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Porphyrin is a kind of photosensitizer for photodynamic therapy of cancer. Many porphyrin derivatives have been used in clinical treatment. Human Serum Albumin (HSA) is the carrier of drug transportation. Therefore, investigation on the interaction of porphyrin with HSA is very important to understand the pharmacokinetic of the porphyrin. In this paper, a new water-soluble carboxyl porphyrins, meso-tetrakis(carboxyl) zinc(Ⅱ) porphyrin (2-Zn), was synthesized and characterized. Its interaction with human serum albumin (HSA)was investigated by UV-Vis absorption spectra, fluorescence spectra, circular dichroism (CD) spectra and molecular modeling. The results indicated that the fluorescence quenching of HSA by 2-Zn was a static process with the quenching constants are 1.96×104 L·mol-1 (298 K) and 1.37×104 L·mol-1 (310 K) and the binding constants were calculated to be 1.93×104 L·mol-1 (298 K) and 1.50×104 L·mol-1 (310 K). According to the Van’t Hoff equation, the thermodynamic parameters were characterized by negative enthalpy (ΔH=-16.132 kJ·mol-1) and positive entropy (ΔS=27.905 J·mol-1·K-1), which indicated that 2-Zn binds with HSA mainly via electrostatic interaction along with the hydrogen bonding and hydrophobic interaction. Site marker competitive binding experiment confirmed that 2-Zn mainly binds at site Ⅱ. The distance between HSA and the receptor (2-Zn) and the efficiency energy transfer were obtained to be 4.01 nm and 0.163 respectively, based on the Forster theory on resonance energy transfer. Synchronous fluorescence, absorption and CD spectroscopy showed that the interaction of HSA with 2-Zn induced a conformational change of protein, and the amount of α-helical structures were decrease. Furthermore, the binding details between 2-Zn and HSA were further studied with the molecular docking, which was in good agreement with the site marker competitive binding experiments and thermodynamic parameters.

Near-infrared(NIR)As a fast and non-destructive testing technology, spectroscopy techniques is very suitable for pharmaceutical discrimination. Autoencoder network, as a hot research topic, has drawn widespread attention in machine learning research in recent years. Compared with traditional surface learning algorithm models, Autoencoder network has more powerful modeling capability as a typical deep networks model. Based on the unsupervised greedy layer-wise pre-training, autoencoder trains the network layer by layer while minimizing the error in reconstructing. Each layer is pre-trained with an unsupervised learning algorithm, learning a nonlinear transformation of the input of each layer which is the output of the previous layer. Pre-whitening process could get the inner structural features of the data more effectively. The supervised fine-tuning is followed with the unsupervised pre-training which sets the stage for a final training phase. The deep architecture is fine-tuned with respect to a supervised training criterion with gradient-based optimization. In this paper, firstly, the preprocessing step and pre-whitening transformation were used to treat near-infrared spectroscopy data of erythromycin ethylsuccinate, The pre-whitening transformation would reduce the correlation of the features, which gave each feature the same variance. Experimental results showed that the pre-whitening process had improved the classification accuracy of Sparse Denoising Autoencoder (SDAE) effectively. The SDAE with two hidden layers combined with pre-whitening was used to build the classification model for the identification of counterfeit pharmaceutical. The BP neural networks was compared with SVM algorithm for the classification accuracy and mean absolute difference (MAD). SDAE algorithm had higher classification accuracy than BP neural networks which had the same network structure with the SDAE networks, and SDAE algorithm also performed better than the SVM algorithm when the train datasets achieved a certain amount. As to the generalization performances, SDAE algorithm had less mean absolute difference of classification accuracy than SVM and BP Neural Networks. This result showed that SDAE algorithm could be effectively used to discriminate the counterfeit pharmaceutical.

A new kind of imitation of turquoise named “Violet” and “White buffalo” which is a type of associated mineral of turquoise have appeared on the market recently and are becoming increasingly popular. Conventional instruments, X-ray fluorescence spectrometer, Infrared spectrometer, X-ray powder diffraction, Scanning electronic microscope, UV-Vis have been employed to discuss the gemmological characteristics of this kind of imitation of turquoise in this paper, to study their chemical composition, mineral composition, microstructure, spectral characteristics and color emerging mechanism. The X-ray fluorescence spectrum shows that the chemical composition of the sample is complicated. The basic elements of different-colour samples are basically identical which contains of Ca, Al, P, Cu, Si, K, Fe, Ba. It can be deduced from the intensity of infrared absorption bend that the major anion group of this kind of imitation of tuequoise is PO3-4. The analysis for X-ray powder diffraction data indicated that the major mineral of the sample is crandallite and woodhouseite. Meanwhile, the scanning electron microscopy showed that the structure of the dense sample is determined by numerous of scaly, leaf-shaped and irregular granular aggregates. With the study of absorption spectrum, the conclusion is drawn that Fe3+ electronic transitions are the main factor for coloring of the sample and color varies with the content of Fe3+.

Extreme learning machine (ELM) has been applied in near infrared spectral analysis as a novel chemometric method which attracted the attentions of various researchers. However, the dimension of spectral data is usually very high while more hidden nodes should be incorporated in original ELM model for spectral data. Thus the problems of high dimension and high colinearity in the output matrix of hidden layer of ELM model are inevitable. The solutions obtained with the existing Moore-Penrose generalized inverse can be ill-conditional due to the high dimension and high colinearity in the hidden layer output matrix. This study aims to propose an improved ELM to build spectral regression model. The proposed method firstly uses extreme learning machine (ELM) to relate spectral variables to response variable; then the output of each hidden node are treated as new variables; VIP-SPLS ( improved stacked PLS based on variable importance in the projection) proposed by our group recently is used to build the regression model between those new variables and the response variable. In this paper, this method is called as improved ELM (iELM). VIP-SPLS model can fully utilize the output information of each hidden node and can effectively solve the problems of high dimension and high colineariy. At the same time, VIP-SPLS also has the advantage of model ensemble. Therefore, the performance of ELM model used for spectral data can be improved if the VIP-SPLS is incorporated to relate the hidden layer output matrix and response variable. The proposed method is applied to a commonly used benchmark NIR spectral data for evaluation. The results demonstrate that the precision improvement of iELM model is 29.06% to PLS model and 27.47% to original ELM model, respectively.

Surface broadband emissivity in the thermal infrared region is an important parameteras for the studies of the surface energy balance. This paper analyzed and offered an equation to estimate the surface broadband emissivity for the spectral domains 8～14 μm against the MODIS data, and then, the distribution characteristic of surface emissivity for Taklimakan Desert was obtained with this equation. Firstly, along two highways crossing the Taklimakan Desert, twenty sample sites were selected and their spectral of broadband emissivity were observed with Fourier Transform Infrared spectrometer (FTIR). Secondly, using the Moderate Resolution Imaging Spectrometer (MODIS) land surface temperature and emissivity product MOD11A1 and MOD11C1, derived emissivities in three thermal infrared channels 29 (8.4～8.7 μm), 31 (10.78～11.28 μm) and 32 (11.77～12.27 μm) and MODIS surface reflectance products MOD09A1, derived reflectance in near-infrared channel 7 (2.105～2.155 μm), developing an empirical regression equation to convert these spectral emissivities and reflectance to a broadband emissivity. The FTIR data were used to determine the coefficients of the regression equation, another part of FTIR data were used to investigate the accuracy of equation. It was found that the equation consist of MODIS channels 29, 31 and 32 has more accuracy; furthermore, the accuracy is improved when channel 7 data was added in the regression equation. The root mean square error (RMSE) and Bias were 0.004 5 and 0.000 1, respectively. Comparing to other six equations originated from literatures, which also estimate the surface broadband emissivity from narrowband emissivities. The RMSE and Bias of our equation are lower one order and two orders of magnitude than other six equations, respectively. Lastly, our equation is applied in the Taklimakan Desert area to build a distribution image of emissivity based on MODIS data. It demonstrates that the emissivity of Taklimakan Desert is in the range of 0.880～0.910 over the central regions, the averaged value is 0.906; The emissivity is in the range of 0.910～0.940 where the areas covered by spare vegetation; The emissivity is in range of 0.950～0.980 where the regions near to the oasis.

Precipitable water vapor (PWV) shows great significance in remote sensing quantitative application and ecological research. Aiming at solving the problems of traditional methods, an Improved Continuum Interpolated Band Ratio (ICIBR) algorithm was proposed based on the ratios of apparent reflectance of multi-channels in this paper. The ICIBR algorithm considers the absorption characteristics of water vapor absorption in three MODIS near infrared channels (Bands 17, 18 and 19) and the relationship between the PWV and the ICIBRs of above three channels were simulated by using the MORTRAN model. Then the PWV retrieval model for MODIS data was constructed. Texas, Oklahoma region, a typical arid/semi-arid areas, located in North South America were selected as the study area and four different MODIS 1B data were obtained to perform PWV retrieval experiments using the ICIBR algorithm. Last, the corresponding GPS PWV ground observation data provided by SuomiNet and the MODIS PWV product (MOD05) were obtained to verify the experiment results. Evaluation and comparison results showed that the PWV retrievals showed a higher consistency (r=0.967) with the GPS ground measured PWV data with smaller RMSE~0.276 cm and a total of 71.08% of observation points falling within PWV Expected Errors (EE~±0.05+0.15PWVgps). Moreover, the ICIBR algorithm showed an obviously great improvement in PWV estimation, which can effectively reduce 61% overestimation of PWV retrievals than MOD05 PWV products. This new algorithm is more simple and practical with an overall more reliable retrieval accuracy.

As a sensing device, microfiber knot resonator (MKR) has been widely used in sensing field due to its advantages of high sensitivity and fast response. It is important to analyze the spectrum of MKR for detecting environmental parameters in that the spectrum can reflect the change of ambient environment. In this paper, the relationship between spectrum of MKR and seawater temperature has been studied theoretically and experimentally. Firstly, the dependences of the propagation constants of fundamental mode (HE11) on the fiber diameter and the probing wavelength are studied theoretically in this paper, including two orthogonal polarization states of fundamental mode. The calculated results show that the propagation constant increases with the increasing fiber diameter and the decreasing probing wavelength. Simultaneously, the dependence of propagation constant of fundamental mode on seawater temperature is studied. The results show that the larger propagation constant corresponds to the higher seawater temperature, which indicates that the seawater temperature affect the mode propagation constant. Thus, the seawater temperature can be obtained by detecting the resonant spectrum. Secondly, the experimental system for seawater temperature sensing is set up, with which the resonant spectrum of MKR for seawater temperature sensing are obtained. The experimental results show that two sets of resonant peak exist on the spectrum, which are corresponding to TE and TM modes of fundamental mode. The sensing sensitivities of the two polarization modes are 5.54 pm/°C and 5.24pm/°C, respectively. Finally, the reason for resonant spectrum of the two polarization states is discussed, which is that the separation of the two modes resulting from the twisted coupler of the knot zone, and the resonant intensity of the two polarization modes is analyzed. The resonant intensities of the two modes are different and one increases while the other one decreases with the increasing probing wavelength. It is determined by the coupling coefficients and attenuations of the two coupled states，which are affected by the probing wavelength. The experimental and theoretical results agree well.

A set of coal samples were used for laser-induced breakdown spectroscopy (LIBS) experiment to measure the coal calorific value. Traditional channel normalization method didn’t consider the physical / chemical mechanism of coal, which would limit the model in precision, accuracy and repeatability. Thus a new calibrated model based on the kinds of the effects of spectral deviation was proposed in this paper. The model selected 19 groups of coal samples, where the random 15 groups were used to establish quantitative analysis model of calorific value while the remaining four for inspection and evaluation. The model based on spectral deviation factors, and the transmission theory combined with the stark broadening formula was used to deduce the absorption effect mechanism and the deviation correction method under the condition of LIBS. The mutual interference between elements and the mechanism of matrix effect were being analyzed while K coefficient method was used to correct mutual interference between the elements in the LIBS. The establishment of numerical model with the electron density, the plasma temperature and the element concentration was used to deeply corrected spectrum deviation caused by matrix effect. Thus taking into consideration of the effect of self-absorption, interfere of inter-elements and matrix effect, the calibration model was established, while R2=0.967, RMSEP=0.49 MJ·kg-1, RMSE=0.45 MJ·kg-1, MRE=2.42%, ARE=1.64%, RSD=5.79% and RSDP=8.10%. Compared with the 0.405, 8.28 MJ·kg-1, 4.14 MJ·kg-1, 22.85%, 52.48%, 18.28% and 32.85% of traditional channel normalized-multiple linear regression method, it demonstrated that the precision and accuracy have been improved significantly and model has good application value.

The heterojunction was formed between two kinds of coupling semiconductors , which improved the charge separation efficiency of system, widened the spectral response range of catalysts and improved photocatalytic propertirs of catalysts. The process of preparation of semiconductors coupling was easily affected by preparation methods and reaction temperature and so on, which would cause the changes in crystal structure and surface properties of coupling semiconductors, thus photocatalytic quantum efficiency of coupling semiconductors was increased. In this article, the following three aspects were mainly discussed. (1) About the coupled system of halogen bismuth oxide and oxide, because generaling BiOX with the semiconductor material compound, the efficient heterojunction structure could be formed, photocatalytic performances of the photocatalytic degradation of pollutants were improved. (2) About the coupled system of AgX and BiOX, compared with the pure AgI or BiOI, composite photocatalytic materials of AgI/BiOI had higher photocatalytic reactivity in visible light. (3) About the coupled system of halogen bismuth oxide and other compounds, after Bi2S3 coupled with BiOX, photoproduction electronic migrated in the two kinds of catalysts, the separation efficiency of electrons and holes was improved, and photocatalytic performances of coupling compound were improved. In addition, in recent years, the latest research progress of the preparation method, the influencing factors of the photocatalytic performance and improving the utilization efficiency of visible light of semiconductors coupling at home and abroad was reviewed in this paper. Finally, the main problems and the future striving direction in semiconductors coupling were presented.

Triphenyltin Chloride (TPhT) is one of the only two kinds of metal compounds known in the endocrine disruptors. TPhT is widely used in industry, agriculture and transportation fields, which can cause great impact on soil, marine and inland freshwater environment. This experiment collects the Raman signal of TPhT standard powders by applying laser confocal Raman spectroscopy to explore the feasibility of the method and to acquire optimized parameters. In this paper, we combined the application of laser confocal Raman spectroscopy with TPhT physical property. Due to different functional groups of TPhT molecules shows different vibration modes, the Raman spectra was divided into three wavenumber areas(1 500~3 200, 900~1 500 and 100~900 cm-1) to attribute and analyze their assignments of the Raman peaks, obtaining the characteristics of TPhT vibration modes and the corresponding characteristic peaks. Finally, a standard Raman spectra library was established with the spectral range between 100~3 200 cm-1. The results showed that the Raman spectra with high signal-to-noise ratio can be obtained in a short time when the laser power options decayed to 0.5% of the original power (500 mW) after 10s exposure and 2 times integration. Strong Raman signals observed in the Raman spectroscopy at 212, 332, 657, 997 and 1 577 cm-1 could be used as the characteristic peaks of TPhT in Raman detection. Otherwise, the co-occurrence of the Raman peaks at 657 and 997 cm-1 can be considered as the presence of TPhT in complex environmental samples. Experimental results are given to identify the presence of TPhT. The results would be used to identify the presence of TPhT residual in real environmental samples, which provide a theoretical basis and data base on Raman spectroscopy.

A radiometric calibration method is described in this paper by using the deep convective clouds (Deep Convective Cloud, DCC) target for FY-2 visible channel. The deep convective cloud can be used as the radiometric calibration transfer object. The on-operational FY-2 radiometric calibration bias and the long-term degradation trend are evaluated according to the AQUA/MODIS instrument as the baseline of radiometric reference and DCC. The results show that: (1) There are different degrees of degradation for FY-2D, FY-2E and FY-2F, among which FY-2D has the biggest degradation due to the longest period. The annual rates of degradation for FY-2D and FY-2E are quite similar, 1.67% and 1.69% respectively, whereas the rate for FY-2F is lower with 0.81%; (2) During the period of satellite eclipse, the instruments are not stable and this phenomenon could be detected by the DCC method;(3) There are bias in the the operational radiometric calibration between FY-2 and AQUA/MODIS, which is treated as the the radiometric reference usually. The radiometric calibration method based on DCC could work well in the radiometric calibration for FY-2. The results will help us to understand the degradation of instrument and for quantitative application usage.

Hollow waveguides(HWG)have recently emerged as a novel concept serving as an efficient optical waveguide and a highly miniaturized gas cell. Compared with conventional multi-pass gas cells, HWG gas cell has the advantages of facilitating gas exchanging because of its small size and fast responding speed. In this paper, we poposed an ammonia sensor based on tunable diode laser absorption spectroscopy(TDLAS) using HWG as the gas cell. The sensor employs wavelength modulation spectrum(WMS) with simultaneous detection of the second harmonic(2f) signal and the first hamonic(1f) signal. Normalization of the 2f signal by the 1f signal enables the sensor for calibration free measurement. The sensor performance is tested with gas standards and the result shows good linearity with correlation coefficient of 0.999 8, and the detection limit is 26 ppb with an integration time of 18 s. The sensor based on HWG gas cell is suitable for sensative and real-time monitoring ammonia in the air.

Vegetation water content is an important indicator of vegetal state, and a vital parameter of studying agriculture, ecological and hydrological. The diagnosis of vegetation water content has great significance for forest fire forecast and natural vegetation drought condition monitoring. The correlation analysis of the vegetation spectral reflectance and vegetation water content shows that the relativity between the spectral reflectance of different wavelengths and the vegetation water content varies considerably. The spectral reflectance of red band of visible light (620～700 nm) and the near-infrared band(800～1 350, 1 600～1 950, 2 200～2 400 nm) had a higher correlation with the vegetation water content. The slope angle indexes were used as parameters for estimating the vegetation water content based on analyzing the relation between the slope angle indexes and vegetation water content. An evaluation model of vegetation water content was set up by utilizing statistical linear regression model method. The band of 660, 850, 1 630, 2 200 nm were selected as RED, NIR, SWIR1 and SWIR2 band value of the slope angle index based on the analysis of the correlation between spectral reflectance and vegetation water content. A large amount of vegetation spectral information and vegetation water content were collected in the study area(the upstream of Minjiang River), and the linear regression model of the slope angle index (SANI, SASI, ANIR) and vegetation water content (FMC) was build. The linear regression model of ANIR and FMC has the highest of linear fitting and the linearity is up to 0.791. The near infrared angle index(ANIR)was improved on the basis of the analysis the linear regression results of angle slope vegetation index and water content. Near infrared angle normalized index (NANI) and near infrared angle slope index (NASI) were defined, and the linear regression model was established. Compared with the slope angle index (SANI, SASI, ANIR) which were proposed by Palacios-Orueta, NANI had more advantages in the vegetation water content inversion in the study area. The determination coefficient (R2) of the inversion model increased from 0.791 to 0.853, and root-mean-square error (RMSE) reduced from 0.047 to 0.039. Angle slope index had higher linear fitting and estimation accuracy by improving the angle of slope index. NANI and FMC linear regression model was established to estimate the vegetation water content in the study area. In this paper, the main innovation point is that the slope angle index NANI and NASI has been proposed on the basis of predecessors’ research results, and the improved angle slope index has higher linear fitting and estimation accuracy compared with SANI, SASI, ANIR.

Wild relatives play a very important role in enriching germplasm resources and improving the quality and yield of cultivated species. In this paper, the genetic relationship between Panax notoginseng and its wild relatives has been investigated by using Fourier transform infrared (FTIR) spectroscopy in order to provide theoretical bases in the variety improvement of P. notoginseng as well as the development and utilization of germplasm resources. The FTIR spectra of P. notoginseng and its wild relatives (P. japonicus var. major, P. stipuleanatus, P. vietnamensis, P. japonicus var. bipinnatifidus) as well as Panax notoginsenosides were collected. The original infrared spectra of P. notoginseng and its wild relatives were pretreated by automatic baseline correction, smoothing, ordinate normalization and second derivative. The genetic relationship between P. notoginseng and its wild relatives has been studied together with the aid of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). By comparing the infrared spectra of P. notoginseng with that of panax notoginsenosides, some common peaks such as 3 400, 2 930, 1 635, 1 385, 1 075 and 927 cm-1 has been found. It showed that the peak heights of P. notoginseng samples may relate with the content of panax notoginsenosides. The original infrared spectra of P. notoginseng are similar to its wild relatives and the absorption peaks of the functional groups of C—H, CO, O—H, C—N and C—O were presented. There were some differences in the fingerprint region (1 800～500 cm-1) of the second derivative spectra of these five species samples. The characteristic absorption peaks such as 1 385 and 784 cm-1 has an obviously differentiation. Then the fingerprint region of second derivative spectra is subjected to be analyzed by PCA and PLS-DA. By comparing the 3D score plots of these two methods, the classification result of PLS-DA is significantly better than PCA. In addition, the result of HCA which based on the six principal components of PLS-DA has shown that P. japonicus var. major and P. vienamensis have close relationship with P. notoginseng while P. stipuleanatus and P. japonicus var. bipinnatifidus are far from P. notoginseng. The use of Fourier transform infrared spectroscopy combined with chemometrics methods could effectively investigate the genetic relationship between P. notoginseng and its wild relatives. Furthermore, it could provide reference for the research of medicinal plants.

Raman spectroscopy gas detection technology which uses a single wavelength laser detection of gas samples without contact and loss is suitable for dissolved gas detection in oil-immersed transformers. Combined with the features of Raman spectral lines, the analysis of the detection mechanism in Raman spectroscopy technology has been made. Raman spectral lines have been presented in the expression of the convolution of Lorentz function and Gaussian function, which shows preferable Raman spectrum peak linear outline basic characteristics. In this paper, the basic features of the peak height, the peak position and the half peak width, are the main targets of peak searching, and the fundamental purpose of this paper is to obtain qualitative and quantitative analysis of the sample. Therefore, the experimental data of Raman spectroscopy is designed based on the comparison method to realize the automatic peak seeking model to achieve the detection target. Therefore, according to the automatic peak searching model based on the comparison method is designed to achieve the detection target. The simulation results of using peak searching model in the Voigt linear model show that spectrum peak height and peak position are in conformity with the theory in the test experiment. Based on the establishment of Raman spectroscopy detection platform aiming at the dissolved gas in transformer oil, the analysis of the experimental data show that the actual values of the half peak height and width are (8.7，11.5)(cm-1)in the Voigt linear model with deviations. Setting the value as 10.257 cm-1 to modify the parameter, then compared with the research results, it shows that the modified Voigt linear model and the peak searching model have better adaptability and practicability. Combined with the gas detection in the experimental platform in the experimental platform of Raman spectroscopy,detection of seven kinds of transformer fault characteristic gas and analysis of peak searching have been completed effectively. In terms of methane gas, the linear relationship among the unit gas content, Raman characteristic peak intensity and the area has been obtained, which has laid a foundation for the quantitative analysis of the dissolved gas in transformer oil.

Spectrum analysis of crop that affected by subsurface waterlogging is the foundation of surface waterlogging monitoring by remote sensing in large scale. It is also the prerequisite of achieving non-destructive, quick identification of wheat subsurface waterlogging damage with spectral features. But there is no papers report about spectrum analysis of crop affected by subsurface waterlogging so far. The paper analysis the spectral features difference of 8 species (zenmai9023, xinong223, luo6010, fumai168, emai23, emai19, guangyuang11-2, nongda195) between affected subsurface waterlogging and normal in leaf and canopy model using field experiment. The results shows that the leaf spectrum reflectivity value of wheat affected by subsurface waterlogging is high in 645～680 and 1 428～1 456 while it is lower in 757～917 and 1 641～1 684 nm. The main reason is that the subsurface waterlogging causes the leaf chlorophyll photosynthetic capacity decreased and leaf water loss. It is suggested to use NDWI (normal differential water index) value to indicate the damage degree of wheat subsurface waterlogging disaster. The changes of the NDWI index value is analyzed with time when difference wheat species demerged by subsurface waterlogging. It is found that the main factors caused the changes of the NDWI index value with time is related with resistance of variety. The canopy spectrum reflectivity value of wheat affected by subsurface waterlogging is low in all spectrum area. It is suggested to use average value in 670～240 nm to indicate the degree of wheat subsurface waterlogging disaster.

This paper proposes a method to monitor atmospheric HCHO and CHOCHO with high temporal resolution based on differential optical absorption spectroscopy (DOAS) in Shanghai urban area. Based on the characteristic absorbing structure of HCHO and CHOCHO, different fitting intervals were chosen for spectral analysis in order to avoid the absorption of interfering gases and reduce the residuals of spectral analysis. The resulting optical thickness of the target gas is used to obtain HCHO and CHOCHO concentrations, which were averaged at (4.0±1.6) and (3.4±1.2) μg·m-3 in October 2013, respectively. The averaged concentrations of HCHO in workdays were higher than those in holidays due to the impacts of its anthropogenic emission sources, while no obvious differences of averaged CHOCHO concentrations between workdays and holidays were observed. Diurnal patterns of HCHO and CHOCHO were alike. In the early morning, both the HCHO and CHOCHO concentrations peaked at 06:00—07:00, and then decreased rapidly to the minimum around 09:00. Afterwards, the concentrations increased continuously until sunset and kept in a relatively stable level in the evening. To explore the possible emission source and formation mechanism of atmospheric HCHO, four typical periods, i.e. steady-state stage at night, morning rush hours, photochemical reaction stage and evening rush hours, were classified for source apportionment.NO2 is regarded as the indicator for primary source of ambient HCHO.As the intermediate products of photochemical reactions, HCHO has a similar formation mechanism in common with CHOCHO. Therefore, it is reasonable to use CHOCHO as an indicator for secondary source of ambient HCHO. The linear regression analysis showed a good agreement between modeled and observed HCHO concentrations, the correlation coefficients R2 ranged from 0.60 to 0.81. Secondary sources of HCHO were estimated to contribute to one third of ambient HCHO concentrations in Shanghai urban area.

Celestial spectrum contains a great deal of astrophysical information. Through the analysis of spectra, people can get the physical information of celestial bodies, as well as their chemical composition and atmospheric parameters. With the implementation of LAMOST, SDSS telescopes and other large-scale surveys, massive spectral data will be produced, especially along with the formal operation of LAMOST, 2 000 to 4 000 spectral data will be generated each observation night. It requires more efficient processing technology to cope with such massive spectra. Automatic classification of stellar spectra is a basic content of spectral processing. The main purpose of this paper is to research the automatic classification of massive stellar spectra. The Lick index is a set of standard indices defined in astronomical spectra to describe the spectral intensity of spectral lines, which represent the physical characteristics of spectra. Lick index is a relatively wide spectral characteristics, each line index is named after the most prominent absorption line. In this paper, the Bayesian method is used to classify stellar spectra based on Lick line index, which divides stellar spectra to three subtypes: F, G, K. First of all, Lick line index of spectra is calculated as the characteristic vector of spectra, and then Bayesian method is used to classify these spectra. For massive spectra, the computation of Lick indices and the spectral classification using Bayesian decision method are implemented on Hadoop. With use of the high throughput and good fault tolerance of HDFS, combined with the advantages of MapReduce parallel programming model, the efficiency of analysis and processing for massive spectral data have been improved significantly. The main innovative contributions of this thesis are as follows. (1) Using Lick indices as the characteristic to classify stellar spectra based on Bayesian decision method. (2) Implementing parallel computation of Lick indices and parallel classification of stellar spectra using Bayesian based on Hadoop MapReduce distributed computing framework.

Based on Gaussian Process (GP), a wavelength selection algorithm named Synergy Interval Gaussian Process (siGP) model is proposed in this paper by using near infrared spectroscopy technology. Full spectrum is divided into a series of unique and equal spacing intervals, before selecting optimal several intervals to establish GP model. Due to the GP model with nonlinear processing ability, the method reduces the disadvantages of nonlinear factor. Taking the near infrared spectrum data of moisture content and pH in solid-state fermentation of monascus as performance verification object of this new algorithm, the prediction correlation coefficient (Rp) of moisture content and pH are 0.956 4 and 0.977 3, respectively. The root mean square errors for prediction set (RMSEP) are 0.012 7 and 0.161 0, respectively. Data points participating in modeling decrease respectively from the original 1 500 to 225 and 375. In the prediction for independent samples, it shows good accuracy. Comparing with traditional synergy interval partial least squares (siPLS) algorithm, the results show that the siGP achieves the best prediction result. The prediction correlation coefficient of moisture content and pH in new algorithm has increased respectively by 3.37% and 3.51% under the model of Gaussian Process, with increases of 29.4% and 34.8% in the root mean square errors for prediction set. This study shows that the combination of siGP and GP model can select wavelength effectively and improves the prediction accuracy of the NIR model. This method is reference for realizing the online detection and optimization control.

Xenon flash lamp is used by most analytical instruments and medical equipments as light sources whose spectral characteristics exert a tremendous influence on the property of instruments and medical equipments. In order to study the effects of pulsed Xenon discharge circuit parameters and geometric parameters on the spectral characteristics, theoretical analysis has been conducted to Xenon lamp emitting process with the gas discharge theory. The spectroscopic detection system has been designed to conduct experimental detection to the spectral characteristics of pulsed Xenon lamp with different parameters. The experimental results show that the emission spectra of Xenon flash lamp contain both line and continuous radiation. The line spectrum results from the electron energy gap transition, while the cw radiation related to ionic recombination and bremsstrahlung. The relative spectral intensity increases linearly with the discharge voltage, and has a positive correlation with storage capacitor. When low discharge voltage, the shorter the arc length, the higher the relative spectral intensity .while the discharge voltage becomes higher, the result is reversed. The work in this paper has great significance for xenon flashlamp on the parameters selection and industrial application.

Grassland fire disaster is an important influence factor to grassland ecological system in China. Therefore, it is crucial to study on the monitoring, prediction and management of grassland fire. Remote Sensing (RS) provides detailed data and saves a lot of manpower, material resources and financial resources on the research of grassland fire. However, it is difficult to identify the grassland fuel and soil with Remote Sensing. In this paper, we introduced fractal into the spectral analyses of the field-derived spectra (FDS) of grassland fuel and soil to solve the problem above. The study area laid on the Westward of Changling, Jinlin province, China. Study subjects included soil and dominant species: Leymus chinensis, Reed, Chloris virgate, Kalimeris integrifolia and Artemisia mongolica. FDS of study subjects were measured with ASD FS3 and continuums of FDS were calculated by Matlab 2010. Meanwhile, Box-counting values of FDS and continuums were calculated by Matlab 2010. According to the spectral and continuum analysis, it is difficult to identify soil, Leymus chinensis, Reed, Chloris virgate, and Artemisia mongolica because of the similar spectral curves. However, the Artemisia mongolica can be identified for the strong reflection. For typical fractal characteristics of FDS and continuum, clustering analyses of study subjects were done according to box-counting values of FDS and continuum. The results of clustering analyses show that Box-counting values of FDS and continuum are important indexes to identify the study subjects. This study provides a new thought to identity the grassland combustibles and soil with Remote Sensing.

Based on Plasmon-enhanced Raman Spectroscopy (PERS) technology, the detection system for rapid field determination of synthetic edible pigments in food，such as beverage，meat productsand preserves，is presented. The hardware framework of this detection system is mainly composed of double central control chips (ARM and FPGA), a sample pretreatment module (containing a nanoparticle giving device), a laser diode source, and a spectral data acquisitioning module; Besides, the software can run the sample pretreatment module automatically while receiving the Raman spectrogram. To verify its performance in edible synthetic pigments, the rapid detection system was applied to determine carmine, citrine, allure red in three actual samples (i.e. preserves, sodas, sausage). The relative standard deviations of the sample detection were in the range of ±5%, which indicated that the system developed in this paper have the advantage of favorable sensitivity, excellent repeatability and short testing time. Thus, it meets the requirements for rapid field determination of synthetic pigments in food.

This research collected the transmission hyper-spectral data of eggs with hyper-spectral imager. Haugh unit value was used as freshness norm. With the help of MATLAB and SAS software combined with stechiometry method, the hyper-spectral data of sample eggs was analyzed and processed. The prediction model of egg freshness was established based on hyper-spectral technology. The research chose the band range from 500 to 1 000 nm as sensitive band. The hyper-spectral data of abnormal samples were removed by using mahalanobis distance. Differential correction was done on hyper-spectral data. After the comparison, there was a high linearity between the second-order differential data of hyper-spectra and haugh unit value. Therefore, this paper conducted a further research on the second-order differential data of hyper-spectra. And it was treated with wavelet denoising, smoothing and standardizing. This paper chose the newly proposed CARS variable selection method to do dimensionality reduction on hyper-spectral data. And thirty-two characteristic parameters were extracted. They were used to establish partial least square prediction model based on all band and multiple regression model based on characteristic parameters on white shell eggs. The correlation coefficients of white shell eggs were 0.88 and 0.93 respectively, and the corresponding mean square errors being 7.565 and 6.44. Inspections were conducted on PLS prediction model based on all band hyper-spectral second-order differential and multiple regression model based on characteristic parameters by using eggs of validation set. The accuracy rates of these two models to discriminate white shell eggs’ freshness and non-freshness were 100% and 88% respectively.

Three-dimensional fluorescence spectroscopy is an emerging sensitive technology to detect organic pollution in water bodies. Based on this technique, a research group from Tsinghua University developed a novel instrument as a tool of pollution early-warning and pollution source identification，it has been put into use in A city in South China, for aqueous fingerprint monitoring and pollution sources identification under abnormal conditions. As a new monitoring method, it broke the limitation that traditional water quality monitoring technology could not provide directivity information of pollution source, and could detect abnormity of water quality quickly and identify pollution source accurately. In this paper, the process to identify pollution source during an abnormity incident of water quality in S River captured by the instrument was studied. When the instrument captured unidentified aqueous fingerprints during on-line monitoring, pollution intrusion process was inferred based on the variation of aqueous fingerprint figure and peak intensity. Then the pollution source identification was achieved by comparing the fingerprints between the polluted water body and possible pollution sources by the instrument. The source identification was verified with the changes of other water quality parameters such as pH, aniline, TOC and TN. The results showed that this early-warning and pollution source identification technique can quickly detect and release warning of abnormity of water quality and identify pollution sources accurately via monitoring aqueous fingerprints. The abnormity incident studied in this paper might be caused by dumping raw materials by a chemical plant located in upstream of the river.

The synergistic antioxidant mechanism of chlorogenic acids (CQAs) was studied in this paper through cyclic voltammograms (CV), oil-water partition coefficient (P), FT-IR, XRD and circular dichroism (CD). The antioxidant capability of CQAs isomers and their mixture was determined by using ABTS free radical quenching ability assay. The results showed that the bigger the antioxidant activity disparity between the CQAs molecules was, the higher the content of high antioxidant activity CQAs was, the better the synergistic effect of the CQAs combination mixture became; The oxidation potential (Epa) of CQAs combination mixture kept constant in the synergistic experiments, which indicted the oxidative coupling interaction don’t exist between the CQAs; The charge transferred (Q) and antioxidant activity exhibited high correlation (0.92); the practical Q was higher than the theoretical Q in the synergistic process and this confirmed that the CQAs (dicaffeoylquinic acids) regeneration of high antioxidant activity happened; the CQAs mixture with the absolute difference value of oil-water partition coefficient of 0.13 gave the good interface effect and high synergistic degree; the interaction and the regular arrangement between the CQAs combination were not discovered through FT-IR, XRD and CD. Therefore, the regeneration mechanism of CQAs molecules and the interface effect of reaction system were the main cause of the phenomenon of the synergistic antioxidant of CQAs.

In this paper the cluster structures of [Zn-(CH3CH2OH)n]2+ have been investigated with spectroscopic experiment and theoretical calculation. According to the fluorescence spectroscopy experiments, the fluorescence peak of ethanol molecules was found between 275~330 nm. A new peak appeared between 350~380 nm after the metal ions (Zn2+) was added into ethanol solution due to the generation of new clusters of molecules, and the original fluorescence peak of ethanol molecules became weak owing to the destroyed structure of ethanol molecules induced by Zn2+. The cluster structures of Zn2+ in water solution were investigated by using different methods. By comparing the results, a more accurate and fast B3LYP method of DFT was found and applied to optimize the possible structures of [Zn-(CH3CH2OH)n]2+. The results suggested that the first solvation shell of the system is up to six ethanol molecules, and thermodynamic parameters also shows the six kinds of molecular clusters which are likely in the solution. Moreover compared the theoretical fluorescence spectroscopy with experimental fluorescence spectroscopy, new clusters [Zn-(CH3CH2OH)n]2+ have been generated, with [Zn-(CH3CH2OH)n]2+(n=1~3) as main constructions.

This paper conducted fluorescent spectra of dissolved organic matter of Spring China North Yellow Sea by using three-dimensional fluorescence spectroscopy (EEMs) combined with parallel factors analysis (PARAFAC). The results showed that the fluorescent dissolved organic matter (FDOM) in colored dissolved organic matter (CDOM) of North Yellow Sea could be resolved to four components. Two components, c1 (260,315/425) and c2 (295,355/490) were identified as humic-like fluorescence components while c3 (275/310) and c4 (230,290/345) were protein-like components. The different linear correlations showed different degrees of similarities in the source or the chemical constitution, the relationship of the humic-like fluorescence components were most closely, followed by protein-like components. Four fluorescent components and total fluorescence intensity (TFI) in the horizontal and vertical directions had a higher intensity in coastal area than that of the area far away from the coast. Additionally, besides terrestrial input, the seawater mass movement also affects the distribution of CDOM in North Yellow Sea. Cluster analysis not only further illustrated the TFI characteristics of different areas of the North Yellow Sea, but also reflects the overall uniformity of the CDOM. Fluorescence Index (FI), Humification Index (HIX) and Biological Index (BIX) at some extent indicated the source of North Yellow Sea CDOM and the conclusion was that the near shore side gets greater influence from terrigenous sources while the sea side is more impacted by biological activity, which matched with the prior distribution patterns of fluorescence components.

This paper proposes a novel wood species recognition scheme based on the spectral reflection features of wood surface, aiming to address the following three issues in terms of the noise filtering, feature selection and radian’s optimal design . First, noises occur in some bands of wood spectral reflection curve so that these noisy bands should be deleted. Second, the wood spectral band is 350~2 500 nm, which is a 2 150D vector with a spectral sampling interval of 1 nm. Therefore, both noise filtering and feature selection should be performed to wood spectral data. In this paper, to simultaneously and efficiently solve the two problems of feature selection and noise filtering, both a feature selection procedure and a noise filtering procedure are performed by solving the eigenvalues of dispersion matrix. This scheme is novel and produces a good outcome. Third, to make the spectral reflection curves picked up by the spectral instrument have the best pattern recognition information; an optimal design is performed for the indoor radian’s mounting height. The genetic algorithm is used to solve the optimal radian’s height so that the spectral reflection curves have the best classification information for wood species. Therefore, the optimal design scheme for the radian’s mounting height can improve the pattern classification accuracy of the wood species to some extents, which is novel with excellent executive feasibility. Many experiments made with our developed software system on the five ordinary wood species in northeast region of China (i.e., including Betula platyphylla, Populus davidiana, Pinus Sylvestris, Picea jezoensis, Larix gmelinii) are performed for approximately 105 times. It indicates that the overall recognition rate reaches to a good recognition accuracy of 95% for five wood species with an ideal recognition velocity. The selected feature wavelengths by using of our feature selection algorithm based on dispersion matrix are mainly in the near infrared band.

It has been a long time that the imitations of turquoise appear in gem market. Pressed dyed carbonate in the early stage and dyed variscite, chalcedony as well as magnesite appeared in succession. These kinds of materials do not have the color and characteristics of natural turquoise. Their physical and optical properties are quite different from natural turquoise. One kind of turquoise imitation newly appears in market is selected to be studied in this paper. The conventional gemological method, infrared absorption spectroscopy and X-ray diffraction were employed to study its mineral compositions, gemological properties and structural characteristics. The results show that: the brecciated structure can be observed and the distribution of the iron wire is very simple on the surface of the sample. Deep blue granules are visible on the surface of blue samples and a few black spots can be observed on the surface of white ones, which are typical indication of pressing process. The blue imitations of turquoise has been stained and pressed. The index of this kind of turquoise imitation range from 1.54 to 1.58, which is lower than that of natural turquoise and it show bluish white fluorescence, which can be used as important evidence to distinguish it with natural turquoise. The data of X-ray diffraction shows the sample mainly consists of enstatite and quartz. The results of infrared absorption spectroscopy show that the absorption spectra of this kind of turquoise imitation is in accordance with the typical absorption peaks of enstatite: the absorption peaks which locates near 1 088 and 799 cm-1 are associated with Si—O, Si—O—Si stretching vibration in quartz; the absorption peaks near 2 947 and 2 882 cm-1 is related with stretching vibration of CH2 of exotic organic resin; the absorption peaks near 1 736 and 1 510 cm-1 are resulted from the bending and stretching vibration of CO and CH2.

The detection of gas pollutants in atmosphere and indoor air is very important to human health and safety. Monoxide carbon (CO) is a common gas pollutant with high toxicity that mainly comes from the inadequacy oxidization of carbon such as oil, coal and petrol inadequacy combustion, auto-gas and some natural disasters whose limit value in air is lower than 6.0 mg·m-3 in the national standard. Due to its toxicity and uneasy detection, it is one of very dangerous component in the silent killer. Recently, several methods, including infra-red absorption, gas chromatography, potentiometry, Hg replacement, spectrophotometry, I2O5 and PdCl2 nake-eye, semiconductor sensor have been reportedly used for the detection of CO. To our best knowledge, there are no SPR absorption methods for CO, based on the NG SPR absorption. In this paper, the reaction between CO and HAuCl4 was studied with absorption spectrophotometry and transmission electron microscopy (TEM)while a simple and rapid SPR absorption method was developed for the determination of trace CO. In pH 7.2 phosphate buffer solutions, monoxide carbon reduced HAuCl4 to form nanogold (NG) particles with the size of about 45 nm that exhibited surface plasmon resonance (SPR) absorption peak at 540 nm and three energy spectral peaks at 1.70 keV, 2.20 and 9.70 keV for gold element. The analytical conditions were examined, and a pH 7.2 phosphate buffer solution with a concentration of 40 mmoL·L-1 PO3-4, a concentration of 40.0 μg· mL-1 HAuCl4 and a reaction time of 5 min was selected for use. Under the selected conditions, the SPR absorption peak value was linear to CO concentration in the range of 0.2～8.75 μg· mL-1, with a detection limit of 0.1 μg· mL-1 CO. According to the procedure, the influence of coexistent substances on the determination of 1.0 mg·L-1 CO was tested, with a relative error of ±5%. Results indicated that 200 times SO2-3, PO3-4, SO2-4, CO2-3 and NO-3, 100 times Zn2+, K+, BrO-3, Na2S, ethanol, methanol, 80 times Ni2+, Cr3+, Co2+, Ca2+, Mg2+, Fe3+, glucose, Pb2+, Al3+, SeO2-3, Na2S2O3, formaldehyde, 50 times Mn2+ do not interfere with the determination. It showed that this SPR method had good selectivity. The CO content in air samples was determined with the SPR method, with a relative standard deviation (RSD) of 1.8%～4.2%, the SPR method results were agreement with that of the gas chromatography (GC).

The accuracy of the calibration reference source polarization states directly influences the precision of the polarized optical remote sensor calibration, and thus affects the inversion accuracy of the characteristics of targets. In this paper, 870 nm horizontal linear polarized light has been chosen as the tested calibration reference light, modulated by rotating quarter-wave plate(QWP). The intensity as a Fourier series of the rotation angle of the plate and its coefficients were demodulated with the Fourier transform method, Stokes parameters can be calculated with these coefficients. The mean, standard deviation, composite uncertainty and relative deviation of measured data compared with the theoretical value of the ten measurement results were presented. In order to improve the accuracy of the measurement, the correction model for the quarter-wave plate retardance deviation Δδ, fast axis angle deviation Δα and the transmission axis alignment deviation Δβ of analyzing polarizer has been constructed. In this model, detection deviation of Stokes parameters is described as a function of Δδ and Δβ, Δδ and Δβ were determined by the function and magnitude of the deviation. Combined with quarter-wave plate fast axis angle deviation which was the result of simulation to adjust the experiment device, and then detecting the calibration reference source polarization states again. The experimental results show that, the difference between measured value and theoretical value of Stokes parameters reduced to less than 1.41% from 3.77% relative to without correction. The experiment principle, device and deviation correction model of this research can significantly improve the accuracy of detecting the polarization state of the calibration reference light source.

Cerium doped Y2SiO5 (YSO) is an important scintillator material due to its high density, non-hygroscopic, excellent light output and fast decay time nature. in the paper, Y2SiO5∶Ce3+0.2%(YSO∶Ce) was grown with high-temperature solid-phase method. The time-resolved excitation and emission spectra and fluorescent decay curves at low temperature and room temperature (RT) were measured and discussed. There were two types of luminescence, one was the crystal defect emission, the center at 320 nm; the other one was doped Ce3+ ions 5d→4f emission, the center at 440 nm. Only when the excitation energy (Ex) was greater than the band gap width (Eg), the crystal defect emission can be observed corresponding to slow process, and the emission intensity was higher at low temperature. The crystals defect emission was hardly observed in the time-resolved emission spectra when the temperature rose to room temperature because of temperature quenching. Regions from 60~300 nm corresponding to emission due to 5d→4f transitions in the activator Ce3+ ions peaks at 440 nm, a plurality of excitation peaks were observed. Among them, the excitation with energy less than 6.1 eV(Ex

As one of the most important water treatment agents, polysilicate coagulant, has been playing an important role in coagulation- flocculation, but it is prone to lose stability due to self-polymerization and the forming of silica gel. Therefore, research on the preparation of stable polysilicate coagulant has attract great attention. A new method to prepare a stable polysilicate coagulant (PSPF), was proposed in this paper. Its structure and morphology were characterized by using Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) respectively. Fe species in PSPF was analyzed via Fe-Ferron complexation timed spectrophotometric method. The performance of PSPF was assessed by measuring micro-polluted water treatment efficiency. Primary chemicals, such as ferrous sulfate, sodium silicate, potassium dihydrogen phosphate, sodium carbonate, were used. The influence of those parameters affecting the preparation of PSPF, such as nSi/nFe, nP/nFe and nOH/nFe molar ratios were examined. The results showed that nSi/nFe of 1∶4, nP/nFe of 1∶6 and nOH/nFe of 1∶10 under 60 ℃ water bath for 30 min was the optimum condition for preparation. The FTIR spectrum indicated that PSPF was a kind of high molecular polymer, containing new groups (e.g., Si—O—Si and Fe—O—Si), which could increase the molecular weight，molecular chain and coagulation-flocculation efficiency. PSPF presented a cluster appearance similar to a network structure, which was conductive to adsorption-bridging capacity and precipitation sweeping. The increase of Fe(b) and Fe(c) as a result of Si increasing in PSPF improved the polymerization and solidification. The coagulation behaviors of PSPF that were largely affected by the coagulant dosage and pH, indicated that for pH and dosage at 6 and 8 mg·L-1, respectively, the residual turbidity and UV254 removal efficiency could achieve 0.33 NTU and 58.6%, respectively.

The classification of stellar spectra is an important job in data processing of astronomy, which is mainly used for searching celestial spectra with known types in massive data survey. This paper focuses on LAMOST M dwarfs fine classification based on measurement of residual distribution. Residual distribution measurement is a measurement method used to measure the distance between two spectra. In the process of calculating the distance between two spectra, normalized processing should come first. Then the residuals of the sampling points of corresponding wavelength are calculated. Eventually the standard deviation of the residual distribution as the distance between the spectra is calculated. In this paper, the M star of LAMOST DR2 is used as the experimental data of classification. The experimental results show that the spectra data can be classified more accurately with the measurement method of residual distribution than the use of other traditional classification methods. The effect of spectral classification is affected by signal to noise ratio, outliers, residual standardized coefficient and other factors.

Rapid identification and classification of mine water inrush is important for flood prevention work underground. This paper proposed a method of KNN combined with PCA identification of water inrush in mine with the laser induced fluorescence spectrum with an immersion probe laser into water samples. The fluorescence spectra of 4 kinds of water samples were obtained. For each set of data preprocessing, the processed data in each sample from 15 sets of data as the training setwith a total of 60 groups. The other 20 groups were used as the prediction set. The data were processed by principal component analysis (PCA), and then the KNN algorithm was used to classify and identify the principal component analysis. During the experiment, the pretreatment method in the principal component number is 2 while the correct rate has reached 100% by KNN classification algorithm.

The oil pollutants detector is designed in this paper. The pulse xenon lamp is used as light source; the step type multi-mode pure silica fiber is chosen to transmit the excitation and emission light. The asymmetric Czemy-Turner light path of high precision grating monochromator is adopted. The detector is applied to determine the fluorescence spectrum of diesel, gasoline and kerosene. The optimal excitation /emission wavelengths are: 290/330 nm (diesel)，270/300 nm (gasoline) and 280/330 nm (kerosene). The detection limits are: diesel (0.025 mg·L-1), gasoline (0.042 mg·L-1) and kerosene(0.054 mg·L-1). The relative errors are: diesel(2.55%), gasoline(2.06%) and kerosene(1.71%). Experiment results show that the designed detector has high accuracy of measurement. The different concentration of diesel, gasoline and kerosene mixed solution is configured, and three dimensional fluorescence spectra being measured. The self-weighted alternating trilinear decomposition is adopted to decompose the spectrum data. The predicted concentration and recovery rate show that self-weighted alternating trilinear decomposition has high resolution for mixed oil substance.

In order to realize real-time, online monitoring of the component of steel and other metal smelting process, we designed a remote double-pulse laser-induced breakdown spectroscopy (LIBS) analysis system which can realize non-contact remote measurement and component analysis for long distance sample. The paper first tests the system on solid standard steel samples, which provides basis for online monitoring the component of molten steel. The experimental results show：laser focal spot is about 1mm in long distance; double-pulse ablation depth is deeper than single pulse’s; the optimum delay of double-pulse is non-consistent in different distances; the enhancement effect of double- pulse in 3.1 m is better than that in 2.1 m，and the maximum enhancement is 5.19 of Ti(Ⅰ) 319.99 nm; the calibration curve of R2 is about 0.99, RSD being less than 5%, RMSE being less than 0.021%, LOD being less than 500 ppm for most elements in 2.1 m, which is better than that in 3.1 m.

In this paper, we propose the surface plasmon resonance intensity modulation of the metal copper deposited on a titanium oxide thin film. Surface plasmon resonance (SPR) has a broad application prospect in various types of detectors and sensors. We first used the electron beam evaporation to obtain TiO2 thin films with the same thickness, and then we prepared copper film with the thickness ranging from 5 to 80 nm with magnetron sputtering method. The results show that the TiO2 film with different thickness copper thin films own different surface plasmon resonance modulation capability. The thinner copper film shows more obvious regulation. When the copper layer thickness is more than 20 nm, the resonance enhancement effect began to weaken because of better extinction capability.

Flesh browning mostly happens in plum fruit during the post-harvest storage period, which is an important factor affecting the storage quality of plum fruits. Traditional methods used to discriminate plum browning involve the destruction of the intact fruit, which are highly subjective and error-prone. Therefore, the near-infrared (NIR) spectroscopy technique was applied to achieve rapid and non-destructive identification of plum browning and non-browning in this paper. The near infrared diffuse reflectance spectroscopy of 124 plum samples were collected in the band number of 4 000~12 500 cm-1. These samples were classified into two groups, browning (n=70) and non-browning (n=54). In order to find a new way to effectively discriminate plum fruits with flesh browning, three qualitative identification methods: the qualitative test, Mahalanobis distances discriminate analysis (DA) and Back Propagation-artificial neural networks (BP-ANN) were used to compare their capacity of recognizing browning plums and non-browning oneswhile the last two approaches were based on the principal component analysis (PCA) method. These results showed that DA and BP-ANN could be used to conctruct effective classification models for identifying plum browning, and the first ten principal components extracted from original spectra were applied as input variables to build DA and BP-ANN models. The optimal method was obtained with BP-ANN, which gained an accuracy of 100% for calibration set and 97.56% for prediction set, and the identification accuracy rate reached 100% and 98.57% for non-browning samples and browning ones, respectively. It could be concluded that NIR spectroscopy technique combined with chemometrics methods has great potential to recognize plums of browning and non-browning rapidly, non-destructively and effectively.

Mo-doped zinc oxide (ZnO∶Mo) films were deposited with direct current magnetron sputtering on quartz substrates at room temperature. The effects of Mo doping content on the crystal structure, surface microstructure，optical properties and plasmon characteristics of the ZnO films were investigated with X-ray diffraction(XRD),atomic force microscopy (AFM),Spectrophotometer and Raman spectrometer. The XRD pattern reveals that pure ZnO film exhibits good crystallization and c-axis oriented while heavy doping leads to increasing film defects. That results decline the film crystalline quality. When Mo doping content exceeds 3.93 Wt%, the ZnO films transform c-axis oriented into amorphous. The AFM pattern indicates that the surface of amorphous MZO film is extraordinarily flat. The Rq is 498 pm. The transmittance spectra reveal that all samples have an average transmittance of 80% in the visible light range. The optical band gap energy (Eg) increases from 3.28 to 3.60 eV as the Mo doping content increase. The absorbance spectrum indicates that ZnO surface plasmon resonance absorbance perk moves to short-wavelength as the Mo doping content increase. The Raman spectrum suggests that heavy Mo doping make the Raman scattering intensity decrease significantly. This paper obtains amorphous ZnO thin film by Mo doping. That broadens the application field of ZnO thin film materials. Meanwhile, we study the effect of Mo doping concentration on ZnO thin films surface plasmon, which provides important reference value for the preparation of oxidized zinc base photonic devices.

Given that AOTF (Acousto-Optic Tunable Filter, AOTF) spectral imaging analyzer is widely used in a wide spectral region of the visible and infrared spectrum, the spectral bandwidth, diffraction efficiency and power efficiency of the AOTF need to be improved to meet higher standards. Ultrasonic transducer is the core component of AOTF. Its 3 dB working bandwidth determines the spectral diffraction range of AOTF, so it is making two different thicknesses high-low frequency ultrasonic transducer to improve AOTF spectral bandwidth on the same of the acousto-optic medium. Because between the operating frequency of ultrasonic transducer and input impedance there exists non-linear relationship, they have different input impedances at different frequencies. When the between driving signal source’s output impedance and ultrasound transducer have mismatched, It would produce energy consumption and lead to cannot bring the maximum power transfer to the ultrasonic transducer, so that the spectral diffraction efficiency of AOTF is reduced, and it affected spectral imaging quality. So going through to study theoretical ultrasonic transducer impedance frequency characteristics deeply in this paper designed a new broadband impedance matching network, which has important application reference value of the spectral diffraction efficiency improving. By ADS simulation and actual matching circuit experimental test, experimental results show that impedance matching network’ power efficiency reach to more than 90%, spectral diffraction efficiency get up to 90% in the 60~200 MHz bandwidth, and improve to spectral imaging quality within 420~1 150 nm waveband. The higher diffraction efficiency of the matching network has important implications for the current application AOTF based on spectral imaging technology.

With respect to the problem of long period and low precision in using traditional methods to predict rice seeds germination rate, a novel method based on continuous polarization spectroscopy was proposed to achieve rapid and nondestructive prediction .The paper set different aging rice seeds as prediction targets and ten minutes as prediction time, using polarizer to modulate optical fiber collimating light source to linearly polarized light which issuing into rice seeds extract vertically before rotating the analyser every 5 degrees . The transmission spectrum was predicted through the optical fiber spectrometer. After normalization pretreatment to the polarization spectrum, the article gave the characteristics of polarization angel and wavelength by 0 degree, 5 degrees, 25 degrees, 620, 788 and 576 nm according to the contribution of polarization angel and wavelength when predicting different germination rate rice seeds and inputted obtained continuous polarization spectrum by wavelength, polarization angel, transmissivity to construct rice seeds germination rate prediction model using three modeling methods to build rice seeds germination rate prediction model in comparison, including Partial Least Squares Regression (PLSR), Back Propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN).1 520 sets of experimental data were measured in total at different polarization angels through using rice seeds with different aging days (0, 2, 4, 6) respectively, setting 912 sets of data as calibration set and 608 sets of data as predicion set. The modeling results show that RBF model’s prediction accuracy is the highest. Its correlation coefficient is 0.976; the mean square is 0.785; and the average relative error is 0.85%. The research results show that the continuous polarization spectroscopy technique through multidimension spectral information can achieve rapid and accurate prediction of rice seeds germination rate.

Near-infrared quantum dots have unique optical properties, such as high fluorescence quantum yield, long fluorescent life, tunable fluorescence emission wavelength, half peak width and large stokes shift, resisting light bleaching etc. The advantage of “near infrared biological window” gives them great potential application value in biological fluorescent tags, solar cells, quantization calculation, photocatalysis, chemical analysis, food detection, vivo imaging and other fields. At present, the luminescence mechanism research of near-infrared quantum dots is still not comprehensive enough. In this paper, the luminescent principle of three different types of near-infrared quantum dots is summarized, including core/shell structure quantum dots (CdTe/CdSe, CdSe/CdTe/ZnSe, etc), ternary quantum dots (Cu-In-Se, CuInS2, etc) and doped quantum dots (Cu∶InP, etc). The luminescence mechanism of Type Ⅱ core/shell structure is most likely to attribute to the interband recombination luminescence, the ternary structure of quantum dots light emitting mechanism is considered to be due to the intrinsic structure defects, and the luminescence mechanism of doped quantum dots is deemed to result from the impurity defects. The existing problems of near-infrared luminescent principle of quantum dots are also discussed and their development tendency is explored t in this review. A systematic study of luminescence mechanism of near-infrared quantum dots will not only help to understand the luminescent properties of near infrared quantum dots, but also contribute to improve the synthesis methods of quantum dots with similarly high quality.

In scientific research and engineering application, improving the power of fiber device is an important topic, which leads to observably rise of temperature in fiber core at the same time. In this paper, Thermal effect and its influence on absorption spectrum and lifetime of Erbium-doped fiber are studied with numerical modeling. Lorentz broadening of sub-levels is used to build the mathematical relationship between temperature and absorption spectrum. The McCumber Theory is applied to deduce the lifetime of Erbium-doped fiber in different temperature. Temperature experiments of absorption and emission spectrum from 25 to 900 ℃ are carried out, which show that the wavelength of absorption peak near 980nm increase at rate of 0.625 nm/100 ℃, the ratio of absorption peak near 1 530 nm declines at a rate of 0.001 9 dB·(m℃)-1 and the broadband of absorption spectrum near 1 530 nm increase with rising temperature. The linear variation of lifetime and peak absorption in experiment proves that the theoretical model is reasonable when the temperature is below 600 ℃.

With characteristics of rapidness, non-destructiveness and high precision in detecting plant leaves, hyperspectral technology is promising in assessing the contents of leaf pigments and other biochemical components. Because the spectral absorption features of carotenoid and chlorophyll are overlapped in visible light region and that foliar carotenoid content is far lower than chlorophyll content, studies about constructing vegetation indices (VIs) for carotenoid is rare at home and abroad though carotenoid is one of the most important photosynthetic pigments. Hyperspectral data has abundant spectral information, so this paper proposed a multiple spectral indices collaborative algorithm to construct VIs on the basis of band-combination traversal and correlation analysis. Through a large number of simulated leaf reflectance spectra under different biochemical components contents run on PROSPECT model, a radiative transfer model, we successfully constructed a new kind of stable vegetation index (VI) for assessing carotenoid content at leaf level: RVIDNDVI. Our results indicate that RVIDNDVI is composed of two parts: (1)Narrow band NDVI constructed with 532 and 405 nm is high correlated with both carotenoid content and chlorophyll content while narrow band NDVI constructed with 548 and 498 nm is highly correlated with carotenoid content. The influence of chlorophyll content on RVIDNDVI can be eliminated with the ratio combination of these two indices. (2) The influence of mesophyll structure parameter can be weakened by subtracting the reflectance at 916 nm, which has strong correlation with mesophyll structure parameter. RVIDNDVIonly has high sensitivity to carotenoid content (the correlation coefficient is -0.94) at leaf level and R2 of its exponential fit is 0.834 4. The estimation of RVIDNDVIto carotenoid content can be verified with the validations of both simulated data and measured data.

In view of the existing chromatism in the AOTF multispectral imaging system causes different wavelength imaging clarity under different focal length , even under the fixed wavelength, due to the horizontal direction (diffraction) spectral broadening, which leads to clear horizontal lines and blurring vertical lines. This paper made a concrete analysis of the chromatism of AOTF, pointing out the causes of imaging blurring. According to the imaging characteristics of AOTF multispectral imaging system, this paper proposed an improved algorithm based on SSIM, which can be used to evaluate the image sharpness. The target of the original algorithm was two images (one as a reference image, the other one as the test image), while the algorithm proposed in this paper made similarity analysis between the adjacent lines in one target image, small similarity in change edges declares great contrast, thus the image is clearer. The image quality caused by chromatism in the AOTF multispectral imaging system can be compensated by automatic focusing system, which mainly based on this algorithm. It has small calculated amount and fast speed. The feasibility and practicability of the compensation strategy were verified through experiments. The results show that this strategy can effectively solve the image blurring caused by chromatism in the AOTF multispectral imaging system, which has an important application value.

The process of polymer synthesis and processing affects the degree of polymerization, crystallinity, orientation and the uniformity of blending components, which ultimately influences the performance of polymer products. Therefore, real-time monitoring of material properties is very important in the process of polymer synthesis and processing. Real-time measurement technology with Raman spectroscopy has been widely applied in the field of polymer synthesis and processing, since it features non-destructive testing, easy sampling and high reliability. It has no specific requirements for the sample’s thickness, shape and state, and it realizes the measurement in high temperature, high pressure and toxic environment directly. Real-time Raman spectroscopy can analyze material chemical composition and physical properties. It controls the process of polymer synthesis and processing to improve the performance of polymeric products. In this paper, the research progress in real-time measurement of polymerization conversion, polymer particle size and distribution, crystallinity, orientation, and components content in multiphase blend polymers with Raman spectroscopy is discussed. The application of real-time measurement technology with Raman spectroscopy has great potential.

Osteoarthritis is a common arthritis disease caused by cartilage tissue damage and degeneration, which is one of the large epidemics that affect human health. The early detection of the pathological changes of articular cartilage can greatly improve the cure rate of disease, but the relevant clinical diagnosis technology has not been developed. In recent years, the applications and researches of terahertz technology are increasingly valued and it has drawn great attention in the field of medicine. Compared with traditional methods, the terahertz radiation is low-energy and non-ionizing whose spectral-fingerprinting capability is well-known in the biological world. Meanwhile, THz technology has a great potential in diagnosis of articular cartilage early degeneration. This paper briefly introduces the physiological and pathological conditions of the articular cartilage, the current clinical techniques of articular cartilage detection. It mainly summarizes the terahertz technology used for detecting articular cartilage, including detection of animal and human cartilage respectively. At last, the challenges and development prospects of terahertz technology in articular cartilage detection are discussed.

LAMOST-DR1 is the first data released by Guoshoujing telescop, which has the largest number of stellar spectra in the world at present. The data set provides the data source for searching for special and rare celestial objects like cataclysmic variable stars.Meanwhile, it requires more advanced astronomical data processing methods and techniques. A data mining method for cataclysmic variable spectra in massive spectra data is proposed in this paper. Different types of celestial spectra show obvious difference in the feature space constructed with Laplacian Eigenmap method. The parameters of artificial neural network are optimized with particle swarm optimization method and the total LAMOST-DR1 data is processed. 7 cataclysmic variable star spectra are found in the experiment including 2 dwarf nova, 2 nova like variables and a highly polarized AM Her type. The newly found spectra enrich the current cataclysmic variable spectra library. The experiment is the first attempt of searching for cataclysmic variable star spectra with Guoshoujing telescope data and the results show that our approach is feasible in LAMOST data. This method is also applicable for mining other special celestial objects in sky survey telescope data.

A laboratory cement quality analysis apparatus based on laser-induced breakdown spectroscopy (LIBS) has been developed for rapid analysis of cement composition and ratio values. In this paper, the overall structure, the optical system, the sample preparation process, as well as the spectral data analysis methods are introduced. The calibration model is established with internal standard method. A comparison as to the measurement results between LIBS and X-ray fluorescence spectrometry (XRF) has been made and being analyzed. It shows that by using the LIBS apparatus, the mean absolute error of CaO, SiO2, Al2O3, and Fe2O3 in cement raw materials is 0.46%, 0.25%, 0.13%, and 0.05%, respectively, while the mean absolute error of the ratio value such as KH, SM, and IM in cement clinker is 0.02, 0.05, and 0.04, respectively. The generated cement plasmas are verified to be in the local thermal equilibrium (LTE) condition by calculating both the plasma temperature and the electron density.