The concentrations and pollution characteristics of metal elements in fine particulate matter (PM2.5) during haze-fog days in winter in the urban area of Beijing were investigated. Aerosol samples of PM2.5 were collected in Beijing for 23 consecutive days from January to February, 2013. The concentrations of PM2.5 were measured by filter membrane weighting method. The samples were treated by ultrasound exacting procedures and the total amounts of the 36 metal elements were determined by inductively couple plasma-mass spectrometry (ICP-MS). The pollution characteristics of metal elements were analyzed by enrichment factor (EFs) method. The concentrations of PM2.5 significantly increased in haze-fog days, which were much higher than the 24 h mean value (75 μg·m-3) in National Ambient Air Quality Standard issued by Ministry of Environmental Protection of the People’s Republic of China. Among the 36 metal elements, the concentrations of 27 metal elements in PM2.5 were higher in haze-fog days than those in normal days. As, Cr, Pb, Ti and V were the principal inorganic components of PM2.5 in winter in the urban area of Beijing. In haze-fog days, the metal elements could be roughly divided into 3 groups by EFs, including high-enrichment group (EFs were higher than 10, including As, Cr, Cu, and Pb), medium-enrichment group (EFs were between 10 to 1, including Sb, Sn, etc) and low-enrichment group (EFs were close to 1, including Ti, V, etc). It was concluded that ICP-MS has good precision and accuracy in determining multi elements in atmospheric particulate samples, and can meet the requirements of monitoring principal and trace elements of pollutants simultaneously. In haze-fog days, the increase of As, Cr, Pb were strongly correlated with the pollutions caused by anthropogenic activities, such as vehicle exhaust emissions, and oil and coal combustions in winter. This study can probably provide a useful analytical method for the elements characteristic detection of PM2.5 in haze-fog days, and provide scientific basis for decreasing air pollution in Beijing and protecting the people’s health.

In order to study the color-genetic mechanism of brown jade, samples collected from Xinjiang were studied using XRD, ICP-MS, Raman and HRTEM. The results are as follows: the main mineral composition of brown jade is tremolite. ICP-MS data show that the concentration of the brown color has a good relationship with Fe element, samples with deeper color have higher Fe content. Fe occurs as iron mineral distributed in the tremolite particles or the microcracks. The grain size of the muddy iron mineral is extremely small, and the content is low. Experiments were designed to enrich the iron mineral with Stokes law. Then we studied the enriched samples with TEM and got the electron diffraction patterns of iron mineral, and identified them to be goethite. We conclude that the brown coloration of nephrite is due to goethite distributed in the tremolite particles or the microcracks. Raman spectra show that the rare mineral particle at the surface of the sample is rutile, and because of the low content, rutile has a little influence of the coloration.

In the present paper, yielding and propagation characteristics of the stimulated Raman scattering(SRS) and four wave mixing(FWM) were researched in a non-zero dispersion shifted single-mode optical fiber(NZDSF) and standard single-mode optical fiber(SMF) combination under the same pump light and coupling conditions. At 20 m length of NZDSF, The first order Stokes 544.6 nm of SRS gains and is transported by LP01 mode. At 30 m length, 537.7 nm of FWM emerges but 544.6 nm of SRS decreases whose mode is gradually transformed into the LP11 mode. After 80 m length of NZDSF, because the splice point between NZDSF and SMF is a biconical fused taper and their transmission mode interferes with each other, FWM is suppressed and the mode is converted to LP01 again. The phenomena and mechanisms are conducive to inhibiting of optical nonlinear distortion in a dense wavelength division multiplexing system and to developing optical soliton communication system.

Based on the error theory, “M+N” theory illustrates the effect of “N” factor such as external disturbance in the spectral analysis and provides a method to improve measurement accuracy. Multi-component ideal simulation model was established using the method that combines source spectra measured under different voltage with multi-component linear absorption spectral model. Meanwhile, source voltage was chosen as the “N” factor influencing the accuracy of measured component concentration. “N” factor distribution mode of samples was set to two groups with three different modes under different signal-to-noise ratio. It is concluded that prediction accuracy of component concentration is certainly influenced by “N” factor distribution range of calibration set and prediction set, which is system error. It was verified by the experimental results that “N” factor can be used to improve measurement accuracy based on “M+N” theory. Moreover, this research supplies a theoretic guidance for reducing interference and improving measurement accuracy of complex measurement system.

According to ADA theory and Kramers-Kronig relationship, the real and imaginary parts of complex refractive index were estimated by the datasets of microcystis aeruginosa algal absorption and particle size distribution. Then, the optical properties of microcystis aeruginosa were calculated based on the Mie theory. The relative deviations between modeled and measured scattering efficiency factors are 10.08%, and 4.8% between absorption efficiency factors. The agreements between simulation result and actual measurement show that homogenous spherical model can be used to reproduce the absorption and scattering property of microcystis aeruginosa.

Traditional and classical depth profile analysis is very time consuming and difficult to analyze irregular coating sample. In the present work, under optimal parameters, two irregular unknown coating samples were analyzed by laser-induced breakdown spectroscopy. Depth profile analysis curve indicates that zinc diffusion process is adopted for this two unknown samples. Zn and Fe are major elements, and small amount of Cr is used to passivate metal surface. Under the interface, the substrate is medium-low steel alloy, a set of certified reference material for medium-low steel is used to establish calibration curve, and chemical composition of substrates and the concentration variation for Cr and Mo from surface to interface were investigated in detail.

Laser-induced breakdown spectroscopy (LIBS) was applied to directly detect the coal particle flow. An investigation of the impact of different spectrometer detector angles measured from the laser incident axis on the plasma properties was conducted on the self-built two-phase particle flow experiment bench. Coal particle flow was used as the experimental sample. An analysis of the variation in the emission intensity and relative standard deviation (RSD) was carried out. The atomic spectral lines of C, Si and Al which are representative elements in coal were chosen as the analytical lines. Then the influence of the spectra acquired with different collection angles was discussed. The results demonstrated that the strong and stable intensity signal can be acquired in the collection angle range from 30° to 45°.

Coherent anti-Stokes Raman scattering is a nonlinear coherent four-wave mixing effect. But it is hard to quantitatively spectral analyze material components by detecting coherent signal. In the present paper, the resonant and non-resonant signals of mixture of ethanol and water with different volume ratio were experimentally studied with a single-frequency coherent anti-Stokes Raman scattering spectral method. By analyzing the experimental results, we found that the intensity of resonance signals at frequency 2 876 cm-1 is increased with the increase in volume ratio of ethanol and it is quadratic, but the intensity of non-resonant signal linearly increased with the volume ratio of ethanol. So the non-resonance intensity signal is linear with the concentration of the number of molecules N. Therefore, detecting the non-resonance signal will provide a way for the quantitative spectral analysis of the specific ingredients in the mixture.

Nephrite samples coming from various origins were investigated using Fourier transform near-infrared spectroscopy (FTNIR). The result shows that samples from some specific origins display four absorption bands of OH stretching vibrations in the 7 000~7 400 cm-1 region, which are ascribed to overtone vibrations respectively of OH(MgMgMg), OH(MgMgFe2+), OH(MgFe2+Fe2+) and OH(Fe2+Fe2+Fe2+), are related to Mg2+ and Fe2+ ion located in M1 and M3 positions of tremolite crystalline structure. With the raise of Fe2+/(Fe2++Mg2+) ratio in tremolite, splitting and redshift of OH vibration bands proceed, gaining an increase in wavenumber, band number and band intensity. Significance of the OH NIR spectra in nephrite origin identification is discussed.

The interaction between polyvinylpyrrolidone (abbreviated as PVP) and europium benzenesulfonate was investigated by using temperature-dependent FTIR and fluorescence spectra. Europium compounds show strong coordination ability with amide groups of PVP. In order to remove the undesired H—O—H scissoring bands from the adsorbed water in FTIR spectra, temperature-dependent FTIR was utilized to reveal the spectral behavior of amide Ⅰ band after being coordinated with europium ions from europium compounds. As a result, the bond order of the carbonyl groups decreased, leading to a red-shift of amide Ⅰ band in the FTIR spectra. Furthermore, fluorescence spectra demonstrate that the interaction between PVP and europium benzenesulfonate enhanced the solubility of europium benzenesulfonate in chloroform. As a matter of fact, europium benzenesulfonate, which is insoluble in chloroform, becomes soluble in chloroform containing 10 Wt% PVP. In the fluorescence spectrum of europium benzenesulfonate crystals, only the f—f transition peaks of europium ions can be found. However, the emission peak of the π*—π transition of benzenesulfonate group appears in concentrated aqueous solution of europium benzenesulfonate. The phenomenon shows that part of benzenesulfonate undergoes decomplexation from europium benzenesulfonate complex and isolated benzenesulfonate occurs in the aqueous solution. Energy transfer pathway from excited triplet state of benzenesulfonate to europium ions is blocked in isolated benzenesulfonate. As a result, π*—π transition band of benzenesulfonate can be observed in the florescence spectrum. The decomplexation provides larger coordination space around europium ions, which favors the coordination between europium benzenesulfonate and PVP.

The aim of the present study is to detect the surface temperature of the green roofs by using infrared thermography, and then analyze the roof greening’s effect on thermal environment. The slope roof greening, lightweight roof greening and water storage roof greening’s infrared thermal images were shot in a cloudy day, a day after the rain and a sunny day, and then the same day and different days’ surface temperatures of the three typical green roof were compared. The results show that the green roofs’ surface temperature are mainly under the influence of solar radiation and air temperature, and water storage green roof’s surface temperature is also influenced by its water content. Roof greening can effectively reduce the roof surface temperature and decrease the range of temperature fluctuation in any weather conditions.

The purpose of this study is to establish the cell and subcellular basis for the diagnosis of colorectal cancer by researching the FTIR( Fourier transform infrared spectra) of the normal tissue (30 cases) and cell line (SW 620), normal nuclei and malignant nucleus with Fourier transform infrared spectrum technology. The results of the peak position and the relative intensity obtained by statistical analysis indicate that the bands at 2 925, 1 240 and 1 085 cm-1 shifted toward higher wavenumber (p<0.05) in the FTIR spectra of cell lines (SW 620) and malignant nucleus. The 1 400 cm-1 band shifted toward lower wave number (p<0.05). The relative intensity ratios of I1 650/I1 460, I1 400/I1 460 and I1 240/I1 460 increased in the FTIR spectra of cell lines (SW 620) compared with normal tissue (p<0.05), but inversely, I1 740/I1 460 decreased significantly (p<0.01). The relative intensity ratios of I1 650/I1 460, I1 400/I1 460 and I1 240/I1 460 also increased in the FTIR spectra of the malignant nucleus. The above statistic differences are the basis for diagnosing colorectum cancer by FTIR spectroscopy.

Maize subnormal kernels are generated from haploid induction and have lower weight and viability than normal kernels. The germination percentage of subnormal kernels is below 40%. In the present work, a new approach to discriminating the viability of the subnormal maize kernels based on near infrared spectroscopy was developed and the feather selection method based on Kolmogorov-Smirnov (KS) test was applied into near infrared spectra analysis for the first time. The partial least squares model was established and validated with 600 spectral samples from 200 maize subnormal kernels, half of which have the ability to geminate within 3 days through the standard germination test and the other half cannot geminate within 7 days. The spectra were collected by a FT-NIR spectrometer in the diffuse reflectance mode. Ten models established via different preprocessing and feather selection methods were compared. Each model with 1 134 different parameter sets were evaluated through Monte Carlo cross validation. The optimum model was obtained by using 482 wavelengths in the range of 4 027 to 5 500 cm-1 and 6 858 to 9 088 cm-1 via the combination of the smoothing, vector normalization, KS feature selection, and low-signal-to-noise-ratio wavelength elimination. The highest correct discrimination rates for the seeds with germination ability and without germination ability arrive at 92.20% and 84.86%, respectively.

The present study an improved fusion algorithm was proposed based on the Tetrolet transform. It was used to solve the problems that the infrared and visible light images fusion speed is slow, the contrast of the fused image is low and it is easy to bring artifacts to the fused image. First of all, the visible light image was converted to the lαβ color space to get three irrelevant color channels. Secondly, the component l and infrared image were decomposed by the Tetrolet transform. The neighborhood energy and proximity were introduced to the low-pass coefficients fusion rule. The Tetrolet coefficients were observed by the pseudo-random Fourier matrix. The observation value was weightedly fused. Thirdly, the fused observation value were iterated by the CoSaMP optimization algorithm to get the fused Tetrolet coefficient. The fused gray image was got after the Tetrolet reconstruction. Finally, the final fused image was obtained by mapping the grey image to the RGB color space. The experiment results testified the algorithm validity for the image fusion.

Autoencoder network (AN) is a nonlinear dimension reduction manifold learning algorithm which can find out nonlinear low-dimensional manifold structure from high dimensional spectra data effectively. In the present paper, a nonlinear infrared (IR) spectra modeling method AN-PLS was proposed by combining AN and partial least squares (PLS) to reflect the nonlinear correlations existing between IR spectra and physicochemical properties of samples. In AN-PLS, AN and PLS were adopted to deduct the dimensions of IR spectra and build regression calibration model, respectively. The AN-PLS was then applied to correlate the near infrared (NIR) spectra and the mid infrared (MIR) spectra with the concentrations of insoluble dietary fiber in bamboo shoots. The results indicate that AN-PLS can predict the concentrations of insoluble dietary fiber in bamboo shoots with a lower cross validation RMS error (RMSECV) and higher determinative coefficient (R2), than other common spectra data preprocessing methods combined with PLS or sole PLS. It can be concluded that AN-PLS can effectively model the nonlinear correlations between IR spectra and physicochemical properties of the samples. And it is feasible to accurately detect the concentrations of insoluble dietary fiber in the bamboo shoots by coupling NIR and MIR spectra with AN-PLS modeling method.

In the present paper, the authors used NIR to determine routine chemical components, namely total sugar, reducing sugar, nicotine, total nitrogen, starch and volatile alkali. Orthogonal signal correction (OSC) was employed as spectral pretreatment and the principal component regression (PCR) models for 6 chemical components were established with Monte Carlo cross-validation modeling strategy. RPD value for each model was calculated to evaluate the methods. The orientation of PCR projection is the largest variance direction and has no relationship with the concentration. OSC can not only get rid of uninformative concentration but also solve the problem of noise, baseline drift and stray light. Compared with conventional PCR, OSC-PCR sustains the accuracy of the predicting model and improves the stability of the model significantly. It proves that NIR coupled with OSC-PCR method can be applied to the determination of routine chemical components, which is of great significance in evaluation of tobacco quality and analysis of tobacco aroma components.

In order to quickly and accurately determine ammonia concentration from the mainstream smoke of cigarettes burning, a detection system was designed based on characteristic infrared absorption method. According to the characteristics absorption curve of ammonia in the mid-infrared, the system was aligned with the strongest absorption peak of wavelength position by a 10.4 μm infrared fixed wavelength laser. Infrared light irradiated mainstream smoke of cigarettes, and interference fringes were collected by the infrared detector array. Combining spectrum database and spectrum analysis algorithm, the concentration of ammonia was solved by Bill Lambert law for the mainstream smoke. Standard spectrum data was selected from the group consisting of NIST spectrum database, combined with noise reduction processing for interfering gases in a variety of mainstream smoke. Finally, the ammonia concentration information was real-time displayed. Using UnicornTM fixed wavelength lasers, and static Fourier transform interferometer with standard suction engine, experiments tested five different brands of cigarettes for its ammonia concentration in its mainstream smoke. For ten cigarettes randomly selected from each brand, it was detected by ion chromatography and the system in the experiments. The experimental results show that the ammonia concentration of detection system is basically the same as the standard value, meanwhile the system is faster, stronger with anti-interference ability.

The dust samples from concerned person and van in a traffic accident were identified using Fourier transform mid-infrared photoacoustic spectroscopy. The results showed that the dust spectra from the left elbow was significantly different from other locations, but was significant similar to the dust in the right side of the concerned van, which directly verified that contact occured between the concened person and van, and it provided direct evidence to allocate the responsibility in this traffic accident. Therefore, Fourier transform mid-infrared photoacoustic spectroscopy can be applied in micro analysis, which provides an alternative method in evidence analysis.

Fourier transform infrared spectroscopy (FTIR) technique was applied in the early rapid detection of Plasmodiophora brassicae in root of Chinese cabbage while the symptom had not appeared. The Chinese cabbage root was inoculated with Plasmodiophora brassicae. Chinese cabbage root infected with Plasnmodiophora brassicae and uninfected root samples showed their difference in FTIR spectra. The absorption peaks at 1 227, 1 143 and 1 105 cm-1 were only found in the infected root samples, and combined with the variation in the peak area at these absorption peaks they could be used for early rapid detection for clubroot of Chinese cabbage. In addition, the polymerase chain reaction was used to verify the veracity of Fourier transform infrared spectroscopy (FTIR) technique. The results show that the detection results are consistent with each other, and they could detect the Plasmodiophora brassicae 5 days after inoculation. Results clearly demonstrated that the PTIR technology is a highly sensitive, convenient and quick one for the early rapid detection of clubroot of Chinese cabbage, and provides a new thought and method for the early detection of plant disease.

Visible and near infrared (Vis-NIR) spectroscopy was used to accomplish a rapid and noninvasive quantification of the two common adulterants, soybean oil and rapeseed oil, in fish oil. Different contents of soybean oil were added into fish oil of three brands and different contents of rapeseed oil were added into fish oil of another three brands, the Vis-NIR spectra of adulterated samples were collected, pretreated by five spectral preprocessing algorithms (smoothing, standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-derivative, and 2nd- derivative), and used to establish partial least square regression (PLSR) models. The correlation coefficients for prediction (Rp) of 0.938 6 and 0.959 3 were obtained for the adulterant detection of soybean oil and rapeseed oil respectively, and their optimal models were full range spectral PLSR model and MSC-PLSR model. Successive projections algorithm (SPA) was then used to analyze the full range spectra of fish oil samples adulterated with soybean oil and rapeseed oil respectively, and 11 and 15 spectral characteristic wavelength variables were obtained. The Rp of 0.941 2 and 0.932 6 were obtained based on SPA-PLSR models for the adulterant detection of soybean oil and rapeseed oil, respectively. The overall results indicate that Vis-NIR spectroscopy is a feasible way to determine the adulterants of soybean oil and rapeseed oil in fish oil rapidly and non-destructively.

In order to explore a rapid identification method for transgenic soybeans, non-transgenic and transgenic soybeans were tested as the experimental samples via near infrared spectroscopy (NIR) and principal component analysis (PCA) combined with back propagation artificial neural network (BP-ANN) model. The spectrum data was collected after NIRS scanning the samples, and then analyzed by PCA plus BP-ANN model. The accumulative reliabilities of the six components were 99. 03% through the PCA. Then BP-ANN model was used to further test these six components and a three-layer BP-ANN model was developed. The final result achieved a 100% recognition rate of all 22 test samples respectively. In conclusion, the measure of NIRS and PCA combined with BP-ANN model has proved to be a rapid and accurate method to detect transgenic soybean nondestructively.

In order to achieve rapid nondestructive determination of phosphate content in hairtail surimi and surimi-based food(kamaboko), near-infrared quantitative models were established by fitting the near-infrared diffuse reflectance spectral data of haitrail surimi and kamaboko with its phosphate content. Prediction and correlationcoefficients of thecalibration and validation were used to evaluate the quality of the model. The model was further developed by partial least squares(PLS). The result showed that correlationcoefficients calibration in the model of hairtail surimi and surimi-based food(kamaboko) were 0.983 and 0.960, respectively and prediction of calibration were 0.032 and 0.101, respectively. The correlationcoefficients of thevalidationswere 0.951 and 0.954, respectively. The predictions of validation were 0.058 and 0.097, respectively. This study demonstrated that NIR spectroscopy could be used to determine the content of phosphate in hairtail surimi and surimi-based food(kamaboko) to a certain extent.

Surface enhanced Raman scattering (SERS) is a surface sensitive and vibrational phenomena characterized by increased Raman scattering from surface plasma resonance (SPR) interaction between molecules that are located on or near roughened metal surfaces and metal nanostructures. First, the commonly fabricating methods (MNPs in suspension; self-assembly; template techniques and nanolithographic) and performance of SERS substrates are reviewed in detail. Second, some applications of SERS to biomedicine and environmental analysis are introduced. It is shown that SERS substrate with highly enhancement factor, good reliability and reproducibility can be fabricated, so that the potential application of this technique can be achieved.

The present paper studied surface-enhanced Raman spectra (SERS) of serum from breast cancer using chemometrics methods of principal component analysis (PCA) and linear discriminant analysis (LDA) respectively. Firstly, the structure of peptidyl backbone and side chain of protein, which is destroyed in the serum from breast cancer, is explained. Meanwhile the structure of lipid and also the environment of glucid are changed. Then, we studied the SERS of 18 serum samples from breast cancer patients who were clinically diagnosed and 20 serum samples from healthy volunteers control using the method above mentioned. It was showed that the method of PCA cannot discriminate the two groups completely, but after using the method of LDA the analysis result is satisfactory.

Raman spectra of aragonite and calcite were studied in the temperature range of 18～388 ℃ and pressure range of 79～2 014 MPa, and 19～351 ℃ and 96～1 823 MPa, respectively in the diamond anvil cell. The relationships among the Raman shifts of aragonite and calcite, the system pressure and temperature were obtained. The Raman shifts of aragonite and calcite become higher with the pressure increasing, and become lower with the temperature increasing except the Raman shift of 704 cm-1 of aragonite. The absolute values of νi/T of aragonite and calcite for the lattice modes are greater than those for the internal modes of the CO3 groups, and the values of νi/T and νi/P of aragonite and calcite are various. The Raman shifts of the internal CO3 modes are related to the C—O bond length and the C—O bond is more compressible and less expansible in calcite than in aragonite, besides, they may be also related to the dynamical effects of the CO3 groups accompanying the aragonite-calcite transition.

Quartz and calcite grains in fault gouge at the Shenxigou section of Wenchuan earthquake fault zone were investigated by micro-Raman spectroscopic measurement. All the peaks shift towards high-frequency due to the tectonic stress during fault slipping. The 464 cm-1 peak arising from the micro-fractured quartz grains coexisting with clay minerals such as chlorite shifts by 2.50 cm-1, while the 464 cm-1 peak arising from the weakly fractured and pure quartz grains shifts by 3.78 cm-1. This likely resulted from that the microfissure filling in loose clay minerals increases the surface area of the quartz grains, in which the stress existing in the crystal lattice was accelerated to be released. The 1 085 cm-1 peak arising from calcite grains shifts by 2.70 cm-1 towards high-frequency, as a result of the Wenchuan Ms 8.0 earthquake. The compressive stress in the fault plane was estimated to be at least 496 MPa during the fault slipping when the big earthquake happened according to the Raman peak frequency shift of calcite grains and the available experimental data. The results showed that the Raman spectra of minerals within fault zone should provide information of mechanisms on the fault plane when fault slipping takes place.

In the present work, two algorithms of support vector classification (SVC) were utilized to analyze and classify Raman spectra of nasopharyngeal cell lines C666-1, CNE2 and nasopharyngeal normal cell line NP69, and achieved great sensitivity and specificity which are all up to 90%. This is coincident with our previous LDA classification model. The final results show that both of these two SVC algorithms can well classify the cell lines, and meanwhile may be helpful to the realization of Raman spectroscopy to be one of diagnostic techniques of nasopharyngeal carcinoma.

In the present study, the alliin and methiin as the precursor of the primary functional active components in garlic were tested by using Fourier transform infrared (FT-IR) spectroscopy and confocal micro-Raman spectroscopy. Significant IR and Raman absorption peaks were detected in the range of 3 200～2 800 cm-1 and 1 700～200 cm-1, respectively. For alliin eight intense IR absorption peaks were observed at 3 080, 1 617, 1 582, 1 496, 1 418, 1 342, 1 301 and 919 cm-1, respectively, and nine strong Raman vibration peaks were assigned at 3 088, 1 636, 1 404, 1 290, 1 051, 790, 745, 693 and 588 cm-1, respectively, as its characteristic peaks. In parallel, for methiin eight strong IR absorption peaks were revealed at 1 644, 1 481, 1 395, 1 370, 1 233, 1 068, 1 004 and 892 cm-1, respectively, and nine intensive Raman vibration peaks were presented at 1 644, 1 310, 1 073, 1 011, 998, 893, 846, 702 and 676 cm-1, respectively, as its characteristic peaks. It is concluded that the IR and Raman spectra of alliin and its homologue, methiin, show obvious differences, and these two methods are provided for the rapid and simple analysis of alliin and its homologues.

In order to find a fast and scientific method to identify the Pu’er ripe teas with different origins and fermentation years, using the surface enhanced Raman scattering (SERS) technique was used to compare the similarities and differences between those teas with different origins and fermentation years in the Raman range of 600～1 800 cm-1. The results show that the trend of the SERS spectra are the same on the whole, but because of the diversity of different types of Pu’er ripe teas, there are so many differences between the SERS spectra, and we can use the differences to analyze and identify Pu’er ripe teas with different origins and fermentation years.

It is necessary to find the largest inner circle of a complex polygon in many applications. The present paper develops a method for finding the largest inner circle of a polygon based on Voronoi diagram, and then improves the algorithm by medial axis simplification (MAS) and parallel computing. Data partition is a key issue in parallel computing of vector data. The algorithm complexity equalization strategy (ACES) is then presented. By several experimental tests of large quantity of lakes in Alaska we conclude that the approach developed in this paper performs effectively and efficiently by using MAS and ACES methods.

Using a thermal camera to obtain canopy temperatures for winter wheat, an infrared crop water stress index (ICWSI) was calculated in the main water-requirement stage. The performance of a BP neural network was tested with ICWSI values for three different periods in one irrigation circle as independent input factors and observed winter wheat yield after harvest as the output. The topology of the neural network was 3-5-1, and after data normalization, convergence performance was enhanced. Results showed that the maximum relative error was only 3.42%. To confirm the superiority of this method, a common nonlinear regression model was also built to compare the predictions with ICWSI values and the observed yield of winter wheat, but the maximum relative error of this model was higher (18.87%). Comparison between these two mathematical methods shows that the approach of combining thermal camera technology with a BP neural network prediction model, which is more precise for nonlinear prediction, was sufficiently better than other models to predict the winter wheat yield successfully and accurate enough to meet production requirements.

Seasonal snow reflectance in the spectral range 350～2 500 nm was collected by a portable spectroradiometer in the field. The experiments were carried out in Fuyun County, Xinjiang Province. Spectral characteristics of snow in the region were drawn by the contrast with ice. And the influence of solar elevation angle, slope, snow depth and contamination on snow reflectance was analyzed. The observations suggest that snow reflectance is high in visible light range and decreases gradually with increasing wavelength. As the solar elevation angle increases, snow reflectance decreases and the maximum variations are observed around 1 090 and 1 300 nm. For the existence of slope, snow reflectance rises significantly. As snow depth increases locally, snow reflectance increases and the maximum variations are observed around 400～500 and 1 250～1 320 nm. As contamination content increases, snow reflectance decreases and troughs and crests of spectral curve flatten. The measurement and analysis of seasonal snow reflectance are of great significance for monitoring and retrieval of snow characteristics.

Alpine ecosystem of source region of Yarlung Zangbo plays a vital role in maintaining ecological security of its middle and lower reaches with great population. The eco-environmental quality deteriorated evidently in recent years. The present paper explored spectral characteristics of grassland features, plant community and the corresponding spectral pattern of vegetation succession during the process of degradation. The result showed that the difference in spectral features of Kobresia littledalei, Stipa purpurea, Kobresia pygmaea and Potentilla fruticosa, as main vegetation types of alpine meadow, alpine steppe and brushwood, mainly occurred in the position and shape of red edge, chlorophyll absorption feature and cellulose absorption features. During the process of degradation, reflectance values decreased gradually and spectral features of soil background appeared with deterioration of chlorophyll absorption and water absorption features. Vegetation indices capturing features of Chlorophyll absorption and red edge can be used to monitor grassland degradation.

Early detection of early blight on tomato leaves using hyperspectral imaging technique based on spectroscopy and texture was researched in the present study. Hyperspectral images of seventy-one infected and eighty-eight healthy tomato samples were captured by hyperspectral imaging system over the wavelength region of 380～1 030 nm and then were dimensioned by principal component analysis (PCA). Diffuse spectral response of region of interest (ROI) from hyperspectral image was extracted by ENVI software. At the same time, four features variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from each PC image of the first eight PCs including contrast, correlation, entropy and homogeneity, respectively. Then PCA and successive projections algorithm (SPA) were used to build least squares-support vector machine (LS-SVM) model to detect early blight on tomato leaves. Among the six models, LS-SVM model based on spectroscopy performed best with the discrimination of 100%. It was demonstrated that it is feasible to detect early blight on tomato leaves by hyperspectral imaging technique.

Yellow rust and powdery mildew are two important diseases of winter wheat in China. The coincidence of their occurrence in field poses a challenge in their management and prevention. In the present study, the leaf spectra of the two diseases were measured by a spectrometer. Based on these data, we assessed the feasibility of differentiating the two diseases and evaluating their severity degrees. The disease sensitive bands and spectral features were identified through correlation analysis and independent t-test, including band regions at 665～684, 718～726 nm etc. and spectral features of DEP550-770, SIWSI etc. Based on these bands and spectral features, the models for disease discrimination and severity retrieving were developed according to FLDA and PLSR analysis, respectively. The results showed that the selected bands and spectral features can differentiate the yellow rust and powdery mildew explicitly, which yielded an OAA of 80%. It is noted that the discrimination model performed especially well (OAA=95%) in classifying those diseased leaves with damage proportion over 20%. The retrieving model of disease severity that were constructed by spectral features achieved reasonable estimates, with the RMSE for both diseases less than 15%. The leaf level models for discriminating powdery mildew and yellow rust and estimating their disease severity serve as a basis for further study in diseases differentiation and detection at canopy level.

Polarized excitation emission matrix(PEEM), combining advantages from both polarized technology and excitation emission matrix (EEM), is a analytical tool with higher selection, accuracy and sensitivity, which could provide complete information for molecular characterization. The degradation of benz［a］pyrene by potassium ferrate was further studied by PEEM in this paper, and the degradation features were also deeply discussed through polarized values and PEEM changing. According to the experiment, for t=15 s, the degradation rate was 60%; while for t=40 s, above 90%. Polarized value characterized more clearly the difficult degradation of benz［a］pyrene. The first order kinetic equation also obtained: y=0.277x－0.004(r=－93.90%), by fitting the time polarized curve. These results are accord with the psevious results obtained from EEM’s characterization, which provide a very useful conference for method selectivity in researching process and mechanism of pollutants’ degradation.

Parallel factor analysis method was used to analyze the three-dimensional fluorescence spectra of dissolved organic matter in the water of Taihu Lake. Based on analysis of the error squared sum of excitation and emission wavelength, the core consistency and the simulation of three dimensional fluorescence spectrum and residual spectrum when the factor of the PARAFAC model is 3, 4 and 5, results show that the best factor number of parallel factor model was three. The three fluorescence components were identified, including humic-like component, tryptophan-like component and tyrosine-like component.

A new method was put forward to identify rice syrup adulteration in honey using three-dimensional fluorescence spectra technology combined with chemometric methods. The information of the three-dimensional fluorescence spectra was compressed and extracted using characteristic parameters method and the principal component analysis. Then, it can analyze honey adulteration combined with linear discriminant analysis (LDA) and the error back propagation neural network (BP-ANN) methods. The results showed that the prediction recognition rate of the LDA model was 94.44%, but that of the BP-ANN model can reach 100% when the number of principal components were four in the recognition experiment of adulterated honey, indicating that the BP-ANN model was more suitable for honey adulteration identification. This work demonstrated that the three-dimensional fluorescence spectroscopy combined with the BP-ANN model can be rapid, nondestructive, and accurate in identifying adulterated honey.

The thermoluminescence characteristics and selected frequency luminescence characteristics of an orthoclase were studied using RGD-3B thermoluminescence dosimeter and BG2003 luminescence spectrograph. The result indicates that the orthoclase has three thermoluminescence peaks and the temperatures of these peaks are 148, 197 and 310 ℃ respectively. The 197 and 310 ℃ thermoluminescence peaks appear when it has been irradiated. The temperature of peak value deceases with the decease in warming rates. The luminescence signal is obvious at 290, 300, 310, 340, 400 and 480 nm wavelength with heat excitation. The luminescence signal is obvious at 280, 290, 300, 310, 320, 400, 460 and 480 nm wavelength with 532nm light excitation. The certain linear relation exists between the photon counts from 400 nm and irradiation doses. It can also be used as a dosimeter. It has application potential in luminescence dating.

In order to avoid the ink spilling problem and make full use of the color reproduction ability of the printer simultaneously, a novel multi-dimensional nonlinear interpolation ink restriction method based on spectral gamut maximization is proposed. Four ink-paper combinations were studied and the maximum gamut was determined judging from the ink holding performance of the specially designed printed samples whose spectral reflectance ranging from 380 to 730 nm was acquired by a spectrophotometer. The principal component analysis (PCA) method was employed to reduce the dimension of the spectral data and the convex hull algorithm was used to visualize and model the maximum gamut of each printing system. The research shows that the proposed ink restriction method can solve the ink spilling problem perfectly, meanwhile, the maximum gamut covering ratios of each ink-restricted gamut are higher than 95%, no matter calculated in spectral, colorimetric or device color space. Therefore, this research provides a reliable and accurate ink restriction method for the ink-jet printing industry.

In the present paper, Ag nanoparticles embedded bismuthate glass composite was prepared by using a novel thermo-chemical reduction method. Red shift of surface plasmon resonance (SPR) absorption of the Ag nanoparticles was observed by utilizing UV-visible absorption spectra. The Raman spectra evidenced the evolution of the structural units of the glass after introduction of Ag nanoparticles. Z-scan and optical Kerr shutter technique with femtosecond laser as excitation source was applied to investigate the third-order optical nonlinearities of the nanocomposite in near-infrared region. The results show that the silver-bismuthate nanocomposite possesses Kerr response in scale of sub-picosecond, and its nonlinear refraction γ was dramatically increased up to 29 times under the influences of hot electron effect and local field effect.

In the present paper, a novel algorithm is proposed to integrate both spatial and spectral information for automatic EE (Integration of spatial-spectral information based endmember extraction, ISEE). At first, the image is divided into some subspaces for improvement of spectral contrast. Then, the subset of the image is projected to the feature space related to the image endmembers, and the candidate endmember spectra are extracted through orthogonal subspace projection analysis. At last, the endmember spectra are refined under the constraint of image spatial context and spectral information. The performances of different endmember extraction methods are compared using both synthetic hyperspectral image and real hyperspectral image. The experimental results demonstrate that ISEE incorporated with spatial information is effective, and the endmember spectra extracted by ISEE are more accurate than by some common EE methods.

In order to reduce the limitation in background statistics estimation of unstructured background detector, a small target detection algorithm based on object-oriented analysis was proposed. After segmenting the whole imagery into many fairly homogenous regions using adaptive iterative method, multivariate normality test was applied to choose several optimal object sets which obey the law of normal distribution well. Then, the selected objects would be combined with GLR to perform target detection. This method could make the local background well fit a normal distribution and effectively separate the target signal from background, and meanwhile avoid the contamination effect through the selection of optimal objects. A simulation experiment was conducted on real OMIS data to validate the effectiveness of the proposed algorithm. The detection results were compared with those detected by the unstructured background detector GLR and improved GLR which incorporated K-Means clustering. The results show that the proposed algorithm has better detection performance and lower false alarm probability than other detection algorithms.

The present research attempts to establish a soil salinization monitoring model through a combination of remote and near sensing technologies. An electromagnetic induction instrument (EM38) was used to measure the electronic conductivity of soil samples collected from the delta oasis between the Weigan River and the Kuqa River in the north rim of the Tarim basin. Hyperspectral images were obtained via ASD Field Specpro FR and were transformed via 11 different approaches including root mean squares, logarithm, inversion, inversion-logarithm, continuum removal, and first order differentiation, etc. After the transformation, the obtained soil spectra that correlate well with soil electronic conductivity as measured by the EM38 were used to calculate five salinity indexes (salinity index 1, 2, and 3, normalized differential salinity index, and brightness index). Our analyses suggest that the salinity index 2 obtained via first order differentiation transformation of the spectra with the wavelengths of 456, 686 and 1 373 nm generated the highest correlation with salinity information derived via the EM38. By incorporating near sensing information (soil electronic conductivity information obtained via EM38), the current research provides a potentially more accurate approach to monitoring and predicting soil salinization in the future.

When light launches into the water, the emitted polarized light is closely related to water physical and chemical properties by reflection, scattering and absorption. It can reflect the concentration of water composition, and can be used to monitor water quality in the complex lake water for important information. Taking Chao Lake as an example, three-band polarized hyperspectral semi-analytical model of lake chlorophyll was established by the hyperspectral multi-angle polarization measurement data on water surface on the basis of the theory of polarized light and bio-optical model. The result shows that the correlation between polarized hyperspectral three-band combination and chlorophyll concentration was discovered to be 0.76, while RMSE and MRE were found to be 5.14 μg·L-1 and 31.44% respectively, which are increased by 4.1%, 2.05 μg·L-1 and 5.46% respectively than the traditional three-band radiation model. This indicates that the three-band polarized semi-analytical model has strong predictive ability for chlorophyll concentration, which reflects the advantages of polarized hyperspectral information for monitoring lake water quality.

Precise spectral characterization is fundamental for thematic information extraction. Because different types of water have different spectral features, which result in the difficulty of automatic retrieving water information precisely from the whole film. The present paper explored a new approach to water adaptive extraction based on local end member spectral characterization (LESC). Firstly, through the spectral index calculation the primary water identification was achieved. Secondly, through spatial analysis and automatic end member extraction, we can get the water end member in part of region. Thirdly, according to the end member spectral, we can calculate local end member spectral similarity and histogram of similarity. Finally, through the histogram spectral analysis the optimal segmentation threshold was determined and according to the results the segmentation threshold was adjusted to fulfill water information extracting automatically and accurately. Experiments results show that through local end member spectral characterization the precision of extraction result can be promoted. The proposed method can extract all types of water information precisely and is not affected by different spectral feature.

The UN Intergovernmental Panel on Climate Change (IPCC) pointed out that nitrogen deposition was being increasingly intensified along with climate warming. In this paper, we conducted a multi-factor experiment from 2005 using Artemisia frigida, a Mongolian traditional medicinal plant in Inner Mongolia steppe, to study the effect of simulated warming and nitrogen addition on 12 essential element contents and their allocation in reproductive branches (vegetative branches) by ICP-AES. The four treatments were: CK (ambient), IR (warming), N (nitrogen addition) and IRN (warming plus nitrogen addition). The simulated warming was 2 ℃, and the total amount of added NH4NO3 was 10 g N·m-2·a-1. In this study, our result showed that warming and nitrogen addition influenced the essential element absorption and distribution. Compared to ambient, there were different variations in 12 essential element contents at warming condition, but the negative effects were more than the positive effects. Nitrogen deposition significantly increased absorption of P and Mn, but no interactive effect of warming, Nitrogen deposition, and different part on all of 12 element contents. In addition, absorption and allocation of N, K, Ca and Mg were significantly different between reproductive branches and vegetative branches (P<0.05).

In order to provide the test analysis technology to support the technology development of China high-purity quartz, taking the 4N high-purity quartz made by ourself as experimental samples, we studied the ICP detection technology for the quality of high-purity quartz. The results showed that the digestion and soluble ore is the key of the ICP detection technology for the quality of high-purity quartz. The ICP detection results of high-purity quartz are influenced by the factors including sample weight, reagent combination, reagent consumption and reagent purity. The optimal condition is that: the sample weight is ≥2 000 mg; reagent combination is HF+HNO3; reagent purity is high purity level(MOS or BV-Ⅲ); HF dosage is 25 mL; concentrated HNO3 are used by three times, and the total amount is ≥5 mL. In addition, the samples are not screened by steel sieve in order to avoid iron pollution. And the high-purity quartz should be dissolved in the super-clean laboratory, which may be good for reducing the detection error.

Fe(III) modified sodium alginate beads (Fe(Ⅲ) modified Na) were firstly adopted as solid phase for micro-extraction of inorganic arsenic species from water samples. In combination with hydride generation-atomic fluorescent spectrometry (HG-AFS), speciation analysis of inorganic As(Ⅲ) and As(Ⅴ) in water samples could be accomplished because Fe(Ⅲ) modified Na had various micro-extraction behavior for inorganic As(Ⅲ) and As(Ⅴ) respectively. As a result, Fe(Ⅲ) modified Na could be developed into a practical solid phase for speciation analysis of inorganic As(Ⅲ) and As(Ⅴ), with the advantages of being efficient, simple, and environmental-friendly.

A new, rapid and sensitive method for the determination of bovine serum albumin (BSA) by flow-injection coupled with chemiLuminescence detection was establish, which based on BSA enhancing effect on weak chemiLuminescence system of Luminol-H2O2 in alkaline solution. Under the optimized conditions, linearity for BSA was found in the range of 2.5×10-9～1.0×10-6 mol·L-1, with the detection limit of 5.8×10-10 mol·L-1. And the sampling detecting rate was about 112 samples·h-1. The relative standard deviation(RSD)was found to be 0.8% for 11 replicate determinations of 1.0×10-7 mol·L-1 BSA. The proposed method has been satisfactorily applied to the determination of BSA in the bovine serum samples. The possible mechanism of Luminol-H2O2-BSA was studied by chemiLuminescence spectrum and UV-visible spectrum.

The approaches of scanning electron microscopy (SEM), X-ray fluorescence spectrometry (XRF), X-ray diffraction spectroscopy (XRD) and Fourier transform infrared spectroscopy (FTIR) were mainly used to analyze the loess characteristics of arid farmland in northwest China, and the Tessier sequential extraction method was utilized to determine the Pb content of different chemical speciation. The results showed: that the specific surface area is 291 m2·g-1 and the silt particles account for 80.87% of total loess samples. The sample structure, composed of particles, appears on clear surface with chip thickness of 3～5 μm. The dominant mineral phase is quartz and potassium mica, with small amounts of K-feldspar and serpentine. In addition, the kaolinite and calcite are important components indicated from FTIR spectra with low content of organic matter. The content of Pb in loess sample is higher than that of Shaanxi province and nationwide, the residual speciation of Pb accounts for 64.04% of total Pb content. The achievements are significant for the remediation of polluted soil in future.

By using the artificial neural network method based on the Lick line index to develop atmospheric physical parameters measurement, Kurucz synthetic spectra preprocessing was used to adapt to the requirements of the final LAMOST spectral data, Lick line index with the corresponding atmospheric physical parameter was input, and training artificial neural network was used to get the training model through DR8 spectroscopic data for testing. The artificial neural network is aimed to achieve the best experimental effect by adjusting the relevant parameters. The experimental results show that through the artificial neural network method to get the atmospheric physical parameters is feasible.

Aiming at the wide-spectrum high-resolution echelle spectrometer, the dispersion characteristic of the echelle grating and the cross-dispersion principle is discussed. And an optical design method of resolution preferential echelle-prism cross-dispersion is described, including the design of high-resolution main dispersion, the auxiliary dispersion for the separation on the overlapped orders, and the main-auxiliary dispersion calibration and revision. According to the requirements of commercial spectrometer, a design example is reported. The simulation and the experiment results show that when the spectral span is 400～900 nm, the resolution for Hg 546 nm is 51 000, and that for Na 589 nm is 44 000.

The fundamental parameter method, empirical coefficient method, artificial neural network and some other methods are commonly used to establish the physical model between the count rate and the content of elements in the energy dispersive X-ray fluorescence analysis technique. Besides, through a large number of theoretical and experimental proof, as a new method of dealing with complex nonlinear problems, GMDH (group method of data handing) is better than most of statistical methods of calculation. And is a self-organizing learning in feed forward network, which could auto filter and determine its structure in the training process. Here, we are going to improve GMDH and give a quantitative prediction of the results. And both the reference values and forecast values of relative errors will be less than 5%, which make the method simple, reasonable, and reliable.

A new in-situ instrument to simultaneously and rapidly detect the nitrates and nitrites of water is presented based on the sequential injection analysis (SIA) and spectrophotometer. The optimal time and injection speed for incomplete chemical color reaction of nitrite with reagent and deoxidization efficiency of cadmium column on changing nitrate to nitrite were studied and selected to obtain the rapid detection speed (the detection time for a sample can be as short as 5 mins for nitrates and 4 min for nitrites) of nitrates and nitrites in situ. In this instrument, the liquid-core waveguide (LCW) was adopted to replace the quartz cuvette as the colorimetric cell to obtain a relatively low detection limit(nmol·L-1) and very small sample consumption, and all water sample was filtered before sample injection and measurement. These measures make this in-situ detection instrument have the advantages of complete automation, very quick detection speed, low weight and power consumption, and good versatility and extensibility. Only by replacing the reagent or LCW, it also can be used to measure the trace element of water or other nutrition elements such as phosphate, silicate etc, in different sea areas in profile or fixed-point.

In the present article, the measurement principle of a multi-target multi-spectral thermometer developed previously is improved in that the brightness temperature approximation method is adopted to overcome the second measurement method initial value selection difficulty. The thermometer is used to measure the explosion true temperature of 3KG TNT explosive in open fields. The experimental results show that this pyrometer can measure changes in the explosion flame true temperature in the whole process, and functions well in the wavefront instantaneous temperature measurement and burning fireball temperature measurement. Finally, through the analysis of the factors which affect the thermometer measurement accuracy, it was concluded that the main error sources restricting the measurement accuracy improvement are still the true temperature algorithm error and the calibration method error. The analysis result has provided the direction for the high-precision thermometer development.

In the present paper, the abilities of resistance to random error of the two non-source temperature calibration methods put forward previously were researched by adding random error of different sizes to blackbody radiant flux to simulate different precisions of the multi-spectral high temperature measurement system. Experimental results show that the non-source calibration method based on cure similarity principle (NCCSP) has strong anti-random error ability and it is suitable for large random error system. When the random error is very small, its precision is lower than the non-source calibration method based on the MSP transfer function (NCCTF), but when the random error increases to a certain range, the NCCSP accuracy is much higher than the latter. The NCCTF in a certain random error range has a high calibration accuracy of the extrapolation, but its resistance to random error is weak and can be applied to the measurement system of small random error.