In electromagnetic radiative transfer calculations, the accuracy and the computation timeare usually determined by the representation of scattering phase function. Accurate calculations are time consuming even for non-spherical particles. In order to get a better fit to exact calculations and simulate the backward-scattering peak of non-spherical particles, we developed a new empirical expression of non-spherical based on the fundamental theory of electromagnetic scattering and radiation transmission. This empirical expression of phase function is an algebraic expression with one single free parameter(asymmetry factor), and can be expanded into Legendre polynomials. We compared the Henyey-Greenstein* phase function and the new empirical expression with the T-matrix method for dustlike aerosol with different geometric shape, and found the new empirical expression provided a more realistic description for the scattering of non-spherical particles. Furthermore, the calculated value for ratio of scattering intensity at 90 degree to the scattered intensity in the backward direction is more reasonable when the ratio of the horizontal to rotational axes and the diameter-to-length ratio is larger than 0.5. We also investigated the effectiveness in approximating scattering from polydispersed particles by comparison between the new empirical expression, the Henyey-Greenstein* phase function and the T-matrix method for four of the log normal distribution polydispersions. The results show that the new empirical expression fits the T-matrix method much better than the Henyey-Greenstein* phase function. For the new empirical expression, the RMSE is small for 100% data except for the ellipsoidal oceanic aerosol at the wavelength of 633 nm. Similarly, the effectiveness of the new empirical expression is significant when we calculate the ratio of scattered intensity at 90 degree to the scattered intensity in the backward direction of non-spherical aerosol. In summary, the new empirical expression provides more accurate calculation for scattered intensity of non-spherical particlesin the backward direction, and is helpful in electromagnetic radiative transfer calculations, and the reformatting radiative transfer models in terms of the new empirical expression should require relatively less effort.

This proposesa metal nanostructure composed of a metal nanodisc and a metal nanosphereon top of it, which canbe applied for surface enhanced Raman scattering. Due to the excitation of the breathing mode surface plasmon resonance of the nanodisc, this nanostructure can form a gap mode with efficient longitudinal electric field enhancement under the illumination of a radially polarized vector beam. The simulation based on finite element method is carried out to investigate this gap mode and an electrical filed enhancement of 100 times relative to the valid transverse electrical field is demonstrated. In order to present more clearly the spectrum characteristic and the surface electric field distribution of this new nanostructure, the other structures including a single metal nanodisc, a single metal nanosphere, metal film and a metal nanosphere-on the metal film-are also studied under the same simulation configuration. Since the metal nanosphere can be regarded as the tip of a metal probe, the gap mode proposed here is expected to find application in tip enhanced Raman scattering.

As a high-throughput element distribution analysis technique for metal surface with a large size original position statistic distribution analysis (OPA) combined with spark source atomic emission spectrometry has been developed for more than ten years with the advantage of rapid analysis speed, simultaneous locating acquisition of multi-element signals and large information of statistic analysis. This technique has been widely used in the casting billet segregation analysis for medium and low alloyed steel. In this paper, OPA technique with spark source was used for the distribution analysis of main alloy elements such as Al, Cr, Co, Ti, W, Mo, Nb in FGH96 super-alloy turbine disk prepared by casting& forging deformation. The quantitative statistic analysis of these elements has been obtained by the application of appropriate calibration curves. Fix point analysis in different turbine disk position from wheel hub to flange was done by direct-reading spectrometer and a good agreement of the results of two methods has been obtained. It was found that the statistic segregation degree of the most of the elements in turbine disk is less than 5% except the element of Nb. The distribution in different position of turbine disk was varied with different type of cooling in disk. A certain segregation of carbide forming elements such as Ti and Nb existed in disk flange and the content of elements increased and on the contrary the inhomogeneousdistribution of Co, W, Mo occurred in the middle part of turbine disk. Carbides with a large size were also observed by scanning electron microscope combined with energy spectrum analysis and it can be a further evidence to explain the existence of the segregation of Nb and Ti in disk flange. The element distributionquantitative analysis results will guide the preparation of FGH96 alloy turbine disk by new casting & forging deformation technology and help improve performance.

Fourier transform infrared spectroscopy (FTIR) enables the simultaneous measurement of multiple pollutants at highspeed and is thus a powerful technique that can be used for the rapid detection and online monitoring of air pollutants. Air pollution monitoring through FTIR is mainly affected by water vapor, which interferes with the measurement of pollutants that share the same spectral region with water vapor, particularly NOX and SO2. One of the approaches for increasing the accuracy of a method for measuring pollutants is removing water vapor interference from a sample spectrum by subtractingthe background water vapor spectrum. This method is greatly useful in analyzing spectra with water vapor interference. Water vapor spectra in different concentrations do not ideally follow the Beer-Lambert’s law because of several nonlinear effects, such as water molecule clusters and instrument line shape function. Thus, a numerically calculated water vapor spectrum by Beer-Lambert’s law presents substantial error. Therefore, the background water vapor spectrum is usually directly measured by the same FTIR instrument that measures the sample spectrum at the same water vapor concentration as that of a sample spectrum. The background water vapor spectrum can be measured by two methods. One is adjusting water vapor concentration in a water vapor/nitrogen mixture toresemble a sample spectrum. This method is time consuming and extremely difficult to use in the field. The other method involves premeasuring multiple water vapor reference spectra with different concentrations and fitting. After sample spectrum measurement, two reference spectra are selected from premeasured water vapor spectra, and a background water vapor spectrum is linearly fitted by these reference spectra. The fitting method can obtain a highly approximated water vapor background spectrum when the water vapor concentrations in the reference spectra are extremely close to the sample spectrum’s water vapor concentrations and the water vapor concentration of sample spectrum is in the middle of two reference spectra. Currently, the fitting method cannot be applied in the rapid automatic elimination of water vapor interference due to lack of automatic algorithm. Thus, this study proposes an automatic algorithm, which includes reference spectrum selection and background spectrum fitting, for the fitting method. In the reference spectrum selection, the sample spectrum deducts the premeasured water vapor spectra from low to high concentrations. Two reference spectra are selected by the criteria according to the number of wavenumbers of negative absorbance in the subtracted spectra. The background water vapor spectrum is fitted through the iterative least square method, which gradually deletes the wavenumbers that are interfered by pollutants, and a background water vapor spectrum, which has an absorption feature that is consistently identical to that of water vapor in the sample spectrum, is linearly fitted by the remaining wavenumbers. The water vapor interference in the sample spectrum is eliminated by subtracting its background water vapor spectrum. In this study, we automatically remove water vapor interference in an air spectrum that contains NO2. Results show that the proposed algorithm accurately remove water vapor interference. After the elimination of water vapor interference, NO2 can be detected by its absorption peak located at 1 629 cm-1. The detection limit of NO2 remarkably improves when compared with detecting by its weaker absorption peak located at 2 917 cm-1 that is not interfered by water vapor.

Dynamic parameter measurement is the essential of performance for special weapons, such as amphibious and underwater special weapons. The information about velocity of moving target underwater is one of the important factors for evaluating underwater weapons performance. There are many disadvantages of the traditional connect measurement method, for example, aluminum foil target and comb target have poor reliability, small effective area and low repeatability in an underwater environment. At the same time, the methods of Doppler and sonar are very expensive. In order to solve these problems, we propose a real-time and non-contact method to obtain weapon velocity parameters based on the split-type reflective and with laser screen as effective area. The law of underwater spectral transmission is determined by analyzing the law of Lambert-Beer and the function of body scattering and other mathematical principles. The optimal laser peak wavelength was obtained. A 1 m-diameter cylinder was created as a scatterer to simulate the scattering of in water. The total number of traced space rays was 1×105. And the light energy of the irradiance at the receiving surface located at 1, 3, 5, and 7 m was obtained. So the optimal peak power of the system laser source was obtained also. On the basis of this, the optical system adopted principle of determining the distance measuring time principle and the one-dimensional retro-reflective technology, which consisted of 532 nm, fiber-coupled semiconductor lase, fiber-coupled laser beam expander used Powell lens and retro-reflector. The laser emission part and the signal processing part were located on the water, and the effective area of the laser screen was located under the water. The laser was emitted and the signal was recovered by the optical fiber. One end of the transmitting optical fiber was coupled with the light source, and the other end of the optical fiber was coupled with the Powell lens to form a fan-shaped light screen under water. One end of the receiving optical fiber was distributed at Powell prism exit, and the other end was coupled with the PIN-type photoelectric sensor. One-dimensional tooth-shaped retro-reflector was designed and manufactured, the light would be returned along the original direction, and the other dimensional direction would observe the principle of specular reflection. The receiving system was placed near the vertical point of the emission point in order to collect most of the reflected light. The problem of the current reflector was solved, that is to say, reflection characteristic disappeared, because the refractive index of the water was different from the air. The experiment adopted a laser with a wavelength of （532±5） nm, power stability <1%, optical noise <0.5%. After it was collimated, the laser was coupled to a single-mode fiber with a length of 2 m, then was widened to a 60° fan sharp laser screen by a Powell lens. The beam expander module used nylon as the waterproof material, and the receiving optical fiber was uniformly distributed around the light source to form an annular fiber bundle, and the other end of the optical fiber was evenly arranged and directly coupled with the PIN photodiode. In front of the photodiode, a center wavelength 532 nm optical filter was added, FWHM = （3±1） nm and the transmittance was 70%. The effective size of the PIN photodiode was 5.0 mm×5.0 mm. Adopted multiple adjustable optical signal conditioning circuits to adapt to different sizes of targets. The system performed different velocity of targets measurement. The steel file was used as a launching device. The signal was collected through fiber; then processed by conditioning circuit, finally, transmitted to the computer. The waveform and the average velocity were obtained. The distance between the two laser screens was a constant value of 300 mm, and the peak value of the waveform was used as a timing moment. The higher SNR waveform signals were acquired successfully. That system has been tested for different target speed parameters, and the waveform signal and the target speed value of higher signal-to-noise ratio have been successfully obtained. The experiments of different target were set up, and the signal waveform with high signal-to-noise ratio was successfully obtained. The absolute error was obtained by comparing the underwater moving target model with the simulation results. The experimental results showed that the proposed method can achieve the test requirements of 1 m×1 m in effective area, the minimum measurable target size of 5 mm. The accuracy of system was more than 0.2%, though compared with the results of initial velocity measurement and empirical formula.

The finite element modes of the quartz tuning fork in the Quartz Enhanced Photoacoustic Spectroscopy (QEPAS) system were calculated and the first 6 modes and modal frequencies of the quartz tuning fork were obtained. The 4th order symmetrically oscillating vibration mode was recognized as an effective vibration. Single factor method was used to analyze the effects of fork’s arm length l1, fork’s arm width w1, fork’s arm thickness t, fork’s arm cutting angle θ, fork’s arm diameter d and fork’s arm round hole height h on low effective resonance frequency (Fre). The results of the sensitivity sensitivity were as follows: l1>w1>d>θ>t>h. Considering the actual design situation, four design parameters of quartz tuning fork l1, w1, d and h were screened. Using Box-Behnken experimental design and RSM (Response Surface Methodology) method, Fre was set as a function target to establish l1, w1, d and h quadratic regression response surface model, and get the interaction between the parameters. The Design-Expert software was used to inverse the design parameters of the response surface model. The results showed that the error in the calculation area of 15 000 Hz≤Fre≤25 000 Hz is small, and basically meets the calculation requirements of QEPAS system. The proposed research and design methods have some generality, which can provide references for the design of quartz tuning fork structure in QEPAS system.

A series of Sn2+, Dy3+ and Sn2+-Dy3+ co-doped fluorophosphate glasses (FPGs) for white light emitting phosphor have been prepared by the melt quenching method. Under the UV light excitation, FPG: Sn2+ and FPG∶Dy3+ can obtain blue and yellow light, respectively. The emission color of FPG∶Sn2+-Dy3+ can be tuned from blue to white color by properly adjusting the concentration of Dy3+ ions under the excitation of 280 nm UV light, which can be attributed to the energy transfer from Sn2+ to Dy3+ ions. The energy transfer mechanism was investigated and analyzed according to the photoluminescence, lifetime decay and CIE chromaticity coordinate. In addition, the FPG∶Sn2+fluorophosphate glass shows the highest color rendering index of 94 and the quantum efficiency of 81.3%, and the Sn2+-Dy3+ co-doped fluorophosphate glasses show better white color coordinates. By controlling the concentration of Dy3+, the FPGs can present a white light with a CIE chromaticity coordinate of (0.311, 0.330), which is very close to the equal energy point. The corresponding quantum efficiency and the luminance are 56.3% and 6 706 cd·m-2, respectively. The results of this study demonstrate that the FPGs are promising candidate for commercial white light emitting applications.

Saffron and natural bezoar are two traditional Chinese medicines widely used in clinical practice. Due to their lower yields, high medicinal value and price, market demand and other factors, more and more adulteration and counterfeit goods not only seriously damage the health of patients but also hinder market normal operation. However, the empirical methods based on observation, smell and soak have become increasingly difficult to distinguish high imitation counterfeits. In addition, the traditional physical and chemical detection techniques through chemical extraction, chromatography and mass spectrometry are cumbersome and time-consuming, and have high requirements and reliance on testing environments, professional ability and equipment. They cannot meet the actual needs of on-site, rapid and simple identification. Thus, it is urgent to explore new and effective detection methods and identification techniques. Terahertz radiation has very low energy and terahertz time-domain spectroscopy (THz-TDS) possesses not only the high specificity of pure compounds but also the “macroscopic fingerprint characteristics” of the hybrid system to identify the diversity and complexity of the chemical composition in the mixture. In addition, as a common statistical analysis method, principal component analysis (PCA) mainly replaces the original variables with a few comprehensive variables that can explain the variance of the original data to the greatest extent and can perform pattern recognition on different kinds of samples. In this work, 18 pieces of saffron and safflower samples as well as 20 groups of natural and artificial bezoar were respectively compressed by using pellet compression. The absorption spectra of two kinds of valuable Chinese medicinal materials and their counterfeit products, saffron and safflower as well as natural and artificial bezoar, were measured using THz time-domain spectroscopy in the range of 0.3~2.5 THz. Finally, the principal component analysis (PCA) was used to identify the obtained data set. In order to improve the identification ability of PCA, on one hand, the data set was mapped to a set of bases (feature vectors) for simplification, and larger eigenvalues were selected instead of describing the original main spectral information; on the other hand, in order to eliminate the impact of noise on the classification process, we adopted Savitzky-Golay（S-G）smoothing before PCA to remove the redundant and irrelevant spectral features; the discriminant analysis was then performed using Fisher’s diagnostic line. Comparing the principal component scores with and without S-G smoothing, classification results with S-G smoothing were obviously distinguished and the first two principal components could basically reflect the differences between spectra. It could be clearly seen that in the unprocessed score plots, the overlapping of the two types of samples is severe, whereas only a relatively small number of sample points overlap in the smoothed score plots, indicating the role of SG smoothing in spectral identification. The classification results showed that the saffron and safflower had obvious clustering trends, and the accuracy of classification identification of saffron and safflower were both 100%; while there was a slight overlap of artificial bezoar and natural bezoar even though the intra-class samples basically gathered together, and the classification accuracy was 100% and 90%, respectively. Furthermore, the principal component score of the sample can also reflect the internal characteristics of the sample and the clustering information. Among them, the saffron sample contains higher compounds of crocin, crocetin and other content, so that better degree of polymerization has been obtained and the distribution is relatively concentrated; on the other hand, the compounds contained in the natural bezoar are more complex. Consequently, the clustering effect is poor and the distribution range is wide. The reliable results based on the THz-TDS and PCA not only distinguish between saffron and safflower as well as natural and artificial bezoar, but also provide the means and theoretical basis for enriching the quality standard of Chinese herbal medicine.

Terahertz spectroscopy can provide the lattice vibration information of solid materials, which is different from previous measuring methods. And particularly, it is suitable to test polymorphs. Thus it has an important meaning for controlling and detecting the polymorphs of drugs. The polymorphs should affect the properties, such as stability,of the drugs in storage. Nalidixic acid, the earliest quinolone antibacterials used, was investigated in this work. Two polymorphs NA-Ⅰ and NA-Ⅱ were synthesized and were confirmed by X-ray powder diffraction experiments. Different absorption spectra in the range of 0.2~2.4 THz of two synthesized polymorphs of nalidixic acid were obtained with terahertz time-domain spectroscopy at room temperature. Significant differences existed in the terahertz spectra of NA-Ⅰ and NA-Ⅱ. Five characteristic absorption peaks observed in the terahertz spectrum of NA-Ⅰ were at 0.94, 1.41, 1.88, 2.05, and 2.16 THz, while six peaks of NA-Ⅱ were at 0.72, 0.96, 1.38, 1.80, 2.04 and 2.16 THz. The most obvious difference was that there was a shoulder peak at 2.04 THz for NA-Ⅱ, while the intensity of the peak at 2.05 THz for NA-Ⅱ was much larger. Unlike other spectroscopies, such as infrared spectroscopy, the characteristic peaks in terahertz spectra were not related to specific functional groups. Thus the assignment of the characteristic peaks in terahertz spectra was essential. Theoretical calculations for the two polymorphs were performed based on density functional theory. The calculated characteristic peaks were in accord with those in the experiments. The vibrational modes in this low frequency region were mainly skeletal vibrations. Also the difference of the two spectra was explained by the different packing modes of the two crystals. This work provides evidence for identification of drug polymorphs using terahertz spectroscopy.

The MAI on TG-2 space laboratory, which was launched on 15 September 2016, is the first on-orbit Multi-angle Polarization Imager in China. The capability of MAI is mainly used to obtain macroscopic and microphysical features of clouds. On-orbit calibration of spaceborne remote sensing instruments is a key prerequisite for the quantitative application of observational data and extends throughout the life of the instrument. Laboratory calibration has been performed prior to MAI launch with high accuracy. In order to monitor the status of MAI after launched, aiming at the problem that MAI has no onboard calibration system, a method of on-orbit monitoring and inter-calibration of TG-2/MAI 565, 670 and 763 nm channels based on Metop-B/GOME-2 hyperspectral data has been presented. The method first obtains the data of same observation target at the similar time and near geometric condition of MAI and GOME-2 based on spatial, temporal and geometric collocation criterion. Then, the GOME-2 reflectance is convoluted with the spectral response function of the MAI visible channels to obtain the reference reflectivity of visible channels. Finally, compare the reference reflectivity with the MAI reflectivity to achieve the onboard calibration of MAI. The process of calibration mainly includes: (1) Forecasting the orbit of TG-2 and Metop-B from December 2016 to February 2017 to obtain the collocated observations between MAI and GOME-2. The temporal matching interval is set to 900 s, and 8 collocated samples of MAI and GOME-2 are obtained, including 2 455 matched pixels. (2) The spatial location of matched pixels is checked, and the cross samples with MAI pixels over 338 covered by a single GOME-2 pixel is reserved to ensure that a single GOME-2 pixel is filled as completly as possible by the MAI pixels. (3) The limit of GOME-2 observation zenith angle is set to 30°, and the geometry of the observation sight detection condition matching pixels is set to the ratio of cosine of the two instruments observed zenith angle is close to 1, and the difference is not more than 0.05, and takes full advantage of MAI multi-angle observation, which allows each MAI pixel with up to 14 viewing angles. Therefore, the optimal matching viewing angle could be chosen; (4) In the target uniformity checking, the condition of uniformity detection for matched pixels is set to the ratio of the reflectance of all MAI piexls coveraged by a GOME-2 piexl standard deviation and the average is less than 0.5. And 469 GOME-2 pixels are reserved. (5) The reflectance of each wavelength corresponding to the above GOME-2 pixels is convoluted with the spectral response function of the MAI visible channel to obtain the corresponding GOME-2 reference reflectance of each MAI channel. (6) Based on the large difference of the spatial resolution of GOME-2 and MAI pixel, the reflectance of all MAI pixels covered by each GOME-2 pixel is averaged and taken as MAI reflectivity, which significantly reduces the dependence of calibration results on target uniformity. (7) And the inter-calibration coefficients are derived by regression analysis of the GOME-2 reference reflectivity and the MAI reflectivity. Onboard inter-calibration of the MAI is achieved. To analyze the influence of matching and screening conditions on the calibration results, the simple variable method is used to adjust the threshold of each test condition in pixel matching and screening process. The results show that the calibration results do not change significantly when the matching and screening conditions are more stringent. The MAI reflectance and the GOME-2 reference reflectance are compared, and the results indicate that both reflectivities have a significant linear relationship with the correlation coefficients all better than 0.97. The mean values of their differences are 1.6%, 4.2% and 2.3%, and the standard deviations are 3.1%, 4.1% and 2.4% for the three channels, respectively. Therefore, on-orbit monitoring and vicarious calibration of the MAI visible bands can be achieved by the inter-calibration method, which lays the foundation for the quantitative application of MAI data.

In recent years, the silicon semiconductor clusters have experimentally and theoretically attracted great attention because of their potential applications regarded as cluster-assembled optoelectronic materials. Especially, the appropriate transition-metal atoms can stabilize the silicon clusters by doping to the surface of clusters, accordingly novel physical and chemical properties of transition-metal doped silicon clusters will be produced, e. g., optical property, magnetic property, and superconductor, etc. In this work, the geometric structures and electronic properties of HmTiSin (m=1～2; n=2～8) clusters are systematically studied using the density functional theory (DFT) B3LYP method, and the changing regularity, dissociation channels and HOMO-LUMO gaps of the ground-state structures of the TiSin(n=2～8) clusters and their hydrides are discussed in detail. The results show that the Ti atom in the TiSin(n=2～8) clusters will gradually move from convex to surface and to interior sites along with the increasing number of the Si atom. For most of hydrogenated HmTiSin clusters, their stable structures keep the structural framework of TiSin clusters, while the H atoms prefer energetically to be attached on the silicon atoms, rather than the Ti atom. The analysis of dissociation energies as well as HOMO-LUMO gaps show that the adsorption of two H atoms on clusters’ surfaces will eliminate the number of dangling bonds in these clusters, and largely improve the structural stability of clusters. The second-order energy differences (Δ2E) can explore the chemical stability of the magic clusters, and it is found that the Δ2E is very sensitive to the cluster size, and has the local oscillation behaviors along with the increasing cluster size, in which the TiSi2 and TiSi6 clusters possess relatively higher stabilities than their neighboring ones, whereas the hydrogenated H1TiSi7 and H2TiSi7 clusters are the most stable in all of clusters studied here. In addition, we simulate the infrared spectra of these hydrogenated clusters, and assign the main vibrational peaks for further experimental references. These studies will provide significant theoretical references for further experimental synthesis and measurements of the transition metal-doped silicon-based nanomaterials.

The targets have strong infrared radiation near the 2.7 and 4.3 μm bands, so these two bands are also the best bands to detect the flying target, but since these two bands are not atmospheric windows, they are not included in most remote sensors. It is of great value to study the typical features of infrared absorption spectrum. However, due to the lack of necessary data acquisition ability, it often faces the problem of lack of data. There are frequent volcanic eruptions in various parts of the world. Whether volcanic eruptions have an impact on typical target detection or not is lacking in relevant analysis and research. In this paper, we get the data which transformed from the meteorological satellite data, through the wave band conversion model, based on the theory of atmospheric radiation transmission and the multivariate statistical analysis. The High temperature pixel is regarded as a mixed pixel of flame and background, and the target radiation is separated from the background to describe the thermal radiation of high temperature target pixel. When the aerosol mode is fixed, the observation zenith angle, atmospheric precipitation and the atmospheric profile are the influencing factors of the independent variable. For the background radiation brightness, the observation zenith angle, atmospheric precipitation in the atmosphere, atmospheric profile and background temperature are the influential factors of independent variables, and multivariate statistics are used to establish the relevant models. The volcano was detected by using the statistical characteristics of the third channel data of the FY-3 VIRR to obtain the apparent multidimensional features and quantitative analysis in time dimension, and the data of the same volcano are analyzed at different times. In spatial dimension, the spatial distribution characteristics of radiation brightness and luminance temperature of the crater are statistically calculated. The resolution of normal meteorological satellite is quite low, if we use pixel resolution to represent the volcanic area, the actual area of the volcano will be significantly exaggerated. So in this paper we propose a sub-pixel characteristics analysis method to increase the quantitative analysis accuracy. A combination of a mixture of pixels is considered as a combination of flame and background, and a linear spectral mixture model is used to accurately calculate the area and temperature of a volcano’s high temperature point by the emissivity of the mixed pixel. The results show that the 2.7~2.95 μm crater may interfere with the high temperature target in the weak background environment. In 4.2~4.45 μm band, the crater has been proved to be a potential disturbance that can not be ignored. Its energy is much higher than the general surface type.

Microalgae are rich in carotenoids, vitamins, proteins, polyunsaturated fatty acids and other nutrients required by humans and animals. In addition, some microalgae also play critical roles in aquatic ecological environment. Thus, it is important to carry out microalgal research for practical applications. Traditionally, analysis of microalgal components requires cell disruption and extraction with organic solvents followed by gas or liquid chromatography of the extracts. However, these traditional methods are generally time-consuming, requiring expensive instruments and sophisticated operations. So it is urgent to develop a more effective method for rapid and convenient microalgal analysis. Infrared spectroscopy mainly relies on the absorbance of radiation at molecular vibrational frequencies to observe the changes of the molecular compositions, characteristics, structures and concentrations. Actually, this technique has emerged as a promising tool to distinguish and quantify various cellular components such as proteins, lipids, nucleic acids, carbohydrates, chlorophyll and carotenoids in microalgal systems. Compared with the conventional detection methods, it has the advantages of simple, fast, non-invasive, and multiplex measurements. Especially, with the development of microscopic imaging technology, infrared microspectroscopy shows the powerful potential for spatially-resolved and real-time in situ measurements of biological systems at the single cell or tissue level. In recent years, with the development of synchrotron radiation technique, the advanced synchrotron infrared microspectroscopy and imaging technique has been applied and it can provide higher sensitivity and spatial resolution than the traditional infrared spectroscopy, so to a certain degree it resolves the contradiction between high throughput analysis and high spatial resolution observation. In this study, the principle and advantages of infrared spectroscopy and micro-spectroscopic imaging were firstly illustrated, especially the application of this technique combined with chemometrics in the field of biology. Then, the recent progress on the application of infrared spectroscopy in discrimination, metabolism, breeding, water environment protection, medicine and health relating to microalgal research was introduced and discussed in review of a variety of recent literature. For instance, infrared spectroscopy in combination with chemometrics can identify, discriminate or classify different microalgal strains. This technique can also be applied in the research of microalgal growth and metabolism based on its advantages of fast and multicomponent analysis. It can even provide a non-destructive and high-throughput method to screen the lipids, β-carotene or astaxanthin hyperproducing microalgal strains based on infrared spectroscopic tool. In addition, microalgae have been reported to be potentially proper biosorbents for the treatment of heavy metals and dyes wastewater. Actually, infrared spectroscopy has already been applied as a proper approach to study biosorption of heavy metals or dyes from wastewaters using microalgae. Being a powerful tool, it has not only distinguished and quantified various vital components quickly and conveniently, but has also provided an effective method for quality detection and authenticity measurement of microalgal foods and drugs. Although there are still some deficiencies for the application of infrared spectroscopy in microalgal research, the promising potential and comprehensive resolution for it are discussed in this study. Finally, based on the latest progress and application of infrared spectroscopy, some prospects of this technique for microalgal research were also put forward at the end of the review, such as industrial large-scale culture of microalgae, selection and breeding of hyperproducing strains, the physiology of microalgae, the molecular structure and function of organelles, etc.

Surface plasmons are collective oscillations of surface electrons in metal, as well as the related electro-magnetic fields, which are induced by incident lights. So far, the spectral characteristics of the surface plasmons of gold micro-nano particles have been widely studied and applied, at the visible light regime. In addition, the spectral properties in the mid-infrared wavelength region have also drawn great attention in the community, due to its great potential in the research of good-quality sensors. So far, to systematically adjust the paramters including the resonance wavelength, the maximum absorptance and the full width at half maximum intensity, etc. in the design of the surface plasmon-based photoelectric sensing devices, is still a key issue in the community. Compared with single paticles, array-based structures possess great advantages in tuning the above mentioned parameters due to the characteristics of periodicity. Therefore, in this work, an array structure based on gold micro-nano particles is proposed. With the finite difference time domain method, the reflectance spectrum, transmittance spectrum and absorptance spectrum in a wavelength range of 4～18 μm are systematically studied, by varying the structural parameters of the gold array including the particle radius, the particle height, the particle separation, and the particle shape. The results in this work reveal a resonance characteristics within the wavelength range of 8~10 μm that occurs when the incident light resonates with the induced surface plasmons of the gold micro-nano array. The results also indicate that the resonance wavelength, the absorptance peak intensity and the full width at half maximum intensity of the resonance spectrum can effectively be adjusted by tuning the structural parameters of the gold array. Based on the spectral characteristics demonstrated in this work, the proposed gold micro-nano array structure may be utilized in the future design of photoelectric sensors at the mid-infrared wavelength regime.

As near-infrared spectroscopy has many advantages, such as fast analysis, non-destructive testing and field detection, it has been widely used in many fields. However, there are some shortcomings such as low signal-to-noise ratio, weak absorption intensity and overlapping peaks in near-infrared spectroscopy. NIR spectroscopy can not be qualitatively/quantitatively obtained from the spectrum. Therefore, NIR spectroscopy can only be used as an indirect analytical technique. The research of infrared spectral modeling method becomes the core of analyzing near infrared spectroscopy. Deep learning is a new branch of machine learning and has been successfully applied in many fields. The network structure of deep learning and the non-linear activation ability make the model especially suitable for high-dimensional and nonlinear large-scale data modeling. In order to further enrich the NIRS modeling method and improve the accuracy of NIRS, it is necessary to develop a new modeling method using NIRS. The qualitative analysis of near-infrared spectroscopy is studied in this paper. A model based on Stacked Contractive Auto-Encoders(SCAE) is proposed to identify the same drugs produced by different manufacturers on the market. With contractive Auto-Encoder (CAE) based on Auto-Encoder network by adding Jacobi matrix as a constraint, self-coding network is used to reduce the dimension of the data to learn the internal characteristics of the data, and Jacobi matrix contains information in all directions. The extracted features can be invariant to a certain degree of perturbation of the input data and improve the ability of self-encoding network to extract features. SCAE is a multi-layer CAE neural network. As the input layer of the latter layer of CAE network, all the parameters of the network are obtained by adopting the layer-by-layer greedy training method. After the training, all the networks are regarded as a whole, Fine-tuning by backpropagation algorithm, and finally using Logistic/Softmax classifier for qualitative analysis. The experimental data were collected by the National Institutes for Food and Drug Control, with Cefixime Capsules as the second classification experimental data and Isosorbide Dinitrate Tablets as a multi-classification experimental data. The spectral curves were obtained by measuring the absorbance of each sample at different wavelengths with a Bruker Matix spectrometer, and then the deviation from the spectral samples was obtained by OPUS software to eliminate the drift and other factors. Next, we established the model by experimentally determining the Lamda of the constrained Jacobi matrix ratio coefficient of 0.003. The modeling process was divided into five stages, namely: pre-treatment stage, pre-training stage, fine-tuning stage, testing stage and contrast stage. In order to verify the performance of SCAE network in terms of classification accuracy, algorithm stability and modeling time, the algorithm was compared with BP neural network, SVM algorithm, sparse Auto-Encoders (SAE), Denoising Auto-Encoders(DAE) for comparison. In terms of classification accuracy, stack compression self-coding network has the highest classification accuracy and algorithm stability at different ratios of training set to test set. In terms of modeling time, SVM algorithm has a great advantage over other algorithms in terms of running time because it does not need pre-training and feature extraction. However, stack compression self-coding network modeling speed is better than other contrast algorithms except SVM. In summary, the use of stack compression self-coding network for drug identification is effective and feasible.

Phenomenon of adulterated traditional Chinese medicine (TCM) are still common in TCM market today. Unscrupulous traders used fibrous root powder pretending to be main root and rhizome powder of Panax notoginseng, and such behavior has serious influence on the quality and efficacy of Panax notoginseng. In this study, we have established a rapid method to discriminate the main root, rhizome and fibrous root powder and detect saponins content of Panax notoginseng in order to provide some research bases for rapid quality assessment of Panax notoginseng. A total of 60 Fourier transform infrared (FTIR) spectra of the main root, rhizome and fibrous root powder of Panax notoginseng were collected, and ultra-high performance liquid chromatography (UPLC) was used for measuring the content of notoginsenoside R1, ginsenoside Rg1, ginsenoside Rb1 and ginsenoside Rd of samples. The origin data of identify model were processed by ordinate normalization and second derivative, and 2/3 of the 60 individuals were selected to form the calibration set by using Kennard-stone algorithm as well as the other 1/3 were used as validation set. Calibration set data were used to establish the discriminant model of support vector machine (SVM) and the cross-validation was used for screening optimal parameters c and g, and validation set data were used to verify the results of SVM discriminant model for external validation. The origin data used to predict saponins content were calculated by first (1D) and second derivative (2D), Savitsky-Golay smoothing with five, seven, nine, and eleven points. 2/3 of the 60 individuals were selected to form the calibration set and the rest were used as validation set. The orthogonal signal correction-partial least squares regression (OSC-PLSR) model was established by calibration set and the validation set was utilized to verify the results of the model for external validation. Results showed that, (1) with second derivative processing, the overlapped peak of FTIR spectra were efficiently separated and the resolution of the spectra has been improved. (2) The optimal parameters c and g of support vector machine calculated by cross-validation were 2.828 43 and 4.882 81×10-4 respectively and the optimal accuracy rate of calibration set was 100%. (3) The parameter of support vector machine model was set as the optimal parameter and the accuracy rate of validation set was 100%, and all samples in validation set have been identified correctly. (4) The prediction content of greatest model of notoginsenoside R1, ginsenoside Rg1, ginsenoside Rb1 and ginsenoside Rd was close to the content measured by UPLC. The result indicated that, FTIR combined with support vector machine could effectively identify the main root, rhizome and fibrous root powder of Panax notoginseng. OSC-PLSR could accurately predict the content of four saponins of Panax notoginseng. In summary, the FTIR spectroscopy could provide a rapid and effective method for the quality control of Panax notoginseng.

Order-disorder transition is one of the important properties of spinel, but seldom research can be found about it. One nature Burma pink spinel were tested by Raman spectroscopy, excited by 785 nm laser in the liquid nitrogen atmosphere to obtain the clear and sharp Raman spectra. In this way, one can avoid the 469 nm fluorescence peak induced by the 532 nm laser, and the thermal affection of the room temperature test. One nature pink Cr-doped MgAl2O4 spinel were used in this study. The Raman spectra were obtained after the samples were heated at different temperatures to present the process of Raman spectra variation during spinel order-disorder transition. The parameter analysis showed the results that: the Raman peaks of MgAl2O4 spinel are caused by 5 vibrational mode, Eg, T2g(1), T2g(2), N3 and Ag, at 407.8, 312.4, 667.5, 720.0 and 769.0 cm-1; The parameter of Raman peaks have a sudden change at around 800 ℃, including the increase of HWFM of all the peaks and the relative intensities of the other peaks and the main peak Eg; the appearance of N3 peak; and position movement of T2g(1) (to the low wavenumber direction) and T2g(2)(to the high wavenumber direction) peaks with the disappearance of peak symmetry. This study indicates that the peak parameters of Raman peaks can be used to show the order-disorder transition of spinel, and it can be widely used in the identification of heat treated spinel gems.

It is a pursuing goal to find an experimental method for determining the aromatic size of aromatic molecules for scientists. It is a new attempt to measure the aromaticity degree of half-sandwich complexes using Raman spectroscopy.The calculations for the geometries optimization, electrostatic forces, stabilization energies, nucleus independent chemical shifts (NICS) and values of the Raman spectroscopy for (η6-C6X6) and half sandwich complexes ［(η6-C6X6)M］n+(X=F~Br, M=Ti~Mn, n=1, 2) were conducted using density functional theory (DFT) method within Gaussian09 process package. The results showed that, for all the (η6-C6X6) and half sandwich complexes, the values of NICS were all negative. These kinds of molecules all were aromatic. There were strong Ring Stretching Vibration Raman Spectroscopic Frequency (RSVRSF) peaks with A1g/A1 symmetric in the (η6-C6X6) and its half sandwich complexes. The RSVRSF values and the absolute NICS values of the (η6-C6X6) and its half sandwich complexes gradually decreased with the order of F, Cl, and Br, showing a highly positive correlation, and the correlation coefficients were above 0.99. It is theoretically predicted that the determination of the aromatic degree is possible by the experiment determination of the RSVRSF values for the (η6-C6X6) and its half sandwich complexes.

Foodborne diseases caused by food-borne pathogen have become a global public health problem. Rapid and accurate detection of food-borne pathogenic microorganisms has become a key to the prevention and control of food-borne diseases and is also the key technology to ensuring food safety. Surface-enhanced Raman spectroscopy (SERS), as a powerful and attractive analytical tool, has the advantages of simplicity, rapidness and high sensitivity. This review summarizes the recent trends and developments of SERS in the detection of food-borne pathogenic microorganisms. A brief tutorial on SERS, the SERS enhancement theory and SERS enhancement substrate are given first of all. Then we summarize the recent trends and developments of SERS applied to the detection of foodborne pathogen in food and medical diagnosis. In addition, microfluidic SERS platforms for foodborne pathogen are discussed as well. In the field of food safety analysis, SERS combined with pattern recognition methods can rapidly and effectively identify common food-borne pathogenic microorganisms. Some studies have reported to apply SERS to detect food-borne pathogenic microorganisms in different food samples, which demonstrates the advantage of SERS as “fingerprint”. In medical diagnosis, SERS can rapidly detect food-borne pathogenic microorganisms in pathological samples (such as blood and urine). The application of SERS makes the rapid diagnosis of food-borne diseases possible due to the shortening of the sample analysis time. With the development of microfluidic technology, microfluidic platform combined with SERS technology is called “chip lab”, which can improve the controllability, stability, specificity and sensitivity for detection of food-borne pathogenic microorganisms. The review summarized and compared these different studies of SERS methods, which could be used to detect food-borne pathogenic microorganisms based on different isolation methods, different substrates or different target capture methods. These researches have demonstrated that the application of SERS in foodborne pathogenic microorganisms could overcome the shortcomings of traditional methods, and provide an effective, rapid and sensitive analytical tool for real-time monitoring of food safety and diagnosis of foodborne diseases. At the same time, there are still great challenges for the application of SERS technology in foodborne pathogenic microorganism analysis. (1) Most researches do not focus on the actual samples. However, there is really difference between the standard culture medium and the actual samples for SERS analysis. (2) There are differences between the results of different methods, mainly due to the difference of SERS substrate, the difference of the target adsorption modes, the difference of stability and so on. So further studies are needed for optimization conditions. (3) It is expected to establish standardized SERS methods to replace the traditional techniques, which could fully show the advantages of SERS including rapidness, sensitivity and simplicity. An outlook of the work done and a perspective on the future directions of SERS as a reliable and rapid analytical tool are given for a broader application prospect in food safety, biomedicine and other fields in the future.

Carbaryl is a broad-spectrum and efficient carbamate pesticide. In this study, we proposed a surface-enhanced Raman scattering (SERS) approach for quantitative and qualitative analysis of carbaryl residues in pakchoi. Density functional theory(DFT) calculations with Gaussian 03 using B3LYP/6-311G basis sets were executed. The experimental vibrational spectrum and theoretical spectrum of carbaryl were contrasted for its assignments of Raman peaks. Magnesium sulfate, PSA, carbopack and C18 were used to remove the influences of fluorescent substances in pakchoi. The limit of detection can achieve the standard of 0.976 mg·L-1 for carbaryl pesticide residues in pakchoi. Primordial spectra were pretreated by three methods of MSC, SNV and Normalization, and then the spectra were used to construct the pesticide residues models by the method of Partial Least Squares (PLS). Based on the results of PLS, carbaryl residues extracted from pakchoi can be predicted by the MSC model with a lower root mean square error of prediction (RMSEP=1.71 mg·L-1) and higher correlation coefficient in the prediction set (Rp=0.986 5) value. It shows that the model of MSC can accurately predict the carbaryl pesticide residues extracted from pakchoi. Five unknown carbaryl concentration pakchoi samples were prepared for prediction model precision, and the values of relative deviation were calculated to be between 1.98% and 7.28%, and the predicted recovery rates were calculated to be between 95.73% and 107.28%. The T value is 0.397, which is smaller than t0.05, 4=2.776. These demonstrate that there is not evident difference between the measured and predicted values. This study illustrates that SERS method serves as an efficient method for the detection of carbaryl pesticide residues extracted from pakchoi quickly and reliably.

Rapeseed oil in the process of processing and storage are vulnerable to oxygen, temperature, light and other factors, resulting in oxidative rancidity phenomenon. In order to judge the oxidation degree of oil accurately and realize the intelligent evaluation of the quality of rapeseed oil under different oxidation modes, the intelligent evaluation model of rapeseed oil oxidation state was established based on the three dimensional synchronous fluorescence spectrometry combined with parallel factor analysis and BP neural network method. With cold pickled oil as raw materials, the samples were treated in the normal temperature, Schaal oven and high temperature oxidation mode respectively. During the period, the three dimensional synchronous fluorescence spectrum data and physical and chemical indexes of the rapeseed oil were collected. When the physical and chemical indexes exceeded the limits of the national standard, the data were stopped.The results of three dimensional synchronous fluorescence spectra showed that there were significant differences in the evolution of fluorescent substances in rapeseed oil in different oxidation modes. Oxidation mechanisms of rapeseed oil changed significantly with the temperature. The characteristic fluorescence peak position of rapeseed oil had no significant changes at normal temperature between 1 day and 350 days, only with a slight change of fluorescence peak near Ex 620 and 660 nm. After oxidation of 26 days in Schaal oven, the fluorescence peak near 620 and 660 nm decreased significantly, and a new fluorescence peak was formed between Ex 350 and 450 nm. The fluorescence peak of Ex at 620 and 660 nm disappeared after 48 h of high temperature oxidation, and a significant fluorescence peak produced at Ex 400~550 nm. Compared with the oxidation of Schaal oven, the fluorescence wavelength shifted to a certain extent, which was caused by the poor stability of the substance produced by the oxidation of oil in the high temperature mode. The parallel factor analysis method was used to decompose the three-dimensional synchronous fluorescence spectra. When the number of components was 6, the load value of excitation wavelength was the largest when Δλ=60 nm, and the difference between the different samples was the most significant.The two-dimensional fluorescence spectra of Δλ=60 nm band were selected for intelligent evaluation, which were used as the input values of the BP neural network model. The polar components were used as the output values to model the three kinds of oxidation mode data respectively. The experimental results show that the correlation coefficient R of the training set, the verification set and the test set model corresponding to the three oxidation modes can all reach above 0.9. The correlation coefficient R of the validation set and the test set model in the normal temperature oxidation mode is 1, showing that the output value and the target values are close and the prediction effect of the model is better. The correlation coefficients of the three training models, i.e. the corresponding training set, the validation set and the test set model, are 0.999, 0.913 and 0.988 respectively, and the root mean square error is small, which shows that the model can accurately determine the different oxidation status of rapeseed oil. Therefore, three-dimensional synchronous fluorescence spectroscopy combined with parallel factor analysis, BP neural network method to establish rapid detection model can achieve different oxidation state discrimination of rapeseed oil, which provides a new method for the evaluation of rapeseed oil oxidation degree, and also provides a new method for evaluating the quality of other edible oils.

As the source of life, water is closely related to the survival of human beings. In recent years, there have been more and more reports on water pollution. Water pollution has become a serious problem, which can not be ignored. Two isomers of naphtol, 1-naphthol and 2-naphthol, were used as the research object in the experiment, and a new algorithm, which was used for qualitative and quantitative analysis of naphthol in water by analyzing the three-dimensional fluorescence spectrum of the mixture, was proposed. Using FS920 steady-state fluorescence spectrometer to scan the mixed solution and get the required experimental data. Then, a series of preprocessing steps for data are needed to remove the effects of Raman scattering and Rayleigh scattering. Independent component analysis (ICA) which is always used to solve the problem of blind source separation (BSS) will be applied to solve the problem in quantitative and qualitative analysis of fluorescence spectrum. BBS is an algorithm that uses the measured mixed signals as the processing objects to realize the decomposion of the source signals in the unknown system, as well as, to get the mixed matrix. The problem in identification and measurement of a single substance in a mixture is similar to the problem in blind source separation. The fast independent component analysis (FastICA) algorithm based on the maximum negative entropy is used to decompose the experimental data. The three-dimensional fluorescence data of all samples need to be expanded into a vector along the direction of the emission wavelength, and a matrix whose size is N×M can be obtained (N is the number of samples and M is the number of wavelength). This matrix is used as the input of fast independent component analysis to extract independent component, and the output is the expansion fluorescence spectrum of the single component material and a mixed matrix. The key to the fast independent component analysis algorithm is using Newton iterative algorithm to obtain the solution matrix, but the complex derivation of iteration process makes this algorithm have some problems, such as large computation and slow iteration. In order to overcome the shortcomings of fast independent component analysis, the differential method, also called double point chord cut method, is proposed to replace the complex derivation problem in the iterative process. In order to verify the feasibility of the algorithm, five times independent component extraction experiments were carried out on the spectral data with the improved algorithm and five times independent component extraction experiments were carried out on the spectral data with the original algorithm. The average running time of original FastICA algorithm is 17.78 seconds, and improved FastICA algorithm is 3.22 seconds, which is 14.56 seconds lower than original algorithm. The experiment result proves that differential method instead of the complex derivation problem in the iterative process can effectively reduce the amount of calculation and improve the speed of the iteration of the fast independent component analysis algorithm and the convergence is more stable. It can be seen from the experiment result that the fluorescence spectrum which was obtained by the decomposition are closer to the real spectrum. The mixture matrix obtained by FastICA is related to concentration matrix, which is the basis for quantitative analysis of materials. But the relationship between the mixture matrix and the concentration matrix may be nonlinear. Therefore, it is necessary to take the nonlinear fitting method to realize the fitting between the two. Support vector regression (SVR) machine can realize nonlinear regression, so SVR will be used to obtain predicted concentration. The mixed matrix decomposed and the actual concentration matrix are as the input and output of support vector regression machine respectively. The parameters of SVR are crucial to the prediction. Genetic algorithm (GA) is used to optimize the parameters and radial basis function (RBF function) is selected as the kernel function of SVR. Then the regression model is established by using the algorithm to realize quantitative analysis of the fluorescence spectrum. The fitting correlation coefficient (r) of 1-naphthol is 0.998 6 and 2-naphthol is 0.998 8; the recovery rate of 1-naphthol is 96.6%~104.2% and 2-naphthol is 96.8%～105.5%; the prediction of root mean square error (RMSEP) of 1-naphthol is 0.119 μg·L-1 and 2-naphthol is 0.100 μg·L-1. The results of the prediction are satisfactory and meet the requirements of the prediction. The experiment proved that the improved fast independent component analysis algorithm based on negative entropy combined with support vector regression algorithm can accurately identify and measure 1-naphthol and 2-naphthol in mixture, and this algorithm can also increase the speed of analysis for the hybrid system.

Carmine is a widely used food pigment in various food and beverage additives. Excessive consumption of synthetic pigment shall do harm to body seriously. The food is generally associated with a variety of colors. Various pigments will interfere with each other, which increases the difficulty of detection of pigments in food. Under the simulation context of various food pigments’ coexistence, we adopted the technology of fluorescence spectroscopy, together with the PSO-SVM algorithm, so as to establish a method for the determination of carmine content in mixed solution. Carmine and amaranth solid powders were purchased from reagent company. Carmine was selected as pigment to be detected, and amaranth was interfered pigment, carmine monochromatic solution with different concentrations and mixed solution after adding amaranth. The carmine concentrations of 0.1~30 μg·mL-1, interfered pigment amaranth concentrations of 0.1~10 μg·mL-1 were arbitrarily added. Using the FS920 steady state fluorescence spectrometer produced by Edinburgh Instruments Company, the fluorescence spectra of the carmine monochromatic solution and the mixed solution after the addition of amaranth were measured. The optimal excitation wavelength of carmine was λex=326 nm. The optimal emission wavelength For λem=430 nm. The six different concentrations of monochromatic samples and mixed pigment samples were selected. Among them, the concentration of amaranth was set at 2 μg·mL-1, and the concentration of carmine was 3, 4, 5, 6, 7, 8 μg·mL-1. Observe the relationship between the emission spectra and the fluorescence intensity of the six samples at the excitation wavelength λex=326 nm. In the monochromatic samples, the carmine concentration and fluorescence intensity were linear well. The fluorescence intensity of the six samples decreased first and then increased and then decreased again with the increase of the carmine concentration. It is proved that the fluorescence spectrum of the mixed solution is not simply superimposed by the spectrum of the components, but rather the competition and interaction between the carmine solution and amaranth solution in the process of absorbing the light spectrum. With 25 sets of carmine and amaranth mixed solution, seven of them were selected as prediction samples and the remaining 18 groups were used as training samples. The concentrations of carmine in the seven predicted samples were 1.0, 2.0, 4.0, 6.0, 9.0, 12 and 15 μg·mL-1, and the concentrations of the intercalating matter amaranth in the range of 0.1~10 μg·mL-1. The fluorescence intensities corresponding to the optimal excitation wavelength λex=326 nm of each sample were selected as the input of the detection model, and the predicted concentration of carmine was taken as the output. After initializing the PSO parameters, the optimal parameters c and g of SVM were trained. The optimal parameters were input into the PSO-SVM model. The predicted results of the seven predicted samples were: 1.146 9, 1.860 6, 3.854 4, 6.146 9, 9.133 8, 11.857 6, 14.859 8 μg·mL-1. The results of PSO-SVM analysis showed that the average recovery of carmine was 100.84%, and the root mean square error of prediction (RMSEP) was 1.03×10-4, and the correlation coefficient between model output and real value was 0.999. Under the same conditions, the concentrations of seven samples predicted BP method were 1.140 1, 2.139 8, 3.188 2, 6.436 2, 8.882 7, 11.860 1 and 12.664 3 μg·mL-1. The average recoveries was 98.56% The RMSEP was 4.65×10-3 and the correlation between the output and the true value was 0.972. Compared with the predicted results of reverse transmission, the correlation coefficient of PSO-SVM was 2.7% higher, the average recovery rate for 0.6%, and the root mean square error was nearly one order of magnitude lower. According to the analysis results, it can effectively avoid the interference caused by pigment with the combination of the fluorescence spectrum technique and PSO-SVM, accurately determining the content of carmine in mixed solution with an effect better than that of BP.

In this paper, we analyzed the mineral properties, element composition and spectroscopy characteristics of orthopyroxene megacrysts in Heilongjiang Muling area via EPMA, Raman and FTIR analysis. The species, element composition and constitution water content of orthopyroxene megacrysts were determined. According to the EPMA results, we found that the orthopyroxene megacrysts in this area belong to orthopyroxene. The Raman spectra showed that the main bands of orthopyroxene megacrysts are 1 015, 678, 345 and 145 cm-1. The FTIR results showed that orthopyroxene megacrysts have three distinct peaks at 3 550～3 590, 3 510～3 520 and 3 410～3 420 cm-1. These absorption peaks are consistent with mantle derived pyroxene. The FTIR micro-region analysis showed that the constitution water is distributed unevenly in different orthopyroxene megacrysts but uniformly in the inner part one orthopyroxene megacryst, and there is no obvious change in the core-edge.

In the ultraviolet spectrophotometry, we know that the absorption peak of nitrate nitrogen is around 202.0 nm, and the absorption peak of nitrite nitrogen is around 210.0 nm. It can be seen that the absorption peaks of the two are very close, and the absorption curves overlap seriously and interfere with each other badly. It is difficult to use single wavelength to determine the content of the two without the separation, and the national standard method is too complicated and time-consuming. In order to monitor the nitrate nitrogen and nitrite nitrogen in environmental water and drinking water more accurately, rapidly and eco-friendly, and to avoid many shortcomings in the national standard method, in this paper we studied the rapid analysis method of the two in water. This method combines with ultraviolet spectrophotometry and the second derivative spectrometry which are more rapid and accurate. And it does not need any pre-separation treatment. Nitrate nitrogen and nitrite nitrogen series solution were prepared by guarantee reagent. Using deionized water as a reference, UV-Vis spectrophotometer was used to scan the UV absorption spectrum in the range of 195～250 nm. After that, we used the Origin software to do the second derivative spectra of the obtained spectrogram, and used the Savitzky-Golay method in the Origin software to smoothen the second derivative spectra. By observing the above two groups of second derivative spectrogram, we found that the second derivative absorbance at different concentrations of nitrite nitrogen samples at 223.5 nm was 0 and nitrate nitrogen samples at 216.5 nm was also 0. The second derivative absorbance of the UV spectra of the mixed samples were observed by experiments. We found that they conformed to Lambert-Beer law. Then the nitrate nitrogen and nitrite nitrogen mixed samples were prepared. The UV absorption spectra of the mixed samples were scanned. We did the second derivative spectra of the obtained spectrogram and smoothened them. After that, we observed the second derivative spectra of mixed samples. It could be seen that when the concentration of nitrate nitrogen is the same and nitrite nitrogen concentration is different, the concentration of nitrite nitrogen will affect the second derivative absorbance of nitrate nitrogen. However, the second derivative spectra of various samples at 223.5 nm are almost overlapping, indicating that the concentration of nitrite nitrogen at this wavelength will not have any effect on the second derivative absorbance of nitrate nitrogen. At this wavelength, the value of the second derivative absorbance of the mixed samples increase linearly with the increase of nitrate nitrogen concentration. Therefore, 223.5 nm was chosen as the determination wavelength of nitrate nitrogen in the mixed samples. In the same way, the determination wavelength of nitrite nitrogen is 216.5 nm. Linear regression analysis of the nitrate nitrogen single component samples was performed between the second derivative absorbance and the concentration at 223.5 nm. The linear relationship was good, and the regression equation of the obtained standard curve was C=438.69A+0.015, R2=0.995 9. In the same way, we obtained that the regression equation of the nitrite nitrogen at 216.5 nm was C=-657.29A+0.068 8, R2=0.998. In order to test the application of this method in actual water sample measurement, we took three kinds of water samples from New River, Tang River and Dai River in Qinhuangdao to carry out experiments. The results showed that the recovery rate was between 96.7% and 103%, the relative standard deviation was 1.46~3.68. The method is relatively accurate and easy to operate and costs less. The rapid on-line monitoring of nitrate nitrogen and nitrite nitrogen can be realized at the same time.

Ancient porcelain is a remnant of history and has non-regenerability, so the ideal ancient ceramic analysis technique should be nondestructive. In order to objectively and effectively identify ancient ceramics kiln, a non-destructive method has been developed based on ultraviolet, visible and near-infrared diffuse reflectance spectroscopy to identify ancient ceramic kiln objectively and effectively. In view of the lack of description of the target characteristics in the traditional single-band ancient ceramic kilns, for example, the diffuse reflectance spectroscopy data can reflect the color characteristics of ancient ceramics in the visible region, but the ceramics fired in the same kiln will have different color property, only based on the diffuse reflectance of the visible light band to identify the source of the kiln unreasonable, in the ultraviolet and near-infrared and ultraviolet light band, the ancient ceramic interior molecules and the band light after the diffuse reflectance spectral data reflect the ancient rich sample structure and material properties carried by the ceramic can effectively improve the expression of the features by combining the UV and near-infrared spectral reflectance spectroscopy data. Therefore, we propose a multi-band feature extraction method using ultraviolet, visible and near infrared. During the actual experiment, the average identification accuracy based on multi-band linear feature fusion kiln is 92.9%, which is 1.8% higher than the average accuracy of 91.1% for single-band kiln identification. The experimental results verify that the multi-band method is effective; In the process of feature extraction, wavelet transform is often used to process the spectral signal. However, since the ancient ceramic reflection spectrum wave signal is in the ultraviolet region, the waveform of the diffuse reflection spectrum in the visible and near-infrared region is not only fluctuating but also changing greatly in frequency. Therefore, it is very difficult to select the wavelet basis. In this paper, The feature extraction method is characterized by adaptively distributing the intrinsic mode functions of different frequency wavelets, selecting the appropriate intrinsic mode functions to extract the spectral characteristics of different wavelength bands of the ancient ceramics, but there exists an over-decomposition phenomenon in the decomposition process, that is to say, the intrinsic mode function of the false component. The average correlation coefficient and the mean contribution rate of all the intrinsic mode functions of all the samples and the decomposition are taken as the criteria for selecting the intrinsic mode function. The experimental results show that with the decomposition order increases, the average correlation coefficient and the mean variance contribution rate decrease, and when the decomposition order is 4, the contribution rate of correlation coefficient and variance are 0.30, but when the decomposition order is 5, the contribution rate of correlation coefficient and variance is only 0.15 and 0.18. Therefore, the fourth-order decomposition is chosen for feature extraction in different bands. On this basis, calculate the distribution matrix of different spectral features, use intra-class and class scatter matrix traces, calculate the weight of different band features when the feature is fused, the greater the weight, indicating that the greater the contribution of such features to the identification; Finally, the k nearest neighbor classifier is used to classify the ancient ceramics from different kilns. By comparing objectively and quantitatively the proposed method with similar methods, Zhu Xufeng used non-linear feature fusion method, and the average identification accuracy of kiln is 86.97%, and the method of this paper is 7.53% higher than this method. Liu Feng used covariance matrix to solve the feature weight of multi-band method, and the average identification accuracy of kiln is 89.63%, and the method of this paper is 4.87% higher than this method. The experimental results show that the proposed method is effective and feasible. It can be used as an effective auxiliary appraisal method for ancient ceramic kiln identification.

Iron rich porcelain has an important position in the history of Chinese ancient ceramics. Optical Coherence Tomography (OCT) combined with Optical microscope (OM), laser Raman spectroscopy (LRS) and X-ray fluorescence spectroscopy (XRF) is used to analyze the Iron rich porcelain dated to Jin and Yuan dynasty and excavated from Qingliangsi site in Baofeng County, Henan Province. The structure of iron rich glaze, the phase information of crystal and chemical composition of glaze and body are obtained, and the relationship between the OCT image and microstructure of section of glaze layer and the firing process of iron rich porcelain are discussed. The results show thatthe surfaces of sauce glazed porcelain, persimmon leaf red glazed porcelain and the red spot area of black glaze existiron-rich crystal layer, but from the surface of black glaze we have not found the existence of iron-rich crystal layer. ε-Fe2O3 is the main iron oxide in iron-rich crystal layer of sauce glaze layer, while α-Fe2O3 is the main one in persimmon leaf red glaze layer, and thered spot area of black porcelain has all of ε-Fe2O3, α-Fe2O3 and magnetite. The reasons for the above differences are related to thechemical composition of glaze and the firing process. It is easier for the area of high content alkaline-earth metallic oxide andiron oxide in glaze layer to precipitate iron oxide crystal. And the firing atmosphere of black-glazed porcelain is reducing condition, while sauce glazed porcelain and persimmon leaf red glazed porcelain are inclined to oxidizing condition. Moreover, the firing temperature of persimmon leaf red glazed porcelain is higher than that of sauce glazed porcelain. This study combining OCTwith OM, LRS and XRF has great significance for enhancing the overall understanding of iron rich porcelain and providing more abundant clues for confirming the production sites and periods of iron rich porcelain.

Herbicides have been widely used because of their rapid and effective weeding. But it will also cause a certain degree of pollution to the environment and the crops, and it is often found that the misuse of herbicides causes the fruit trees to be poisoned in the process of agricultural production. Metolachlor is a selective amide herbicide which is widely used in upland crops, vegetable crops, orchard and nursery. According to the relevant literature, the methods of Metolachlor residue detection mainly include Gas chromatography (GC), Gas chromatograph-mass spectrometry (GC-MS), Solid phase extraction (SPE) and so on. The analysis of metolachlor residues based on absorption spectroscopy has not been reported in literature. This paper presented the absorption spectroscopy and its derivative spectrometry to detect the metolachlor pesticide residues in the apple juice. First, the absorption spectrum of different concentration of metolachlor were recorded by spectrophotometer, and it was found that there was a distinct absorption spectrum peak at 266 nm. The relationship between the pesticide concentration and absorbance was obtained by fitting analysis on the absorption spectrum of metolachlor, and the function equation was deduced as y=2.147 09x+0.031 98, and the correlation coefficient was 0.998 5. Second, the absorption spectrum of mixed solution of apple juice and metolachlor were studied by spectrophotometer. Compared with the absorption spectrum of apple juice, the metolachlor characteristic peak at 266 nm was also found in the absorption spectrum of mixed solution. The model function between absorbance and metolachlor concentration in apple juice was further obtained as follows: y=0.704 9+0.826 8x, its correlation coefficient was 0.991 1. It can be seen that when the residual amount of metolachlor in apple juice was very low, the absorption spectrum characteristic peak of the pesticide was not obvious. Third, in order to further improve the detection effect, the first derivative processing of the absorption spectrum was carried out, and the first order derivative absorption spectrum of the mixed solution were then obtained. Compared with the derivative absorption spectrum of apple juice, the derivative absorption spectrum of mixed solution of apple juice and metolachlor pesticide had two distinct spectral peaks, which were located at 269 and 276 nm, respectively. In order to further analyze the relationship between the metolachlor content and the peak value of the derivative absorption spectrum of mixed solution, the metolachlor content and the absorbance of derivative spectrum were linearly fitted, and the corresponding prediction model in 269 nm was deduced as y=0.005 3-0.090 6x, and the correlation coefficient was r=0.992 5. The prediction model corresponding to 276 nm was deduced as y=-0.000 769-0.302 8x, and the correlation coefficient was r=0.990 6. At last, in order to verify the accuracy of the prediction model obtained from the absorption spectrum and its first derivative spectrum, five different concentrations of the mixed solution of metolachlor and apple juice were configured and tested under the same experiment condition. The absorbance value at 266, 269 and 276 nm were substituted into the model function respectively, and the predictive value of the metolachlor concentration can be obtained, the average recovery rate can be further calculated according to the actual metolachlor concentration. The calculation results were as follows: the average recovery rate of the absorption spectrum at 266 nm was 104.68%, and the average recovery rate of the derivative spectrum at 269 nm was 104.59%, and it was 105.18% at the absorption peak of 276 nm. The limit of detection (LOD) and limit of quantification (LOQ) parameters were calculated by analyzing the detection model of metolachlor in apple juice. The LOD and LOQ of the original absorption spectrum were 0.014 8 mg·mL-1 and 0.049 2 mg·mL-1, respectively. And the minimum values of LOD and LOQ corresponding to the first derivative absorption spectra were 0.001 5 mg·mL-1 and 0.004 9 mg·mL-1, respectively. The results showed that absorption spectroscopy was fast and feasible for the detection and analysis of metolachlor residue in apple juice, and the detection effect was better after the derivative operation of absorption spectrum.

Hyperspectral imaging technology has been used to establish the prediction model of moisture content in dehydrated scallops, and the model performance is affected by sample division method and modeling method. The method of sample division determines whether the selected sample is representative, and the modeling method determines how to use the sample to build the model, but the internal relationship between the sample division method and the modeling method has been rarely reported. It is important to explore the effects of different sample division methods and modeling methods on the prediction of the moisture content of scallops, and it can also provide reference for the study of spectral modeling of other samples. In this paper, the hyperspectral data of 270 scallops were extracted from spectral images captured by a hyperspectral imaging system in the 380～1 030 nm range. The samples were divided by RS, KS, SPXY and CG. The prediction models were established by PLSR and LS-SVM. The performance indexes of each model were calculated and compared. The results showed that the best sample division method is RS when using PLSR building prediction model (the RPD is 4.079 6) and SPXY is most suitable for LS-SVM model(the RPD is 4.175 6). The advantages and disadvantages of the division of the sample set are related to the modeling method, and the best choice should take modeling method into account. In this commonly used four sample division methods and two modeling methods, SPXY method is used to classify the sample set of moisture content and combine with LS-SVM method to optimize the effect and precision.

In order to make up some flaws of sensory evaluation of tea quality, computer vision technology as a fast and nondestructive method was used to evaluate tea quality in this paper. Biluochun green tea samples were studied, and the tea samples were divided into four grades based on the evaluation results by experts for tea samples. The median filter and Laplace operator were used to preprocess the images of tea samples, and tea appearance features such as color features and texture features were extracted from the preprocessed images. Random forest（RF）method was used to analyze the significance of tea appearance features. The most important features and the optimal amounts of tree pruning of decision tree were investigated to develop the sensory evaluation model of tea quality. And the performance of RF model was compared to that of SVM model. The results show that nine more important features such as hue mean, hue standard deviation, greed channel mean, mean grey, saturation mean, red channel mean, saturation standard deviation, vision mean and uniformity were selected, and the result was consistent with the sensory evaluation profile; the optimal model was obtained when 9 most importance feature variables were selected and tree pruning of decision tree were 500. The overall recognition rate of the model was 95.75%, Kappa coefficient was 0.933, and OOB error was 5%. Compared with SVM model, the average recognition rate and Kappa coefficient of RF algorithm were improved 3.5% and 0.066, respectively. The 9 significant image features selected were consistent with the feature description of tea sensory evaluation terms by NY/T 863-2004. The study indicated that the developed model with a few significant appearance feature variables selected by RF method has high accuracy, and the model is simplified without lowering accuracy. The precision and stability of RF model are superior to that of SVM.

Polysorbate 80, also known as Tween 80, is an amphipathic non-ionic surfactant commonly used as excipient in the food, personal care and pharmaceutical industries. Particularly, it has been widely used in Traditional Chinese Medicine (TCM) injections to enhance solubility and clarity. In recent years, more attentions have benn paid to the quality and application of polysorbate 80 due to incidence of adverse reactions. Some declared that adding of polysorbate 80 might increase the side effects. To avoid abuse of this excipient, it is essential to control the levels of polysorbate 80 in therapeutic formulations within a target level. Assay of polysorbate 80 in TCM injections has become a hot and difficult issue nowadays. Content of polysorbate 80 in preparations could be quantified directly by spectrophotometry, size-exclusion chromatography coupled with evaporative light scattering detector (SEC-ELSD) or liquid chromatography combined with mass spectrometry (LC-MS) method. Acid hydrolysis followed by high performance liquid chromatography coupled with ultraviolet detector (HPLC-UV) or gas chromatography (GC) determination was also used as an indirect measurement of polysorbate 80. But it's hard to find a unified conversion formula or reference standard for accurate quantification owing to the fact that the surfactant is a complex mixtures of oligomers, whose composition and ratio of chemical components vary by different manufacturers. In addition, it becomes a challenge to measure polysorbate 80 in TCM injections without false-positive interference due to the complex matrix of the preparation. Taking Shengmai Injection as example, a novel method for determination of polysorbate 80 in TCM injections was proposed based on absorption coefficient, which offered a solution for the above problems. After optimization of detection wavelength, chromogenic reagent and of the shaking and standing time of the liquid-liquid extraction, absorption coefficient (E1%1 cm) of cobalt thiocyanate complex of polysorbate 80 was obtained from 6 different bands of instruments as 104.23 with RSD of 2.08%. Shengmai Injection was first diluted by 10 times. To 1.0 mL of the test solution, 10 and 20 mL of dichloromethane were added accurately to form a polysorbate/thiocyanate complex, followed by cyclotron oscillation for 3 minutes. The mixture was transferred into a separating funnel and was stranded for 30 minutes. The first 1 mL of the lower layer was discarded and the next 15 mL was collected. Then the absorbance was measured at 320 nm. Finally the content of polysorbate 80 was calculated by using the previously obtained absorption coefficient based on Lambert-Beer law. With no interference from the negative sample, the established method provided precision and reproducibility (% relative standard deviation) less than 3% and accuracy (% spike recovery) of 98.42%. To further validate the accuracy of the method, 10 batches of Shengmai Injection from 2 manufacturers were determined by both the absorption coefficient method and the standard curve method, and the results were compared with the actual feeding content of polysorbate 80 in the sample. Results of paired t test indicated that there were no significance difference between the results obtained by two methods, or between the results of the absorption coefficient method and the actual feeding content of polysorbate 80 (p>0.05). The investigation selected 320 nm as the detection wavelength to improve the sensitivity, which was never adopted in previous work. In this way, remarkable decrease of matrix interference was achieved, thus the problem of misfit determination results and the actual feeding content of polysorbate 80 was solved. With no need for reference standard or standard curve, the search offered a feasible tool for test standard of polysorbate 80 in TCM injection. All in all, the proposed method was sensitive, accurate, rapid and simple, which can provide key constants and new ideas for quality control of preparations containing polysorbate 80.

Psammophytes are the main plants for improving the environmental quality and controlling desertification because of its large resource quantity, rapid growth and wide planting area, but their feeding value has not been fully utilized. At present, due to large-scale and intensive development of animal husbandry, there is a lack of pasture, and the supply of commercial pasture is seriously insufficient, resulting in an ever-increasing contradiction between livestock and pasture. Because of the stubble harvest of the psammophytes, it is crucially important to fully explore its potential feeding value, develop and produce the unconventional feeds and promote the development of animal husbandry and ecological restoration in desert and semi-desert areas of China. In this study, 14 kinds of nutrients (such as crude protein (CP), ether extract (EE), crude fiber (CF), K, Na and Ca, et al.) and 17 kinds of amino acids (such as leucine (Leu), lysine (Lys), methionine (Met), cystine (Cys) and threonine (Thr), et al.) in 6 kinds of common psammophytes (Salsola passerina, Reaumuria songarica, Artemisia ordosica, Alhagi sparsifolia, Caragana korshinskii and Agriophyllum squarrosum), which were collected from Alxa Left Banner, Inner Mongolia, were analyzed by using the spectroscopy technology. Compared with 4 kinds of conventional feeds (high-quality corn grain, NT-2 paddy, GB-2 soybean and GB-3 alfalfa meal), the contents of nutrients and amino acids and evaluation of the nutritional and potential feed values of 6 kinds of psammophytes were obtained. The results showed that: (1) The CP, EE and mineral element contents of the 6 kinds of psammophytes were all at a relatively higher level, and the CF contents in psammophytes were higher than 13% of dry matter in the diet of lactating cows, equivalent to or better than the above 4 kinds of conventional feeds. (2) 6 kinds of psammophytes in this study, contained 17 kinds of amino acids, and the composition was more balanced based on the results of amino acid chemical score (CS) . The essential amino acid contents and nutritional values of Artemisia ordosica, Alhagi sparsifolia, Caragana korshinskii and Agriophyllum squarrosum were higher than those of high-quality corn grain and NT-2 paddy. Leu and Lys were the first limiting amino acids of the Reaumuria songarica, Alhagi sparsifolia, Caragana korshinskii and Salsola passerina, high-quality corn grain, NT-2 paddy, respectively, while Thr and Met+Cys were the first limiting amino acids of Artemisia ordosica and Agriophyllum squarrosum, GB-2 soybean, GB-3 alfalfa meal, respectively. The results of this study will provide a reliable experimental and theoretical basis for guiding the balance of amino acids in feed and synthesizing amino acids. (3) The results of principal component analysis showed that the nutritional values of Salsola passerina, Caragana korshinskii and Agriophyllum squarrosum were higher than that in conventional feed (GB-3 alfalfa meal), and the nutritional values of the 6 kinds of psammophytes were higher than that of high-quality corn grain and NY-2 paddy. In conclusion, the 6 kinds of psammophytes in this study not only had the characteristics of rapid growth, large above-ground biomass and resistance to mow, but also greater developing potential for forage, which can serve as a good source of feed for desert and semi-desert areas. The results provided a reliable experimental data and theoretical basis for the guidance of amino acid balance and amino acid synthesis.

Due to the dry climate and scarce precipitation in desert areas, water content is one of the factors that restrict the growth of vegetation. The stress factors include ozone stress, salt stress, and water stress involving terrestrial and aquatic plants. Water stress has a greater effect on plant growth and yield than any other stress. Along with the development of hyperspectral remote sensing technology, there have been many scholars at home and abroad who have been using hyperspectral data to study the effects of stress on vegetation. However, these research objects mainly focused on beet, cotton, corn, rice and other crops. There are few studies on the stress of saline vegetation in arid areas. Haloxylon is one of the typical halophytic vegetation in desert and semi-desert area, which is also known as Haloxylon ammodendron. It belongs to Chenopodiaceae, shrub or small tree, widely distributed in the desert and semi-desert regions. The plants’s root is well developed. It has a great effect on breaking wind and fixing sands and has the characteristics of salinity tolerance, drought resistance and so on, which has extremely high ecological value and economic value. In this paper, we selected the Haloxylon ammodendron as the research object. We developed the annual Haloxylon ammodendron, and set three water gradients, forming the plant with different water stress. The spectral characteristics of leaves were studied by using the original spectra, the red-edge position, combined with vegetation index and two-dimensional correlation spectra. This provides reference for using hyperspectral remote sensing to monitor saline vegetation in arid area. The results showed that: (1)By analyzing the leave reflectance of Haloxylon ammodendron under different water treatnment, we have found that: with in the range of visible to mid-infrared bands, Haloxylon ammodendron’s leaf spectral reflectance of different water stress was significantly different. In the visible region (350~610 nm), the leaf reflectance of Haloxylon ammodendron under various water stress was 100 mL>500 mL>200 mL. This was because of the fact that the water content of 100 mL and 200 mL promoted the synthesis of chlorophyll of this plant, which leads to the decrease of reflectance in these wavebands. However, too much water (500 mL) had no greater effects on the chlorophyll synthesis of this plant. In the red light region (611~738 nm), the leaf spectral reflectivity of Haloxylon ammodendron under different water stress decreased in turn as water content increased. In 738~1 181 and 1 228~1 296 nm wavebands, the leaf spectral reflectance of Haloxylon ammodendron under various water stress was 200 mL>100 mL>500 mL. In 1 182~1 227 nm wavebands, the leaf spectral reflectance of Haloxylon ammodendron under various water stress was 100 mL>200 mL>500 mL. This was because of the fact that the leaf spectral reflectance in the near-infrared region is mainly affected by the cell structure of leaf. It leads to the difference of leaf spectral reflectance of Haloxylon ammodendron under different water treatment. In the mid-infrared bands of 1 300~1 365 and 1 392~1 800 nm, the leaf spectral reflectance of Haloxylon ammodendron under various water stress was 100 mL>200 mL>500 mL. This indicates that, within the water content of 500 mL, the more water content was, the stronger the water absorption capacity of cell sap and cell membrane of leaves was. Thus the leaf reflectance decreased. By calculating the first derivative of the original spectrum and extracting the red edge position parameters, it was found that the red-edge position of the plant under different water treatment did not shift. This was because of that fact that Haloxylon ammodendron formed a special environmental adaptation mechanism under the influence of long-term drought stress. Water is insensitive to its red-edge position. (2) We selected several vegetation indices to analyze the changes of Haloxylon ammodendron’s leaf spectral indices under different water treatment and found that: when water content increased from 100 mL to 200 mL, WI/NDWI, MSI and NDII indices changed significantly, which can be used to study the spectral characteristics of Haloxylon ammodendron under the influence of water content. (3) The spectral characteristics of Haloxylon ammodendron treated by different water stress were analyzed by two-dimensional correlation spectra. We concluded that: when water treatment was 100 mL, at the bands of 536, 643, 1 219 and 1 653 nm, the absorption peaks were sensitive to the water perturbation. When water treatment was 200 mL, at the bands of 846 and 1 083 nm, the absorption peaks were sensitive to the water perturbation. When water treatment was 500 mL, in the bands of 835 and 1 067 nm, the absorption peaks were sensitive to the water perturbation. In conclusion, in the near-infrared bands, the sensitivity of the absorption peaks to the water perturbation increased when Haloxylon was stressed by 200 and 500 mL water content compared with 100 mL water content. The two-dimensional synchronous correlation spectra of Haloxylon ammodendron under the water treatment of 100 mL water content revealed that the positive cross-peaks were formed at 1 044 and 1 665 nm bands, 1 072 and 903 nm bands, 903 and 1 264 nm bands, 1 230 and 1 061 nm bands, which indicating that the spectral intensity of these bands changed simultaneously with the disturbance of water.

The use of hyperspectral technology to invert and monitor vegetation is based on the identification of its spectral characteristics. The spectral reflectance and the SPAD values of leaves of typical plants, collected in different mining disturbance zones of Coal mining subsidence areas in semi-arid regiens, were simultaneous measured by Field Spec 3 spectrometer and the SPAD-502 chlorophyll meter to explore the spectral characteristics of the plants in the same zone with the SPAD values changing and the spectral differences of typical plants in different mining disturbance zones when the SPAD values was in the same interval. Furthermore, the correlations between SPAD values of leaves of typical plants and the spectral index were analyzed by using Matlab software. The results showed that the spectral characteristics of the same plant species in different mining disturbance zones were distinct with the SPAD values changing, and the spectral curves of different sample zones were clearly distinguished in visible band. In visible band, the green peak of spectral curves of Cleistogenes squarrosa, Artemisia ordosica, Caragana microphylla and Populus tremula in the non-mining zone were absent when the SPAD values was very low, and it appeared with the increase of SPAD value, but the position was red-shifted, when the SPAD value was above 30, the spectral features of the blue valley, green peak, red valley and red edge were obvious, and the higher the SPAD value of leaves of Chinese pine, the lower the spectral reflectance, while no rule was observed in the other zones. In addition, the spectral reflectance of samples with lower SPAD values of Cleistogenes squarrosa, Artemisia ordosica, Chinese pine and Caragana microphylla in the different mining disturbance zones was significantly higher than that of the samples with higher SPAD values from 400 to 700 nm band in general, but the trend of leaves of Populus tremula was just the opposite. The reflectance of the higher and lower groups of SPAD values of Cleistogenes squarrosa in the drawing zone, Artemisia ordosica and Caragana microphylla in the non-mining zone, and Chinese pine and Populus tremula in the compression zone were similar. Compared with the non-mining zone, the differences between the reflectance of the higher and lower groups of SPAD values of the Cleistogenes squarrosa, Artemisia ordosica, and Caragana microphylla in the mining disturbance zones significantly reduced. Moreover, the correlation between the SPAD value and the spectral index of the samples in the mining disturbance zones was enhanced in some bands than that in non-mining zone. The maximum correlation coefficient values between the SPAD value and the spectral index of Artemisia ordosica, Chinese pine, Caragana microphylla and Populus tremula had become lager in neutral zone than that in the non-mining zone, while that of Cleistogenes squarrosa was just opposite. At the same time, the maximum correlation coefficient between SPAD value and NDVI in non-mining zone was higher than that of DI, and the band combinations were mostly in the near infrared, but in the other zones, the band combinations were mostly in visible band. The results of this study provided theoretical support for identifying the differences in the spectral characteristics of typical plants in different mining disturbance zones, further monitoring the health status of plants and accurately controlling the ecological environment in mining area.

Wheat is a major food crop and occupies an important position in Chinese agricultural production, transportation, and food processing. Unsound kernel seriously affects wheat quality and food security. Wheat unsound kernel is mainly produced during production, storage, and packaging. At present, the manual sorting method is the main method for detecting wheat kernel quality in China. It is subjective, time-consuming, laborious, and costly. Therefore, the rapid and accurate identification method of the wheat unsound kernel will increase productivity and ensure food security. So the method for rapid and accurate detection of wheat unsound kernel was proposed by using hyperspectral image technology and the method of characteristic band selection. In this paper, near-infrared hyperspectral imaging system was used to collected hyperspectral reflection image of 1 000 wheat kernels (including healthy kernels, sprouted kernels, mildewed kernels, and kernels infected with Fusarium head blight, their respective amount are 250) at 862.9~1 704.2 nm (a total of 256 bands) and the average reflectivity of each sample were extracted from region of interests of hyperspectral images as classification characteristics. This paper conducts pre-processing for the extracted full-wave bands spectral information through window smoothing, first order derivative and vector normalization. It will also amplify hidden signals of the original spectral data and erase random errors. On the basis of pre-processing, feature extraction by applying discriminant partial least squares (DPLS) and orthogonal linear discriminant analysis (OLDA) to lower the redundancies of the data. Finally, it establishes identification model for 4 kinds of wheat through pattern recognition (BPR). The experiment results showed that the average identification accuracy of the model for wheat unsound kernel established by using full-wave bands spectral information is 97.8%. The analysis also proves that it is feasible to detect wheat unsound kernel by using near-infrared hyperspectral imaging technology. Though full-wave bands spectral information achieved better detection effect, the high costs of hyperspectral imaging equipment and large amount of hyperspectral full-wave bands spectral information data fail to meet the high requirement of calculation for site equipment. Therefore, this paper uses successive projections algorithm (SPA) to select characteristic bands among full-wave bands data and lower the number of band from 256 dimensions to 10 dimensions to improve the operation and calculation speed of the system. So 10 characteristic bands were taken to establish identification model for wheat unsound kernel. The experiment results showed that the average identification accuracy of the 10 characteristic bands is only 83.2%, which means that though the 10 characteristic bands improve the real-time capability of the system, but they show worse identification accuracy. In order to achieve the identification effect that is basically equivalent to the characteristics of the whole band, this paper uses the combination of spectral features and image features to establish identification model of the wheat unsound kernel. All kinds of relevant information (morphological information, texture information, spectral information) of the kernel of the above 10 selected wave bands are integrated. The experimental results showed that the combination of spectral information and image information in 10 characteristic bands can achieve an average identification accuracy of 94.2%. Its identification effect is basically consistent with the use of full-wave bands spectral data. This paper uses hyperspectral imaging system to explore the feasibility of wheat unsound kernel detection. From the analysis of the above experiment, it can be seen that near-infrared hyperspectral imaging technology shows better results in the detection of wheat unsound kernel. It can guarantee the identification accuracy of the system while improving calculation speed so it offers an effective research orientation for later development of equipment that is able to detect wheat unsound kernel in a quick way.

In this paper, Lingwu Long Jujube VC content was regarded as the research object, and a combination of hyperspectral imaging technique with chemometrics method was used to explore a rapid and nondestructive detecting method for fruit internal components. Vitamin C content of Long jujube was measured by high performance liquid chromatography (HPLC). A total of 164 Lingwu long jujubes of hyperspectral images in region of 400~1 000 nm were acquired. Then spectral curves were obtained by ENVI 4.8 software from the region of interest (ROI). The models were built for chemical value and spectral data by UnsecramblerX 10.4 software. Outliers were to be eliminated by Monte Carlo cross validation method; Samples division was set partitioning based on joint X-Y distance(SPXY) method to improve the prediction performance of the model; The spectral's pretreatment was analyzed, such as Moving Average, Median Filter, Normalize, Baseline, multiple scatter correction (MSC), Detrending and standard normal variate (SNV) and so on; To reduce the amount and dimension of data, the feature wavelengths were extracted by competitive adaptive weighting algorithm (CARS), uninformative variable elimination ( UVE) and continuous feeding Shadow algorithm (SPA) ; Compared to the models of full spectrum (FS) and the feature wavelengths extracted by CARS and UVE of PLSR and SVM built, the optimal model was determined. A total of 7 abnormal samples were eliminated using Monte Carlo cross validation method. After eliminating abnormal sample data, the samples were divided into calibration set and prediction set by SPXY method, and calibration samples is 117, and prediction samples is 40. The spectral pretreatment were studied by the 7 methods. The results showed that the model effect without spectral pretreatment was the best, and its Rc was 0.8779, and RMSECV was 0.0481; Without a preprocessing method by CARS, UVE and SPA method to reduce the dimensions, a total of 16 feature wavelengths were selected by CARS, which were 415, 487, 406, 631 636, 655, 660, 665, 670, 684, 689, 694, 723, 732, 747 and 881 nm. A total of 32 feature wavelengths were selected by UVE, which were 415, 406, 627, 631, 636, 651, 655, 660, 665, 670, 675. 679, 684, 689, 694, 699, 703, 708, 742, 747, 751, 756, 761, 766, 771, 775, 780, 785, 790, 795, 919 and 924 nm. A total of 3 feature wavelengths were selected by SPA, which were 401, 665 and 684 nm. Comparing models of the full band spectrum with the models of extracted characteristic wavelengths of PLSR and SVM, the UVE-SVM model is the best, and its R2c is 0.847 1 and R2p is 0.714 9, which indicates that UVE effectively reduces the dimension of the spectrum and simplifies the data processing. This study explores the application of hyperspectral imaging technology in the field of fruit, explores a new method for nondestructive testing of Lingwu Long Jujube VC content, provides a theoretical basis for visible and near infrared hyperspectral model established for the rapid detection of other components of fruit.

Spectral polarization detection can obtain rich target parameters including spectrum, radiant intensity, polarization state and so on, which is beneficial to improving the ability of target detection and identification. Identification method of camouflaged objects based on long wave infrared hyperspectral polarization characteristic was introduced in this paper. The measuring system of long wave infrared hyperspectral polarization was set up. The measuring experiments of polarization characteristic of two types of camouflaged objects at different temperatures were carried out. Effective experimental data were obtained and analyzed. The results showed that: due to the influence of basal material thermal conductivity, the upgrade of spectral degree of polarization of two types of camouflaged objects with the same coating is significantly greater than spectral radiance at the same difference of temperature. As the difference of temperature increases, the upgrade difference of spectral degree of polarization increases. The band selectivity rule is also showed. The identification problem will be effectively solved by use of the upgrade difference of spectral degree of polarization of two types of camouflaged objects.

Seriphidium terrae-albae is a kind of plant widely distributed in various mining areas of Fuyun County, Xinjiang, China. The traditional exploration methods are difficult to play a role due to the existence of plant information and other obstacles, and some new methods and new ideas are urgently needed. The remote sensing plant geochemistry method is a kind of natural information source that smartly utilizes plants, transforming the plant from the barrier information to the useful information. Help people quickly and economically obtain the useful information about minerals under plant barriers. Because of its large area, being fast and non-destructive and other advantages, it has attracted more and more attention of scholars, and has become the current research hotspot. In recent years, although some scholars have synthetically considered “absorption coefficient” and “contrast coefficient” to prove that Seriphidium terrae-albae can be used as a good indicator for the exploration of concealed deposits. The plants in the upper part of the deposit can absorb the ore-forming elements in the soil better, but at the same time they form geochemical anomalies in their bodies, and the information is more visible than other plant anomalies. However, no one has studied whether the geochemical anomalies in Seriphidium terrae-albae can be found from the spectral point of view, then providing some references for the exploration of concealed deposits. Therefore, our study first tries to look for the feature bands or eigenvalues closely related to geochemical anomalies, and then construct the prediction model of hidden deposit based on plant spectrum. First, the method adopted was to measure the reflectance spectra of plants growing in the upper part of deposit and background area by ASD FieldSpec3 spectrometer respectively. Then the spectra of plants growing in these two regions were analyzed and compared from five aspects, including the original spectrum, the first derivative spectrum, the second derivative spectrometry, the first derivative fractal dimension and the second derivative fractal dimension. Finally the 10 characteristic bands that were notably different were selected including R′824, R′834, R′1 533, R′1 573, R′1 633, R′1 643, R″1 284, R″1 703, the first derivative fractal dimension and the second derivative fractal dimension. These characteristic bands can be used as botanogeochemistry marks for seeking exploration of concealed deposits. Taking these ten optimized bands as input parameters, random forest (RF) and partial least squares support vector machine (PLS-SVM) were used to construct a prediction model that seeks the position of hidden deposits based on abnormal spectrum of plant. The results showed that these two models can obtain satisfactory results, but compared with the random forest model, the partial least squares support vector machine model has a better robustness and stronger generalization ability. The results also indicated that it has great potential in looking for hidden deposit using extraordinary spectrum of plants, due to the advantages of being simple and quick. Our team has built a “very low altitude detection platform” using dynamic delta wing and HySpex imaging hyperspectral sensor, which can realize the observation of “sub-meter”. But the problems will be our next research focus as follows, how to effectively solve the problem of “spatial scale” and “spectral scale”? How to better apply the model established on the ground test ground to the very low altitude detection platform, and how to extract the plant anomaly information in a large area and quickly in the research area?

Laser-Induced Breakdown Spectroscopy (LIBS) has been widely used in more and more fields as a new measurement method of material composition. However, compared with the traditional analysis methods, the analytical performance of LIBS needs to be further improved. The basis of LIBS is the laser-induced plasma. It is helpful to optimize the experimental parameters of LIBS system and lays the theoretical foundation for improving the detection capability of LIBS. The laser induced plasma is a non-steady radiation source associated with the space. Spatial-resolved spectroscopy is one of the most important ways to explore the physical properties of plasma. In order to study the characteristics of laser induced plasma, a Q-switched Nd∶YAG laser operating at the wavelength of 1 064 nm was used to ablate the alloy steel and the plasma was generated. The two-dimensional distribution of plasma emission was measured by the spatial resolution device. It is analyzed that the spectral signal collected in the experiment is the integrated intensity of the spectrum along the line of sight of the signal detector. So the plasma parameter calculated by the integrated intensity is the average of the observed path. In order to investigate the emission characteristics from the inner layer to the outer layer of the plasma, we measured the transverse spatial distribution of integrated intensity firstly. Then, assuming that the plasma is optically thin and cylindrically symmetrical, a method of Abel inversion based on cubic spline functions was performed on the integrated intensity. And the radial spatial distribution of the emissivity of the plasma from the inner layer to the outer layer was obtained. The atomic emission lines of Fe Ⅰ: 374.55 nm and Mn Ⅰ: 403.08 nm were selected to analyze the spatial distribution characteristics of the plasma emission. It has shown that the distribution of the integrated intensity presents a greater intensity value in the central location and smaller intensity at the edge of the plasma. This is due to the expansion of the plasma. The radial distribution of the spectral emissivity of the plasma was obtained by Abel inverse transformation. It has shown that the emissivity increased and then decreased from the inner to the outer of the plasma. A minimum value of emissivity appears at the center of the plasma as a result of the lower electron density in the central region of the plasma source. Eleven atomic lines of Fe in the plasma emission spectra were selected to calculate the plasma temperature by Boltzman method. The corresponding integral spectral intensities and emissivity were used respectively. The two-dimensional distributions of the transverse and radial spatial distributions of the plasma temperature were obtained. They have the similar variation rule. It can be seen that transverse spatial distributions of the plasma temperature decreases monotonously with the increase of the distance from the sample surface. And the plasma temperature gradually decreases from the center of the plasma to the edge which is the result of the expansion of the plasma and the interaction with the ambient gas. From the radial spatial distribution of the plasma temperature, it can be seen that the temperature of the plasma gradually decreases from the inner layer to the outer layer due to the expansion and cooling of plasma. Therefore, the radiation characteristics of the plasma can be obtained by using the Abel inverse. It provides an experimental basis for further understanding of the physical mechanism of the laser induced plasma. It also lays a theoretical foundation for improving the analytical performance of laser-induced breakdown spectroscopy.

As the last stage of China’s Chang’e (CE) lunar program, the Chang’e-5 lunar rover will land on the surface of moon, obtain lunar samples and then return back to Earth. The Lunar Mineralogical Spectrometer (LMS) is one of CE-5’s onboard payloads, which is an important data source for the lunar exploration project. LMS spectral data is used to identify the composition of lunar minerals to aid in rock classification and stratigraphic analysis-all of which provide data required to support research on moon formation, geologic evolution and rock-water interactions. Compared with the CE-3 VIS/NIR imaging spectrometer (VNIS), the CE-5 LMS extends the spectral range from 450～2 400 to 480～3 200 nm. In addition to identifying the major minerals such as pyroxene and olivine, it can also detect absorption peaks around 3 000 nm characteristic of hydrous minerals. In addition, Chang’e-5 will sample thematerialbelow the surface of the moon, and LMS can detect the area before and after sampling, to analyze the spectral characteristics of lunar soil under different depths and weathering degrees, then compared with the laboratory spectra of the later return samples. In order to ensure the reliability of LMS lunar data, a pre-flight LMS ground validation experiment was carried out, using a variety of minerals and mineral mixed samples, collecting the detection data of LMS under different test environment, combining with a standard instrument to analyze the spectral quality. In this paper, spectral uncertainty parameters of all experiment samples were calculated and evaluated. Moreover, the LMS spectral data were consistent with those simultaneously obtained from standard comparison spectrometers under the same conditions, indicating that LMS could effectively identify the spectral profile and absorption peak of the targets.

Internal standard method is one of the most popular quantitative analytical methods for quantitative analysis of laser induced breakdown spectroscopy (LIBS). In order to improve the precision of quantitative analysis, the rule of relative fluctuation characteristic of spectral line intensity ratio between analysisline and excitation line,energy differential (ΔE), and wavelength difference (Δλ) has been studied. Under thermodynamic equilibrium conditions, the mathematical model considers the electron energy level difference of atomic emission spectrum generated from high level to low level, plasma temperature, partition function, and the factors that influence the intensity of plasma, the influence of excitation energy differential on relative fluctuation of spectral line intensity has been studied. Under the condition of -2 eV0, so ΔE spectrum line pairs with ΔE<0 should be chosen. With theoretical analysis, it has been discovered that if |ΔE| is close to zero, the relative fluctuation of spectral line intensity is smaller. In experiment, the Nd∶YAG pulsed laser was used to breakdown samples works with wavelength of 1 064 nm, pulse energy is 85 mJ, repeat frequency is 1 Hz, and pulse width is 13 ns. Andor company Mechelle 5000 spectrometer is used together with Andor New iStar model ICCD for spectrum gathering with working wavelength of 200~975 nm, optical resolution is better than 0.05 nm.The laser induced plasma spectra is verified by iron-based alloy. In the experiment, Fe is internal standard element, Cr/Mn are analysis elements. In this paper, the spectrum line with transition probability more than 106 of the NIST spectrum line library is chosen, and the non-resonant line with similarity resonance energy difference is compared and analyzed. Results show that the method of choose spectrum line with similar excitation energy level or similar wavelength has some limitations. For Cr and Fe, |ΔE| at 0.14 and 1.51 eV for the spectral line intensity of relative standard deviation (RSD) were 6.7% and 4.6% respectively, and theoretical value and practical value of the spectral line intensity were 1.14 and 0.59; RSD were 6.3% and 4.4% with |Δλ| of 11.7 nm and 50.8 nm, respectively. Difference between theoretical and practical values of the spectral line intensity was 1.69 and 0.62. Analysis showed that, influence of stimulated energy level difference of Cr/Mn is obvious than wavelength difference.To analysis element Cr/Mn, when internal standard element is Fe, the absolute wavelength value increases RSD decreases. Within a large scope of 1.50 eV and 90 nm spectrum range, larger |ΔE| can get smaller relative fluctuation characteristic of spectral line intensity ratio, and maximum RSD of Cr and Fe is 2.06%; large |Δλ| can get smaller relative fluctuation characteristic of spectral line intensity ratio, and maximum RSD of Cr and Fe is 1.35%. It can be concluded from the experimental results that, when the spectrum line is selected, the spectral line principle with similar excitation energy level and wavelength has certain limitations. If |ΔE| or |Δλ| is larger, RSD will be smaller. The spectral line intensity ratio which has similar theoretical and practical value can be a spectral line selection basis. Additionally, if theoretical value and actual value of spectral line intensity ratio are more similar, relative fluctuation of spectral lines intensity can be reduced.

Biosorption, with many advantages such as low-cost of sources, good adsorption effect, easily desorption, good recycling and being environmental-friendly, has been regarded as a cost-effective technology for heavy metals uptake at low metal concentrations. In this paper, the potentials and mechanisms of biosorption of lead ion, copper ion, cadmium ion, zinc ion and chromium ion in the single-ion aqueous solution using tartary buckwheat tea powders were investigated by spectral analysis. Scanning Electron Microscope (SEM), Energy Dispersive Spectrometer (EDS) and Fourier Transform Infrared Spectroscopy (FTIS) were used to characterize tartary buckwheat tea powders before and after the adsorption processes to identify the functional groups and elements which had changed, furthermore, to explore the possible mechanisms of the biosorption. The models of the adsorption isotherms (Langmuir, Freundlich, Temkin and Dubinin-Radushkevich) and the adsorption kinetics (pseudo-first order, pseudo-second order and intraparticle diffusion equation) were used to fit the adsorption behaviors. Response surface methodology is a collection of statistical and mathematical techniques based on fitting a polynomial equation to the experimental data. It can be well applied when a response or a set of responses of interest are affected by several factors. The response surface methodology was applied to evaluate the combined effects of various factors, namely initial metal ion concentration (A), adsorbent particle size (B), adsorbent dose (C) and contact time (D) on the removal rates of lead ion, copper ion, cadmium ion, zinc ion and chromium ion from aqueous solution using tartary buckwheat tea powders. The results of isotherm models indicated that the biosorption was mainly heterogeneous adsorption, accompanying other adsorption behaviors. The models of kinetic revealed that biosorption processes fitted a pseudo-second kinetic well, which suggests that the adsorption rates were controlled by effects of film diffusion and intraparticle process and the surface of tartary buckwheat tea powders changed into smoothed and melted. The lead ion, copper ion, cadmium ion, zinc ion and chromium ion onto surface of tartary buckwheat tea powders were confirmed by Energy Dispersive Spectrometer. The Fourier transform infrared spectra results exhibited that -OH, -CH2, -CH3, CO, -NH, -C-O, CH played major roles on removal lead ion, copper ion, cadmium ion, zinc ion and chromium ion using tartary buckwheat tea powders in single-ion aqueous solution. The results showed that the five values of the nonlinear models of coefficient constant were Adj R2Pb=97.10, Adj R2Cu=98.44, Adj R2Cd=94.55, Adj R2Zn=92.71 and Adj R2Cr=97.02, respectively for removal rates of lead ion, copper ion, cadmium ion, zinc ion and chromium ion in the aqueous solution using tartary buckwheat tea powders under conditions of various factors, which could navigate the design space for various factors on effects of biosorption the metal ions from aqueous solution. The effects of factors were in order as A＞D＞B＞C on removal rate lead ion, A＞C＞D＞B on removal rate copper ion, A＞B＞C＞D on removal rate cadmium ion, B＞C＞A＞D on removal rate zinc ion and C＞B＞D＞A on removal rate chromium ion, respectively by tartary buckwheat tea powders from single-ion aqueous solution. The study of results provided evidences that tartary buckwheat tea powders can be used for removing lead ion, copper ion, cadmium ion, zinc ion and chromium ion from single-ion aqueous solution.

In the process of cement production in the industrial field, the content of each component in the cement directly affects the quality of the cement. Therefore, it is of great significance to quickly and accurately monitor the content of each component in the cement. In this paper, the laser induced breakdown spectroscopy (LIBS) technology is used to detect the powder cement, and the powder cement are put in a two-dimensional moved platform without any pretreatment. The spectral data is processed by normalization and principal component analysis(PCA) firstly, which is used as the input of the model. In order to analyze the elements of Ca, Si, Al, Fe and Mg in cement, we build the models based on Partial least squares(PLS) and Support Vector Regression (SVR) as the comparison of methods. In addition, the comparison of measurement methods is between cement powder detection and cement tablet detection. The experimental results show that in this type of experiment, the SVR method is more advantageous than the PLS method because of the relationship between the element concentration and the strength of its characteristic line of the cement samples. The accuracy of the direct measurement of the cement powder is close to that of the tablet type, and it demonstrated the feasibility of on-line analysis of cement powder using LIBS technology under this type of experiment.

To elucidate the xenia effects of mineral elements on different pollination combinations in Castanea henryi, using the chinquapin cultivars “Huali No.1”, “Huali No.2”, “Huali No.3” and “Huangzhen” as materials, we investigated the xenia effects of mineral elements in C. henryi by auto dicsrete analyzers and atomic absorption spectrometry. Twenty combinations of self-, cross-, and natural pollination were undertaken. The results revealed that eight mineral elements of Castanea henryi seeds were significant differences. Xenia obviously has its mineral elements, especially iron and znic. The fruit of “Huali No.2”דHuangzhen” showed the highest iron and zinc content, which were 162.13 and 41.79 μg·g-1, respectively. The fruit of “Huangzhen”דHuali No.1” showed an increased manganese content of 165.67 μg·g-1, which provides a reference for the utilization of this variety as a manganese fertilizer.Through principal component analysis, the best combination was “Huali No.2”דHuangzhen” in the 19 combinations. The results can give a basis for planting design of varieties and improving fruit quality in C. henryi.

Carbon dioxide is one of the most important greenhouse gases playing an important role in climate change and human activities, so the detection of carbon dioxide concentration has an important significance. Highly sensitive detection of carbon dioxide gas was achieved by using near infrared tunable diode laser absorption spectroscopy combined with self-designed portable miniaturized cylindrical mirror multi-pass absorption cell. The cylindrical mirrors multi-pass absorption cell was optimized and designed by using a light transmission matrix programmed with Matlab software, compared with traditional Herriott multipass cell, which has the advantages of high mirror utilization and longer optical path length in the same volume. An effective optical path length of 14 m was achieved with 15 cm physical length. In present work, a DFB diode laser emission at 1.57 μm was used as a light source. Direct absorption spectroscopy method was used to detect the CO2, and the Allan variance was used to analyze the system performance. The results showed that the detection sensitivity of the system can be achieved 5.3 μL·L-1 with the average time of 5 s, and a detection sensitivity of 33.1 μL·L-1 can be achieved by averaging in 235 s. In addition, CO2 in the atmosphere was measured by the developed CO2 sensor, the measured results showed that the concentration of CO2 in the atmosphere is about 383.4 μL·L-1. The tunable diode laser absorption spectroscopy (TDLAS) system based on cylindrical mirrors multi-pass absorption cell, combined with the cylindrical mirrors multi-pass cell can achieve long optical path in a small volume and tunable diode laser absorption spectroscopy technology has the advantages of high sensitivity, high resolution and fast response, greatly reduces volume and improves detection sensitivity of the system, which has a broad application in the field of gas detection.

Wedge filter spectral imager, with no moving components and low complexity, has become an important development direction of low cost miniature imaging spectrometer. Based on the state of the art hyperspectral lossless compression standard CCSDS123, we propose a lossless data compression method for the wedge filter spectral imager. The proposed method redefines the local difference vector in CCSDS123, taking fully advantage of the spatial-spectral co-modulation characteristics of the wedge filter spectral imager. To compress the raw data from a wedge filter spectral imager, the compression encoder firstly predicts the sample value using its local sum and local difference vector, then computes a prediction residual and the corresponding mapped prediction residual, finally encodes the mapped prediction residual via a sample-adaptive entropy coding approach. The proposed method can effectively compress the raw data from a wedge filter spectral imager by using the local correlation in the spatial-spectral space. To verify the compression performance of the proposed method, experiments are taken on 6 raw datasets containing different scenes. The results show that the proposed method surpasses the original CCSDS123 method by about 21.62% higher compression ratio on the test datasets with almost the same computational time.

The portable micro-X-ray fluorescence analysis with polycapillary optics has advantages of being non-destructive, and it has been widely used in the analysis of cultural relics. However, due to the irregular or curving surface of archaeological objects and the shortcoming of polycapillary optics in focusing X-rays, the distances between the irradiation spot of samples and exit of polycapillary optics are variables. As a result, the sizes of focused X-ray spot can't keep constant, which reduces the accuracy of measurement and the resolution of elemental mappings of scanning area. In this paper, we propose a new type of portable micro-X-ray fluorescence spectrometer that consists of a new closed loop feedback system and SDD X-ray detector, 30W lower power X-ray tube, polycapillary optics, CCD and so on. In particularly, the closed loop feedback system is composed of Laser Displacement Sensor (LDS), stepper motor, sample stage and computer programs developed by LabVIEW languages. During measuring, the LDS accurately controls the distances between the irradiation spot of samples and exit of polycapillary optics. Based on this closed loop feedback system, our portable micro X-ray fluorescence spectrometer can keep the sizes of focused X-ray spot constant. On the other hand, we provide different alternative sizes of focused X-ray spot by controlling the distances between the exit of polycapillary optics and measured spot of samples in our spectrometer. In order to test the feasibility of this instrument, the concentration of elements and elemental mappings of K, Ca, Zn, Fe and other elements in the irregular colored glaze of a piece of ancient porcelain have been measured by our portable micro X-ray fluorescence spectrometer under enabling and disabling LDS conditions. From the results, it can be concluded that the concentration of elements is very close to the real values and the resolution of elemental mappings is better when LDS is enabled. This indicates that the closed loop feedback system based on LDS can accurately reduce the measurement errors caused by the irregular or curving surface of archaeological objects. Therefore, this portable micro X-ray fluorescence spectrometer developed by our laboratory has potential application prospects in non-destructive analysis of cultural relics.

In recent years, China has launched a variety of Earth observation satellites with many newly-developed sensors onboard. Meanwhile, researches on the cross-comparison of these China-made new sensors are in progress. Nevertheless, no study has been published with respect to the comparison between Gaofen-2 (GF-2) PMS2 and Ziyuan-3 (ZY-3) MUX sensor data up to now. The quantitative relationship between the two sensor data is unclear, and it is uncertain whether the two sensor data can be used for the same project directly. To meet this special requirement, this study carried out a cross-comparison between the GF-2 PMS2 and ZY-3 MUX sensor data based on three synchronous image pairs of the two sensors. The cross-comparison was performed using two methods. The first one is making use of image statistics based on large areas in common between the image pairs. A pixel-by-pixel comparison method was used to investigate quantitative relationship between GF-2 PMS2 and ZY-3 MUX sensor data based on the whole test area. The other method is a comparison based on the region of interest (ROI) in common to avoid the problem due to the difference in spatial resolution between the GF-2 PMS2 (4 m) and ZY-3 MUX (6 m). The ROIs had appropriate size and were selected from homogeneous areas that excluded complicated terrain conditions. A linear regression model was adopted for the Top of Atmosphere (TOA) reflectance-based comparison between the ROIs of the GF-2 PMS2 and ZY-3 MUX images. Through the two methods, we obtained the quantitative relationship models between GF-2 PMS2 and ZY-3 MUX sensor data. This comparison study found that the results obtained by two methods, i. e., pixel-by-pixel comparison and ROI-based comparison, are almost consistent. However, the ROI-based comparison achieves a higher accuracy because the spectral information of the corresponding pixels may be offset when using the pixel-by-pixel comparison due largely to the mis-registration of image pixels. This will lower the accuracy of the pixel-by-pixel comparison method. The results showed that the TOA reflectance of GF-2 PMS2 and ZY-3 MUX sensors has a high degree of agreement, with R2 values greater than 0.9 for all the four bands. However, the higher R2 values in blue and green bands indicated that the TOA reflectance between the two sensors in both bands has a better agreement than that of red and near-infrared bands. Scatter plots showed that almost all data points lie under the one-to-one line in the spectral feature space with GF-2 data in x-axis and ZY-3 data in y-axis. This suggested that the GF-2 PMS2 sensor data generally have higher TOA reflectance than ZY-3 MUX, especially in blue and green bands. It should be noted that the difference of TOA reflectance between the two sensor data can be affected by land cover types in red and near infrared bands. In the image pairs dominated by bare soil, the difference between the TOA reflectance of two sensors decreases with increasing wavelength, while for vegetation-dominated image pairs, the difference increases with increasing wavelength. In order to further examine the differences caused by the land cover types, more ROIs of pure vegetation and pure bare soil were extracted separately. The results showed that the signal difference between the two sensors is mainly affected by bare soil in the red band and by vegetation in the near infrared band. The more vigorous the vegetation grows, the greater the difference between the two sensors is. The band-by-band comparison has yielded the conversion equations for each corresponding bands of the two sensors, which were applied to convert the TOA reflectance between each corresponding bands of the two sensors. The validation of the conversion showed that the obtained conversion equations have high accuracy. It can be observed that the GF-2 PMS2-simulated ZY-3 MUX data are almost identical with the actual ZY-3 MUX data with R2 values close to 1 and RMSE less than 0.01. The conversion has resulted in a significance reduction in RMSE by up to 64.79%, as well as a significant decrease in ME. This study showed that such a conversion can significantly improve the agreement between the two sensors data. The converted data are more conducive to the synergy between the GF-2 PMS2 and ZY-3 MUX sensor data. The analysis showed that the differences in TOA reflectance between the two sensor data result probably from the differences in their spectral response function and spatial resolution. We found that the spectral response curve of ZY-3 MUX is smoother with no obvious fluctuations than that of GF-2 PMS2, which is fluctuant in all of four bands. Such a difference in the spectral response functions may have led to the difference in TOA reflectance between the two sensors. In addition, the spatial resolution of GF-2 PMS2 is 4 m, which is higher than ZY-3 MUX’s 6 m. A higher spatial resolution will help GF-2 PMS2 sensor to detect subtle spectral information of small ground objects and thus cause the difference in TOA reflectance between the two sensors.

When near infrared spectral information is much larger than the sample size, it is both important and challenging to make automatic variable selection of spectral information and coefficient estimation to establish a sparse linear model between spectra and sample concentration. In this paper, adaptive Elastic Net, a variable selection method, is used to establish a quantitative calibration model between near infrared spectroscopy and o-cresol content, which is a kind of trace component and is difficult to measure in the production of polyphenylene ether. Then, the model performance is compared with the Elastic Net method. Under the circumstance that the number of variables is much larger than the sample size, although Elastic Net method can achieve variable selection, due to the fact that its coefficient estimation does not have the Oracle property, the interpretability and prediction accuracy of the model are affected. The adaptive Elastic Net method solves the above problem and improves the model performance by applying adaptive weights to L1 penalty. In order to verify model performance indicators of adaptive Elastic Net method, the number of selected independent variables (NSIV) is used to evaluate the model complexity and the complex correlation coefficient R2 is used to evaluate the interpretability of the model. Meanwhile, the prediction accuracy of the model is evaluated by using the mean relative prediction error (MRPE) and the prediction correlation coefficient (Rp). The performance indicators of Elastic Net Method are: NSIV=529, R2=0.96, MRPE=3.22％, Rp=0.97; adaptive Elastic Net method’s performance indicators are: NSIV=139, R2=0.99, MRPE=2.00%, Rp=0.99. The results show that adaptive Elastic Net’s model is better than that of Elastic Net. A simpler sparse linear model with better interpretability and higher prediction accuracy can be obtained by the adaptive Elastic Net regression.

The purpose of this research is to cluster and identify forty-one Camellia varieties by Fourier Transform Infrared (FTIR) spectroscopy of genomic DNA. We discovered FTIR spectra of genomic DNA are different among forty-one varieties tested. FTIR spectra can therefore act as a fingerprint for Camellia. Anova confirmed that the differences among FTIR data are significant. We set up the standard clustering and identifying model of forty-one Camellia varieties by hierarchical cluster combined with principal component analysis. The accuracy rate of clustering by using average spectra from one hundred and twenty-three genomic DNA samples is 92.68%. The identification accuracy rate is 100%. Clustering results showed that the forty-one Camellia varieties fall into nine categories based on a 1.0 cluster distance limit, and into three bigger categories at a 15.0 cluster distance limit. The genetic relationship analysis illustrates that the current Chuxiong population of C. reticulata Lindl. comes from Chuxiong, Tengchong, and Dali. We concluded that hierarchical clustering combined with principal component analysis based on FTIR spectra of genomic DNA can be used to quickly cluster and identify Camellia.