in front and back of the lens, from which it was found that camera system sensitivity to SO2 is less influenced by the incident angle when the filter is placed in the back of lens that; (3) the influence of solar zenith angle on SO2 concentration image inversion is analyzed, and the necessity of real-time calibration of SO2 concentration inversion curve is clarified; (4) the UV imaging remote sensing detection apparatus is designed based on the above theoretical analysis and the experiment research of UV imaging remote sensing monitoring SO2 emissions are carried out. The images of the SO2 concentration is obtained by using the 2-IM method to fit the artificial sky background by using the standard calibration of the SO2 Optical depth; (5) DOAS technology is used to monitor the SO2 emissions simultaneously. And the SO2 concentration results obtained by DOAS technology and by the UV imaging remote sensing technology are compared, which shows that the tend of the concentration information calculated by the two methods are consistent. This proves the accuracy of the measurement results of the UV imaging remote sensing monitoring technology and shows the great application prospect of this technology in remote sensing monitoring of industrial smokestacks and ship exhausts pollution.

Through observing and analyzing these celestial spectra, astronomers can obtain effective astronomical information to research the theory of astronomy and astrophysics. And a large scientific engineering project in China, the LAMOST survey program can obtain a large number of celestial spectra every observation night. For the massive data, it is very important to find an automatic method to analyze the celestial spectrum and measure various physical parameters of the celestial body. In fact, many scholars had been attracted to research this topic before, however, the current algorithms and corresponding results that were presented by them cannot meet the accuracy of manual measurement, and it means that we should find more appropriate algorithms to improve the effect of automatic processing. In this paper, we study deeply the applications of the Kernel Ridge Regression (hereinafter referred to as KRR) method in the automatic measurement of the stellar atmosphere physical parameters (including temperature, gravity and chemical abundance), especially the applications in the spectral data released by LAMOST. The KRR is a further development of the Ridge Regression algorithm, and the Ridge Regression is a variant of the Least Squares Method with a regularization term, it has an ability to solve high dimensional multi-collinearity problems. Therefore, the KRR method is suitable for processing high-dimensional celestial spectral information. In this paper, 20 000 stellar spectral data identified as stars are randomly selected from the release data of LAMOST for experimental testing. The data set contain spectra with low SNR and high SNR (the average SNR in g, r, i-band are from 6.7 up to 793). In this paper, we preprocess all the spectra firstly, including three steps: one is the de-noise phase based on the wavelet transform; In order to avoid some inaccuracies in the spectral flux value, the second step is spectral flux normalization; Because the dimension of each spectrum is up to several thousand dimensions, the principal component analysis method (PCA) is used to reduce the spectral dimensions as the third step. Then, we establish a regression model between the spectral data and the normalized three stellar parameters based on the KRR method. Finally, we design many different combinations of experiments to test and analyze the KRR model, and compare its results with the results of the classical algorithm SVR. All the experimental results using KRR method show that the average absolute error of temperature, gravity and chemical abundance is 82.989 7 K, 0.185 8 dex and 0.121 1 dex, respectively, it is better than the result of the SVR which is 144.230 8 K, 0.188 6 dex and 0.124 6 dex, respectively. In particular, the KRR method has a large advantage in temperature test results, which indicates that the KRR method can be effectively applied to the automatic measurement of the stellar spectral parameters.

LAMOST first-tage regular survey has been successfully observed more than 150 000 galaxy spectra, which will provide necessary data for mining the precious and rare objects to improve the existing cosmogony theories. The advanced information technologies will provide effective tools to mine these rare samples from the vast amounts of spectral data. In this paper, the spectrum J152238.11+333136.1 is particularly chosen from LAMOST DR 5 by using the outlier mining method based on the DoPS method and intensively discussed the rare characteristics of P-Cygni profiles. Firstly, the basic information, the description of suspected P-Cygni profiles and the related data mining methods are introduced briefly. The P-Cygni shapes are shown in the wavelength of Hβ and ［OⅢ］λ4860, and inverted P-Cygni shapes are shown in the wavelength of NeⅢλ3869 and HeⅠλ5874. Secondly, the authenticity of the feature and its generation mechanism are discussed from the following four perspectives. (1) Homologous observation. The homologous observation of Sloan survey in 2004 (11 years before) did not present corresponding features on the spectrum, which might be due to ongoing evolutionary activities or fiber positioning errors; (2) By analyzing the spectral quality and reducing the residual of the skylight, it is analyzed whether the P-Cygni features are caused by observations or data processing. The inverted P-Cygni shapes in NeⅢλ3869 and HeⅠλ5874 have lower credibility. Meanwhile, by comparing the target spectrum with the super skylight, and the spectral characteristics of the spectrum observed by the adjacent fibers, the possibility that the P-Cygni profiles is caused by the sky background reduction process is illustrated; (3) Spectral sub-type differences. Near-infrared homologous observations such as IRAS and WISE showed that it is a Seyfert 2 galaxy. However, the emission line intensity ratio ［NⅡ］/Hα, ［OⅢ］/Hβ shows that it is HⅡ region. Considering the characteristics of optical and infrared photometry images, we infer that the two galaxies of the target may be on going the merging activities; (4) From the perspective of the physical mechanism that leads to the P-Cygni profiles, the possibility of the outflows triggered by galaxies, the outflows triggered by the ionized gas of star formation (star burst), and the super-wind by the Wolf-Rayet galaxies are carefully analyzed.

In this study, hyperspectral images were used to detect a fungal disease in apple leaves called Marssonina blotch (AMB). Estimation models were built to classify healthy, asymptomatic and symptomatic classes using partial least squares regression (PLSR), principal component analysis (PCA), and linear discriminant analysis (LDA) multivariate methods. In general, the LDA estimation model performed the best among the three models in detecting AMB asymptomatic pixels, while all the models were able to detect the symptomatic class. LDA correctly classified asymptomatic pixels and LDA model predicted them with an accuracy of 88.0%. An accuracy of 91.4% was achieved as the total classification accuracy. The results from this work indicate the potential of using the LDA estimation model to identify asymptomatic pixels on leaves infected by AMB.

People consume tea brewed from the leaves of the Camellia sinensis plant for about 50 centuries. Health benefits of the tea have been investigated for about three decades. Especially green tea shows antitoxic and lots of properties with its determined ingredients. Turkey is not only the one of the best consumer and but also good producer of the tea as being 5th producer all over the world. It grows eastern region of the Turkey and high quality tea is imported and exported. To have quality tea, grooving soils are also crucial. In the current research, Tea leaves and their own grown soils were collected from 20 stations where the most tea producer cities as Trabzon, Rize and Artvin tea fields of the eastern of Black Sea Region in Turkey. The cultivated tea and their own grown soil samples were analysed by using EDXRF Spectrometry. In the soil samples, the elements Mg, Al, Si, P, S, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Sr, Ba, Pb on percent level and the elements Mg, Al, Si, P, Cl, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn and Sr in the tea leaves were detected by using SKRAY 3600-EDXRF.and also the obtained data were evaluated with the Kriging interpolation of geostatistical method. Element content were investigated in the soil and tea samples according to the geological situations and also the relation of elemental difference between the tea and the own grown soil. Pb/Zn ratio was also anaysed in the samples.

To understand the endowment characteristics of the organic sulfur in coking coal and know about the chemical mechanism of microwave to organic sulfur in coal are of great importance to the enrichment of the coal desulfurization theory, optimization of the microwave desulfurization process of coal and development of new technology of fine coal processing. XPS and XANES were used to represent the main structure types of organic sulfur and the relative content of organic sulfur in coking coal. Based on the difference of coal density characteristics, we explored the types and content changes of organic sulfur. The benzothiophene and 3-thiophenecarboxylic acid were selected for microwave irradiation experiments at 915 and 2 450 MHz. The variation characteristics of sulfur structures in the model compounds were studied by Raman spectroscopy. The quantum chemistry simulation was performed using Materials Studio to calculate different directions. Materials Studio was used to simulate the configuration parameters of model compounds under energy fields in different directions. We compared the conformation changes of the model compounds after microwave radiating, and analyzed the response mechanism of the sulfur chemical bonds in the coal to the microwave. The XPS and XANES characterization results showed that sulfur in coking coal is dominated by organic sulfur, and thiophene is the most important form of organic sulfur. With the increase of the densities of coking, thiophenic content decreases relatively, a thiol (ether), and (sub) sulfone gradually increase, and the content of three types of organic sulfur tends to be average. After applying the applied energy fields in different directions, the chemical bonds length and bond level of sulfur in benzothiophene and 3-thiophenecarboxylic acid were not obvious, indicating that the microwave energy fields have limited stretching and torsion of chemical bonds. However, there had influence on the bonds angle and dipole moment in the molecular structures of the model compounds by different energy fields application directions. Raman spectrum showed that the vibration peaks of sulfur chemical bonds in the model compounds were red-shifted after microwave irradiation at 915 and 2 450 MHz. Therefore, microwave radiation affected the local structures of the model compounds microenvironment, and changed their molecular configuration and molecular polarity. At the same time, the vibration recovery force of the crystal lattice was reduced, the interaction force between atoms was weakened, and the fracture of sulfur chemical bonds and the dissociation of sulfur were promoted. It was found that the 915 MHz microwave radiation had more obvious effects than 2 450 MHz.