Nicotinamide adenine dinucleotide (reduced form) (NADH), as an important coenzyme molecule in enzymes, is ubiquitously involved in the organism and plays a key role in cellular energy metabolism. Metal ions can affect the NADH participating enzymes reactions. Aluminum ions (Al3+) are toxic to the nervous system and can cause a range of neurodegenerative diseases. Therefore, the study on the interaction between NADH and Al3+ is significantly helpful to understand the influence of Al3+ on the TCA cycle and enzymatic reaction in vivo. In this paper, the effects of Al3+ on the intrinsic fluorescence characteristics and molecular conformations of NADH in aqueous solutions have been investigated using UV-visible absorbance and steady-state fluorescence spectroscopy combined with time-correlated single photon counting technique (TCSPC). UV-visible absorption spectrum shows that NADH’s combination with Al3+ does not change the absorption characteristics of NADH’s adenine and reduced nicotinamide chromophores. We chose 340 nm as the excitation wavelength to avoid the fluorescence resonance energy transfer (FRET) between the NADH’s two intrinsic chromophores. It was proved that Al3+ could be coordinated to the two oxygen atoms on the hydroxyl group of NADH pyrophosphate bridge, which would make the NADH structure relatively more rigid, there by inhibiting the non-radiative processes such as the molecular rotation of NADH molecule in solution, resulting in the increase in the average fluorescence lifetime of the NADH molecule. Therefore, a fluorescence enhancement of the NADH molecule was observed by increasing Al3+ concentrations. Furthermore, we used the intrinsic fluorescence lifetimes amplitude ratio of NADH to characterize the two main conformational forms of the NADH in solution: folded form in which the adenine and nicotinamide groups are stacked parallel to each other, and unfolded form where the two bases apart from each other. It was found that Al3+ would break the original equilibrium of two conformations of NADH, making coenzyme NADH have a clear preference from the unfolded towards folded conformation, and eventually a new dynamic equilibrium would be achieved. Interestingly, it was revealed that the amplitude ratio increased logarithmically with the concentration of Al3+ when the concentration ratio of Al3+∶NADH was not more than 1∶2, showing a potential application prospect in Al3+ detection.

Space weather events such as magnetic storms can cause significant changes in the concentration of the neutral components O and N2 in the thermosphere. Therefore, the ratio of the column density of oxygen atom to nitrogen molecule O/N2 is often used as a sign of the ionosphere’s disturbance. The results have shown that the ratio of the density of O and N2 columns density O/N2 and the ratio of the column emission intensity 135.6/LBH of far ultraviolet airglow OⅠ 135.6 nm and N2 LBH have a good correlation, so far ultraviolet optical remote sensing is very important for monitoring space weather. Since the 1970s, research on ionospheric exploration with far ultraviolet airglow has been conducted abroad, and several related satellite coordinates have been launched, especially in the United States, Japan, Sweden and other countries. Among the airborne prediction and optical detection instruments operating in orbit, only some work in the microwave and visible wavelength, and there are no detection instruments working in far ultraviolet longer, until the successful launch of FENGYUN-3D meteorological satellite in November 2017. The ionospheric photometer on the satellite is the first airborne far ultraviolet airglow remote sensing detection load in China. It provides us with a series of far ultraviolet detection data with independent intellectual property rights, and lays a foundation for us to carry out the research of ionospheric O/N2 characteristics. Firstly, we described how to retrieve the ratio of column density O/N2 of the neutral component in the thermosphere by using the ratio of column radiation intensity 135.6/LBH. Secondly, in the msise-00 atmospheric model, the ratio coefficient between 135.6/LBH and O/N2 is calculated by AURIC, and then the ionospheric O/N2 is retrieved by using the far ultraviolet airglow data observed by ionospheric photometer in real time, which further verifies the disturbance of the ionospheric neutral component during the magnetic storm. In the process of data processing, Chebyshev filter is used to process the out of band stray light in the data, which further suppresses the influence of the stray light signal on the FUV spectrum signal. Finally, the inversion results of O/N2 retrieved by Ionospheric Photometer are compared with the results of Global Ultraviolet Imager (GUVI) loaded by foreign optical remote sensing. The results show that they have the same response to a magnetic storm, and the product RMS error of O/N2 is about 0.319 6. In this paper, we have made an initial analysis of the possible causes of the differences, which lays a foundation for the follow-up work. This studyis the first time to use the data of the far ultraviolet airglow remote sensing payload independently developed in China for data inversion and analysis, which is of great significance to the development of the ionospheric far ultraviolet remote sensing technology in China.

China has abundant genetic resources under the huge population base. Foreign countries may illegally plunder these resources to obtain benefits. There may be some security risks in the process of illegal plunder, such as the spread of infectious diseases. It has become a new problem of biosafety to strengthen the protection of Chinese citizens’ genetic resources and promote normal and legal international information sharing and scientific research cooperation. In order to strengthen the entry and exit management of human cells and their products, prevent the loss of genetic resources and the introduction of harmful substances, and promote medical scientific research and international exchange and cooperation among various countries, a non-invasive, fast and safe cell spectral identification technology is proposed. In this paper, the physicochemical mechanism of cell supercontinuum is described, the effect of cell concentration on the supercontinuum is discussed, and the non-invasive detection and extraction of the supercontinuum fingerprint spectrum of biological cells is realized. The experimental results show that the supercontinuum fingerprint spectrum of cells is mainly concentrated in the visible region of 500~700 nm. The cell samples in the experiment are all cultured separately, so there is no influence between the samples. The subjects of the experiment are 293T cells, HCC827 cells and HT29 cells. The medium of the three types of cells in PBS medium, each type of cell has three concentrations (5×105, 5×106, 5×107 cells·mL-1) and three samples are cultured independently under each concentration, a total of 27 samples are obtained individual cell samples. The supercontinuum spectra of 24 cell samples were tested and a prediction model was established. Another three samples were used as unknown samples for model prediction. The principal component analysis is used to reduce the dimension of the test samples’ original data, and the reduced dimension data are classified by support vector machine regression. The root mean square error of the training set is 0.097 2, R2=0.995 1, and the root mean square error of the verification set is 0.097 2, R2=0.931 4. It is found that cell concentration affects the extraction of the supercontinuum fingerprint spectrum. In this paper, when building the model, considering the universality of the application of the technology and the limited concentration parameters of the experimental samples, the influence of cell concentration on the recognition rate of the prediction model is not considered. In the later stage, if a certain concentration threshold is taken as the concentration starting point of cell detection, the recognition rate of the model will be more accurate and scientific. Under controlled experimental conditions, supercontinuum spectroscopy can be applied to noninvasive and noninvasive identification of biological cells.

Raman spectroscopy can provide the spectral information related to the specific molecular structure of the substance, and recognize the tiny biochemical variation of biological tissue. It is a promising diagnosis technology in clinical applications due to the advantages of fast, real-time, non-destructive detection without sample pretreatment. Compared with the routine histopathological analysis, Raman spectroscopy can detect the fresh tissue directly and it may simplify the analysis procedure and shorten the diagnosis time. Some changes may take place in molecular composition and structure of pathological tissues, which provides a theoretical basis for the application of Raman spectroscopy in histopathological diagnosis. Raman spectroscopy may provide the objective diagnosis information and achieve the rapid, low-invasive diagnosis based on the differences of molecular composition and structure between normal and pathological tissues. This paper reviewed the development of Raman spectroscopy for the pathological diagnosis of tissues in the past decade and focused on the crucial results. Some key issues in the ex vivo and in vivo studies of Raman spectroscopy in pathological diagnosis were emphasized. Firstly, for the Raman detection of ex vivo sample, the applicability of several tissue samples, such as formal infixed paraffin-embedded samples, frozen samples and fresh tissue samples, was mainly evaluated; And the emphasis of Ramandata collection in the ex vivo studies of Raman spectroscopy, including applicable laser source, spectra range, and the way of spectra collection were summarized. Meanwhile, we focused on the two application forms of Raman spectroscopy in the detection in vivo, which involved the detection of Raman spectroscopy combined with the medical endoscope, and the direct detection in open surgery. The Raman system suitable for clinical application was also taken into consideration in this review, and the fiber probes used in the current in vivo Raman studies were major. Furthermore, the analysis methods of Raman data were discussed, the pathological diagnosis models, which can provide the excellent diagnosis results in the sample set with small size, have been established through spectral preprocessing, feature extraction, classification and identification. It is necessary to optimize the analysis method to achieve the correlation between Raman spectraand biochemical information and incorporate the effects of individual differences into the classification model to improve the model. In this paper, the crucial issues of Raman spectroscopy in pathological diagnosis were discussed in order to provide a reference for further research. More extensive ex vivo and in vivo studies are required to put the Raman spectroscopy technology into clinical practice.

Cell is the basic unit of structure and biological activities of animals and plants. An important feature of cellular processes is that the biochemical components involved in it have spatiotemporally interacted with each other. However, it is difficult to evaluate molecular interactions in living cells using traditional biochemical methods (e. g., yeast two-hybrid system, pull-down system, etc. ). The rapid development of optical technology provides new genetic research tools for studying the spatiotemporal dynamics of biomolecules in living cells. FRET-FLIM (Frster resonance energy transfer-fluorescence lifetime imaging microscopy) technology has unique advantages in real-time detection and analysis of conformational changes and dynamic interactions of macromolecules in living cells, such as achieving real-time “visualization” study of living cells with the high spatial and temporal resolution, obtaining more reliable results with high sensitivity, and accomplishing simple and fast analysis program based on brief mathematical operations etc. This paper outlined the theoretical backgrounds of FRET-FLIM, and then summarized the advantages and disadvantages of FRET-FLIM technique compared with traditional methods for protein interaction studies. In addition, we emphasized the recent studies and applications of FRET-FLIM in protein interaction, cell biology, and disease diagnosis. Finally, this paper prospected the future research trend of FRET-FLIM in the field of life sciences and provided a comprehensive and systematic reference for related research in the structure and processes of living cells.

Imaging spectrum technology can obtain the image and spectral characteristics of the target at the same time, and it is easy to identify the traditional camouflage materials whose spectrum are different from the background spectrum. In recent years, imaging spectrum has been developed rapidly, which has experienced the leap from multispectral technology to hyperspectral technology. Owing to the application of ISR UAV technology in various countries, hyperspectral sensors are expanded from spaceborne to airborne, which can identify military camouflage targets in a closer range, and pose a huge challenge to the survival ability of military targets with important value. At present, the main design idea of hyperspectral camouflage materials is to composite materials with similar color and spectral reflection characteristics (within the detection range of sensors), so as to achieve “the same color and spectrum” with the environmental background to avoid the high spectrum reconnaissance. Green vegetation is the most common camouflage background, and it is also the spectral simulation object of most researches in this field. Its spectral reflection curve has four main characteristics in the visible near-infrared range: “green peak”, “red edge”, “near-infrared plateau” and “water absorption band”, assign to leaf tissue structure, chlorophyll and water. The photothermal stability of chlorophyll in vitro is poor, which cannot be directly used as camouflage material, so it is one of the current researches focuses on finding and synthesizing molecules with good stability, chlorophyll-like structure and spectrum. In addition, chrome green and cobalt green are commonly used camouflage pigments, which have spectral reflection characteristics similar to green vegetation, like “green peak”, “red edge” and “near-infrared plateau”. Researchers composited them with superabsorbent fillers to introduce “water absorption peak”, roughly simulating the reflection spectrum of green vegetation, but there are still some problems to be solved in order to achieve accurate simulation. Based on the Vis-NIR reflection spectra of green vegetation, this paper elucidated the material selection, which simulating spectrum properties of green vegetation in the visible and near-infrared region respectively, introduced the problems existing in the accurate simulation of the vegetation spectra, and the research work of improving the spectral similarity and weather ability through modification and composition. The prospects for the future developments are also discussed.

Terahertz biomedicine is a hot spot in the field of spectroscopy. The main difficulty lies in how to effectively avoid the interference of moisture and perform sensitive analysis and detection of samples in the liquid environment. Metamaterials terahertz sensors have become an important research method in the field of terahertz biomedical sensing due to their advantages such as high sensitivity, fast detection, and trace analysis. A terahertz liquid-phase sensor chip based on a single-open resonance ring metamaterial was designed and developed. In order to effectively solve the strong absorption of terahertz waves by water, a microfluidic channel with a depth of 50 μm was etched by photolithography. The sensor chip integrates a metamaterial substrate and a PDMS flow channel. In the THz frequency band, there are two resonance peaks located at 0.771 and 2.129 THz. Compared with the THz time-domain spectrum of the blank sensor itself, the addition of liquid caused the phase delay and amplitude of the time-domain peak to decrease. At the same time, because the refractive index of water is greater than that of ethanol, the results of the THz transmission spectrum show that the frequency shift of water is greater than ethanol and peak 2 lagers than peak 1. The above results show that the constructed metamaterial liquid-phase sensing chip is a sensitive refractive index sensor, and it also proves the feasibility of the sensor in measuring liquid samples. In addition, using this chip to study PBS solutions of different concentrations, it was found that the addition of ions in the aqueous solution will cause the red shift of the resonance frequency (using water as a reference). As the ion concentration increases, the amount of change in the resonance frequency will increase in turn, with the largest red shift of 10× PBS, Peak 1 is 22.9 GHz, and peak 2 is 30.5 GHz. Comparing the sensing performance of the two resonance peaks, peak 2 has better sensing capabilities, but Peak 1 is more sensitive to low-concentration ion solutions. Therefore, as a sensitive refractive index sensor, the constructed microfluidic sensor and detection system can develop a sensitive label-free THz sensing platform to provide new ideas for terahertz biomedical research.

As a type of nitromethylene nicotine insecticide, midacloprid has been widely used in agricultural production due to its wide-spectrum, high efficiency and low toxicity. However, its excessive residue poses a threat to human health. We first analyze the terahertz transmission spectrum of the metamaterial structure and explains the reason for the formation of the resonance frequency. Secondly, both the metamaterial structure and the silica substratecoated with 500 mg·L-1 imidacloprid solution were measured, respectively, and the influence of the silica substrate was excluded. Next, we prepared imidacloprid solutions with three gradients and 15 concentrations, which were respectively: 100~500, 10～50, 1～5 mg·L-1, and then the terahertz time-domain spectra of these imidacloprid films sprayed on the metamaterial were measured. According to the different peak frequency redshifts of terahertz transmission spectrum, the identification of different solution concentrations was realized, and the functional relationship between the peak frequency redshifts and the concentration of imidacloprid was established. The experimental results show that the detectable concentration of imidacloprid film can be as low as 1 mg·L-1 by THz spectroscopy, along with the increase of imidacloprid concentrations, and peak frequency of the red shift curve increased. Lastly, the refractive index of different concentration imidacloprid solution was substituted into CST software for simulation, and results demonstrated that the transmission curves of different concentration imidacloprid had different redshifts. The above experimental and simulation results show that the peak frequency modulation can be used to detect the low concentration imidacloprid by using THz-TDS with metamaterial. This study provides a new method for the detection of pesticide residues in the food matrix.

The stability of the cell microenvironment is an essential condition to maintain healthy cell proliferation, metabolism, and functional activities, and the abnormality of microenvironment components can cause pathological cell changes. In this paper, the fluorescence emission characteristics and mechanism of white blood cell (WBC) in vitro in polysaccharide microenvironment were considered by fluorescence spectroscopy, and the effect of Polysaccharide on the WBC biological activity was further analyzed. The results showed that under 407 nm laser irradiation, white blood cells emit fluorescence at 450 nm. When lipopolysaccharide or dextran is added, the position of fluorescence emission peak of leukocyte will not change, but the fluorescence peak will be affected. The addition of lipopolysaccharide (LPS) can weaken the fluorescence peak intensity of leukocytes, and the higher the LPS concentration (in the range of 0～500 μg·mL-1), the lower the fluorescence intensity. While the addition of dextran can enhance the WBC fluorescence intensity to a certain extent, and the higher the concentration, the stronger the fluorescence. It believes that the 450 nm fluorescence emitted by WBC comes from the nicotinamide adenine dinucleotide (NADH). NADH is oxidized to NAD+ with the increase of cell in vitro, which leads to the decrease of fluorescence intensity and apoptosis. The hydroxyl radical (·OH) generated by adding lipopolysaccharide will react with NADH. Therefore, lipopolysaccharide accelerates the consumption of NADH, resulting in weakened leukocyte fluorescence and accelerated cell apoptosis. Dextran is mainly composed of glucose monomer, which can reduce NAD+ to NADH, thus delaying the WBC apoptosis. It believes that lipopolysaccharide can accelerate the WBC apoptosis, improve the probability of inflammation and even tumor, while dextran can protect WBC. This study is used as a valuable reference for the study of tumor occurrence, development, and treatment.

Advanced glycation end products (AGEs) are a kind of compounds with various structures. When the blood sugar is higher than a normal value, it will be produced in large quantities and cannot be metabolized by the body’s own metabolism, and has a memory function of long-term abnormal blood sugar. Studies have shown that AGEs is one of the important factors causing diabetes and its complications. By detecting the accumulation of AGEs in vivo, the occurrence and development of diabetes and its complications can be predicted. The existing in vitro AGEs detection methods have problems of complicated operation, long detection time, high cost and difficulty in promotion; The in vivo AGEs detection methods have problems such as skin pigmentation, age, and hemoglobin interference. Therefore, based on the good optical properties of the cornea and the autofluorescence characteristics of AGEs, a fluorescence spectroscopic detection method for advanced glycation end products of the cornea was proposed. A set of corneal AGEs fluorescence spectrum detection system was constructed. The system consisted of microfiber spectrometer, integrated LED excitation light source, Y-type 12+1 fiber and PC-side spectral processing display software. The fluorescence spectrum detection system was used to collect data from 17 volunteers (9 males, 8 females, 4 diabetics, the youngest 15 years old and the oldest 81 years old) in darkroom conditions. The fluorescence spectrum data that excitation light central wavelengths were 370 and 395 nm were obtained. In order to accurately identify the useful information of fluorescence spectrum, the required fluorescence spectrum data segments (450~700 nm) were intercepted, and then processed them by removing background noise, normalization, wavelet transform, and so on. The above methods could amplify and identify the non-obvious fluorescence peaks in the fluorescence spectrum. The experimental results show that the fluorescence spectrum of corneal is detected within 420~600 nm when the LEDs with wavelengths of 370 and 395 nm are used as excitation sources. Moreover, the fluorescence spectra have peaks in the range of 450~500, 500~550 and 550~600 nm, respectively. According to the principle that the fluorescence peak of fluorescent substances is independent of the excitation wavelength, it is shown that the fluorescence spectra of two different excitation wavelengths are all produced by AGEs. The peak fluorescence intensity of diabetes mellitus patients and normal people were analyzed. The results showed that the fluorescence intensity of diabetes mellitus patients was significantly higher than that of normal people, which indicated that it was feasible to detect advanced glycation end products of the cornea by fluorescence spectroscopy.

LED has the advantages of high efficiency, small size, low power consumption, long life and son on, and it can easily achieve wide spectral regulation, which makes it stand out in the field of agriculture. Plant-growth light-emitting diodes (LEDs) can be divided into two categories, one is monochromatic LED, and the other is a White light-emitting diode (WLED), WLEDs used for plant growth are either mixed with monochromatic LED or used alone to realize plant supplementary lighting. Most of the WLEDs on plant growth is composed of a blue LED chip or a UV LED chip packed with phosphors, that is, phosphor-converted WLEDs, however, the obtained spectra are concentrated in the bluish visible light, and the efficiency of photosynthesis to plants is relatively poor. The absorption spectra of the plant are not full-band but selective, based on the particularity of the absorption spectra of plant, the color rendering performance of the spectra of WLEDs is regarded as the standard to judge whether the spectra are suitable for plant growth, the average color rendering index Ra, special color rendering index R9 (saturated red light) and R12 (saturated blue light) were considered as the main performance evaluation parameters of WLEDs on plant growth. In order to design WLEDs with good performance and can be used in the field of agriculture, common commercial YAGG was selected as green color conversion material and (Sr, Ca)AlSiN3 was selected as red color conversion material. (Sr, Ca)AlSiN3 red phosphors were prepared by traditional high temperature solid state method and the spectral performance was analyzed. By importing the built LED structure models into the simulation software LightTools and introducing the characteristic parameters of green phosphor particles, red phosphor particles and blue chip respectively, the simulation models of WLED were built based on a single blue LED chip (450 nm) and two blue LED chips (450+470 nm) separately , the color rendering performance of the spectral power distribution of WLEDs under different correlated color temperature was studied under the two excitation modes. In order to verify the difference of the color rendering index of the spectra obtained by the two excitation modes, WLEDs were prepared by combining (Sr, Ca)AlSiN3 and YAGG phosphors. Eventually, a real WLED was encapsulated by coating the combination of Sr0.8Ca0.12AlSiN3∶0.08Eu2+ and YAGG phosphors on two blue chips and led to an optimal spectrum of Ra=91.2, R9=96.1, R12=78.9, LER=126 lm·W-1 which contains the blue and red light needed for plant growth.

Silver is widely used in medical materials, photography, electronics, imaging and other industries because of its unique properties. However, silver ions are listed as one of the most toxic heavy metal ions, which poses a serious threat to the environment and human health. In order to detect the concentration of silver ions in an aqueous environment sensitively and accurately, a fluorescence aptasensor for detecting the concentration of silver ions in aqueous environment is proposed, which takes advantage of the excellent optical quenching property of nanogold and the stronger ability of double chain aptamers to capture silver ions based on the principle of fluorescence energy resonance transfer. Aptamer modified with SH bonds were mixed with nanogold to form stable nanostructures, and aptamer labeled with FAM were added to form a working solution to detect the concentration of silver ions. In the absence of silvers, two aptamers cannot hybridize with each other due to the repulsive force between the mismatched bases C—C, and the reaction system has strong fluorescence. In the presence of silver ions, the mismatched C—C in the double-stranded aptamers can form a stable C—Ag+—C base pair with the silver ions through the interaction between metal ion and base. The generation of this composite structure will shorten the distance between the gold nanoparticles and the fluorophore, so that the fluorescence signal will gradually weaken with the increase of silver ion concentration. According to the change of fluorescence intensity before and after adding silver ion, the concentration of silver ion can be detected. At the same time, in order to improve the sensitivity and stability of the sensor, the concentration ratio of gold and nucleic acid aptamers in the working solution, the concentration of sodium chloride, the pH of the buffer and the culture temperature were optimized. The results showed that when the volume ratio of 0.012 5 g·L-1 nano gold to 5 μmol·L-1 aptamer was 5∶1, the concentration of NaCl was 260 mmol·L-1, the pH of the buffer was 7, and the culture temperature was 30 ℃, the initial fluorescence intensity of the working solution was the strongest, the detection limit of silver ion was 10 nmol·L-1, and the correlation coefficient was R2=0.99. Based on the characteristics of high recognition ability and strong affinity of aptamers to silver ions, the sensor showed good selectivity to silver ions and was not disturbed by other substances. In addition, the sensor has good specificity for silver ion concentration detection, and has the advantages of simple operation, sensitivity and no introduction of toxic solvents, so it has a good application prospect for silver ion concentration detection in the water environment.

The spectroscopic parameters of gas absorption lines are an important factor that can influence gas parameters’ measurement accuracy based on laser absorption spectroscopy technology. However, the spectroscopic parameters in the molecule spectral database exist big uncertainties, resulting in the obvious measurement errors when used in laser absorption technology measurement. To obtain the spectroscopic parameters of H2O lines that can be used in the combustion diagnosis, time-division multiplexing technique is adopted to measure the absorption spectroscopy near 1.4 μm in the environments where the temperature, pressure and H2O concentration are known. The line strength, broadening coefficients and their temperature-dependent exponents of two H2O lines (7 185.60 and 7 454.45 cm-1) are measured, and the measured line strength of the two lines is less than 2.61% and 4.65% compared to the value in the HITRAN database. The uncertainty of the measured line strength of two lines is less than 4%.

There are many biosurfactant substances in human urine, and the adsorption relationship between these substances and calcium oxalate crystallites with different morphologies has not received widespread attention. In this study, the commonly used anionic surfactant sodium dioctyl sulfosuccinate (AOT) was selected as the adsorbing substance. The adsorption differences of AOT onto calcium oxalate dihydrate (COD) with different morphologies were studied to calcium oxalate stone’s formation mechanism. The crystalline phase transition of COD with different morphologies (Rod, Blunt, Flower, Cross, Bipyramid) before and after AOT adsorption was analyzed using an X-ray powder diffractometer Fourier-transform infrared spectrometer. Zeta potential changes on crystal surface after AOT adsorption was measured using a Zeta potential analyzer. The adsorption quantity of different AOT concentrations onto various COD crystals was detected using a colorimetric method, and the adsorption curves were drawn. As c(AOT) increases, the number of COD adsorbed increases gradually, and finally reaches saturation, and the adsorption curves are all S-type. The order of maximum adsorption of AOT by different morphologies of COD is: COD(Rod) (41.0 mg·g-1)>COD(Blunt) (37.5 mg·g-1)>COD(Flower) (35.0 mg·g-1)>COD(Cross) (27.2 mg·g-1)>COD(Bipyramid) (20.9 mg·g-1). The larger the specific surface area of the COD crystal was, and the more active sites were provided, so the stronger the adsorption capacity would be; the (100) surface of COD, which is rich in Ca2+ ions, is beneficial to AOT adsorption; the larger the internal energy of COD crystal was, the lower the adsorption amount would be. COD crystals’ stability in suspension adsorbed is obviously increased after AOT adsorption, and the rate of COD transition to COM is obviously reduced. Based on the adsorption characteristics of AOT on the surface of COD crystals with different morphologies, we propose a molecular model for the adsorption of AOT onto COD crystals. The adsorption of AOT by COD crystals is closely related to crystal morphology. The morphology of COD crystal that easily adsorbs AOT is more likely to adhere to injured cells’ surface with negative charges, thus leading to an increasing risk of calcium oxalate stone formation.

Quasi-Stellar Object (QSO), the most distant celestial body observed by humans, has important scientific value for the universe evolution.Quasars are far away from the earth, and their redshift values are large, which results in few features appearing in the optical observation window. Hence, constructing a QSO template is a difficult task, and then making the automatic identification of QSO become an urgent problem. The abnormal characteristics extraction and analysis of QSO spectra are helpful to solve the above problems, there by further providing strong evidence for exploring the mysteries of the universe. The outlier detection method, one of the main research contents in the data mining field, can detect rare data objects and anomalous characteristics from massive size data. Therefore, outlier detection can facilitate novel schemes for identifying rare QSOs and achieving validation. As a new generation of big data distributed processing framework, Spark provides an efficient, easy-to-implement and reliable parallel programming platform for analyzing and processing massive celestial spectra. The overarching goal of this paper is to investigate parallel detection methods based on sparse-subspace for QSO anomalous characteristics. We aim to optimize the performance of parallel abnormal detection through the virtue of the high-performance data processing capacity of the Spark programming model on clusters. This research embraces the following three modules, namely, QSO spectral feature reduction, sparse-subspace construction and search of QSO spectral data, and parallel algorithm design and analysis of QSO abnormal characteristics extraction. The QSO spectral feature reduction module exhibits superb performance in speeding up abnormal characteristic’s detection efficiency by the attribute correlation analysis. Specifically, some spectral feature lines with clustering structure are identified, which are usually concentrated in dense regions and are meaningless for detecting anomalous spectral features. The module aims to prune the redundant feature lines so as to narrow the search range of abnormal quasars. The second module is the sparse-subspace construction and search module, which extends the particle swarm optimization method to search sparse subspaces so as to obtain the anomalous features quickly. At the heart of this module is the determination of the sparse-subspace that contains QSO spectra anomalous features, where the subspace density of QSO spectra is measured by a threshold of sparse coefficients. In the third module, a parallel detection algorithm for abnormal spectral data under the MapReduce framework is proposed. The algorithm consists of two MapReduce: parallel data reduction strategy and sparse-subspace parallel search technique. Finally, the detectedanomalous features of some QSOs are analyzed, measured and verified by human eyes, which fully demonstrates that the sparse-subspace can provide effective support and strong evidence for identifying candidate sources of special and unknown QSOs.

The spectral reflectance describes the surface color characteristics of the object. In order to obtain more accurate color information of the object, the spectral reflectance reconstruction in the image processing field has become a hot topic. We take the spectral reflectance as the main research target. Sequentially, we propose the algorithm which reconstructs the spectral reflectance of the measured object in the visible region to enhance the accuracy of color reproduction. This article attempts to employ compressed sensing (CS) theory in the spectral experiments to reconstruct the spectral reflectance. This article first introduces the theory of compressive sensing and then combines the theory of compressive sensing with the principle of spectral reflectance. According to the theoretical framework of spectral reflectance reconstruction based on compressed sensing. The appropriate sampling value is selected.The compressed sensing sample value is the compressed value, the wavelet base is used as the orthogonal matrix, and the Gaussian random matrix is used as the measurement matrix, the orthogonal matrix and the measurement matrix ensure irrelevance, the original spectral reflectance is linearly projected from the high dimension to the low dimension, then a low-dimensional observation signal is obtained, the simple orthogonal matching pursuit algorithm (OMP) reconstructs the low-dimensional to high-dimensional high-precision observation signals from low-dimensional observation signals, and the obtained spectral reflectance has the same dimensions as the original spectral reflectance. Finally, the compressed inverse method and the traditional spectral reflectance reconstruction algorithm are compared with thepseudo-inverse method and the polynomial regression method. The experimental results showthat the color difference and the root mean square error obtained by the compressed sensing reconstruction algorithm are smaller than the measured value of the pseudo-inverse method and the polynomial regression method, In other words, the reconstruction accuracy is significantly improved, the spectral curve reconstructed by compressed sensing can reach or be closer to the peak of the original spectral curve, which is closer to the original spectral curve on the whole visible range. The spectral curve reconstructed by the polynomial regression method and the pseudo-inverse method does not reach the original peak, and there is an overall deviation. Inconclusion, the experimental resultsshow that compressed sensing uses low-sampling data to achieve the effect of full sampling. Compressed sensing improves the accuracy of spectral reflectance reconstruction while reducing the amount of computation. The compression reconstruction effect proposed in this paper is significantly better than the traditional polynomial regression method and the pseudo-inverse method. Compressed sensing theory can be applied to practical multispectral imaging systems.

Compressed sensing (CS) is a new technology of signal compression and sampling. Orthogonal Matching Pursuit (OMP), a greedy tracking algorithm, is widely used in sparse signal reconstruction in the compressed sensing field. In connection with the characteristics of high-dimensional small samples of near-infrared spectra signals and sparse prior signals, a novel near-infrared spectra variable selection method named Orthogonal Matching Pursuit Based Variable Selection (OMPBVS) is proposed, based on the compressed sensing theory, to further improve the flexibility and reliability of near-infrared spectra variable selection. By sparse reconstruction of the original spectral signal, OMPBVS can compress the regression coefficient of most variables to zero, and then indirectly realize the selection of spectral variables. In the specific process, the spectral matrix is adopted as the sensing matrix, the predictive variable as the observation variable and iteratively calculated residual and the inner product of the atom, and the inner product of the largest atom is chosen. During each iteration, the signal is projected onto the subspace spanned by all selected atoms, and then the coefficients are updated for all the selected atoms, enabling the residual error and all the selected atoms to be orthogonal. With the residual calculation to be the essence of Grammar-Schmidt Orthogonalization, the orthogonal projection can reduce the number of iterations and ensure the accuracy of signal reconstruction. OMPBVS can reduce the spectral dimension to the sample size scale, and its variable selection capability is comparable to LASSO. However, compared with LASSO, the optimization method of OMPBVS loss function is a forward selection algorithm, which reduces the number of iterations and can precisely control the number of selected variables. Variable selection experiments were performed on the beer dataset and Wheat kernels dataset to compare the performance of six variable selection methods: PLS, MCUVE, CARS, WMSCVS, LASSOLarsCV, and OMPBVS. There were 60 samples in the beer dataset, 36 samples of the training set and 24 samples of the test set were divided by Kennard Stone (KS) method, and the prediction variable was Original extract concentration. The Wheat kernels data set consisted of 523 samples, 415 training samples, and 108 test samples. The predicted value was protein content. The OMPBVS method selects the number of variables, RMSEC and RMSEP from the beer dataset as 2, 0.205 2 and 0.159 8, respectively. When on the Wheat kernels data set, the number of selected variables, RMSEC and RMSEP were 9, 0.450 2, and 0.412 5, respectively, and the variable selection ability and model performance was better than the other five methods, indicating that OMPBVS is an effective NIR spectral variable selection and quantitative analysis method. OMPBVS variable selection method has good generalization ability in the case of small samples, which can reduce the number of selected variables and improve the robustness of variable selection. Besides, spectral preprocessing methods based on SNV and MSC can reduce the number of selected variables to a certain extent and improve the interpretability of the model.

Vapor hydrogen peroxide is a strong oxidant. The final product is water and oxygen. It has the advantages of no residue, safety, rapid disinfection and wide material compatibility. Vapor hydrogen peroxide sterilization is widely used in pharmaceutical, medical, health, and biosafety fields to ensure the safety of drugs, medical devices and food, especially used for disinfection and prevention of respiratory infectious diseases such as COVID-19, MERS, SARS and H1N1. In order to ensure the sterilization effect, hydrogen peroxide detector is used to monitor the concentration of hydrogen peroxide. Based on the tunable laser absorption spectroscopy technology of 1 255 cm-1 quantum cascade laser, a vapor hydrogen peroxide concentration measuring device is developed. The concentration range is 0~1 800 ppm. The main parts of the detector are isolated from hydrogen peroxide by V-type optical path structure and optical window to avoid hydrogen peroxide corrosion. In view of the fact that the error of transmittance function approximated by the first-order Taylor series is large in the case of high concentration and high absorbance, the second-order Taylor series is used to approximate transmittance function, and the second-order function of the second harmonic signal with respect to the gas concentration is derived. The second harmonic signal is the voltage value, which is calibrated and traced by potassium permanganate titration. Finally, the measurement formula is obtained, which fits the high and low concentration hydrogen peroxide well, and the maximum fitting error is 3%. When the humidity changes, the second harmonic signal does not change, excluding the influence of moisture on the measurement of hydrogen peroxide, which is suitable for the measurement of high concentration VHP at atmospheric pressure during the sterilization process.

The potential risk of nanomaterials to the environment and human health has caused widespread concernin recent years. Because of their small size and high reactivity, nanoparticles are likely to interact with biological macromolecules in vivo when they enter into the organisms. Therefore their original physical and chemical properties will change. Then, they are easily recognized and phagocytized by cells, and further bring about harm to the tissues and organs. In the process of interaction, biomacromolecules are also affected by nanoparticles, which may cause damage to their structure and interfere with the normal performance of their specific functions. Therefore, the toxic effect of nanomaterials on biomacromolecules is an important theoretical basis for studying their biosafety. Structure and function changes of human serum albumin (HSA) when exposed to carbon quantum dots (CQDs) and the mechanism for their interaction were studied by a variety of spectroscopic methods. According to fluorescence spectra and UV-Vis absorption spectra, CQDs quenched the intrinsic fluorescence of HSA by forming non-fluorescence complexes. Calculation results of binding constant (KA) and the number of binding sites (n) showed that there was only one binding site for CQDs on HSA, and synchronous fluorescence spectrum and binding sites competition experiment revealed this binding site was close to the only tryptophan residuein HSA, which located in ⅡA subdomain of protein. Based on Frster resonance energy transfer (FRET) theory, binding distance r between CQDs and HSA was calculated to be 2.89 nm, which is less than 8 nm, indicating that there was a high possibility of fluorescence quenching caused by non-radiative energy transfer between CQDs and HSA. From the calculated thermodynamic parameters, it can be inferred that hydrogen bonds and van der Waal’s force played an important role in the interaction of HSA with CQDs. According to the results of resonance light scattering(RLS) spectra, three-dimensional fluorescence spectra and circular dichroism (CD) spectra, CQDs could change the secondary and tertiary structure of HSA, causing α-helix conformation content of protein increase, promoting HSA further curling and folding, making hydrophobicity of microenvironment around tryptophan residue increase. Structural changes further affected the aggregation state and some physiological functions of protein, resulting in the decrease of the aggregation degree and esterase-like activity of HSA and a slight improvement of its free radical scavenging ability. This work can provide a reference for the evaluation of biological and environmental safety of nanomaterials, and it also provides a relatively systematic set of spectral analysis methods for exploring the interaction between nanoparticles and proteins.

Generally, the multivariate calibration model based on spectroscopy is only for the same instrument, the same test conditions and the same batch or similar samples. However, with the increasing demand for spectral application, the problem that different samples cannot share the spectral model has become the fundamental technical bottleneck limiting spectral technology application. In the visible near-infrared spectrum analysis, after the change of the instrument, the test environment and the sample, the established spectral model is no longer suitable. So the model transfer is needed to solve this kind of problem. The model transfer is the key technology bottleneck to limit the application of spectral technology. Therefore, this paper summarizes the current research situation and discusses future development direction. First of all, the model transfer problem is divided into two categories: the first is the model mismatch of the same sample under different instruments or different test environments, called the first type of model transfer; the second is the model mismatch between different samples, called the second type of model transfer. These two kinds of problems are different in nature. To solve the first type of model transfer can ensure the accuracy and stability of homologous samples. And to solve the second type can realize the automatic transfer and matching application of spectral model between different products. Then, the commonly used model transfer algorithms are sorted and classified, including model updating, spectrum based correction algorithm, result based correction algorithm, and the application of each category of model transfer algorithm is listed. Model updating is the most direct method for recalculating model coefficients, which can meet the new changes by expanding and adjusting the model. Spectrum based correction algorithm is based on the calculation of the transfer matrix to achieve spectral correction. Result based correction algorithm is based on the calculation of pre-test results and actual results coefficients, so as to achieve the correction of prediction results. Finally, it is pointed out that the second type of model transfer should be studied in the future, especially the automatic model transfer by machine, so as to realize the real spectral velocity measurement.

In recent years, deep learning has been studied more and more in the field of data mining, and the integrated learning algorithm in deep learning has been applied to classification and quantitative regression more and more, but the application of integrated learning in the field of infrared spectrum analysis is little. In this paper, an integrated learning quantitative regression algorithm based on Blending model is proposed. GBDT algorithm, linear kernel support vector machine (LinearSVM) and radial kernel support vector machine (RBF SVM) are used as the basic learners, and the prediction results of the basic learners are fused by LinearSVM. The first derivative preprocessing was carried out for the spectral data. The prediction results of the model were analyzed and compared by using the GBDT, LinearSVM, RBF SVM and the Blending integrated learning model respectively. RBF SVM model is the best model for predicting the content of active substance and hardness, R2 is the highest, the RMSEP is the smallest, and the RPD is the largest, and the GBDT model is the worst. The R2 of tablet quality predicted by Blending model is the highest, reaching 0.837 4, while the RMSEP of RBF SVM is the lowest, 2.140 6, and the RPD of RBF SVM, 7.487 8, is the largest. For the boiling point, flash point and total aromatics of diesel oil, Blending model is the best one, which is better than the single model. For the cetane number, GBDT model and RBF SVM model are better than Blending model. For the density property, the single model and the integrated model have better prediction results, except that the R2 of LinearSVM model is 0.944 5, R2 of other models are all higher than 0.99. For the prediction of freezing point properties, RBF SVM and LinearSVM are both better than Blending model. For the prediction of viscosity, only RBF SVM is better than Blending model. It can be seen from the results that the Blending model integrates the characteristics of GBDT, LinearSVM and RBF SVM model, compared with the single model, the prediction of Blending is better or optimal. It is proved that Blending integrated learning model has strong applicability for infrared quantitative regression, and has a high prediction accuracy and generalization ability. It is of great significance for further research on the application of integrated learning algorithm in infrared quantitative regression.

Fourier infrared spectroscopy (FTIR) technology is an important method for material characterization. However, it is limited by the diffraction limit. The spatial resolution limit of traditional Fourier infrared spectrometer is on the order of micrometers and cannot be applied to the characterization of nanomaterials. Nano Fourier transforms infrared spectroscopy (Nano-FTIR) is an emerging super-resolution spectroscopic surface analysis technology, which exhibits tremendous performance in nanomaterial characterization research with the characteristics of nano-scale spatial resolution, wide spectral range and high chemical sensitivity. Qualitative and quantitative research on the source of the high spatial resolution of Nano-FTIR signals and the extraction process of spectral signals in the system can provide the important basis for the design and development of Nano-FTIR instruments and the interpretation of sample spectral characterization results. Based on the typical instrument structure and basic working principles, an equivalent research model is established in the multi-physics finite element analysis software COMSOL, and the important details of the model and the numerical calculation process are explained separately. In the simulation study, the model first calculated the electromagnetic field enhancement in the model space based on Maxwell’s electromagnetic wave theory, and then simulated the “line sweep” process of the probe at the interface between two materials with large differences in dielectric constants, discussed the near-field enhancement of the tip-sample and the spatial resolution of the signal. Subsequently, a numerical model was proposed with the scattered power of the probe and sample as the research object, and the process of the modulation and demodulation extraction of the signal by the probe tapping, different incident angle of light and demodulation frequencies were also discussed. Finally, in order to verify the rationality of the model, the spectral responses of SiO2 thin film samples of three thicknesses of 20, 100 and 300 nm in the wavenumber range of 900～1 250 cm-1 were simulated, and the simulated spectra were compared with the measured results. The results show that as the thickness of the sample increases, the spectral signal is correspondingly enhanced and predicted spectra in agreement with the experimental spectra. Spectra predicted by our model are more consistent in peak shape compare with spectra simulated by the tip-sample electric field strength used to some previous studies. The proposed numerical model can be used for the prediction of Nano-FTIR spectra, in addition, the model also has certain generality, and can be used for Tip-enhanced Raman spectra and Terahertz spectra based on scattering near-field optical microscopy (s-SNOM) technology.

Precise wind field data is essential for improving the accuracy of the numerical weather forecast, and tropospheric winds are not satisfied with the requirements as one of the key measurement objectives for improving weather forecasts. Although meteorological satellite-based imager derived winds by tracking the motion of characteristic targets in continuous cloud field images is an effective observation method that has improved numerical weather prediction forecasts on both regional and global scales, an error still exists in the ambiguity of the vector height assignment. Satellite-based infrared hyperspectral sounder has the capability of atmospheric vertical detection of temperature and humidity profiles, which can provide more accurate vector height assignment of wind field by analyzing atmospheric motion vectors among multiple vertical layers, improving the ambiguity of the vector height assignment. We proposed a method of tropospheric 3D winds measurement on cross-platform polar meteorological satellite-based infrared hyperspectral sounders of FY-3D/HIRAS and NOAA-20/CrIS, collocated vapor channel images through nadir overpass observations of both instruments, derived wind field by calculating the motion of dense optical flow field, combined ERA-Interim reanalysis data to verify the mean absolute deviation(MAE) and root mean square error(RMSE) of wind speed and the MAE of wind direction after quality control. The vertical wind fields of 200, 300, 400, 600, 650 and 1 000 hPa are calculated through observations of HIRAS and CrIS at 00:00, 06:00 and 12:00 UTC on February 20, 2019, the results show that, the trend of the variation of wind speed range is consistent with ERA-Interim reanalysis data, the wind speed range decreases as the height decreases, the upper layers are more sensitive to wind speeds above 20 m·s-1, while wind speeds measured near the surface are concentrated within 10 m·s-1. The MAE of wind speed is mostly less than 3 m·s-1 while the maximum value is less than 4 m·s-1, the RMSE of wind speed is mostly less than 3.5 m·s-1 while the maximum value is less than 4.5 m·s-1, the MAE of wind direction is mostly less than 30° while the maximum value is less than 40°. The wind field error mainly comes from the observation deviation of radiation data due to different instrument parameters, along with the positioning deviation of data image re-projection due to different spatial resolution.

Steel slag is a solid waste in the process of steelmaking, and slag is a by-product in the ironmaking process of the blast furnace, it has the problem of hard to use and added value. In the face of the above problems, the development of an inexpensive composite rubber filler is used for rubber field. Magnetic hot braised slag, unmagnetic hot braised slag, mineral powder and grinding aid-modified compound agent were used to prepare modified steel slag-mineral powder composite rubber filler and used in composite rubber system. The effects of amount of magnetic hot braised slag, amount of unmagnetic hot braised slag, amount of mineral powder and amount of grinding aid-modified compound agent on the properties of modified steel slag-mineral powder base rubber composites were studied, respectively. At the same time, the influence mechanism is analyzed. The results showed that the modified steel slag-mineral powder composite rubber filler has the best reinforcing-flame retardant performance with the amount of magnetic hot braised slag is 150 g, amount of unmagnetic hot braised slag is 150 g, amount of mineral powder is 150 g, and amount of grinding aid-modified compound agent is 9 g. According to the mass ratio of modified steel slag-mineral powder composite rubber filler and carbon black is 20∶30 preparation of modified steel slag-mineral powder base rubber composites, its tensile strength 21.83 MPa, tear strength 46.23 kN·m-1, shore A hardness 62, abrasion loss 159 mm3, limit oxygen index 19.8% and burning-out time 187 s. The grinding aid and modified compound agent not only reduces particle size and improves particle size uniformity, but also improves the surface structure and properties of modified steel slag-mineral powder composite filler, which is beneficial for modified steel slag-mineral powder composite filler to disperse evenly in composite rubber system and improve the compatibility. The chemical reaction between steel slag and mineral powder under the action of grinding aid and modified compound agent changed the phase composition of steel slag and mineral powder and improved the reinforcing and flame retardant properties.

Tunable diode laser absorption spectroscopy (TDLAS) can realize the simultaneous measurement of multiple parameters such as temperature and component concentration, which has the advantages of small size, fast response and high environmental adaptability, so the technology has gradually become a major tool of combustion flow field diagnosis. TDLAS mainly includes direct absorption spectroscopy and wavelength modulation spectroscopy. Intensity normalized wavelength modulation spectroscopy is suitable for gas turbine flow field parameter measurement under severe application environment conditions, such as beam deflection effect caused by vibration, turbulence and strong thermal radiation background. Based on the TDLAS technology, carry out the 1f normalized wavelength modulation technique methods for measuring the parameters of gas turbine combustor temperature, the concentration of components research and laboratory test. A two-dimensional measurement scheme of temperature and concentration of H2O and CH4 in gas turbine combustor along the airflow direction was designed, and a single nozzle bench test of cold and hot state verification test was carried out. The measurement adopted 1f normalized WMS to restrain the rack vibration and the background noise of thermal radiation. The DFB laser in 1 392, 1 469 and 1 343 nm of butterfly package was used. The light output of the three lasers was time-division multiplexing. The absorption lines at 7 185.6, 6 807.83 and 7 444.3 cm-1 of H2O were selected and used in pairs to measure the temperature and H2O concentration in a certain range under the hot state. The DFB laser in 1 654 nm of butterfly package was employed for the measurement of the cold-state CH4 concentration. The laboratory verified the reliability of the measurement system. The CH4 standard gasin the range of 4%～6% was measured and compared with the actual value. The maximum relative deviation of concentration measurement was 3.72%. A temperature step within the range of 900～1 500 K was set in the high-temperature furnace, pure water vapor was filled in, and the temperature measurement value and concentration under different temperatures and pressures set were calculated. The results showed that the maximum relative deviation of temperature measurement was 3.07%, and the maximum relative deviation of water vapor was -2.00%, which reflected the reliability of the measurement system. In the gas turbine experiment, a set of the miniaturized measuring instrument was integrated, and the measurement structure of multi-beam laser transceiver was designed. In the experiment, two electric displacement tables with measuring structure were used to move at intervals of 5 mm and measure the 300 mm×60 mm combustion area in the combustion chamber of a gas turbine so as to obtain the results of hot and cold states under some conditions. By bicubic interpolation, a two-dimensional flow field map with a resolution of 0.5 mm was drawn. The results showed the real state of CH4 and flame distribution in the measurement area. In this paper, a new research method and technical means were proposed for the study of the mixing of fuel and air and the combustion characteristics of gas turbine nozzles.

The application of underwater wet welding technology is becoming more and more extensive. Due to the special welding conditions, the welding quality in deep water should be improved. This paper simulates water depths of 0.3, 20 and 40 m respectively by building an underwater wet welding experiment platform and adjusting air pressure by pressure tank. After defining the arc initiation stage, the spectral information and voltage and current data of the welding arc initiation stage under three water depth environmental conditions were collected. When collecting spectral information, the delay trigger function of the spectrometer is used to collect the spectral data at the time of arc initiation of 5, 10, 15, 20 and 25 ms, respectively, and the collected arc spectral data is analyzed for diagnosis after sorting out. In the diagnostic analysis, the recognition results of each element particle were obtained by combining the relevant data of the NIST atomic spectral database and characteristic spectral lines. For the elemention state of high price state, the degree of excitation ionization will be affected by the change of arc temperature due to its large ionization energy. Therefore, it is not only necessary to identify and diagnose its components by spectrogram, but also need to calculate the number density of its components. Eighteen kinds of particles of arc plasma components to be considered in the calculation are determined by combining the spectral diagnosis information of underwater wet welding arc and the reaction process of underwater wet welding. The number density of the plasma component under three water depths is obtained by solving the equations composed of the Saha equation, dissociation ionization equation, quasi-neutral equation and gas pressure equilibrium equation. The variation law was analyzed to explore the influence of different water depth on the number density of welding arc plasma and its factors. The results show that the variation of particle number density is nonlinear under different water depth, and the variation amplitude of arc number density increases rapidly with the increase of water depth. As the water depth increases, the arc will be compressed, but the arc cannot be compressed indefinitely. The ionization of particles is affected by the temperature. The higher the temperature is, the stronger the ionization will be. However, when the temperature rises to a certain degree, each ionization has its ionization limit, and the particle number density will not increase indefinitely. Based on the calculation of arc number density, the mechanism of underwater welding arc is studied, which provides theoretical basis for improving welding stability and arc simulation.

Conformal coating is a protective coating widely used for protecting printed circuit board (PCB) from harsh environmental conditions. The conformal coating’s thickness is one of the key parameters for evaluating the quality of conformal coating, and thus the thickness needs to be measured before PCB assembling. In this paper, we proposed to utilize spectral domain optical coherence tomography (SD-OCT) combined with an image segmentation algorithm to achieve a rapid and non-destructive thickness measurement on conformal coating. The spectral domain OCT system was built with a broadband SLD light source (bandwidth: 180 nm) so that an axial resolution of 1.72 μm was obtained. Meanwhile, an image segmentation algorithm based on boundary tracing was designed to segment the conformal coating in OCT image for rapid and accurate thickness measurement. To evaluate the proposed method, the measurement results in this paper were compared with the traditional metallographic examination, analyzing the consistency of the detected boundaries as well as the measured thickness between these two methods. In addition, we also compared the method proposed in this paper with the edge detection algorithm based on image gradient previously reported by our group in terms of the accuracy and efficiency, so as to justify the advantages of this newly developed method. The results show that the measurement method designed in this paper is in good agreement with the traditional metallographic method, and the thickness of the coating can be accurately measured. Based on the three-dimensional imaging capability of the system, the thickness topographic map of the conformal coating can be visualized clearly, which cannot be realized by the metallographic section method. Compared with the method proposed by our group previously, this method can provide more accurate and efficient measurement, paving the way for real-time and inline thickness measurement on conformal coating.

Plant polysaccharides’ chemical structures are similar to that of glucosaminoglycan, a stone inhibitor in urine; thus plant polysaccharides may be used to prevent and treat kidney stones. However, the applications of natural polysaccharides are limited due to their large molecular weights and volume. In this study, the effects of four degraded Gracilaria lemaneiformis polysaccharides (GLP1, GLP2, GLP3 and GLP4) with molecular weights of 49.6, 16.2, 8.2 and 3.8 kDa, respectively, on crystal growth of calcium oxalate (CaOx) were studied. 1H NMR, 13C NMR and gas chromatography-mass spectrometry (GC-MS) spectra showed that the four GLPs were composed of β-D-galactose and 6-O-sulfate-3,6-α-L-galactopyranose. X-ray diffraction (XRD) patterns showed that GLPs induced the formation of calcium oxalate dihydrate (COD) crystals, and the diffraction peaks of COD appeared at crystal plane spacing d=0.617, 0.441, 0.277 and 0.224 nm. However, only calcium oxalate monohydrate (COM) crystals were formed in the absence of polysaccharide, and the diffraction peaks of COM appeared at d=0.593, 0.364, 0.296 and 0.235 nm. Because COD is more easily excreted out from the body than COM, COD formation is beneficial to reduce the risk of stone formation. Fourier-transform infrared spectroscopy (FTIR) showed that the asymmetric stretching vibration νas(COO-) and symmetric stretching vibration νs(COO-) of carboxyl groups from oxalate had different degrees of blue shift as the molecular weight of GLP decreased or GLP concentration increased, in which νas(COO-) increased from 1 618 to 1 642 cm-1 and νs(COO-) increased from 1 318 to 1 328 cm-1. That is, GLP4 induced full COD crystals. Scanning electron microscope (SEM) examination showed that as the molecular weight of GLP decreases, the proportion of COD increased, and the dispersion degree of the crystal increased, making the crystal much blunter. As GLP molecular weight decreased or GLP concentration increased, the surface charges of CaOx crystals induced by GLPs changed to be more negative. The greater absolute value of Zeta potential is conducive to inhibiting crystal aggregation. The inductively coupled plasma emission spectra (ICP) results revealed that the four GLPs increased the concentration of soluble Ca2+ ions (c(Ca2+)) in the solution and decreased the amount of CaOx precipitation. At a concentration of 1.0 g·L-1, the c(Ca2+) in the supernatant were GLP4 (37.88 μmol·L-1)>GLP3 (19.70 μmol·L-1)>GLP2 (16.05 μmol·L-1)>GLP1 (10.55 μmol·L-1), respectively. The four GLPs can inhibit COM growth, induce COD formation, reduce the aggregation degree of crystals, increase the absolute value of Zeta potential on crystal surface and the concentration of soluble Ca2+ ions in solution, and reduce the amount of CaOx crystal formation. The regulatory ability of GLP was negatively correlated with its molecular weight. All these results showed that GLPs, especially GLP4 with the smallest molecular weight, might be potential drugs for the preventing and treating CaOx stones.

To improve the of quality and yield of sweet cherry fruits, sweet cherry trees were cultivated under rain-shelter to avoid the problems of low fruit setting rate, falling fruit and fruit malformation in southern China. Sweet cherry trees under rain-shelter cultivation had an obvious negative effect on photosynthesis. In both plants and algae, photosynthetic pigments such as chlorophylls and carotenoids play irreplaceable roles in light harvesting and mediating stress responses to a variety of endogenous stimuli. This research aimed to detect changes of photosynthetic pigments in leaves that affect photosynthesis of fruit trees quickly and conveniently. The experiment took sweet cherry leaves in two different cultivation pattern, open-field and rain-shelter cultivation, as the research objects, and determined its Raman spectrum in the range of 200~3 500 cm-1. The analysis is performed, and the characteristic peaks are calibrated and designated from three wave number bands of 400~800, 800~1 250 and 1 250~1 650 cm-1. According to the Raman spectrum characteristic value, it is concluded that the sweet cherry leaves have a relatively small Raman scattering. Sensitivity is mainly concentrated in the 500~1 700cm-1 band. The analysis of Raman spectrum in the range of 960~1 800 cm-1 found that characterizes the carotenoids (lycopene, β-carotene and lutein) mainly contains 4 main peaks, which are 1 526, 1 157, 1 005 and 960 cm-1, the Raman intensity of leaves of sweet cherry tree under open-field cultivation is significantly lower than that of rain-shelter cultivation. 1 157 and 1 526 cm-1 are also the Raman spectrum characteristic peaks of chlorophyll. Overall analysis shows that photosynthetic pigment content in leaves of sweet cherry under open-field cultivation is lower than that of under rain-shelter cultivation. The characteristic spectral lines of 1 157, 1 520 and 1 526 cm-1 correspond to symmetrical stretching vibrations of C—C single bond and CC double bond, and their relative strengths can be used as the basis for judging the content of cellulose, carotenoids and chlorophyll in sweet cherry leaves. Fourier transform infrared spectroscopy (FTIR) characterizes that the vibration peak position and vibration intensity of chlorophyll are weak, the vibration coupling is complex and difficult to identify. The second derivative treatment of the infrared spectrum of the chemical composition in sweet cherry leaves was used to derive the peak position and enhance the resolution of the spectrum. The characteristic peaks of β-carotene at 1 437 and 1 551 cm-1 are obvious. Compared to rain-shelter cultivation, sweet cherry leaves showed lower absorbance at these two characteristic peaks, indicating that the content of β-carotene in leaves sweet cherry tree under open-field cultivation is less than that of rain-shelter cultivation. These findings provide the theoretical basis for the spectroscopy research of photosynthetic pigments in plant leaves under different cultivation pattern.

At present, there are a lot of unhealthy phenomena in the cherry market, which have seriously damaged the economic benefit of famous cherry brands. As a kind of spectrum tracing technology, Raman spectrum tracing technology has been paid more and more attention because of its advantages of fast-speed, high efficiency, pollution-free and non-destructive analysis. And the long short-term memory (LSTM) network is a kind of feedback neural network with memory, which is a variant of the recurrent neural network. LSTM network overcomes the problem of gradient disappearance in the recurrent neural network, and is suitable for solving sequence-sensitive problems and tasks. At present, it is widely used in speech recognition, image recognition and handwriting recognition. However, there are few studies on the application of LSTM network in origin tracing. Therefore, a technology that can identify cherries of different origins quickly and non-destructively is urgently needed. Based on this, this study in this paper proposes a fast and non-destructive identification technique for cherries from different origins by using LSTM network and Raman spectroscopy. In this study, 369 cherries from the United States, Shandong and Sichuan are used to obtain the spectral data of cherries from different regions with the Raman spectrometer under the 785 nm laser. Moreover, the Raman spectral data after baseline correction is taken as the network input data, and a discriminant model is built based on the LSTM network to realize rapid identification of cherries from different origins. In addition, the sample discrimination accuracy A, sample precision P, sample recall R, and sample F values are used as evaluation standards to explore the effects of different prepossessing methods on the sample discrimination accuracy. The results showed that when the ratio of the sample training set to the test set is 85∶38, the LSTM network model that directly uses the original Raman spectral data has poor ability to identify the origin, and the identification accuracy is only 79.87% on average. But when prepossessed Raman spectral data are used, the average accuracy of the model remains above 92%. And the model has the best discrimination accuracy after using Stravinsky-Golay (SG) and multiplicative scatter correction (MSC) prepossessing methods, and the discrimination accuracy reaches 99.12%. At the same time, the accuracy rate, recall rate and F value of LSTM network discrimination model are all high when the preprocessing method named SG+MSC is used. It means that the LSTM discrimination model proposed in this paper can perform well in distinguishing cherries from different regions, which provides a new way of tracing the origin of cherries.

The visible near-infrared (Vis-NIR) hyperspectral imaging technology was used to identify the intact and damaged Lingwu long jujube rapidly. In this study, damage grades, including Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅴ of Lingwu long jujubes were obtained by using quantitative damage devices. Hyperspectral images of intact and damaged samples were collected by using a hyperspectral imaging system. Region of interest (ROI) was extracted from the image and average spectral values of samples were calculated. Sample set partitioning based on joint x-y distance (SPXY) was used to divide all samples (420) into calibration sets (315) and prediction sets (105) in a ratio of 3∶1. The partial least squares discriminant analysis (PLS-DA) classification model was established for the original spectrum, and the accuracies of the calibration set and prediction set were 72.70% and 86.67%, respectively. The original spectrum of Lingwu long jujube was preprocessed by means of moving average (MA), Savitzky Golay (SG), multiplicative scatter correction (MSC), orthogonal signal corrections (OSC), baseline and de-trending. PLS-DA classification model was established after pretreatment. The results showed that in the PLS-DA classification model established by spectrum preprocessed by different pretreatment algorithms. Through analysis and comparison, it was found that MSC-PLS-DA was the optimal model combination. In the established classification discrimination model, the accuracies of the calibration set and prediction set were 76.19% and 86.67%, respectively. The accuracy of the calibration set was 3.49% higher than that of the original spectral modeling, and the accuracy of the prediction set was not higher than that of the original spectral modeling. Original spectral and spectral after pretreatment was used to extract feature wavelengths using the successive projections algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and interval variable iterative space shrinkage approach (iVISSA), and established the PLS-DA classification model based on the feature wavelengths. The results showed that MSC-CARS-PLS-DA was the optimal classification model, the accuracy of the calibration set was 77.14%, the accuracy of the prediction set was 89.52%. The modeling accuracy was improved by 4.44% and 2.85% respectively compared with the original spectral modeling accuracy. The above research showed that the Vis-NIR hyperspectral imaging technology combined with MSC-CARS-PLS-DA model could realize the rapid identification of lingwu jujube damage grade.

Origin is an important environmental factor affecting crop production, and tracing the origin is of great significance for food safety. The chemical analysis method is generally used in traditional agricultural product origin detection, and its operation is cumbersome, destructive and time-consuming. In this study, northern cold mung beans were used as the research object. Near-infrared spectral data of mung bean in two states of seed and powder were obtained in the main origins of high-quality for Baicheng, Dumeng and Tailai. A new nondestructive detecting method for mung bean origins were established by optimizing the NIR characteristic spectrum wavenumbers. Firstly, in the range of 10 105.37~4 078.655 cm-1 wavenumber with strong absorbance value, the raw spectral data of mung beans from different regions was preprocessed by using multivariate scattering correction (MSC) method to eliminate spectral interference information. Then competitive adaptive reweighted sampling(CARS) algorithm is applied to optimize the characteristic spectral wavenumbers of mung bean seed and powder states from different origins to reduce the feature vector dimension of the spectral curve. Finally, a feed-forward neural network (BP) adaptive inference mechanism was used to establish a non-linear mapping model between the origin of mung bean and its spectral characteristic wavenumber, and the encoding vector output by the network was parsed to the original name as the output result of the detection of the origin of the mung bean. The results show that: (1)Preprocessed with multiple scattering corrections in the raw spectral, the error of the spectral curve of mung bean powder is reduced from 12.87 to 3.20, and the error of the spectral curve of mung bean seed is reduced from 153.04 to 27.73, which provides effective and reliable spectral data. (2) Through the competitive adaptive reweighting sampling algorithm, the important characteristic wavenumbers of mung bean spectral curve are extracted. From the 2 114 original wavenumbers of seed and powder state, 61 and 107 characteristic wavenumbers are optimized respectively, and the total number of wavebands is reduced by 94.94%, which is taken as the characteristic index of mung bean origin recognition. (3) The MSC-CARS-BP mung bean origin detection model was put forward innovatively. Based on the optimized spectral characteristic wavenumber as the quantitative basis, the origin detection of mung bean seed and powder was carried out respectively. The accuracy of the prediction set was 92.59% and 98.63%, and the correlation coefficient was above 0.99. This method can use near-infrared spectrum processing technology to achieve the goal of non-destructive detecting of mung bean origin, and provide technical support and reference for automatic and rapid traceability of agricultural products origin.

Creaminess is one of the most favorite sensory properites of fermented milk for consumers, reflecting the pleasant characteristics. To develop fermented milk with high creaminess intensity without additional additives, the key textural characteristics of fermented milk with high creaminess intensity should be revealed. However, the definition and evaluation criteria for creaminess have not been unified, and the key textural characteristics of fermented milk with high creaminess intensity have not been clearly clarified. Previous studies have shown that the gelation process and gel strength, the stability and the apparent viscosity of fermented milk may be the key textural characteristics that affect the sensory perception of fermented milk. In this study, the spectroscopy methods were applied to analyze the key textural characteristics of fermented milks with different creaminess intensities. Five samples with different creaminess intensity selected from the descriptive sensory evaluation were used in this research. The gelation process and gel strength of fermented milk were measured by diffusing wave spectroscopy. The stability of fermented milk was studied by multiple light scattering spectrum. In addition, the rheological technology was used to analyze the apparent viscosity of fermented milk. First, the multi speckle diffusing wave spectroscopy technique was used to investigate the changes of the mean square displacement of particles during the gelation of fermented milk gels. The results showed that the critical points of the elasticity index of the gel were 108, 115, 106, 132 and 143 min, respectively, indicating that the gelation time of fermented milk basically increased gradually. The reason might be due to the sufficient rearrangement and aggregation of casein micelles to form a more uniform gel network, which enhanced the perception of creaminess. The final value of elasticity index represents the gel strength of fermented milk. The results showed that the creaminess intensity of fermented milk was stronger when the gel strength was at a moderate level. Furthermore, the stability of fermented milk was measured by using multiple light scattering technology. It was found that the turbis can stability indexes of fermented milks were 2.2, 2.1, 1.9, 2.0 and 1.4, respectively, indicating that the stability of fermented milk was positively correlated with the perceived intensity of creaminess of fermented milk. At last, the apparent viscosity of fermented milk was measured by rheological technology. The result showed that the apparent viscosities of fermented milks were (0.362±0.016), (0.271±0.013), (0.251±0.021), (0.479±0.031) and (0.343±0.024) Pa·s, respectively. The results showed that there was no correlation between the apparent viscosity of fermented milk and the perceived intensity of creaminess. In summary, the gelation time and stability of fermented milk can significantly affect its creaminess perception. This study provides a theoretical basis for determining the key texture characteristics of the creaminess off ermented milk and developing fermented dairy products with enhanced creaminess intensity.

Composting is one of the effective ways for wheat straw utilization. However, the relevant study on solewheat straw composting is still constrained. Dissolved organic matter (DOM) is widely believed to be the most activeorganic matters during composting, and then it can be seen as an indicator for investigating the composting process of straw materials. In this study, total organic carbon analysis, UV-Vis spectroscopy and EEM-PARAFAC were applied to study the evolutions of abundance, structure and composition of DOM during wheat straw compost. The dissolved organic carbon (DOC) of DOM were decreased by 23%, with a major decrease in an early stage of composting, suggesting DOM was an active portion of organic matters. UV-Vis spectra analysis showed that the absorbance of DOM was significantly decreased after composting, suggesting the degradation of aromatic compounds. DOM present obvious changes of fluorescence peaks in EEM during composting, where the strong protein-like peaks were transformed to the humic-like peaks. It indicated that the composition of DOM was changed. SUVA254 and HIX values of DOM varied dynamically, but both were generally increased with composting. It implied that the aromaticity and humification degree of DOM were transformed to be stronger after composting. These results revealed that the non-humic matters were readly to be degraded, and the relative content of humic matters was elevated, resulting in an increase of aromaticity and humification of DOM during composting. EEM-PARAFAC analysis showed that the relative abundances of protein-like compounds (C3) were decreased by 46%, while the relative abundances of fulvic acid-like (C1) and humic acid-like (C2) substances were increased by 45% and 80%. The composition of DOM had been transformed from C1∶C2∶C3=41∶17∶42 to 53∶27∶20 during composting. The results revealed that the protein-like compounds were dominantly degraded, while humic-like matters were somewhat formed or slowly degraded and transformed to be the dominant one. The correlation analysis showed strong positive correlations between HIX, C1 and C2 (r=0.806~0.853), suggesting that HIX could effective reflect the humic characteristics of compost DOM. The results obtained here are greatly help for better controlling wheat straw composting and improving the number of straw manures.

It is essential to estimate above-ground biomass (AGB) quickly and accurately, and AGB is an important indicator of crop growth evaluation and yield prediction. Due to the saturation of AGB in multiple growth periods estimated by traditional vegetation indexes (VIs). Therefore, the study attempts to use VIs combined high-frequency information extracted by image wavelet decomposition (IWD) based on discrete wavelet transform (DWT) technology and wavelet coefficients extracted by continuous wavelet transform (CWT) technology, explore the estimation capabilities of VIs, VIs+IWD and VIs+CWT for AGB. Firstly, the hyperspectral and digital images of the unmanned aerial vehicle (UAV) and measured AGB were acquired during the potato budding stage, tuber formation stage, tuber growth stage, and starch accumulation stage. Secondly, three high-frequency information were extractedby using digital images through IWD technology, wavelet coefficients were extracted by using hyperspectral reflectance through CWT technology and six hyperspectral vegetation indexes were constructed. Then, the correlation between vegetation index, high-frequency information and wavelet coefficients and AGB was analyzed, and the top 10 bands with high absolute values of correlation coefficients at different scales were selected. Finally, the partial least square regression (PLSR) was used to construct and compare AGB estimation models with VIs, VIs+IWD and VIs+CWT. The results showed that: (1) 6 vegetation indexes, 3 high-frequency information and 10 wavelet coefficients selected in each growth period were significantly correlated with AGB, and the correlation decreased after increased in the whole growth period, in which the wavelet coefficients was the highest, the nextwas high frequency information, and the vegetation index was the lowest. (2) The three estimation models of each growth period were compared and analyzed, the estimation effect of VIs+CWT was the best, and that of VIs was the worst, indicating that the model based on wavelet analysis has wide applicability and strong stability. (3) The AGB estimation models constructed by PLSR method with three variables in each growth period reached the highest accuracy in the tuber growth period (VIs: modeling R2=0.70, RMSE=98.88 kg·hm-2, NRMSE=11.63%; VIs+IWD: modeling R2=0.78, RMSE=86.45 kg·hm-2, NRMSE=10.17%; VIs+CWT: modeling R2=0.85, RMSE=74.25 kg·hm-2, NRMSE=9.27%). The PLSR method through VIs combined with IWD and CWT technology were used to improve the accuracy of AGB estimation, which provide a reliable reference for agricultural guidance and management.

Potato blackheart disease is an internal defect, which decreases the quality and yield of potato processed products such as fries, chips and whole powder. At present, the classification of potatoes mainly focuses on their external quality, rather than internal defects. The purpose of this research was to develop a fast non-destructive detection technology that could be used to detect potato blackheart disease. A visible and near infrared (VIS-NIR) transmission spectroscopy platform was built for potato detection. The spectral transmission characteristics of healthy and blackheart potatoes were analyzed, and the spectral discrimination model parameters were further optimized. Based on the potato grading line and the PG2000 high-speed spectrometer, the transmission spectra of 470 potatoes, including 234 healthy potatoes and 236 blackheart potatoes, were collected using left-to-right transmission method, of which the light source and the optical fiber probe were located on the left and right sides of the fruit plate of grading line respectively. A partial least squares discriminant analysis (PLS-DA) model was established. Furthermore, the principal component analysis (PCA) and spectral morphological features were combined to select essential wavelengths for model optimization. According to the VIS-NIR transmission spectra, there were significant differences between healthy and blackheart potatoes in absorbance values and spectral morphological characteristics. The average spectral absorbance values of blackheart potatoes in the range of 650～900 nm were higher than that of healthy potatoes. The average spectrum curve of blackheart potatoes was relatively smooth without obvious absorption peaks. However, obvious absorption peaks around 665, 732 and 839 nm appeared in that of healthy potatoes. The average spectral difference of blackheart and healthy potatoes reached the maximum at 705 nm. Based on the PLS-DA method, a potato blackheart disease discrimination model was established, which had a significant effect on detecting blackheart disease. The area under the receiver operating characteristic curve (AUC), total discrimination accuracy, RMSECV and RMSEP of the model were 0.994 2, 97.16%, 0.28 and 0.26, respectively. Moreover, a useful wavelength combination consisting of 6 wavelengths (658, 705, 716, 800, 816 and 839 nm) was obtained. The total accuracy of the simplified model could reach 96.73%, which was similar to that of the full-band model. It is shown that the left-to-right transmission method can accurately and rapidly identify blackheart potatoes. The study provides an important theoretical, and practical basis for improving the online detection technology of internal potato defects.

Quantitative prediction of disease degree of rice panicle and neck blast is essential on accurate prevention and control measures. The study of field canopy scale can provide a theoretical basis for hyperspectral sensors. In this paper, the rice which was damaged by panicle and neck blast was regarded as the research object, and hyperspectral canopy reflectance was acquired by SVC HR768i spectral radiometer at two different periods during the filling stage. The percentage of rice plants diseased represented disease degree index. The canopy spectral data were preprocessed by nine-point smoothing and resampled at 1 nm intervals. Vegetation indexes were calculated and hyperspectral characteristic parameters were extracted by continuum removal (CR) and first derivative reflectance. Were totally analyzed between each period, the response ability of different spectral transformation, vegetation index and hyperspectral characteristic parameters to disease degree through correlation analysis, and prediction models of disease degree were established through the random forest (RF) based on vegetation index and hyperspectral characteristic parameters, respectively. The two single-period prediction models were compared to select the common input to generate a disease degree prediction model which mixed data in two periods. The results demonstrated that: (1) Canopy hyperspectral reflectance processed by continuum removal (CR) method could effectively enhance the spectral information which isclosed related to the disease degree. The sensitive bands were the near-infrared region (960～1 050 nm) and (1 150～1 280 nm), and the correlation coefficient was above 0.80. (2) In the correlation analysis between hyperspectral characteristic parameters and the disease degree, the correlation coefficient of absorption valley parameters extracted by CR was higher than other parameters, and that of area (A3, A4), depth (DP3, DP4) and slope (SL4, SR4) in the absorption valley V3(910～1 100 nm) and V4(1 100～1 300 nm) was above 0.74. (3) The absorption valley parameters which played a role as the model input showed the best result in the mixed data of two periods and that of every single period. In addition, the prediction accuracy reached a peak at the later filling stage, with R2=0.91 and RMSE=0.02 in the validation set. (4) The prediction accuracy of the mixed data of two periods was between that of two single-period, with R2=0.85, and RMSE=0.03 in the validation set. The results revealed the spectral response mechanism of rice panicle and neck blast at different periods during the filling stage and it was practical to predict disease degree by combining absorption valley parameters extracted by CR with the random forest model, which can be used to rapidly, accurately and nondestructively predictthe disease degree of rice panicle and neck blast and provided a theoretical basis for precise application of pesticides. Beyond that, it also provided some technical reference for aviation and aerospace remote sensing monitoring in the future.

In order to accurately and reliably obtain LAI of rice of different fertilization gradients and varieties through vegetation index (VI), an improved QGA-ELM algorithm was proposed in this paper for LAI inversion of rice. This model firstly determined by 8 fold cross-validation extreme learning machine (ELM) optimal number of neurons in the hidden layer and hidden layer activation function types, and by introducing a dynamic rotation Angle combination strategy, single point chaos crossover operation and mutation operation, deterministic selection strategy, quantum catastrophe operations to improve the quantum genetic algorithm (QGA), finally using the improved QGA ELM algorithm optimization neural network input layer to hidden layer connection weights and threshold of the hidden layer. In order to validate the model, this paper, in turn, to establish multiple linear regression, BP, ELM, QGA-ELM, improved QGA-five ELM algorithm model, and compared the inversion effect on different data sets, the results show that: (1) Compare the QGA-ELM evolution algorithm and the improved QGA-ELM algorithm, in this paper, the improved algorithm can enhance the searching capability model and avoid precocious, algorithm and can find better results. (2) By comparing the inversion effects of five algorithms on different data sets, it is verified that the relationship between NDVI, RVI and LAI is mainly non-linear, and the inversion effect of ELM neural network model is better than that of BP neural network model and multiple linear regression model. (3) By comparing the inversion effects of the five algorithms on different data sets, the improved QGA-ELM algorithm in this paper has the highest inversion accuracy and the lowest error in most cases, and the improved algorithm has significantly improved the inversion accuracy and generalization performance. (4) The improved QGA-ELM algorithm has the highest inversion accuracy and the lowest error in all fertilization gradients, and the accuracy is higher, which can provide a basis for LAI inversion of rice under different growth conditions. (5) Five model for Qinghexiang LAI inversion precision are higher than the dragon rice 18, and the improved QGA-ELM algorithm on different rice varieties still has high inversion accuracy, and the inversion precision tiny difference on different rice varieties, is far lower than the other four kinds of models, can adapt to different rice varieties LAI inversion requirements, greatly improve model stability, provide a reference for different rice varieties of inversion.

The terrestrial carbon cycle is the most important constitution and plays a prominent role in global carbon cycle, and soil carbon sequestration makes an important contribution to the global climate change. The full soil profile is a highly dynamic component of the ecosystem, with pronounced depth-dependent processing of soil organic carbon (SOC), such that accumulations and losses of carbon above ground are on different temporal trajectories than changes below ground. But many studies do not consider the spatial variability of soil properties in the vertical direction, especially in the Qinghai-Tibet Plateau, mainly because of the difficulties and expense of collecting soil material in that kind of terrain and transporting it to the laboratory. Visible near-infrared reflectance spectroscopy (Vis-NIR) is an increasingly popular measurement method that is enabling the rapid, real-time and accurate proximal sensing of soil properties, including SOC. However, thesoil moisture content has been shown to affect soil spectra, might mask or alter the absorption features of SOC. EPO and PDS are two effective methods to correct soil spectra effects, but we still unknow the feasibility of those two methods on fresh profile samples. In this study, we compared EPO and PDS on a set of 26 soil cores (1 m depths and 5 cm diameter) in the Sygera Mountains on the Qinghai-Tibet Plateau, China. Spectra were acquired from fresh, vertical faces 5 cm×5 cm in the area from the centers of the cores to give 386 spectra in all. We also got the spectra and SOC contents from the 386 dry samples. The statistical models were built to predict of the SOC in the samples from the spectra by Random Forest. The bootstrap was used to assess the uncertainty of the predictions by the EPO and PDS. Our results show that PDS is an effective strategy to mitigate the effects of soil water content on vis-NIR spectra for the fresh soil core samples from arable and grassland. While EPO neither shown significantly out performed those wet core samples from grassland. There were somewhat differences along with the profile on prediction accuracy of SOC between EPO and PDS. Both EPO and PDS show significantly available on surface layers of samples from arable and grassland. The EPO and PDS illustrated the dependence of land use type and soil depth. Our work would be a benefit to therapid and accurate estimation of the vertical partitioning of SOC content in the field in alpine mountains using Vis-NIR spectroscopy.

Three-dimensional excitation-emission matrix fluorescence technology and parallel factor analysis were conducted to investigate the spectral characteristics and pollution source of dissolved organic matter (DOM) in a subsurface flow-surface flow combined constructed wetland in north China. This study provided a scientific basis for further revealing the chemical behavior and ecological effects of DOM in constructed wetlands. A similar fluorescence pattern was observed from different stages in the constructed wetland. Both humic-like peak and protein-like peak appeared with different intensities. The fluorescence intensity of humic-like and protein-like compounds decreased by coagulation sedimentation. The fluorescence spectrum of effluent from subsurface flow wetland suggested that the intensity of protein-like peaks, microbial metabolic by-product peaks, and tryptophan-like peaks significantly decreased. It stated that subsurface flow wetlands could achieve the effective degradation of proteinoid substances in the river. However, the subsurface flow wetlands possessed poor degradation ability on humic-like substances. The weaker intensity of the protein-like peak and the humic acid-like peak in the surface flow wetland was observed, and the weakest intensity spot was found at 3 km downstream of the subsurface wetland. This trend was attributed to the microbial degradation on the surface of the membrane and the adsorption of aquatic plant roots. Five fluorescent components were identified by PARAFAC, including fulvic-like component C1(240, 330/430 nm), humic-like component related to microbial activity C2(285, 330/380 nm), tryp to phan-like component C3(230/350 nm), microbial metabolic by-product component C4(280/320 nm) and terrestrial humic-like component C5(270, 380/470 nm). Multi indexes of the fluorescence spectrum were calculated for the DOM source analysis in the wetland. Both the fluorescence index and the autochthonous index of water samples indicated that the main source of DOM in the wetland was from biological metabolism and the terrestrial input had negligible influence. The humification index showed that the wetland had weak humification. Spearman correlation analysis suggested that the five fluorescent components were homologous and closely related to the migration and transformation of nitrogen in the water.

In recent years, a relatively unique turquoise variety has been found in the Xiaolinpa ore district in Qingu Town, Zhushan County, Shiyan City of Hubei Province. The color of this turquoise variety is mostly light green, light yellow green, or light apple green. The rough material is slippery feel and brittle. As a result, it is called “oily turquoise” by a local citizen. Compared to the turquoise with similar structure fineness, the density of turquoise is generally lower, and the hardness is smaller, with the Mohs hardness ranges from 3 to 4. This type of turquoise has not been significantly improved in hardness and structural density after being filled with a traditional organic binder, and cannot be used as a gem-quality turquoise material for jewelry. The conventional gemological instruments, infrared absorption spectrometers, X-ray powder crystal diffractometers, electron probes microscopic analysis, and environmental scanning electron microscopes were used to study the chemical composition and microstructure characteristics of the “oily turquoise”. The results show that the specific gravity of the “oily turquoise” range 2.04 from 2.22, which was lower than that of typical turquoise. It shows inert fluorescence under long-wave and short-wave UV light. The infrared absorption spectrum of the “oily turquoise” is mainly distributed in the range of 3 700 to 3 090 and 1 638 to 466 cm-1, of which the peaks at 3 509 cm-1± and 3 462 cm-1± have sharp OH-induced absorption spectra, and the broader crystallization water-induced absorption spectra at 3 277 cm-1± and 3 090 cm-1 ± are consistent with the absorption characteristics of the turquoise functional group region. It has weak OH-induced infrared absorption peaks in kaolinite or montmorillonite at high frequencies of 3 700 and 3 622 cm-1. Relatively broad absorption band around 1 638 cm-1, with moderate intensity agree with the absorption peak of the bending vibration induced by H2O in turquoise. The shape and position of the absorption peaks in the fingerprint region of the “oily turquoise” are quite different from those of typical turquoise; they are mixed absorption spectrum peaks of Si—O and P—O. The main chemical composition of the “oily turquoise” is SiO2, Al2O3, FeOT, P2O5 and CuO, with small amounts of MgO, CaO and Cr2O3. The content of SiO2 is 25.60% to 30.90%, and the content of Al2O3 is 26.55% to 28.29%, FeOT content is 5.35% to 5.90%, P2O5 content is 22.00% to 23.52%, and CuO content is 5.10% to 5.87%. Compared with another typical natural turquoise, the “oily turquoise” contains a higher content of SiO2, which is higher than 25%. The main constituent mineral of the “oily turquoise” is turquoise, and it contains a certain amount of montmorillonite and montmorillonite-kaolinite. The low hardness and slippery characteristics of the “oily turquoise” are relevant to montmorillonite that results in the insignificant optimization effect.

Understanding the spectral characteristics of moso bamboo leaves damaged by Pantana phyllostachysae Chao can provide theoretical guidance for developing applicable and effective technologies to monitor the ecological safety of the bamboo forest. Compared with the traditional multispectral data, hyperspectral remote sensing can sense the subtle changes of host spectrum among different severity of Pantana phyllostachysae Chao. However, the related researches were still rare, and the spectral change mechanism of the host needs to be further summarized. Therefore, this study analyzed the spectral differences among healthy, damaged and off-yearmoso bamboo leaves based on 552 field measured spectrums. The characteristic variables that can act as indicators of leaves health status were selected. Finally, the model for monitoring the damage of leaves caused by Pantana phyllostachysae Chao was established using the XGBoost algorithm. The results show that: (1) with the increase of pest damage, the reflectance of damaged leaves gradually appeared “green low and red high” in visible-band, while the reflectance noticeably decreased in near-infrared band, and the reflectance of damaged leaves in shortwave infrared band was significantly higher than that of healthy leaves, especially in the two typical water vapor absorption bands (1 450 and 1 940 nm); (2) the reflectance of off-year leaves in visible and near infrared bands was significantly higher than healthy and damaged leaves; (3) the spectral characteristics of indentation-only leaves only slightly changed in comparison with healthy leaves, while the red band reflectance of leaves with red-brown disease spots increased to some extent, andthe leaves with gray-white disease spots completely lost the basic spectral characteristics of vegetation; (4) according to the feature importance score determined by the XGBoost algorithm, the contribution of each characteristic variable was PRI＞FDVI576, 717＞NPCI＞DSWI＞VOG 1＞RVSI＞NDWI; (5) the overall average accuracy of the model to detect the damage by the pest was 74.39%, and the accuracy for healthy, mild damaged, severe damaged, off-year, and moderate damaged leaves was 94.55%, 74.93%, 84.12%, 71.10%, and 33.48%, respectively.

Cellulose is a renewable natural hydrophilic polymer, and its huge hydrogen bond grid forms a variety of different crystal structures. There are five crystalline variants of cellulose (cellulose Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅹ), of which cellulose Ⅱ is formed from cellulose Ⅰ (natural cellulose) after regeneration or mercerization, is the lowest surface free energy and the most stable performance among the five crystal varieties, mainly due to the antiparallel chain structure of cellulose Ⅱ, which is opposite to the parallel chain structure of cellulose I, and has additional intermolecular hydrogen bonds compared with cellulose I. Therefore, in view of the sensitivity of near-infrared spectroscopy (NIRS) to the hydrogen-containing group, and the crystalline structure of cellulose contains a large number of hydrogen bonds, this makes it possible for NIRS to analyze the degree of hydrogen bond destruction of cellulose by hydrogen containing functional groups, and to detect and quantitatively evaluate the crystalline structure of cellulose qualitatively. So far, there are very few studies on the hydrogen bonding of cellulose crystal variants, and the hydrogen bonding of bamboo cellulose Ⅱ and its derivatives has not been reported at home and abroad. In the study, cellulose Ⅰ was prepared from bamboo, and bamboo-based cellulose Ⅱ was obtained through mercerization, which NIRS investigated hydrogen bonds, the results were compared with bamboo powder and bamboo-based cellulose Ⅰ. Besides, the crystallinity of bamboo powder and bamboo-based cellulose was quantitatively evaluated by NIRS. The results can be drawn as follows: (1) the differences of NIRS among bamboo powder, bamboo-based cellulose Ⅰ and Ⅱ varied little, hydrogen bonding were quantitatively remarkable, but were qualitatively slight in the amorphous region; (2) compared with bamboo powder, the crystal structure of bamboo-based cellulose I remained unchanged, while bamboo-based cellulose Ⅱ occurred two absorbance peaks in the semi-crystalline region; (3) in the crystalline region a strong hydrogen bonding absorbance peak reflected the first overtone of hydroxyl group stretching vibration was observed at the wavenumber of 6 292 cm-1 assigned to the intermolecular bond of O2—H2…O6 of cellulose Ⅰ, which shifted to 6 354 cm-1 for bamboo-based cellulose Ⅱ. We deduced the absorbance peak in cellulose Ⅱ was assigned to the intermolecular bond of O2—H2…O2 due to anti-parallel structure of cellulose confirmation; (4) a good correlation among crystallinity was obtained by NIRS with the results of XRD analysis. The above research shows that the hydrogen bonding in the crystalline region of cellulose shifts in the near-infrared characteristic band and forms double peaks in the semi-crystalline region, which were the main characteristics of bamboo-based cellulose Ⅱ different from bamboo-based cellulose Ⅰ. Simultaneously, it is feasible to use NIRS to study the hydrogen bonding of various celluloses and predict their crystallinity.

This paper implements the design of a set of LIBS-Raman test systems in a laboratory environment. Based on the system’s LIBS and Raman spectroscopy techniques, it is used to verify the comprehensive detection capabilities of mineral samples in the Martian simulated environment. This system uses the Cassegrain telescope structure for long-range LIBS excitation and the bypass reflection optical path for Raman spectrum excitation. The wavelengths of excitation lasers are 1 064 and 532 nm, respectively. The Cassegrain telescope was then used to collect both spectra. In order to fully simulate the physical conditions of the minerals on the surface of Mars, we have designed a set of gas cavities. By placing samples in the gas chamber, the conditions on the surface of Mars can be simulated to the greatest extent. In order to verify the ability of this LIBS-Raman instrument to analyze Mars minerals, we performed experimental analysis using 8 typical samples (malachite, azurite, realgar, orpiment, aragonite, calcite, anhydrite and gypsum). There are huge differences in elements and molecules in these samples, among which malachite and azurite have molecules with different valence states and atomic ratios; the number of atoms of realgar and orpiment molecules is different; aragonite and calcite have the same molecular formula, but their crystal structures are significantly different; the difference between anhydrite and gypsum mineral is reflected in the presence or absence of crystal water in its molecules. These differences were studied using LIBS and Raman techniques to verify the effectiveness of using this combined instrument to analyze mineral types and components under Martian conditions, and to study the advantages and disadvantages of LIBS and Raman techniques in the analysis of material composition. Experimental results show that the system can effectively analyze mineral species and composition under Martian conditions. This comparative experiment also verified that LIBS could quickly distinguish element types in the analysis of specific mineral elemental composition in Martian material, but there are obvious limitations for molecular information detection; Raman technology can compensate for this limitation to a certain extent. The combination of the two will effectively improve the recognition efficiency of minerals with different molecular composition and structure under extreme conditions. The successful verification of the system can complement the further Mars exploration program and help the laboratory establish a valuable database.

Nanhong agate is a special kind of agate, because of its moist texture and bright color, it is accepted by most consumers. Special band with transparent edges and the obvious inner boundary is often found in Nanhong agate, which is rarely mentioned in the study of Nanhong agate. This article uses micro-laser Raman spectroscopy and scanning electron microscopy to analyze this band. The test results show that the difference between the outer side (section Ⅰ) and the inner side (sectionⅡ) of the Raman spectrum is the change in the strength of the 503 cm-1 peak and the degree of the splitting of the 400 cm-1 peak. The results of the two samples showed that the strength of the 503 cm-1 peak gradually decreased from the outside to the inside and disappeared, and the regular change of the 400 cm-1 peak in the section Ⅰ and Ⅱ. The characteristic peak of 503 cm-1 represents a kind of silica mesophase mineral-moganite, and it also shows that the content of moganite gradually decreases from the outside to the inside until it disappears. The splitting degree of 396 and 402 cm-1 in 400 cm-1 increases with the decrease of the peak strength of 503 cm-1, and the difference in the splitting degree of 400 cm-1 reflects the crystallinity of agate, so it is inferred that there is a certain correlation between its crystallinity and the content of moganite. WeSelect YND-6 # 2 to calculate the content of moganite, by simulating the relationship function between the peak intensity ratio of I(503 cm-1)/I(465 cm-1) and the content of moganite, the maximum of moganite content is calculated as 11.67%, indicating that it forms a non-evaporating environment. Scanning electron microscopy results showed that the size, order of arrangement, and self-profiling of the particles increased from section Ⅰ to sectionⅡ, indicating that the crystallinity increased from section Ⅰ to section Ⅱ. In summary, the regular changes in the content of moganite and the degree of crystallinity in the band of Nanhong agate, as well as the difference in micro-morphological and appearance between the band and the inner side, indicate that it is in different growth stages.

Organic carbon is one of the indispensable indexes in multi-target geochemical surveys, land quality geochemical surveys and other project studies. Accurate quantification of organic carbon is an important part of the geochemical survey with great significance. However, the traditional gravimetric method and volumetric method have long processes and slow speed, which can no longer meet the requirements of large-scale and rapid determination of geochemical samples. In this paper, dilute hydrochloric acid was used to remove inorganic carbon in the samples, and a high-frequency infrared carbon-sulfur analyzer was used to determine the organic carbon in different types of geochemical samples. This paper optimized the conditions such as the sample weight, the choice of acids and fluxes and their optimal concentration and dosage by the comparative experiments. Additionally, the first-level geochemical reference materials also are utilized to establish the calibration curve. A hydrochloric acid pretreatment-infrared absorption spectrometry analysis method of organic carbonin geochemical samples was established by this paper. The experiments showed that the blank value of ceramic crucible could be reduced by burning at 1 200 ℃, which could reduce the impact on the results. The test accuracy was high when choosing the sample weight of 50.0～70.0 mg, and solving the problem of the splash when melting. 0.40～0.60 mL dilute hydrochloric acid (1+7) was chosen as it can remove the inorganic carbon and reduce the loss of organic carbon. The results also showed that the fluxing action was preferably when 0.40～0.50 g pure iron filings and 1.50～1.70 g tungsten particles were used as the mixed flux. The calibration curve of organic carbon established by national geochemical standard reference materials showed good linearity (R2=0.998 5). The detection limit of this method is 69 μg·g-1, and the relative standard deviations (RSD, n=12) of the measurement results is less than 8%. This method has been verified by 63 different types of national geochemical standard reference materials (soil, rock, and sediments), and the results are consistent with the standard values. The method provides the advantages of convenient operation with low detection limits, high sensitivity, and reliable results. It is believed that this method is suitable for the determination of organic carbon in geochemical samples.

During the flotation process of fluorite, NH+4 and F- in the slurry have important effects on fluorite’s flotability of fluorite. At present, the research about the effects of inevitable cations on floatation mainly focus on the activation or inhibition mechanism of metal cations, but there is less research about the effect of the complex cation (NH+4) and the anion (F-) on the adsorption mechanism of sodium oleate to fluorite. Therefore, the action mechanism of NH+4 and F- on fluorite flotation by sodium oleate is studied in this paper by means of infrared spectroscopy analysis, combined with the flotation experiment of pure fluorite, Zeta potential and solution chemical calculation. The results show that NH+4 has a strong activation effect on fluorite and increases the recovery rate of fluorite under acidic condition, and when the pH value is 6, the recovery of fluorite increased to about 94% at different NH+4 concentrations. However, under alkaline conditions, NH+4 had a certain inhibitory effect on fluorite flotation, and the recovery rate decreased gradually with the increase of pH value. But F- inhibits fluorite to some extent, when the pH value is 6, the inhibitory effect of different F- concentrations is significantly enhanced, resulting in a decrease of fluorite recovery. However, under alkaline conditions, F- has little influence on the flotability of fluorite. The action mechanism is that the adsorption of the cation (NH+4) and F- dissociated from fluorite surface in solution form NH4F under acidic conditions to improve the electropositivity of fluorite surface, and enhance the adsorption between fluorite surface andmonomer, dimer and acidmolecule-ion association of oleic; while under alkaline conditions, the hydrolysis of NH+4 results in the formation of NH3·H2O, which reduces the positive electrical properties and leads to the weakening of the adsorption of sodium oleate on the fluorite surface. F- inhibits the dissociation of F- on fluorite surface, thus inhibiting the chemisorption of oleate acid ion on fluorite surface. FT-IR analysis results show that the chemisorption occurred between sodium oleate and fluorite surface. NH+4 had a strong activation effect on fluorite surface under acidic conditions because the characteristic peaks of —CH3, —CH2—, —COO- are red-shifted and their peak intensity is enhanced, showing a strong chemisorption effect. However, under alkaline conditions, only the characteristic peak of —CH2— appears red shift, and the intensity of the characteristic peak is weakened, indicating that under weak alkali conditions, the chemisorption of NH+4 on the fluorite surface is weak and inhibited the adsorption. When F- is added, only the antisymmetric expansion vibration peak of —CH3 appears and no red shift of the peak position has occurred. Therefore, no chemisorption of any group on the fluorite surface occurs after the addition of F-, which inhibits the flotability of fluorite.

Rocks in nature are usually aggregates of various minerals. Due to the low spatial resolution of hyperspectral sensors, the hyperspectral data obtained are mostly the mixing spectrum of mineral components. Affected by factors such as noise interference and intimate mixing characteristics of minerals, endmember extracting and quantifying analysis of minerals is still a hotspot and difficulty topicin current research. Based on the deep learning theory, this study improves the autoencoder structure and proposes a new stacked sparse autoencoders method (SSAE), which provides a new idea for mineral hyperspectral unmixing. First of all, according to the characteristics of mineral mixing spectrum, three improvements have been made: first, the bias term of autoencoder neural network is removed; second, the batch normalization (BN) layer is added in front of the activation function of each hidden layer, and the Relu activation function is used for the final output layer; third, spectral angular distance (LSAD) is used as the objective function instead of the mean square error (ＬMSE). The proposed model obtained the parameters by optimizing the objective function through gradient descent method. Then, two simulation datasets with different mineral combinations and different mass fractions are established by using the Hapke model. The datasets include ten pure minerals, kaolinite, pyrophyllite, montmorillonite, chlorite, muscovite, calcite, hematite, dolomite, potassium feldspar and limonite. Finally, SSAE method is used to test the datasets. Test results of SSAE are compared with the results of six cases in the process of autoencoder network improvement as well as the results of VCA and SISAL. Experiments show that the accuracy of SSAE endmember extraction is greatly improved than before. The SSAE method can successfully identify all endmembers of two data sets. The mean errors of Spectral Angle Distance(SAD) are respectively 0.059 7 and 0.034 4, which is less different from the result of the VCA and is better than there sult of SISAL. SSAE method provides a new ideal for hyperspectral unmixing, and has a better promoting effect on the geological application and quantitative analysis of hyperspectral remote sensing.

Nephrites from Dahua Yao Autonomous County, Guangxi Zhuang Autonomous Region and nephrites from Luodian County, Guizhou province have formed mining scales, both of which have many similar and different characteristics in gemology, spectroscopy and mineralogy. In recent years, a large number of nephrites from these two localities began to appear at domestic and foreign markets. In order to analyze spectroscopic characteristics and color-causing mechanisms of nephrites from these two different sources, the standard gemological methods including refractive index testing, hydrostatic specific gravity testing, an observation by the naked eye and gem microscope were used to their research gemological properties of them. Also, Fourier transforms infrared spectroscopy (FTIR), laser Raman spectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) was also used to research spectral characteristics and chemical composition of these nephrites. The research result shows that the infrared spectrum of samples from Dahua County showed main absorption bands at 1 033, 932, 771, 699, 524, 490 and 427 cm-1, and Fourier transform infrared spectrum of Luodian nephrites samples mainly showed absorption bands at 1 032, 932, 773, 700, 525, 490 and 426 cm-1. Infrared absorption bands at 1 033, 1 032 and 932 cm-1 are induced by O—Si—O anti-symmetric stretching vibration, O—Si—O symmetric stretching vibration and Si—O—Si anti-symmetric stretching vibration. Infrared absorption bands at 773, 771, 700 and 699 cm-1 are induced by Si—O—Si symmetric stretching vibration. Infrared absorption bands at 525, 524, 490, 427 and 426 cm-1 are induced by Si—O bending vibration and M—O lattice vibration. Unlike previous studies, the absorption band at 850 cm-1 is first discovered in Dahua nephrites, which may be attributed to minor amounts of diopside pyroxene in the sample. This absorption band has not been reported in the previous studies on Dahua nephrites. According to results of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), the main chemical compositions of nephrites samples from Dahua are SiO2 (58.91%), MgO (25.77%) and CaO (13.67%). The main chemical compositions of Luodian nephrites samples are SiO2 (57.07%), MgO (24.85%) and CaO (17%). The average content of calcium in Luodian nephrites samples is a little higher than that of Dahua nephrites samples. A spot of FeO, MnO, Al2O3, Na2O, K2O, P2O5 and TiO2 were also found in our samples. The value of Mg/Mg+Fe is 97.3% in Dahua and 98.8% in Luodian nephrites respectively. This means the main mineral composition of both location nephrites is tremolite. According to our research, Dahua nephrites and Luodian nephrites have different green color-causing mechanisms. Testing results of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) showed that the content of vanadium in Luodian nephrites samples increased with the green deepening, which showed that the color-causing ion of Luodian green nephrites is probably vanadium (V). This article discussed the green color-causing mechanism of Dahua nephrites for the first time. We think that chrome (Cr) and ion (Fe) are color-causing ions of Dahua nephrites because the content of chrome (Cr) and ion (Fe) increased with green deepening. The trace element values can be used to identify the location of the two site nephrites from Dahua and Luodian. Identifying a location tree can be used to distinguish all the known sites of nephrite in China now.

Possibly Derived from ancient Pinaceae pinus or Cedrusplants, ancient Araucariaceae Agathis plants or ancient Sciadopityaceae Sciadopitys plants, Baltic ambers are the natural fossilized resins undergone a variety of geological reworkings. Incorporating succinic acid Baltic ambers are based on labdanoid diterpenes with regular configurations, which are including communic acid, communol and biformenes. The price of raw Baltic ambers fluctuates wildly for several reasons. Therefore thermal optimization products of Baltic ambers have emerged in the market for many years. The huge price difference and collection value difference between natural Baltic ambers and thermally optimized Baltic ambers have already caused alarm to Chinese consumers. The research object is a representative Baltic amber chunk with wax-like appearance, which was cut into two sets of the same size, 6 pieces of each set. Thermally optimized Baltic amber samples (Jin Bao-mi, Jin Po, Hua Po, Xue Po, Lao Mi-la, Bai La) of one set was obtained under multiple experimental conditions, in addition, two samples in this set were further sectioned and heat-treated in the second stage. Through the infrared spectrum analysis, the spectral characteristics of thermally optimized Baltic ambers were obtained which can be used as the differential basis on distinguishing natural Baltic ambers and thermally optimized Baltic ambers. The results showed that thermally optimized Baltic amber samples in nitrogen or oxygen environment underwent polymerization and esterification reactions, and thermally optimized Baltic amber samples in acidulous aqueous solution underwent polymerization and hydrolysis reactions. Using formula Ratio(I1)=ACarboxylic acid carbonyl CO stretching vibration 1 710 cm-1/AC—H unsymmetric bending vibration 1 456 cm-1to determine whether the Baltic amber has been thermally optimized is very persuasive. This method is applicable to the identification of Jin Bao-mi, Jin Po, Hua Po, Xue Po and Lao Mi-la. When I1≥1, the Baltic amber samples are not thermally optimized. When I1＜1, the Baltic amber samples are thermally optimized. As an added note, this criterion should not be used to Bai La. Raman spectrum results also show that the maturities of thermally optimized Baltic ambers are increased in different degrees. The maturities from high to low are Lao Mi-la, Xue Po, Hua Po, Jin Po, Bai La, Jin Bao-mi. This research aims to provide a scientific basis for identifying thermally optimized Baltic ambers, improving the utilization rate of Baltic amber raw chunks and developing the preventive protection technology for the Baltic amber museums.

Ancient jade artifacts are not only ornaments, but also contain rich cultural meanings. In order to explore the relationship among the material properties, processing techniques and object functions of archaicjade, this paper selected 10 jade wares unearthed from No.1 tomb in Hougudui, Gushi, Henan province, using non-destructive techniques such as X-ray fluorescence spectroscopy (XRF) and confocal microscopic laser Raman Spectroscopy (LRS), ultra-depth-of-field Optical Microscopy (OM), combinewith Scanning Electron Microscope (SEM) and silicone resin molding to analyze them comprehensively. Firstly, the chemical composition and phase structure of the samples were obtained by spectral information, and the mineral type was determined. Then, the samples were quantitatively observed by means of microscopy combined with silica gel mulching technique, and representative micro-traces were engraved for secondary observation to determine the processing technology, and therelevant data has been collected. The results show that the mineral types of this batch of jade wares include tremolite and mica. Most of the samples of tremolite were ritual wares, while the samples of mica were burial jade. The processing technology is analyzed from the following four aspects: engraving process, drilling process, cutting and grinding process and other special processes. There are two kinds of engraving tools, one is the rotary wheel, and the other is hand-held hard tools. Rotary wheel is the main one in this experiment, and the regular changes of the surface trace, the angle of incision and the depth of the nick engraved by two different tools are summarized. Drilling methods are double-faced drilling, the drilling tools are solid drilling tools, and some samples are drilled with jade sand. Most of the samples have been polished, and one of them has a narrow gap cut by a hard flake. In addition, the other special processing technology was characterized, and the differences between two kinds of shallow relief techniques are discussed, and the cutting bore technology using solid drill positioning. The above results show that the selection of ancient jade making technology is influenced by the mineral properties of jade materials and the function of jade type. This paper also discusses the source of jade, the times of tomb owner, and the transformation of slice cutting tools in the processing of jade in the Central Plains in the Spring and Autumn period.

Determining the types of rock is crucial to understand the origin and history of geological units. The recognition and classification of lithologies are usually performed by experienced researchers based on the colors and morphologies with the accuracy not guaranteed, particularly when trying to distinguish between units with similar lithology. Different from the subjective judgment, spectral means can provide multiple optical parameters among different frequencies. Information of the samples can be determined by establishing the relationship between optical parameters and the physical properties, to achieve qualitative and quantitative evaluation. In this study, terahertz (THz) spectroscopy was employed to discriminate the lithology of rock gathered from different regions. Based on the relations between effective refractive index (n) and attenuation coefficient (a), the samples can be classified into four gatherings, with the result in agreement with their lithologies. Besides, the variation tendency of THz parameters was plotted with the componential and structural properties, indicating that the variant THz responses were resulted by varying compositions and structures for different classifications of rocks. The mineral composition is the main factor affecting the absorption and refraction of the THz wave. Element content can be estimated with the THz parameters for Fe and Mg. For sandstone with stable elements, both the absorption and refraction of THz wave are negatively correlated with the porosity. Organic content in oil shale has a reverse effect on THz absorption and refraction, in which the higher oil yield results in a stronger absorption and lower refraction. Herein, the refractive index is positively correlated with the absorption for granite, limestone and sandstone, while the relation between them is negatively correlated for oil shale. Our results prove that the THz technique is a promising means for determining the lithology and petrophysics properties, which will be a significant supplementary in geology research.

Fermentation is a crucial process that affects the quality of black tea. The quality of fermentation is mainly judged on artificial experience, hence making it difficult to accomplish an accurate and objective evaluation. The objective of this study is to investigate the fermentation quality of Congou at different times and temperatures using non-destructive techniques and intelligent discrimination methods (chemometrics). For this purpose, different congou fermentation samples were prepared at different fermentation timings. Thereafter, these samples under went testing by hyperspectral detection technology and chemometrics methods. First, the hyperspectral imager (400~1 000 nm) was used to collect the hyperspectral data of Congou fermentation samples. Next, according to the on-site production information, such as temperature, tea tenderness, withering condition, rolling process, fermentation leaf color, aroma, and so on, six different fermentation samples under different time series were divided into three categories according to the degree of fermentation (light, moderate, and excessive fermentation). Standard normal variate (SNV) and multiple scatter correction (MSC) were selected to preprocess the full-band spectrum.Principal components analysis (PCA) was applied to the preprocessed spectral data to obtain the three-dimensional load maps of the first three principal components (PCs). Thereafter, a better SNV preprocessing method was selected according to the spatial distribution characteristics of the samples in the map. The k-nearest neighbor (KNN), random forests (RF), and extreme learning machine (ELM) discriminant models were established by using the optimal PCs of the full-band spectrum as the model input.The recognition rates of KNN, RF, and ELM were 63.89%, 94.44%, and 86.11%, respectively. The results showed that the recognition rate of the non-linear model (RF, ELM) was higher, and the performance of the RF model was better than that of the ELM model. 31 characteristic wavelengths were extracted by successive projections algorithm (SPA) for PCA dimension reduction.The SPA-KNN, SPA-RF and SPA-ELM discriminant models were constructed, and their recognition rates were 83.33%, 91.67%, and 91.67%, respectively. After the variables were screened by SPA, the performances of SPA-KNN and SPA-ELM models were found to be significantly improved, and the recognition accuracy of the SPA-RF model was slightly decreased. As compared with the model established by the characteristic wavelength, the RF model established in the whole band showed the best performance, and the discrimination rates of light fermentation, moderate fermentation and excessive fermentation of Congoureached at 100%, 83.33%, and 83.33%, respectively. The research results provided a theoretical and scientific basis for advancing the realization of intelligent and digital processing of black tea.