Surface enhanced Raman spectroscopy (SERS) is a technique in which target molecules are adsorbed onto a nanometerrough metal surface, significantly enhancing the Raman signal. SERS has the advantages of high sensitivity, no interference from water, simple operation, rapidness, non-destructive testing and so on, and has become a research hotspot in food, chemistry, medical science and so on. Liquid food (e.g., milk, edible oil, drinks, honey, wine) is indispensable for human survival and daily life. The safety of liquid food is related to consumers health and the enterprises benefit, so it is very important to detect the safety of liquid food quickly and in real-time. Liquid food-related safety indicators such as antibiotic residues, pesticide residues, pigments, illegal additives, etc. Usually have strong Raman activity, and using the “fingerprint” characteristic of SERS technology, trace substances in liquid food can be quickly and accurately detected qualitatively and quantitatively. Compared with other spectroscopic techniques, the non-interference of water in SERS technology is simpler to detect and analyze the matrix of aqueous solution samples (e.g., milk, beverage and wine). It has more potential to realize online real-time detection of liquid food quality and safety, and it is a frontier analytical technique with great application potential and prospects in the field of liquid food quality and safety detection. This paper briefly describes the enhancement principles of SERS technology and summarizes the relevant SERS substrate research in the field of liquid food safety testing, and focuses on the application and research status of SERS technology in the field of liquid food quality detection, summarizes recent research work and progress in liquid food-related safety (e.g., lipid oxidation, antibiotic residues, pesticide residues, wine origin identification) in terms of sample pre-treatment methods, substrate types and detection limits; discusses the advantages and limitations of SERS technology, as well as the main challenges and future development prospects.

Proteins and sugars are important components of food. During food processing, storage and transportation, the amino group of protein molecules is easily combined with the carbonyl group of reducing sugar through covalent bonds, and the glycation reaction occurs. Maillard reaction-based glycation includes three stages: the early stage, the middle stage and the advanced stage. The glycation reaction modifies the main or side chains of proteins, changes the structure, static charge and hydrophobicity of proteins, and affects the chemical activities of the main and side chain groups of proteins, thus changing the physicochemical properties of proteins, including improving the emulsifying, foaming, gel and other functional properties, enhancing the antioxidant and other nutritional properties, while reducing the sensitization and enhancing the antibacterial and storage properties. It is very common and important in food processing, storage and transportation. The changes in protein structure during the reaction are the important causes of the alteration of nutritional and functional properties. In recent years, chemical analysis (free amino content and surface hydrophobicity determination, etc.), spectrometry (ultraviolet spectroscopy, fluorescence spectroscopy, Fourier transform infrared spectroscopy, etc.), chromatography (circular dichroism, size-exclusion high-performance liquid chromatography, etc.), mass spectrometry (matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS), liquid chromatography-electrospray tandem mass spectrometry (LC-MS2), etc.) and other techniques have been explored to analyze the structural changing rules of protein during glycation reaction, playing the important roles in the mechanism study of glycation reaction as well as regulation of glycation degree and nutritional and functional properties of glycation products. In particular, the emergence and mature application of orbital ion trap mass spectrometry (LC-Orbitrap-MS2), Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS2), hydrogen-deuterium exchange Fourier transform ion cyclotron resonance mass spectrometry (HDX-FTICR-MS2), and other technologies have made the research on glycation sites more in-depth. These technologies make it possible to reveal the mechanism of protein glycation reaction by quantifying the number of bonded sugar molecules and the degree of glycation substitution at each reaction site. This study reviewed characterization and detection methods for the protein glycation degree, primary, secondary and tertiary protein structures and functional group structure. This study aims to provide a reference for the deep research of protein glycation reaction and its application in food processing.

The reflectance spectrum has a high dimension and has nothing to do with illumination and observation. It can truly and objectively describe the color information of an object. The characteristics of the object itself determine it, so it is called the "fingerprint" of the object. However, the amount of reflectance spectra data is more than ten times that of the traditional three-color system, and these huge spectral data cause huge burdens in terms of storage, data processing, and data transfer, and they spend too much computing time. Suppose the high-dimensional spectrum can be mapped to the low-dimensional space through mathematical transformation methods, and ensure that the low-dimensional space data can better represent the information covered by the original spectrum. In that case, the multi-spectral data can be effectively compressed and the processing efficiency of spectrum-based color reproduction can be improved. PCA treats all wavelengths in the visible range equally, and the reconstructed spectrum is only a mathematical approximation to the original spectrum, which often leads to the problem of small spectral reconstruction error and significant colorimetric reconstruction error. A multispectral dimension reduction algorithm based on the weight function of spectral color difference is proposed in this paper. The dimensionality of Munsell was reduced to one dimension by PCA and then restored to 31 dimensions, and the average spectral color difference between Munsells original spectrum and its reconstructed spectrum was used as a weight function. Taking NCS as the training sample and NCS, Munsell and 3 multispectral images as the test samples respectively, the performance of the proposed method of this paper and the classical PCA and the other four weighted PCA are analyzed and compared. CIELAB color difference under the conditions of multiple Lighting and viewing (D65/2° and A/2°) and root mean square error (RMSE) evaluate the colorimetric and spectral reconstruction accuracy between the original spectra and the reconstructed spectra of the test sample respectively. The experimental results show that: compared with PCA, the proposed method has greatly improved colorimetric reconstruction accuracy at the expense of a small amount of spectral reconstruction accuracy. The improvement of colorimetric reconstruction accuracy is very important for spectral color reproduction. The results also show that the colorimetric reconstruction accuracy of the proposed method is better than that of the other four existing weighted PCA.

Spectral images, which theoretically have a wider range of applications, store more information than RGB images. However, due to the high cost of spectral imaging equipment and complex data processing, spectral images are mainly applied in remote sensing, military and other fields. In recent years, scholars have proposed solutions to reconstruct spectral images by mathematical methods using RGB images, which can greatly improve the application range of spectral images. However, there are many problems in current spectral reconstruction models, such as the loss of image details and insufficient spectral accuracy. Therefore, this paper proposes a spectral reconstruction method from RGB images based on a dual attention mechanism to improve the quality of spectral image reconstruction from image detail and spectral accuracy. The proposed spectral reconstruction method designs a sparse signal depth reconstruction network, focusing on the sparse characteristics of RGB images, and achieves sparse to complete signals reconstruction by accurately extracting multi-level features of image information and mining more semantic information. Regarding network structure, the designed spectral reconstruction network first uses small parameter convolution to extract shallow feature information of RGB images. Then, the effective multi-frequency channel attention mechanism was used to calculate the correlation between each channel in the feature layer, and the effective distribution of feature response was realized by inter-layer weighting. At the same time, the layer feature weighted fusion attention mechanism is introduced to learn the dependence between features of different layers, and the weights are optimized through different layers weighting to extract effective spectral depth features. Finally, based on the extracted depth features, the hyperspectral image is transformed into a specified dimension by convolution. The experiment uses the python 3.7 programming language, pytorch 1.2, as the deep learning model framework and combined spectral image error and RGB image error as loss functions for the training of the spectral reconstruction network. The proposed method and 7 mainstream spectral reconstruction methods are compared and verified on the NTIRE 2020 and CAVE datasets. From a subjective perspective, the spectral image details recovered by this method are clearer, and the error is smaller. From the perspective of objective indicators, the spectral images reconstructed by this method are reduced by 18.9%, 16.6%, and 22.2% in RRMSE, RSAM and RERGAS indicators, respectively, compared with the methods with better reconstruction performance in the existing literature. The RPSNR indicator improved by 4.5%. Therefore, the experimental results prove the effectiveness of the proposed method from RGB image spectral reconstruction.

With the plasma jets aid, the liquids atomization characteristics can be improved to a certain extent. It can be seen that plasma-assisted atomization has the potential to be applied in the field of ultra-fine water mist to suppress gas explosions. However, since various active particles in the plasma jet have a promoting effect on combustion, it is necessary to quantitatively analyze the active particle species in the plasma in the presence of gas. In this study, a DBD discharge study was performed on premixed methane and air under atmospheric pressure using helium as the carrier gas. The needle-ring dielectric barrier plasma generator ionizes the helium/methane-air mixture at a discharge frequency of 10 kHz and atmospheric pressure and generates a stable plasma jet. Diagnosis of the active particle types, vibration temperature and electron excitation temperature of the plasma jet under the conditions of different peak voltages and different mixing volume flow ratios by emission spectroscopy. The results show that the main active particles in the plasma jet are the OH group, the second positive band of N2, the CH group, HeⅠ, and a small number of O atoms. Among them, the methane ionization region is mainly concentrated in the 400 to 600 nm range between. Increasing the peak voltage and the helium mixing volume flow ratio can effectively increase the content of active groups in the isojet. Using the continuous band of the second positive band of N2 to do the least square linear fitting, the vibration temperature of the plasma jet is calculated, and the vibration temperature of the atmospheric pressure helium/air-methane plasma jet is between 2 000 and 4 000 K. The vibration temperature shows an increasing trend with the increase of peak voltage and helium mixing ratio. Using five spectral lines with a large difference in excitation energy of HeⅠatoms to do the least squares linear fitting, the electron excitation temperature of the plasma jet is calculated, and the electron excitation temperature of the atmospheric pressure helium/air-methane plasma jet is obtained. Between 3 500~13 000 K. With the increase of the peak voltage, the electron excitation temperature shows an increasing trend. With the helium mixing ratio increase, the electron excitation temperature shows a decreasing trend. The analysis shows that the increase in the helium volume flow rate Larger will make the airflow in the jet generator faster, take away more heat in the generator, and cause the electron excitation temperature to drop.

In the non-invasive blood glucose measurement (NBGM) based on near-infrared spectroscopy, the established blood glucose prediction model could not work well for a long time as it is greatly affected by the fluctuation of human skin status. It can be a limitation for the real clinical application of the method. Cut aneous blood perfusion (CBP) is a parameter closely related to the physiological state of the skin, which directly affects the water flow in the skin. It is also difficult to be controlled by external means like the approaches to control the temperature or contacting pressure. As for measuring skin spectra, CBP affects the thickness of the dermis indirectly by changing the migration of water, and the skin spectra change greatly. In this paper, Monte Carlo simulation is used to simulate the diffuse light intensity, photon penetration depth and average optical path of a three-layered skin model when the thickness of the dermis changes ±30 microns at 1 000~1 700 nm. The spectral changes were investigated. The differential processing can be used on the diffuse attenuations of two neighbored source-detector separations (SDSs) to eliminate the influence of dermis thickness change. The appropriate SDSs are acquired to perform the differential measurement for 1 000~1 700 nm wavelengths. It has been found that 1 200 nm should be an optimal wavelength to get away from the affection from the varying dermis thickness because the attenuation at all SDSs varies little. At the wavelengths near 1 450 nm, where the water has strong absorption, the attenuation will change rapidly with SDS in certain SDSs, requiring a critical SDS selection. For the commonly used wavelengths in NBGM, 1 200, 1 300 and 1 600 nm, the SDSs can be set in which is less than 0.1 cm or greater than 0.4 cm since there the attenuation changes slowly with the SDS and the differential on two SDSs can work well to reduce the influence of the change of dermis thickness. Moreover, the SDSs should be selected to primarily sense the information of the dermis considering the different percentages of photons in the dermis, where 80% photon percentage is taken as the threshold to choose the SDSs. Finally, for 1 200, 1 300 and 1 600 nm, two appropriate SDSs can be picked in 0.03~0.1 cm to suppress the influence of dermis thickness change and achieve the desired measurement accuracy by the current instruments. This study could be a solution to reduce the influence of CBP.收稿日期>2022-02-22

Wheat is one of Chinas main food crops, which significantly impacts the development of the national economy. However, high temperature and UV stress led to a significant decline in its yield.When stresses occur, the polysaccharides in the cell wall will change to different degrees. As an important component of such polysaccharides, pectin plays a major role in determining intercellular porosity, identifying pathogens, and maintaining structural integrity. At present, common pectin detection methods include a gravimetric method, titration method, acid extraction method, etc. Most of these methods are damaging detection, whose determination steps are cumbersome, and the samples loss is large. In recent years, spectral detection technology has been widely used in the field of plant physiological information detection due to its advantages of simplicity, rapidity, high resolution, and strong real-time performance. Therefore, in this study, the hyperspectral and chlorophyll fluorescence spectrum detection technology were used to determine pectins content. Taking Ningmai 13 as the research object, the hydroponics method was adopted. The high temperature and UV stress environment was simulated during wheat growth by adjusting the temperature of the artificial climate incubator and the irradiation intensity of the UV lamp. At the tillering stage of wheat, hyperspectral data and chlorophyll fluorescence spectrum data of leaves were collected, and the pectin content in leaves was determined. The two original spectral data were smoothed and denoised by wavelet analysis. The coincidence band with the highest correlation coefficient between the two spectral data and pectin content was (620, 651) by correlation coefficient analysis. The training set and the validation set were divided in a ratio of 3∶1.The hyperspectral inversion pectin model, the fluorescence spectrum inversion pectin model and the double spectrum inversion pectin model were established by the PLS least squares method. The research results show that the inversion effect of pectin content in wheat leaves by double spectrum is good. The models correlation coefficients of the training set and verification set are 0.994 9 and 0.944 5. The conclusions of this study are helpful in explore the response of polysaccharides in the cell wall of wheat under adversity stress. They also can provide references and help for predicting the degree of stress environment of field crops and controlling the planting environment accurately.

Nephrite, also known as He tian Yu, is one of Chinas earliest and most popular natural jade material. Thanks to Chinas element connoted in the “Jin Xiang Yu” gold medal used in the 2008 Beijing Olympic Games, the jade has been re-understanding by people and shows increasing demand in the market. As a result, more nephrite has been exploited with more tailing produced, leaving the situation of resource wasting and environmental pollution. Therefore, it is of great theoretical and practical significance to the high-value utilization of nephrite tailings. The main mineral composition of nephrite is tremolite, with ideal crystal formula of ［Ca2Mg5Si8O22(OH)2］. It is a typical double-chain silicate, which can be used as an important natural raw material for silicate glass. The nephrite glass was obtained by firstly melting the raw material at 1 500 ℃ for 2 h, then pouring the melt into an iron mould immediately and finally annealing at 600 ℃ for 2 h. Results showed that with the increase of MnO content, there was a deepening tendency in color of the tailings glass to brownish yellow, transparent, glass luster, clean inside, no crack, refractive index, and increasing tendencies in dielectric content and relative density. Four nephrite tailings glass samples with spectral characteristics were investigated by XRF, FTIR, Raman spectrometer and UV-Vis-NIR spectrometer, and the effects of Mn2+ content on the quality and color of nephrite tailings glass were discussed. The results show that the spectral peaks near to 1 370 and 1 500 cm-1 in both infrared and Raman spectra were attributed to the gas molecules dissolved in nephrite tailings glass melt; When the content of MnO is 1%, the infrared spectra of sample Tb-3 is in the range of 450~500 cm-1, and the peak intensity of vibration spectrum is significantly stronger than that of the other three samples. The difference between infrared and Raman spectra indicates that the energy of the Si-O bond in the internal structure of Mn2+ nephrite tailings becomes stronger firstly and then weaker, leading to the densest internal structure at MnO of 1%. Compared the chemical composition and UV-vis-NIR spectra with the results of chemical analysis, it is suggested that the color of nephrite tailings glass is due to the combination of Fe and Mn elements. The charge transfer generated by Fe2+-Fe3+ pair makes the blue-violet region (400~460 nm) produce a wide absorption band, and the outermost d-d electron jump of Mn2+ makes the blue-green region (480~550 nm) produce a wide absorption band generating the transmittance in the yellow, orange region, and further producing brownish-yellow. This research confirmed the preparation process and spectral characteristics of Mn2+ doped nephrite tailings glass, discussed its application prospect, and provided a scientific direction for the high-value utilization of nephrite tailings, suggesting important theoretical research and application values.

To achieve high-precision spectral retrieval under high-temperature conditions, we compared and analyzed the accuracy and stability of seven different spectral line-shape profiles and developed a spectral line-shape profile selection strategy suitable for high-temperature diagnosis. Firstly, two H2O absorption spectra in the temperature range of 1 100~1 600 K were measured experimentally, and seven different spectral profiles were used to fit them respectively. The integral absorbance, velocity-dependent line-width and Doppler half height and half-width (FWHM) of each measured spectrum were obtained, and the gas temperature value was calculated according to the line-intensity ratio method. The comparative analysis show that under the condition of high temperature and atmospheric pressure, the fitting accuracy of the Gaussian profile is the worst, and the gas temperature inversion accuracy of the Gaussian profile is the highest; Setting the Doppler half-height width parameter in the line-shape profile as a constant can not only effectively improve the stability and accuracy of spectral line parameter inversion, but also improve the temperature inversion accuracy of each spectral line-shape profile. Compared with the non-Voigt profile (speed-dependent Voigt profile, Rautian profile, Speed-dependent Rautian profile and Hartman tran profile), the integral absorbance and velocity-dependent line-width obtained by the Voigt profile are small, and the relative error of calculated temperature are large. Finally, the running time of seven line-shape profile fitting programs was compared. Under the condition of ensuring accuracy, the speed-dependent Voigt profile calculation speed is the fastest. Therefore, the gas temperature is obtained by Gaussian Profile. The Doppler half height and half width are calculated according to the temperature and fixed as a constant, and the speed-dependent Voigt profile is used to fit the measured spectrum, which can effectively improve the inversion accuracy, velocity and stability of high-temperature spectrum.

To identify the intrinsic relationship between the distribution of seed composition and the change in seed vigour using a nondestructive testing method, this study chooses starch, a major component of corn seeds, as the research object. Combining the terahertz (THz) time-domain spectral reflection imaging technology with moving-window correlation coefficient imaging, a visualised map showing the spatial distribution of starch in corn seeds with different vigour degrees is constructed. Using the Zhengdan 958 corn variety as an example, a test is conducted to apply an artificial ageing method to prepare samples aged 0, 18, 36, 54 and 72 h. Then, the THz spectrometer is used to scan the reflection imaging attachments to obtain THz images of the samples. The THz image at 16.35 cm-1 was used as a benchmark, and the endosperm and seed embryo regions of the seeds were accurately extracted using the threshold segmentation method. By comparing the average absorbance of THz in different tissue regions, it can be obtained that the endosperm and seed embryo spectra differ significantly, and there is an obvious common absorption peak near 51.96 cm-1 for endosperm and starch pure substances. The moving window correlation coefficient method (window width 20, moving step 10) was applied to calculate the correlation coefficient between the terahertz time-domain spectrum of the seeds and the pure maize starch spectrum on a pixel-by-pixel basis and to construct a pseudo-colour heat map based on the correlation coefficient values and the coordinate information to visualise the maize seed starch distribution. Statistics on the percentage of pixel points with a correlation coefficient of >0.8 in the starch distribution map and covering five ageing stages and six spectral region windows lead to the conclusion that within the range of 29.83～67.36 cm-1 of the starch in the endosperm area of the seed exhibits an overall downward trend in vigour decline, presenting a positive correlation with vigour. Test results show that the THz time-domain spectral imaging technology can preliminarily realise the nondestructive detection of the spatial distribution characteristics of starch in corn seeds as seed vigour changes when it is used with the moving-window correlation coefficient pseudo-colour imaging analysis method. This technique provides a new perspective and method for studying the relationship between the chemical composition of seeds and their vitality and for non-destructively analysing changes in the vital activity of seeds and their own physiological and ecological patterns.

The problem of heavy metals exceeding the standard in Chinese medicinal materials is becoming increasingly serious, which will hinder the high-quality development of the Chinese medicine industry in the future. Therefore, research on efficient, accurate and convenient methods for the identification of excessive heavy metals is of great value for understanding the safety of traditional Chinese medicine. X-ray fluorescence spectrometry (XRF) instruments have the advantages of non-destructive testing, fast and accurate, and convenient sample preparation, and are widely used in elemental analysis. Due to the low threshold of heavy metals in traditional Chinese medicinal materials (for example, the 2020 edition of the Chinese Pharmacopoeia stipulates that the lead exceeds the standard at 5 mg·kg-1), there are many types of traditional Chinese medicines, complex matrices, and lack of national standard samples. Conventional classification algorithms are difficult to identify excessive problems accurately. This paper combines transfer learning with a multi-class support vector machine (TrAdaBoost SVM) method. The spectral feature information of national soil standard samples similar to honeysuckle is used for data enhancement, and the standard soil sample and a small amount of traditional Chinese medicine samples are mixed with establish Transfer learning and support vector machine classification models. Through the experimental verification, the classification optimization method combining transfer learning and TrAdaBoost-SVM, compared with the traditional SVM and AdaBoost classification algorithm, the accuracy rate of identifying the heavy metal element lead (Pb) exceeding the standard has been significantly improved. Through the prediction verification of the test dataset, the prediction accuracy of the TrAdaBoost-SVM model is 96.7%, which is higher than that of the traditional SVM and AdaBoost classification models. The method of combining transfer learning and TrAdaBoost-SVM proposed in this paper can establish a classification model under the condition of small samples and can accurately predict the excess of heavy metals in traditional Chinese medicine, which has certain theoretical significance and application value.

Near-Infrared (NIR, wavelength range: 780~2 500 nm) on-line spectral analysis technique has the advantages of miniaturization, rapid detection, and stable and reliable results. Therefore, this technique is widely used in the field of industrial detection. Because the spectroscopic system is significantly affected by ambient temperature, the detector is always cooled in traditional on-line spectroscopic systems. However, measurement errors, such as wavelength drift, are still generated from the optical components when temperature changes. In addition, PCs are always used for system control and spectra acquisition, which significantly increase system instability. In order to solve these problems, this paper proposes an on-line thermostatic control spectroscopic system based on an STM32 single-chip microcomputer. Firstly, STM32 single-chip microcomputer is used to control the near-infrared spectroscopy for spectra data acquisition, configuration setting, preprocessing of the spectral data, and calculation of the physical and chemical parameters of the sample. Secondly, a constant temperature control system has been developed based onthe STM32 single-chip microcomputer, in which the proportional-integral-differential (PID) control algorithm is used. The closed-loop thermostatic control has been realized for the whole spectrometer, including the optical and the circuit part. At last, an industrial communication interface including Modbus protocol communication and 4~20 mA current signal communication has also been developed based on the STM32 single-chip microcomputer. The system test results show that the whole system can be operated stably without PC for a long time. The measure errors originating from the changing of ambient temperature also reduce obviously. During a test of 48-hour system operation, the temperature of the spectrometer is controlled stably at around (5±0.25) ℃. A much smaller relative standard deviation of absorption spectra is obtained when the thermostatic control is applied. The system integrates spectra acquisition, pretreatment, calculation of physical and chemical parameters, industrial communication and thermostatic control, which can satisfy the requirements of industrial on-line detection.

Laser-induced breakdown spectroscopy (LIBS) is widely used because of its advantages, such as no sample preparation, small sample damage, online detection, and fast detection. The process of laser-induced plasma is very complex and is affected by many factors.The incidence angle of a pulsed laser is one of the key factors. The change of pulsed laser incident angle will change the laser focal spot shape on the sample surface, affecting the laser-induced plasma process. The change of angle between the pulsed laser and the normal direction of the target will directly affect the plasma expansion. Although the laser incident angle is one of the key factors affecting the laser-plasma, however, there are few studies on the incident angle of pulsed laser at low pressure, the effect of pulsed laser incident angle on laser-plasma is still unclear, and the internal mechanism of the influence of pulsed laser incident angle on laser-plasma needs further study. The present study studies the influence of pulsed laser incident angle on the focal spot formed by pulse laser on the target surface. The results of the experiment and simulation show that the focal spot size increases with the pulsedlaser incident angle, resulting in the power density of the pulsed laser decreasing. Secondly, the influence of pulsed laser incident angle on laser-plasma at different ambient pressure is studied by coaxial imaging. The experimental results show that the radiation intensity of the laser-plasma core decreases against the laser incident angle. When the incident laser direction deviates from the target surface normal directionof 0°~15°; the reduction of radiation intensity is only 3.05%. When the direction of the incident laser deviates 60° from the normal direction of the target surface, the reduction of radiation intensity can reach 25.415%, which means that the pulsed laser incident angle has an obvious influence on the plasma core radiation intensity. Finally, the microstructure of pulsed laser ablation pits generated by laser from different angles at 10-4 is analyzed. The results show that the ablation volume of the target increases with the laser incident angle, but the ablation efficiency of the target, i.e. the ablation amount of target per unit spot area, is against with the pulsed laser incident angle. It explains that the radiation intensity of the laser-plasmacore decreases against the laser incident.

To study the influence of magmatic intrusion on coal chemical structure, the chemical structures of four highly metamorphic coals in the magmatic contact zone in the Yunjialing coal mine in the Hanxing mining area were characterized using Fourier Transform Infrared Spectroscopy. The results showed that the aromatic structures of all samples were dominated by tri-substituted benzene rings, and the highest proportion was 67.4% observed in the parting; the farther away from the intrusive body, the lower the tri-substituted contents of benzene rings, the di-substituted contents of benzene rings gradually increase, whereas the content of the benzene ring tera-substituted and pen-ta-substituted both show a law of increase firstly and then decrease. In terms of oxygen-containing groups, the farther away from the intrusive body, the more active aromatic esters and carboxyl groups, while the C-O content in phenol changes less and the content of alkyl ethers and aryl ethers decreases. The proportion ofCC stretching vibration in the aromatic or condensed ring gradually increases. Due to the influence of magmatic intrusion, the samples fatty substances content is very low. The farther away from the intrusive body, the proportion of symmetric and antisymmetric stretching vibrations of methyl groups first increases and then decreases. The variation rule of methylene symmetry and methylene antisymmetric stretching vibration is opposite to that of the methyl group. Regarding hydroxyl groups, hydroxyl self-association hydrogen bonds are the main type of hydrogen bonds in coal samples, while in the parting, hydrogen bonds formed by hydroxyl and ether oxygen are the main types. With the deepening of magmatic intrusion, the latter gradually decreased in coal samples. The farther away from the intrusive body, the lower the hydrogen bond content formed by hydroxyl-π hydrogen bonds, the rule of the hydroxyl-N hydrogen bond content first increases and then decreases. The results show that magmatic intrusion dramatically impacts the chemical structure of organic matter in coal, and has a differential impact on the chemical structure of organic matter in coal and parting. The results will provide theoretical guidance for exploring the evolution mechanism of the macromolecular structure of highly metamorphic coal.

Determining the amount of gold and impurity in electroformed gold ornaments is mainly performed by the cupellation method and the inductively coupled plasma atomic emission spectrometry (ICP-AES). However, the gold contents of electroformed gold ornaments are different according to these two methods, especially whether the gold ornaments are prepared by electroformed process from cyanide or non-cyanide bath. In the paper, the content of 26 impurity elements for gold samples was determined by glow discharge mass spectrometry (GDMS). The measured results are high for every impurity according to the instruments relative sensitivity factors(RSF. The ratios of the instrument RSF and corrected RSF range from 0.96 to 1.90 for 16 elements except the copper and chromium elements. The ratios for these two elements are 3.01 and 3.91 respectively. Thus, the results show that the relative sensitivity factors of the instrument can be used for quantitative analysis of impurities from gold ornaments. Besides, the GDMS method was used to determine gold contents, with fire assay and ICP-AES as controls. The gold content of GDMS method is consistent with that of the fire assay and ICP-AES for the electroformed gold ornaments from the non-cyanide bath, but the gold content of GDMS method for electroformed gold ornaments from the cyanide bath is still consistent with that of the fire assay, but that is lower than that of ICP-AES. It indicates that the GDMS and fire assay methods are suitable for the accurate determination of gold content for the electroformed gold ornaments from cyanide/non-cyanide baths, whereas the ICP-AES method is only suitable for the electroformed gold ornaments from the non-cyanide bath. Besides, compared with the ICP-AES method, the carbon and nitrogen elements can be tested by GDMS method. It was revealed that the contents of carbon, nitrogen, sodium, and potassium elements determined by the GDMS method for electroformed gold ornaments from cyanide bath were significantly higher than those for the electroformed gold ornaments from the non-cyanide bath. It can be used to infer the possible preparation process of the electroformed gold ornaments.

The biological application of thiosemicarbazide transition metal complexes is the focus of current research. In order to better understand the interaction mechanism between thiosemicarbazide aryl ruthenium complexes and has, two thiosemicarbazide aryl ruthenium(Ⅱ) complexes were synthesized. The fluorescence quenching mechanism of two thiosemicarbazide aryl ruthenium complexes with human albumin was studied by time-resolved fluorescence spectroscopy and steady-state fluorescence spectroscopy. Fluorescence spectroscopy results indicate that these two thiosemicarbazide aryl ruthenium complexes could quench the endogenous fluorescence of has, and the fluorescence quenching effect of HSA was linear with the concentration of the complex. It was found that the fluorescence quenching efficiency of complex 2 was stronger. The quenching and binding constants of the two complexes interacting with HSA decrease with the increase in temperature. Therefore, the interaction process between the complexes and HSA was a static quenching process, and the fluorescence quenching ability of complex 2 was stronger. Through the analysis of thermodynamic parameters, the main binding forces between the two complexes and HSA are hydrogen bond, and van der Waals force, and the binding process between the two complexes and HSA was spontaneous. Finally, the infrared absorption and circular dichroism spectra show that the two thiosemicarbazide aryl ruthenium complexes have different effects on the secondary conformation and microenvironment of HSA. The infrared absorption spectra show that combining the two complexes with HSA causes the rearrangement of the secondary structure of HSA, Circular dichroism spectra showed that the addition of these two complexes reduced the secondary structure stability of HSA. The above studies show that exploring the effect of thiosemicarbazide aryl ruthenium complex on the structure and function of human serum albumin can reveal its possible mechanism of action with HSA after entering the body as an antitumor drug. Therefore, it provides a theoretical reference for researching and developing aryl ruthenium complex anti-tumor drugs with thiosemicarbazide as the ligand.

Due to its unique advantages of rapid, high efficiency, non-contact detection and no need for sample pretreatment, Raman spectroscopy is an ideal method for high-throughput, non-destructive on-line detection of large quantities of samples and a suitable method for rapid detection of ethanol concentration of liquor products. However, different from pure water, besides Raman characteristic peaks of ethanol and water, there is apparent fluorescence interference within the spectra of most liquor under laser excitation, which negatively influences the quantitative determination of ethanol concentration. Therefore, during the determination of ethanol concentration based on the Raman characteristic peak intensity ratio method, the data points for fluorescence background fitting need to be manually selected before data processing, which possess strong subjectivity and low data processing efficiency and is difficult to fully meet the technical requirements of the high-throughput samples on-line screening. In order to solve the above problems, based on the self-built laser-induced polarized Raman spectroscopy detection system, a series of experiments were carried out on the polarization characteristics of the Raman peaks and fluorescence background of four kinds of liquor samples under excitation of linearly polarized laser with different polarization orientation. Based on the polarization difference of them, a quantitative determination method of alcohol concentration in liquor products based on polarized Raman spectroscopy is proposed. The experimental results show that, with the aid of differential polarization Raman spectroscopy, the correlation coefficient of the polynomial fitting of three times is over 0.99, which can achieve accurate inversion of ethanol concentration in the range of 3%~97%vol. The accuracy of ethanol concentration in four kinds of liquor samples is significantly higher than the inversion results of traditional methods, which significantly improves the accuracy and efficiency of alcohol concentration determination of liquor products.

In this paper, the natural sand grains were modified by wet modification with acetic acid, and the modified sand grains obtained were used as sorbent in the micro solid-phase adsorption column. The atomic absorption spectrometry (AAS) as the detection method was connected to the micro-column, and the adsorption behavior of lead ions on the modified sand grains in the column was analyzed. At the same time the adsorption conditions were optimized. Identification of the surface modification characterized and performed based on FTIR and SEM. The results showed that the saturated adsorption of the modified sand grains for lead ions was superior to that of natural sand grains. The saturated adsorption of the modified sand grains reached 28.7 mg·g-1 under the following condition, including the size of the grains was 38～74 μm, the sample pH was about 6, the loading flow rate of the solution through the micro-column was 1.5 mL·min-1, the adsorption temperature was room temperature. The adsorption capacity of lead ions on modified sand grains increased by 13%, and the adsorption rate could reach 92.6%. Different concentrations of HCl, H2SO4, and HNO3 dilute solutions were selected for desorption experiments, and the experimental results showed that the desorption effect of 0.01 mol·L-1 hydrochloric acid solution is good, so that 0.01 mol·L-1 HCl solution was selected as the best desorption agent. The desorption rate reached 97.3% under optimal conditions.

Milk fat content of high and low will affect peoples health. The experiment in milk fat content analysis indicators, application of image processing technology analysis of hyperspectral data, extracting the region of interest (ROI) from hyperspectral images using the ENVI software, different preprocessing methods were used to establish Partial Least Squares Regression (PLSR) model for spectral data and the best preprocessing method was obtained by comparison, Then, different numbers of principal components were used for feature extraction of the pre-processed data and Support Vector Regression (SVR) model was established. The optimal number of principal components was obtained through comparison. Finally, the SVR prediction model was established for the data after feature extraction to analyze the fat content in milk. Since the traditional SVR model has a poor prediction effect and cannot meet peoples basic requirements, this paper proposes a hybrid strategy improved whale optimization algorithm to optimize the SVR prediction model. The evaluation parameters of the SVR model optimized by hybrid strategy whale optimization algorithm are compared with those optimized by genetic algorithm, traditional whale optimization algorithm and elite reverse learning whale optimization algorithm. The results show that the training set and prediction set coefficient of determination (R2) of the SVR model optimized by hybrid strategy modified Whale optimization algorithm are 0.998 and 0.995, respectively. The reciprocal 1/RMSE values of Root Mean Square Error (RMSE) were 13.766 and 6.191, and the reciprocal 1/MAE values of Mean Absolute Error (MAE) were 13.910 and 11.422, respectively. The training set and prediction set parameters R2 of the SVR model optimized by the traditional whale optimization algorithm are 0.998 and 0.989, 1/RMSE is 13.526 and 5.849, and 1/MAE is 13.616 and 7.037, respectively. The training set and prediction set parameters R2 of the SVR model optimized by the whale optimization algorithm improved by reverse learning strategy are 0.998 and 0.988, 1/RMSE is 12.474 and 6.421, and 1/MAE is 15.003 and 10.554, respectively. The above results show that the hybrid strategy improved whale optimization algorithm is feasible to optimize the SVR prediction model, and the optimized SVR model has a better prediction effect.

Food safety has attracted public attention because of pesticide residue contamination. It has certain theoretical and practical significance to develop fast, accurate and highly sensitive new methods for detecting pesticide residues. In this paper, a colorimetric and fluorescent dual-mode optical sensor has been designed for sensitive detection of pesticide residues by using the difference of plasma absorption spectrum of aggregation and dispersion gold nanoparticles and the internal filtration effect between fluorescent molecule rhodamine 110 and gold nanoparticles. Gold nanoparticles (AuNPs) with a diameter of about 13 nm and a negative charge on the surface were synthesized by the citrate reduction method. They were dispersed in an aqueous solution and showed wine red. The maximum absorption wavelength of the solution was 520 nm. Pesticide molecules can induce the aggregation of dispersed AuNPs by forming Au-N or Au-O coordination bonds, resulting in the color changes of the solution gradually from redwine to blue-purple. Pesticide content can be detected according to the change of absorbance of the solution at 520 nm. The significant color change of the solution can be observed even with the naked eye. The detection method has the advantages of simplicity, rapidity and low cost. Although the single colorimetric detection mode is simple, false positive is possible. In order to further verify the accuracy of the results and improve the detection sensitivity, fluorescent dye rhodamine 110 with the positive charge is introduced into the dispersed AuNPs solution, which can adsorb on the negatively charged AuNPs surface. In this state, the AuNPs are still well dispersed in the solution. The fluorescence spectrum of rhodamine 110 overlaps with the absorption spectrum of AuNPs, leading to the fluorescence internal filtration effect (IFE). The fluorescence intensity of the solution is very weak, even with no fluorescence emission. In the presence of pesticide molecules in the solution, they compete for adsorption with the fluorescent dyes on the surface of AuNPs to induce the aggregation of AuNPs. The color of the solution changes from redwine to blue purple. At the same time, the fluorescence of rhodamine 110 molecules released into the solution is restored. The colorimetric and fluorescence dual-mode detection of the targets is realized according to the changes insolution absorbance and fluorescence intensity. As a model molecule, phoxim was used to test the sensors performance. The limit of detection for color imetry and fluorescence were 15.0 and 4.0 nmol·L-1, respectively. The test results of actual samples showed that the sensor had certain application potential in the food safety field.

To realize the rapid, nondestructive detection of solid soluble content (SSC) in Mandarin, a hand-held nondestructive detection system was developed based on visible/near-infrared technology. Wide spectra range LED light source combined with a narrowband response micro-spectrometer was designed as a core in the handheld nondestructive detection terminal. The cloud data system of the fruit spectrometer based on Internet of Things technology was also developed, including a user database, equipment database, test database and model database. The data system was connected with the detection terminal through a communication module to realize functions, including modifying parameters of spectra collection, uploading and downloading the cloud data and invoking cloud model. Based on the spectra collected by the system, anovelone-dimensional convolutional neural network (1D-CNN) model was proposed to predictmandarin soluble solid content. The network contains 7 layers: input, convolution, pooling, full connection, and output. Mandarin spectra of the master machine were collected to build the 1D-CNN SSC prediction model, and the 1D-CNN model was compared with traditional regression methods to evaluate the model performance. The Rp and RMSEP of the 1D-CNN model were 0.812 and 0.488 respectively, better than that of partial least squares (PLS), artificial neural network (ANN) and support vector machine (SVM).Transfer learning method based on the 1D-CNN model of the master machine was adopted to transfer the model to the slave machine, the influence of the number of samples from the slave machine on model transfer was studied, and a small number of slave machine spectral samples for model training achieved good model transfer effect, modeling transferring result with root mean square error of prediction of the slave machine being 0.531.The results demonstrated that the detection system has the advantages of fast detection, low cost and simple operation. The 1D-CNN network based on the detection system could effectively extract effective features of Mandarin spectra and perform regression analysis. With the help of the transfer learning algorithm, the effective transfer of the 1D-CNN model between different devices can be realized, which could meet the demand fornon-destructive testing of solid soluble content in the mandarin industry.This research provided a reference for developing and applying handheld spectrometer non-destructive testing system of fruit internal quality.

As a common mineral, analcime has been widely applying in industry. The compound methodsof analcimearealso mature, but the natural gem-gradeanalcimeis still very rare on Earth. In this paper, the object of discussion is the natural gem-gradeanalcime from the Daye mining area of Hubei province. The purpose is providing its identification and characteristics of origin. These analcimein this area are mostly colorless and transparent, with complete crystal shape and clear crystal surface pattern. The maximum crystal size can reach 36 mm; of course, the clarity of these is excellent too. According to the backscattered electron imagingand energy spectrum analysisresults, determined the crystal is homogeneous analcime. The white inclusion is wairakites with fascicular structure, a set of cleavage and metasomatic relict texture according to the electronic probe microanalyzer results calculation. The characteristics of Raman spectra show that: the diffraction peaks of 81, 139, 201 and 298 cm-1 are caused by the lattice vibration of zeolite. The strong peak of 298 cm-1 may represent the vibration of metal-oxygen. The bending vibration of O-Si-O causes the extremely strong peak of 491 cm-1. The shift of Si-O and AlO tetrahedrons may attribute the diffraction peak of 390, 671 cm-1. A set of peaks at 1 105 cm-1 indicates the stretching vibration of Si-O, which is a typical peak position of natural zeolite. The weak peak of 1 624 cm-1 represents the bending vibration of water, and the strong peak of 3 557 cm-1 represents the stretching vibration of water. The samples infrared spectra mainly show that: the infrared absorption 788, 1 259 cm-1 are caused by the stretching vibration of silica tetrahedron. The bending vibration of water causes 1 646 cm-1; The weak absorption of 3 635 cm-1 is caused by the tensile vibration of water. For some complete samples, only one absorption can be seen at 3 635 cm-1, which means there is only one form of water molecule in the lattice. In other samples with pocket and chlorite, the 3 635 cm-1 absorptions can be seen on the left side between 3 635 cm-1 with strong absorption, which means the type of water molecules in the lattice are various, and the number is slightly larger than the rest, but still fewer overall. Therefore, according to the composition and spectrumtest, it is speculated that the gem-grade calcite in Hubei Daye belongs to high-temperature sedimentary analcime, which are the metasomatic products of wairakites, and later experienced local chloritization.

In recent years, gold nanoparticles (AuNPs) have been widely used in developing and utilising colorimetric sensors due to their extremely high extinction coefficient and distance-dependent color. Common salt-induced AuNP aggregation is carried out using charge shielding, the aggregation process is easily disturbed, and the color is often unstable after aggregation. DNA dyes achieve AuNPs aggregation by charge neutralization, which has the advantages of less dosage and faster aggregation speed. Fast and stable. Therefore, it is necessary to screen for common DNA dyes. In this paper, eight common DNA dyes, including EB, AO, TO, SG, PG, TOTO-1, TOTO-3 and YOYO, were systematically screened for inducing the rapid aggregation of AuNPs. Experiments found that the amount of dye to induce AuNPs agglomeration was between 0.18 and 2.6 μmol·L-1, about 10 000 times lower than the traditional method of inducing aggregation with NaCl and Cys dosages of 60 mmol·L-1 and 20 mmol·L-1, respectively. In addition, the aggregation efficiency was evaluated by examining the “IC50” value of DNA dye-induced AuNPs aggregation (that is, the concentration of 50% of the maximum absorbance change (A680/A520) of AuNPs aggregation induced by the inducer). Among the eight DNA dyes screened, SG The “IC50” values of, PG, TOTO-1, TOTO-3 and YOYO molecules were between 0.12 and 0.30 μmol·L-1, which were relatively small, and the aggregation efficiency of AuNPs was high. Since the number of positively charged N atoms plays a key role in the aggregation of AuNPs, the more positively charged N atoms, the less the amount of neutralizing AuNPs. The number of positively charged N atoms was calculated through the microspecies and microspecies distribution in Marvin View. The results show that pH=7, SG, PG, TOTO-1, TOTO-3, And YOYO molecules have more positively charged N atoms, so the above dye-induced AuNPs aggregation efficiency is high. At the same time, the binding ratio of double-stranded DNA (dsDNA) base pairs to DNA dyes was calculated by the Job curve. The results showed that the binding ratios of the 8 kinds of DNA dyes screened to dsDNA base pairs were similar under the same conditions. Based on the binding ratio, the binding constants of dsDNA and DNA dyes were calculated by combining the experimental fitting curves. The calculation showed that the binding constants of SG, YOYO, TOTO-3, PG and dsDNA were relatively large, ranging from 2.75×109 to 3.12×1010 L·mol-1, and the binding ability to DNA is stronger. Taken together, SG, PG, YOYO, TOTO-3 and other dyes are effective in rapidly inducing AuNPs aggregation and colorimetric sensing.

Offshore oil spill accidents cause a great waste of oil resources and seriously threaten the ecological environment. Therefore, it is important to use fluorescence spectroscopy to detect oil film thickness quickly and nondestructively for effective evaluation of oil spills. Based on laser-induced fluorescence (LIF) technology, the fluorescence spectra of oil film of 0# diesel oil and 5# white oil on sea water surface were detected, and then the oil film thickness was quantified. Firstly, SG was used to preprocess the original spectral data to reduce the background noise in the original spectrum. Then, interval random frog (IRF) combined with iteratively variable subset optimization (IVSO) was used to select the wavelength of the obtained full spectral data to eliminate redundant variables. The characteristic wavelength of the spectrum screened out twice was used as the independent variable input data of partial least squares regression (PLS) to establish the oil film thickness inversion model. In the first step of the method, the characteristic bands are screened from the full spectral data by IRF, and the characteristic wavelength variables are further screened by the combination of characteristic spectral bands by IVSO to effectively improve the prediction ability and stability of the oil film thickness inversion model based on the selected characteristic wavelengths. IRF-IVSO was compared with four wavelength optimization methods: full spectrum and moving window partial least squares (MWPLS), interval random frog (IRF), variables combination population analysis (VCPA) and iteratively variable subset optimization (IVSO). The characteristic wavelengths of 0# diesel oil and 5# white oil screened by IRF-IVSO accounted for 4.48% and 19.40% of the total spectral data, respectively. The full spectrum and the characteristic wavelengths screened by the above wavelength optimization method were used as input to establish a PLS model for analysis and discussion. The results show that the prediction ability and efficiency of different models established by using the feature wavelength selection method combined with PLS are significantly higher than that of the full spectrum. Among them, the oil film thickness inversion model established by IRF-IVSO combined with PLS has the best prediction effect. This model can realize effective inversion of 0# diesel oil and 5# white oil with the thickness of 0.141 5～2.291 8 and 0.052～0.980 mm, respectively, and the correlation coefficient RP of diesel oil film test set can reach 0.961 1. The RMSEP of the test set is 0.137 5, the correlation coefficient RP of the white oil film test set is 0.971 2, and the RMSEP of the test set is 0.079 0. This study shows that IRF-IVSO can effectively and stably screen characteristic wavelength variables by combining interval band screening and single variable selection, and the oil film thickness inversion model established by combining PLS can achieve reliable prediction.

Due to the lack of confirmation technology for rapid identification of rice origin, 186 rice samples from Wuchang, Northeast and South of China, were identified by near-infrared mid-infrared, and Raman combined with chemometric analysis in this study. Firstly, the recognition effects of three algorithms of k-nearest neighbor method (KNN), linear discriminant analysis (LDA) and least squares-support vector machine (LS-SVM), combined with five preprocessing methods on three single spectrum rice origin identification models are compared. The results show that the Raman spectrum model of the LS-SVM algorithm combined with the SNV+2nd preprocessing method is the best, and the accuracy of the calibration set and validation set are 100% and 93.48% respectively. In order to further improve the accuracy of the identification model, the rice-origin identification models of data layer fusion, feature layer fusion and decision-layer fusion based on near-infrared spectroscopy, mid-infrared spectroscopy and Raman spectroscopy are established innovatively. The results show that the recognition accuracy of the three spectral information fusion model levels is greatly improved compared with the single spectral model. In the data layer fusion rice origin identification model, the LS-SVM algorithm combined with SNV+2nd preprocessing method is the best model. The accuracy of the calibration set and validation set are 100% and 95.65% respectively, which is 2.17% higher than that of a single spectral optimal model. In the decision-level fusion identification model, the LS-SVM algorithm combined with the SNV+1st preprocessing method is the best model. The accuracy of the calibration set and validation set are 100% and 97.83% respectively, which is 4.35% higher than that of a single spectral optimal model. In the feature layerfusion origin identification model, the LS-SVM algorithm combined with SNV+2nd preprocessing method is the best identification model. The recognition accuracy of the calibration set and validation set are both 100%, which is 6.52% higher than that of the single spectral optimal model. The results show that it is feasible to use near-infrared spectroscopy, mid-infrared spectroscopy and Raman spectroscopy combined with chemometrics to identify rice origin, and the rice origin identification model based on Raman spectroscopy combined with LS-SVM algorithm is the best. The recognition accuracy of the three spectral information fusion model levels is greatly improved compared with the single spectral model. Among them, the feature level fusion method is more suitable for the data type of this fusion and can quickly and accurately identify the origin of Wuchang rice, Southern rice and Northeast rice. This study provides a new method for rapidly and accurately identifying rice-producing areas.

The water absorption rate of flour is an important quality parameter for evaluating flour quality and predicting the processing characteristics of flour-based products. Determining the water absorption rate is mainly conducted using a gluten analyzer according to international or national standards, which is time-consuming and labor-intensive. Therefore, this study proposes using visible near-infrared spectroscopic analysis technology for rapid and non-destructive detection of the water absorption rate of flour. The water absorption rates of 150 wheat flour samples were determined according to the national standard method, and the value rang from 53.10% to 74.50%. The spectral information of the flour samples was collected using a visible near-infrared spectrometer, with an effective spectral range from 570 to 1 100 nm. Partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) was used to correlate the spectral information with the water absorption rate of flour. Quantitative analysis prediction models for the water absorption rate were established, and the optimal modeling methods were selected. Based on the selected modeling methods, competitive adaptive reweighted sampling (CARS), interval random frog leaping (iRF), iterative variable selection using the retained informative variables (IRIV), and successive projections algorithm (SPA) were employed to extract feature wavelengths and select the optimal feature wavelength extraction algorithm. Five spectral preprocessing methods, including normalization (NL), first derivative (1st Der), baseline correction (BL), standard normal variate (SNV), and detrending (DT), were applied to preprocess the spectral data of the feature wavelengths. The optimal spectral preprocessing method was determined. The results showed that the PLSR model built after preprocessing the spectra of the 24 feature wavelengths (only 2.26% of the original wavelengths) extracted by the CARS algorithm using the NL spectral preprocessing method achieved the best performance. The correlation coefficient (R2p), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD) for the prediction set were 0.889 4, 1.458 5, and 2.641 3, respectively. The model built using the feature wavelengths extracted by the CARS algorithm not only improved the models performance but also significantly increased the computational efficiency, reduced instrument manufacturing costs, and alleviated the challenges of miniaturizing the spectrometer. This study provides a foundation for the non-destructive and rapid detection of the water absorption rate of flour using visible near-infrared spectroscopy.

Single-cell Raman spectroscopy (SCRS) technology has the advantages of being rapid, sensitive, and label-free to study cell structure at the single-cell level. A cell growth detection method based on Spectral Clustering and SCRS was proposed in this paper. SCRS data of 600 synchronous culture fermentation-engineered bacteria E. Coli were collected as experimental data, and SCRS data of 300 fermentation-probiotic bacteria-Bacillus subtilis, were collected to verify the methods applicability. Firstly, the growth curve of OD600 was measured for the synchronously cultured colonies as growth period labels at the microbial population level. Secondly, t-SNE was applied to visualize the SCRS data of the population cells, guiding Spectral Clustering to cluster the high-dimensional SCRS data. Silhouette Coefficient and CH index were applied to evaluate the best clusters and assign labels to each SCRS data cluster. Finally, the intersection of SCRS data cluster labels and growth period labels was fitted by cubic spline interpolation to accurately identify the heterogeneous growth period data co-existing in the population and achieve accurate identification of growth periods of single-celled microorganisms. The results showed that the cell growth analysis method based on spectral clustering and SCRS could effectively detect 9% and 4.3% heterogeneous data of the optimal clusters in the three growth periods by using a 2-dimensional embedding space dimension and nearest neighbor-based spectral clustering similarity calculation method according to the cell growth curve of synchronous culture population. The study proposed a method of unsupervised detection of single-cell growth, with the help of spectral clustering without tags, can directly according to the features of SCRS data modeling, and can be of the arbitrary shape of high-dimensional SCRS data clustering and the advantages of fast convergence, realized with two kinds of fermentation engineering bacteria and probiotic fermentation cells lag, the accuracy of logarithmic phase and stable phase identification. In a real sense, it can detect cell growth from the single cell level and provide more accurate and real-time control guidance for fermentation engineering, which has important engineering application value.

Rice is the most important cereal crop in China. To accurately realize the variety identification and appearance quality evaluation of geographically iconic rice is not only related to consumers interests but also to the reputation of retailers and manufacturers, which is a widespread concern. Firstly, to realize the integrated application of milled rice variety recognition and appearance quality detection, a multi-parameter detection system for milled rice variety and appearance quality was established. The system uses an NIR spectrometer with a diffuse reflectance accessory to collect the spectral information of rice flour, which can realize the classification of milled rice varieties based on NIR spectroscopy. Multi-parameter detection of milled rice appearance quality was realized based on the image method using the Complementary metal-oxide-semiconductor (CMOS) camera. The detection objects included cracks, length/width, chalkiness, broken grains and yellow grains. Based on the above system, this paper proposed a milled rice variety classification method based on spectral-image feature model fusion to improve the classification accuracy of milled rice varieties. In this method, the NIR spectral features and multi-image features were used as the input parameters, the milled rice variety number was used as the output parameters, and a variety classification fusion model was established based on the Partial least squares (PLS) method. In the modeling process of each fusion scheme, the variable projection importance analysis (VIP) method was used to achieve the optimal selection of the input parameters. Then the optimal fusion model was determined by comparing the classification accuracy of different fusion schemes. Finally, the multi-parameter detection experiment of milled rice appearance quality and the performance comparison experiment of different milled rice variety classification methods were carried out. Experimental results showed that the detection system established in this paper could realize the multi-parameter detection of milled rice appearance quality, including broken rice rate, length-width ratio, fissured rice rate, chalky rice rate, and yellow-colored rice rate, for which the detection accuracy range was 89.2%~97.0%. The proposed milled rice variety classification method based on the spectral-image feature model fusion could improve the classification accuracy of milled rice varieties. Compared with the NIRS method, which has a better effect than the traditional methods, the classification accuracy of Wuchang, Xiangshui, Yinshui, and Yuiguang rice varieties can be improved by 2.5%~7.5% using the new variety classification method.

In the terahertz spectroscopy experiment, the tested solid samples surface is usually parallel and smooth to improve the systems signal-to-noise ratio. However, the objects surface in its natural state may show particular morphology such as depression and bulge, which will affect the terahertz spectrum in practical applications such as security inspection. These effects are related to the size of the particular morphology, but the most easily overlooked is that these effects are also related to the spatial distribution of terahertz waves. In this paper, firstly, we establish a model according to the terahertz transmission process of concave surface samples based on Gaussian optics. The influence of the regular cylinderconcave surface on the terahertz transmission spectra is studied. The Gaussian optical parameters of the terahertz spectrum system are measured by the small-aperture fitting method, and the parameters such as the beam waist radius of the terahertz wave are obtained. Then, polytetrafluoroethylene with a regular cylinderconcave surface is selected as the experiment material. The theoretical model value of transfer function amplitude is compared with the experiment value to verify the models applicability. The necessity of taking a terahertz wave as the Gaussian beam when there are defects such as a concave surface is confirmed through the experiment. Finally, it is inferred from the model that the quantitative effects of depression depth and depression radius on terahertz transmission spectra, in the directions parallel and perpendicular to the propagation direction of terahertz wave: with the increase of depression depth, the spectral transfer function amplitude period becomes smaller and smaller. It is a monotonous quantitative relationship that will not be affected by the depression radius. The quantitative detection of depression depth can be realized using this quantitative relationship between depression depth and the spectral transfer function amplitude period. When the available spectra width is 1.2 THz, the minimum detection limit of depression depth is 0.53 mm; With the increase of depression radius, the average spectral transfer function amplitude decreases first and then increases. There is no monotonic function relationship between them, which is affected by depression depth. When the depression radius is greater than 5 mm, the mean value of the spectral transfer function amplitude no longer increases with the increase of the radius. The influence of the depression on the spectral transfer function amplitude is also related to the depression position. These two phenomena are mainly related to the Gaussian distribution of the terahertz waves. The conclusions of this study can be used in the nondestructive testing of surface defects of nonpolar materials by terahertz wave. They can also be used to design the surface morphology of samples to make them have the desired spectral transfer function.

This article reveals materials and techniques used in the mural in Lam Rim Hall of Wudang Lamasery, which is situated on a hillside of the Yin Mountains in Baotou City of Inner Mongolia, China. The mural was drawn on paper and preserved on the south wall of the hall. It has suffered significant deterioration, such as craquelure, flaking, and paint loss, due to environmental aging or human influences. Before restoring, several spectral techniques, including XRD, Laser particle size analysis, Raman, FTIR-ATR, Hyperspectral, and THM-Py-GC-MS, were used to investigate the compositions and properties of the mural.The results showed that the mural keeps a typical characteristic of Tibetan Buddhism wall painting, while the specificity of times and region about materials and techniques. Both coarse and fine plaster is dominated by quartz, albite and calcite, the composition is consistent with the local raw soil, but with different particle sizes and mixed with different plant fibres. A paper layer is between the plaster and paint layer, hyperspectral was used to extract the red Tibetan marks on the paper, and the legibility substantially improves with MNF and Band Math. Raman analysis showed that vermilion, red lead, chrome yellow, emerald green, and synthetic ultramarine were used in the frame, and the latter three were synthesized in a European lab and did not enter China until the late 19th century. FTIR-ATR and THM-Py-GC-MS results indicated that the surface is coated by a dry oil film, but still, it needs further work to determine its source and processing, and alkyd resin is also be detected as a kind of restoration material.

Hyperspectral remote sensing technology can show the spectral characteristics of rocks and minerals in more detail, which provides a powerful means for hyperspectral rock and mineral identification. The traditional hyperspectral rock and mineral identification model based on specific absorption characteristic band depends on high a priori knowledge and is difficult to meet the requirements of distinguishing different types of rocks. Therefore, exploring and establishing an accurate and efficient hyperspectral rock automatic identification model is of great significance. Three typical sedimentary rocks (21 mudstone, sandstone and limestone) were collected as target samples in the Lufeng Dinosaur Valley area. The hyperspectral data of sedimentary rock samples in the range of 350~2 500 nm were obtained with the aid of the ASD fieldspec3 ground feature spectrometer. The original spectrums first-order differential and continuous removal transformation were carried out, and the spectral characteristics were analyzed. The continuous projection algorithm (SPA) was used. Competitive adaptive reweighted sampling algorithm (CARS) and iterative retained information variable method (IRIV) select the characteristic wavelengths in the original spectrum and transformed spectrum and then establish support vector machine (SVM) and random forest (RF) recognition models based on the full band and characteristic wavelength data respectively. The results show that the three feature variable selection algorithms have a good dimensionality reduction effect on hyperspectral data, and the number of feature wavelengths selected from the original spectrum and the two transform spectra is between 7~59. It is obtained that the combined continuum removal SPA-SVM model method performs best for identifying three types of target sedimentary rocks, and its recognition accuracy is 0.952 4. At this time, 10 characteristic wavelengths are selected for the input model, which accounts for only 0.5% of the whole band, which greatly reduces the amount of calculation of the model. Two characteristic wavelengths are located in the Fe2+ and Fe3+ absorption bands near 550 nm, Two Fe3+ absorption bands near 900nm and five water molecules and hydroxyl absorption bands near 1 900, and 2 200 nm can better reflect the spectral absorption characteristics caused by the difference of chemical composition of sedimentary rocks. The experimental results show that the automatic recognition of hyperspectral sedimentary rocks using spectral transformation and characteristic variable selection algorithm is feasible and can provide a reference for hyperspectral rock and mineral recognition methods.

Recently, A kind of jade has appeared in gem wholessale markets, which is similar to veins turquoise, called “Middle East turquoise” and brings trouble to the normal order of the jewelry market. This paper analysed Microlithography, X-ray powder crystal diffraction, infrared spectroscopy, Raman spectroscopy, micro-UV visible spectroscopy and trace elements to determine its mineralogy and spectral characteristics. The results showed that “Middle East turquoise” is a kind of quartzite jade, mainly composed of transparent-microtransparent blue and white spherical minerals, with opaque lignite minerals, glass luster, the refractive index of 1.53～1.54, the relative density of about 2.48~2.60. Under short and long wave ultraviolet light, the blue part is blue and white fluorescence. Micrographic analysis shows that the blue and white annules are mostly crystalline radioactive pulp, part of the iron oxide brown; the center of the annule is 0.05~0.3 mm granular single crystal quartz. X-ray powder crystal diffraction analysis found that “Middle East turquoise” also contains low crystallization. FTIR spectroscopy shows that the characteristic absorption peak of “Middle East turquoise” coincides with quartz jade and 1 179, 1 104, 798, 781, 690, 540, 488 cm-1, resulting from Si-O asymmetric extension vibration, Si-O-Si symmetric extension vibration and Si-O bending vibration. Raman spectroscopy shows that the Raman shift with quartz 466 and 210 cm-1 in the blue annual and central part of the sample, and the brown-red part of the sample has not only quartz Raman shift but also the Raman shift 302 and 551 cm-1. Micro-UV visible spectrum and trace element analysis show that the blue of “Middle East turquoise” positively correlates with Cu element content, showing a 600~700 nm absorption band. While the appearance of “Middle East turquoise” is similar to wire turquoise, its mineral composition is not turquoise but a collection of crystalline quartz and chalcedony, containing a small amount of needle ore, so it should not be confused with turquoise. It can be named “quartzite jade”.

The hydrothermal sediment samples from the 49.2°E-50.5°E hydrothermal fields of the Southwest Indian Ocean ultra-slow spreading ridge were analyzed for the mineral compositions and elemental geochemical characteristics. Moreover, the results show that the mineral compositions of the hydrothermal sediments in the study area are mainly pyrite and chalcopyrite, also possessed slightly sphalerite. Meanwhile, the silicon chimneys and residual oxides are composed predominantly of amorphous silicon, calcium carbonate, and iron hydroxides (such as goethite). The transition trend of the mineral assembles from the hydrothermal sediments to the residual oxides indicates that the changing process of its ore-forming temperature decreasing gradually. Furthermore, the main and trace element geochemical characteristics of the hydrothermal sediments were analyzed by XRF and ICP-AES. Geochemical characteristics of the main and trace elements show that the contribution of seawater increases gradually and reduces the influence of hydrothermal fluid. The formation environment also changes gradually from the hydrothermal plume flow condition to the low temperature conditions of the marine aquatic. In the analysis of rare earth elements (REE), the hydrothermal sediment samples contained the highest contents of rare earth elements ［∑REE: (26.37～32.86)×10-6］, residual oxides samples contained the second highest contents of rare earth elements ［∑REE: (5.58～30.43)×10-6］, and silicon chimneys samples contained the lowest contents of rare earth elements ［∑REE: (0.92～6.96)×10-6］. Besides, all of the samples enrich the light rare earth elements (LREE) and deplete the heavy rare earth elements, with Ce negative anomaly (δCe: 0.34～1.00) and Eu positive anomaly (δEu: 0.87～4.24). Geochemical characteristics of rare earth elements suggest that the samples not only inherited part of the geochemical characteristics of hydrothermal fluid but were also affected obviously by seawater, and the source of REE in the hydrothermal sediments has diversity characteristics. Comprehensive analysis shows that the metallogenic environment of the study area is predominantly in the low temperature due to limited weak hydrothermal activity, and the ore-forming fluid mainly originated in seawater. Therefore, the metallogenic process is greatly influenced by the ambient seawaters mixing action.

Potato is an important food crop after rice, wheat, and corn, and its optimal cultivation and production are essential to ensure food security. Crop above-ground biomass (AGB) is widely considered to be closely related to crop growth status and is often directly involved in crop yield prediction and health status parameter assessment. Existing studies show that the remote sensing vegetation index loses sensitivity to crop parameters at medium to high crop cover, i. e., the “saturation phenomenon”, which limits accurate monitoring studies of AGB at mid to late crop growth. The main work of this study is to use a new vertically growing crop AGB model (VGC-AGB) combined with hyperspectral remote sensing for AGB estimation of potatoes at multiple growth stages. In response to the “saturation problem” of remote sensing spectral indices for crop biomass monitoring in multiple growth periods, VGC-AGB defined two parameters, leaf dry matter content (Cm) and vertical organ dry matter content (Csm), to describe the average dry matter content of potatoes leaves and stems, respectively. The aboveground biomass of leaves (AGBl) was calculated by leaf area index (LAI)×Cm, and aboveground biomass of vertical organs (AGBv) was calculated by the product of planting density (Cd), potato plant height (Ch) and Csm, i. e., Cd×Ch×Csm. Based on the 2019 potato field experiment at the National Precision Agriculture Research Demonstration Base, ground-based ASD hyperspectral data, measured plant height, AGB, and LAI data were obtained for four critical growth periods of potato. Hyperspectral reflectance data were used to construct hyperspectral feature parameters and compare the performance of three potato AGB estimation models of (1) hyperspectral feature parameters+plant height, (2) ground-based measurement parameters+VGC-AGB model and (3) hyperspectral feature parameters+VGC-AGB model, respectively. The results show that the new VGC-AGB model combined with hyperspectral remote sensing data can provide higher performance estimation results of potato AGBl, AGBv, and total AGB than the traditional AGB estimation method of hyperspectral remote sensing vegetation index + plant height, and the technique can provide technical support for rapid and nondestructive monitoring of potato AGB.

Chlorophyll is a critical evaluation content of sugarcane growth monitoring, especially when diseases infected sugarcane. Accurate estimation of chlorophyll content is beneficial for the early detection and control of diseases, which is of great importance in practical production. In order to determine the best prediction model of chlorophyll content in sugarcane leaves, this study infected sugarcane leaves with mosaic from July to November, 2021 through artificial inoculation of pathogenic bacteria. Among them were 35 infected plants and 35 healthy plants, and two leaves were collected for each pot. Repeat the measurement of the leaf hyperspectral data using the spectrometer. The chlorophyll content of chemical leaves was measured to establish a hyperspectral data set of sugarcane leaves. In this study, five pre-processing methods, SG, MSC, SNV, 1st D and 2nd D, were used to establish the PLSR detection model and determine the best preprocessing method. Based on the optimized pretreatment results, correlation coefficient, SPA and RF were used to select the characteristic bands of chlorophyll content in sugarcane leaves, and the selected bands were combined with BPNN, SVR and KNN to establish chlorophyll prediction models. The results showed that the PLSR model based on SG treatment has the highest accuracy R2p=0.995 2, and the RMSEp=0.235 3 mg·cm-2. The model combined with the BPNN algorithm using RF screening was the optimal prediction model of chlorophyll content in sugarcane leaves under mosaic disease stress, the decision coefficient of the SG-RF-BPNN model was R2p=0.996 4, and the RMSEp=0.205 8 mg·cm-2, which had high accuracy and predictive power. It will provide a theoretical basis for the accurate and injury-free disease stress detection of large-scale sugarcane.

In response to the problems, including Cantonese tangerine peel appearing on the market with shoddy and year of aging falsification, a hyperspectral identification method based on the Black Widow Optimization (BWO) algorithm that supports vector machine (SVM) was proposed to address these problems. In the current study. The Samples hyper-spectral image data (385~1 014 nm) were collected with the Cantonese tangerine peel with four aging years (5~20 years) as the experimental object. The average spectral data of the sample region of interest were extracted by lens and reflectance calibration. Firstly, Savitzky-Golay Smoothing (SG), the Multiple Scattering Correction (MSC), and the Detrended Fluctuation Analysis (detrend) algorithm were utilized to perform spectral noise reduction for the data. Furthermore, the successive projections algorithm (SPA) and the competitive adaptive reweighting sampling mixed stepwise regression (CARS_SR) algorithm were used to extract the feature wavelengths. Finally, the root mean square error (RMSE) was proposed as the fitness function. The partial least-regression (PLS), the particle swarm optimization (PSO)-SVM, and the grasshopper optimization algorithm (GOA)-SVM were used to identify the aging year of Cantonese tangerine peel. Additionally, the identification models optimal parameters were obtained using the BWO algorithm optimized SVM model (BWO-SVM). It was found that the SG_detrend algorithm has a relatively excellent noise reduction ability for the hyperspectral data of Cantonese tangerine peel. The feature wavelengths could be extracted via the CARS_SR algorithm. Compared with PLS, PSO-SVM, and GOA-SVM, more optimal control parameters for the identification model could be gained using BWO-SVM. The accuracy of 97.59%, RMSE of 0.060 2, and R2 of 0.952 9 for the identification of aged vintage Cantonese tangerine peel were achieved with the SVM model. This research provides a novel method to achieve rapid and nondestructive identification of aged vintage Cantonese tangerine peel and also provides a theoretical basis for the development of portable identification instruments and online production equipment.

Due to the influence of the field environment, winter wheat canopy spectra collected in the field contain a large amount of noise unrelated to the target information, which limits the ability of hyperspectral data to estimate the information of winter wheat plants. In order to limit the influence of noise information on spectral information and explore the methods to improve the estimation ability of spectral information on the water supply of winter wheat plants, this study obtained the hyperspectral data of winter wheat and its leaf water content information through field experiments, processed and analyzed the hyperspectral data by discrete wavelet algorithm, combined with correlation analysis algorithm and partial least squares algorithm, and quantitatively analyzed the influence of five types of wavelet bases on the discrete wavelet algorithm to separate The results show that: (1) the discrete wavelet algorithm can be used to separate the available spectral information from noise, and (2) the wavelet bases can be used to separate the available spectral information from noise, to provide theoretical and methodological support for the processing and analysis of the spectral data in the field. The results show that (1) the sensitive bands are mostly distributed in D1-D5 scales, and the distribution intervals of sensitive bands are relatively consistent among wavelet bases. However, there are some differences in band positions and correlation strengths, which indicates that the choice of wavelet bases can influence the correlation strengths and band positions of high-frequency information and winter wheat leaf water content. (2) The available spectral information and noise information both show a pattern of increasing and then decreasing with the increase of decomposition scale. The interference strength of noise information on the estimation ability of high-frequency information decreases with the increase of scale, and the estimation ability of high-frequency information on the water content of winter wheat leaves decreases with the increase of scale. (3) The accuracy and stability of the model are the results of the combined effect of available spectral information and noise information, in which the estimation model constructed based on the D5 scale of Meyer wavelet basis is the optimal model with R2=0.625 and RMSE=1.562 for modeling accuracy and R2=0.767 and RMSE=1.828 for validation accuracy. Spectral processing and analysis, and provide some reference for the processing and analysis of spectral information that is heavily influenced by noise, and also provide basic support for the detection of the water content of crop leaves within regions with high annual water vapor content, such as southwest and south China, or in the north in summer.

Purely organic room temperature phosphorescence (ORTP), due to its wide Stokes shift, low fabrication cost and unique long after glow emission, has attracted extensive attention in many applications such as data encryption, anti-counterfeiting, organic light emitting diodes and cell imaging. At present, designing organic materials with high phosphorescence and extremely long luminescent time is still a great challenge. Based on the heavy atom effect, a purely ORTP molecule was designed and synthesized in this work. This compound was white powder under ambient conditions, and can emit a bright yellow phosphorescence when the UV lamp was turned on. The maximum excitation at 366 nm, the corresponding maximum emission at 544 nm, and two shoulder peaks at 590 and 640 nm, respectively, were observed. The lifetime was 103.55 ms, and the afterglow was close to 2 s. To explore the influence of heavy atom introduction on phosphorescence, a theoretical simulation was performed by (TD) DFT. The band gap of HOMO/LUMO was only 0.02 eV, which indicated that the molecule was easily excited. Compared with similar halohaline-free compounds, It is proved that the introduction of heavy atoms is helpful in increasing the rate of spin-orbit coupling (SOC) and intersystem crossing (ISC) between singlet and triplet states. XRD spectroscopy was performed to further explore the origin of BFCzB ultralong phosphorescence and investigate the molecular packing model and the presence of interactions. InBFCzB molecules, three types of intramolecular interactions, including C-Br…π (3.373 1 ) halogen bond, C-Br…N (3.170 5 ) halogen bond and C-F…H-C (2.587 7 ) hydrogen bond were observed, which effectively limited the rotation and vibration of the molecules, thereby reducing the non-radiative energy attenuation. In addition, some intermolecular interactions between halogen atoms and adjacent molecules were found in BFCzB. C-F formed major interactions with the carbazole rings of neighboring molecules, C-F…H-C (2.527 1 ) hydrogen bond and C-F…π (2.933 5 and 3.049 4 ) halogen bond. C-Br…H-C (2.846 6 ) hydrogen bond and C-Br…π (3.531 4 ) halogen bond were also observed between Br and its neighbors. π…π stacking (3.399 2 ) was found in the carbazole group of the neighboring molecule. These intramolecular and intermolecular interactions worked together to inhibit molecular motion, further reducing the nonradiative attenuation of triplet excitons and achieving ultra-long phosphorescence. Moreover, in this work, the production of singlet oxygen (1O2) during phosphorescence of BFCzB molecular quenching in water was verified by the TMB colorimetric method. Therefore, the photodynamic antibacterial were carried out. This study can provide some reference for the design, synthesis and application of purely ORTP molecules.

The comprehensive and accurate characterization of the characteristics of non-metallic inclusions in steel is conducive to the discovery and recognition of new inclusions and is also the prerequisite for the regulation of non-metallic inclusions and the improvement of steel quality. This paper uses scanning electron microscopy with energy spectrum (SEM-EDS), Raman spectrum, high-resolution transmission electron microscope(TEM) and micro-region X-ray diffraction (μXRD), combined with the inclusion of electrolytic extraction technology and image analysis technology, the characterization of zirconium deoxidization non-metallic inclusions in steel shape, size, quantity, distribution, composition, crystal structure, characteristic parameters such as comparative analysis the advantages and disadvantages of four kinds of methods for characterizing the inclusions. The results show that the inclusion in zirconium deoxidized steel was mainly composed of Zr, O and a small amount of Al by SEM-EDS method. Based on the stoichiometric relationship between zirconium oxide and aluminum oxide, the inclusion was analyzed to be composed of 94% ZrO2 and 6% Al2O3. The inclusion size distribution in zirconium deoxidized steel is normal. The average inclusion size is 0.62 μm, and the number of inclusions is the largest in the range of 0.7~0.8 μm. The three-dimensional morphology of non-metallic inclusions in steel can be observed using SEM combined with electrolysis. The EDS method can be used to qualitatively analyze the composition and distribution of elements in inclusions individually. The composition of inclusions with single valence can be quantitatively analyzed. However, for non-metallic inclusions with many valence states and unknown valence states, the EDS method alone cannot accurately analyze the phase and composition of inclusions. The presence of monoclinic zirconia in zirconium-deoxidized steel was detected by Raman spectroscopy combined with electrolysis extraction of inclusions. TEM diffraction pattern calibration and energy spectrum analysis of a single inclusion detected Zirconia with monoclinic phase. Two phases, including monoclinic and tetragonal zirconia, were detected by μXRD combined with electrolytic extraction of inclusions, and the lattice parameters of zirconia inclusions were obtained. These three methods detected no aluminum-containing phase. Raman spectroscopy, TEM and μXRD can be used to qualitatively analyze the phase and composition of inclusions after electrolytic extraction, but the three methods cannot accurately characterize the phase with low content. TEM and μXRD can characterize the crystal structure and lattice parameters of the inclusion. TEM and SEM can only characterize individual inclusions one by one. μXRD and Raman spectroscopy can characterize the phase of all the inclusions in the detected region, a statistically significant method to characterize inclusions. Therefore, the inclusion characteristics can be characterized comprehensively and accurately by SEM-EDS analysis combined with μXRD analysis.

High voltage bushing is one of the core components in a power system. Therefore, it is with significant meaning to monitor the insulation status. However, current commercial equipment for online monitoring is not suitable for bushing with special locations and small sizes. As a result, developing a detection system for bushing specifically is necessary. In-situ detection is one of the theoretically feasible ways for bushing monitoring. Compared with metal material, photoacoustic cells made of insulating material can avoid floating potentials and following partial discharge. This paper studied the feasibility of insulating material-based photoacoustic cells. First, the influence of the material on the performance of photoacoustic cells was studied, and potential problems of photoacoustic cells made of insulating material quartz were discussed. Then, using COMSOL Multiphysics, the theoretical performance of quartz photoacoustic cells was simulated from both the thermal and acoustic sides. Finally, a detection system based on quartz photoacoustic cells was established to testify to the simulation outcome. The simulation results indicated that quartz-based photoacoustic cell was as capable as the conventional brass photoacoustic cell to detect trace gas quantitatively. Besides, the detection limit of the established detection system in this word could reach the level of 0.16 μL·L-1, which could meet the relevant standard. Therefore, such insulating material-based photoacoustic cell is with the potential for in-situ detection of bushing.

Maize is one of the important food source, which is widely planted in China. The selection of excellent maize varieties is the key to agricultural production and breeding. However, there are wide varieties of maize on the market at present. In this paper, the extreme learning machine (ELM) model of maize varieties identification based on hyperspectral image technology was proposed to solve the problem of maize varieties identification. In this study, eight varieties of maize seeds were regarded as research objects, and 480 experiment samples were divided into training sets and test sets in a 2∶1 ratio, with 320 and 160 samples respectively. The images of maize seeds in the 935.61~1 720.23 nm were obtained by a hyperspectral acquisition system. Regions of interest (ROI) of 10×10 pixels in germ were selected after correction, and the average spectrum in the region was extracted as the original spectral data. Due to the large noise at both ends and less effective information of the original spectrum, in order to enhance the signal-to-noise ratio, spectral bands of maize seeds in the range of 949~1 700 nm were selected as effective bands for analysis. Due to the strong interference of irrelevant information during data collection, the spectral bands information after denoising was processed by Savitzky-Golay smoothing. The smoothing point was set to 3. Maximum normalization (MN) was used to pretreat based on SG smoothing. After pretreatment, feature wavelength variables were extracted by competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and CARS+SPA, CARS-SPA. The wavelength reflectance was used as the input matrix X, and the sample varieties 1, 2, 3, 4, 5, 6, 7, 8 were used as the output matrix Y. (SG+MN)-ELM, (SG+MN)-CARS-ELM, (SG+MN)-SPA-ELM, (SG+MN)-(CARS+SPA)-ELM, (SG+MN)-(CARS-SPA)-ELM were established. The experiment results showed that (SG+MN)-(CARS-SPA)-ELM model had the best identification performance compared with others, and the average identification accuracy of training sets and test sets was 98.13%, indicating that CARS-SPA secondary screening feature wavelength variables were more sensitive, which could represent all wavelengths information. The ELM model had better qualitative identification performance. It could realize the identification of maize varieties. This study provides a new idea and method for rapid and accurate identification of maize and other crop seeds.

With the rapid development of hybrid rice breeding technology, hybrid rice varieties are becoming increasingly diverse, and their quality and price vary widely. The use of intelligent means for rapid classification, grading and quality detection of hybrid rice seeds has become a hot spot in hybrid rice research. In this paper, we first investigate the effect of different preprocessing methods on the accuracy of a 1D Convolutional Neural Network (1D-CNN) classification model constructed based on the near-infrared spectra of 10 hybrid rice seeds. The results show that the overall validation and prediction accuracy can be up to 95.4% and 92.9% respectively when the near-infrared spectra are preprocessed with the Savitzky-Golay convolution smoothing algorithm (SG). Secondly, the three most important feature wavelengths were selected by the random forest feature wavelength selection algorithm to build a single-wavelength grayscale image dataset and a 3-wavelength reconstructed pseudo-color image dataset, and the hybrid rice seed classification model based on the convolutional neural network VGG and the residual network ResNet of the image dataset was constructed and studied. The results show that the VGG model based on the pseudo-color image dataset can obtain the optimal classification effect, and the classification accuracies of its validation set and test set are 92.8% and 92.8%, respectively. Compared with the ResNet classification model based on the pseudo-color image dataset, an improved value of 3.6% is achievedin the validation set and 4.9% in the test set. In order to further improve the classification accuracy, a hybrid rice seed classification method based on the fusion of image information and spectral information is proposed. This methodextracts spectral features using the 1D-CNN network branch and extracts dimensionalspatial features using the 2D-CNN network branch. 2Branches-CNN model is then constructed based on the fusion of image and spectral features, and the classification accuracy reaches high values of 98% and 96.7% for the validation set and test set. The classification effect of the 2Branch-CNN model for each type of hybrid rice seeds is also evaluated by calculating the confusion matrix. The results of this paper show that the classification accuracy of the convolutional neural network model can be effectively improved by image-spectrum fusion, and the construction of a two-branch convolutional neural network model based on image-spectrum fusion will provide new ideas for rapid screening and classification of hybrid seed varieties.

The existing convolutional neural network soil organic matter (SOM) prediction models suffer from low modeling effectiveness and low prediction accuracy under small sample data sets. In order to predict the content of organic matter in the soil more accurately, this paper proposes a hyperspectral prediction model of red soil organic matter with an improved Self Attention Temporal Convolutional Network (SATCN) using 206 collected soil samples as the research object. In this paper, after Savitaky-Golay (SG) smoothing is performed on soil samples, four transformations are performed: first-order differential (1DR), second-order differential (2DR), standard normal variable (SNV) and multivariate scattering correction (MSC). The modeling effects of Long Short-Term Memory (LSTM), Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM) under different spectral preprocessing were compared and analyzed. The results show that the first-order differential preprocessing method of the spectrum after SG processing has the best modeling effect. A shallow network structure is applied in the temporal convolutional network (TCN) architecture, a self-attention layer is added to the TCN residual structure to improve the model feature learning capability, and L2 regularization is added to each convolutional kernel weight to prevent overfitting. First-order differentiation is selected as the spectral preprocessing, and four models of ResNet-13, VGGNet-7, TCN and improved temporal convolutional network (SATCN) are constructed to compare and analyze the modeling effects of the four models, as well as the modeling effects of SATCN models at different network depths. The results show that the shallow SATCN modeling is better than the deep model in the case of first-order differential spectral preprocessing; the self-attention residual structure in the SATCN model not only enhanced the important features of the spectral sequence, but also significantly improved the model feature learning ability and prediction accuracy. Compared with modeling methods such as CNN and TCN, the proposed SATCN model has higher accuracy and excellent model estimation capability with a coefficient of determination (R2) of 0.943, root mean square error (RMSE) of 3.042 g·kg-1, and relative analysis error (RPD) of 4.273 for the validation set. In summary, the best budget SOM content of this paper model is the SATCN prediction model based on the first-order differential spectral preprocessing after SG smoothing, which provides a more accurate prediction of soil organic matter in Guangxi woodlands.

As an emerging technology in remote sensing, hyperspectral imaging provides massive content for remote sensing image processing analysis and computer vision. Hyperspectral images advantages lie in the wide and high resolution of the electromagnetic spectrum, which can show the inherent spectral reflection characteristics of ground objects in a more comprehensive and discriminating manner, and are widely used in ground object classification, target recognition, anomaly detection, etc. However, its huge amount of data and redundant information causes considerable difficulties for hyperspectral image processing, storage and transmission. Band selection is a data dimensionality reduction method that can effectively reduce the amount of image data without changing the physical information of the original image. In order to achieve a better classification effect of ground objects, the visual saliency model is applied to the band selection method. Firstly, the target saliency algorithm based on image space distribution is introduced to process the band image to obtain the target saliency map. Secondly, using the target saliency map to analyze the degree of separability between ground objects in each band image is defined as band saliency. Spectral clustering algorithm is used to divide bands into several subspaces before band selection. Then in the subspace, the bands are sorted in descending order according to the saliency of the bands, and the bands with better target saliency performance in each subspace are selected to form the band subsets. Finally, the method is verified on the hyperspectral image data collected by GF-5, the effective target saliency algorithm is screened, and the classification accuracy is compared with the commonly used band selection algorithm. The experimental results show that the band selection subset based on the LC target saliency algorithm has excellent classification results in the SVM classifier, with overall classification accuracy and Kappa coefficient of 87.780 0% and 0.805 3. This method outperforms the results of the other three band selection methods and the results of all bands.

Viscosity is an important index reflecting paper celluloses degree of polymerization and physical properties. Accurate real time information on viscosity is important for repairing and protecting precious paper materials. However, the traditional papers viscosity analysis method mainly uses chemical means, which takes a long time and will inevitably cause secondary damage to the paper. To solve this problem, hyperspectral remote sensing, with its rich information and real-time, contactless characteristics, is an effective way to obtain the papers viscosity content without damage. First, obtain experimental papers with different aging degrees in the laboratory to measure their viscosity contents, collect hyperspectral data of paper samples, preprocess paper samples hyperspectral data through spectral noise reduction, spectral transformation, and spectral information expansion, establish a spectral database of papers viscosity contents under different aging degrees, and respectively build spectral difference index, ratio index and normalization index under different spectral transformation methods. Correlation analyses were carried out the 12 best spectral indices with the strongest correlation with viscosity were selected. Finally, the selected spectral indices were used as independent variables to build a regression model on the viscosity content. We also selected the spectral index and model that best characterized the change of the papers viscosity content by comparing model accuracy. The results show that: (1) Compared with the original spectrum, the proportion of the highly correlated feature subset of viscosity extracted after spectral transformation processing greatly improved along with the mean and median of the correlation coefficient; (2) The correlation between the spectral information parameters (obtained by spectral information expansion) and viscosity is higher than that of the original spectral segment, and most of the 12 optimal spectral indices extracted have the participation in the expanded information parameters; (3) The correlation between the best spectral index, extracted under different spectral transformation results, and the viscosity content above 0.89, and the three representative spectral indices selected from them effectively reflected the change of papers viscosity at 400~500 mL·g-1; (4) After logarithmic first-order differential treatment of the paper spectrum, the normalized index constructed by spectral integration and spectral absorption depth has the largest correlation with viscosity, reaching -0.917 and the R2 of the model established by this index in the training set and the test set is 0.84 and 0.76 respectively, with MRE and RMSE in the test set as 0.089 and 40.29 mL·g-1, respectively. The research results can provide scientific theory and technical support for the remote sensing inversion of the papers viscosity content, and have important reference significance for the construction of the non-destructive analysis system of paper cultural relics.

To monitor the quality change of cucumber during storage, a feature extraction method of 3D fluorescence information of storage room gas (feature excitation wavelength and feature emission wavelength) is proposed using multivariate statistical analysis coupled with wavelet packet energy during different cucumber storage dates. Firstly, these 3D fluorescence data were handled by removing Rayleigh scattering and polynomial Savitzky-Golar (SG) smoothing to remove the effects of scattering and noise signals. Secondly, the pre-processed 3D fluorescence data were handled by principal component analysis (PCA) to obtain the principal component matrix, and Wilks statistics were constructed by using each principal component variable, then the principal component corresponding to the minimum value (the 11th principal component, PC11) was selected. Then eight feature excitation wavelengths were extracted according to the combination coefficient of each original variable (excitation wavelength) of the principal component. Thirdly, the emission spectrum is divided by 10nm interval and gets 26 bands; the 3-scale based wavelet packet decomposition (WPD) was carried out for each band, and the wavelet packet energy of each band after decomposition was calculated. And then, according to the analysis results of 8 days test data, the band with the highest energy was selected as the primary feature emission band. Fourthly, partial least squares regression (PLS) was used to analyze the primary emission bands combined with the physicochemical indexes (hardness, chlorophyll content and weight loss rate) of cucumber, and seven feature emission wavelengths were selected according to the regression coefficient, which greatly simplified the calculation. At the same time, according to the hardness data of cucumber, the turning point of its trend change could be found; and according to the cucumber chlorophyll content data and its first derivative, the chlorophyll decline the most significant points could be also found; and then combined with the sensory analysis in the process of test, ultimately determine the quality of cucumber stored at the fifth day become bad rapidly. Therefore, the fifth storage day was chosen to monitor the reference date. Finally, Mahalanobis Distance (MD) between different storage days and monitoring reference dates was calculated using the extracted feature fluorescence information, and the MD monitoring model was constructed. The results show that the MD decreased gradually to 0 with the storage time approaching the monitoring reference date, which was consistent with the quality change process of cucumber during storage. Therefore, the above feature wavelength extraction method based on multivariate statistical analysis combined with wavelet packet energy and the MD monitoring model is expected to be an effective method for quality monitoring of cucumber during storage.

The organic filling treatment of turquoise has a long history, and there has been much research on two filling methods: immersion (acrylates) and injection (epoxy resins), while frozen jelly filling (acrylates) is a relatively new filling method with a weak research base. Using conventional gemological methods, FTIR, 3D fluorescence spectrometer and UV-VIS spectrum analysis, combined with the frozen jelly material used for the treatment, this paper presents a systematic comparative analysis of the gemological and spectroscopic characteristics of natural turquoise raw material and its corresponding frozen jelly-filled turquoise. The results showed that the color and density of turquoises were significantly enhanced by the frozen jelly filling, with glue residue and white “pine flower” on the surface; frozen jelly-filled turquoises showed weak-moderate blue fluorescence under the LW UV lamp and weak fluorescence under the SW, with obvious luminescence in the glue residue positions, while raw turquoises were inert under both LW and SW. The IR spectra of the frozen jelly solution showed CO stretching vibration peak ［(1 722±5) cm-1］, C-O stretching vibration peak ［(1 156±5) cm-1］, CC stretching vibration peak ［(1 637±5) cm-1］, benzene ring structure and C-OH absorption peaks, indicating that its composition may be a copolymer of compounds containing benzene ring, carboxylic acid, alcohol and other structures with methacrylate. The infrared spectrum of frozen jelly-filled turquoises showed the organic group vibrational peaks corresponding to the frozen jelly solution, effectively identifying filled turquoises from raw materials. 3D fluorescence spectra showed that raw turquoises did not appear obvious fluorescence centers throughout the excitation wavelength range, frozen jelly-filled turquoises all appeared characteristic fluorescence centers with emission wavelengths around 465, 445 and 410 nm, and excitation wavelengths in the range of 360～400 nm, corresponding to the fluorescence centers of the frozen jelly solution, indicating that the fluorescence was caused by glue, which can be taken as important evidence of frozen jelly filling. The UV-Vis absorption spectra of both raw turquoises and frozen jelly-filled turquoises were consistent with the characteristics of natural turquoises, indicating that no organic dyes were added during the jelly-filling process.

The agricultural watershed of Dianbu River is the largest tributary of Nanfeihe River, so it is important to explore the temporal and spatial evolution mechanism of dissolved organic matter (DOM) in this watershed to understand its aquatic ecosystem. In this study, we comprehensively utilized the 3-D fluorescence spectrum and UV-visible spectrum of water bodies to track the DOM characteristics of this watershed. PARAFAC method was adopted to extract DOM components from the measured 3-D fluorescence spectral matrix. Moreover, we analyzed the DOM fluorescence components and the proportion of water bodies in a specific period. DOM's temporal and spatial characteristics in Dianbu River agricultural watershed were analyzed by combining the fluorescence parameters (FI, autobiogenic index BIX and humification index HIX) and UV-visible absorption characteristic parameters (α254, E2/E3). The results show that the watershed water DOM in the specific period (September 2020-April 2021) contains two effective components of fluorescence, one kind of humus (fulcrum acid-like component) and one kind of protein (Low excitation tryptophan-like component), the proportion of two components were 53.9% and 46.1% respectively; The fluorescence parameters of the three water bodies (FI, BIX and HIX) showed significant seasonal variations in different seasons (autumn in September, winter in January and spring in April), indicating that DOM was generally influenced by terrigenous and endogenous substances, but had strong humification and weak authigenic characteristics. The characteristic absorption parameters of the UV-visible spectrum (α254, E2/E3) indicated that the relative concentration and molecular weight of DOM in winter water in January were lower than those in the other two seasons (autumn in September and spring in April), while the molecular weight and relative concentration of DOM at each sampling point showed no obvious regional variation trend from upstream to downstream in the watershed. In this study, the three-dimensional fluorescence spectral characteristics and UV-visible spectral absorption characteristics of water bodies in the watershed were combined to trace the component sources of DOM in the Dianbu River in a specific period and its spatiotemporal distribution characteristics were analyzed to provide a reliable theoretical basis for the comprehensive management of the ecological environment in the watershed.