The ground surface NO2 concentration (cNO2) and tropospheric NO2 vertical column density (NO2 VCDtrop) in Shanghai area were measured by ground-based active and passive DOAS methods from May 2015 to May 2016. The hourly cNO2 measured by LP-DOAS was positively associated with Air Quality monitoring data (r=0.81), while the NO2 VCDtrop retrieved form MAX-DOAS measurements agreed well with GOME-2 and OMI satellite observations (r=0.89, 0.88). The results of both active and passive DOAS were reliable. In daytime, Mixing Layer takes up a majority of Planetary Boundary Layer, where most of the air pollutants’ transportation, dispersion, dilution and deposition occur. The concentration of pollutants such as NO2 mixes well within the Mixing Layer and reduces nearly to zero in free troposphere above the Mixing Layer. A new method was proposed to estimate the Mixing Layer Height (MLH) by combining the active and passive DOAS observations. The feasibility of this method was discussed in details in this paper. The calculated MLH was significantly correlated to Planet Boundary Layer Height (PBLH) deduced from GDAS meteorology database (r=0.93). Both MLH and PBLH ranged from 0.1 to 2 km. The diurnal variation showed a single peak. The MLH reached the maximum during 12:00 to 15:00, whereas the PBLH peaked at 14:00 due to the lower temporal resolution. The monthly averaged MLH and PBLH had similar seasonal variation, which were higher in Sept. 2015 and Feb. 2016, and lower in July 2015 and March 2016. The ratio of MLH and PBLH was 0.98±0.59, which was consisted with the relationship between them. The deduced MLH was also highly correlated with PBLHLidar obtained from the co-located Lidar results (r=0.75). PBLHLidar value was slightly higher than MLH, but they tended to be the same at the beginning and end of daytime. The validations with other methodologies suggested that this method was evidently reliable.

Para-xylene(PX)is an important chemical raw materials. In order to study the influence of external electrical field on molecular structure and infrared spectra of PX, the method B3LYP of the density functional theory at 6-311++G(d, p) level has been used to calculate geometrical parameters and infrared(IR) spectra under different external electric fields ( from 0 to 0.030 a. u.) in this article. The results show that the most strongest absorption of IR spectra of PX is produced by C11-H14 and C15-H17 stretching vibration; the molecular geometry parameters is strongly dependent on the external field intensity; the significant negative (“red”) and positive (“blue”) frequency shifts, the redistribution of molar absorption coefficient are observed, i. e., vibrational Stark effect(VSE) is obvious.

The wavelength-integral type solar radiation observation instrument currently used in meteorological service observation has met the bottleneck with insufficient detail of solar radiation and large differences of data, so it has been unable to meet the needs of applied scientific research on the fine observation of solar spectral radiation. Observation methods as well as instrumentation research and development of the precision spectroradiometer with high spectral resolution have become the frontier science and technology issues in solar radiation observation of meteorological field. Under aforementioned scientific research background, our research group has been supported by National (Meteorological) Industry Research Special Funds for Public Welfare Projects. In order to solve the problem of fine observation of solar radiation in the meteorological application field, in-depth scientific research and technology development have been carried out. This paper focuses on the discussion about the instrument development achievements and analysis methods of observation data. Firstly, the spectroradiometer system used for ground-based solar spectral irradiance observation is introduced. The optical bench of the spectroradiometer adopts a spectrograph structure based on flat field concave grating, which has the excellent optical characteristics such as low stray light, high light collection efficiency and high reliability, so it is especially suitable for long-term unattended outdoor meteorological observation. The aberration correction characteristic of flat field concave grating used in the spectroradiometer system is more suitable for the wide spectrum range application from 300 to 1 100 nm, and there is almost no change of spectral resolution covering the entire measured spectrum range, that means the bandwidth of different wavelength channels is basically the same. The spectral resolution (FWHM, Full Width at Half Maximum) is approximately 2 nm when 25 μm slit is used, and the sampling wavelength interval is less than 0.5 nm. So, this spectroradiometer is an advanced spectral irradiance measuring instrument for solar radiation observation, and its spectrum range and spectral resolution match the solar observation demand very well. Secondly, on the basis of the observation data, the observation methods of solar spectral irradiance in meteorology application are expounded and analyzed. The spatial-distribution energy of the solar irradiance is collected by a receiving unit with different device parametric models. The movement process and distribution forms of solar spectral radiation in the hemisphere sky are dimensional constrained and made into three different spectral irradiance components. The three spectral irradiance components are Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI) and Diffuse Horizontal Irradiance (DHI). Based on data of GHI, DNI and DHI, the data features and application purpose of three observation forms are expounded. GHI is the actual irradiance intensity level on the earth surface, which is suitable for solar resource assessment. DHI reflects the motion of atmosphere and cloud phase state. DNI as a direct transmission energy form can be used to calculate sunshine duration and analyze atmospheric parameters. Furthermore, the calculus function relationships among observation forms, spectral signature and geographical parameters (longitude, latitude, altitude and air mass) and meteorological parameters (cloud amount and atmospheric absorption) are further analyzed. Compared with the traditional wavelength-integral type radiation observation, the solar spectroradiometer increases the dimensions of the wavelength information channels for the radiation energy observation. From the spectral irradiance data of the direct normal irradiance form, it can be seen that the radiation energy varies significantly at different wavelengths, which are closely related to the movement process of the atmosphere. Therefore, solar spectral irradiance data not only provide more detailed information for service observation, but also provide more various information channels of radiation energy. Using the radiation quantity information of the feature wavelength channels, the atmospheric parameters such as Aerosol Optical Depth (AOD), Total Ozone Column (TOC) and Water Vapor Column (WVC) can be calculated by the inversion models. The precision solar spectroradiometer provides the solar spectral irradiance with high resolution grade in the order of nanometer as the basic service data, and the detailed information on the distribution and vary of solar radiation can be used for research on meteorological models and climatic models, the assessment of photovoltaic resources and the ecological environment, etc. And it also provides impactful scientific research data and helpful observation tools, and abundant interested information can be extracted in radiation energy distribution on wavelength for variety of ecosystem flux monitoring and evolution relationship research, especially in the areas of climate, agriculture and ecology.

Spectra of altitude triggered lightning channel in the range of 400～660 nm had been obtained by Slit-less spectrograph. The spectra of mini-return stroke and upward positive leader channel were analyzed. The difference of the spectra between metal and air of the lightning channel was discussed. It is shown that duration of the spectra of metal channel was about 140 ms, while the air portion channel was only 0.167 ms. The reasons why the metal channel has high brightness and long duration were given through analyzing the excitation energy and ionization energy of Fe, N, O elements. When the current intensity is same, there are more excited particles in the metal than in the air channel which leads to trigger lightning channel to generate more radiation, brighter channel and stronger spectral intensities. And what is more, the duration of the recombination processes of metal channel was also longer in the subsequent phase of dissipating the plasma channel. Through comparing the spectra structure and temperature of mini-return stroke and lower and upper air portion of positive leader channel to the natural lightning in Guangdong, it depicted that the spectra of mini-return stroke channels were mainly composed of transitions between low excited states of NⅡ ions, and had NⅡ 444.7 nm, NⅡ 517.9 nm, NⅡ 616.8 nm which were the characteristic lines of the general intensity natural lightning in Guangdong area. The spectra with high excitation energy in the lower air portion channel of upper positive leader began to disappear. The spectral with low excitation energy such as Hα, Hβ, and OⅠ 615.8 nm appeared, and had spectral structure of the late stage of lightning return stroke. The temperature of the mini-return stroke and lower air portion upward positive leader channel were 21 000 and 20 000 K, lower than natural lightning temperature.

Dielectric barrier discharge (DBD) at atmospheric pressure has many advantages such as multi-category, depth and breadth, normality, etc. In this paper, a series of parameter diagnoses for the coaxial electrode discharge test were carried out. Argon ionization experiments were carried out under the conditions of 11.4 kHz discharge frequency and 5.4～13.4 kV (interval 1 kV) discharge peak voltage by using self-developed DBD combustion actuator at standard atmospheric pressure. Atomic emission spectrometry (AES) was employed to test and analyze argon plasma excitation and spectroscopy; two-line method and Boltzmannmethod were employed to test electron excitation temperature; electronic density was calculated according to Stark broaden law; the variations in electron excitation temperature and electron density with the increase of the peak discharge voltage were obtained. The results showed that the electron excitation temperature does not monotonically increase with the increase of applied voltage, which indicates the main characteristics of the micro-discharge are not dependent on the external voltage strength, but on the gas composition, gas pressure and discharge model; Increasing discharge power could only increase micro-discharge number; the maximum electron excitation temperature is up to 3 500 K; the electron density which achieves 108～109 cm3 tends to be a quasi linear trend with the increase of applied voltage. The degree of ionization is weak. The exploration of these parameters is of great significance to the study of plasma.

The paper aims to study the interactive mechanism of alisol B 23-acetate (23B), alisol A 24-acetate(24A) and content of 23B∶24A=1∶1 mixture with p53DNA which is tumor-suppressor gene, and explore the molecular mechanism of the antitumor effects of alisol acetates. The interaction of 23B, 24A and 24A-23B mixture with p53DNA was investigated by Ultraviolet and Visible Spectroscopy (UV-Vis), Fluorescence spectroscopy and Molecular simulation. UV-Vis showed that p53DNA was partially inserted by alisol mononers and its mixture, in which the decrease degree between p53DNA’s UV-Vis absorbance was 24A∶23B (1∶1)>23B>24A. Fluorescence spectroscopy demonstrated that the mode of interaction of p53DNA and alisol mononers and its mixture was inserted, in which the bonding strength was 24A∶23B (1∶1)>23B>24A. Molecular simulation illustrated that the sequence of binding of alisol mononers and its mixture with p53DNA was 24A∶23B (1∶1)>23B>24A, in which 23B formed a hydrogen bond with Adenine nucleotides (DA4) of p53DNA while 24A-23B mixture formed four hydrogen bonds with the DA4 and Thymine Nucleotides (DT19). The sequence of binding of alisol mononers and its mixture with p53DNA was 24A∶23B(1∶1)>23B>24A, in which the mode of interaction between them and p53DNA were all partial insertion, indicating that 24A and 23B had synergistic effects on anticancer target p53DNA. In addition, the parental rings C14- of alisol acetates and their steric hindrance and the oxygen in phosphoric acid of DA4 in the f chain in p53DNA were the binding site of the interaction between alisol acetates and p53DNA, which was the active center of the antitumor effects of alisol acetates. The key to the synergy of alisol acetates is that the the hydroxyl group on the side chainn C19- of 24A and the nitrogen and oxygen on adenine of DA4 in the f chain in p53DNA and the oxygen atom in thymine of DT19 in the e chain in p53DNA.

Horseradish Peroxidase(HRP) is a widely used biocatalyst. However, activity change mechanism of HRP after irradiation has not been reported. In this research, the effects of irradiation on the catalytic activity of HRP and the mechanism of enzyme activity were studied by UV-Vis absorption、synchronous fluorescence and circular dichroism(CD) spectroscopy. The experimental results showed that irradiation could promote HRP reduction, while its catalytic activity would change. The UV-Vis absorption spectra date showed that the maximum absorption peak of the Soret band of HRP was red shift after irradiation and the oxidation peak intensity at 498 nm was decreased, and consequently HRPFe(Ⅲ) was reduced to HRPFe(Ⅱ). The effect sequence of wavelength on the photoreduction was 280 nm> 254 nm> 498 nm> 403 nm; the activity of the enzyme was 403 nm> 498 nm> 254 nm> 280 nm; while irradiated by 280 nm light , Phe, Trp, Tyr, Cys free amino acids and glutathioneall could promote the photoreduction process. Enzyme activity was closely related to the degree of photoreduction, and Fe(Ⅱ) could not provide empty orbit coordination with H2O2, so light-induced reduction of Fe(Ⅲ) led to decreased activity. Synchrotron fluorescence and circular dichroism spectroscopy revealed that the conformation of HRP did changed after photoreduction, so the conformational change was the reason for the change of enzyme activity. The factors that affect the decrease of enzyme activity by ultraviolet light are the contribution of light-induced active center Fe(Ⅲ) reduction and protein conformational changes. The results of this study have important theoretical and practical significance for a further understanding of the effects of light on hemoglobin(enzyme) structures and functions.

The polarized phase function is one of the important optical parameters, which is very sensitive to the aerosol complex refractive index, particle size and shape. The multi-angle multi-spectral polarization remote sensing is an effective means for obtaining the aerosol polarized phase function. As a ground-based high-accuracy polarization instrument for aerosol remote sensing, the new generation CIMEL dual-polar sun-sky radiometer CE318-DP has been introduced into the worldwide AErosol RObotic NETwork (AERONET). Meanwhile, as the main instrument of the Sun/sky-radiometer Observation NETwork (SONET) with the extension of multi-wavelength polarization measurements, it has accumulated polarization data for many years over observation stations with different aerosol types. However, the retrieval has been based only on the degree of linear polarization or the polarized radiance up to now. Compared to the degree of linear polarization and the polarized radiance, the Stokes parameters Q and U contain information not only on intensity of linear polarization but also on the orientation of polarization. This study introduces an algorithm to retrieve the aerosol polarized phase function based on the Stokes parameters Q and U of skylight from the multi-angle multi-spectral polarization measurements of the CE318-DP. Considering that the Stokes parameter U is changeless with different aerosol properties for the CE318-DP standard polarization observation scenario PPP (Polarized Principal Plane), which is difficult to be utilized, a new ALMP (ALMucantar Polarization) observation scenario is tested to obtain the Stokes parameters Q and U, then to be applied in retrieval of polarized phase function. As to the typical biomass burning and water-soluble aerosols, the results of -P12/P11 in the channels centered at 340 to 1 640 nm were presented and analyzed systematically. Moreover, the applicability of the inversion algorithm in clear and hazy sky conditions was also tested. For the visible and near-infrared channels, the results were in agreement with the truth values not only for the PPP but also for the ALMP geometries. One of the reasons for the obvious deviation of the results in the ultraviolet (UV) bands was also discussed, which was the assumption of the approximately equivalent “ratios of atmospheric single scattering and atmospheric scattering” based on the initial aerosol parameters and the real aerosol parameters could not be satisfied in the UV bands. The inversion model should be improved to be applied to the short-wave channels in further studies. On this basis, some subsequent researches can be engaged in to utilize the features of multi-spectral -P12/P11 to improve the retrieval of aerosol microphysical properties.

Many real applications require accurate color reproduction, such as textile, printing, art painting archiving and online product exhibition. The most accurate and informative way to represent a natural object’s color is to use its spectral reflectance. However, the very professional measuring instrument of spectrophotometer has the drawbacks of being expensive and having low measuring resolution and slow measuring speed. As digital cameras have become the most widely used devices for color acquisition, developing accurate reflectance estimation methods from RGB responses has received increasing attention. The aim of spectral reflectance estimation is to build estimation function between RGB tristimulus values and the spectral reflectance vector from training samples. Regression methods have been widely used for this problem. Recent studies have shown that natural objects’ reflectance resides on a lower dimensional submanifold which is embedded in the high-dimensional ambient Euclidean space. Due to the limitation of high dimensional and low training sample size, previous global regression approaches could not exploit the local manifold structure well and are prone to be over-fitting. Local linear regression method can improve the problem of overfitting, but the local learning method is susceptible to the influence of outliers, which will lead to under-fitting. Aiming at this problem, this paper proposes a spectral reflectance estimation method based on locally weighted linear regression, which gives different weights to each local training sample within a k-nearest neighbor constraint. The experimental results show that the method based on locally weighted linear regression can make more effective use of local information, alleviate the over-fitting and under-fitting and reconstruct the spectral reflectance more accurately.

Oxygen is the key material of metabolism of human body and the first need of human life activities. Explosion in coal mine production and natural disasters such as seismic hazards and geological hazards will lead to collapse, burying and accumulation, and then a complex enclosed environment will take shape. If there are signs of life in the enclosed environment, the disaster relief work will be particularly important. In particular, the measurement of oxygen content and it’s concentration change with time in enclosed environment is of very important significance to the operation of the disaster rescue robot. Therefore, an experimental system for quasi-continuous modulation laser absorption spectroscopy measurement is established, whose research object is closed in door oxygen. We measured the closed indoor oxygen concentration’s change during 24 hours based on tunable diode laser absorption spectroscopy (TDLAS) and wavelength modulation spectroscopy (WMS)The quasi continuous modulation laser absorption spectrum method can accurately measure the closed indoor oxygen change.

According to the form of its engraved grid, FBG sensor can be divided into uniform type, chirp type and so on. The spectrum distributions of FBG are different for different grid forms. At present, it has been reported that existing structural parameters were mainly analyzed by the literature. The functional model was studied for obtaining the spectral distribution, which can be from any kind of FBG, and its parameter design was realized. In order to realize the control of the echo spectrum distribution by FBG, a mathematical model of the segmented modulation index was established by using transfer matrix method. The spectrum distribution of the echo was controlled by the combination of different refractive index modulations in each segment, and the spectral characteristics under different refractive index distributions were studied. It provided theoretical support for obtaining the Bragg spectrum distribution in any form. In the system, coupled-mode theory and matrix transmission algorithms were used in combination. Compared with the traditional uniform FBG, σ and k were no longer constants, but rather σ(z) and k(z) as a function of form, so for any FBG structure did not have an analytic solution. However, if the FBG was divided into m sections, m sub-FBGs could be obtained from the concrete σ(z) and k(z) functions on each small section, so that the overall effects of the FBG could be obtained by the matrix transmission method. The FBG was divided into m small sections in the z-axis direction, and m sub-FBGs could be obtained from the specific σ(z) and k(z) functions in each small section, so that the overall effects of the FBG can be obtained by the matrix transmission method. When the size of sub-FBG segmented meets the boundary conditions, the coupling mode theory can still be applied. At the same time, it can express the multi-section coupled equations through the matrix function in the form of positive and negative modes. It can be seen from this that the entire FBG composed of arbitrary refractive index modulation does not have the generalized form, but it can be resolvable for the segmented sub-FBGs. And the matrix transmission algorithm can be used to calculate the positive and negative modes of the m-segment sub-FBGs. So the refractive index modulation function of any type of FBG can be transformed into a transmission matrix group. The reflectivity distribution field can also be analyzed. Finally, the equivalent positive and negative modes of the whole FBG can be obtained, so as to realize the control of the echo spectrum distribution. As can be seen from the theoretical part, the spectrum distribution characteristics of echo are mainly determined by the coupling coefficients of the forward and reverse guided modes, the position of the core and the number of segments. They can be represented by σ(z) and k(z). Through MATLAB simulation analysis showed that the two parameters have significant modulation effects on the reflectivity function in the range of (0,1). As the order of control parameters increases, the slope of reflectivity modulation will also increase. In case of k(0.38, 0.48), σ(0.52, 0.58), it is monotonically tuned for reflectivity modulation. The distribution of the reflectivity function under different control parameters was obtained. The quantitative effects of the coupling coefficient on the control of the echo spectrum were discussed. Taking two specific pitch Λ1 and Λ2 as an example, after splitting the whole FBG into m sub FBGs, Λ1 and Λ2 were placed on different sub-sections. The spectrum distribution patterns of FBG were analyzed according to different grid layouts. If the spectrum characteristics of FBG change by the parameters and distribution forms of Λ1 and Λ2, and they are resolvable, the Bragg spectral characteristics can be considered as controllability. Through the parameter control any spectrum distribution can be achieved. In the experiment, AVESTA’s Ti: Sapphire femtosecond laser (Its center wavelength 800 nm, frequency 1 kHz, peak pulse energy 800 nJ.) was used to fabricate four different structured fiber gratings. Four kinds of refractive index modulation FBG segment structure were employed. Respectively: (1) Λ1 and Λ2 were evenly distributed alternately in the m section; (2) Λ1 and Λ2 were evenly distributed alternately in the m/2 section, and the rest of section were randomly distributed; (3) Λ1 and Λ2 were randomly distributed in the m/2 section, and the rest of section were randomly distributed, too; (4) The refractive index of the entire fiber grating segment was randomly distributed. Echo spectrum distribution of the above four FBGs was tested and compared, so the spectrum properties of Bragg was studied by the segmented refractive index modulation. The experimental results showed that when the FBGs in the form of matrix group are distributed in the m section, they are consistent with the traditional series homogeneous FBG test and have two obvious Bragg characteristic peaks, and they are located at 1 551.485 and 1 563.572 nm, respectively, and have a high signal-to-noise ratio. It is consistent with the test results of two series-connected FBGs with fixed pitch and is also a special solution to the piecewise modulated function. In case 2, its characteristic peak positions are 1 551.499 and 1 563.551 nm, its absolute error is better than 0.030 nm. Its half-width is better than that of case 1, but its noise power increases greatly and the signal-to-noise ratio decreases. In case 3, the absolute error of the characteristic peak position is better than 0.050 nm, and the sharpness of the characteristic peak is further increased, and the noise power is further increased, and the signal-to-noise ratio is the worst. When the matrix group is distributed in the m/2 segment, the refractive index modulation characteristic information can still be obtained obviously in the test spectrum, that is, there are two Bragg characteristic peaks, but the peak-peak value decreases, and the noise spectrum increases, and the half-width narrows. At the same time, the trend of stochastic distribution is more obvious than that of alternation. Thus, the characteristic peak, half-width and power spectrum in the echo spectrum can be modulated by controlling the matrix group distribution. The method can accurately control the Bragg spectrum distribution under the pre-designed refractive index modulation matrix to obtain the target echo spectrum.

Convolutional neural network (CNN) was widely used in image classification and recognition but its application in near infrared spectroscopy has not been reported. Therefore, the near-infrared spectroscopy classification modeling method based on CNN was studied in this paper. Taking into account the characteristics of near-infrared spectral data, an improved CNN modeling method was presented in this paper, which improves the CNN classical model Lenet-5: ①The square matrix convolution kernel was transformed into a vector convolution kernel for one-dimensional near-infrared spectroscopy. ②The C5, F6 and output layers of the lenet-5 structure were changed to single-layer sensing machines in order to simplify the network structure. At the same time, the method of sampling points was used to reduce the dimensionality of near infrared spectrum and speed up the convergence rate. The influence of convolution kernel size on modeling results was also studied in this paper. NIR-CNN model was established by the near-infrared spectroscopy of 600 central tobacco samples from northeast, Huanghuai and southwest China. The accuracy of the model was 98.2% and 95% for the training set and test set. The experimental results showed that the application of CNN could accurately and reliably identify the near infrared spectrum data. This method provided guidance for the scientific and rational utilization of raw materials of tobacco enterprises, and it was important to maintain the quality stability of cigarette products. The method of near infrared spectroscopy based on CNN could also be applied in the classification of other agricultural products.

Wheat is one of the main strategic stored grain varieties in China. But wheat is susceptible to fungal infection, which affects its safety as food. Early detection of harmful fungal infection in wheat is the precondition to control its hazard. However, current methods for mold detection, such as plate counting and fluorescence staining, are usually laboratory-intensive and time-consuming, and can not fulfill the need for on-site testing. Concerning this issue, this work intends to apply array fiber spectrometer series and chemometrics to establish an on-line method for detection of wheat mildew, and to provide a reference for further development of on-line sensing instruments for grain quality and safety. Sterile wheat kernels were inoculated with 5 different spore suspensions of fungal strains respectively, which were F. moniliforme 83227, F. proliferatum 195647, F. nivale 3.503, A. parasiticus 3.3950 and A. ochraceus 3.3486. Wheat samples were then stored at 28 ℃ and 85% relative humidity after inoculation to accelerate mildew process. At storage stage of 0, 1, 3, 5 and 7 d, Vis/NIR spectra of samples were collected by an on-line sensing system which was mainly conposed of an array fiber spectrometer and a diffuse reflectance probe. Total colony count of samples was also determined according to plate count method. Spectra of samples were measured at moving speed of 0.15 m·s-1 with integral time of 20 ms. Each sample was collected three times and the acquisition band ranged from 600 to 1 600 nm. Then original spectra of samples were firstly pre-processed by smoothing, multivariate scatter correction and derivative transformation to eliminate spectral noise. Subsequently, principal component analysis (PCA) was used to discriminate wheat samples with different mildew degrees (storage stage). Finally, liner discriminant analysis (LDA) and partial least squares regression (PLSR) were employed to develop qualitative and quantitative analysis models for fungal infection in wheat. Wheat samples undergone three stages during the storage period according to colony counts, which were not mildew, mildew and serious mildew. Analysis of original and second derivative spectra indicated that fungal infection resulted in significant changes in wheat spectra. PCA results showed that there was a certain trend of separation between wheat with different mildew degrees. The overall recognition rate obtained by LDA for the classification of samples with different mildew degrees was more than 90.0%. Colony counts in wheat samples was predicted by PLSR and coefficient of determination for the prediction set (R2p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value obtained were 0.859 2, 0.401 Log CFU·g-1 and 2.65, respectively. The combination of array fiber spectrometer series and chemometrics is feasible for on-line detection of wheat mildew. In further studies, natural infected wheat samples and samples contaminated with more representative fungal strains should be incorporated to enhance the robustness and applicability of the calibration model.

Potato is the fourth important grain crop coordinated with wheat, rice and corn. At present, China is actively promoting the development of potato staple foods, but the uneven quality of potatoes has seriously hampered the process of the main food industry of the potatoes. Therefore, rapid non-destructive testing of potato quality is of great significance to the industrialization of processing. Domestic and foreign scholars have conducted a number of related researches on the detection of potato internal quality based on the visible/near-infrared diffuse reflectance principle. This method is commonly used, but the rough skin of the potato has a great impact on the detection. Another detection method is the transmission spectrum. This method can better reflect the internal quality information of the sample. However, the total transmission spectrum of the potato varies with the size of the sample and results in a large change in spectral intensity. Considering the above two reasons and average quality of potato, this study uses partial transmission spectrum as the detection method. This method can not only avoid the influence of the potato epidermis, but also obtain the internal information of the sample while maintaining the same path length. The spectral acquisition system consists of spectral acquisition units (spectroscopes and coupling lenses) and light source units (halogen lamps and lamp cups) which are arranged side by side. During testing, the two parts are attached to the sample surface to ensure that the spectral acquisition unit does not receive reflected light from the potato surface. Based on this system, partial transmission spectra of 120 potatoes are collected ranging from 650 to 1 100 nm. The prediction model of dry matter, starch and reducing sugar content was established using partial least squares regression after pretreat by detrend, multivariate scattering correction (MSC), standard normal variable transformation (SNV) and first-order derivative (FD). The result shows that the prediction models of dry matter and starch content using multiple scatter correction pretreatment are effective. The determination coefficients of validation set are 0.854 0 and 0.851 0, respectively, and the root mean square errors are 0.521 9% and 0.484 8%, respectively. The reducing sugar prediction model using first-order derivative pretreatment has the best result. The determination coefficients of validation set is 0.768 6 and the root mean square error is 0.025 1%. In order to optimize the model, three methods such as competitive adaptive reweighted sampling (CARS) are used to filter the characteristic wavelengths, and an optimized partial least-square prediction model is established. The result shows that the prediction effect of potato quality parameters has been greatly improved. The determination coefficient of validation sets for dry matter, starch, and reducing sugar prediction models after CARS screening are 0.877 6, 0.865 3 and 0.887 7, respectively. And the root mean square errors of the validation set are 0.449 2%, 0.930 2% and 0.016 7%, respectively. The use of CARS feature wavelength extraction can simplify the model and remove irrelevant variables and collinearity variables. This will improve the accuracy and stability of the model, especially for low-component content parameters such as reducing sugars. Finally, in order to verify the robust of the potato quality parameters prediction model, 30 potato samples are selected for external validation of the prediction model. The determination coefficients between model predicted values and standard physicochemical values of potato dry matter, starch, and reducing sugar are 0.849 9, 0.867 1, and 0.877 6, respectively. The root mean square errors are 0.660 9, 0.480 9, and 0.016 9, respectively. The average relative errors are 2.03%, 1.77% and 7.58%, respectively. The present study shows that the partial transmission spectrum carries the internal information of the potato and it is significantly related to the contents of dry matter, starch, and reducing sugar. The visible/near-infrared partial transmission detection system can achieve rapid and non-destructive prediction of multi-parameters of potatoes, especially good prediction results of dry matter content and starch content, but there is a large relative error in the prediction of individual samples with very low levels of reducing sugars. The next step of the study needs further optimization and improvement.

Non-destructive detection of plant leaves water content is of significance to plant physiological, biochemical research, irrigation management and drought monitoring. In this paper, Gaia Sorter Near-Infrared Spectrometer (900~1 700 nm) was used to detect the water content of 60 fresh corn leaves in different growth stages using PLS and SMLR models. The results demonstrated that the R2/SEP of validation set were 0.975/1.18, 0.980/1.02, all achieving better predictive results, which could bring out the determination of a single corn leaf average water content. The results of the SMLR model established with the preferred characteristic wavelength (1 406 nm, 1 692 nm) indicated that the use of high-throughput near-infrared camera combining filter method achieved the feasibility of corn leaves canopy or high-altitude remote sensing measurement. Simultaneously, the imaging analysis of the water content in different regions of the leaves was carried out, and the results revealed that the correlation coefficients between the measured mean values and the predictive mean values of the mesophyll and the main vein of the six leaves were 0.85, and the predictive results were in accordance with the actual situation.

China has huge rice reserves. In order to develop a rapid and accurate method for harmful mold infection detection in rice, near infrared (NIR) spectroscopy was applied for qualitative and quantitative analysis of the process of rice mildew in this study. Sterilized rice samples were firstly inoculated with four mold Aspergillus spp. species (A. flavus 3.17, A. flavus 3.3950, A. parastiticus 3.3950, A. glaucus 3.0100), respectively. Then the rice samples were stored under appropriate conditions (28 ℃, 80% RH) for mould growth. NIR spectra of samples were collected during the storage on different days (0, 2, 4, 7 and 10 d). Analysis models of mold infection in rice were developed by principal component analysis (PCA), discriminant analysis (DA) and partial least squares regression (PLSR), respectively. The results indicated that rice samples infected by different mold species could be effectively distinguished by NIR spectroscopy, and the average classification accuracy was 87.5%. The degree of mildew intensified during storage. The average correct classification accuracy of storage time (mildew degree) was found to be 92.5% for samples infected by one mold species, and 87.5% for samples infected by the four mold species. The PLSR prediction results of mould cell concentration in samples was: R2P=0.882 3, root mean square error of prediction (RMSEP)=0.339 Log (CFU·g-1) and residual predictive deviation (RPD)=2.93. Overall, the results demonstrated that the NIRS can be used as a rapid and non-destructive method for harmful mold infection detection in rice, ensuring the safety of grain storage and transportation.

Underwater laser-induced breakdown spectroscopy (LIBS) and underwater laser Raman spectroscopy have been successfully applied in deep sea research. These two technologies have similar devices and complementary detecting targets. The combination of these two technologies is expected to be an improvement for deep-sea study. In this paper, a prototype of a combined underwater LIBS-Raman detection system was developed. The whole system was integrated into a pressure housing of L790 mm×Φ270 mm with two optical windows and water proof connector at the end cap. The underwater pressure vessel connected the deck control terminal with a water proof cable, through which power supply, system control, and signal delivery were provided. The main components inside the pressure vessel included a pulsed laser, two spectrometers, an embedded computer and power supply conversion devices. A dual-wavelength pulsed laser was used as the same excitation source for both LIBS and Raman spectroscopy. The 1 064 nm wavelength was used for LIBS excitation and the 532 nm wavelength was used for Raman spectroscopy excitation. The laser was split into two paths based on wavelength and illuminate to sample through different quartz window. The produced LIBS and Raman signals were collected by back-scattering optical set-up and coupled into the AvaSpec-ULS2048 and QE65000 optical fiber spectrometer respectively. Experiments have been carried out with calcite samples in laboratory, and the results showed LIBS and Raman spectra could be obtained simultaneously for solid samples. Field experiments were also carried out at the Qingdao coast, and the results proved the feasibility of developing a combined LIBS-Raman system for underwater detection. It is hoped that this prototype could be used in hydrothermal area detection in the near future after further optimization.

PM2.5 is one of the important pollutants, so it is of great significance to the study of cytotoxical effects of PM2.5 from different regions. In this study, Human lung carcinoma epithelial cells (A549) were exposed to the average daily volume of PM2.5 from three different regions in vitro for 2 h. Afterwards, the cells were examined on the single cell level by the confocal Raman microspectroscopy and atomic force microscopy, respectively, to obtain the Raman spectroscopy, and the morphological and biomechanical properties of the cells. In addition, the cytokine concentration was measured by the multiplex enzyme linked immunosorbent assay. Compared to the untreated cells, statistical results revealed several interesting phenomena, including (1) the intensity of the Raman peaks of the treated cells increased at 1 002.97, 1 127.29, 1 172.83 and 1338.1 cm-1; (2) the lamellipodia of treated cells shrunk or partially disappeared; (3) the treated cells became higher; (4) the adhesion work decreased while the elastic energy increased for the cells treated by the PM2.5 from Suzhou region and (5) the treated cells had an inflammatory response. Our work demonstrated that the toxical effects of PM2.5 from different regions on A549 were different, which provides useful information for the air pollution control and prevention and control of lung diseases.

Rhenium is an important material for research and engineering applications due to the high bulk modulus, high melting point and excellent creepresistance. Theresponse of the shear elastic constant C44 of rhenium to temperature and pressure is important to the design and application of rhenium and its alloy. Raman scattering technique has unique advantages in studying the shear elastic constant C44 of hexagonal closest packed (hcp) metal under different temperatures and pressures. However, it is difficult to obtain the signals of low wavenumber Raman scattering for hcp metals due tothe strong reflection and shallow penetration depth of metals, especiallyunder a certain temperature and pressure loading. In this work, the lateral-incidentRaman scattering technology was carried outtoreduce the influence of metal strong reflection on Raman spectra. Consequently, we successfully measured E2g modes of polycrystallinerhenium under different pressure and temperature. The shear elastic constant C44 is 133 GPa at ambient condition. And we obtainedthe response of the shear elastic constant C44 of rhenium to temperature and pressure. The results show that the elastic shear coefficient C44 of polycrystalline rhenium increases with the increase of the pressure, and decreases with the increase of temperature. And, this work proposes a good research method for the shear elastic constant of metal material with Raman scattering.

The components of saliva are closely related to the corresponding components in blood. Using saliva instead of blood for detection can greatly reduce analysis time, limitation and potential safety hazard, thus it is of great importance to clinical research and control of drugs. In this work, combined with a portable Raman spectrometer, quantitative detection of pioglitazone hydrochloride (an oral hypoglycemic drug) in saliva has been realized by surface-enhanced Raman spectroscopy (SERS). With this method, the characteristic Raman peaks for pioglitazone hydrochloride can be easily observed. Furthermore, the SERS method can be used to quantitatively detect the pioglitazone hydrochloride in saliva. The concentration of pioglitazone hydrochloride is linearly correlated to its Raman intensity with a coefficient of determination (R2) of 0.992 3, and the minimum detectable concentration can be as low as 10 μg·L-1.

Plant breeding is important for increasing the yield and improving the quality of crop. Modern techniques of plant breeding require various phenotypic traits obtained from a large number of crops in order to cultivate varieties with useful traits. Researchers have been interested in developing nondestructive, fast and efficient plant phenotyping techniques, which could bridge the knowledge gap between the plant phenotype and the genotype. The chlorophyll fluorescence imaging can provide information related to the plant photosynthesis as one of the useful tools in the current phenotyping techniques, which can be used in plant responses to biotic and abiotic stresses and promote the selection of useful plant traits. This paper presents a brief introduction of fluorescence imaging system, methods of fluorescence image analysis and applications in plant phenotyping, and demonstrates challenge issues, technical solutions and potential applications of chlorophyll fluorescence imaging in plant phenotyping.

The three-dimensional fluorescence spectroscopy was combined with the self-weighted alternating three linear decomposition (SWATLD) algorithm to detect the three kinds of mixed pesticides. In acetonitrile solvent, preparation of carbaryl, metolcarb and Triazophos, three mixed solution of different concentration ratio as a measurement sample (carbaryl, metolcarb and Triazophos by three optimal excitation wavelength / emission wavelengths were 285/325, 305/345, 265/305 nm), three-dimensional fluorescence spectra were obtained by fluorescence spectrometer, and after blank subtraction, excitation and emission correction, the influence of instrument error was effectively removed and real scattering spectra of samples were obtained. In this paper, the self-weight alternating three linear decomposition algorithm is applied to analyze the measured spectral data, and the average recovery rate of the three pesticides is 96.9%±1.9%, 99.8%±1.0% and 100.8%±3.2%. According to the results of the SWATLD algorithm, calculation of three kinds of pesticide of the root mean square error (RMSEP) value is 0.616×10-2, 0.539×10-2 and 0.374×10-2 μg·mL-1, lower than the parallel factor analysis (PARAFAC) method to predict the results of the RMSEP value, and the minimum detection limit was 0.005~0.022 μg·mL-1 range. Compared with the PARAFAC algorithm, the advantages of the SWATLD algorithm are highlighted. It shows that the algorithm has good decomposition ability for the three kinds of pesticide mixtures with severe spectral overlap.

The study objects of this paper were PAHs fluorene and acenaphthene. A method combining three-dimensional (3D) fluorescence spectroscopy with Krawtchouk moment and generalized regression neural network was proposed for quantitative analysis of PAHs. By using the 3D fluorescence spectra data of samples measured directly, the corresponding grayscale images of 3D spectra could be obtained. The Krawtchouk moments were directly calculated based on the grayscale images of 3D spectra, and the quantitative models for the PAHs were established on the mean impact value and the generalized regression neural network. The average relative errors of the 8 groups mixed samples of fluorene and acenaphthene were predicted to be 0.98% and 2.15%, respectively. The results showed that the proposed method can extract the characteristic information of the spectra effectively and predict the concentration of PAHs simply and accurately.

When flow cytometry is used to analyze the polychromatic fluorescence of cells, multiple fluorescence spectra were often obtained, mixed with multicomponent fluorescence spectra. In this paper, the fluorescence spectra of cyanobacteria including many unknown fluorescence spectra were detected by flow cytometer with serious spectral overlap. In order to extract the main components and their concentrations from cyanobacteria spectra, a method of principal component analysis combined with multivariate curve resolution was used to process the fluorescence spectra of cyanobacteria. At first, the number of main components of cyanobacteria was given by principal component analysis, and then Evolving Factor Analysis was adopted to find the starting and end position of each component and to estimate the initial spectrum of pure components, finally Alternating Least Square combined with the pure components spectral unimodality and non-negativity was used to correct the initial estimation of pure components and concentrations. In the simulation and experiment, it was proved that the method could accurately estimate the number of pure components in the mixed spectra and fit the spectral peaks, and then accurately estimate the concentration of each component. This method can not only be applied in the spectral analysis of cyanobacteria, but also used for other multiple spectral mixture analysis.

The absorption spectra of lutein in ethanol/water solution at different temperatures were measured by UV-Vis spectrophotometer and the spectral changes with time were studied. The results showed that in 1∶1 ethanol/water solution, the probability to form H-aggregate in two samples with different concentration (25.6 and 10.2 μmol·L-1) decreased with increasing temperature. In 1∶2 ethanol/water solution the stable aggregated structure didn’t change with temperature. Kinetic detection of absorption spectra of 1∶1 ethanol/water solution indicated that the absorption of H-aggregate increased exponentially as time went by. It was concluded from analysis that the hydrogen bonds of water play an important role in the formation of aggregates. The increase of water molecules and hydrogen in ethanol/water solution leads to the formation of stable aggregated structure.

Today rare earth elements (REE) are of great interest to global economy, and become more and more important in modern technology. However, little research of reflectance spectroscopy and hyperspectral detection of REE has been conducted particularly due to the scarcity of its distribution. In this paper, electronic configuration and energy transition mechanism of REE were explained in the initial stage, for the partial filling of the 4f electron shell combined with numerous 4f-4f intraconfigurational electronic transitions made spectral features of REE unique in visible and infrared wavelength. Then combined with our study, spectral characteristics of fifteen rare earth elements, REE-bearing minerals (REE fluorocarbonates, REE phosphates, REE silicates), REE-bearing solutions were analyzed. The study indicated that spectra of fifteen rare earth elements were different from each other, and the typical distribution of REE (light REE versus heavy REE) in minerals and solutions directly affected their spectral signatures. The spectra of REE-bearing solutions were the combination of spectra of water and REE, and the intensity of absorption features became weaker with the decrease of REE concentration. In addition, hyperspectral REE detection in space borne was hindered by the suitable data source. Some researchers used an in situ instrument (HYSpex sensor) conducting REE mapping, which indicated that hyperspectral mapping of REE was feasible and efficient for Nd, Er, Dy, Ho, Sm, Tm analysis. Hyperspectral imaging spectroscopy of drill core was carried out using sisuROCK instrumentation, and Nd was extracted using its special spectral absorption bands. Finally, future study of REE reflectance spectroscopy and hyperspectral detection was pointed out, and this paper gives a good enlightenment for future exploration of REE using hyperspectral remote sensing.

It is a challenge to timely detecte microleakage of natural gas which is stored in underground repository or pipeline. The response and other remote sensing characteristics of stressed vegetation were used to indirectly detect the microleakage point of natural gas via controlled experiments in the field. In detail, the canopy reflectance of stressed area and control area of soybean and grassland were measured respectively. Singular values were removed and spectrum was smoothed, then spectra of the canopy reflectance was analyzed using the method of continuous wavelet based on first-derivative, which showed that wavelet energy coefficients of stressed and control canopy reflectance at 685 and 715 nm were good features to separate the stressed and control groups. (DW685－DW715)/(DW685+DW715) (DW) was designed in this paper using 685 and 715 nm and compared to PRI, NPCI, NDVI, and D725/D702, which showed that better performance, universality and robustness were possessed by DW in identifying the stressed grassland and soybean. The results showed that it is feasible to indirectly detect natural gas microleakage points through hyperspectral technology, which can provide technical support and theoretical basis for future engineering applications.

To improve the accruacy of field hyperspectral simulation and build the field spectral mixture model, the experiments for detecting mixture spectra of different detected angles and different ratios of leaf cover area were conducted to obtain the mixture spectra by using ASD FieldSpec3 Hi-Res field spectrum instrument with the reflex platform. Based on the theory of equidistant/homalographic model, the computation of weight coefficients for the model was expanded to calculation of three-dimensional weight coefficients. Taking account of the impact of zenith angle on weight coefficient, the three dimensional coefficient model was proposed for simulating mixture spectra, and comparing with the measured spectra for analysis. The results showed that the three dimensional coefficient model had more accurate computation of weight coefficient and had smaller error compared to equidistant/homalographic model. Especially for inclined detected angle, it had better simulation that it reduced the error 0.016 1 in RMSE and 0.07 in SAD. The three dimensional coefficient model considered the influence of detected angle, thus enhancing the estimation of mixture spectra and providing a new method for building field mixture model and unmixing mixed spectra from satellite data.

For the purpose of high sensitivity and specificity testing in the field of molecular biology and environmental monitoring, areflective single-ended optical fiber refractive index sensor model based on the characteristic identification of reflection spectrum is proposed, and the sensing mechanism and theoretical model of single-mode fiber-no core fiber (SM-NCF) cascaded structure based on multimode interference principle are given. No-core fiber is essentially a multimode fiber with special structure, where, the no-core fiber structure itself serves as the core, and the external environment medium serves as a cladding in practical application, which constitutes an optical waveguide structure compared with the ordinary multimode fiber, and it is unnecessary to use hydrofluoric acid to corrode the cladding of multimode optical fiber in the chemical way, so it will not reduce the mechanical properties of optical fiber, and it will not destroy the transmission condition of the core mode, therefore, it is better to realize the sensing monitoring of the refractive index of surrounding environment. When the refractive index of external environment where no-core fiber is surrounded changes, the effective refractive index of the waveguide structure and the cladding will change as well, causing the longitudinal propagation constant and the mode field distribution of the transmitted light to change accordingly, finally resulting in changes in different wavelength and optical power transmission. The above-mentioned effects are reflected in the reflection spectrum, which are the corresponding changes of the resonant wavelength, the trough valley intensity and the half-wave width of the interference valley, so the measurementof external environment refractive index can be achievedby identifying the characteristics of the reflection spectrum. Based on the beam propagation method (BPM), the numerical simulation of no-core fiber with a length of self-imaging distance and non-self-imaging distance is carried out in this paper, and the internal light energy distribution of the single-mode-no-coremode fiber sensing part is obtained. Then the real single-mode-no-core mode fiber sensing head is made with different lengths respectively, which is self-imaging distance and non-self-imaging mentioned as before. One end of the no-core fiber acts as the sensing area is welded with a standard single mode optical fiber, and a magnetron sputtering technique is used to plategold film on the other end of the no-core fiber in order to enhance the intensity of the reflection spectrum. On this basis, the optical fiber refractive index test system based on the SM-NCF terminal reflection type is built up and relevant experimental research is carried out. The results show that when the length of the no-core fiber is 15 millimeter (which corresponds to the self-image distance), the reflection spectrum of the single-mode-no-core-mode fiber sensing head gradually shifts to the longer wavelength as the refractive index of the liquid increases from 1.331 5 to 1.390 2, and the refractive index sensitivity corresponding to the resonant wavelength of the reflected peak is about 197.57 nm·RIU-1 and the correlation coefficient is 0.93. The peak reflection intensity also shows a decreasing trend, and the refractive index sensitivity is about -62.80 dB·RIU-1. When the length of the no-core fiber is 20 millimeter (which corresponds to the non-self-image distance), the reflectance spectrum of the single-mode-no-core-mode fiber sensing headexhibits a distinct bimodal phenomenon with the gradual increase of the liquid refractive index, and both of the two interference dips shift to the long-wave direction gradually. One of the obvious interference dip is dip 2. which has a refractive index sensitivity of 133 nm·RIU-1 from the aspect of resonant wavelength change, and the correlation coefficient is 0.96; On the other hand, the peak reflection intensity also shows a gradual downward trend, and the refractive index sensitivity is about -31.66 dB·RIU-1. The comparative analysis shows that the terminal reflection type optical fiber sensor of single-mode-no-core mode sensing head with the self-image distance length has higher sensitivity no matter from the prospect of the resonant wavelength deviation or the peak intensity of reflection. It can be seen clearly thatthe half-wave width from the reflection spectrum of single-mode-no-core mode fiber sensing head, when compared with the self-image distance length, there is a significant narrowing trendcorresponding to the non-self-image distance length for the same refractive index liquid environment. Compared with the single mode-no-core-single mode transmission fiber sensor structure, when the length of the sensing area is the same, the reflective structure of single mode-no-core fiber can realize the two adjustment of the round trip to the light wave signal. The terminal reflection type single mode-no-core fiber sensor improves the shortcomings of the traditional transmission type refractive index sensor, making contact with the liquid to be measured more convenient, and, havings the advantages of simple structure, being easy to fabricate, strong ability to resist electromagnetic interferenceand being convenient for remote telemetry, etc., hence it can provide useful support for subsequent research and application in the field of biochemical and environmental monitoring.

The paper aims to obtain the characteristic wavelengths of moso bamboo leaves damaged by Pantana phyllostachysae chao, with which the pest can be identified effectively and accurately. 105 hyperspectral data collected in Shunchang County, Fujian Province were randomly divided into two groups, i.e. the experimental group (71) and verificantion group (34). Selecting wavelengths which were highly significant differences between different pest levels group by One-way ANOVA. The wavelengths were screened by combining the wave band of the commonly used remote sensing satellite. The ability to discriminate between the pests of the selected wavelengths was analyzed by the Euclidean distance method, spectral angle mapping method and correlation coefficient method. According to the analysis results, the characteristic wavelengths were obtained and verified. The results showed that: (1) the spectral reflectance of moso bamboo leaves damaged by P. phyllostachysae were significantly lower than that of healthy leaves, and the higher the pest level is, the lower the reflectance will be; (2) with the increase in pest level, the spectral reflectance curves’ “green peak” and “red balley” of Pinus massoniana gradually disappeared, and the red edge was leveled; (3) the characteristic wavelengths of 703.43~898.56 nm (original spectrum) and 497.68~540.72, 554.53~585.25, 596.24~618.23 nm (first derivative spectrum) were determined, which had good response ability at different pest levels. Our findings will not only provide the theoretical guidliances for “ground-space” coupling, but also provide important basis for establishing the system of pest remote sensing monitoring technology.

Stereochemistry plays an important role in the interactions between functional bio-macromolecules and exogenous small-molecule drugs. Ginsenoside Rh2 is an effective constituent of ginseng with chirality of hydroxyl group at carbon-20, which has been found closely related to its multiple biological effects such as anti-tumor activity. However, the stereoselectivity of ginsenoside Rh2 interacting with serum albumin (SA), a vital drug carrier in the body, has been rarely reported. In the present study, interactions between SA and the C-20 eipmers of ginsenoside Rh2 were investigated under simulative physiological condition by molecular docking method and spectroscopic analyses, including UV absorption, fluorescence and synchronous fluorescence spectroscopy. Results showed that both 20S- and 20R-Rh2 could form 1∶1 type non-covalent complex with SA mainly via hydrogen bond and hydrophobic forces. According to the influence on spectra of SA, both epimers led to some increase in hydrophobicity for the light emitting residues of SA, as well as the change in micro-environment around some residues (including tyrosine and tryptophan) responsible for intrinsic fluorescence of SA, and fluorescence quenching mainly by a static mode. However, 20S-Rh2 displayed relatively larger binding constant and free energy in contrast with 20R-Rh2, suggesting stereoselective characteristics of the eipmers. The stereo-selectivity of ginsenoside Rh2 interacted with serum albumin may be related with stereochemistry of C-20.

Multi-spectral radiation thermometry is a method for obtaining true temperature inversion by Planck formula by measuring a number of spectral radiant intensity information of a point to be measured. However, the multi-spectral radiation thermometry equations obtained by the Planck formula are the underdetermined equations, that is, N equations, and N+1 unknown (N unknown spectral emissivity ελi and a waiting true temperature T). At present, a set of emissivity models (emissivity-wavelength or emissivity-temperature model) are often assumed. If the assumed model is consistent with the actual situation, then the inversion results can meet the requirements, and if the assumed model does not match with the actual situation, the inversion result will have very large errors. The emissivity model is affected by many factors, including temperature, surface state, wavelength and so on. It is difficult to determine the emissivity model in advance. Therefore, the restriction of unknown spectral emissivity has been the major obstacle to the theory of multi-spectral radiation thermometry. The direct inversion of true temperature and spectral emissivity without any assumption of spectral emissivity has always been a hot and difficult issue in the theory of multi-spectral radiation t thermometry. Through the analysis of reference temperature model, the essence of multispectral radiation thermometry inversion is to find a set of spectral emissivity, so that the true temperature of each channel equation is the same, if different, we’ll continue to find the appropriate spectral emissivity, until the true temperature of each channel is equal. Therefore, it is proposed to convert the solving process of the multi-spectral radiation thermometry reference temperature model into a constrained optimization problem. That is, under the constraint of spectral emissivity 0≤ελi≤1, the spectral emissivity is constantly searched through the gradient projection algorithm. After taking the emissivity into the multi-spectral radiation thermometry reference temperature model equations, the variance of the temperature inversion values is calculated until the temperature values obtained for each of the spectral channel equations should be approximately equal. In this case, the variance of the temperature inversion value of each spectral channel is the smallest, so that the inversion problem of true temperature and emissivity of multi-spectral radiation is converted into a constrained optimization problem. The gradient projection method is the main method to solve this kind of problem. However, in order to satisfy the constraints of Ax≥b, we decompose 0≤ελi≤1 into two constraints of ελi≥0 and -ελi≥-1, so as to satisfy constraints optimization problem of Ax≥b. In this way, the true temperature and spectral emissivity can be directly solved by the gradient projection algorithm without any spectral emissivity assumption. Six kinds of materials with different spectral emissivity distribution modes (increasing, decreasing, convex wave, concave wave, “M” wave and “W” wave) were selected as the research objects, to verify the adaptability of the new algorithm to the inversion of the spectral emissivity distribution of different materials. Using the minRosen function of Matlab, the initial values of the spectral emissivity are all chosen to be 0.5 (taking the middle value to improve the computational efficiency). The simulation results of six different spectral emissivity models showed that the new algorithm does not require any prior knowledge of the emissivity and the inversion of different emissivity models by the new algorithm is better. The absolute error is less than 20 K and the relative error is less than 1.2% when the true temperature is 1 800 K. The new algorithm has the advantage that there is no need to consider any prior knowledge of spectral emissivity, high inversion precision and suitable for various emissivity models. It further improves the theory of multi-spectral radiation thermometry and has a good prospect in the field of high temperature measurement.

With 3-nitrobenzonitrile and 3-mercapto-1-propanesulfonate as the starting materials, three kinds of water-soluble phthalocyanines with four 3-sulfopropylsulfonyl groups were synthesized by tetracyclization in the presence of metal acetate. The photophysical properties were measured by UV-Vis absorption spectra, fluorescence spectra and so on. The fluorescence quantum yield and the singlet oxygen yield were calculated. Compared with ZnPc, the shape of the fluorescence emission spectrums of the water-soluble phthalocyanines synthesized by introducing electron-withdrawing groups did not change, but the maximum fluorescence emission wavelengths had more than 10 nm red shifts. Among the three kinds of water-soluble phthalocyanines, zinc phthalocyanine had the highest fluorescence quantum yield and copper phthalocyanine had the lowest fluorescence quantum yield. Their fluorescence in aqueous solution showed a double exponential decay, which could be attributed to the protonation or deprotonation of excited states. The singlet oxygen quantum yield of zinc phthalocyanine was maximum, followed by hollow phthalocyanine, and copper phthalocyanine was minimum. The results of spectral analysis showed that the synthesized zinc phthalocyanine and hollow phthalocyanine have high singlet oxygen quantum yield and high photostability and are expected to be used as photosensitizers for photodynamic therapy and photoimmunotherapy.

Glaciers are extremely sensitive to climate change. And glacier changes have great impacts on the regional climate, ecology, water resources and so on. Remote sensing images are often used to study glacial changes. For plateau mountainous areas, the images usually have a larger area of the mountain shadows. Shadows cause loss or distraction of the information reflected by the ground target, making remote sensing image difficult to understand. Therefore, the identification of glaciers in the mountain shadow area based on remote sensing images becomes a technical difficulty. In this study, a large mountain glacier on the Qinghai-Tibet Plateau was chosen as experimental subject. Based on Landsat 8 OLI data, this study first analyzed the reflection characteristics of different bands for glacier and non-glacier in shadow area. The results showed that due to the fact that direct light is blocked and target objects in a shadow area are mainly irradiated by the scattered light, the blue band which has shorter wavelength and higher intensity of the scattered light is preferred band for glacier identification in shaded area. For longer wavelength band, the reflectance of ground target in the entire shadow region is very low, and it is difficult to distinguish between glaciers or non-glacial regions. On this basis, a shaded glacier information enhanced index is proposed. Compared with the conventional glacier information extraction methods, the proposed method can give a result to identify the segmentation threshold more clearly in the histogram; and get the best result both in accuracy of the extracted boundary and the total area. For large-scale glacier extractionin the plateau mountainous area, it is recommended to use the proposed method which can be helpful in improving the overall work efficiency.

To achieve the spectral reproduction technology of halftone prints manuscript, the number of primary ink and the ink composition used in the manuscript should be specified before hand. However, there are still many problems to be solved in the primary ink spectral prediction for prints manuscript, and existing methods of spectral prediction have many disadvantages. To solve this problem, the algorithm of primary ink spectral prediction based on constrained non negative matrix factorization ISPNMF, and the optimizing algorithm for black ink spectral prediction have been put forward innovatively. The short comings of multiple optimal solutions and local minima of the basic non-negative matrix factorization were overcome, and the unique global optimal solution was realized by the algorithm of ISPNMF. The interference of prediction black inkcaused by the colored inks mixed absorption was eliminated by the optimizing algorithm for black ink spectral prediction, and the optimized result was close to the actual black ink spectrum. The accuracy of the algorithm was verified by using the samples of simulating different brands ink. In the experiments, Konica Minolta C1085 and HP indigo 5600, two kinds of four-color digital printing machine, with its toner and ink paste mimicking different brands of ink, were used. And the IT8.7/3 color card was printed in 230 g white cardboard, then X-rite I1 Pro2 was used to obtain the spectral reflectance data of two proofs as the experimental samples, to explore and verify the accuracy and practicability of the algorithms. The experimental results showed that, the number and spectrum of primary inks used in the printed manuscript can be accurately predicted in the linear empirical space. The GFC of prediction results of color inks were all up to 99.9%, and the SAD were all less than 0.045. The GFC of prediction results of black inks, which were optimized, were also up to 99.9%. The algorithms can not only predict theprimary ink of prints manuscript accurately, but also can match the actualprimary ink precisely. It is of great significance to the realization of the spectral replication of prints manuscript.

With its high brightness and high collimation, the application of synchrotron radiation micro-infrared spectroscopy in polycrystalline diamond can significantly improve the accuracy of the analysis of nitrogen concentration and the residence time in the mantle. The results of synchrotron radiation micro-infrared mapping of the polycrystalline diamond demonstrated that nitrogen impurity in the sample had high content (500～1 300 μg·g-1) and high aggregation, which indicated that the diamonds from western Yangtze craton are type IaAB. The residence time in mantle of the sample was between 0.06 to 0.12 Ga. In general, the sample had different growth zones and multiple diamond grains which crystallize early respectively and aggregate later. In addition, some of the grains briefly ceased to grow during the polycrystalline diamond formation which formed in a volatile material situation.

The study was based on the long-term black soil positioning test (beginning in 1979), and according to the input and output of soil nutrient, the soil nutrient balance of different fertilization treatments was analyzed. The dynamic characteristics of Hu molecular structure were analyzed by infrared and nuclear magnetic resonance spectroscopy, and the relationship between soil nutrient budget and the change of Hu structure was analyzed. The result shows: different fertilization measures changed the molecular structure of the inert component Hu in the soil while changing the nutrient content of black soil. From 1980 to 2014, organic and inorganic fertilizer (MNPK) treatment to meet the crop nutrient absorption, the total soil nutrient content was surplus, and the soil organic carbon content was increased and soil C/N ratio was decreased without CK treatment. At the same time, the ratio of 2 920/1 620 and 2 920/2 850, aliphatic C/, aromatic C, alkyl C/, alkoxy C and hydrophobic C/, hydrophilic C increased in the soil Hu, indicating that the molecular structure of black soil Hu became aliphatic and simplified. Fertilization can increase soil active organic carbon and reduce the content of inert organic carbon components. Organic fertilizer (M) and nitrogen, phosphorus and potassium fertilizer (NPK) could not meet the nutrient uptake of crops, and the soil nutrient deficit appeared. Infrared spectrum display, with the increase of fertilization years, the Hu of 2 920/1 620 decreased with organic manure (M) and nitrogen phosphorus potassium fertilizer (NPK) treatment, indicating that the content of aliphatic C decreased. Nuclear magnetic resonance spectroscopy showed that the treatment of alkyl C / alkoxide C decreased with NPK and non - fertilization (CK), indicating that the activity of organic carbon decreased. Although the treatments of fertilizer application without fertilization (CK) increased the soil Hu lipid C content and decreased the aromatic C content, with the increase of fertilization years, the treatment of nitrogen, phosphorus and potassium fertilizer (NPK) resulted in the change of soil Hu structure. There is a correlation between the nutrient deficit and the structural parameters of soil Hu, N, P2O5, K2O nutrient budget and total nutrient budget and 2 920/1 620 positive, aliphatic C, aromatic C was negatively correlated with the nutrient status, that can affect the structure of soil Hu. In the case of saving economic cost, the application of organic fertilizer in the rotation cycle, combined with the application of nitrogen, phosphorus and potassium fertilizer per year can meet the crop nutrient absorption, improving soil humus inert component structure while improving crop yield.

In order to solve the problem of lower prediction performance caused by the difficulty in retrieving the key features from hyperspectral data of soil available potassium, this paper proposes a novel hybrid feature selection algorithm based on Random Forests. Firstly, wrapper-based feature selection methods were applied to rapidly remove the redundancies and preserve the related features. Secondly, an Improved-RF feature selection algorithm was applied to further accurately select the wavelength variables from the pre-selected feature sets. In this step, characteristic wavelength with strong robustness and discriminative could be selected through improving the dipartite degree between the key and redundant features and using an iterative feature selection method. Therefore, the problem of low prediction performance in the soil available potassium inversion model could be better solved by using our hybrid feature selection algorithm. In order to verify the validity of our algorithm, 124 representative soil samples collected from the Dagu River Basin were chosen. Using our algorithm, the optimal feature subset which contained 13 sensitive bands have been selected and used to build soil available potassium content inversion model. This work compared the model performance of full bands, current feature selection algorithms and our algorithm. The comparison results indicated that our algorithm not only selects minimum numbers of wavelength features and reduces the dimension of full bands, but also achieves better prediction performance with lower RMSEP (9.661 5), higher R (0.936 9) and RPD (2.14). As an effective method of soil available potassium inversion model, the algorithm proposed in this paper can provide theoretical basis for the design of real-time soil nutrient sensors.

In order to distinguish between the crops and trees in the main grain producing areas more quickly and accurately, maize, wheat and poplar which are the main vegetation planted in the Huang-Huai-Hai Plain are used as the research object. Obtain the original spectral reflectance and calculate the data by using the original spectral feature point extraction, first derivation, second derivation and vegetation index. Extract the range of feature points, characteristic bands obtained by analyzing the original spectral reflectance, position, amplitude, area and differential value sum in blue, red and yellow edges by first derivation, and vegetation index by empirical formulas. Compare the accuracy of four methods in distinguishing between the three vegetable types based on the principle that the smaller the overlapping range is, the higher the accuracy of parameter will be, and choose the most suitable characteristic index as the identify indicator which has the smallest overlap in different vegetation types. The results showed that among the four methods of manipulation spectral data, first derivation had the highest accuracy in identifying corn, wheat and poplar compared with the original spectral feature point extraction, second derivation and vegetation index. Among the indexes obtained by the first derivation, the amplitude, area and differential value sum in yellow edge region had the higher recognition accuracy. The recognition accuracy of the amplitude in yellow edge was up to 97.5%, and the area and differential value sum in yellow edge was up to 98.1%. The results were verified with 167 other sets of data, and the verification results showed that the recognition accuracy of the amplitude in yellow edge was up to 96.4%, and the area and differential value sum in yellow edge was up to 97.6%. The result was different from that result which was obtained by average reflectance curve of spectrum from the single plant in different growth state, and this method could effectively preserve the difference between individual spectral reflectance curves. Thus, it could be seen that extracting the yellow edge parameters through the first derivation was effectively used in distiguishing vegetation where the crops and trees were planted in the same place, and among all the parameters, area and differential value sum in yellow edge had the highest recognition accuracy.

Boletus is rich in nutrition, which is favored by consumers all over the world. Due to the differences of species and environmental factors, the quality of boletus of different species and origin vaires. At present, the shoddy, which undermines the sales of genuine boletus and the mushroom market, not only poses a health risks to consumers, but also restricts the international trade of boletus. In this study, the data fusion strategy was used to identify the species and origin of boletus, in order to provide a rapid and effective solution for tracing the source of edible fungi and correctly evaluating their quality. The test samples Boletus griseus, B. umbriniporus, B. edulis, Leccinum rugosicepes and B. tomentipes of five species of boletus fungi fruiting bodies collected from Baoshan, Kunming, Yuxi and Honghe Prefecture of Yunnan province. The chemical information was collected with Fourier transform infrared spectroscopy (FT-IR) and UV-Visible spectrophotometer (UV-Vis). The Kennard-Stone algorithm was used to divide the raw data of samples into calibration sets and validation sets. The calibration set established partial least squares discriminant analysis (PLS-DA) models based on FT-IR, UV-Vis, low-level, mid-level and high-level data fusion. The determination coefficients R2cal, predictive ability Q2, root mean square error of estimation (RMSEE) and root mean square error of estimation (RMSECV) were used to evaluate the robustness of the model. The results showed that: (1) The peak position, peak shape and number of peaks of FT-IR and UV-Vis absorption peaks of different species and origin were similar, and there were differences in absorption intensity. This showed that the chemical compositions of boletus were similar, but the content was different. (2) Two-dimensional scatter plots of PLS-DA model. It can be seen that mid-level fusion is better than low-level fusion to identify sample species and origin. (3) In each model, the mid-level fusion model has a larger Q2 and a minimum RMSECV, it showed that the model has the strongest robustness. (4) The test sets used to verify the model generalization ability, the correct rate of FT-IR, UV-Vis, low-level, mid-level and high-level data fusion model of samples kind identification were 92.86%, 35.71%, 97.62%, 100%, 95.23%, respectively; the correct rate of origin identification were 71.43%, 61.90%, 61.90%, 97.62%, 76.19%. The results showed that the data fusion is better than the independent model to some extent. Among them, the correct rate of mid-level data fusion is 100% in species identification, and the accuracy in origin identification is 97.62%. Mid-level data fusion model has better identification effect and generalization ability. FT-IR and UV-Vis combined with mid-level data fusion strategy can achieve the rapid and accurate identification of the boletus species, the fast and effective identification of origin. It can be used as a new method for traceability and quality evaluation of edible fungi.

When vegetation index is used to retrieve water content, it is important to find the vegetation index which has the highest correlation between measured spectral data and vegetation water content. In this paper, Fukang science experimental base of Xinjiang University was selected as the study area. Based on spring wheat field spectral data and leaf water content data, this paper selected 5 typical water vegetation indices that have higher grey correlation degree with leaf water content through grey correlation analysis. And used 2 kinds of methods including the partial least squares regression (PLSR) and back propagation artificial neural network (BP ANN) to establish the quantitative inversion models of soil water content. The results showed that: (1) The first derivative of spectrum can effectively remove the noise influence and highlight the spectral characteristic information, especially in the range of 750～830, 1 000～1 060, 2 056～2 155 nm, which significantly improves the correlation with LWC. (2) The grey correlation method can better characterize the relationship between water vegetation indices and leaf water content, and the first 5 water vegetation indices based on original spectrum were two band ratio vegetation index, and the water vegetation indices based on the first derivative spectra were basically two band normalized difference vegetation index. (3) Among the two established models, R2 of PLSR and BP neural network model established by the first derivative reflectance were 0.80 and 0.81 respectively, which showed that the two models have good stability in inversion of leaf water content; the RMSE of the two models were 0.55 and RPD were 2.01 and 1.41 respectively, which indicated that the prediction accuracy of PLSR model was higher than that of BP neural network model. From the validation of the model, the PLSR model has some advantages in estimating leaf water content of spring wheat, which provides a reference for hyperspectral quantitative inversion of it.

In order to explore the feasibility of detecting wheat kernel black tip (BT) disease and investigating an optimized classification model based on mainstream machine learning algorithms, a large amount of 2 760 wheat kernels spectral data of Vis/NIR bands (579~1 099 nm) were collected by self-made spectral acquisition platform. After pretreated with standard normal variate correction (SNV) of 600~1 045 nm bands, 7 kinds of data sets were established. Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) of spectral data dimensionality reduction methods, and four machine learning algorithms, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Random Forest (RF) and AdaBoost, were adopted to develop eight classification models. Results showed that Vis/NIR spectrums combined with all the machine learning methods could be used to detect BT disease with accuracies ranging from 93.3% to 98.6%, which indicated that Vis/NIR would be the more effectively compared to NIR. As SPA-SVM possessed a high average classification accuracy and PCA-AdaBoost showed better generalization performance than other algorithms, considering practical purposes, these two algorithms were adopted as optimized models in 2-category classification, 3-category classification and 4-category classification for various degrees of BT detection. Results indicated that the classification accuracies declined gradually with the classification number increasing, but the detection accuracy of non-diseased wheat kernel tended to be stable with an accuracy of more than 87.2%. Taken together, SPA-SVM performed better than PAC-AdaBoost in wheat BT disease detection. The models and conclusions of this research are intended to lead to the streamlining of VIS/NIR spectroscopy in automated wheat black tip inspection as well as to provide criteria for high speed sorting.

This paper established the method for preconcentration and determination of heavy metals such as Zn, Cu, Co, Mn, Ni Cd, Pb in water samples by solid phase extraction (SPE) with inductively coupled plasma atomic emission spectrometry(ICP-AES). In this experiment, Dowex 50WX8 strong acid cation exchange resin was used and by optimizing the conditions of preconcentration and eliminating the interference of coexisting ions, the optimum sample pH, sample flow rate, eluent type and concentration, sample volumes were determined with 3.0~4.0, 3.0 mL·min-1, 3.0 mol·L-1 HNO3 and 200 mL, respectively. For this method, the detection limit and quantitation limit of each element were 0.09～0.45 and 0.31～1.50 μg·L-1, respectively. The recoveries and relative standard deviations RSD (n=6) were 95.3%～104.2% and 1.25%～4.12%. The water samples of different parts were determined by this method and compared with the direct determination by ICP-MS, the results of the both methods were basically identical. All of these indicated that the detection limit and quantitative limit in this method could meet the requirements for determination of heavy metals in water samples. The accuracy and precision of the method were high which were suitable for the determination of Zn, Mn, Cu, Co, Ni, Cd, Pb in water samples.

An analytical method for the accurate determination of 12 kinds of trace elements in wild Artemisia Selengensis by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) was established. In the MS/MS mode, AsO and SeO were formed by mass shift reaction of As and Se with O2, and AsO and SeO were used to eliminate mass spectral interferences. Using NH3/He as the reaction gas, Cr, Mn, Fe, Co, Ni, Cu, and Zn were reacted with NH3/He to form clusters ions, by measuring the cluster ions to eliminate mass spectral interferences. Cd, Hg and Pb were measured using standard mode. The calibration curves of analytes in the 0～200 μg·L-1 range has a good linear relationship, and the detection limit is 0.64～49.61 ng·L-1. By analyzing the national standard material celery (GBW 10048), the difference test was carried out by t test, and the results showed that there was no significant difference between the measured value and the certified value, and the method was proved to be accurate and reliable. The spike recovery was 92.0%～106.1%, and the RSD was 1.6%～4.9% for twelve elements. Analyzing from 4 samples of wild Artemisia Selengensis in different areas of China, according to the results, different sources of 12 elements content in the samples vary, the contents of Mn, Fe, Zn are much higher than the rest of the 9 kinds of elements, and poison rational elements of As, Cd, Hg, Pb content are very low. The method can accurately determine a variety of trace elements in the wild Artemisia Selengensis, and provide scientific theoretical basis for the edible nutrition and safety of the wild Artemisia Selengensis.

X-ray photon-counting detector is the core of the spectral CT imaging technique, and it could choose to record different energy X-ray photons by detector energy threshold, which is helpful to analyze the physical properties of different materials. In this paper, we used a spectral CT system based on photon-counting detector to study the K-edge characteristics of high purity metallic materials. By setting different energy thresholds for the detector, we could obtain the projection images of the metallic materials in different energy ranges. The attenuation characteristics of different energy X-ray could be analyzed by the gray information of projection images to identify the K-edge characteristics of metallic materials. The final experimental results demonstrated that the X-ray spectral CT system based on photon-counting detector can recognize the K-edge characteristics of metallic materials interacting with specific energy X-ray photons. The energy threshold for photon-counting detector can be calibrated by calculating the linear correspondence between K-edge peak energy threshold and K-edge theoretical energy value.

Heavy metal pollution in the soil affects the yield and quality of crops. The traditional detection method has complicated procedures, high detection costs, and slow detection speed. The X-ray fluorescence (XRF) analysis technology to detect heavy metal content in soil has the advantages of being simple in processing, on-site, rapid and non-destructive. Due to the complex soil background including much noise and irrelevant information, before the establishment of the XRF correction model, the pre-processing of the spectrum can effectively remove irrelevant information and maintain useful information, which has an important influence on the accuracy of the XRF prediction model.This article focuses on the effects of spectral pre-processing method on the accuracy of heavy metal content prediction model. Firstly, forward interval partial least squares (FiPLS) was taken as a correction model to compare the detection accuracy of the heavy metal model in eight different conditions, namely non-pre-processing, detrending processing (DT), standard normal variable transformation (SNV), multiple scatter correction (MSC), wavelet denoising (WT), SNV+DT, convolution smoothing (SG) + first derivative and convolution smoothing (SG) + second derivative. The preliminary results showed that the multiple scatter correction pre-treatment method had desirable effects. Compared with the original spectrum, the determination coefficient R rised from the original 0.988 to 0.990, and the prediction of root mean square error (RMSEP) and the relative error respectively declined from the original 20.809 and 0.166 to 19.051 and 0.121. Secondly, on the basis of the multi-dimensional scattering correction pre-processing method, the localized weighted linear regression multiple scatter correction (LWLRMSC) and partial least squares multivariate scatter correction (PLSMSC) were proposed in terms of the restriction of describing non-linear relationships with linear representations, and the modeling effects of LWLRMSC and PLSMSC were compared. LWLRMSC was based on the weighted idea. In the prediction of the value of a point, the proper kernel function and weight distribution strategy were selected to perform linear regression of the prediction point, and the under-fitting condition of the simple linear regression was resolved. PLSMSC, based on the PLS modeling idea and taking into account the maximum correlation between the independent variable and the dependent variable, reduced the fitting error and distortion. The results showed that PLSMSC has the best pre-treatment effects. The R values of the predicted and actual values of the five heavy metals (Cu, Zn, As, Pb and Cr) were 0.989, 0.973, 0.991, 0.989 and 0.986, with the RMSEP respectively being 8.805, 58.360, 7.671, 12.549 and 20.851. Compared with the traditional MSC method, PLSMSC not only has a significant improvement in accuracy but also has better generalization performance. It can eliminate spectral noise and improve the contribution of effective information, thus providing theoretical support for the soil heavy metal content model to select the suitable pre-treatment method.

We have developed a setup that could produce gas phase metal compound molecules and ions by discharging assisted laser ablation. The ionic and neutral radical products have been investigated by the time-of-flight mass spectroscopy (TOF-MS) and laser-induced fluorescence spectroscopy (LIFs), respectively. The results showed that this setup could produce gas phase metal compound radical molecule ions with a high yield and low temperature. Therefore, this source may help researchers in laser spectroscopic research on metal compound molecules.

Fourier transform infrared spectroscopy is an important method for monitoring air pollution emissions from pollution sources. Automatic baseline correction method for gas spectra is of great significance to air pollution monitoring applications, such as rapid detection and long-term on-line monitoring. One difficulty in the current automatic baseline correction is accurately correcting the spectra, which include broad peaks. The broad peakscontain low-frequency content in the frequency domain; thus, the method for extracting baseline information based on low-frequency filtering is prone to baseline distortion because of the difficulty in selecting the appropriate separation parameter. Automatically identifying the baseline point and fitting the baseline of the spectrum based on a preset baseline function can prevent the selection of separation conditions; however, the result of baseline correction is highly sensitive to the baseline function adopted. If the degree of freedom in the baseline function is excessively small, the baseline function cannot fit the baseline drift in the spectra accurately, and the error will be considerable after baseline correction. Meanwhile, if the degree of freedom in the baseline function is excessively large, in particular, when a false degree of freedom does not exist in the natural baseline drift, the fitted baseline may have baseline distortion. Many types of baseline functions exist, including linear, polynomial, spline interpolation, and exponential functions. At present, consensus is lacking regarding the selection criteria for baseline functions. In this study, we proposed a baseline function for gas spectra for extractive atmospheric monitoring based on the degree of freedom of the natural baseline drift; we aimed to avoid false degrees of freedom or lack of necessary degrees of freedom in the baseline function. We found that the degrees of freedom of major baseline drift in the gas spectrum can be approximated in specific order terms of wavenumbers (0, 1st-, 2nd-, and 4th-order terms). An automatic baseline correction method based on a polynomial baseline function with above (0, 1st-, 2nd-, and 4th-) order terms was proposed in this study. In the experiment, a measured air spectrum, which contained broad peaks of water vapor, was used as a sample to test the performance of the baseline correction method. The baseline correction result of the proposed automatic baseline correction method was compared with the that of two types of iterative polynomial fitting methods that were proposed by Lieber and Mahadeven-Jansen (LMJ) and by Liu and Koenig (LK). The experiment results indicated that compared with the LMJ and LK methods, the proposed method avoided the baseline distortion in the best possible manner, and the proposed method also showed the lowest average variance between the corrected baseline and the absorbance zero line. Our research showed that in automatic baseline correction, an effective baseline correction result can be obtained by establishing the baseline function with the freedom of the natural baseline drift.

Raindrop spectra describing the variation of precipitation particle concentration with particle size is the most basic micro-physical characteristics parameter. Using the information of raindrop spectra, we’ll have a clearer understanding of the development of natural precipitation process. From the relative position that the raindrop is in the rectangular beam, the variety of transmission intensity of rectangle beam with raindrop size were theoretically analyzed, and the results showed a nice linear relationship. It pointed out that the type of precipitation (drizzle, rain, snow, soft hail, hail as well as mixed precipitation) can be obtained by measuring raindrop size and fall time, and the information can be calculated, such as precipitation intensity, amount of precipitation and particle spectrum distribution.

The interactions between Fc(COOH)2 (λmax=255 nm) and BSA (λmax=277.5 nm) were studied by UV-Vis spectroscopy. The results showed that, there was a good linear relationship between absorbance and concentration for Fc(COOH)2 in range of 10~190 μmol·L-1 and for BSA in range of 100~1 900 mg·L-1, respectively. After the reaction of Fc(COOH)2 and BSA , λmax shifted to 275 nm to show the existing reaction. When the concentration of Fc(COOH)2 or BSA was fixed, the absorbance of Fc(COOH)2 or BSA increased with the increase of BSA or Fc(COOH)2 concentration, which showed the hydrogen bonding form between Fc(COOH)2 and BSA and the growth of molecular chain, and the increase of absorbance was attributed to the fact that bigger molecule absorbs more energy and release. Meanwhile, the stability of new formed molecule bond was affected by reaction time, after reacted for 0.1 to 24 and 96 h, the absorbance at 275 nm changed from 1.062 to 1.045 and 0.986 for Fc(COOH)2 (70 μmol·L-1) and BSA (1 900 mg·L-1), and the absorbance changed from 0.813 to 0.794 and 0.750 for Fc(COOH)2 (190 μmol·L-1) and BSA (700 mg·L-1), respectively.