Optical measurement is one of the most important means of Space Object Identification. Due to the long observation distance, space objects in the field-of-view usually turn out to be unresolved points. Compared with orbit determination and photometry measurements, spectral measurements provide useful information in the wavelength dimension, which greatly improves the ability of material discrimination. This ability has great potential since it can provide reliable evidences for space object characterization. This paper introduces some typical results of space objects spectral observation and characterization in recent years, including multi-band photometry measurement, hyperspectral measurement, laboratorial spectral measurement, and characteristics modeling and simulation. Multi-band photometry measurement can bring low resolution spectral data, which is a common method for space object classification in large area. Hyperspectral measurement focuses on detailed spectral structure in the reflected light, from whom material composition of specific objects can be derived. Laboratorial spectral measurement can simulate the process of real observation under controlled conditions, while providing database of materials’spectral data. Characteristics modeling and simulation is used to generalize the attribute data to study the spectrum changing process. By analyzing domestic and overseas experiences, the capacity and weakness of current researches as well as several ideas of future works are discussed, offering a reference for future studies.

Water vapor is one of the basic atmospheric parameters, and the vertical structure of the atmosphere is of great importance to process studies. Differential absorption lidar is the techniques which provide high resolution and accuracy for water vapor profiles daytime and nighttime, and that is the most potential instrument. Differential absorption lidar (DIAL) operates in the 720~730 nm region of the 4ν overtone vibrational bands of H2O where previous using tunable dye, or Alexandrite ring laser injection seeded, however, photomultiplier acts as detector. The represent is airborne lidar LEANDRE II. And the DIAL transmitter is based on an injection-seeded, Ti:Sapphire laser or Ti:Sapphire amplifier operated at 820 nm, Si-APD act as detector. University Hohenheim mobile lidar can perform the measurements of the 3-dimensional structure of the water vapor field from 300 m to 4 km altitude. The high-power DIAL at the Schneefernerhaus research station has successfully demonstrated its measurement capabilities of vertical structure of water vapor from 3 to 12 km above sea level. The development of an OPO at 935 nm in the spectral region of the 3ν overtone vibrational band of H2O was stimulated by the need to develop an airborne water vapor DIAL with high measurement sensitivity at tropopause height, particularly in case of very dry air from the lower stratosphere. In this 935 nm wavelength range, the line strengths of suitable water vapor absorption lines are more than a magnitude higher than near 720 nm or 830nm. Based on single-frequency, a diode-pumped Nd∶YGG laser system or optical parametric oscillator emitting at 935 nm, differential absorption lidar has recently been developed to space borne measures water vapor profile of upper troposphere/lower stratosphere (UTLS) region. DBR diode laser and semiconductor optical amplifier as transmitter, APD as Geiger counter, micro-pulse DIAL for measuring water vapor in the lower troposphere has been developed and validated at field campaigns, and the fourth generation product has been constructed and tested. The application required a single-frequency laser transmitter operating at near infrared region of the water vapor absorption spectrum, capable of being on/off wavelength seeded and locked to a reference laser source for DIAL measurements. The system is based on extended-cavity diode lasers and distributed-feedback lasers. It is achieved by locking the laser wavelength to a water vapor absorption line using compact water-vapor reference cells. or the wavemeter readout for frequencies of laser1 or laser 2 counts as the error signal, product of the error signal and the PID adds as a correction to the applied voltage on the piezo controllers and injection current to tune more stably and smoothly frequencies of the diode laser. Second, precise knowledge of spectral properties of water vapor absorption, laser emission, and atmospheric scattering is necessary. However, to get high accuracy one has to consider methods of treating the problem of Doppler-broadened Rayleigh back scattering and correcting absorption section of water vapor in DIAL experiments. At last, the backscatter signal in the near-field channel rapidly drops to a level at which the results are affected by noise and electromagnetic interference. So the matching of near- and far-field channels in the lower part of the operating range with both detection channels is necessary.

The safety and quality of agro-food has attracted increasing attentions. Due to the problems of traditional detection methods, such as being time-consuming, operation complicated, solvent-consuming and high-cost, it is essential to develop rapid and non-destructive detection methods. Terahertz(THz) ,which lies between the mid-infrared and microwave ranges, has unique properties such as transient, transmission, broadband, coherence and low-energy. THz wave can indicate structures and characteristics of many biomolecules, and be used in the quantitative and qualitative analysis of organic biomolecules as “fingerprint”. Therefore, compared with other spectroscopy techniques such as NIR, Raman and X-ray, THz has become an extremely competitive tool for agri-food products inspection. In this paper, the characteristics of THz wave and the principles of THz spectroscopy and imaging technique were introduced at first. Then THz data analysis process and the commonly used methods in its main steps were given, in which the quantitative and qualitative model construction and evaluation were emphatically introduced. Thirdly, the application and research progress of THz technique in the agro-food quality inspection which contains the detection of pesticide residues, antibiotic, toxins, identification of adulteration、 transgenetic agro-food and foreign body, and prediction of the content of nutrient contents were discussed. Finally, the challenges that THz technique in the agro-food quality detection has to face were discussed systematically, based on which the research directions were pointed out, and the future outlook for THz technique was also discussed. The published literatures have indicated that THz technique is an effective solution in the agro-food quality inspection, especially THz wave can penetrate many commonly used nonpolar dielectric materials, which gives it superiorities in detection of packaged agro-food products. With the development of information technology, such as machine learning and intensive learning, on one hand, the THz data analysis technique needs to be strengthened. Through data self-learning, we can reveal the scientific rule from the data point of view, and build an analytical model with higher accuracy, lower computation cost, stronger real-time performance and better generalization ability. On the other hand, based on molecular vibration theory and combined with knowledge of chemistry and Physics, the mechanism of the terahertz spectroscopic characteristics is studied, which can be used in the model construction. Thereby through the combination of these two sides, the soft power of the terahertz will be improved. Meanwhile developing cost-effective and portable THz spectrometer with high sensitivity is a future research trend. It is expected that the application of terahertz technique will gradually begin from the laboratory state to the engineer application.

The hydroxyl (OH) radical is the most important oxidizing agent in the photochemical reactions which helps to understand the atmospheric components and photochemistry events in mesosphere. OH radical’s solar resonance fluorescence Ａ２Σ＋－Ｘ２Π(０, ０) is the excited emergent light by solar radiation around 308 nm. Hyper-spectral Resolution Spectrometer for Mesospheric OH Radical is developed to detect OH ultraviolet solar resonance fluorescence in mesosphere and separate target signal from complex background signal. The spectral range is 308.2~309.8 nm and its spectral resolution is 0.008 25 nm. Limb observation mode detects atmospheric scattering signal which consists of atmospheric molecules, aerosols and cloud scattered by solar energy. Hyper-spectral Resolution Spectrometer for Mesospheric OH Radical is based on Spatial Heterodyne Spectroscopy technique. SHS technique receives rather high spectral resolution around Littrow wavelength and is applicable to fine detection of atmospheric components. Adding cylindrical lens front or behind the optical system results in several split-fields of view. Each split corresponds line of detector imaging plane. Limb observation can obtain limb-scattered signal at different height simultaneously using layered imaging in spatial dimension with SHS technique rather than traditional limb detector scanning at different height. In order to validate detection ability and sensitivity to observation geometry of Hyper-spectral Resolution Spectrometer for Mesospheric OH Radical , a ground-based limb observation experiment is built up to detect atmospheric limb-scattered signal around 308 nm. Simulating limb mode geometry in a clear sky, limb-scattered radiation is detected in an open place. Interferogram error correction and spectrum restoration are needed due to the fact that the instrument is based on SHS. Spectrum restoration and calibration are done to a serial interferogram data at 10 min interval in a period of observation time to obtain final spectrum. The source of scattered radiation is the atmospheric molecule’s scattering of sunlight, so the spectrum should contain high-resolution features information of solar spectrum. Choose 3 feature windows from high-resolution solar spectrum and analyze correspond band in observation spectrum. It turns out that the feature windows match completely. The results can validate detection ability and fine spectrum extracting ability of Hyper-spectral Resolution Spectrometer for Mesospheric OH Radical. Radiative transfer model is set using real time aerosol optical thickness measured by solar radiometer, real time solar zenith and azimuth angle and atmospheric profiles with corresponding date, longitude and latitude. A comparison is taken between simulation spectrum and observation spectrum. Their residual is rather small. The residual between them due to the mismatch between atmospheric parameters setting and actual situation. Real time temperature and pressure profiles are considered to bring in radiative transfer model in the future. These results validate limb-scattered radiation detection ability and fine spectrum extracting ability and sensitivity to observation geometry of Hyper-spectral Resolution Spectrometer for Mesospheric OH Radical. The experiment results not only validate the feasibility in detecting multi-band and broad-band limb-scattered signal and OH target signal on orbit, but also provide theoretical and experimental foundation for orbital limb-scattering signal detection.

With the increase of space activities in various countries, the number and variety of space objects also gradually increased. How to identify and catalog the space object is a critical research issue in the field of space object surveillance for different countries. The research on non-cooperative space object mainly aims to get the information like surface materials, attitude, shape and critical payload information. And the acquisition of surface materials information is the basis for researching space object optical characteristics as well as state recognition. A multi-color photometric measurement system for space object’s surface materials is set up. To reduce the influence of stray light on the measurement results, the entire system is deployed in an optical darkroom. The light source adopts a solar simulator with spectral grade level A. The detector uses a FieldSpec4 spectrometer manufactured by the ASD company in America. The wavelength range is 350~2 500 nm, and the spectral resolution is 1 nm. The spectrometer’s optical fiber is located on the electrically controlled turntable, which can be able to simulate different observation geometry to obtain various data for the same sample. By using Johnson-Cousins UBVRI five-color spectroscopy system, ten kinds of color-index data of eight common surface materials (e.g., GaAs, anodic Al, anodic Kapton, black paint, epoxy paint, aluminized Kapton, titanium blue paint, white paint) under different observation geometry conditions are measured. And every color-index data includes 30 groups of experimental data. Through the traditional 1-sigma uncertainty box method (namely, for a given material with several groups of experimental data, calculating the mean value and standard deviation for each kind of color-index. Then drawing color-index uncertainty box, based on the mean value as rectangle center and twice of standard deviation as the side length), in the ideal identification situation, four kinds of materials (GaAs, anodic Al, anodic Kapton, titanium blue paint) can be identified through the R-I and B-R color index uncertainty box. Two kinds of materials(epoxy paint, white paint) can be determined through the B-V and B-R color index uncertainty box. That above color-index cannot identify the remaining two materials (black paint and aluminized Kapton); however, there are two main problems in using 1-sigma uncertainty box method. The first one is that it is necessary to know the prior information about which band that the test materials are sensitive to, so as to determine which kind of color-index to be used. The other problem is that the identification rate is easily affected by the number of test samples and has poor reliability. The extreme learning machine (ELM) is a kind of machine learning algorithm that uses randomized hidden layer nodes and least-squares method to train data. The algorithm has the advantages of learning efficiency, good generalization performance and not easily falling into optimal local solution. It is widely used in the data classification and regression analysis. Therefore, the ELM algorithm is introduced to solve the problem. Color-index data are randomly divided into a training dataset and testing dataset according to the proportion of 2∶1. A total of three random experiments are carried out. Each kind of material is numbered in the order of 1∶8 in the training dataset, namely, each number from 1 to 8 has 20 groups color-index data respectively, and each group includes ten kinds of color-index data. As for the training dataset, the same number is assigned to the material according to the known attribution materials type. Regard determination coefficient and calculating time as the judgment indicators to judge the accuracy and real-time capability of ELM algorithm. The results show that: whether identify the single kind of material or all testing dataset, the determination coefficient of training dataset is all above 0.98 and determination coefficient of the testing dataset is above 0.96, meaning that at most three groups color-index data cannot be identified in each experiment. In the aspect of calculating time, the total time can be completed within 0.07 s, even up to 0.002 s. The identification efficiency and reliability are much higher than the traditional 1-sigma uncertainty box method, which shows that the ELM algorithm can accurately and quickly identify space object’s surface materials. Relevant research can provide technical support for state information inversions such as shape, attitude and critical payloads of space objects.

In the research field of arc plasma spectrum diagnosis, combined with the advanced image sensing technology, the Fowler-Milne method uses spectral image to obtain temperature information of arc plasma. Because of its high time and space resolution, the Fowler-Milne method is widely used in arc plasma temperature measurement. However, the relationship between line emission coefficient and temperature is not monotonous, and traditional Fowler-Milne method selects one ArⅠ spectral line to complete the measurement, which leads to the decrease of line intensity, and the process of measurement needs researchers to determine the temperature range of different locations to complete the calculation of temperature. The whole process can’t be automatically completed by software. In view of this problem, based on PLTE conditions of arc plasma, applying the partial LTE model of arc plasma, modified Fowler-Milne method based on two spectrum line, which combines the ArⅠ spectral line emitted by Ar atoms in an outer low temperature region of arc, and the ArⅡ spectral line emitted by Ar ionization ion in high-temperature area of arc, to determine the different temperature range of the arc plasma, and then the whole temperature is calculated by the temperature corresponding to the intensity of the ArⅠ spectral line in low-temperature area and high-temperature area, eliminating the adverse effects of single ArⅠ spectral line emission coefficient field. A light splitting system was also designed and built, dividing the light of arc into two beams by a spectroscope. Two sets of reflectors and narrow-band filters were used to collect the image of two sets of arc spectral images though one exposure, of which parameters such as the focal length aperture are exactly the same, achieving good time and spatial consistency and reducing the error of emission coefficient fusion. In order to verify the feasibility of measurement system and arc image extraction, black and white chessboard was used as a target, and the extraction of corners extracting of two image proved the system satisfies the demand of collecting two groups of arc spectral images, and was also used to normalize two images for the extraction of arc image in the later stage. Based on the assumption that the plasma arc has axisymmetric properties, with the brightness information of spectral image CMOS collected as the integration of emission coefficient under different angle projection, after the median filter noise reduction processing, ML-EM method was used to reconstruct the 3D emission coefficient distribution from the 2D luminance distribution. In the experiment, ArⅠ696.5 nm spectral line and ArⅡ480.6 nm spectral line with little self-absorption effect were selected. The OD0.4 ND filter was added in the pathway of 696.5 nm spectral line, to make the maximum brightness value of the two spectral images consistent. 150 A welding plasma arc was measured in the experiment. After three - dimensional reduction of ML-EM method, the two spectral line emission coefficient fields were fused. At the pixel point location where the ArⅠ spectral line reaches the maximum value, ArⅡ spectral line reaches εrp, which were used to determine the high-temperature zone or low-temperature zone. The measurement of the plasma arc of 150 A showed that 696.5 and 480.6 nm spectral line can automatically identify the high temperature zone in welding arc plasma, making it more possible for the arc temperature real-time monitoring to be realized.

Laser has many advantages in good monochromatic, high brightness, strong directional and good coherence. Therefore, the integral of laser spectrum can be applied to the field of micro displacement measurement, which is based on the interference principle. In the process of gravimetry, gravity differences caused by different geological structures make the micro nano-scale displacements of detection mass. Therefore, development of micro displacement measurement system with nanometer precision is vital. However, traditional capacitance displacement measurement method (CDMM) is not sufficient to prevent electromagnetic interference. In comparison, the optical interference method (OIM) has the advantages of anti-electromagnetic interference, strong environmental adaptability, and higher precision than CDMM. For the traditional OIM system, the optical path is complex and difficult to integrate, which is unfavorable to the miniaturization and integration of the gravimeter. Therefore, developing a compact OIM system to measure the micro displacement with nanometer precision has become an urgent requirement. A laser interferometric method based on variable phase retardation was designed to achieve sub-nanometer resolution displacement measurement, which has the advantage of compact structure and easy integration compared to traditional OIM system. This system was composed with diode laser, polarizer, analyzer, birefringent crystal group and spectrometer. This paper studies the following aspects: firstly, the measurement system scheme is determined. The structure with dual optical path of polarized light interference is introduced, and the wedge birefringent crystal group is used as the core device, which transforms relative displacement among crystals into differential phase delay between ordinary light (o) and extraordinary light (e). Integrate the laser spectrum, and then the displacement change is transformed to the variance of the synthesis light intensity. Secondly, the physical model of displacement measurement is set. According to the design of birefringent crystal group geometry structure, displacement process and optical path, the relationship between light intensity variance and measured displacement is determined. The third is the optimization of system parameters. In order to make the system measurement error (ME) and measurement range (MR) to meet the practical requirements, using the established physical model, the ME and the MR are set up as a function of crystal cutting angle α and laser wavelength λ. According to application requirements, appropriate bounds of ME and MR is determined, and then α and λ is obtained. Finally, crystal process, systems build and the system measurement test were carried out. In detail, α and λ are chosen as control parameters to optimize and simulate the system, jointly considering “approximate linearization” and “laser intensity fluctuation error”. Meanwhile, jointly considering “laser wavelength fluctuation error” and “laser intensity fluctuation error”, and using the maximum displacement related to system MR, to optimize system ME. Eventually, yαis selected as 20 ° and λ is 635 nm. In testing experiments, displacement measurement is carried out by means of piezoelectric ceramic actuator generating micro-displacement with 10 nm intervals, which includes linear calibration, MR and ME of the system. In addition, a two-hour continuous measurement is carried out using this system when measured position is fixed, and the displacement detection limit is determined by Allan variance. Experimental results show that the displacement MR is longer than 150 nm, and displacement ME is around 0.5 nm, and a detection limit is 0.32 nm @ 23 s, and the linearity determination coefficient R2 is 0.999 85. In conclusion, the system using self-made birefringent crystal group as the core device with adjustable phase retardation can be used as the displacement measurement unit of mass in gravity detection. This system has the advantage of strong environmental adaptability compared with CDMM, and laconic structure and compact light path compared with the conventional laser interference system, so as to facilitate the miniaturization and integration of the gravimeter.

LIDAR plays significant roles in monitoring the vertical distribution characteristics of clouds and aerosols and studying their impacts on the global climate change. For the space-born LIDAR, discrimination between clouds and aerosol is the first step of cloud/aerosol vertically optical property retrieve, and to a great extent, the retrieval precision depends on the accuracy of cloud and aerosol classification algorithm. Based on the optical and geographic characteristics of aerosols and clouds observed by LIDAR, in this study, the CALIOP aerosol and cloud products over China in the year of 2016 were trained as the sample sets. An effective cloud/aerosol classification algorithm was developed by combining the support vector machines (SVM) and decision tree methods. Our algorithm includes 3 parts: cloud and aerosol discrimination, ice-water cloud classification and aerosol subtype classification. (1) The cloud and aerosol were discriminated by the classification confidence functionsof 5-D probability density function (PDF) with parameters of γ532, χ, δ, Z and lat. (2) Randomly oriented ice (ROI) and water cloud were classified based onthe SVM. And by constructing the PDFs with γ532, χ, δ, Z and T, feature layers misclassified by SVM were corrected, and a small portion of the horizontally oriented ice (HOI) clouds were removed from the water clouds. (3) Based on the optical and geographic characteristics of aerosol subtypes, decision tree classification was used for the determination of aerosol subtypes. Our retrieval results showed a good agreement with the CALIOP VFM products. For the cloud and aerosol discrimination results, the consistency ratios between our retrieves and VFM products for aerosol and cloud are up to 98.51% and 88.43%, respectively. And the consistency ratios in the day are higher than those at night. For the cloud phase retrieval results, water clouds can be well separated, and the consistency ratio of water cloud between our retrieves and VFM products is as high as 93.44%. The consistency ratio of HOI is low due largely to the confusion between HOI and ROI. For the aerosol subtype classification, most aerosol subtypes could be well recognized by our algorithm. However, the consistency ratios of the mixed subtypes (e. g. polluted continental and polluted dust) between retrieval results and VFM products are relatively lower. Moreover, the cloud/aerosol, cloud phase and aerosol subtype classifications were also compared with the VFM products under three typical air conditions, i. e. haze, dust and clean. Under the haze condition, our results for most of the smoke aerosols agree quite well with the corresponding results from VFM. Under the duststorm condition, our algorithm can effectively discriminate the most of dust and polluted dust aerosols. For the clear day, our results for the few existing cloud and aerosol layers are quite consistent with the VFM results. This paper is an important improvement of the cloud and aerosol classification algorithms, which can simplify the processing and improve efficiency with satis factory accuracy. In the future work, we will build day/night and seasonal training sample sets, and consider more ice cloud phases and aerosol properties in the cloud/aerosol classification retrieval algorithm.

In recent years, haze phenomenon is becoming more and more serious in China. It not only harms people’s health and air traffic caused by security risks, but also affects a lot of optics equipment used outdoor. In order to quantitatively analyze the effect of different haze levels on the performance of the trace gas concentration detection system based on the elastic light modulator, the test system was designed. The effect of different PM2.5 concentration sample gas on the inversion of equivalent VOC gas concentration was quantitatively analyzed. In the experiment, different concentrations of PM2.5 were collected in the field, and then mixed with the standard gas to be prepared. Finally, the comparison of the transmitted light spectrum and the measured gas concentration inversion data was made. The effect of PM2.5 concentration on the system was quantitatively analyzed. In the experiment, formaldehyde and benzene were used as the gas to be measured. Six kinds of concentrations of the gas to be measured were prepared, and 6 haze concentration levels (from No. 1 to No. 6) were tested. The experimental results showed that when the PM2.5 concentration increases, the system light energy absorption rate is significantly reduced. When the PM2.5 concentration level is less than No.3, the attenuation change is relatively slower, and when it is greater than No.3, the attenuation effect is enhanced. When the PM2.5 concentration is less than 150 g·m-3, the test accuracy is better than 90%; When the concentration of PM2.5 exceeds 150 g·m-3, the effect of inversion of VOC concentration becomes stronger. When 350 g·m-3 is reached, the test error is nearly 30%. It can be seen that PM2.5 concentration has a significant effect on the gas concentration detection of the optical modulation system.

Terahertz wave technique is very prospective in the biomedical application field, where there exists a practical problem that it is hard to register a terahertz time-domain spectroscopy (THz-TDS) detection point and its accurate corresponding position in pathology image. In this paper, complex human gastric tissue is taken as the experimental subject to search for accurate detection approach. This paper uses tissue microarray technology to process paraffin-embedded gastric tissue, and then contrasts the Terahertz absorption coefficient and refractive index spectrum of detection subjects which are divided into registered experimental group and unregistered control group using principal component analysis method. Meanwhile, this paper contrasts the gastric tubular adenocarcinoma THz-TDS discrimination methods using support vector machine and logistic regression respectively. The research result reveals that the detection accuracy brought by tissue microarray technology helps better discriminate tumor samples and normal samples.

Dielectric barrier discharge system (DBD) is a typical nonlinear gas discharge system, which not only has many applications in the industrial production such as low temperature plasma production and luminescence, but also has attracted widespread attention for its physical phenomenon such as non-linear phenomenon and self-organization phenomenon. In order to study the influence of gap distance and gas components on plasma parameters of discharge filaments in DBD system, a discharge cell is redesigned to ensure the other experiment conditions are the same. The discharge cell of this experiment is a flat multiplicate gas gap, which is composed of three thickness of 1.2 mm square glass frames with the discharge area side length as 40, 30 and 20 mm respectively. The multiplicate gas gap is placed in a vacuum chamber with adjustable gas composition and pressure, and three discharge filaments can be generated whose discharge gap distance is 1.2, 2.4 and 3.6 mm respectively. The instantaneous photos taken by high speed video camera indicate that the discharge types of three filaments are all streamer discharge. Arranging the optical path on the line perpendicular to the plane of gap, the focus lens is used to obtain a clear image, and optical fiber probe is moved to achieve spatial resolution acquisition data. The emission spectra of the N2 second positive band (C3Πu→B3Πu) of the three filaments are collected in the experiment by spectrograph. The molecule vibration temperatures are calculated based on emission intensity; based on the relative intensity of the N+2 line at 391.4 nm and the N2 line at 394.1 nm, the electron average energy of the three kinds filaments are investigated. The changing trends of molecular vibration temperature and electron average energy are obtained by changing the Ar content. The results show that, with the increase of Ar content in the 0%~60% range, the molecule vibration temperatures show a trend from rise to drop; The overall trend is that the thinner the thickness of the discharge area, the higher the molecule vibration temperature of the filament in same Ar content. The molecule vibration temperature of the filament in 1.2 mm thick gap is the highest, the second in 2.4 mm thick gap, that in 3.6 mm thick gap is the lowest; As Ar content increases, the electron average energy increases and then decreases; the thinner the thickness of the discharge area, the higher the electron average energy of the filament in same Ar content. The electron average energy of the filament in 1.2 mm air gap thickness is highest, the second in the 2.4 mm gap thickness and the electron average energy of in the 3.6 mm gap thickness is lowest. This paper has an important reference significance for studying plasma parameters and industrial production in DBD system.

Because of high working pressure and uniform discharge, atmospheric dielectric barrier discharge (DBD) has become the main technology of non-equilibrium plasma discharge in recent years. Electrode structure isthe key factor in the ionization characteristics. Therefore, optimizing the electrode structure to improve ionization characteristics is very important for the development of plasma discharge equipment and the optimization of its performance. In order to enhance the ionization characteristics of dielectric barrier discharge at atmospheric pressure, high activity, low temperature plasma uniformity, coaxial dielectric barrier discharge device has been designed based on the ionization characteristics test and parameter diagnosis. Experimental researches on the effect of electrode structure of three grounding electrode on the spectral parameters were carried out by using photoelectric technology, and argon ionization tests were carried out under the conditions of a standard atmospheric pressure, discharge frequency 11.4 kHz and varied discharge peak voltage increasing from 5.4 to 13.4 kV (with 1.0 kV interval). Atomic emission spectroscopy (AES) was used to detect and analyze the excitation and splitting spectra of argon plasma, and the influence law and effect of electrode shape and applied voltage on the characteristic parameters of dielectric barrier discharge were obtained. The influence ofthe spectral parameters of threaded electrode, toothed electrode and cylindrical electrode, as well as the applied voltage on the characteristic parameters of DBD were studied. The results showed that the discharge intensity of the plasma of tooth shaped electrode is greater and the discharge effect is significant. The average energy utilization ratio of the electron is low, and the electron excitation temperature is weaker than that of cylindrical electrode. Under testing voltage conditions, the electron excitation temperature does not increase with the increase of applied voltage. It indicates that the main features of the micro discharge channel do not depend on the applied voltage supply, but depends on the electrode structure, gas composition and gas pressure. The increase in applied voltage only increases the number of micro discharge in unit time. The integration of electronic excitation temperature can reach up to 3 500 K, which accords with the typical characteristics of low temperature plasma.

A model for predicting spot color ink formula for PET film printing is proposed based on the correlation between absorbance and the concentrations of primary inks. Firstly, according to the methodology with the concept of light propagation and the multiple internal reflectance of ink layer and substrate, a method for obtaining the spectral transmittance of the thin film prints with high transmission characteristic is established by making use of the reflectance of the film prints with back and white substrates. The absorption spectrum of the film prints in the visible spectrum is obtainedby the spectral transmittance. Then, the regression equation between the absorbance of spot color inks and the concentrations of a primary color ink is established to determine characteristic wavelengths with strong linear correlation. After that, according to Lambert-Beer law, the prediction model is established by using the absorbance of the spot color samples and the absorbance of the primary color samples at the characteristic wavelengths, in order to obtain the proportions of primary color inks. Finally, the deviation between the predicted formulas and the actual concentrations is analyzed. In order to evaluate the accuracy of the prediction method, the remade samples are compared with target samples in terms of chromatic difference and the spectralroot mean square error. Some dual-componentspot color samples produced on PET filmby gravureare used as experimental subjects to verify the proposed method. The analysis of spectrum shows that the reflectance on the black substrate is significantly different from the reflectance on the black substrate, but both have the same trend with the variation of primary ink concentration. The transmittance curves of spot colorsamples are situated between the curves of primary color samples and movecloser to the primary color with higher concentration. The absorption spectrum of samples increase in the regionof 400～580 nm with the decrease of concentration of primary ink A, and decrease in the region of 580～700 nm as the concentration of A decreases. Except for the region of 570～590 nm, the linear correlation coefficients R2 between absorbance of spot color inks and the concentration of primary color A are higher than 0.95 and the average value is 0.990 0, which means a strong linear correlation in the visible spectrum range. The absorbancevalues at the wavelength 520 nm (R2 of 0.994 2) and 700 nm (R2 of 0.998 5) of the primaryinks A and B are selected to predict the formulas of spot colors by using the least squares method. The results show that the 6 groups of predicted concentrations are with 2.5% deviation from the actual concentrations of target samples, which means no significant difference. The maximum chromatic difference between the targetsamples and remade samples is estimated to be 1.98, the minimum to be 0.30, and the average value to be 0.85, which satisfies the requirement of spot color reproduction. 5 of the 6 groups are smaller than 1.5, which satisfies the requirement of faithful reproduction. The maximum RMSE is 2.95%, and the minimum is 0.49%, and the average value is 1.40%, which means a high precision color reproduction in the visible spectrum. It is confirmed that the proposed method could effectively improve the printing quality and the spot color matching precision, which may provide a scientific method for the predictive study of spot color ink formula of PET film printing.

Sewage reclamation is one of the effective countermeasures for solving the water shortage problem. Nanofiltration (NF) process is an effective method for reclaiming secondary effluent since it can provide high-qualitied water. However, during the nanofiltration process, complicated and dynamic membrane fouling occurred, which can cause the decrease in flux and effluent quality. Tracking the dynamic evolution of NF membrane fouling in water treatment is important for controlling the membrane fouling depending on different fouling stages. Organic matters are important indicative contaminant for the dynamic evolution of fouling layer. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) is one of the most significant methods to characterize the change of functional groups in fouling layer. However, the peaks of ATR-FTIR are complex and the variations between different samples are tiny, especially when the fouled membrane samples are similar in time. It is difficult to directly discriminate the variation and trend of different ATR-FTIR spectra and cannot be the convictive evidence for fouling stages recognition. To investigate the dynamic evolution of membrane fouling, this study categorized the NF membranes samples obtained at different fouling time through combining the ATR-FTIR spectra and clustering analysis. Considering the influence of distance measurement method between categories, normalization of ATR-FTIR peak absorbance, correlation between peaks, and interaction between peaks and samples, this study utilized the correspondence analysis as the pretreatment of the ATR-FTIR spectra to obtain the scores of different membrane samples along main dimensions and then clustered the samples based on normalized Euclidian distance. During the 1-month NF experiment using sewage secondary effluent, because of the deposition of foulants, membrane fouling occurred and 13 fouled membranes were obtained at different time. Based on the hierarchical clustering of ATR-FTIR spectra, the fouling process can be clearly divided into the stages of: virgin membrane, stage Ⅰ(3 h～8 d), stage Ⅱ (10～15 d), and stage Ⅲ (20～30 d). The results of the clustering analysis was further interpreted by X-ray photoelectron spectroscopy (XPS) and adenosine triphosphate (ATP) content on the membrane surface. It was shown that with the evolution of membrane fouling, the organic composition and coexisting microorganism amount on the membrane surface changed concordantly. The characteristics of different stages may be interpreted as: in stage Ⅰ, the membrane was initially covered by organic foulants, and microorganism began to gather; in stage Ⅱ, the proportion of polysaccharide-like substance increased and the gathering of microorganism became stable; in stage Ⅲ, the membrane fouling became mature and the hydrogen bond characteristics of organic foulants became more evident. In this study, the variation of different ATR-FTIR spectra was detected sensitively through clustering analysis. The study provides an objective, automatic and measurable auxiliary method for recognizing and characterizing membrane fouling stages. Besides, it is meaningful for investigating the ATR-FTIR spectra of a series of samples in not only membrane fouling research but also other fields such as materials science and adsorption research.

Nitrogen (N), phosphorus (P) and potassium (K) are important biochemical components of plant organic matters, and estimating their contents are useful for monitoring plant metabolism processes and health. Visible and near-infrared (VNIR) spectroscopy has been applied to monitor plant biochemical parameters with many modeling methods, in which support vector machine (SVM) has been proved to be a potential approach for modeling the nonlinear relationships between the reflectance spectra and biochemical parameters of plant organic matters, and the successful application of SVM relies on the proper selection of kernels. This study aimed to compare the performances of radial basis function (RBF), polynomial and sigmoid kernels based support vector machine regression (SVR) models in estimating the contents of nitrogen (cN), phosphorus (cP) and potassium (cK) of diverse plant leaves using laboratory-based VNIR spectroscopy. The cN, cP, cK and VNIR reflectance of leaf samples in eight plant species(rice, corn, sesame, soybean, tea, grass, shrub and arbor) were measured in laboratory. Three transformation methods, namely the first derivative(FD), standard normal variate (SNV) and logrithmic reciprocal transformation (Log(1/R)) were used for spectral transformation. The SVR models using three aforementioned kernels were calibrated and validated with 1 000 bootstrap sample datasets. The average determination coefficients (R2) as well as ratio of performance to standard deviate (RPD) were calculated to compare the performances of three different kernels. The results showed that, the RBF kernel based SVR model with FD and absorbance transformation obtained the best accuracy for cN and cK estimations (cN: mean R2=0.64, mean RPD=1.67; cK: mean R2=0.56, mean RPD=1.48), and the RBF kernel based SVR model with FD transformation obtained the best accuracy for cP estimations (cP: mean R2=0.68, mean RPD=1.73). The study indicated that RBF kernel based SVR model has great potential in estimating biochemical component contents of diverse plant leaves with VNIR spectroscopy.

In crop production, the unreasonable use of chemical pesticides to prevent from plant diseases is widespread, which affects product quality and food safety seriously. Therefore, it is of great significance to identify plant disease quickly and adopt appropriate control measures to improve the quality of crops. In this paper, the healthy leaves, rust spot and green area near the spot of rust diseased leaves of broad bean, corn, allium fistulosum and garlic were studied by a tri-step IR spectroscopy method, including Fourier transform infrared (FT-IR) spectroscopy, second derivatives infrared (SD-IR) spectroscopy and two-dimensional correlation infrared (2D-IR) spectroscopy. The results showed that tiny differences were observed in the intensities and shape of several peaks in the original spectra of each crop leaves. And several peak intensity ratios in the original spectra were different. The peak intensity ratio A1 410/A1 646 of the healthy leaves, green area near spot and rust spot of rust diseased leaves of broad bean were 0.698, 0.624 and 0.616 respectively, and the corresponding ratio A2 926/A1 646 were 0.665, 0.638 and 0.552 respectively. The corresponding ratio A1 649/A1 055 of corn were 0.813, 0.696 and 0.691 respectively, and the corresponding ratio A1 382/A1 055 were 0.552, 0.478 and 0.465 respectively; the corresponding ratio of A2 926/A1 055 were 0.574, 0.467 and 0.469 respectively. The corresponding ratio A1 382/A1 061 of allium fistulosum were 0.843, 0.821 and 0.704 respectively; the corresponding ratio of A2 923/A1 061 were 0.707, 0.680 and 0.489 respectively. It can be seen that the intensity ratios of the rust spot and the green area near the spot of rust leaves were lower than that of the healthy leaves. More significant differences were exhibited in their SD-IR spectra in the range of 1 800～800 cm-1, and clearer differences in the position and intensity of auto and cross peaks were observed in the range of 860～1 690 cm-1 in 2D synchronous correlation spectra. The healthy leaves of broad beans showed 4 strong auto-peaks and 2 strong positive cross peaks, and 5 strong auto-peaks and 4 strong positive cross peaks were revealed in the green area near the spot of rust diseased leaves, and 2 strongest auto-peaks, 5 medium strong auto peaks and 5 strong positive cross peaks were appeared in the rust spot of rust diseased leaves. The intensity of auto-peaks of the rust spot of broad bean rust leaves were the strongest while the intensity of auto-peaks of the healthy leaves were the weakest. There were 9 strong auto-peaks and 12 strong positive cross peaks in the healthy leaves of corn, and 11 strong auto-peaks, 3 strongest positive cross peaks and 11 medium strong positive cross peaks in the green area near the rust spot on the diseased leaves, and 6 strong auto-peaks and 3 strong positive cross peaks in the rust spot of rust diseased leaves. There were 9 strong auto-peaks and 8 strong positive cross peaks in the healthy leaves of garlic, and 2 strongest auto-peaks, 9 medium strong auto-peak and 10 strong positive cross peaks appeared in the green area near spot of rust diseased leaves, and 6 strong auto-peaks and 1 strong positive cross peaks in the rust spot of rust diseased leaves. The intensity of auto-peaks of the green area near the spot of rust diseased leaves of corn and garlic were the strongest while the intensity of auto-peaks of the rust spot of rust diseased leaves were the weakest. There were 9 strong auto-peaks and 5 strong positive cross peaks in the healthy leaves of allium fistulosum, and 8 strongest auto-peaks and 3 strong positive cross peaks in the green area near the spot of rust diseased leaves, and 3 strong auto-peaks in the rust spot of rust diseased leaves. The auto-peaks of the healthy leaves of allium fistulosum were the strongest, while the auto-peaks of the rust spot of the rust leaves were the weakest. It is demonstrated that FT-IR combined with SD-IR and 2D-IR spectroscopy could be used to discriminate the crops rust leaves rapidly and effectively. Tri-step IR spectroscopy might provide a spectral method for detecting crop disease.

Unsaturated fatty acids (UFA) are basic composition of fresh meat fat. The composition and content of UFA in fresh meat directly determine its flavor and quality. Differing from being time consuming and causing sample destruction of Gas chromatography, Near-infrared spectroscopy can be used to determine UFA in meat rapidly and non-destructively. NIR diffuse reflectance spectra of sixty-three fresh meat samples including donkey meat, beef, mutton and pork were acquired in the band of 4 000～12 500 cm-1 at temperatures of 5, 10, 15, 20, 25, 30 and 35 ℃. Gas chromatography was used as the reference method to determine the composition and content of UFA in samples. Partial least square (PLS) Calibration models for individual UFA of palmitoleate, linoleic, oleic and tetracosenic acid, and total UFA (TUFA) were developed with all band spectra data of intact and minced samples (diameter of 3 mm) at different temperatures, respectively. The better performances of PLS models for palmitoleate and TUFA were attained with spectra of minced samples at 5 ℃; for linoleic and oleic acid with spectra of minced samples at 35 and 25 ℃ respectively; and for tetracosenic acid with spectra of intact samples at 15 ℃. The influence of sample temperatures on the performances of models for the five indexes was irregularly. Then forward and reverse interval PLS (FiPLS and RiPLS) with interval size of 1 762, 881, 440 and 220 variables were performed to select optimal bands based on all band PLS models. For palmitoleate, linoleic, oleic acid and TUFA, the method of RiPLS with interval size of 220 variables gained better prediction, while for tetracosenic acid the performance of FiPLS model with interval of 440 variables was better than the else iPLS models and all band PLS models. The optimal bands were 4 425～4 636, 4 849～5 272, 5 486～5 696.7, 7 398.6～7 818, 8 031.1～8 666.5, 9 947～10 363.6 and 12 495.5～12 498.4 cm-1 for palmitoleate; 4 000.6～4 423.9, 5 273.4～5 698.6, 7 398.6～9 090.8, 10 576.7～10 787.8 and 12 495.5～12 498.4 cm-1 for linoleic; 4 000.6～4 423.9, 4 637～4 848.2, 7 398.6～8 242.3, 8 455.4～9 090.8, 9 947～10 787.8 and 12 495.5～12 498.4 cm-1 for oleic acid; 4 849.1～5 272.4 cm-1 for tetracosenic acid; and 4 000.6～4 423.9, 4 637～5 698.6, 9 097.5～9 515.1, 9 940.3～10 575.7, 11 646～12 060.6 and 12 273.7～12 498.4 cm-1 for TUFA. The spectra data of optimal bands were compressed by PLS. The latent variables obtained from compression were used as input to Least squares-support vector machine (LS-SVM) models for the five indexes. The performances of LS-SVM models were optimal in comparisons with iPLS models. The correlation coefficients and root mean square error of calibration and leave-one-out cross validation, and ratio of prediction to deviation of cross validation (RPDcv) of the optimal models were 0.974, 1.403 mg·(100 g)-1, 0.973, 1.428 mg·(100 g)-1 and 4.31 for palmitoleate; 0.99, 2.233 mg·(100 g)-1, 0.99, 2.263 mg·(100 g)-1 and 7.21 for linoleic; 0.982, 8.194 mg·(100 g)-1, 0.982, 8.223 mg·(100 g)-1 and 5.19 for oleic; 0.921, 0.224 mg·(100 g)-1, 0.92, 0.225 mg·(100 g)-1 and 2.52 for tetracosenic acid; and 0.996, 24.21 mg·(100 g)-1, 0.995, 26.045 mg·(100 g)-1, 10.01 for TUFA. The RPDcv of linoleic, oleic acid and TUFA models were all more than 5, and the one of palmitoleate was near 5, and the one of tetracosenic acid near 3. The prediction performances of NIR models for the five indexes were satisfied. The results show that the method of combination band selection and PLS compression with LS-SVM can optimize the prediction performance of NIR quantitative models for individual UFA and TUFA in fresh meat.

Many wild nocuous fungi are similar to the edible in morphology and biological characteristic, which easily leads to serious food safety incident because it is difficult for farmers to distinguish them just by experience. The progress of wild edible production makes a great contribution to rural economy of Yunnan province where the yield and export volume are highest in China. Rapid authentication of wild edible fungi variety is beneficial for wild edible industry towards healthy development. Meanwhile, the authentication also contributes to the analysis of the genetic relationship between edible mushroom and their breeding. Seven kinds of fungi were collected from Yunnan and other seven origins around Yunnan. Fingerprint of caps and stipe were obtained with Fourier transforms infrared (FTIR) spectrometer, respectively. Cap model, stipe model, low-level data fusion model and mid-level data fusion were established using prepressed spectra according to low- and mid-level fusion strategy combined with decision trees, discriminant analysis, logistic regression classifiers, support vector machines, nearest neighbor classifiers and ensemble classifiers that every model was computed 10 times. The optimal classification algorithm was selected based on the accuracy of training set. Hierarchical cluster analysis (HCA) was executed using the mid-level fusion dataset to judge genetic relationship between seven fungi. The results indicated: (1) The best algorithm of caps, stipe and low-level fusion is linear discrimination that accuracy is 92.8%, 96.4%, and 97.6%, respectively. Subspace discriminant is the most optimal in mid-level fusion that accuracy is 100%. (2) The average accuracy of all samples is 93.61%, 95.54%, 96.99% and 99.88% based on the best model of stipe, cap, low-level data fusion and mid-level data fusion. The performance of mid-level fusion is better than other three models, which indicated that the model could distinguish the highly -similar samples by reducing the influence caused by their origins. (3) The result of HCA based on mid-level fusion dataset displayed that the distance between Boletus magnificus and B. edulis was very close, which showed their chemical information were similar and genetic relationship was close. (4) The result of HCA based on mid-level fusion dataset displayed that the distance between Boletus magnificus and Leccinum duriusculum was very long, which showed their chemical information were different and genetic relationship was inferior. In a word, mid-level data fusion strategy combining FTIR spectra of different parts, subspace discriminant and HCA could effectively distinguish different kinds of edible fungi and judge the genetic relationship, which is a novel method used for variety authentication and genetic relationship judgment of wild edible fungi.

Epilepsy is a common chronic neurological disorder which is characterized by the occurrence of unprovoked and recurrent spontaneous seizures. Kainic acid (KA), a neurotoxin, was stereotaxically injected into the right hippocampus of rats to induce the post-status epilepticus (SE) model of temporal lobe epilepsy. Then the neurons in CA1 subregion of the hippocampus in the post-SE rats were detected by synchrotron radiation based-Fourier transform infrared microspectroscopy (SR-FTIR) at 24 hour after status epilepticus, determining the distribution and concentration of main biochemical molecules in the epileptic neurons. The infrared imaging of the biological molecules showed that the protein and lipid functional groups (bands at: 1 655 cm-1, 2 800~3 000 cm-1) were mainly localized in the cell body of the control CA1 neuron, whereas exhibited intracellular low concentration but high concentration in the region surrounding the cell body in the epileptic neurons of the hippocampal CA1 subregion. Moreover, the nucleic acid functional groups (bands at: 1 055~1 054 cm-1) were mainly located in the cell body of neurons in the control and epilepsy rats, and there was no significant difference in the distribution and concentration of the nucleic acid functional groups between the control and epileptic neurons. Additionally, the secondary derivative spectra for amide Ⅰ (assigned to 1 655 cm-1) in the CA1 neurons showed that there was an additional negative peak near 1 653 cm-1 in the epileptic hippocampus compared to the control neuron. These findings suggested that the disorders of biochemical composition in the hippocampal neurons in epilepsy rats emerge earlier than their morphological damages.

Methanol gasoline is a new type of environmentally friendly diesel fuel, and the performance and quality of methanol-gasoline are based on the diesel methanol content. In this work, mid-infrared spectroscopy was successfully used to evaluate the methanol contents in methanol-gasoline samples with the aid of chemometric approaches. First, the mid-infrared spectral data obtained were pre-processed by smoothing standard normal, multiple scatter correction (MSC), baseline correction and normalization, and the partial least-square (PLS) quantitative calibration models were established, and the best pre-processed method was found. It was found that the PLS model pre-processed by MSC was much better than others, and the r and RMSEP evaluated were 0.918 and 2.107, respectively. In order to simplify the model and improve the prediction accuracy, uninformative variable elimination (UVE) was used to select the optimal wavelengths, and the experimental results showed that the prediction ability was greatly improved. Different quantitative calibration models by UVE selected wavelength, such as partial least-square (PLS), principal component regression (PCR) and least square-support vector machine (LS-SVM) for measuring methanol content were established and their prediction results were compared. It was found that the UVE-PLS model was much better than others, and the r and RMSEP evaluated were 0.923 and 2.075. It suggested that infrared spectroscopy in the detection of methanol content in the methanol gasoline is feasible and can bring good prediction results. UVE is an effective method for methanol gasoline in the infrared spectrum of band selection method, which is significant for the development of oil chemical industry.

Coherent Anti-Stokes Raman Spectroscopy (CARS) and Coherent Anti-Stokes Hyper-Raman Spectroscopy (CAHRS) are widely used in the study of molecular interfaces and biological membrane surfaces. However, forsuchahigher order nonlinear optical process, the number of molecular microscopic polarization tensor elements areso larger and the relationships is so complexthatthe quantitative analysis of CARS and CAHRS is more difficult. In this paper, we present the simplified scheme for microscopic hyperpolarizability tensor elements of CARS and CAHRS. First, the CARS microscopic hyperpolarizability tensor elements βi′j′k′l′ are expressed as the product of the differentiation of Raman microscopic polarizability tensor α′i′j′. Then the CAHRS microscopic hyperpolarizability tensor elements βi′j′k′l′m′ are expressed as the product of the differentiation of Raman microscopic polarizability tensor α′i′j′ and the differentiation of hyper Raman microscopic polarizability tensor β′i′j′k′. The ratios between the βi′j′k′l′ and the ratios between the βi′j′k′l′m′ are then obtained from the ratios of α′i′j′ and the ratios of β′i′j′k′. Using these relationships between the microscopic hyperpolarizability tensor elements of CARS and CAHRS obtained in this paper, the generalized orientational functional and generalized orientational parameters of CARS and CAHRS are obtained and ready to be used for quantitative analysis of interfacial molecular orientation information.

Royal jelly is a natural nutrient health food that has antioxidant, anti-aging, regulate cardiovascular system and immune function. In recent years, royal jelly has been widely applied in food, biomedicine and other fields. Because the collection process of royal jelly is time-consuming and the quality of the royal jelly is difficult to detect and the quality of royal jelly is uneven in the market. It is very important to realize the rapid identification of the quality of royal jelly. Therefore, the content of two components of moisture and protein on the quality of royal jelly is explored in this paper, and the quantitative analysis model of principal component regression (PCR) and partial least squares (PLS) method is established for the moisture and proteir of royal jelly by Raman spectroscopy, and the feasibility of the quantitative analysis of royal jelly is explored. In the experiment, the determination of the moisture and protein chemical vollues in royal jelly adopts the vacuum drying method and kjeldahl method as specified in the national standard of royal jelly. The royal jelly spectrum is collected by the DXR laser confocal microscopy Raman spectrometer. The TQ Analyst analysis software is used to pretreat the full spectrum of royal jelly and establish a quantitative analysis model. Among them, four spectral preprocessing methods includes first derivative, second derivative, Standant Normal Variate Transformation, Multipicative Scatter Correction and Savitsky-Golay, convolution these four spectral preprocessing methods are combined into a variety of different pretreatment methods. A lot of experiments have been carried out on royal jelly samples to find out the best models and treatment methods. The results show that the effect of the quantitative model by using the principal regression method to establish moisture and protein of royal jelly is not ideal. The results of the quantitative model of moisture show that the best spectral processing method of PCR is Savitsky-Golay smoothing (7), but it is only 0.741 3. The coefficient of prediction set is 0.661 6, RMSEC is 0.656, RMSEP is 1.34, and the modeling effect is not ideal. The quantitative model of protein show that the best spectral processing method of PCR is Savitsky-Golay smoothing (7), the coefficient of correction set is 0.675 0, the coefficient of prediction set is 0.566 8, RMSEC is 0.548, RMSEP is 0.957, and the modeling effect is bad. Therefore, the model based on PCR have a certain prediction possibility for the content of moisture and protein in royal jelly, but the modeling effect is poor, the prediction accuracy is low, and the robustness is poor. The PLS method is used to establish quantitative model of moisture and protein, and S-G(7)+second derivative +SNV is the best spectral processing method for moisture of royal jelly, the coefficient of correction set and prediction set are 0.992 7 and 0.948 8, RMSEC and RMSEP are 0.162 and 0.442 respectively. And S-G (7)+first derivative +SNV is the best spectral processing method for protein of royal jelly, and the coefficients of correction set and prediction set are 0.991 6 and 0.879 5, respectively, RMSEC and RMSEP are 0.143 and 0.497, respectively. The modeling effect is ideal. The results show that it is feasible to detect moisture and protein content in royal jelly by Raman spectroscopy combined with partial least squares, and the established quantitative model has good robustness and high prediction accuracy. Through the above experiments, we can conclude that under the influence of unavoidable external factors, the combination of various pretreatment methods can improve the accuracy and robustness of the model. It is more effective than single spectral pretreatment method to correct spectra, and the optimization effect is more obvious. It effectively improves the parameters of the model, and improves the accuracy of model prediction. It is also shown that Raman spectroscopy is feasible for rapid detection of royal jelly quality, and it has high accuracy and speed and has great prospects in the rapid detection of royal jelly quality.

Considering the defects of uneven distribution, easy oxidation and poor stability during the preparation of metal particles on SERS substrates, we have prepared graphene-silver nanoparticle (GE/AgNPs) composites with uniform distribution using thermal evaporation and high temperature annealing. At the same time, we have investigated their optical and Raman enhancement activities. The Raman spectrum stability test of GE/AgNPs composite structure proves that graphene plays a role in isolating oxygen and catalytic deoxygenation, which is beneficial to the time stability of SERS substrates. (1) The fabrication of graphene-Ag nanoparticles hybrid structure. Firstly, the Ag nanoparticles were uniformly deposited on the SiO2/Si substrate by thermal evaporation and high temperature annealing. Then, the graphene was prepared on the Cu foil by chemical vapor deposition. Finally, the graphene was transferred to the target substrate by a wet transfer method. And the effects of annealing sequence on GE/AgNPs substrates were investigated experimentally. (2) Characterization of graphene, Ag nanoparticles and GE/AgNPs composite substrate. In this paper, optical microscopy, scanning electron microscopy and Raman spectroscopy were used to characterize the properties of samples. The graphene after transfer was completely covered on the SiO2/Si substrate, with a flat surface, but in a few places still with wrinkles and impurities. According to the Ostwald ripening theory, silver particles with an average particle size of 40~60 nm could be obtained by controlling the annealing temperature and time, and the distribution was uniform. In addition, in different annealing sequences, graphene provided a diffusion barrier to the diffusion of silver nanoparticles, resulting in larger irregular particles. (3) Substrate stability test and simulation analysis. Through the Raman mapping test of the substrate itself, the Raman enhancement effect of graphene was mainly due to the enhancement of the electromagnetic field between the silver nanoparticles, and the changes in the peak and FWHM of the graphene Raman characteristic peaks were analyzed. The SERS stability of GE/AgNPs composites and Ag nanoparticle substrate were investigated using rhodamine 6G (R6G) solution with a concentration of 10-6 mol·L-1 as probe molecule. The results showed that the GE/AgNPs composite attenuated slowly from 1 to 33 days, and the Raman signal was still about 35.1%~40.6% of the original signal after 33 days. However, on the pure Ag substrate, nanoparticles oxidized in the airquickly, and the SERS performance decreased significantly, only 5.9%~11.3% after 33 days. In addition, the enhancement factor of the GE/AgNPs composite was about 6.05×105. And the finite difference time domain (FDTD) was used to calculate the electromagnetic field distribution and the theoretical enhancement factor of the composite structure was 5.7×105. The difference between experimental and simulation results was mainly due to the chemical enhancement of graphene.

The water inrush is one of the most important elements that can influence the mining safety, and being able to recognize the category of water inrush sources accurately and rapidly will greatly enhance the mining safety condition when water inrush happens accidentally. Therefore, it is extremely important and necessary to create a model system that can recognize water inrush sources effectively. The water chemistry analytical method is the widest used method to recognize water inrush sources among traditional methods; in this method, we build a model system by using ph, ionic concentration, conductivity and so on, then use that model system to recognize water inrush sources. However, the water chemistry analytical method has disadvantages that usually be time-costing and of low accuracy. This essay will deal with this problem and introduce the AdaBoost method that uses LDA as weak classifier based on LIF technology because of the rapidnessand high sensitivity of LIF technology. In this research, there are nine kinds of waters from a certain mine in the Huainan City considered and fifty independent samples in each kind of water, limestone water, high pressure water from floor of coal seam and gob areas, and seven different proportion mixture of those two kind of water. Emit laser from the 405nm laser emitter into laboratory water samples and collect experiment statistics of fluorescence spectrum, analyze these 450 water samples by select 360 samples (40 samples of each kind of water source) as a training set first and set other 90 samples as a training set. In this essay, we use three different kinds of arithmetic to build three different model systems and compare results from each model system. First of all, we use decision-making tree to recognize and classify different fluorescence spectrum, we get the best outcome and the accuracy rate is 91.11% at that time when the node number is 8. Then, we use the AdaBoost arithmetic and set the decision-making tree as the weak classifier according to the shortage of the decision-making tree, and we get the best accuracy rate of classifying training sets of 97.78% when selecting a decision-making tree whose node number is 9 as the weak classifier. And last, we introduce a AdaBoost arithmetic base on setting LDA arithmetic as the weak classifier to get better classifying results according to the generalization shortage of AdaBoost arithmetic which bases on decision-making tree, and finally we get the spectrum accuracy rate of 100% when iterate 150 times. As we can get from our experiment, classifying arithmetic that integrates the learning arithmetic is much better than other traditional classifying arithmetic, for instance, compared with the decision-making tree arithmetic, AdaBoost arithmetic which sets decision-making tree as its weak classifier can enhance the accuracy rate of classifying testing set from 88.89% to 97.78% and enhance the accuracy rate of classifying training set from 99.72% to 100% when the node number is 9; then compared with the AdaBoost arithmetic which sets decision-making tree as its weak classifier, the AdaBoost arithmetic which uses LDA as its weak classifier can enhance the accuracy rate of classifying sample water fluorescence spectrum testing set from 97.78% to 100% and enhance the accuracy rate of classifying sample water fluorescence spectrum training set to 100% as well, and we can get better recognition outcomes and make our model system have better generalization by using such strategy at the same time. Therefore, it is extremely fair to say that using AdaBoost-LDA arithmetic to classify fluorescence spectrum to recognize and alarm water inrush sources is effective and feasible.

Water resources are related to national economy and people’s livelihood, detection of water quality anomaly has attracted more attention because of the water pollution events happened in recent years. In this paper, the detection method of water quality anomaly with UV-Vis Spectrum based on supervised learning was proposed to solve the problems of existing methods, which behaved as a high detection limit and poor adaptability method. The pretreatment of orthogonal projection was used to correct the gap between different batches of spectral data. Afterwards the Partial Least Squares Discriminatory Analysis was adopted to extract the features from the data set. Outliers were found by comparing the alarm signal with the best threshold from the training set. Finally, Sequential Bayesian Method was used to update the probability of Contaminate Intrusion Events and to get the alarm sequence. The results showed that the proposed method had the lower detection limit than unsupervised method and the pretreatment of orthogonal projection improved the adaptability of detection method based on supervised learning for baseline changing.

Reflectance spectra of eight common scleractinian coral species were chosen to represent the reflectance of corals in the sea area of Luhuitou Sanya Bay in the north-western South China Sea (SCS). Reflectance spectra of these scleractinian corals, ulva and coral rubble were measured using a fiber spectrometer. Then spectral reflectance analysis and derivative spectroscopy were used to study the difference in the reflectance spectrum between reef building scleractinian coral, ulva and coral rubble in this sea area. The results showed that reflectance peak of ulva appeared at 561.4 nm, and its value was as high as 48%; reflectance difference of ulva and scleractinian coral was very significant in 500~700 nm; reflectance of coral rubble was significantly higher than that of coral reflectance spectrum with significant differences. Derivative analysis results showed that the distinguishable bands of scleractinian coral, ulva and coral rubble were as follows: scleractinian coral and ulva, first order derivatives are mainly in 485～487, 505～510, 515～529, 559～578, 587～593, 598～603 and 667～670 nm bands. The second order derivatives are mainly in 494.4～505.7, 524～534.5, 543.6～561.4 and 567.2～579.7 nm bands. The fourth order derivatives are mainly in 515.8～430, 621～627.1, 628.8～635.6, 639.3～645, 661.8～669.8, and 678.4～682.4 nm. First order derivatives of scleractinian coral and coral rubble are mainly in 400～413.7, 414～418, 484.8～486.9, 506～509.6, 514.5～528.9, 576.9～587.6 and 602.7～653.4 nm bands. The second order derivatives are mainly in 451.6～461.6, 564.5～570.7 and 677～685 nm. The fourth order derivatives are mainly in 412.6～425.3, 459.8～467, 467.7～470.6, 535.6～540.8, 583.8～591.4, 654.4～659.8 and 670.8～680 nm bands.

The study on spectral mixing mechanism has a certain instructive significance to unmixing. With a full-wavelength spectrometer, the research made a controlled acquisition of the spectral reflectivity of pure snow pixels, pure desert-vegetation pixels and snow-desert vegetation mixed pixels in the mode of rule and irregular distribution during the accumulation period and ablation period. The ratio of snow area to desert vegetation area of images was calculated by K-means clustering algorithm and spectral variation characteristics of mixed pixels were analyzed; to obtain more precise spectral characteristic information, the absorption characteristic parameters to the response bands were calculated; the images were collected by quasi-synchronous Tetracam ADC3 and the typical indices were calculated. It’s verified at the micro-scale that the mixed pixels are mainly located at the boundary between one category to another. The results are seen as follows: the spectral reflectivity of coarse-grained frozen snow is obviously higher than that of new snow which is obviously higher than that of aged snow in the ranges of 1 456～1 697 nm. Because of the ice cover, the collected desert vegetation spectra is actually a mixture of spectral information of snow, ice crystals and vegetation branches; the spectral properties of vegetation covered with new snow are actually the mixed spectral information of snow and vegetation branches; there is no “red edge” effect like conventional green vegetation. When the acquisition angles are 5° and 10°, the spectral reflectivity is lower than that at a vertical angle. When the acquisition angle is bigger than 10°, the spectral reflectivity increases if the angle becomes bigger.

By using conventional gemological test methods, then combining the UV-Visible spectroscopy(UV-Vis) and Fourier transform of infrared spectral technology (FT-IR), this research focuses on the natural Utah red beryl and Russian hydrothermal synthetic red beryl, which are studied by the gemological characteristics, the UV-visible absorption spectrum, the mid-infrared absorption spectrum (MIR) and the near-infrared spectrum(NIR) characteristics. The results showed thatit is difficult to discriminate the natural red beryl and the hydrothermally synthesized red beryl through the conventional gemological test methods. Also, there is limited ability of UV-visible absorption spectrum to identify the natural and synthetic red beryl. At the same time, the mid-infrared absorption spectrum (MIR) of these two kinds of gems has no obvious difference. Their absorption position and absorption intensity are basically similar, which only show the vibration characteristics of the silicate crystal structure of beryl. As for spectrum of 2 000~9 000 cm-1 scope, there is obvious difference between the natural red beryl and hydrothermal synthesis red beryl. Therefore, the near-infrared absorption spectrum could be used as aunique identification characteristic to differentiate them. Further studies have shown that the natural red beryl sample contains little structural water. However, there exists a very weak absorption band between 3 300～3 600 cm-1 and this might be other forms of water in the natural red beryl sample. It could be concluded that the natural red beryl sample contains certain water, and it might be the channel water. The near-infrared spectrum characteristics of the hydrothermal synthetic red beryl samples show that it has strong water vibration absorption between 3 500~4 000 and 5 000~5 800 cm-1. There exist two types of water in the range of 5 000~5 800 cm-1, which include the weak absorption peak (type Ⅰ water) and the strong absorption peak (type Ⅱ water), which can be attributed to the combined vibration of flexural vibration and stretching vibration of water; the weak type Ⅰ water absorption peak and the strong type Ⅱ water absorption peak are also shown in the range of 7 000～7 500 cm-1, which can be attributed to the double frequency vibration of water. This means the hydrothermally synthesized red beryl is mixed with type Ⅰ structural water to type Ⅱ structural water. It could be concluded that the near-infrared absorption spectrum (NIR) characteristics of hydrothermal synthesis of red beryl samples in the range of 3 500~4 000 and 5 000~5 800 cm-1 can be used as the basis for distinguishing natural and hydrothermal synthesis of red beryl. According to whether or not the red beryl has water, the state of occurrence of water, and the relative intensity and frequency of different types of water, the UV-VIS spectroscopy, the mid-infrared absorption spectroscopy (MIR), and the near-infrared spectroscopy (NIR) can be used as an important basis for accurately providing diagnostic evidence for distinguishing natural and hydrothermal synthesis of red beryl.

In recent years, the advanced scientific research on the extraction and preparation of high value-added coal based chemicals has found that Fulvic Acid Platform Compounds (FAPC) are a kind of cluster characteristic assembly molecular assembly with special functions in modern agriculture, biomedicine and functional materials, then it has a good application prospect. It is of great scientific significance to study the basic theory and chemical composition of the preparation of fulvic acid platform compounds. In this paper, the preparation and extraction methods of fulvic acid platform compounds based on different homologous substances were briefly introduced. The selective dissociation law of coal base compounds, the composition of functional groups of fulvic acid platform compounds and the spectral analysis of self-assembled structure characteristics were described in detail. The results showed that the selective preparation and extraction of fulvic acid platform compounds could be realized by acid-base reaction, ion exchange, biotransformation and microwave. Through the analysis of IR, NMR, fluorescence spectrum, electron and vibration spectra and molecular simulation, the molecular structure, functional groups and molecular chains of the prepared fulvic acid platform compounds were identified from different spectral characteristics. The self-assembled space configuration, group structure and construction method were effectively detected and characterized. A variety of spectra showed that the fulvic acid platform compounds were mainly composed of carboxyl, hydroxyl, phenolic hydroxyl, quinone and carbonyl molecules. The active small molecules produced by selective dissociation form self assembling group due to their molecular orientation. They are characterized by friendly interface, micro and nano size, electrical energy storage and good biocompatibility. In this paper, the advantages and disadvantages of the preparation of fulvic acid platform compounds are revealed through comparative analysis. Finally, future theoretical research and application prospect of fulvic acid platform compounds are prospected.

All-silicon tandem solar cells based on silicon quantum dots (Si-QDs) are considered to be one of the most promising high efficiency solar cells. In recent years, Si-QDs films with low Si-QDs density and many defects were reported. Thence, the photoelectric conversion efficiency of Si-QDs solar cells waslimited. Microwave Annealing (MWA) is considered to be a useful method to prepare nanostructured materials. The non-thermal effect of MWA can reduce energy for nucleation and improve the microstructure and photoelectric properties of the films. In this paper, SiCx thin films containing Si quantum dots were prepared via magnetron co-sputtering technique and MWA with different pulse power.The phase structure and spectral properties of Si-QDs films were characterized by grazing incidence X-ray diffraction (GIXRD), Raman, photoluminescence (PL) and spectrophotometer. The influence of different pulse power on the Si-QDs density and performance was studied systematically.Thin films with highdensity and good performance weredeposited by improving the magnetron sputtering process.The GIXRD and Raman spectra all showed that the Si-QDs existed in the samples, and their intensities first increased and then decreased. By Scherrer’s formula, it was estimated that the size of Si-QDs increased initially and then decreased, and the maximum size of Si-QDs (7.98 nm) wasobtained when the sputtering power was 80 W. The centers of Raman peaks areat 511 cm-1. This is ascribed to Si-Si lateral optical vibration modeand its intensity is also increased first and then decreased. The optimum Gauss peak fitting was used for the Raman spectra. It showed that the crystalline fraction was higher than 62.58%, and the highest crystallinefraction (79.29%) was gained when the power was 80 W. The above analysis showed that Si-QDs formed in the films and the size of Si-QDs first increases and then decreases. The maximum number of Si-QDs was acquiredwith the power of 80 W. The optical bandgap was estimated by Tauc formula. These bandgaps were going to decrease and then increase with the increase of power. The bandgap reachedminimum value (17.2 eV) with the power of 80 W. The Si-QDs size was inversely proportional to the band gap, indicating that the Si-QDs in the films had good quantum confinement effect. The luminescence properties of the samples were analyzed by the PL spectra, the optimum Gauss peak fitting was used. It was found that there were 6 luminescence peaks. Combine with the results of Raman spectrum, the luminescence peaks between 463~624 nm were derived from the role of the Si-QDs. The luminescence peaks between 408 and 430 nm originated from the defect state inside the films withoutthe shift of peak position, while the intensity varies. The distribution of the energy band gap were calculated according to the wavelength of the luminescence peak. Thus, the types of the defect state were determined, the luminescence peak at 408 nm is attributed to the electron radiation transition of ≡Si°→Ev, and the luminescence peak at 430 nm is attributed to ≡Si°→≡Si—Si≡ defect state luminescence. The Si-QDs size on the luminescence peak shift was also studied. The results show that blueshift (redshift) of luminescence peak occurred with the size of Si-QDs becoming smaller (larger). In conclusion, SiCx films with Si-QDs prepared at the sputtering power of 80 W exhibited the best performance. The research results laid the foundation for the follow-up study of Si-QDs solar cells.

In this paper, according to the feasibility and reliability of using the hyperspectral data to retrieve SOM from hyperspectral data, combined with the high efficiency of differential processing in extracting spectral information, a new method based on differential algorithm for soil organic matter modeling In this study, the content of soil organic matter can be obtained by differentiating the multi-spectral remote sensing images directly, which aims to provide the direction for the future study of soil organic matter rapid measurement is proposed. In this paper, Landsat 8_OLI multi-spectral remote sensing image data is used to perform the radiation calibration, geometric correction, atmospheric correction, mosaic and cropping of multi-spectral remote sensing images. The first order differential and second order differential are processed by IDL software. The image can better express the real situation of the object. The first-order differential image can distinguish the water body from the soil better. The original remote sensing image contains a lot of information, including the noise. The differential image processed by the remote sensing image excludes the original image In the study area, five-point method was used to collect soil samples, indoor potassium dichromate oxidation-volume method to measure soil organic matter data, and multispectral data was used to analyze soil organic matter data from the ground to analyze soil organic matter It is found that there is a sensitive band in the correlation between the first-order differential data and soil organic matter content, indicating that the first-order differential processing can transform the original remote sensing image data in some obscure soil in the multi-spectral range. Organic information is released; select a high correlation number established based on the raw remote sensing data, first-order differential data, single-band multi-spectral data of the second order differential linear and multi-band multi-spectral linear model, and select the best model to estimate soil organic matter content retrieval. The main conclusions are as follows: (1) By differentiating the original image, it is found that the image after differential processing changes obviously and the image noise of first-order differential processing decreases, which further highlights the hidden information of soil organic matter in the image. The second-order differential processing suppresses soil organic matter information. (2) The data of the original remote sensing images have a low correlation with soil organic matter content. The data of the first-order differential treatment reflect the correlation of the soil organic matter sensitive band, that is, the partial band data, and the second-order differential processing after the remote sensing images of each band data on soil organic matter content of the correlation is weak. (3) Multi-band modeling is superior to single-band modeling, and the first-order differential multiband model has the best prediction accuracy. The model’s coefficient of determination and the coefficient of model fitting are 0. 898 and 0. 854 respectively. The soil organic matter content in this region was well estimated. The fitting accuracy of single-band model and multi-band model was compared comprehensively. It was found that both the single-band model and the first-order differential model had better prediction ability. (4) Based on the first-order differential multi-band model, the inversion of SOM in the study area was carried out. The inversion result is in accordance with the actual situation, which provides a practical method and reference for the mapping of soil organic matter content in arid area.

Xidan Stone, which is a kind of mountain stones scattered across the Yueyang Stream and weathering product from the famous Shoushan Furong Stone, is one of the well-known varieties of Shoushan stone. Washed into the stream by rain, blocks of Xidan Stone have been impacted by the water and river sand for several years to form a round pebble appearance. Because it is easy to be sculptured, Xidan Stone is widely praised by modern sculptors. In order to analyze this stone from different aspects including mineral components, spectroscopic features, chemical composition and color origin, the systematic mineralogical and spectroscopic studies were conducted on the yellow Xidan stone samples from Shoushan rivulet of Fujian Province, with the help of standard gemological methods, X-Ray powder diffraction, infrared absorption spectroscopy, laser Raman spectroscopy and Electron probe microanalysis. The gemological testing results inferred that Xidan stone samples are pebble-shaped which contain light yellow substrate and rough weathered skin. Examined under magnification, the Xidan stone samples have tiny black dot-like inclusions and white grey mineral component. The Xidan stone samples have an average relative density of 2.8 by hydrostatic weighing method and a Moh’s hardness below three. According to the testing results of XRD, the major constituent mineral of Xidan stone is pyrophyllite, which is in the type of monoclinic pyrophyllite (2M type). The characteristic feature is the three diffraction peaks of 4.44 4.44  (020), 4.24  (112) and 4.17  (111) between 2θ=19° and 22°. The two diffraction peaks (112) and (111) lie closed to each other, therefore a diffraction shoulder appears on the right side of the (112) diffraction peak (2θ=21.06°). Another characteristic feature is the 3.06  (003) strong peak (2θ=29.05°) between 2θ=28° and 31°. Infrared spectroscopy is an effective method to tell the mineral composition of weathering skin parts as well as the substrate parts of Xidan stone samples. The FTIR spectrum shows that these two parts share the same mineral component of pyrophyllite. In fingerprint region, the main absorption bands are 1 122, 1 068, 1 052, 949, 853, 835, 812, 541 and 484 cm-1. Infrared absorption band at 1 122, 1 068 and 1 052 cm-1 are induced with Si—O symmetric stretching vibration and Si—O—Si antisymmetric stretching vibration. Infrared absorption band at 949 cm-1 is induced with Al-OH in-plane bending vibration. Mountain-like infrared absorption bands at 853, 835 and 812 cm-1 are induced with Al—OH out-of-plane bending vibration. Infrared absorption peaks at 541 and 484 cm-1 are induced with Si—O—Al stretching vibration and Si—O bending vibration. In high frequency region, the acute infrared absorption peak at 3 675 cm-1 is induced with Al—OH stretching vibration indicating the highly ordered structure of Xidan Stone samples. Laser Raman spectroscopy is an effective and non-destructive way to analyze the inclusions. LRM testing confirmed that the black inclusions are composed by hematite and the white grey mineral component is diaspore. Raman peaks at 224, 291, 409, 494 and 1 315 cm-1 are typicalfeatures of hematite. Raman peaks at 448, 499, 667, 707, 788 and 1 194 cm-1 correspond to the typical features of diaspore. In addition, Raman spectrum of substrate parts of samples shows the characteristic peaks of pyrophyllite at 111, 194 and 261 cm-1, which are induced with O—H stretching vibration. Based on the unit price of mineral balance principle molecule and the total number of positive charges, the average crystal structural formula of Xidan Stone is (Al1.98Na0.02Cr0.01)［(Si3.98Al0.02)O10］(OH)2. Thedata of EPMA testing tell that Xidan Stone samples have stable chemical composition. Samples mainly contain Si(64.88%) and Al(27.55%). Given that the Xidan Stone samples contain fewer Fe(0.02%) but more Cr(0.2%), Fe as well as Crmightcause the light yellow of stream-egg stones.

With the rapid development of wine market, a large number of Chinese high quality wine has been affected by inferior wine. The existence of fake inferior wine not only affects quality wine brand in China, will also do a certain harm to human body. Water adulteration in wine is the most common means of making fakes, therefore, study of wine water adulteration detection method has attracted more attention from the researchers both at home and abroad. Compared to traditional sensory assay methodor physical and chemical testing methods operated in laboratory, visible/near infrared spectral analysis technology is more suitable for rapid detection of wine quality with thequickness, high efficiency, non-destruction and non-contactfeatures. In order to detect the wine water blending problem rapidly and accurately, based on the visible/near infrared spectral analysis technology, this paper constructed a spectral absorption Depth Index (DI) to reflect the water degree blended in wine, and gave the wine mixing water inversion model based on DI Index to estimate the water content. First, this paper chosethree kinds of wine including the Changcheng cabernet wine (CC), Zhangyu cabernet wine (ZY) and Xiaocabernet wine(XA)to create 18 wine samples with 0% pure wine (no water), 4%, 7.7%, 11.1%, 7.7% and 17.2% of distilled water in the three kinds of wine respectively, and to create other 6 wine samples with 0%, 20%, 40%, 60%, 80%, and 90% of distilled water in Changcheng wine. So there were totally 24 wine samples with different ratios of distilled water. Then, the wine spectral data were sampled using the PSR-3500 portable features spectrometer. After the preprocessing of the S-G filtering, special wavelength choosing, and continuum removing of the original spectral data, the visible/near infrared spectral features of wine samples were analyzed, anda spectral absorption depth Index (DI) of wine with distilled water was constructed using the stable spectral absorption property at 837 nm. In order to improve the robustness of DI index, the mean value of the spectral reflectance values near 837 nm small neighborhood was adopted. Finally, the wine mixing water inversion model based on DI index was created using the quadratic polynomial fitting method. To validate the inversion estimate model of the wine with water, the DI index of Changcheng cabernet wine was used, and seven samples were chosen as the prediction set, and the other four samples were chosen as test set in the experiment. Experimental results showed that the precision of R square value of the model is up to 0.999 2 with the quadratic polynomial fitting method, and the average relative error between the estimates of the model and the real value is 0.042 5. Experiments showed that the inversion estimated model based on DI index can not only identify whether the wine blended with water, but also make a quantitative analysis of the water content in wine. DI index was simple, and the DI index can reflect the water degree of different brands of wine. This study may provide a scientific basis for the design and development of low-cost and handheld portable spectrometers for wine detection, further promoting visible/near infrared spectral analysis technology in the quality detection of wine or other relative field.

The oil spectral data are introduced to indicate the performance degradation and the remaining useful life(RUL) prediction in the reliability evaluation of Power shift steering transmission(PSST). Because of the PSST’s stochastic degradation and spectral measurement error, the measured data inevitably contain the stochasticity of the degradation and the uncertainty of spectrum measurement. However, in current studies of RUL prediction based on oil spectral data, no one has been reported to consider the effect of degradation stochasticity and measurement uncertainty on the prediction of RUL. Thus, aimed at reducing the adverse impact of measurement error of oil spectrum data on the remaining useful life(RUL) prediction of Power shift steering transmission(PSST), a degradation modeling method considering degradation stochasticity and measurement uncertainty is proposed. The concept of RUL of PSST is defined based on the concept of first hit time(FHT) of stochastic process. The parameters of the degradation model are estimated using the maximum likelihood method. The degradation state of the PSST is estimated and updated in real time using Kalman filtering technique, and the RUL distributions considering the system degradation stochasticity and spectral data measurement uncertainty are obtained. The experimental results show that the degradation modeling method proposed in this paper can accurately estimate the running state of the device and avoid the limitation of using the condition maintenance time to maintain the equipment. The time interval of condition-based maintenance has extended as 193 Mh (113.5%), and the RUL prediction method considering uncertain measurements is superior to the method without considering.

A Ni(Ⅱ) coordination complex ［Ni(2,2′-biby) (H2O)4］·(tba)2·5H2O(2,2′-biby=2,2′-dipyridine, H2tba=2-thiobarbituric acid ) has been synthesized and structurally characterized by IR, element analysis and single crystal X-ray diffractometry. The results of single crystal X-ray diffractometry displayed that the title compound belongs to orthorhombic with space group of Pbca, and its cell parameters are a=13.662(2) , b=19.470(4) , c=21.590(4)  . The binding of title compound with Herring sperm DNA/BSA has been investigated by absorption and fluorescence spectra, and the results displayed that the title compound binds with DNA in electrostatic interaction mode, and quenches the intrinsic fluorescence of BSA by a static quenching mechanism.

The gaseous product release characteristic of the low-rank pulverized coal (SJC) in northern Shaanxi co-pyrolysis process with heavy oil (HS), coking coal (JM) and direct coal liquefaction residue (DCLR) were comparatively studied by the thermo-gravimetric analyzer (TG) coupled with fourier transform infrared spectroscopy (FTIR). The research showed that the SJC co-pyrolysis process with HS, JM and DCLR were divided into three stages, the first stage was the release of adsorbed substance from surface of the raw material, and the depolymerizing and decomposition reaction occurred in the second stage, and the third stage was the formation of a stabler semi-coke with the temperature increasing continuously. Coal and the additive have a synergistic effect during the second stage. As the major hydrogen donor, the SJC could generate the hydrogen free-radical in pyrolysis process interaction with small molecular free radical produced by HS, JM and DCLR pyrolysis process, and production of tar and gas. Around 450 ℃ temperature range, the pyrolysis process of SJC and SJC+DCLR were reacted more fully, and the majority of N element was transferred into the tar component. The gaseous product as water, phenols, heterocyclic nitrogen-containing compounds and CO of the pyrolysis process were released during the whole temperature interval of the pyrolysis. During the temperature of 400~650 ℃, the main reaction in the pyrolysis process of SJC+JM and SJC+HS was nitrogenous compounds transfer, the peak temperature of CH4 and aliphatic hydrocarbon compound release nearby 450 ℃, while the peak temperature of SJC+DCLR and SJC was 550 ℃. The aromatic compounds release in tar could be promoted by additive JM, HS and DCLR, generating a large amount of aromatic compounds during 400~550 ℃ in the pyrolysis process of SJC+JM and SJC+HS. The results of this study provided a theoretical foundation for the research and development of the new technology of low rank pulverized coal, which is of great significance to its value.

High purified hafnium has important applications in nuclear reactor, plasma cutting machine, optical element and so on, because of its unique physical and chemical properties. The type and content of impurities in high purity hafnium affect the physical and chemical properties of high purity hafnium, and the purity requirement of high-purity hafnium is also higher and higher. This requires higher requirements for the analysis and detection technology of high-purity hafnium. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a combination of laser ablation sampling technique and inductively coupled plasma mass spectrometry. The advantage of this method is that impurities can be avoided in the preprocessing, and the solid sample can be analyzed directly. So, this method is an efficient, fast and precise analytical technology, widely applied in the fields of environment, geology, metallurgy, fuel energy, materials, biomedicine, archaeology and so on. However, the application of testing high purity hafnium by LA-ICP-MS has not been reported while LA-ICP-MS is one of the best methods for the detection of high purity metallic impurities. Ten kinds of impurities (Al, Sc, Ti, Fe, Ni, Cu, Mo, Ag, Sn, W) in high purified hafnium were quantitatively analyzed by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). In order to reduce the fractionation effect of elements in the laser ablation process and improve the signal sensitivity and stability, the laser denudation parameters were optimized. Experiments showed that the optimal laser ablation parameters were that He flow rate was 600 mL·min-1, energy 90%, spot size 150 μm, scan rate 60 μm·s-1and pulse repetition 20 Hz. The working parameters of ICP-MS instrument after experimental optimization were that RF power was 1 450 W, RF matching voltage 1.8 V, carrier gas flow rate 0.85 L·min-1, cooling gas flow rate 0.85 L·min-1, sample depth 7.5 mm. Under the best experimental conditions, internal control standard samples were used to establish working curves; the linear correlation coefficients of impurities were between 0.993 6 and 0.999 8. The signal intensity of the blank carrier gas was collected and measured for 11 times. The content of the standard deviation of the 3 times blank signal was taken as the detection limit of the elements. The detection limits of each element were from 0.001 to 0.08 μg·g-1. High purified hafnium was made into a suitable sample of size. The oxide on the surface of the sample was washed with nitric acid. The sample was loaded into ablation pool, and laser ablated by line scanning. Under the best experimental conditions, ten kinds of impurities in three high purified hafnium samples were determined by LA-ICP-MS. The content of impurity elements was 0.17~36.76 μg·g-1. Relative standard deviations were from 1.4% to 20%, which showed that the method has good precision. In the case of W, Student’s t test was made between the determination of LA-ICP-MS and ICP-MS. Student’s t test shows that the t values of the three samples were 2.14, 1.64 and 2.11, which were lower than the critical value of the significant level of 0.05 (t0.05, 12=2.18), so there was no significant difference between the results of LA-ICP-MS method and ICP-MS method. The trueness and precision were favorable, which showed that this method can be used for quantitative analysis of impurities in high pure hafnium.

The selection and optimization of the test parameters is one of the important steps in spectrochemical analysis based on laser-induced breakdown spectroscopy(LIBS). Appropriate test parameters can guarantee the accuracy of the later spectral data analysis. Here, LIBS technology was employed to study the influence of different test parameters of LIBS on the spectral characteristics of main elements in soils, and the universal soil testing parameters were obtained. Based on single variable test, the experiment of laser energy (LE), delay time (DT), and lens to sample distance (LTSD) three factors quadratic central composite design was carried out using the respond surface method (RSM). According to the mainelements(Si, Fe, Mg, Ca, Al, Na, K, etc.) in soil, the combined signal-background-ratio (SBR) of characteristic spectral lines from main elements was named as the objective function (YSBR). The interaction influences among three factors on soil plasma characteristics were explored and the optimized parameters of LIBS were summarized. Results revealed as follows: the factor LE showed a remarkable linear effect to YSBR, and factors of DT and LTSD exhibited an opposite result. The interaction of three factors displayed a non-significant relationship. Meanwhile, the quadratic terms of LE2, DT2 and LTSD2 had a significant surface relationship. Through the RSM analysis, the optimized experimental parameters were: LE: 103.09 mJ; DT: 2.92 μs; LTSD: 97.69 mm; and a peak value YSBR of 198.602 could be obtained. These optimized test parameters are the prerequisite for the LIBS data analysis in the late stage, which can offer important reference value for the soil LIBS detection in the field.

Procymidone, as a new type of agricultural fungicide, has the effect of preventing agricultural products from being affected by pests and diseases, but it is easy to be used improperly to harm the environment and human health during the application process. In order to strengthen the detection of procymidone pesticides, this study uses laser induced breakdown spectroscopy (LIBS) to quantitatively detect the content of procymidone in solution. In order to prepare different density of procymidone samples, this study mixed the ingredient content of 98% procymidone powder with xylene in different proportions and completely dissolved. Since liquid samples are easy to spill and cause dangers during laser striking, so this experiment converted the liquid samples into solid samples, used the graphite to adsorb the procymidone, and then used the eight-channel high-precision spectrometer to collect the LIBS spectrum of the sample, and applied different pretreatment methods to pretreat the spectral data. So as to improve the detection accuracy of procymidone, this research chose the strongest chlorine signalthe in two channels (744.555~935.843, 893.107~1 057.058 nm) and spectral data were preprocessed with normalization, baseline correction, standard normal variable transformation and multiplicative scatter correction methods respectively, and PLS method was used to model. After comparing the data of each pretreatment method, considering the comprehensive consideration, the Baseline method was selected as the optimal pretreatment method. Based on the baseline preprocessing method, uninformed variable elimination (UVE) combined with competitive adaptive reweighted sampling (CARS) algorithm was used to eliminate the wavelength variable without information, and screen out the important wavelength variables related to procymidone, and finally the partial least squares regression was used to establish a quantitative prediction model of procymidone content in solution. The modeling results showed that after the spectral preprocessing and optimized by VUE-CARS method, the number of original 4 096 wavelength variables reduced to 13, and the variable compression rate was 99.68%. The PLS model was established after the UVE-CARS variable was optimized. The correction set and prediction set determination coefficient and root mean square error were 0.990 5, 0.66, and 0.990 3, 0.67, respectively. The model performance was better than the PLS model established by the original spectrum. The results showed that it is feasible to detect the procymidone content quantitatively in the solution by using the coaxial double pulse LIBS technique. After screened by UVE and CARS methods, the characteristic variables and related influence variables of procymidone can be effectively extracted. The redundancy and noise influences variables can be eliminated effectively. The quantitative analysis model can be simplified and the stability of the quantitative analysis model can be improved.

An automatic fast determination method of nitrite in seawater was developed by optimizing the previous self-research of sequential injection analysis (SIA), combining our self-developed Z-type liquid waveguide capillary cell (LWCC) flow cell and tubing-looper, using spectrophotometry and incomplete chromogenic reaction to accomplish the automatic fast determination and make its measurement process more suitable for in-situ analysis and monitoring. The heart of the injection technology is a high-precision syringe pump and a multiposition valve (MVP). Cooperating with MVP, the syringe pump inhales samples and reagents in a holding coil in sequence, and then reversely pushes the mixed solution to the mixing coil and an incomplete chromogenic reaction occurred during this period. The syringe pump finally slowly pushes the mixed solution through the Z-type LWCC flow cell, meanwhile, the absorbance changes of flow solution is detected by a spectrometer, and the nitrite concentration of sample is acquired with Lambert-Beer law. Several key parameters of the fast detection method, such as incomplete chromogenic reaction time, flow rate of mixed solution during detection and salinity were analyzed for stable and fast analysis purposes. The study on the incomplete chromogenic reaction shows that the relative standard deviations (RSDs) of absorbance measurement results are all less than 1.64% within 10～60 s reaction time, indicating the chromogenic reaction time of 10~60 s has no effect on the fast detection method, therefore, 10 s is selected as the fast detection method chromogenic reaction time. The research of mixed solution’s flow rate during detection shows that the flow rate has a large effect on the absorbance detection. Rapid flow rate influences the detection instability, and slow flow rate is not conducive to the fast detection. The stability and repeatability of absorbance measurement results are analyzed under the speeds of 10, 11.6, 13 and 15 μL·s-1, which are relatively stable on absorbance measurement on the basis of experimental verification. The analysis results indicate that the linearity under the above four flow rates are all good, so the fastest speed 15 μL·s-1 is selected for the fast detection method. The absorbance changes of three different concentrations of nitrite (150, 250, 350 μg·L-1) in the 0～35 salinity range are analyzed to verify the sensitivity of this fast detection method to salinity and the adaptation to freshwater and the wide range of seawater. The RSDs are 1.39%, 2.03% and 1.28% respectively, indicating that salinity has no effect on this method. The RSDs measured of parallel 80, 150, and 250 μg·L-1 nitrite standard solutions for 11 times are 2.13%, 1.07% and 1.83% respectively, indicating that this fast detection method has a good precision. The detection limit of this method acquired by taking 10 parallel samples of blank samples is 37 μg·L-1 (about 0.5 μmol·L-1). In order to verify the credibility, the standard curves of same batch nitrite standard solutions are made by using the fast detection method in this paper and the standard method in “Specifications for oceanographic survey”. The R2 of above two methods are both greater than 0.999, and the linear regression equation of the measurement data obtained by the two methods of same concentration sample is y=1.046 1x-0.005 7 with the R2=0.999 6, which shows that the results of the two methods are highly consistent, further verifying the feasibility and reliability of the fast detection method in this paper.The determination rate of this method is up to 50 samples·hour-1. Compared with the traditional manual detection method and flow injection analysis method, the nitrite fast detection method in this paper shortens the time -consumed from a dozen minutes to a minute or so, reduces the sample and reagents consumption in the entire detection process. The fast detection method has a good repeatability, and the whole measurement process is fully automated, and the operation is simpler and more intelligent, which avoids the error caused by manual intervention and makes the nutrients online and in-situ detection system based on spectrophotometry more compact, fast and low-consumption, which is more suitable for in-situ and long time monitoring. The fast detection method in this paper is applicable to other seawater nutrients as long as it is slightly adjusted, having a wide range of applications and good application prospects.

The investment precision casting technology is widely adopted in manufacturing superalloy castings. Single crystal superalloy casting blades have high precision in dimension, without inner defects and surface defects. Knobs, pits and shrinkage cavity on surface of castings are caused by unsuitable temperature field, melted alloy flow field, and interface reaction of melted alloy with ceramic shell. The laser induced breakdown spectroscopy (LIBS) is an efficient surface and interface analysis technique, with obvious advantage in diagnosis of casting surface/interface, with micro destruction, independent on surface planeness of sample. The parameters were optimized in depth analysis of single crystal superalloy by LIBS. A good spatial resolution has been obtained at 2 mm aperture, 500 mJ of output energy and off focus radiation with 1 064 nm pulse Gaussian laser beam; meanwhile spectrum signal distortion brought from ablation and excitation of surface by exceeding secondary diffraction ring with low laser energy density was reduced. Ablated volume per pulse was linear to output of laser energy with good correlation, positively correlated to the aperture diameter, independent on shot frequency. Depth analysis of surface and interface of single crystal superalloy blade was performed by LIBS. Alloy depletion of Al, Ti, Ni, Cr and Co in DD407 single crystal blade was observed with significant depletion of Al and Ti up to 50 μm depth, using SiO2-Al2O3 ceramic shell with mullite phase in cast process. The alloy depletion of edge of the blade was more outstanding than thick part. Precipitation of calcium salt, magnesium salt, sodium salt and carbon matters on blade surface occurs after dissolution to remove ceramic shell and ceramic core. Sodium salt has been removed completely after washed with boiling water and ultrasonic washing; most of calcium matters, magnesium matters and carbon matters have been removed and small amount remained in 3~5 μm depth. LIBS is efficient in terms of composition-depth distribution analysis of alloy casting surface and interface, providing surface/interface quality criteria and presenting bright application prospect.

This paper mainly discussed the optimum conditions of hydride generation atomic fluorescence spectrometry (HG-AFS) for the determination of selenium content in eggs, then the analytical method for the determination of selenium content by mixed acid digestion hydride generation atomic fluorescence spectrometry was established, and through testing the selenium content of ordinary eggs, native eggs and dark eggs, we hoped to provide a theoretical and practical reference for people to choose eggs. In order to improve the precision and accuracy of the method to detect the selenium content in eggs by atomic fluorescence spectrometry, the mixed acid ratio, prereducing agent concentration and KBH4 concentration were compared and analyzed, and the feasibility of test result was detected by calculating the precision, recovery rate and the minimum detection limit. The results showed that the samples were dissolved overnight after the mixture of concentrated nitric acid and perchloric acid with a volume ratio of 1∶1, then heated to clear on the micro control digital display electric heating plate at 200 degrees, then the temperature of the electric heating plate was adjusted to 160 degrees, after the conical bottle temperature was cooled to room temperature, 5 mL of 6 mol·L-1 HCl was added. The conical bottle was heated on the electric heating plate again, and removed after the solution was clear. After cooled to the room temperature, the conical bottle solution was transferred to 100 mL capacity bottle, and 1 mL 10% potassium ferricyanide solution was added, and 10% hydrochloric acid was used to shake and be measured at the same time, and the sample blank control was made at the same time. The analytes were detected by a high-performance hollow cathode selenium lamp with a deoxidizer of 1.5% KBH4 solution and the carrier fluid of 2% HCl. At the same time, the different reasons of selenium content between varieties of eggs were discussed. Under the optimum conditions and the working state, the selenium contents showed a good linear relationship in the concentration range from 0 to 8 g·L-1, and the standard curve equation of selenium is IF=114.19C＋1.30, and the correlation coefficient of the standard curve was 0.999 9, and the minimum detection limit was 0.01 μg·L-1, and the relative standard deviation was 0.07%~0.72%, and added standard recovery was 96.12%~99.1%. The optimum conditions of hydride generation atomic fluorescence spectrometry (HG-AFS) for the determination of selenium content in eggs were established, and the method is simple and easy to operate, with high precision and sensitivity. Using the method to test selenium content of ordinary eggs, native eggs and dark eggs, the results showed that the selenium content of dark eggs, native eggs and ordinary eggs were 0.191, 0.195, 0.141 mg·kg-1, respectively. The selenium content of dark eggs was not significant than that of native eggs (p>0.05), but the selenium content of dark eggs and native eggs were significantly higher than that of ordinary eggs (p<0.05). This study provides a theoretical basis for the scientific detection of selenium content in eggs and people’s choice.

From the perspective of early textiles protection, this article analyzes a large number of domestic and foreign literature materials in this field. By comparing the conventional system analysis methods of fabrics and combining with modern scientific and technological methods, the types of fiber, types of dyes and dyeing processes of traditional costumes were measured and analyzed. Faced with the severe situation of the preservation of dyeing textiles of the costume museum in the late Qing Dynasty. In this study, FT-IR was used to identify the material of garments, and the dyes on textiles were extracted by extraction method. The Lab values of color of the textile surface before and after extraction were analyzed by a reflection spectrophotometer. The structure of dyes was analyzed by high-pressure liquid chromatography and mass spectrometry (LC-MS) to determine the structural components of dyes. To further study traditional costumes, we integrated modern analysis techniques (LC-MS) and concepts (Lab system) into traditional research, used objective science and technology to obtain more effective data and information and then support the conclusions obtained by subjective observation methods, which makes up for the gaps in this research field. Compared with traditional methods of textile identification, the relevant information obtained by modern scientific and technological methods is more detailed and reliable. It is advantageous to provide targeted preservation measures according to the characteristics of different dyes and different fibers, which is of great significance to the protection and preservation of the museum’s collection textiles. Aiming at the special needs of dyeing textiles in the late Qing Dynasty, it is necessary to carry out targeted preservation measures according to different dyestuff characteristics. In order to identify the dyes on a female red coat which may be produced in the late Qing Dynasty, the textile material was analyzed by FT-IR spectroscopy. The results indicated that it was silkworm silk fabrics. A method for extracting dyes from fabrics was established. Acetone, acetonitrile, pyridine/water (1/1, W/W), N,N-dimethylformamide (DMF), 0.1% ethylene diamine tetraacetic acid (EDTA)/DMF (1/1, W/W) and methanol were used to extract the dye from the sample and using the reflection spectrophotometer to compare the color measurement of the color of the textile surface and the color after peeling. In addition, it was found that adding a small amount of EDTA when extracting the dye would increase the stripping efficiency, so the dyeing method might be a mordant method, and EDTA might destroy the complexation between the dye and the mordant metal ion. The color appearance of the textile was measured by using a spectrophotometer, and the extracted dyes were analyzed by LC-MS technology. It was found that the water solution of pyridine had the best extraction effect for the dye. The results of LC-MS analysis indicated that the coat’s red color could be mainly matched by many dyes, and the main one of the dyes might be berberine (yellow) according to the way of dyeing onto the fabric and the same molecular weight. After verification by standard berberine dyes, it was proved that one of the dyes was berberine.

Special stars are stars with anomalous metal abundance, the information of which is of great importance to the study of the origin of the universe, the evolution of the solar system and the evolution of life. Therefore, the search of special stars is an important goal in the large-scale survey project at home and abroad. Stellar spectra contain a wealth of information on the chemical composition, the physical property, and the movement state of stars, which is an important basis for conducting stellar studies. Stellar identification, classification, and the discovery of special stars are largely based on stellar spectral data. With the development of large-scale digital survey projects at home and abroad, such as LAMOST and SDSS, the data amount of stellar spectra has reached an unprecedented height. Such a large amount of data provide strong support for the discovery of special stars. Therefore, how to use these data to find the special, rare and even unknown types of stellar spectra rapidly and accurately is an important issue in astronomical research. Data mining is a technology that combines the pattern recognition, machine learning, statistical analysis and background knowledge of relevant experts to extract the potential unknown valuable information in the past. It has a natural advantage in dealing with big data. More and more data mining methods are applied to the survey data processing and analysis. At present, the data mining algorithms for special stars search mainly include stochastic forest, cluster analysis and outlier detection and so on. However, as the depth of the survey is expanded, the target of observation is getting darker and the signal-to-noise ratio of the observed spectrum accordingly lowers. There is a lot of useless information in the low signal-to-noise ratio spectrum, and the results obtained by directly analyzing and processing the relevant algorithms often have great deviations. Therefore, how to efficiently search out the special stellar spectra from a large number of low-SNR stellar data is an important issue nowadays. Due to the characteristics of the low-SNR stellar spectra themselves, a few studies are being done to search for the special stellar spectra. In order to solve this problem, a method based on principal component analysis (PCA) and the density peak approach is proposed to search special stellar spectra in low-S/N stellar data on the basis of careful study of the relevant methods. In this method, firstly, various types of high-SNR star spectra of O, B, A, F, G, K and M are selected, and then characteristic spectra are obtained by principal component analysis after wavelength unification and flux interpolation; secondly, the stellar spectra are reconstructed to obtain high-SNR spectra by using the first few characteristic spectra; finally, high-SNR spectra are clustered, and the outlier data is the special stellar spectrum. When clustering, this method uses a clustering method based on density peak for clustering and outlier mining with taking into account the characteristics of stellar spectral data itself. Experiments show that the proposed method can accurately search for a relatively smaller number of special stars in the low-SNR stellar data. At the same time, the proposed method can be applied to the spectral data analysis and mining of various galactic survey such as LAMOST and SDSS.

The two-dimensional optical fiber spectral images are the observation results of a spectrometer in an astronomical telescope system, and they are followed by a series of post-processing steps to produce the common one-dimensional spectra. Owing to the optical distortion caused by the spectrometer and CCD, obvious bending can be seen from the two-dimensional optical fiber spectral images, especially at both ends of the fibers. So far for this bending problem, there has been no good solution, nor in any reference to see any relevant work. And this kind of bending can cause great troubles to the subsequent spectral lines extraction and other works, which will affect the wavelength calibration to a great extent, as well as the accuracy of the one-dimensional spectrum. In this paper, a normal mapping method has been used to correct the bending phenomenon of the two-dimensional optical fiber spectral images. The method deals with each optical fiber spectrum in the images separately, and corrects the spectrum into vertical straight. The first step is preprocessing, which is to extract each fiber’s centerline. Then the centerline is taken as a smooth curve to obtain the normal direction at each point. The ideal vertical line is based on a point (usually the most salient point) of the whole centerline, and all the points of the optical fiber centerline are projected onto the ideal vertical line along the relevant normal directions, so as to realize the alignment of the optical fiber centerline points, which is the second step. The third step is to handle the whole fiber spectrum. Because the fiber’s width is 7 pixels in the two-dimensional optical fiber spectral image, the fiber’s centerline moves 3 pixels one by one to the left and right, respectively, realizing the above 2 steps and obtaining the straightening result of the whole fiber spectrum. In the whole process, there are two key problems to be noticed: one is the homogenization problem of the coordinate points, and the other is the accuracy maintenance problem of pixels’ values. The uniformity of the sitting punctuation is due to the use of the normal mapping in this method, which results in uneven density of the points after the formation of the alignment, which is unfavorable to the subsequent processing. The solution is to use the cubic spline interpolation method to achieve the density uniformity of the point on the line, to ensure a series of integer coordinates to facilitate the subsequent processing. To maintain the accuracy of pixels’ gray values, the 64-bit high precision number of pixels’ gray values is always kept in the process of the interpolation calculation. At the end of the implementation of the method, it is necessary to intercept both of the two ends of the spectrum to keep the end-to-end consistency, removing the pixels extending out of the height range of the image, and retaining only the pixel points in the image range. If without this process, the mapped vertical lines are different in length, so that causing difficulties in subsequent processing. This paper deals with the two-dimensional optical fiber spectral images, solves the problem of brightness deviation in the process of extracting optical fiber centerlines by using curve fitting method, obtains the corrected two-dimensional spectra after straightening, and makes a follow-up comparison between the one-dimensional spectra. In the comparison, it can be seen that the spectral line differences before and after correction are more obvious than those of the spectra at both ends of the curve. The experimental results show that this method can completely improve the bending of the spectra. It can be seen that the changes at both ends are large and the changes in the middle are very small, which accords with the basic understanding of the observation. Furthermore, due to the dislocation of pixels and the interpolation of pixels’ values, the effect of the superposition of the pixels’ values changes significantly. Therefore, by observing the movement of the spectral lines after the calibration, it is proved that this method has an important influence on the precise acquisition of the wavelength positions of the characteristic spectral lines. This paper creatively designs the method of automatic bending correction of two-dimensional optical fiber spectra and verifies the validity of the method by experiments.

The signals processing algorithm is presented based on laser spectroscopy direct absorption signal (DAS) and wavelength modulation spectroscopy (WMS) for the trace carbon monoxide (CO) measurement. The simulated transmittance data of the pure CO gas are from the HATRAN database. The DAS intensity, appending WMS 1-f and WMS 2-f signal intensities are used as the raw signals. Aimed to obtain optimized filtering algorithm, those raw signals which were added Gaussian white noise are denoised by using diverse wavelet-bases and decomposition layers. The effectiveness is validated by our CO concentration detection experiment which measures the weak laser absorption spectral line P(4) of second overtone band at 1.578 μm. A 0.95 m Herriott-type cell provides an effective absorption path length of 55.1 m. Comparing the sensing performances without and with using the optimized wavelet, the experimental results show that the signal-to-noise ratios of the system are significantly improved by 1 to 2 orders of magnitude for the DAS, 1-f and 2-f signal. The anti-jamming capability of the system is improved by proposing the suitable wavelet-base and decomposition layer algorithm.

In waste textile recycling, the rapid and accurate determination of fiber type and content is a key part of the recovery program. In this paper, 598 waste cotton-polyester blended fabrics were used as the research object, and the raw near infrared spectra (NIRS) of the samples were tested by portable NIR spectrometer. In the 1 400~1 700 and 1 900~2 200 nm NIR regions, there was a clear difference between the spectra of 100% cotton and 100% polyester samples, and these spectral differences were reflected in the various color fibers. At the same time, the reason why the slant spectrum was produced might be surface effect of the fabric, the coloring method and the fine particles adhered to the fiber surface. Dark samples tended to drift their spectral baselines in the shortwave region. After the derivative pretreatment, the baseline drift was basically eliminated, and the oblique spectrum showed normal spectral characteristics. The quantitative analysis model of waste cotton-polyester blend fabric was established by partial least squares (PLS) method combined with 1st derivative, Savitzky-Golay (S-G) smoothing, mean centering and orthogonal signal correction (OSC) method. In order to verify the reliability of the model, root mean standard error of cross validation (RMSECV) was calculated and 346 external samples were selected to test the model. The RMSECV of the model was 0.002, and the relational coefficient of calibration ( RC ) was 0.998, and the relational coefficient of prediction ( RP ) was 0.997, and the standard error of prediction (SEP) was 1.121, and the prediction accuracy of the model was up to 97%. The error of NIR predictive value and gravimetric determination was within ±3%, and the consistency between the two is more than 90% , while the error was within ±5%, and the consistency was above 95%, and the analysis time of each sample was less than 10 seconds. Therefore, the waste cotton-polyester blend fabric fiber content could be quickly and accurately predicted by using NIR technology combined with the model.

For the radiation heat transfer of particles in high-temperature combustion environment, radiation spectroscopy was presented to on-line measure radiation heat transfer parameters based on Planck’s law. According to the changes of radiation spectrums with wavelength in visible range, the temperature and radiation intensity of radiation heat transfer were obtained directly based on parameter fitting method. In order to verify the measurement accuracy, the measurement system of high-temperature blackbody furnace was built. The results of measurements showed that the relative deviation of temperature measurements and setting value was less than 3%, and the relative deviation of radiation intensity measurements and theoretical calculation value was less than 5%. On this basis, the water-cooled probe for radiation heat transfer parameters measurement of particles in high-temperature combustion environment was designed and applied to measure 200~1 100 nm radiation spectrums of gas-solid two phase flow in high-temperature combustion environment. The across section distribution of temperature and radiation intensity of high-temperature particles were obtained directly by using this method. It can eliminate influence of gas convective heat transfer effectively, and provide data support for research on radiation heat transfer of high-temperature particles.

Stout camphor essential oil has a special scent due to its content of specific aromatic terpenoid compounds. Essential oils prepared from different species, varieties or subspecies have been shown to possess varied compounds. Of the major essential oil components extracted from the Stout Camphor tree, α-terpineol has been shown to be a key component, while essential oils prepared from other camphor species may not contain this terpene alcohol. Therefore, α-terpineol can be used as an index element to represent the purity of Stout Camphor essential oil. However, a method that can quantitatively examine the quality and purity of commercial Stout Camphor essential oils is necessary. By adding an internal standard to essential oil samples, this study aimed to develop a simple and reliable method for determining the level of α-terpineol in Stout Camphor essential oils on the market. Capillary column gas chromatography has the advantages of high resolution and high sensitivity, and is still one of the most important spectral analysis techniques in modern times. Therefore, this study developed a rapid method that used vanillin as an internal standard to determine the level of α-terpineol, a key component of the essential oil extracted from the stout camphor tree, using gas chromatography. The analysis of each sample only required 30 min. The lowest limit of quantitation was as low as 1 μg·mL-1. Add α-terpineol 1.0 and 10.0 mg to commercially available Cinnamomum micranthum Hayata essential oil and stout camphor wood essential oil, and the recovery rates were more than 98% (98%~103%) with a coefficient of variation below 10.8%. We then analyzed 15 commercially-available essential oil samples and one essential oil sample directly extracted from stout camphor wood. We found that the levels of α-terpineol in these samples were within the range of 21.3%～51.6%. In conclusion, this method has a high accuracy, and the α-terpineol levels can be used as an index to rapidly determine the quality of the stout camphor essential oil on the market.

Heavy metal (HM) contamination has become a widespread global problem and posed threat to the aquatic environment due to their toxicity, persistence and bioenrichment in the food chain. In this study, overlying water, sediment, Potamogeton pectinatus L. (P. pectinatus), Phragmites australis (P. australis), and four types of fish in Wuliangsuhai Lake, China, were analyzed for HMs. The Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was used to determine the contents of HMs in collected samples in order to investigate their spatial distribution, enrichment characteristic, risk assessment and the possible sources. The results showed that: (1) The mean levels of Cr, Ni, Cu, Mn, Pb and Zn mainly followed an order of sediment>P. pectinatus (submerged plants)>P. australis (emergent plants)>fishes>overlying water, but for As, the concentration in overlying water was higher than that in P. australis and fishes. The content of Cd, in P. australis was almost 50 times higher than that in normal plants and in fish was 3.3 times higher than the permissible threshold standards in China, leading to potential hazards to fish and human health via food chain bioaccumulation. (2) In sediment As and Cd experienced moderately severe enrichment. For P. pectinatus, the higher bioconcentration factor (BCF) and the lower biota-sediment accumulation factor (BASF) indicated that this species was more likely to accumulate HMs from overlying water and could remove HMs from Wuliangsuhai Lake as a hyperaccumulator. (3) In sediment, the Eri and RI values suggested that Cd posed a considerable high ecological risk and a very high risk to the surroundings. Because of the high HM contamination levels in the northwest part of the lake, the inlet and outlet of the lake were identified as priority regions for metal pollution monitoring and management. (4) The results of source identification indicated that Zn and Cd were derived from mining and industrial wastewater, while As was related to nonpoint source pollution from agriculture. These results will provide important information for improving the aquatic environment, minimizing the potential risks posed by the HMs pollution in Wuliangsuhai Lake and managing the water quality of the Yellow River.

Molecular spectra analysis combined with the chemometrics is becoming a popular method for rapid classification of edible oil. However, when the molecular spectral differences among the different types of samples are tiny, it is usually difficult to identify them with the traditional classification techniques. In this work, a method of molecular spectra analysis based on image recognition for rapid classification of edible oil is proposed. In order to accomplish recognition of different types of edible oil, the attenuated total reflectance infrared spectra of seven types of edible oil are scanned on ATR-FTIR. To enhance the spectral differences among different types of samples and visualize the identification process, the pretreated IR spectra are transformed into two-dimensional spectral image with auto correlation operation. Then, the local extrema are extracted with the method of image expansion and are used as the classification features. The back propagation (BP) neural network is chosen as the classifier to identify the extracted local extrema of the two-dimensional spectral image. Comparative experiments to identify the same samples with the proposed method, PCA-BP and KL-BP have also been done. Comparative experiment results have verified that the classification results with the proposed method (correct classification rate is 94.4%) are obviously better than those with PCA-BP (correct classification rate is 66.7%) and with KL-BP (correct classification rate is 83.3%). The proposed method has provided a new way to classify the edible oil rapidly based on molecular spectra analysis.