Spark-induced breakdown spectroscopy (SIBS) is a qualitative and quantitative analysis technology of substance concentration and composition based on atomic emission spectroscopy. Compared with traditional laboratory analytical techniques such as inductively coupled plasma atomic emission spectroscopy (ICP-AES), atomic absorption spectrometry (AAS) and mass spectroscopy, it has the advantages of real-time, in situ, on-line rapid detection, high sensitivity, low cost, small volume and simple maintenance. Currently, the existing researches on this technology focus on aerosol composition analysis, soil composition analysis, metal particle concentration detection, cement composition analysis and so on. It has extensive and promising applications in environmental monitoring, industrial health, food safety, biomedicine, etc. Starting with the basic principle of SIBS, this paper summarizes the principle of spectral analysis, which is that the electric spark generated by the high-voltage pulse power supply is used to excite the surface of the measured object so that the measured object generates plasma between the positive and negative poles of the power supply. The optical fiber probe of the spectrometer is used to collect the photons and characteristic radiation spectra released through the transition during the plasma cooling process. Because different elements have unique characteristic spectra, the qualitative and quantitative analysis of the composition and concentration of the measured substance can be carried out according to the characteristic spectrum; Then, related factors affecting the spectral image and spectral analysis of SIBS, such as the parameters of pulse power supply, electrode material and incident angle, and the characteristics of plasma itself, are analyzed, and the relationship between some factors and the intensity of spectral signal is pointed out quantitatively; This paper summarizes some technological innovations and application innovations like laser ablation assisted spark induced breakdown spectroscopy (LA-SIBS), high repetition rate laser ablation spark induced breakdown spectroscopy (HRR-LA-SIBS), ultrasonic atomization assisted spark induced breakdown spectroscopy (UN-SIBS), particle flow spark induced breakdown spectroscopy (PF-SIBS), etc. in the development process of SIBS, and briefly explains some application fields, application characteristics and enlightenment to the future development direction of SIBS technology. According to the principal defects of SIBS and some problems exposed in its application, the challenges faced by this technology are listed, such as equipment technology cost, spark energy, environmental noise, sample contamination, etc. Finally, the future research direction and development trend of spark induced breakdown spectroscopy is prospected.

In order to study the effects of the long-term application of compost on the source and composition characteristics of soil DOM in fluvo-aquic soil, this study took the experimental field of long-term application of compost at Quzhou Experimental Station in Hebei Province as the research object. It used three-dimensional fluorescence spectroscopy to explore the differences of the source and composition characteristics of DOM in the soil profile under long-term application of the high dosage of bio-compost (EMI), the conventional dosage of bio-compost (EMII), high dosage of traditional compost (TCI), the conventional dosage of traditional compost (TCII) and chemical fertilizers (CF). The results showed that the distribution of soil dissolved organic carbon (DOC) in soil profiles under different fertilization treatments was quite different, and the application of compost significantly increased the DOC in the 0~20 and 60~80 cm soil layers by 81.94%~171.33% and 61.18%~152.18%, respectively. The fluorescence spectrum index showed that the source of DOM is a mixed source of microorganisms and plants. The increase of compost dosage will increase the degree of DOM’s humification, causing the surface soil’s DOM to migrate from land source to biological source. As the soil depth increased, DOM migrated from land to biological sources. Three-dimensional fluorescence spectroscopy and fluorescence area integration showed that bio-compost and traditional compost increased the content of humic acid-like substances, which increased with the increase of application rate. High-dosage bio-compost and traditional compost increased fulvic acid-like and soluble microbial byproduct-like substances. The application of chemical fertilizers and compost reduced the content of tryptophan-like. The contents of humic acid-like, fulvic acid-like, and soluble microbial byproduct-like substances showed an overall decreasing trend with soil depth; Tyrosine-like protein increased with soil depth; Tryptophan-like substance first increased and then decreased with the increase of soil depth, and the content of tryptophan-like substance was the highest in 20~40 cm. Correlation analysis showed that soil physical and chemical indexes such as TP, TN, CEC, AK, SOC and DOC were negatively correlated with tyrosine-like substances, positively correlated with fulvic acid-like substances, soluble microbial products and humic acid-like substances. Moreover, NO-3-N, TN, pH and SOC were significantly positively correlated with tryptophan-like substances. In a word, long-term application of compost increased the DOM content in the surface layer of fluvo-aquic soil and significantly changed the composition of DOM in the soil and the distribution characteristics on the profile.

Edible oil is a necessity in daily diet, providing heat energy and fatty acid for the human body. It is an important organic matter that promotes the absorption of fat-soluble vitamins. With the improvement of people’s living standards, high-grade edible oil has entered the table of the public and is deeply welcomed. Due to the high selling price of high-grade edible oil in the market, some illegal manufacturers mix cheap edible oil into high-grade edible oil for sale to make huge profits. And this leads to the adulteration of edible oil from time to time, which has aroused widespread concern of the government and the public. In order to protect the legitimate interests of consumers and maintain the normal order of the edible oil market, it is urgent to detect the adulteration of edible oil quickly and effectively. Near-infrared spectroscopy(NIR)has the advantages of simple, rapid, nondestructive and no sample pretreatment, and it is widely used in the analysis of adulteration of edible oil. This paper summarizes the basic principle of NIR technology and reviews the research progress of NIR technology in adulteration detection of edible oils such as olive oil, camellia oil, sesame oil and walnut oil in recent ten years. Different test devices, test methods and data processing methods(pretreatment, wavelength selection and modeling methods) are mainly used to detect the binary, ternary and multivariant adulteration of edible oil to improve the accuracy and application range of edible oil adulteration detection and establish a more effective quantitative detection and qualitative identification model for edible oil adulteration. Then, it summarizes the existing problems in the detection of adulteration of edible oil by near-infrared spectroscopy, such as the detection mechanism of adulteration of edible oil is unclear. The prepared adulterated edible oil samples are difficult to meet the actual complex adulteration forms, the adulteration detection by sampling can only realize part of the spot sampling inspection, and the unified standard specification of adulteration detection of edible oil is not established. At last, it points out the development trend in future to integrate NIR with other rapid detection technologies to obtain more accurate and reliable detection models, and the construction of edible oil NIR database using the internet of things and big data technology to realize spectral data sharing and online upgrade and remote update of adulteration detection models. This paper aims to provide references and solutions for detecting adulteration of edible oil in China.

Mineral classification and identification is an important area in the field of geological research, which is of great significance to geological exploration and environmental evolution. However, the traditional ore classification and identification methods rely on professionals to conduct manual identification through the shape and physical properties of the ore, which has strong subjectivity and low accuracy. Laser-induced breakdown spectroscopy (LIBS) is suitable for geological research due to itselement “fingerprint” characteristics, high sensitivity and fast on-line detection. In this paper, we use confocal laser-induced breakdown spectroscopy combined with machine learning to improve the accuracy of ore classification and recognition. The confocal LIBS system is used to obtain the spectral data of 8 natural ore samples (Gold, Copper, Silver, Hematite, Aluminum, Galena, Apatite and Sphalerite). Principal component analysis (PCA) is used to reduce the dimension of the data, Linear discriminant analysis (LDA), nearest neighbor rule (KNN) and support vector machine (SVM) are used for high-precision classification and recognition of feature spectral lines. Firstly, a standard copper is employed as the sample to conduct the comparison experiments between non confocal LIBS system and the confocal LIBS system for the stability and its influence on the cumulative contribution rate of PCA principal components. The results show that compared with non-confocal LIBS system, the stability of the confocal LIBS system is improved by 63.75%, and the cumulative contribution rate of principal components is increased by 17.81%. Then, the confocal LIBS system is used to obtain the spectral information of the above eight ore samples with data preprocessing, such as denoising. PCA is used to extract the ore feature data, and the first 10-dimensional feature space with a cumulative contribution rate of 99.4% is retained. Finally, the feature data are combined with LDA, KNN and SVM to build a classification model for classification and recognition. The experimental results show that the classification accuracy of PCA combined with LDA and KNN is 95.78% and 92.58% respectively, while that of SVM can reach 97.89%. Therefore, combining confocal laser-induced breakdown spectroscopy with PCA and SVM can provide a fast and accurate classification and recognition method for geological exploration and mineral recognition and has wide application prospects.

In storing drugs in sealed vials, the gas tightness of the vials often deteriorates due to improper storage methods and substandard product quality, which can easily lead to chemical reactions with various gases in the air and cause deterioration of the drugs and affect their normal use. Therefore, the storage status of drugs can be reflected by measuring the concentration of various gases inside the vials. Among them, water vapor (H2O) is a common gas in the air and is very easy to react with drugs, so the measurement of H2O concentration in medicine bottles is one of the important bases to determine whether the drugs inside the bottles deteriorate. In practice, traditional methods or instruments usually require direct contact with the sample to make a judgment. It is difficult to achieve nondestructive testing, and the sample handling process is tedious, time-consuming and labor-intensive, making it difficult to achieve real-time nondestructive measurement of a large number of drug bottles. In order to efficiently detect and monitor the water vapor concentration in sealed drug storage containers (vials) in real-time, a digital orthogonal phase-locked demodulation algorithm for tunable semiconductor laser absorption spectroscopy (TDLAS) is proposed in this paper, and the feasibility and effectiveness of the algorithm are experimentally verified. The drug bottle is made of transmissive polyethylene (PE) with a length of 12 cm, a width of 9 cm and a height of 64 cm, and a distributed feedback (DFB) laser with a central wavelength of 1 391 nm is used as the light source. The effects of different modulation depths and sampling rates on the amplitude of the demodulated second harmonic signal (WMS-2f) are investigated. The stability of the WMS-2f signal at different optical powers is investigated under the optimal system parameters, and the WMS-2f signal of other unknown water vapor concentrations is deduced from the fitting results. The results show that the digital phase-locked demodulation is more compliable, compact and cheaper than the conventional lock-in amplifier demodulation algorithm. The Allan ANOVA shows that the water vapor detection limit is 18 ppm in the state of 160 s, which verifies the stability and reliability of the method.

Multispectral thermometry is based on Blackbody radiationlaw, and the temperature value can be calculated based on the radiation intensity and multiple sets of wavelengths. This method has become widely used in engineering practice, as it overcomes the constraints of the single spectrum and similar colorimetric spectrum requirements for colorimetric temperature measurement. In multispectral temperature inversion, the solution of spectral emissivity and multispectral data processing are the keys to accurate temperature measurement. At present, the solution of spectral emissivity is mostly based on the assumption model of spectral emissivity. When the hypothetical model is close to reality, the accuracy of the inverted temperature and spectral emissivity is very high; otherwise, the inversion result deviates significantly. For the temperature measurement of complex materials and the dynamic changes of material properties during the combustion process, the method of assuming the model of spectral emissivity is groundless; In recent years, the deep learning method based on the neural network has been applied to multispectral temperature measurement, which avoids the assumption model of spectral emissivity, and can establish the nonlinear statistical relationship between temperature and multi spectrum, but it requires massive data and supercomputing power support, and the modeling process is complicated.In order to solve the above problems, this paper proposes a multispectral temperature measurement method named multi-element extreme value optimization (MEVO) measurement method. This method utilizes the correlation between multispectral signals at different temperatures, and by analyses the relationship between the measured temperatures of each channel in the process of multispectral temperature inversion, based on the principle of multispectral radiation temperature measurement and the information correlation between the data of each channel in the process of temperature inversion, establish a multivariate temperature difference correlation function, and establish a high-precision temperature measurement model through the optimization of the correlation function. This method simplifies the modeling process to the optimization problem of multivariate temperature difference function, avoids the assumption of the relationship between spectral emissivity and other physical quantities, reduces the requirement of data sample size for deep learning methods, and simplifies the process of multispectral temperature measurement. A simple 8-channel temperature measuring device was used for experimental verification. In the experiment, we determined that the temperature emitted by the Blackbody furnace was the standard value. The spectral data of the 468~603 nm band in the 1 923.15~2 273.15 K temperature zone was calibrated, and the multispectral thermometry based on the optimization of multiple extreme values was realized. The temperature measurement accuracy is about 0.5%, and the temperature inversion time is within 2.5 s. Compared with the second measurement method (SMM) and the neural network method, the inversion accuracy is substantially improved. Moreover, the inversion speed is significantly faster than the SMM method.

The most significant advantages include multi-component, noncontact detection, short testing period and molecular fingerprint characteristics. Raman spectroscopy has been successfully used in many fields. However, the low Raman-scattering signal limits its further development. In order to improve the sensitivity of Raman liquid detection, a method for enhancing the liquid Raman signal based on a new sample cell is reported in this paper. A concave mirror is added to the bottom of the traditional cuvette. On the one hand, the laser can be reflected and focused again after acting on the sample. On the other hand, the Raman scattering signals of forward and backward are collected by the setup at the same time. Thereby the Raman signal can be improved. Firstly, the influencing factors of Raman scattering intensity and the relationship between the Raman scattering signal intensity and the collection angle excited by unpolarized light are analyzed theoretically. It is concluded that the scattering intensity is the largest when the collection angle is forward and backward (0° or 180°). A new sample cell was designed, and the silver-plated concave mirror (diameter 12.5 mm, focal length 10 mm) and quartz tube (outer diameter 12 mm, wall thickness 1 mm, length 30 mm) was bonded with UV glue to form a liquid sample cell. A 785 nm Raman probe setup was used to conduct relevant experimental research, and different samples (75% ethanol, isopropanol, methanol) were detected and compared with traditional cuvettes. The results showed that the new sample cell is effective for different liquid samples, and the detection sensitivity can be improved, the enhancement factor is nearly 4 times. In order to analyze the repeatability of the new sample cell manufacturing method, the detection effects of the three sample cells were compared in the experiment. Lastly, to verify the detection ability of low-concentration samples, the 75% ethanol solution diluted 20 times was detected using the new sample cell. The result shows that the new sample can obtain effective Raman spectroscopy information. The experimental results prove that the new sample cell can improve the detection sensitivity of liquid Raman spectroscopy. It has a simple structure and a wide range of applications.

Visible high-speed photography is an important way to study projectile penetration. However, the intense flashes emitted during projectile penetration can cause high-speed photography to lose critical images of moments such as target impact and intrusion. Therefore, it is particularly important to analyze the mechanism of penetration spectroscopy and select a suitable optical observation window for the penetration process. For the experiment of a 400 mm diameter high-strength steel ovoid bullet penetrating a 20 cm thickness, 45# steel target at 804 m·s-1, a spectral targeting and acquisition device was designed. The integrated spectra of the whole process of target plate penetration were collected at 25 m from the target plate using a multimode fiber coupled with an objective lens, and the collection area could cover the 431 mm diameter of the target plate. The molten material of the payload and the other samples of the shells were analyzed by LIBS (Laser-Induced Breakdown Spectroscopy) and compared with the components of the intrusion integral spectrum. The smooth integral continuous spectrum in the interval of 615~700 nm consists of two parts: (a) the spreading integral of a small number of metal elements and O Ⅰ and O Ⅱ emission spectra of the bullet target (b) the integral of a small amount of thermal radiation spectrum; the thermal radiation of the intrusion mainly comes from shear strain work and friction work. However, the intensity of thermal radiation in the intrusion spectrum is significantly lower than that in the high-speed impact spectrum, which is caused by the retention of most of the kinetic energy of the projectile after the shear strain and intrusion into the target plate; the visible spectrum emitted during the intrusion process has obvious atomic emission spectra, mainly from the emission spectra of metal atoms and their primary ionization. The most disturbing visible light component comes from the Fe I. plasma line spectra of 588.88~589.53, 766.41~766.43 nm, and due to the Stark widening effect, the line spectrum is Lorentz linear, and it’s FWHM(Full Width at Half Maximum) can reach 27 nm. Therefore, in the experiments of the field environment where sunlight is the main light source when Fe is the main component of the target, the spectrum of 380~450 nm is the best observation window for visible high-speed photography, which can avoid the intrusion luminescence interference and achieve the whole intrusion process photography. The high-speed photography equipment should be ensured by sufficient luminous flux.

Tomato is rich in nutrition, which most people love. It has a long growth cycle and requires a lot of water, and water content is the main factor influencing the tomato plant’s growth and development. It is of great significance to find out the water deficit state of tomato plants quickly for scientific and effective irrigation management of tomatoes, guaranteeing and improving the yield and quality of tomatoes. In this study, hyperspectral imaging technology was used to identify the degree of drought stress on tomato leaves in real-time, and a recognition method of drought stress on tomato leaves based on hyperspectral imaging technology was proposed. Firstly, a red cherry tomato was selected as the experimental variety, and 12 POTS of tomato seedlings were cultured in the laboratory. On the basis of ensuring the same as other management measures, the stress state of the tomato was controlled by controlling the amount of water applied. Then, three treatments (suitablewater, moderate and severe stress) were designed for the degree of drought stress. The hyperspectral images in the 400~1000nm range of young leaves of tomato seedlings with different drought degrees were collected in batches, and each sample’s spectral, texture characteristics were extracted. The spectral features were pre-processed using four methods, namely normalization (Norm), multiple scattering correction (MSC), first derivative (1st) and standard normalized variate (SNV) to remove noise from spectral data. The important feature bands of the spectral features were selected using the successive projections algorithm (SPA), competitive adaptive reweighting algorithm (CARS) and the competitive adaptive reweighting algorithm combined with the continuous projection algorithm (CARS-SPA). The texture features of tomato leaves were extracted by the Gray Level-Gradient Co-occurrence Matrix (GLGCM), and the important variables of texture features were selected by SPA. Finally, a support vector machine (SVM) was applied to fuse the above-mentioned various features to build a tomato drought stress recognition model, and Adaptive boosting (AdaBoost), and K-Nearest Neighbor (KNN) were used to compare and analyze with SVM model. The results showed that the SNV-SVM model based on CARS-SPA wavelength selection has the best classification effect after the fusion of important spectral features and texture features. The classification accuracy of the training set (ACCT) is 94.5%, and the classification accuracy of the prediction set (ACCP) is 95%. The adaBoost model had the second highest classification effect, ACCT 86.5%, and ACCP 87%. KNN model had the worst classification effect, with ACCT 81.5%, and ACCP 79%. Therefore, the method presented in this paper has a good effect on the real-time recognition of the drought stress degree of tomato leaves and can provide a reference for the construction of intelligent drought stress analysis technology.

The timely and accurate location of water pollution sources and fine pollution prevention and control measures are the urgent need to win the battle of water pollution prevention and control, in order to solve the practical problem of accurate classification of permanganate index of surface water samples, in this paper, based on spectral noise reduction and spectral effective information extraction, according to the characteristics of UV-visible spectral data, one-dimensional convolution neural network is proposed to process UV-visible spectral data. In order to verify the feasibility of detecting a one-dimensional convolution neural network to classify the spectral signals of surface water, a section of the Yangtze River was selected as the sampling point. The water from the upper reaches of the Yangtze River, a river and the Jialing River were collected on the same day, and domestic sewage and 500 mg·L-1 potassium hydrogen phthalate solution were used to simulate the polluted water source. Several kinds of water samples were used to simulate the basin’s changes in water pollution on the same day according to different proportions. Collect the spectral data of existing single and mixed water samples, and distinguish them according to the characteristic spectral information of all kinds of water samples. Realize the prediction and classification of surface water permanganate index, quickly determine the pollution source of abnormal water samples through simulation experiments, optimize the model parameters and complete the optimization training. Compared with traditional classification methods such as the K nearest neighbor method and support vector machine, this algorithm has great advantages in spectral preprocessing complexity and qualitative analysis accuracy. 350 spectral data obtained are used to establish a water quality classification model, of which 245 data are randomly selected as the training set and 105 data as the test set. The confusion matrix classification accuracy of the model is up to 99.0%. It not only simplifies the whole spectral analysis process but also retains more effective spectral information, reduces the influence of artificial pretreatment on UV-Vis spectral data, and realizes the accurate classification of the permanganate index of surface water. The experimental results show that this method can accurately classify water samples from different water bodies, locate pollution sources quickly, and provide a research basis for tracing the sources of pollutants that can not stimulate fluorescence. It provides the possibility for rapid and accurate location of surface water pollution sources with the aid of three-dimensional fluorescence technology. It shows that depth learning has great application potential and research value in the UV-vis spectroscopy measurement of actual water samples.

The content of soluble solids (SSC) plays an essential role in the internal quality of cherry tomatoes. However, SSC detection has some problems based on hyperspectral imaging and dielectric properties. There are few SSC non-destructive testing models for cherry tomatoes currently. Therefore, in order to realize the non-destructive detection of SSC in cherry tomatoes, a prediction model of internal quality based on the spectral characteristics of cherry tomatoes and an improved BP neural network algorithm were proposed to solve the problem of rapid non-destructive detection of cherry tomatoes’ internal quality. In this study, cherry tomatoes were selected as the research object, and there were 188 test samples divided into a training set of 150 and a testing set of 38. The cherry tomatoes’ reflective intensity in 350~1 000 nm was obtained using the visible/near-infrared spectral acquisition system, and corrected sample reflectivity was obtained and analyzed. The practical information of the cherry tomatoes’ spectral in 481.15~800.03 nm was intercepted to enhance the signal-to-noise ratio. A BP neural network prediction model was established by comparing the effective wavelengths treated by Savitzky-Golay smoothing (SG). The coefficient of determination (R2) and root mean square error (RMSE) for the test set were 0.578 5 and 0.563 9. On this basis, the network structure of the BP neural network was improved to seek the optimal prediction structure of the BP neural network. The error between the output layer and the expected value was calculated. The network structure parameters were adjusted, and the learning rate and the number of neurons were set to 0.01 and 5 to establish BP neural network model (SG-IBP). The R2 and RMSE of the test set were 0.981 2 and 0.102 3. While the R2 and RMSE of the test set were 0.997 8 and 0.047 9, with 18 feature lengths screened by the competitive adaptive reweighted sampling algorithm (CARS). Meanwhile, the speed was greatly improved. The results showed that the performance of the improved BP neural network model was significantly improved. After feature lengths were extracted by CARS, R2 of the test set was increased by 0.419 3, and RMSE was reduced by 0.516.The speed was also significantly improved. Therefore, the improved BP neural network model, which used CARS to extract characteristic lengths (SG-CARS-IBP), had apparent advantages, and the SG-CARS-IBP model was more suitable for studying cherry tomatoes’ SSC non-destructive detection. This study can provide a reference for efficient non-destructive detection of cherry tomatoes.

Tomato early blight is highly infectious and destructive. The detection and identification of pre-symptom characteristics in the incubation period is the key to Tomato Early Blight monitoring, early warning and scientific control. In this paper, the evolution of early tomato blight was monitored by hyperspectral images, and the data were analyzed combined with visible light images and spectral characteristics. The results showed that the average value of near-infrared spectrum and red edge reflectance of tomatoes infected with early blight decreased with time, and the disease information of the incubation period appeared 36 hours after inoculation. This paper selected the spectral data of 36 h inoculation as the modeling data of Tomato Early Blight incubation period. The principal component (PCA) transformation and multivariate scattering correction (MSC) were used to reduce the spectral dimension or noise of the modeling data. Then the gradient lifting decision tree (GBDT) and support vector machine (SVM) recognition models were established, and the data were imported for training and recognition. The influence of PCA and MSC preprocessing methods on the recognition effect of gradient lifting decision tree (GBDT) and support vector machine (SVM) models is discussed. The influence of common kernel functions on SVM recognition models is further discussed, and the combination algorithm of preprocessing method and recognition model is optimized. The results showed that the accuracy of PCA-GBDT, PCA-SVM (Gaussian kernel), PCA-SVM (linear kernel), MSC-GBDT and MSC-SVM (polynomial kernel) was more than 95%, which could well realize the spectral recognition of Tomato Early Blight incubation period; Among them, MSC-GBDT has the best recognition recall and accuracy, while PCA-SVM (Gaussian kernel) has the highest recognition efficiency. The research shows that the hyperspectral data of the Tomato Early Blight incubation period after noise reduction reduces the noise is more in line with the real distribution, and has a large amount of data. The recognition ability will be insufficient, while combined with a complex recognition model, a higher test result can be achieved; The dimension reduction algorithm can reduce the dimension and amount of hyperspectral data in the incubation period of early tomato blight, and the features after dimension reduction can express the lesion information. When combined with a simple recognition model, the recognition effect is good, while with an overly complex recognition model, it will lead to over fitting the recognition model.

Aiming at the weight difference between strong and weak peaks and the interference of noise peaks in the traditional reverse matching method (RMM), an improved reverse matching method (IRMM) is proposed in this paper. In this method, the weight attenuation function is introduced to optimize the weight proportion relationship between the strong peak and the weak so that the weight of each feature peak in the spectrum is distributed in a reasonable range, which avoids the situation that the weight of the strong peak masks the weak. Moreover, this method realizes the adaptive filtering of noise peaks by the method of dynamic noise filtering of the probability distribution function, which improves the recognition performance of the reverse matching method. In the experiment, many conventional Raman and surface-enhanced Raman spectra were used as verification samples, which were identified and verified based on a large database of conventional Ramanand surface-enhanced Raman. Experiments show that this method (IRMM) has a comprehensive accuracy rate of 91.52% under a large amount of data testing, which is greatly improved compared to the hit quality index method (HQI, 51.08%) and the traditional reverse matching method (RMM, 16.57%).

Tunable diode laser absorption spectroscopy (TDLAS) is a non-invasive spectral detection technology with high selectivity, high response and high resolution. According to the principle of molecular spectral absorption, the change in target gas temperature will affect the change of molecular absorption line strength and then affect the accuracy of gas concentration inversion. In order to improve the accuracy and authenticity of gas concentration measurements in high-temperature atmospheres, carbon monoxide (CO), a common gas in industrial processes, was selected as the target gas. Experiments were designed to detect the gas spectra in multiple temperature regions (from 14 to 1 100 ℃) based on wavelength modulation technique, compared with spectral parameters in the HITRAN database, and the results were calibrated and analyzed. At the same time, the influence of different materials of sapphire window pieces was analyzed in terms of parameters such as the linearity of the detection signal. A cooling gradient measurement was selected as the temperature control sequence for the high-temperature experiment by analysing the data from the temperature rise and fall experiments. Through the high-temperature experiment with a standard concentration of CO, it was found that the second harmonic (2f) amplitude and absorption line intensity had a consistent decreasing trend with increasing temperature, by the theoretical equation of variation law. After analysis, the corrected 2f amplitude and temperature show a non-correlation and a correction for the effect of temperature on spectral detection is achieved. The shortcomings of the correction formula and the proposed improvement method are remedied, and the accuracy of the 2f amplitude correction at variable temperatures is verified. This study provides a reference for the practical application of spectral detection technology in the measurement process of high-temperature environments, especially for the dynamic evaluation of combustion efficiency in high-precision industrial furnaces, which is of great importance.

Dual-energy CT uses two groups of attenuation information under different energy spectra to accurately segment two kinds of basis materials. In practical applications, the internal material structure of the object is complex, and the composition is diversified. It is often necessary to obtain three or more basic material images to understand its internal structure information. Conventional CT is a continuous mixed spectral beam. The projection information obtained does not match the single-energy reconstruction algorithm, and there are errors in the attenuation coefficients of each basic material in the reconstructed image. The density of materials in the industrial field is generally larger and the noise of the basic material in the reconstructed image is more serious, affecting the accuracy of each component’s characterisation, especially for the materials with similar attenuation coefficients. In order to realize dual-energy data decomposition to obtain multiple high-quality basis images, in addition to the influence of noise in the reconstructed image, the selection of the coefficient matrix in the material decomposition model is also very important. However, there is a deviation between the attenuation coefficient value in the reconstructed image and the theoretical attenuation coefficient value. In the reconstructed image, the attenuation coefficients of different materials with similar densities are similar or even equal, resulting in the wrong selection of the material triplet of the pixel to be decomposed, which reduces the accuracy of material decomposition. Therefore, it proposes a 3D block-matching multi-component decomposition method with inter-layer constraints. In the method, a multi-material decomposition model with mass volume conservation and the constraint that each pixel comprises three types of materials at most is introduced. Three-dimensional structure similarity information is added to the selection of pixel material components. Constraint solution is carried out by utilizing the three-dimensional structure information to reduce noise pollution, and an initial basis image, roughly decomposed, is obtained; Then use the three-dimensional block matching method to match the initial basis image, and classify the three-dimensional feature constraints of each basis material image. After classification, select the optimal material composition triplet containing this type of basic material to carry out a multi-material decomposition model and obtain a more accurate component characterization diagram. In two groups of experiments on pure metal phantoms and granite. Compared with the results of the existing methods, the three-dimensional block matching decomposition method under interlayer constraints is more accurate in identifying industrial materials with similar attenuation coefficients, the structure of each component characterization image is complete, the image quality is good, and the detail processing is accurate. The quantitative analysis in pure metal phantom experiments shows that the PSNR and SSIM values of the proposed method are increased by 5%~6% and 31%~35%, respectively, compared with the existing methods. The effectiveness and robustness of the algorithm are verified, and more accurate multi-component decomposition is achieved under the conventional CT system.

Tang Sancai is an important cultural heritage of China. The analysis of the chemical compositions and phase structures of the Tang Sancai body and glaze is helpful to studying the Tang Sancai raw materials and firing technologies. This paper reports the analysis results of chemical compositions and phase structures of the Tang Sancai from the Liquanfang Kiln, the Huangye Kiln, and modern products from the Shaanxi Provincial Museum by the micro X-Ray fluorescence spectrometer and the X-ray diffractometer. The results show that the raw materials of Tang Sancai bodies in the Liquanfang kiln and Huangye kiln come from different clays. The cristobalite (PDF 76-0932) and α-quartz (PDF 46-1045) are the main phase structures in the Tang Sancai bodies of Liquanfang kiln and Huangye kiln. However, a small number of α-Fe2O3 (PDF 16-0653) phase andtraces of mullite (PDF 83-1881) phase existed in the Tang Sancai bodies of Liquanfang kiln and Huangye kiln, respectively.It shows that the difference in the firing technology and raw materials of the two kilns results in the different mineral structures of the bodies. In the glaze of Tang Sancai, the coloring elements Fe, Cu, and Co in the glazes melted in the lead flux, and Fe and Cu blended in the mixed area of the yellow glaze and the green glaze. The XRD patterns of the glazes show that there are mainly amorphous glass phases and traces of α-quartz (PDF 46-1045) in the glass. In addition, a small amount of Pb8Cu(Si2O7)3 (PDF 31-0464) phase existed in the Tang Sancai green glaze of Liquanfang kiln; a large amount of CaAl2Si2O8(PDF 89-1462) phase existed in the Tang Sancai yellow glaze of Huangye kiln; a small number of α-Fe2O3 (PDF 47-1409) phase existed in the Tang Sancai yellow glaze of Liquanfang kiln. It indicates that the differences in the chemical compositions of the glaze raw materials and the firing technologies lead to the different mineral crystals in the Tang Sancai glazes. The concentrations of the main elements of the modern products’bodies and glazes are close to the Huangye kiln samples. However, there are significant differences between the fake and true Tang Sancai in the phase compositions of the bodies and glazes. In general, the combination of micro X-ray fluorescence and X-ray diffraction analysis technology could have broad application prospects in raw material origin, authenticity identification, and firing technology of ancient ceramics.

Cerebral ischemia is a common sudden cerebral surgical disease with a high lethality and disability rate. The rapid and accurate detection of cerebral ischemia is of great significance to the diagnosis and treatment of cerebral ischemia. Inthispaper, we performed spectroscopy on cerebrospinal fluid (CSF) and serum of rats with ischemia time of 0, 0.5, 1, 2, 4, 6 and 24 h respectively, based on attenuated total reflection terahertz time-domain spectroscopy (THz-TDS). The changes in absorption coefficient and refractive index of CSF and serum with different ischemic times were analyzed. The results showed that the absorption coefficient and refractive index of CSF and serum of rats with different ischemic times were somewhat different compared with the control group. Furthermore, according to the absorption coefficient of CSF and serum with different ischemic times, principal component analysis and machine learning algorithms were used to automatically classify and recognize the degree of cerebral ischemia in rats. Especially, the recognition accuracy of the support vector machine classification model based on the absorption coefficient of CSF is relatively high, reaching 89.3%. Combining terahertz spectroscopic detection of CSF and serum ofrats with machine learning algorithms provides a new and effective detection method for the early diagnosis of cerebral ischemia.

Surface plasmon resonance (SPR) is an optical detection technology that has emerged in recent years. It mainly uses the evanescent field generated by the total reflection of the light wave to interact with the metal to perceive the refractive index change of the external medium. Theoretically, any phenomenon that causes a change in the refractive index can be detected using SPR. However, in practical applications, SPR sensors still have shortcomings, such as low sensitivity and weak detection signals, so the research on improving the detection sensitivity of SPR sensors has always attracted much attention. Graphene oxide, also regarded as the oxide of graphene, has optoelectronic properties similar to graphene and is easier to modify than graphene chemically, so it is widely used as the surface of SPR sensor chips coating to improve sensitivity. There are many reports of graphene oxide being used to modify SPR chips to improve sensitivity. However, there are different opinions on the use concentration of graphene oxide, and it needs to be immersed for a long time when preparing graphene oxide-modified sensor chips, which greatly affects the chip fabrication efficiency. In this study, long-chain functionalized PEG was used to modify the surface of the SPR gold chip chemically, and the graphene oxide solution was used to modify the chip by the method of flow scouring. By comparing the refractive index change response signals, it was finally determined that the optimal concentration of graphene oxide was 0.5 mg·mL-1, and the modification time was reduced to less than 20 min. The influence of incident light waves of different wavelengths on the resonance peak of the graphene oxide-modified chip was studied, and the chip’s overall performance was evaluated. The chip’s sensitivity under the incident light wave at 950 nm was 243.2°·RIU-1, and the FOM value was 82.2 RIU-1. This value is 33.3% higher in sensitivity and 36.1% higher in FOM than that of the same chip at the original 800 nm incident wavelength, and it is 4 times in sensitivity and 3.41 times in FOM that of the bare gold chip at 800 nm wavelength. The institute proposed that the rapid preparation of graphene oxide-modified SPR sensor chips has the advantages of low cost and fast fabrication and has broad application prospects in the field of biochemical detection.

At present, ionic liquids’ high production and use costs are limiting their large-scale applications, so how to recycle them has attracted great attention. Crystallization processes are extremely important for developing new recycling technologies for ionic liquids, and cooling rates have important effects on the crystallization processes. Based on these, in this paper, polarizing microscopy, small angle X-ray scattering and Raman spectroscopy were employed to research the phase transitions and the structural changes of 1-dodecyl-3-methylimidazolium tetrafluoroborate (［C12mim］［BF4］) from 60 to 0 ℃ at the cooling rates of 30 and 1 ℃·min-1, in order to reveal the effect of cooling rates on the crystallization process and product. POM results show that ［C12mim］［BF4］ experienced the phase transitions from the liquid state to liquid crystal state and then to crystal state Ⅰ during the rapid cooling process, and that ［C12mim］［BF4］ underwent the phase transitions from the liquid state to liquid crystal state and then to crystal state Ⅱ during the slow cooling process. The crystal state Ⅰ consisted of many “ball-like” crystals with large sizes, while the crystal state Ⅱ was composed of a lot of “needle-like” crystals with small sizes. In addition, SAXS results show that ［C12mim］［BF4］ has two crystal structures, including perpendicular and triclinic bilayer phases. Both were found simultaneously in the crystal state Ⅰ, but only the triclinic bilayer phase appeared in the crystal state Ⅱ. Therefore, crystal state Ⅰ is a mixed phase crystal, while crystal state Ⅱ is a single phase crystal. Furthermore, it can be concluded from the Raman results of ［C12mim］［BF4］ that the ［C12mim］+ in the perpendicular bilayer phase is the G conformation, and that the ［C12mim］+ in the triclinic bilayer phase is the A conformation. In conclusion, ［C12mim］［BF4］ underwent the phase transitions from the liquid state to liquid crystal state and then to crystal state Ⅰ at a rapid cooling process, and a mixed phase crystal composed of the perpendicular bilayer phase and the triclinic bilayer phase was obtained. However, ［C12mim］［BF4］ underwent the phase transitions from the liquid state to liquid crystal state and then to crystal state Ⅱ at a slow cooling, and a single phase crystal consisting of the triclinic bilayer phase was obtained. What is more, the mixed phase crystal includes the G conformation and the A conformation of ［C12mim］+, while the single phase crystal only contains the A conformation. So, the cooling rate has an important effect on the crystallization process and product of ［C12mim］［BF4］. These results provide important experimental data for enhancing the recovery technology of ［C12mim］［BF4］, and are also helpful in investigating the phase transition and structure change of similar ionic liquids.

In recent years, porphyrins and metalloporphyrins have attracted much attention in photodynamic therapy and anticancer, and some have been approved for clinical use. Human serum albumin(HSA) can bind and transport some drug molecules. A detailed study of the binding mechanism of metalloporphyrins and HSA is of great significance in clarifying the action mechanism of porphyrin drugs. This study synthesised three kinds of novel free porphyrins modified with 6-chloronicotinic acid(4, 5, 6) and their Zn complex(4-Zn, 5-Zn, 6-Zn) and characterized by UV-Vis, IR, 1H NMR, elemental analysis, fluorescence spectra and theoretical calculations. The theoretical calculation results showed that the 6-chloronicotinate moieties in the three zinc porphyrins were far away from the porphyrin ring plane. The 4-Zn configuration was more stable than the 5-Zn and 6-Zn configurations with substituents. Under simulated physiological conditions, the bonding modes between three zinc porphyrins and HSA were studied by fluorescence spectra, and the results were calculated according to the Stern-Volmer equation, double-logarithmic equation and Van’t Hoff equation. The experimental results indicated: (1) Three zinc porphyrins could all quench the fluorescence of HSA and the values of Kq calculated by the Stern-Volmer equation were much larger than 2.0×1010 L·mol-1·s-1. Thus the quenching type was static quenching. (2) The binding constants were calculated by a double-logarithmic equation. Except for the 5-Zn at 318 K, other binding constants were all greater than 103 L·mol-1, and the binding sites were close to 1, indicating the formation of a 1∶1 complex. (3) According to the Van’t Hoff equation, the thermodynamic parameters ΔH, ΔS, ΔG were all less than 0, eg. those of 4-Zn were calculated to be ΔH=-123.9 kJ·mol-1, ΔS=-322.9 J·mol-1·K-1, ΔG=-27.7 kJ·mol-1 (298 K), indicating that the reaction process was spontaneous and the predominant forces between zinc porphyrins and HSA were vander waals force and hydrogen bond. The experimental data obtained in this experiment can provide useful information for studying the interaction mechanism between metalloporphyrins and biological small molecules.

Spectral analysis technology has a certain potential in wood species identification, and mid-infrared spectroscopy technology is also widely used in qualitative and quantitative analysis. This research focuses on the identification of wood species by mid-infrared spectroscopy. Based on a deep convolutional neural network, an algorithm that combines cluster analysis (CA), symmetrical lattice image analysis (SDP) and deep learning (DenseNet) is proposed to achieve a high recognition rate with few parameters. With the advantages of DenseNet, the accuracy of wood recognition in mid-infrared spectroscopy is improved. First, 250 sets of mid-infrared spectroscopy data, including guaiacum sanctum, dalbergiabariensis, pterocarpuserinaceus, pterocarpusmacarocarpus, and spiraea, are collected. Through eliminating outliers based on Euclidean distance, the feasibility analysis of the remaining 240 groups as data to be analyzed and classified. The optimal parameters of SDP conversion are determined through the SDP conversion analysis of the original spectral data. The characteristics of original spectral data are filtered out through CA. According to CA, different thresholds determine the characteristics of the three groups of dimensions and related discussions are carried out. The optimal dimensional feature is initially determined by comparing the three sets of feature data, including the intra-class similarity and the inter-class difference between the images after SDP conversion. The determined optimal dimensional feature data is input into the SDP-DenseNet model to obtain model recognition accuracy. Finally, the comparative analysis verifies the validity of the model. On the one hand, the original data and the feature data of the other two sets of contrast dimensions are input into the SDP-DenseNet model to compare recognition accuracy. On the other hand, the optimal dimensional feature data is input into the random forest for recognition to compare the accuracy of traditional machine recognition and SDP-DenseNet algorithm recognition. According to the results, the accuracy of the SDP-DenseNet model filtered by the CA feature is generally higher than that of the SDP-DenseNet model directly input to the original data. The optimal dimension of CA feature selection is 255 dimensions, with the highest recognition rate of 88.67%. In the control group, the recognition rate of 107 dimensions is 77.78%, and the recognition rate of 322 dimensions is 68.89%. In contrast, the SDP-DenseNet model recognition rate of the original data is only 57.78%. The recognition rate of the random forest model corresponding to the optimal dimensionality data screened by clustering features is relatively low, only 66.67%. Therefore, the CA-SDP-DenseNet model proposed in this study can effectively improve the accuracy of mid-infrared spectroscopy in identifying wood species.

The content of metal elements in the root influences the growth of sorghum. Laser-Induced Breakdown Spectroscopy (LIBS) is an ideal technology for rapidly detecting metal elements in crops.In this paper, a quantitative analysis method of metal elements in sorghum roots was established based on laser-induced breakdown spectroscopy and wavelength selection algorithm based on variable dimension particle swarm optimization-combined moving window (VDPSO-CMW). We collected 27 sorghum samples with different Na and Fe concentrations under sodium salt stress. For LIBS spectra of sorghum roots, the VDPSO-CMW algorithm was used to screen the characteristic bands related to Na and Fe, and PLS quantitative analysis model was constructed. After VDPSO-CMW algorithm optimization, the determination coefficient of cross validation (R2CV) of the PLS model for Na in sorghum root was 0.962, which was 6.5% higher than that before optimization. The root means square error of cross validation (RMSECV) was 1.261, which was 37.7% lower than thatbefore optimization; the determination coefficient of prediction (R2P) was 0.988, which was 16.8% greater than that before optimization. While the root means square error of prediction (RMSEP) was 1.063, which was 72.1% lower than that before optimization. After VDPSO-CMW algorithm optimization, the R2CV of the PLS model for Fe in sorghum root was 0.956, which was 7.4% higher than that before optimization; the RMSECV was 5.095, which was 37.1% lower than that before optimization; the R2P was 0.955, which was 4.3% higher than that before optimization; while the RMSEP was 6.438, which was 27.3% lower than that before optimization. The results show that the VDPSO-CMW wavelength selection algorithm can eliminate the LIBS bands affected by self-absorption, spectral line interference, and other factors and improve the accuracy of quantitative analysis. The combination of this algorithm and LIBS technology can not only realize the rapid and accurate determination of Na and Fe in sorghum roots but may also apply to the quantitative analysis of other samples and elements.

Hyperspectral data contain not only critical information but also some interference information and invalid information, and using these data to build the model will reduce the reliability and accuracy of the relational model. Extracting feature wavelengths from full-band data is an effective way to improve the accuracy of prediction models. Ordered Predictive Selection (OPS) is a feature wavelength extraction algorithm that selects effective wavelength variables based on the information vector, and has shown good performance in feature wavelength variable screening. However, the model was built without removing the less important variables, resulting in too many invalid variables being involved in the model and reducing the model’s accuracy. The paper proposes an improved feature wavelength variable selection method based on an information vector and exponential decay function of ordered predictive selection method (AW-OPS) for lamb freshness detection, using lamb hyperspectral data as the research object. The algorithm calculates the information vector and ranks the wavelength variables by the relationship between the spectral data and the physicochemical value data. The exponential decay function (EDF) is used to remove some wavelength variables with relatively low absolute values of information vectors by multiple iterations. Finally, a multiple regression model was established by gradually adding wavelength points to the obtained effective wavelength variables, and the subset of wavelength variables with the lowest value of root mean square error (RMSECV) was selected as the characteristic wavelength variables. For the experiments, the partial least squares (PLS) relational models of lamb TVB-N were constructed by the OPS -and AW-OPS methods after selecting the characteristic wavelengths, respectively, and compared with the effects of FULL-PLS models. The results showed that the OPS algorithm took an average of 175.9 s to run the program, preferentially selected 370 characteristic wavelength variables, with an average OPS-PLS model correlation coefficient(RP)of 0.963 1 and an average root mean square error(RMSEP)of 0.727. while the improved ordered prediction selection method(AW-OPS)runs the program in an average time of 57.6 s, preferentially selects 275 characteristic wavelength variables, and the AW-OPS-PLS model RP improves to 0.973 1 on average, and RMSEP reduces to 0.572 8 on average. The number of full-spectrum wavelengths was 1 414 wavelength variables, and the average RP of its PLS model was 0.920 8, and the average RMSEP was 1.048 3. The AW-OPS-PLS model improved the test accuracy by 21.2% compared to the OPS-PLS model and 45% compared to the full-spectrum-PLS model, proving that the improved AW-OPS is an effective feature wavelength variable screening method that improves the accuracy of the OPS model and the efficiency of the program operation and reduces the complexity of the model.

The determination of the main components of milk is an important criterion for evaluating the quality of milk. Relevant national departments have formulated a series of relatively detailed specifications to ensure the quality and safety of milkand other dairy products. However,thetraditional detection methods are often complex, time-consuming and labor-intensive. Some even cause environmental pollution, making it difficult to meet the rapid detection needs of contemporary dairy production and consumption. In this study, the portable attenuated total reflectionFourier transform infrared spectroscopy (ATR-FTIR) technique was combined with relative humidity (RH) control system to establish a method to measure the infrared spectra of different kinds of milk under the condition of continuous decline of RH. This method provides a new way for non-destructive testing, classification and quality analysis of milk products. The main contents include:(1)Selecting five types of milk of YiLi brand (pure milk, Zhennong milk, skimmed pure milk, high-calcium low-fat milk, and Shuhua milk) as research objects. Whose infrared spectra in the process of evaporation and concentration were collected under continuous decline of RH, and the peak position attribution and qualitative analysis of main nutritional components were carried out. It only takes a few microliters of milk samples for us to obtain the spectral information of the main components, such as water, carbohydrates, fats, and proteins, during the sample concentration process in a short time and achievea relatively comprehensive characterization of the chemical components of milk; (2) Using NWUSA software to build the model and process the infrared spectral data, choosing 4 000～400 cm-1 band as the variables to perform PCA and evaluating the identification ability of the model for different types of milk, it shows that the data of PCA process are well aggregated in the same group. The coordinate axes in different groups are far apart, indicating that the model selection is both reliable and representative. A total of 75 milk samples were used in the experiment, in which the production date and place were random factors, and the type and brand of milk were fixed factors. The results show that the proposed method has the advantages of simple operation, sensitive response, high spectral quality and non-destructive measurement, which is suitable for in-situ, rapid and non-destructive identification and analysis of milk and other dairy products.

A rapid quantification of oxytetracycline hydrochloride in cattle premix using Attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with partial least squares (PLS) was performed. Rapid quantification of the prohibited component of oxytetracycline hydrochloride in cattle premix was carried out. 98.00% (W/W) oxytetracycline hydrochloride was added to the blank cattle premix, and 113 mixed samples were prepared with oxytetracycline hydrochloride concentrations ranging from 0.00% to 5.00%. Pure water was used as the extractant, and the extract was dried by an infrared lamp and used for IR spectroscopy. Three pretreatment methods were employed: Savitzky-Golay convolution smoothing (S-G), standard normal variation (SNV), multivariate scattering correction (MSC), and three variable selection algorithms: Interval partial least-squares (iPLS), Moving partial window least-squares (MWPLS), the bootstrapping soft shrinkage (BOSS). Among them, SNV combined with the BOSS algorithm obtained the best model results: RMSECV=0.337 0, R2CV=0.946 9, RMSEP=0.317 3, R2pre=0.934 6. The model predicted 29 samples with contents ranging from 0.53% to 4.67%, and the average relative error of prediction was 0.126 7. Meanwhile, the variables selected by the BOSS algorithm were mainly concentrated in the absorption regions of the characteristic peaks of oxytetracycline hydrochloride (1 674~1 593 and 1 175~1 017 cm-1), which can provide a valuable reference for the ATR-FTIR technique in the rapid detection of oxytetracycline hydrochloride in feed.

The study was performed to evaluate the effects of thermal modification on the mechanical properties, optimize the modification temperature based on strength class, and provide a basis for the rational application of thermally modified wood in buildings. In this study, a total of 560 poplar wood specimens were tested to determine the effects of thermal modification between 160 and 210 ℃ on mechanical properties, such as bending strength (fm), parallel-to-grain tensile strength along the grain (ft, 0), perpendicular-to-grain tangential (ft, T, 90) and radial tensile strength (ft, R, 90), parallel-to-grain compressive strength (fc, 0), parallel-to-grain tangential (fv, T) and radial shear strength (fv, R), and modulus of elasticity (E0). The Fourier transform infrared spectroscopy was used to analyze the changes in chemical components of thermally modified wood at different temperature levels. The optimization temperature of thermal modification based on strength class was put forward. The results showed that the hemicellulose within wood had the lowest heat resistance under high-temperature condition and was first degraded by thermal exposure followed by acceleration at ≥190 ℃. The thermal resistance of cellulose was relatively higher, which was slightly degraded at the higher temperature, and mainly occurred in the amorphous region, increasing the orderly arrangement of microfibril. It was shown that thermal modification had an obvious adverse effects on fm, ft, 0, ft, 90 and fv of poplar wood. At room temperature, fm, ft, 0, ft, T, 90, ft, R, 90, fv, T and fv, R were determined as 67.0, 86.2, 5.8, 8.9, 7.7 and 6.7 MPa, respectively. At lower temperatures, the chemical components of wood degraded slightly, and the mechanical properties of thermally modified poplar wood specimens decreased slowly. These parameters decreased to 53.5, 78.9, 4.0, 4.8, 6.0 and 5.4 MPa at 180 ℃, respectively. When the temperature was ≥190 ℃, severe pyrolysis occurred to the main chemical components, resulting in the rapid reduction of mechanical properties. At 210 ℃, these parameters represented 44.5%, 56.1%, 43.1%, 29.2%, 34.5% and 26.7% of the values at normal temperature, respectively. fc, 0 and E0 of thermally modified poplar wood increased as the temperature increased from 160 to 180 ℃, then decreased in the temperature range between 190 and 210 ℃. fc, 0 and E0 were 41.4 and 8 568 MPa at 20 ℃, respectively. In the temperature range between 160 and 180 ℃, the crystallization of cellulose increased, leading to the increase of the two parameters. At temperature of 180 ℃, fc, 0 and E0 were 30.7% and 12.8% higher than those at room temperature, respectively. The pyrolysis of cellulose increased with the temperature, resulting in the two values decreasing continuously until they achieved 45.0 and 8 104 MPa at 210 ℃, respectively. The untreated poplar wood cannot be used as a structural material, because E0 does not meet the requirements of the minimum strength class D18 according to European standard BS EN 338. The E0 of the modified wood specimen between 160 and 170 ℃ was higher than that of the untreated, but it was still lower than that specified by the minimum strength grade D18. After that, E0 increased with increasing temperature, and the modified poplar wood reached strength class D18 at 180 ℃. At temperatures between 190 and 200 ℃, the E0 of thermally modified wood specimens was higher than the corresponding value of strength class D18, but they still cannot be used as loading-bearing materials due to the excessive reduction of fv, R. The study can provide a basis for the rational application of thermal modification technology and low-quality fast-growing wood in engineering structures.

In the analysis of trace substances in hair by Surface-Enhanced Raman Spectroscopy (SERS), the characteristic peaks of hair are coupled with the background peaks of the substrate. In the case of coupling, the background peaks are mistakenly identified as the characteristic peak of hair, resulting in the identification error of the analyte to be tested. In addition, the strong background peak has a masking interference on the weak characteristic peak in hair. Therefore, the background peak deduction is an important way to solve the above problems. However, the conventional peak deduction method always leads to serious distortion of the surrounding peaks. In this paper, a Gaussian mixture model is proposed. The model not only characterizes the SERS signal but also makes each characteristic peak independent of the other, and does not interfere with the adjacent peaks in the process of peak deduction, which realizes the deduction of interference peaks and ensures the micro distortion of adjacent peaks. The core problem of the Gaussian mixture model is the solution of model parameters. In this paper, wavelet transform and conjugate gradient methods are proposed to solve the model’s initial parameter problem and optimal solution problem. The wavelet transforms fully extracts the subtle feature information of the signal by mapping the detailed information of the amplified SERS signal and takes the feature information as the initial parameter of the model. The conjugate gradient method is an iterative optimization method, and the model parameters are iteratively optimized. The final convergence result is the optimal solution of the model parameters. In summary, the two methods can accurately establish the Gaussian mixture model, and the single Gaussian function is the characteristic peak of the SERS signal, and the peak shape of the two methods is consistent. The deduction of background peak should include the extraction of effective data, model establishment, and peak deduction. The effective data extraction is to detect the blank and sampled substrate in the same position, thus obtaining a set of SERS signals. The model was established to characterize the SERS signal of the sampled substrate by the Gaussian mixture model, which multiple Gaussian functions can express. Finally, the SERS signal of the sampled substrate was identified by the characteristic peaks of the blank substrate, and the characteristic peaks with similar peak shapes and the same peak position can be deducted. The results show that when the variance ratio is the smallest, the peak position, peak width, and peak intensity of the Gaussian mixture model are the same as those of the hair SERS signal. At this time, the Gaussian mixture model can accurately characterize the information of SERS signal of hair. In seven groups of hair peak deduction experiments, of the hair SERS signal background peak deduction rate reached 50%~100%, while the hair characteristic peak wasalso effectively extracted. The model was used to identify tramadol in the rapid analysis of real hair samples.

Pyrene, a kind of polycyclic aromatic hydrocarbons (PAHs), widely exists in the natural environment. It has strong lipophilicity and carcinogenic effect on the human body. Therefore, the rapid analysis of pyrene content in edible oil has far-reaching significance for quality control. The quantitative analysis of polycyclic aromatic hydrocarbons using Raman spectroscopy and artificial intelligence algorithm is a current research hotspot. One milliliter of edible oil is mixed with pyrene liquid with different fixed concentrations to make samples, and then a thin-layer chromatography plate and gold particles are made. The experiment is carried out by combining thin-layer chromatography, and surface-enhanced Raman scattering (SERS) spectrum to obtain the spectral data. The adaptive iterative weighted penalty least square algorithm is selected for preprocessing, Then the Multi parameter-Principal Component Analysis- Back Propagation Neural Network model was used for quantitative analysis. Firstly, two characteristic peaks are selected in the preprocessed spectrum for peak fitting, and the parameters such as height, half-width, height and area of characteristic peaks are obtained. Normalized the Raman data of the two characteristic peaks and the parameters obtained by fitting, and then use the principal component analysis to obtain the key parameters. The obtained key parameters are input into the BP neural network based on L2 regularization as the input layer to output the predicted concentration. The experimental results show that the R2 determination coefficient of the test set is 0.58 and the root mean square error (RMSEC) is 1.85; The linear regression is used to fit the law between the characteristic peak area and pyrene concentration. The final predicted pyrene concentration has an R2 determination coefficient of 0.26, and a root mean square error (RMSEC) of 2.28; For the pyrene concentration predicted by the Multi parameter-Principal Component Analysis-Back Propagation Neural Network model, the R2 determination coefficient of the test set is 0.99, and the root mean square error (RMSEC) is 0.31. The multi-parameter principal component analysis-back propagation neural network model has higher measurement accuracy and less error. The model is aimed at the nonlinear and high-dimensional relationship between spectral data information and sample concentration. The prediction accuracy and modeling efficiency are higher than similar comparison algorithms. The model fits the characteristic peak to obtain the key variables and takes the Raman displacement of the variable and the characteristic peak as the characteristic vector, so the characteristic vector is sufficient. The model uses PCA to extract the nonlinear characteristics of the Raman spectrum and adopts the advantages of strong generalization based on L2 regularization BP neural network to prevent overfitting, so that it can predict the concentration of naphthalene more accurately and quickly.

Based on the repeatability and intermediate precision of time differences in the standard, through inspection of the data of repeatability and trueness of measurements within the time intervals, repeatability between time intervals, general precision between time intervals and trueness of grand mean of long-term stability test. The long-term stability of analytical instruments can be systematically evaluated according to the standard, the essence of which is to monitor the precision and accuracy of analysis results. However, a large number of test methods in the laboratory that have not been upgraded to standards, the so-called non-standard methods, cannot be directly evaluated for long-term stability according to the above methods due to the lack of repeatability and laboratory reproducibility data. Fortunately, the stimulated repeatability and within-laboratory reproducibility limits were obtained for non-standard test methods after correcting the instrument between different time intervals. Based on the stimulated repeatability limit and within-laboratory reproducibility limit, the long-term stability of the corresponding analytical instrument method can be evaluated. This paper uses the non-standard method for determining 18 impurity elements in pure nickel by glow discharge mass spectrometry (GD-MS) as an example. A set of comparative experiments are designed to obtain the simulation reproducibility limit and simulation reproducibility limit data. The long-term stability time for 18 impurity elements, such as B, Mg, Al and Si in pure nickel samples was evaluated by χ2 statistics after being tested by GD-MS. The results showed that the long-term stability was different for different elements when determined under the same condition. The results met the statistical requirements for most elements within 3 hours. Among these, the long-term stability interval can be up to 6 hours or even 12 hours for elements P, As, V, Sb and Pb. The reliable results can be obtained within that time interval without instrument correction. The evaluation results are consistent with the laboratory experience, indicating that the systematic measurement and characterization method proposed in this experiment objectively reflects the long-term stability of the GD-MS objectively. This method can also be used to evaluate the long-term stability of other non-standard methods, which has significant practical guidance for the quality control for the laboratory.

Natural mineral red clay was used as the color agent in red lime of traditional palace buildings. However, due to red clay mining being restricted, iron oxide red synthesized in the industrial method was commonly used to replace red clay in the restoration of ancient buildings, which caused poor water resistance, cracking, peeling, and other that phenomena happened in red lime. In order to explore the cause of this phenomenon, in this study, red Jia-long lime samples were collected from the roofs of three halls (Zheng hall, Xiwei Hall and Hou hall) of Yangxin Hall in the Forbidden city and determined the main phase composition of the samples by X-ray diffraction (XRD). The results show that the main components of the samples are calcite, iron oxide and quartz, in addition to a small amount of albite and halloysite. Since the quite difference in chromaticity of the samples from each hall, it combined powder microscopic morphology and mapping test, comparing the shape and color of the microscopic particles and the element distribution of the sample section to determine the main color components of the red lime sample. The results show that the powder micromorphology of the samples in Zheng Hall and Hou Hall is uniform and full of dark red particles, accompanied by black or brown-yellow mineral particles, and the distribution of Fe in the samples is not uniform and is inconsistent with the distribution of Al and Si elements. However, the powder morphology of the Xiwei Hall sample shows light color particles accompanied by other mineral components. The distribution of Fe elements in the sample is uniform, consistent with the distribution of Al and Si elements. It is concluded that the raw materials and coloring materials used in red lime samples from Zheng Hall and Hou Hall are lime, red clay and iron oxide red. The raw materials and coloring materials used in red lime samples from the Xiwei Hall are lime and red clay. It is the first time that the identification method between red clay and iron oxide red has been proposed. Scanning electron microscopy (SEM) was used to explore the effect of color components on the micro-morphology of the sample, and it was shown that the structure of the sample containing only red clay was continuous and compact, and the two phases were poorly combined after adding iron oxide red, and there were pores. In order to further explore the influence of red clay and iron oxide red on the performance of samples, the simulated samples with red clay and iron oxide red were prepared respectively. It can be seen that the use of iron oxide red instead of red clay to make Jia-long lime has poor physical and mechanical properties compared to the Jia-long lime made with red clay.

Jadeite is one of the most common jades in the current market, and the processing means to cover up the tiny defects on the surface of jadeite and improve its luster by waxing is commonly accepted by consumers. The waxing of A jadeite, B and B+C jadeite and B and B+C jadeite will fluoresce under a UV lamp after waxing, and how to identify these jadeites quickly and effectively is a problem that needs to be solved now. The common A, B, B+C jadeite and two kinds of optimized waxes in the current market were collected, and the optimized waxes were used to wax this jadeite, and the samples before and after waxing were systematically compared and studied by using a UV fluorescent lamp, and infrared spectrometer and fluorescence spectrometer. The results showed that the A jadeite did not fluoresce under UV fluorescent light, the B and B+C jadeite had weak-medium fluorescence, and all the jadeites emitted similar fluorescence after waxing. Infrared spectroscopy direct transmission method showed that the absorption peaks of 3 064, 3 032, 3 003 cm-1 were generated by the stretching vibration of the functional group ν(C—H) on the aromatic ring of the aromatic compounds in the functional group area of B and B+C jadeite; the high-quality waxes were generated by the stretching vibration of the functional group ν(—CH2—) of the alkane compounds absorption peaks at 2 915, 2 846 cm-1 and possibly 1 681 cm-1 due to the stretching vibration of the functional group ν(CO),respectively. In contrast, only absorption peaks from methyl and methylene groups were detected in Chuan wax. The fluorescence spectra showed no fluorescence response for A jadeite and fluorescence response for B and B+C jadeite. The high similarity of the profiles of some B+C jadeite and B jadeite indicates that the filling emits the fluorescence, and the ions in the dyes cause the fluorescence peaks to be shifted and the fluorescence intensity to change. According to the B+C, jadeite fluorescence peak position can be divided into three categories, excitation wavelengths were 350, 358, 370 nm, and emission wavelengths were 370, 420, 414/434 nm. Two optimized wax fluorescence peak is different, and the quality of wax fluorescence intensity is higher than Chuan wax. After waxing, the fluorescence peak of A jadeite will appear. The fluorescence intensity is related to the concentration of wax on the surface of jadeite, B and B+C jadeite will also appear after waxing, the fluorescence peak of optimized wax, but because the concentration of optimized wax is low, the strongest fluorescence peak is issued by the filling of jadeite and compared to the wax before, the fluorescence peak has redshift. Fluorescence spectroscopy can be used as a fast and non-destructive detection means to distinguish A, B and B+C jadeite before and after waxing, and the A jadeite and B or B+C jadeite after waxing can also be effectively distinguished, perfecting the basis on which fluorescence spectroscopy can be widely used in the jadeite market.

Caveolin-1 (CAV-1) plays a key role in developing cardiovascular diseases such as atherosclerosis. The interaction between quercetin and CAV-1 was studied by multispectral, homology modeling, molecular docking simulation and bio-layer interferometry (BLI) in the simulated physiological environment and different temperatures. The fluorescence quenching data showed that the Kq value (the quenching rate constant) were all much larger than 2.0×1010 L·mol-1·s-1, and the fluorescence quenching constant (KSV) decreased with the increase of temperature, which proves that the quenching process of the interaction between quercetin and CAV-1 is static quenching. Furthermore, the thermodynamic parameters, enthalpy change ΔH＜0, entropy change ΔS＜0 and ΔG＜0 indicated that the bonding process is spontaneous and enthalpy driven, indicating that the main types of interaction are van der Waals force and hydrogen bonding. Through the synchronous fluorescence and three-dimensional fluorescence spectrums analysis of the interaction between quercetin and CAV-1, it was found that the fluorescence intensity of CAV-1 was progressively decreased upon the addition of quercetin, indicating that quercetin interacted with CAV-1. Further analysis showed that quercetin caused the redshift of the maximum emission wavelength of the aromatic amino acid residues in CAV-1, enhanced the polarity of the microenvironment around the CAV-1, enhanced its hydrophilicity, indicating that the addition of quercetin changed the protein conformation of CAV-1. The UV-Vis absorption spectrum showed that a ground-state complex was formed between CAV-1 and quercetin, which further confirmed the static quenching mechanism between CAV-1 and quercetin. The X-ray crystal structure template of CAV-1 was constructed using homology modeling. The molecular docking simulation results showed that the binding force of quercetin and CAV-1 was -7.372 kcal·mol-1. The docking results showed that quercetin could bind to the active pocket composed of amino acids such as Glu20, ASP70, VAL16 and ARG19. There were the van der Waals forces between quercetin and residues GLN21, VAL16 and ARG19 of CAV-1, and hydrogen bonds between quercetin and GLU20 and ASP70. Various forces affected the micro-environmental changes of CAV-1 and led to its fluorescence quenching, which is a key factor involved in the formation of the complex. Finally, the binding of quercetin and CAV-1 was quantitatively studied by the BLI technique. The results showed that quercetin had a good binding activity with CAV-1 with an equilibrium constant (KD) value of 2.50×10-5 mol·L-1. The response signal value increased with quercetin concentration, evidencing the specific binding between CAV-1 and quercetin. This research was helpful in understanding the mechanism of interaction between quercetin and CAV-1 and provide references for research on the therapeutic targets of quercetin in atherosclerosis.

Halloysitum Album is one of the most commonly used mineral drugs. It is often found fake or mixed in the market, which is related to the unclear identification of mineralogy. The purpose of this paper is to observe the characters and identify them by mineralogy. The mineralogical and spectral characteristics of Halloysitum Albumfrom different places on the market were compared using scanning electron microscopy (SEM), X-ray diffraction (XRD), semi-quantitative trace element spectroscopy, differential thermal analysisand infrared spectroscopy(IR). The results are as follows: (1) Halloysitum Album is white, with earthy luster, small hardness, water-absorbing and sticky tongue, and slightly earthy and fishy. The main minerals are kaolinite, illite and quartz. The main impurity minerals are feldspar, calcite and limonite. (2) The SEM results showed that all white stone lipids had grid-like fiber structure, and the main components were O, Si and Al. (3) The results of XRD showed that the main diffraction lines of the samples are 7.44(3), 3.53(4), the main composition is kaolinite, the diffraction lines are 10.45(5), 4.51(3) and illite, and the quartz diffraction lines are 5.04(3), 4.51(3), 3.06(10), secondary diffraction lines show the presence of and small amounts of feldspar, calcite and limonite. (4) The micronutrient spectrum Quantitative analysis showed that Ca, Mg, K, Na and Cr were the main components in the white stone lipids. (5) The differential thermal analysis (DTA) showed an obvious heat absorption effect of kaolinite and illite. (6) The results ofIR showed that the high frequency bands of 3 700～3 000 cm-1 have sharp and strong absorption peaks near 3 440.98, 3 621.18 and 3 620.45 cm-1, respectively. All the samples can be divided into 1 200～1 000, 950～900 and 800～600 cm-1 bands. The bands of the three samples are the same, and the main mineral composition is kaolinite. To sum up, the quality of commercially available Halloysitum Album can be judged effectively using properties, mineralogy, SEM, IR, XRD and differential thermal analysis.

Accurate prediction of soil organic matter content is helpful in evaluating farmland fertility and provide a data for precision agriculture. In order to solve the problems of low accuracy and weak Generalization ability of a single model for rapid estimation of organic matter content in farmland surface soil. The surface soil of typical cinnamon farmland in Shanxi Province was studied,a Stacked Generalization Model (SGM) was proposed based on VIS-NIR hyperspectral data for predicting organic matter content. Firstly, the original hyperspectral data are smoothed by wavelet, and the reciprocal derivative and logarithmic reciprocal derivative transform are performed on the smoothed data. The feature bands are extracted by correlation coefficient and recursive feature elimination method. At the same time, Ensemble learning Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting are introduced in machine learning (XGBoost), and Adaboost are used to predict organic matter content through 5-fold cross-validation. Based on the prediction results of the primary learner, Stochastic gradient Descent (SGD) is used as a meta-learner to establish the SGM stack generalization model. The limitation of low accuracy and instability of a single model is broken through to realize the rapid and stable detection of organic matter content. The results show a good correlation between the spectral information and organic matter content after the penultimate differential transformation, and the maximum correlation is -0.611. Compared with the single model, the decision coefficient (R2) and relative analysis error (RPD) of the stacked generalization prediction model are 0.819 and 2.256, respectively, which are 0.055 and 0.323 higher than the average decision coefficient (R2) and relative analysis error (RPD) of other algorithms, respectively. The mean absolute error (MAE) and root mean square error (RMSE) are 1.742 and 2.308 g·kg-1, respectively, which are 0.406 and 0.389 g·kg-1lower than those of other algorithms. The optimization effect is obvious. It can be used to estimate organic matter content in farmland soil surfaces effectively. The results can provide a basis and reference for the rapid detection of organic matter content in farmland soil surface by hyperspectral method.

Researchers have been committed to transforming low-grade biomass resources, such as corn stalks, into high-value chemicals to improve their utilization value. Therefore, it is very significant to carry out the liquefaction of corn stalks in the presence of polyhydric alcohols with an acid catalyst at atmospheric pressure and study the main components, pyrolysis and fiber properties of the liquefaction residue. In this paper, the chemical groups, pyrolysis property, thermogravimetric loss, crystal structure, and microstructure of corn stalk and its liquefaction residue were analyzed by Fourier transform infrared spectroscopy (FTIR), pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), thermogravimetric analysis (TGA), X-rays diffraction (XRD), and scanning electron microscopy (SEM). FTIR analysis results showed that the characteristic absorption peaks of three components (cellulose, hemicellulose and lignin) in liquefaction residue almost disappeared. It was mainly large molecules produced by interactions of some small molecules produced by the degradation of three components and incompletely degraded cellulose. Py-GC/MS showed that 89 kinds of organic compounds could be identified in pyrolysis products of liquefaction residue, including furans (10.64%), phenols (18.89%), ketones (3.73%), hydrocarbons (35.23%), alcohols (4.17%), aldehydes (4.31%), ethers (1.25%), organic acids (4.79%) and heteroatom-containing compounds (17.00%). The carbon number of these organic compounds was higher than that of similar compounds in corn stalks. TGA analyzed the thermal weight loss of liquefaction residue. In the heating phase, the rapid weightlessness phase and the slow weightlessness phase, the mass loss was about 3%, 45%, and 4%, respectively. Its pyrolysis conditions were more severe than those of corn stalks. Results from XRD revealed that the main and secondary peaks of liquefaction residue disappeared, destroyed the cellulose I latticed and formed an amorphous structure. In addition, from SEM characterization results, the liquefaction residue exhibited a disorganized, rough, irregular, and granulated morphology. In conclusion, the corn stalk’s fibrous structure was destroyed and liquefied under this liquefaction condition. The theoretical foundation and technical support could be provided for preparing wood-based carbon materials from liquefaction residue and then promoted the high value-added utilization of biomass resources.

At present, remote sensing forest fire monitoring mainly focuses on the accuracy of fire point detection by polar-orbiting satellites. At the same time, there is less research on remote sensing monitoring and identification of fire points, smoke characteristics and other comprehensive fire information based on multi-source remote sensing images. The forest fire of May 9, 2020, in Anning City, Yunnan Province, was studied based on the Gaofen-6 wide-field (GF-6 WFV) data and the FY-3D polar-orbiting meteorological satellite medium-resolution spectrometer (FY-3D MERSI) data for smoke, burned areas extraction and fire point identification. Firstly, Based on GF-6 WFV data, six spectral feature indices were selected to identify fire smoke and fire trails by maximum likelihood, support vector machine, and random forest classification methods, and evaluated for accuracy. Then, Based on the 1 km mid-infrared channel data of FY-3D MERSI, the potential fire point identification algorithm is improved, and the basic principles of FY-3C VIRR and MODIS fire point detection are combined with dynamic threshold and context detection method to identify fire points. Then the identification results are optimized by combining the far-infrared channel with 250 m resolution. Finally, the information on smoke, fire points and fire trails extracted from the two kinds of data were combined to explore and analyze the monitoring capability of GF-6 WFV and FY-3D MERSI for forest fires. The results show that the smoke and burned areas can be effectively identified by five feature indices and eight bands of GF-6 WVF data, and the random forest classification is the most effective among the three classification methods, with an overall classification accuracy and Kappa coefficient of 97.20% and 0.955. The improved fire point recognition algorithm for FY-3D MERSI data can effectively improve the recognition accuracy of fire points. Combining the mid-infrared -and far-infrared channels to detect fires can improve the fire detection accuracy from kilometer to 100 meter level. The combined GF-6 and FY-3D MERSI data can effectively extract smoke, burned areas and fire point information from the fire site, and the use of multi-source data can carry out forest fire monitoring and early warning in multiple directions, which is of great significance to improve the capacity of satellite remote sensing forest fire monitoring.

With the rapid development of power systems towards high voltage, large capacity and application sininformation technology, the efficient operation and maintenance of power equipment is of great significance to ensure the safe operation of power systems and steady economic growth. Detecting sulfur hexafluoride gas decomposition products is an effective method for leak detection and fault diagnosis in electrical insulation equipment. Dual-comb spectroscopy, derived from optical frequency combs, has the advantages of high resolution, high precision, wide spectrum and high speed, and is expected to provide a reliable method for quantitative analysis of characteristic gases in leakage troubleshooting of power equipment. In this paper, a dual-comb spectroscopy detection equipment was built by using two integrated erbium-doped fiber optical combs. With precise frequency control and fine temperature control, the frequency fluctuation of the repetition rate was decreased from 18.37 Hz to 607.72 μHz and the stability of the comb teeth was improved to 10-12. The long-term stability and integration enabled the combs strong immunity to environmental disturbance and the combs maintained high coherence within more than 2 hours in outdoor operation as the repetition rate and the carrier envelope phase offset signals of the combs kept phase-locked. In terms of spectral detection, the mixture of CO and CO2 was measured with the help of an ultra-sensitive multi-pass cell and as a result, the absorption peaks of CO and CO2 within the 1 540～1 590 nm band were simultaneously measured with 1 pm spectral resolution on the ms time scale. Taking the characteristic absorption peaks of CO at 1 585.47, 1 581.946 nm, and CO2 at 1 580.5, 1 579.575 nm, for example, the molecular densities of CO and CO2 could be easily derived from Lorentzian fitting, and the uncertainties were reduced to 0.32% and 0.24%, respectively, using multi-peaks fitting, which were nearly one order of magnitude lower than that of single-peak measurement (2%). Our research promotes the application of dual-comb spectroscopy and the related system in non-contact real-time detection of characteristic gases in power equipment. Compared with the traditional contact detection technology, which has the shortcomings of single gas detection, long integration time, and difficulty in long-term online real-time monitoring, the dual-comb spectroscopy is advantaged in simultaneously multi-peaks detection of diverse gases in ms order of time, which can shorten detection time and improve the accuracy. In a word, dual-comb spectroscopy provides an effective method for timely troubleshooting and fault diagnosis for leak detection in power equipment.

To study the impulse coupling mechanism of a pulsed laser ablation aluminum target, the direct measurement of its macroscopic impulse coupling characteristics is one of the means. However, laser ablation involves many physical processes. Therefore, analysing the impulse formation mechanism only by studying its macroscopic mechanical properties is difficult. Plasma plume ejection formed by pulsed laser ablation is an important process to induce the mechanical effect. Hence, based on studying the macroscopic mechanical properties, this paper deeply analyzes the impulse coupling mechanism of pulsed laser ablation by measuring the plasma plume and emission spectrum characteristics. In this paper, a single pulse laser with a wavelength of 1 064 nm is used to ablate aluminum targets. By constructing a fast photogrammetry system and optical emission spectroscopy measurement system, the plasma plume image, the plasma spectral image, and the plasma emission spectrum generated by laser oblique incident ablation of the aluminum target were obtained. Based on optical emission spectroscopy of the plasma plume, the Boltzmann plotting method and Stark broadening method were used to study the variation of plasma temperature and electron number density with the laser fluence at different incidence angles of a pulsed laser, respectively. Moreover, a torsion pendulum system was built to study the trend of the impulse coupling coefficient with the laser fluence along the direction of laser incident at various incident angles. The study follows the research ideas from the plume microscale evolution process to impulse macro mechanical properties analysis. The experimental results show that the luminescence intensity of the plasma plume strengthens with the laser fluence, accompanied by the rise of plume ionization degree. Moreover, the plasma temperature and electron number density increase rapidly, resulting in the impulse coupling coefficient heightening rapidly. When the laser fluence is greater than 15 J·cm-2, the plasma temperature and the electron number density are gradually saturated due to the plasma shielding effect. The change of plasma temperature and electron number density results in the decrease of impulse coupling coefficient with increased laser fluence. In addition, the plasma temperature and the electron number density decrease with the increase of incident angle, which reduces the impulse coupling coefficient. The results show that the coupling mechanism of the ablation impulse can be well analyzed using fast photography and optical emission spectroscopy. The results can provide a reference for optimising key parameters for space applications such as laser space debris removal, space micro-thruster and despinning non-cooperative targets in space.

Landsat Collection 2 Level-2 Surface Temperature (LC2L2ST) was formally released in December 2020 by the U.S. Geological Survey (USGS). However, there are few reports on this new land surface temperature (LST) product. As this product will be the only LST data provided by the USGS starting in 2022, it is necessary to evaluate the product timely. Among various satellite LST products, the quality of the MODIS LST product is well recognized, and widely used. Therefore, this paper, for the first time, performed a cross-comparison between the new Landsat LST product and the MODIS LST product to examine the quality of the new product. Different regions in China (Fuzhou, Taihu, Yinchuan and Dunhuang) were selected as the test areas, and 20 pairs of LC2L2ST and MODIS LST synchronous images were used for the comparison. The images cover different land types, such as vegetation, water, town and deserts across different seasons. A total of 560 homogeneous regions of interest (ROI) were selected from the images of the test areas. The regression analysis was carried out to examine the fit of the ROIs and the quantitative relationship between the two LST products. The conversion model between them was also developed. The results showed that the new LC2L2ST product is highly correlated with the MODIS LST product. Each of the four test areas can achieve a coefficient of determination (R2) greater than 0.98. Integrating the 560 samples from the four test areas also obtain an R2 close to 0.98. Nevertheless, differences between the two products have also been founded. The LC2L2ST is 0.90℃ averagely higher than the MODIS LST (RMSE = 2.29 ℃). However, LC2L2ST can be slightly lower than MODIS LST in late fall and winter seasons but significantly higher than extremely hot summer seasons with a bias close to 7 ℃. The analysis revealed that the differences were related to spatial resolution, sensor viewing angles, land cover types and seasons. In general, the new LC2L2ST product strongly correlates with the MODIS LST, but significant differences were also observed in the summer months. Therefore, the new Landsat LST product must be further tested with in-situ measured LST data. Due to the differences in this paper, the two LST data products need to be converted when they must be collaboratively used. This study developed the conversion equation between the two LSTs based on the 560 ROIs. The verification found that the differences between the two data after conversion were greatly reduced. It is conducive to the cooperative use of the two LST data and providing continuous remote sensing data for long-term LST monitoring.

Water is a basic need for life and health. Human production and life are inseparable from water. Excessive nitrogen and phosphorus in the water body lead to excess nutrients, resulting in eutrophication of the water body, and then the deterioration of water quality has a wide-ranging impact. The application of hyperspectral remote sensing in the inland water quality monitoring field is becoming more and more extensive. Based on this research, to reduce the influence of outdoor water body-specific factors, this study builds an experimental laboratory system by simulating external conditions in the laboratory. According to national emission standards, this study prepares 40 sodium phosphate standard solutions with different concentration gradients in the concentration range of 0～2.5 mg·L-1 and 40 different concentration gradient ammonium chloride standard solutions in the concentration range of 0～20 mg·L-1. After obtaining hyperspectral images of all standard solutions, The spectral responses of water quality parameters total phosphorus and total nitrogen were analyzed. This study finds the sensitive bands corresponding to total phosphorus and total nitrogen at around 420, 720 nm and around 410 nm. Building a hyperspectral water quality inversion dataset using Principal Component Analysis (PCA). By preprocessing hyperspectral image radiometric calibration, Savitzky-Golay filtering (SG filtering), and using the BP artificial neural network method to construct a laboratory hyperspectral water quality inversion model. The coefficient of determination of the constructed laboratory hyperspectral total phosphorus inversion model is 0.980 2, and the determination coefficient of the laboratory hyperspectral total nitrogen inversion model is 0.860 2. Taking an indoor river in Yixing, Jiangsu as the research object. The model is applied to the outdoor hyperspectral image data obtained by the outdoor UAV equipped with the hyperspectral imaging system. The inversion accuracies of the mean concentrations of total phosphorus and total nitrogen are 95.00% and 93.52%, respectively. The outdoor hyperspectral water quality inversion model constructed by using the traditional method to directly draw water from the observation points of the river to be tested the average values of total phosphorus and total nitrogen concentrations obtained at the same five points with an inversion accuracy of 86.87% and 86.48%. This study compares the inversion results of the two groups. It is found that the inversion accuracy of 90% of the spectral inversion results obtained by the laboratory hyperspectral water quality inversion model constructed in this study is slightly higher than that of the outdoor water quality inversion model. It is confirmed that this study can effectively predict the content of total phosphorus and total nitrogen in the river to be measured, and can also provide certain technical support for the hyperspectral remote sensing inversion of total phosphorus and total nitrogen in the water.

Grassland green biomass is an important index for monitoring the grassland ecosystem, and it is of great significance to estimate green biomass efficiently and accurately. Remote sensing technology has been widely used in biomass estimation due to its convenience and low cost advantages. However, traditional optical remote sensing technology is susceptible to the cloud and climatic conditions and unsuitable for high-density vegetation areas. Therefore, Synthetic Aperture Radar (SAR) technology, which is less affected by the external environment and has certain penetration, has been promoted in biomass estimation. However, the current SAR technology is mostly used to estimate forest biomass and crop biomass, and there are few studies on estimating grassland green biomass. Therefore, the Inner Mongolia grassland was selected as the research area, and 11 radar indices, including backscattering coefficient, texture characteristics and polarization decomposition, were extracted from Sentinel-1A SLC images. Two radar vegetation indices (σ１ and σ′１) were introduced based on the existing radar vegetation indices (σ0 and σ′0). Based on the measured data of grassland green biomass, 15 radar indices were modeled and analyzed respectively. The results showed that the mean value and the backscattering coefficient σＶＨ in the texture feature were the best radar indices for estimating grassland green biomass, and their estimation models R2 were 0.54 and 0.60, respectively. RMSE were 47.3 and 44.3 g·m-2, respectively. In addition, radar vegetation indices σ0 and σ１ can also be used to estimate green biomass of grassland with high accuracy, with R2 of 0.53 and 0.42, RMSE of 47.6 and 53.0 g·m-2, respectively. This study proved that SAR technology has strong application potential in high-efficiency and high-precision estimation of grassland green biomass, but it still needs improvement in error elimination.

Pyrolysis extraction was one of the effective ways of high value-added utilization of low-rank coal. It was significant to clarify the pyrolysis characteristics of a thermal extraction for clean and efficient conversion of low-rank coal. The relationship between pyrolysis activation energy and the molecular structure of escaped gas in Lignite extract (CPW) has been researched by the technology of a thermogravimetric analyzer coupled with Fourier transform infrared spectrometer (TG-FTIR) and the method of peak fitting. In this work, the CPW has been used as the raw material, and the non-isothermal kinetics of CPW was studied by the TG method. Under the condition of equal conversion rate without considering the reaction mechanism, the pyrolytic activation energy (Ea) of CPW was calculated and analyzed using the Flynn-Wall-Ozawa method and Kissinger-Akahira-Sunose method. The results indicated: that the pyrolytic activation energies of CPW ranged from 94.04 to 177.40 kJ·mol-1 when the conversion rate (α) was between 0.2 and 0.8, and the average value of activation energy was 130.01 kJ·mol-1. Moreover, with the increase conversion rate, activation energy also increased. The Peak Fit software was used to perform peak fitting of the infrared spectra of CPW in four regions of 700~900, 1 100~1 800, 2 800~3 000 and 3 000~3 100 cm-1 to achieve the fine structure information about various functional groups. In addition, six molecular structure parameters (I1~I6) were introduced to characterize the relationship between molecular structure and pyrolysis activation energy of CPW when the conversion rate was 0.20≤α≤0.80. The results showed that: It is closely related to molecular structure and pyrolysis activation energy of CPW during the different reaction stages. It showed a good linear relationship between the pyrolysis activation energy and molecular structure parameters, including the degree of branching (I1), aromaticity index (I3), degree of substitute (I6), and the linearity R2 is 0.903 4, 0.744 7 and 0.803 1, respectively. The six molecular structure parameters and pyrolysis activation energy (Ea) were analyzed by linear regression at the same conversion rate. The fitting relationship model between the Ea of CPW and the molecular structure indexes was shown as follows: Ea=124.91-88.75I1-318.84I2-19.19I3+40.29I4-14.28I5+1 272.33I6 (R2 up to 0.999 9). Based on the TG-FTIR experiment, the molecular structure parameters and pyrolysis activation energy of CPW were analyzed, which helped to clarify the pyrolysis process and thermal conversion behavior of CPW. They provided the certain theoretical basis for the high value-added utilization of lignite.

In order to realize the rapid monitoring of quality change during tomato storage, 300 tomato hyperspectral images with different storage data were collected by hyperspectral imaging technology (HSI), and a Mahalanobis distance monitoring model for quality change during tomato storage was constructed based on extracting feature wavelengths from the defined effective wavelength bands. Then the quality change monitoring during tomato storage was realized. Firstly, the hyperspectral raw data is preprocessed using multiplicative scatter correction (MSC) combined with Savizky-Golay convolutional smoothing (SG) to eliminate the effects of baseline drift and noisy signals and so on. Secondly, based on the variation trend of the spectral curve in different bands and combined with the weight coefficient of principal components corresponding to the minimum value of Wilks Λ statistic in the whole band, the effective band that can highlight the quality change in the tomato storage process is defined. Thirdly, three feature wavelength screening methods, namely, successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and principal component analysis based on Wilks Λ statistics, are used to extract feature wavelengths in the full band and effective band, respectively; By comparing and analyzing the number of feature wavelengths extracted by the three methods, it is pointed out that the fusion principal component analysis based on Wilks Λ statistics can effectively reduce the data dimension and simplify the operation process. Then, the principal components screened in the full band, and effective band based on Wilks Λ statistics coupled with principal component analysis are analyzed. It is pointed out that the extraction of feature wavelength based on Wilks Λ statistics fusion principal component analysis in the effective band can avoid the masking effect of redundant information on effective information effectively and further reduce the data dimension. Finally, the advantages and disadvantages of the Mahalanobis distance monitoring model for quality change during tomato storage constructed based on the first storage day and the critical day of tomato spoilage are analyzed, and it is pointed out that the model constructed by the first storage day as the monitoring benchmark has higher effectiveness and reliability. The results show that the number of feature wavelengths extracted based on Wilks Λ statistic combined with principal component analysis under the effective band is the least (5 feature wavelengths), and the selected principal components can effectively represent the difference between the quality change during tomato storage. At the same time, it also provides an effective feature wavelength extraction method for monitoring the quality changes during tomato storage by hyperspectral imaging technology.

The essence of measuring water quality COD by UV-vis absorption spectrometry is to model a large number of spectral data, and then introduce the measured spectral data to predict the process. However, there are two characteristic absorption peaks in the measured COD standard solution of potassium hydrogen phthalate at 200～300 nm, and the peak and peak values of the standard solution are also different at different concentrations. This feature is used to select the characteristic wavelength of this band and use it to characterize the spectral information, which reduces the data redundancy and improves the prediction accuracy. Because the measured water quality spectral signal is easily disturbed by the internal and external interference, resulting in a large number of non-stationary noise in the spectral data, and the characteristic absorption peak and its adjacent signal frequency is high, conventional denoising algorithms directly abandon high-frequency signals and can not accurately judge the limits of signal-to-noise components, resulting in the lack of effective signals. A joint denoising algorithm based on fully adaptive noise set empirical mode decomposition CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) and dual-tree complex wavelet transform DT-CWT (The Dual-Tree Complex Wavelet Transform) is proposed. The algorithm uses CEEMDAN to decompose the signal into intrinsic mode function IMF (Intrinsic Mode Function). It makes linear correlation analysis through normalized autocorrelation function and cross-correlation number to determine the boundary between high-frequency noise components and low-frequency signal components. Then the DT-CWT threshold denoising algorithm is used to process the noisy high-frequency IMF component, and the IMF high-frequency component after DT-CWT processing is reconstructed from the IMF low-frequency component demarcated by CEEMDAN, and the final denoised signal is obtained. The experimental results show that the denoising algorithm based on CEEMDAN combined with dual-tree complex wavelet transform is suitable for data processing of UV-Vis spectrum water quality detection. For potassium hydrogen phthalate solution whose chemical oxygen demand (COD) standard solution is 100 mg·L-1, the denoising effect of SNR=24.201 5 dB, RMSE=0.024 0, NCC=0.999 4 and PSNR=37.573 6 denoised by the combined algorithm is significantly better than that of CEEMDAN and double-tree complex wavelet threshold algorithm. Moreover, it effectively retains the characteristic absorption peak of the original COD standard solution, suppresses the translation sensitivity and improves the smoothness of the reconstructed signal. The quality of the reconstructed signal is improved. It provides a new data pre-processing method for detecting water quality COD by UV-Vis spectrum.

Urban air pollutants in China mainly include nitrogen oxides,ozone,sulfur dioxide and particulate matter. NO2 and SO2 are common trace gases in atmospheric pollutants, which directly or indirectly impact ground air radiation,global climate,air quality and human health.Huaibei region is the production base of basic energy and important raw material coal in China. Local atmospheric pollution has become more complex due to long-term coal production. It is one of the research hot pots to acquire atmospheric pollutant concentration quickly. Differential optical absorption spectrometer(DOAS) is an optical remote sensing spectral equipment,which has the advantages of stability, and high sensitivity and is not restricted by building platform. It can obtain the concentration information of a variety of polluting gases at the same time. Because of the complex environment pollution in Huaibei, this paper constructed a mobile mini differential optical absorption spectroscopy(DOAS) system based on the mobile platform, which includes a spectrum acquisition system, temperature control system and GPS positioning system. The GPS positioning system was used to record the longitude, latitude and speed during the movement. The spectrometer was placed in the constant temperature system to ensure the accuracy of the system measurement. During the experiment, the performance of the system is tested, and the navigation observation route is planned first. The mobile DOAS measurement results are compared with the MAX-DOAS to verify the accuracy of the system to realize the rapid, convenient and accurate monitoring of typical atmospheric pollutants in Huaibei. During the measurement period, QDOAS software was used for inversion processing of the originally measured spectra and the relatively clean is selected as the reference spectrum to obtain the spatial distribution of NO2 and SO2 column concentration in the Huaibei region. The range of NO2 concentration is 0.509×1016～15.4×1016 molecule·cm-2, and that of SO2 is 0.353×1016～9.07×1016 molecule·cm-2. The results of mobile Mini-DOAS measurements were compared with MAX-DOAS measurements and TROPOMI data, which showed good consistency (correlation coefficient R2>0.75). Field experiments show that the mobile Mini-DOAS system can accurately obtain the distribution of urban pollution gas column, providing an effective technical means for confirming the source area of urban atmospheric pollution and verifying satellite remote sensing data.

Compared with the complicated enrichment procedure, a rapid, simple and reliable method is urgently needed to determine trace metal elements in drinking water. Total reflection X-ray Fluorescence spectrometry (TXRF) is a convenient and quantitative method for simultaneous analysis of trace multi-metal elements requiring less samples and short measured time and can be analysed directly without samples pretreatment. In this paper, Ga was used as the internal standard. The feasibility of rapid determination of multi-mass concentration gradient and the multi-element metal solution was explored by direct injection-TXRF method and then applied to the low mineral drinking water for the trace metal elements analysis. The experimental results showed that Al, K, Ca, Mn, Fe, Co, Ni, Cu, Zn and Sr can be analyzed immediately. However, the experiment results found that Al, K and Ca, as light elements, are difficult to achieve accurate quantification due to the recovery rate deviating from the standard value, the reason for the high matrix effect and low elements sensitivity. In contrast, other elements achieve the quantitative requirements. It was found that Mn, Fe, Co, Ni, Cu, Zn and Sr showed good accuracy and precision when the concentrations of metal elements were 40 mg·L-1, 4 mg·L-1, 0.4 mg·L-1 and 40 μg·L-1, respectively. The Recovery Rate (RR) was 80%～112%. The relative Standard Deviation (RSD) was 3.6%～10.5%, and the Detection Limit (DL) was 0.001～0.07 mg·L-1. With the decrease in the concentration gradient, the accuracy and precision appeared to have different degrees of decline. When the mass concentration was at the lowest level of 4 μg·L-1 in this experiment, the RR and RSD of most elements (except Mn) significantly deviated from the standard value. This paper used the direct injection-TXRF method to test the RR of drinking mineral water at low, medium and high levels. The results showed that Mn, Fe, Co, Ni, Cu, Zn and Sr in the samples were basically at the concentration of μg·L-1 levels, the average RR ranged from 90% to 110%, and the average RSD was less than 12%, which met the qualification of micro estimation. In summary, Multi-element test results showed that TXRF is more suitable for heavy elements (Z>20) in selecting elements. Water samples with more than ten components of μg·L-1 level can directly achieve rapid and accurate quantitative analysis without complex pretreatment for enrichment. Preconcentration techniques are needed to improve the accuracy of ultra-trace level samples in the environment.