Substantial relics are vital pieces of evidence for people to reconstruct historical details. By studying them, we can know ancient people’s lifestyles,production and the relationship with nature. X-ray spectroscopy (X-ray fluorescence spectrum and X-ray absorption fine structure spectrum) is an important non-destructive analysis method. It can be used to acquire some information such as elemental composition and structure of a complex containing specific ions to provide support for conservation and value perception of relics. Nowadays, these methods are widely used in a variety of substantial relics. This article briefly summarizes and introduces recent applications of X-ray spectroscopy in relic research. In general, X-ray fluorescence spectroscopy plays an important role in the provenance study of ancient ceramics, oil paintings and bronzes. Constructing database based on elemental composition acquired from the X-ray fluorescence spectrum and the effective use of X-ray absorption fine structure spectrum relics the study are the main difficulties andkey topics in the future application research.

The unknown emissivity of materials is a big obstacle to radiation true temperature measurement, which leads to the fact that the true temperature of materials cannot be obtained by a single group of measurement data. People can only calculate the non-true temperature, such as brightness temperature, by assuming the emissivity model of materials. Based on this background, Gardner J and other scientists put forward multispectral thermometry and constantly improve its theory. Nowadays, multispectral thermometry is widely used in high-temperature and ultra-high temperature measurement, high temperature target thermal performance measurement, true temperature dynamic measurement, etc. In 2005, Sun Xiaogang put forward the second measurement method. The secondary measurement method is a kind of multispectral true temperature inversion algorithm, which solves the problem of inversion of true temperature and material emissivity under each wavelength by the iterative operation between two groups of measurement data. It ensures the accuracy of the emissivity and true temperature results under each wavelength by building quantities of emissivity models. However, it needs to build a large number of emissivity models in the mathematical operation and software operation. By matching all emissivity models, the best solution of true temperature is obtained, which not only consumes a lot of time but also takes up a lot of software resources. In this paper, a new fast inversion method of true multispectral temperature is proposed. This paper first reveals the inequality equations between radiational signals and emissivity and then adds the steps of optimizing the emissivity model library in the algorithm of the secondary measurement method. This measure can screen out the unreasonable models in the emissivity model library to reduce the scale of the emissivity model library, saving a lot of calculation time and software resources. This paper carries out the simulation experiment of wavelength in 0.400~1.100 μm, which contains six initial emissivity models.The results of the fast inversion method of true multispectral temperature and secondary measurement method are compared, and the results show that the fast inversion method of true multispectral temperature not only guarantees the inversion accuracy but also reduces calculation time compared with the second measurement method for the same target under the same initial temperature value and same emissivity search range. The fast inversion method of true multispectral temperature reduces 29%~64% emissivity model number and 26%~57% calculation time. After that, this paper carries out the actual experiment of wavelength in 0.574~0.914 μm. The results show that under the same conditions, the fast inversion method of true multispectral temperature can reduce the emissivity model number by 42%~48% and reduce the calculation time by 35%~49% compared with the second measurement method on the premise of ensuring accuracy. The above experiments show that the fast inversion method of true multispectral temperature is feasible, and it has important value for large-scale multispectral true temperature measurement technology and online multispectral true temperature measurement technology.

With the rapid development of China’s aquaculture and aquatic processing industries, aquatic products have become more and more important in the national diet structure, which make consumers have more requirements for the quality of aquatic products. In order to meet consumers’ need for the quality of aquatic products, enterprises and markets need to detect the quality of aquatic products at every links in the supply chain and make it public. Therefore, it urgently needs to develop a technology that can satisfy the fast non-destructive testing of aquatic products.Spectroscopic technology can infer the material properties and component content based on the spectral characteristics of the sample at the characteristic wavelength, which has huge application prospects in the detection of aquatic product freshness, hazardous substance residue, harmful microorganism, quality classification, adulteration analysis and so on.This review discusses and summarizes the advantages and limitations of several commonly used spectroscopic techniques in aquatic product quality testing. It is believed that compared with traditional laboratory testing methods, spectroscopic techniques have the advantages of fast, non-destructive, good test reproducibility, and high accuracy. These characteristics make it possible for online real-time detection of aquatic product quality, which can bring huge economic benefits. However, spectral detection also has the disadvantages of high initial investment, poor universality and need for continuous maintenance. Each spectrum technology also has its own scope and limitations.Therefore, this technology in the quality inspection of aquatic products needs further research and improvement. This review collates the existing relevant research literature at home and abroad, discussing and commentingon the commonly used spectral data preprocessing algorithms and prediction models in the detection process. Focusing on characteristics and current application status of four kinds of spectral preprocessing algorithms and several modeling methods. At present, the application of spectroscopic technology in aquatic product quality testing is mainly in the laboratory research stage, rather than been applied to commodity markets and consumer markets widely. Based on the above analysis, this paper prospects the future development of the application of spectroscopy technology in the quality inspection of aquatic products, think that building a unified, standard and efficient spectral detection model library, combined multiple indicators to correlation analysis, eliminate environmental interference during the spectrum acquisition process, and realizing online real-time detection of aquatic product quality is the future technology development trend.

The types and contents of mineral elements in medicinal plants are closely associated with their growth environment and pertinent development process. Meanwhile, they are also closely related to the product of medicinal substances and their clinical effects. Therefore, they are important indicators for the quality evaluation of Chinese medicinal materials, and it is worth exploring on the relationship of the characteristics regarding elements with the different environmental factors, such as climate, soil, hydrology and so on. In this study, the concentrations of 35 elements in Ca, K, Mg, Na, Fe, Sn, Be, As, Al, V, Sc, Cr, Mn, Co, Bi, Ga, Ni, La, Mo, Ag, Hg, Cu, Nb, Zn, Ge, Se, Tl, Cd, Sb, Ba, Y, Ti, Pb, Zr and Sr were determined by inductively coupled plasma-mass spectrometry and inductively coupled plasma-atomic emission spectrometry techniques, and analyzed using multivariate statistical methods including variance analysis, principal component analysis and factor analysis. The results showed that the method had good selectivity, accuracy, within-day precision, recovery and linearity in their established ranges, respectively. There was a significant difference for 33 elements out of 35 in A. argyi from different locations (p＞0.05). According to the results of PCA, 7 principal components with a cumulative contribution rate of 82.75% were extracted from 35 elements for further analysis. Also, the distribution of A. argyi samples from different locations were relatively concentrated and was feasible for classification and evaluation. The comprehensive evaluation function for the 7 selected elements was F=0.449 1F1＋0.118F2＋0.097 2F3＋0.055 5F4＋0.042 5F5+0.034 5F6+0.030 7F7. By calculating the scores of comprehensive factors, the total values of Qi ai (Qichun, Hubei province) and Bei ai (Anguo, Hebei province) are on top 2. The study established an accurate and efficient analytical method and comprehensive evaluation system for the mineral elements in A. argyi from different ecological areas, provide a scientific basis for quality control of beneficial supplements in A. argyi, and may be valuable for the similar studies for other medicinal species.

In this paper, Energy-dispersive X-ray Spectroscopy (ED-XRF) technology is used in conjunction with the operation chain analysis of the pottery process commonly used in archaeological circles to develop the production process and cultural heritage of the pottery remains unearthed from the Meishan site in Ruzhou, Henan province. The Meishan site is located in Liuzhuang Village, North of Ruzhou City, Henan Province (formerly Linru County). It is a representative site in the Central Plains for studying the Neolithic period to the civilized state stage. There are a large amount of pottery remains with typical archaeological cultural characteristics from the Wangwan Ⅲ culture (BC2300—BC1900) to the state form of Erlitou culture (BC1750—BC1500), which can provide a deeper understanding of major issues such as the process of social complexity and the origin of the state around 4 000 years ago. Therefore, the comprehensive multi-disciplinary study of the pottery unearthed at the site has extremely high academic value and cultural significance. This paper analyzes the chemical composition and physical properties of pottery samples of different cultural types unearthed from the Meishan site and combines the analysis results with the pottery operation chain analysis for comparative analysis. Aiming at the pottery remains of Meishan site, the results of spectrum analysis show that the archaeological cultures in different periods have little difference in the selection of pottery raw materials and have the same origin, suggesting that although the cultural types are different, their understanding and selection of clay are highly consistency. The results of the pottery operation chain analysis show that although the Neolithic Wangwan Phase Ⅲ culture was earlier, its pottery production process is more advanced than the late Erlitou culture, reflecting the continuous and swaying development in the cultural evolution of the Central Plains region. Pottery operation chain analysis can reflect the characteristics and inheritance of cultural content. After using the spectroscopic analysis to exclude the influence of raw materials and firing temperature, the information extracted by it is more persuasive and comparable. Comprehensive research shows that combining spectroscopy analysis and pottery operation chain analysis to carry out multi-disciplinary comparison and comprehensive research can better eliminate interference, obtain a more solid and objective understanding, and provide important research for archaeological cultural evolution.

To elucidate the effect of synergistic fertilizers amendment on the flurorescence characteristics and humification degree of fulvic acid in soil, field trials were conducted in Nenjiang county and Aihui district of Heihe city, respectively. Totally, there were five treatments: (1) balanced fertilization (NE); (2) 25% reduction in balanced fertilization (CK), (3) 25% reduction in balanced fertilization combined with nano-carbon synergist (T1), (4) 25% reduction in balanced fertilization combined with zeolite synergist (T2); (5) 25% reduction in balanced fertilization combined with biochar synergist (T3). Three-dimensional fluorescence region interal (FRI) method was used to determine the change of relative Fmax content among treatments. The application of synergistic fertilizers greatly improved the soil humification degree and exhibited the following pattern: T2>T1>T3>NE>CK. T2 treatment showed the highest soil humification degree and nutrient-supply capability. Especially, the ratio of relative content of the visible fluorescent FA region Ⅴ and ultraviolet fluorescent FA region Ⅲ (PⅤ, ｎ/PⅢ, ｎ) in T2 treatment was improved by 5.81% in Nenjiang county, and was improved by 4.65% in Aihui District as compared with CK. Soil FA was divided into C1 and C2 component using the parallel factor analysis. The Fmax ratio of C2 to C1 component in T2 treatment was improved by 22.09% in Nenjiang County, while was improved by 20.93% in Aihui District as compared with CK. The C2 component exhibited more complex structure and higher condensation than C1 component. The application of synergistic fertilizers improved the proportion of C2 component in FA and exhibited the following pattern: NE＞T2＞T1＞T3＞CK, indicating enhanced soil nutrient-supply capabilities by synergistic fertilizers. Overall, this study indicated that the T2 treatment significantly enhanced soil humification degree as compared with CK, and its positive effect on soil FA was superior to nano-carbon and biochar synergists. Therefore, the application of zeolite synergist would effectively improve soil nutrient-supply capability and ameliorate soil ecological conditions.

The rapid detection for danofloxacin mesylate (DFM) and ofloxacin (OFL) residues in chicken were achieved through synchronous fluorescence technology coupled with chemometric methods. First of all, the synchronous fluorescence spectra of DFM standard solution, OFL standard solution, chicken extract without antibiotics and chicken extract containing DFM and OFL were analyzed, and the wavelength difference (Δλ) of DFM and OFL were respectively determined as 130 and 200 nm, and the fluorescence excitation peaks of DFM and OFL were respectively determined as 288 and 325 nm for the detection of DFM and OFL in chicken, respectively. Subsequently, the effects of the concentrations of sodium hydroxide solution and the type of surfactant on the fluorescence intensities were investigated through the single factor test. The best detection conditions of DFM and OFL residues in chicken were as follows: the concentration of sodium hydroxide solution of 0.1 mol·L-1, and the concentration of SDS solution of 0.1 mol·L-1. Finally, the prediction models of DFM and OFL residues in chicken were established using linear regression, partial least squares regression (PLSR), and multiple linear regression (MLR), respectively. The experimental results showed that the comprehensive evaluation of DFM residues’ prediction model of based on the PLSR algorithm was best among these algorithms. The coefficient of determination for the prediction set (R2P) and the root mean square error for the prediction set (RMSEP) were 0.978 3 and 1.934 2 mg·kg-1. The ratio of prediction to deviation (RPD) was 5.876 5. The comprehensive evaluation of the prediction model of OFL residues based on the MLR algorithm was best among these algorithms. The R2P, RMSEP, and RPD were 0.895 0, 3.859 8 mg·kg-1, and 2.509 1, respectively. The adopted method was simple and fast, and could to realize the rapid detection of DFM and OFL residues in chicken.

Terahertz (THz) waves play an important role in material detection and is a potential biochemical sensor. However, the traditional terahertz time-domain spectroscopy (THz-TDS) system is complex in structure, low in integration and large in space. Therefore, guiding THz wave effectively, realising integrated transmission, and getting high-quality spectroscopy has become a research hotspot of the terahertz spectroscopy system. THz system on chip integrates the generation, transmission and detection of THz on the same chip, and then obtains THz time-domain spectroscopy by coherent detection. It can be used to detect many kinds of samples, especially in detecting trace samples thatare difficult to sample. It does notneed optical alignment, is easy to operate and has a high yield. The two research works in this paper are based on low-temperature GaAs (LT-GaAs) epitaxial wafers. Firstly, a 200 μm diameter copper wire is fixed on the top of the LT-GaAs epitaxial wafer, and the antenna electrode is prepared by vacuum evaporation. At the same time, the antenna gap is obtained, and the THz antenna based on the LT-GaAs epitaxial wafer is developed. The high-quality THz signal is obtained by using the femtosecond laser with a wavelength of 800 nm, which verifies the practicability of the antenna. Then the transmission line and microelectrode are fabricated on another epitaxial wafer by lithography, and the integrated THz system on chip is obtained. A femtosecond laser with a wavelength of 1 550 nm is used to excite the terahertz generation antenna and the system’s detection antenna on chip. The THz waves generated by the antenna propagate on the transmission line, and the high-quality THz time-domain signal is also obtained at the detection end, which proves the feasibility of the system achip. This method omits the steps of corrosion sacrificial layer, transfer and bonding of LT-GaAs film greatly improves the yield of the system a chip, and avoids the problems of fragility and toxicity of corrosive solutionthe process of film transfer. Finally, the influence of applied voltage on THz wave performance obtained from the system on chip is studied. The results show that the higher the voltage is, the stronger THz wave’ssignal strength is. Besides, the fact that THz waves propagate along the transmission line is verified by placing copper foil vertically above the transmission line. The system on chip based on LT-GaAs epitaxial wafer used in this study has the advantages of simple preparation method, short fabrication cycle, safe fabrication process and wide application field, which lays a foundation for detecting liquid samples by combining with microfluidic chips in the future.

In recent years, food adulteration incidents have occurred frequently, which poses a huge threat to food safety. This problem has become the focus of people’s attention and hotspots for discussion. Therefore, the realization of fast, accurate and non-destructive testing of food adulteration is of great significance for ensuring food quality and safety. With the continuous emergence of new food raw materials, additives and food processing technologies, food adulteration is being technical, invisible and diversified, bringing more severe challenges to adulterate identification. At present, there have been some new methods for effective food adulteration detection, including high-performance liquid chromatography, stable carbon isotope ratio method, etc. However, due to the complex pretreatment of the sample and the high technical requirements for the detection instrument operation, these methods’ application has been limited. Hence, A new type of non-destructive technology with high sensitivity and fingerprint characteristics is necessarily required. Terahertz (THz) spectrum refers to electromagnetic waves with a frequency from 0.1 to 10 THz, between microwaves and infrared waves, which has the advantages of fingerprint characteristics, coherence, security, etc. Since the skeleton vibration, dipole rotation, and vibration transition of most macromolecules in organic substances and the weak interaction between them can be reflected in the THz spectrum. The THz technology possesses great potential in food adulteration detection. This paper elaborated THz spectroscopy technology’s detection mechanism and reviewed the latest research progress of food adulteration detection by THz spectroscopy, including genetically modified foods identification, food geographical origin traceability, adulteration detection of dairy products, honey and other foods. Then, THz spectrum technology’s existing application problems in food adulteration detection were analyzed, such as moisture influence, scattering influence, etc. At last, the applications of the THz spectrum technology in the food adulteration detection field prospected, such as the development of low-cost THz sources and detectors to promote the popularization and application of terahertz technology, the use of machine learning algorithms for THz spectrum modeling and analysis to improve model accuracy and analysis speed, combination with other modern detection technologies to achieve advantageous complementarities, etc. This paper is expected to provide reference and guidance THz spectrum technology research in food adulteration detection.

At present, Terahertz (THz) spectroscopy techniques are mainly used for qualitative analysis, but the application of THz technology can hardly be found in quantitative analysis in the detection of edible oil’s quality. This paper presents an approach to analyze edible oil quality based on Attenuated Total Reflection (ATR) and Terahertz Time Domain Spectroscopy(THz-TDS). Firstly, the THz-TDS of edible oil samples with different types and degrees of oxidation were collected, the effective signal band was filtered and the optical constants were extracted, the preprocessing algorithm corrected the optical constants, a variety of chemometrics methods were used to establish quantitative analysis models, in order to quickly and accurately predicted the peroxide value of edible oils. 70 experimental samples were used, including soybean oils, rapeseed oils and corn oils, the peroxide value ranged from 0.41 to 10.23 mmol·kg-1, and the peroxide value distribution of the samples was evenly distributed. A TeraPulse 4000 terahertz pulse spectroscopy system equipped with an ATR detection module belonging to TeraView was used to collect samples’ THz-TDS signals. According to THz-TDS characteristics, the effective band 10 to 86.78 cm-1 was selected for modeling analysis. The frequency domain signals were obtained by fast Fourier transform, and the optical constants were extracted: refractive index and absorption coefficient. Refractive index and absorption coefficient were preprocessed separately through Savitzky-Golay 7-points convolution smoothing, which had achieved the purpose of removing interference signals. The SPXY algorithm was used to divide the calibration set, and prediction set samples in a 3∶1 ratio. The peroxide value analysis models based on refractive index and absorption coefficient were established by the principal component regression algorithm and partial least square algorithm. The root mean square error and correlation coefficient of the model evaluation indexes were analyzed, the peroxide value analysis model based on the refractive index was modeled by partial least squares algorithm had ideal prediction accuracy. When the optimal principal component number was selected to be 6, RMSEC is 0.168%, R2 is 0.981, RMSEP is 0.231%, r2 is 0.977. The principal component regression algorithm modeled the peroxide value analysis model based on the absorption coefficlent, and the prediction model had the best robustness. When the optimal principal component number was selected to be 10, RMSEC is 0.192%, R2 is 0.979, RMSEP is 0.262%, r2 is 0.97. This study verifies it is feasible to detect the peroxide value of edible oil by THz technology, and the more important innovation is a high-precision, stable performance, fast and non-destructive detection method for the evaluation of edible oil quality has been found. Furthermore, this research has important guiding significance for improving the safety of edible oil quality and building edible risk assessment systems.

Engine lubricating oil is the cornerstone to ensure the long-term and stable operation of automobile engines. Accurately evaluating various performance indicators of engine lubricating oil is an essential step in the entire process from production to use. Engine lubricating oil will deteriorate for a variety of reasons after being used for a while. The engine lubricating oil deterioration indicators can be expressed in terms of non-magnetic particulate matter concentration, metal filings content, pH value, viscosity, water content and so on. To detect water content in engine lubricating oil, the traditional detection methods have the disadvantages of complicated operation and poor timeliness. Terahertz has strong absorption of water and is suitable for analysing micro-water content in sample products. In this paper, the transmission coefficients of six engine oils with different water contents were used to obtain the absorption coefficient curve of 1.0~3.5 THz by the transmission terahertz time domain spectroscopy system. The spectroscopic data were preprocessed with Savitzky-Golay(SG).Then, the sample was divided into a calibration set and test set by the Kennard-Stone algorithm after rejecting the odd samples.The interval Partial Least Squares (iPLS), backward interval partial least squares (BiPLS), and synergy interval partial least squares (SiPLS) were used to screen their terahertz time-domain spectral characteristic spectral intervals. They were focusing on the impact of factors such as the number of intervals, the number of PLS components, the number of best principal factors, and the selection of intervals on the PLS model’s properties. It also models and analyzes lubricants with different water contents, compares and selects different models, and establishes an optimal quantitative analysis model. The modeling results indicate that the feature spectrum region filtering can improve modeling performance and reduce model complexity. The characteristic spectrum region screening algorithm eliminates the non-linear or irrelevant variables in the terahertz absorption coefficient spectrum of engine lubricants so that the modeling results can better express the relationship between the absorption coefficient spectrum and its water content. The results show that the optimal model for quantitative analysis of trace water content in generator lubricants was obtained with BiPLS method that separated the whole spectra into 26 intervals and selected ［18 10 4 3 8 12 5 11 24 13 16 21 2］ intervals. The number of major factors is 10. The BiPLS model had a root mean standard error of cross-validation (RMSECV)of 0.003 5 and root mean standard error of prediction(RMSEP) of 0.004 6. The correlation coefficient (r) of the correction set is 0.913 9, and the correlation coefficient (r) of the prediction set is 0.865 7. Overall, BiPLS method could accurately predict the water content of engine lubricants, and the experimental process is simple, the modeling and calculation speed is fast, and the effect is ideal, and it can be applied to the quantitative analysis of the water content of non-contact oil products.

In order to have abetter understanding on the discharge mechanism of Ar/CH4 plasma jet and the state of its internal electrons under atmospheric pressure, a stable Ar/CH4 plasma jet was produced in the atmosphere through a self-designed needle-ring Dielectric Barrier Discharge structure with a discharge frequency of 10kHz, and the plasma jet was diagnosed by emission spectroscopy. The discharge phenomenon of Ar/CH4 plasma jet and the types of active particles inside were analyzed under atmospheric conditions. The effects of different argon-methane volume flow ratios and different peak voltages on the electron excitation temperature, electron density and concentration of CH group active particles in atmospheric pressure Ar/CH4 plasma jet were emphasis studied. The results show that the Ar/CH4 plasma jet is light blue under atmospheric pressure, and filamentous burrs can be observed on the edge of the jet. A harsh ionizing sound accompanies the discharge, and the shape of the jet tip fluctuates greatly; The main active particles in Ar/CH4 plasma jet at atmospheric pressure are CH group, C atom, CⅡ, CⅢ, CⅣ, ArⅠ and ArⅡ. Among them, the spectral lines of carbon-containing particles are mainly concentrated between 400 and 600 nm, and the spectral lines of ArⅠ and ArⅡ are distributed between 680 and 800 nm; It can be found that the concentration of the CH group increases with the increase of the peak voltage, however, the concentration of CH group decreases with the increase of the volume flow ratio of Ar/CH4. At the same time, the concentration of C atoms in the Ar/CH4 plasma jet increases. This means that the increase of the volume flow rate accelerates C—H fracture in the Ar/CH4 plasma jet. Therefore, it can be seen that increasing of the peak voltage and Ar/CH4 volume flow ratio can significantly accelerate the dehydrogenation efficiency of methane molecules, but increasing the Ar/CH4 volume flow ratio dehydrogenation effect is more obvious. Four ArⅠ lines was selected to calculate the electron excitation temperature under different operating conditions. The electron excitation temperature of Ar/CH4 plasma jet is between 6 000 and 12 000 K, and it shows an upward trend with the increase of the peak voltage and the volume flow rate of Ar/CH4; The electron density of the Ar/CH4 plasma jet was calculated based on the Stark broadening mechanism, the magnitude of electron density can reach 1017 cm-3, and plasma jet electron density can be significantly improved by increasing the volume flow ratio of Ar/CH4 and peak voltage. The exploration of these parameters is of great significance to the study of atmospheric pressure plasma jets.

In order to improve the utilization efficiency of pulpwood in Hainan Province, alleviate the shortage of domestic pulping and papermaking materials, and reduce pollution and overall costs in the pulping and papermaking industry, this study aimed to use near-infrared spectroscopy for the analysis of pulpwood. A holographic grating spectroscopic near-infrared spectrometer with a simple structure and easy modification was used to collect the near-infrared spectrum of 205 samples of pulpwood common in Hainan (E. urophlla× E. tereticornis, Eucalyptus urophylla× grandis, Eucalyptus urophylla, Acacia mangium, Acacia crassicarpa Benth.), and the content of holocellulose and lignin were measured according to the traditional laboratory methods. Suitable pretreatment methods were selected in combination with partial least squares to establish analysis models holocellulose and lignin. Then genetic algorithm was usedto eliminate the irrelevant variables and clarifythe feature absorption of holocellulose and lignin in order to optimize the models. The holocellulose model was established by pretreatment methods of Savitzky-Golay 13 points 3 times smoothing, vector normalization, the first derivative of the original spectrum, with 1 150.3~2 362.0 nm bands participated in modeling. The optimal bands included the characteristic absorption of cellulose such as the 2nd overtone of C—H stretching vibration in CH3 between 1 188~1 196 nm, the 1st overtone of O—H stretching vibration between 1 742~1 633 nm, the group frequencies of formation and stretching vibration of O—H near 2 112 nm. The optimal bands also included the characteristic absorption of pentosan such as the 1st overtone absorption of O—H stretching vibration between 1 470~1 495 nm, and the 2nd overtone absorption of CO stretching vibration around 1 906 and 1 911 nm. The RMSEP value of the model was 0.55%, and the absolute deviation range was -0.91%~0.87%. The lignin model was established by pretreatment methods of Savitzky-Golay 13 points 3 times smoothing, MSC, the second derivative of the original spectrum, with 1 137.6~1 872.5 and 2 131.0~2 424.1 nm bands participated in modeling. The optimal bands included the characteristic absorption of lignin such as the 2nd overtone of the C—H stretching vibration in the benzene ring and in the CH3 near 1 143 nm, the 1st overtone of the C—H stretching vibration in the benzene ring between 1 670~1 684 nm, the group frequencies of stretching vibration of C—H and CO near 2 205 nm. The RMSEP value of the model was 0.45%, and the absolute deviation range was -0.76%~0.79%. The two models’ RPD values were 4.71 and 3.47, respectively, which can meet the actual needs of online quick analysis and measurement of the main components of pulpwood. At the same time, this study provides a theoretical basis for the establishment of a near-infrared characterization system for pulpwood, and has a significant significance for the near-infrared technology to help the pulping and papermaking industry to change from automation to intelligence.

The quality of Polygonati Rhizoma medicinal materials is closely related to the original plants’ origin environment. It is necessary to ensure their quality control and drug safety by establishing a simple, rapid and accurate origin identification method for the medicinal materials.In this study, the attenuated total Reflection-Fourier transform infrared (ATR-FTIR) spectra and ultraviolet visible (UV-Vis) spectra of 133 Polygonatum kingianum rhizomes from 9 geographic origins in Yunnan, Sichuan and Guangxi Provinces were collected to establish random forest (RF) modelafter data pretreatment, respectively. ATR-FTIR and UV-Vis spectra data were directly connected in series to complete the RF model of low-level data fusion. Principal components (PCs) and latent variables (LVs) of the two spectra were extracted to achieve RF model ofmid-level (mid-PCs and mid-LVs) and high-level (high-PCs and high-LVs) data fusion. The accuracy (ACC), sensitivity (SEN) and specificity (SPE) of different models were compared to select the best model for origin identification. The results showed that the peaks of ATR-FTIR and UV-Visspectrain P. kingianum were similar, and their absorbance were different. There were 14 common peaks in ATR-FTIR spectra of P. kingianum, which were related to carbohydrate, steroidal saponins, flavonoids and alkaloids. The common peaks of UV-Visspectra in P. kingianum were mainly at 272 and 327 nm, which were related to flavonoids. For the RF models of ATR-FTIR, UV-Vis and low-level fusion, the ACC of the training set and prediction set were respectively (76.34%, 95.00%), (80.65%, 95.00%) and (83.87%, 100.00%), however, the SEN and SPE values were so low that they were not suitable to use. The SEN and SPE of mid-PCs and mid-LVs RF models were greater than 0.91 and 0.98, respectively. The ACC of the training set was 91.40% and 97.85%, respectively, and that of the prediction set both were 97.50%. The ACC of RF training set with high-PCs and high-LVs was 77.42% and 97.85%, respectively, and the prediction set ACC both were 95.00%. The RF model with high-PCs has poor identification effect, and the RF model with high-LVs was over-fitted. In summary, the identification of model from high to low was: mid-LVs＞mid-PCs＞low fusion＞UV-Vis＞ATR-FTIR＞high-PCs. LVs extraction method is better than PCs for origin identification. RF model of mid-LVs established has the highest ACC with the best model performance, and the SEN and SPE greater than 0.98, and, which can provide a theoretical basis for the scientific evaluation of medicinal resources of Polygonati Rhizoma.

ufficient homogenization pretreatment of heterogeneous solid samples is the prerequisite for obtaining reliable analysis results, which is particularly important for infrared spectrum (IR, KBr) analysis with just about 1 mg needed in a single test. Through the multi-angle infrared spectral similarity evaluation of tea samples including different tea types and particle sizes, the relationship between particle size and the degree of homogenization was revealed and used to guide the tea pulverization to ensure that the followed IR spectra could accurately reflect the chemical composition information of tea powder. Three kinds of Yunnan tea production, including Raw Pu-erh tea (Raw-PE), DianHong (YNBT), Riped Pu-erh tea (Riped-PE), were selected to be prepared into four tea samples with different particle sizes respectively. The IR (KBr) and attenuated total reflection method (ATR-IR) spectra of the prepared tea powder were collected 5 times in parallel. The similarity evaluations by Cosine(i) of the infrared spectra obtained were carried out to investigate the influence of pulverized particle size, spectral collection method, tea type and other factors on the spectral correlation coefficient (r). The spectral similarity evaluation results based on different tea types showed that the r value from Raw-PE was significantly higher than that of Riped-PE and YNDH. The r values from the tea powder of YNDH with different meshes fluctuated by up to 18%. The results from different spectral test mode showed that the r values based on ATR spectra were more concentrated, while the results based on KBr spectra were dispersed. The results based on tea powder with different particle sizes showed that the smaller the particle size was, the higher the r value was. Moreover, the r value from tea samples with more than 250 mesh was usually a good result over 0.999. The results show that the r values based on ATR spectra showed good reproducibility and the results from KBr spectra displayed stronger difference recognition ability. The latter is more suitable for the comparative analysis of the composition differences between highly similar samples. The homogeneity of tea powder is closely related to the pulverized particle size and related to the substrate of the tea sample itself. Whatever it can be improved by reducing the pulverized particle size. Generally, the particle size of tea powder less than 60 mesh is difficult to meet the infrared spectroscopy analysis’s homogeneity requirements. An r value over 0.995 based on IR spectra (KBr) of tea powder above 120 mesh could be given, but for the ATR spectra, the tea sample needs to be shredded by more than 250 mesh to give the same evaluation result.

China is one of the earliest countries in the world to use coal and jet. However, during the research of unearthed jet cultural relics, it was found that the texture of raw materials of jet cultural relics sometimes deviated from the jet defined in Gemology. Thus, the concept of jet-like cultural relics is advanced in this paper to have jet, lignite, candle coal and other materials which have a similar texture with jet put into the same cultural concept. At present, there is still a large gap in the scientific research on jet-like cultural relics, among which there especially is no scientific judgment method for the most basic study of material type identification. In this paper, 16 pieces of jet-like relics unearthed from Zhouyuan Hejia Cemetery in Shaanxi, Xianyang Yancun Cemetery in Shaanxi, Turpan Shengjindian Cemetery in Xinjiang, and YiliJirentaigoukou Site in Xinjiang are studied for exploring the application of diffuse reflectance Fourier transform infrared spectroscopy in the composition analysis method of jet-like cultural relics. It can be seen through the result of the infrared spectrum that spectrograms of jet-like relics unearthed from different sites are quite different, while spectrograms of jet-like relics from the same site are similar. The infrared spectra are processed by Norris second derivative method for improving the resolution of absorption peak, a special infrared index I=A820 cm-1/A2 870 cm-1 was selected to have the infrared spectra quantitative analysis of different samples. Combined with the density data of some samples, the coalification degree of jet-like cultural relics can be judged preliminarily. Furthermore, the materials of jet-like relics from different sites could be distinguished by the result of principal component analysis, which is consistent with the judgement of the special infrared index. This paper verifies the feasibility of non-destructive analysis method based on the infrared spectrum in distinguishing materials of jet-like cultural relics. In addition, a research method for the identification of jet-like cultural relics’ origin can be provided if enough infrared spectrum of jet ore samples are collected from different areas.

After silicate impregnation, the content and distribution of elements in the modifier is an important index to evaluate the impregnation effect, which plays an important role in the physical and mechanical properties of the modified Chinese fir. The effects of the biomimetic respiration method on the density, bending strength, compressive strength, three section hardness and 24 h water absorption of silicate modified Chinese fir were studied. The chemical composition and structure of unmodified and modified Chinese fir were analyzed by XPS and FTIR, and the distribution depth and law of silicate modifier in the modified Chinese fir were discussed. The results showed that the average density of the modified Chinese fir was more than 0.721 g·cm-3, the bending strength and compressive strength increased by 170.19% and 286.64%, respectively. The hardness of the transverse section, radial section and tangential section of the modified Chinese fir increased in different degrees. The 24h water absorption of Chinese fir decreased from 91.17%±2.51% to 39.23%±1.62% after silicate modification, which indicated that the dimensional stability of Chinese fir was greatly improved. Compared with the unmodified Chinese fir, the absorption peaks of Na and Si elements appear in the XPS full-spectrum scanning of the modified Chinese fir wood, and the chemical structures of Si—O—C and Na-O appear in the narrow spectrum scanning. At the same time, the absorption peak of Si—O—Si appeared in the FTIR spectrum of the modified Chinese fir wood, the content of free hydroxyl decreased, and the associated hydroxyl increased. XPS and FTIR analysis showed that silicates were impregnated into the pores of Chinese fir wood, and sodium silicate formed a chemical bond and hydrogen bond with hydroxyl in Chinese fir wood. This was also an important reason for the improvement of mechanical properties and water resistance of modified Chinese fir. In addition, it was found by XPS that C, O, Na and Si elements appeared along the transverse direction from the surface to 30 mm. The absorption peak intensity of Si—O—C bonding structure was basically the same from the surface to 30 mm, which indicated that the chemical bond between sodium silicate and hydroxyl groups in Chinese fir wood was formed more evenly from the surface to the middle part.It was found that the relative contents of C, O, Na and Si element in the modified Chinese fir wood were slightly different from the surface to the middle part (30 mm). The results showed that the modifier was in the middle of Chinese fir wood, and the uniformity was good. The research results will provide data support for the impregnation modification effect of Chinese fir, and provide the basis for optimizing the modification process and method, and further improving the physical and mechanical properties of modified Chinese fir.

Passive FTIR infrared remote sensing technology is useful in gas leak detection. The detection limit of trace gas is related to the instrument’s signal-to-noise ratio (SNR). The instrumental SNR is related to measurement parameters, such as spectral resolution, sampling frequency, integration time, and average times. How to optimize the combination of parameters to achieve the best signal-to-noise ratio in combination with actual applications currently lacks system analysis. This paper the oretically analyzes the relationship between these parameters and SNR and categorizes them into three aspects: (1) In terms of spectral resolution, the conclusion, cited from Roland Harig, is that SNR stays the same when the spectral resolution is lower than the full width at half maximum (FWHM), but in order to avoid interference of background gases, the resolution mustavoid too low and is appropriately set in practical application; (2) In terms of the sampling frequency, sampling frequency reduction can reduce calculation cost and narrow the spectral range, but the sampling frequency and spectral range are not related with SNR; (3) In terms of integration time and multiple spectra averaging, under the same time conditions, multiple interferogram acquisitions will introduce a sampling error in zero path difference, making the noise larger than the theoretical calculation, so SNR obtained by long integration time is better than multiple averaging. Conducted sulfur hexafluoride (SF6) leak detection experiment, the 4 cm-1 resolution is selected according to SF6 spectral feature, and 20 kHz sampling frequency is selected by taking into account SNR and detection time, and FTIR inspection system is used to locate the leakage point in the transform substation, which proves the effectiveness of this method.

Near-infrared (NIR) spectroscopy rapid detection technology was used to determine protein content in instant coffee. Support vector machine (SVM) and extreme learning machine (ELM) was applied for validating their practicality in modeling analysis. We proposed the latent variable SVM (LV-SVM) and latent variable ELM (LV-ELM) methods combined with latent variable analysis technique. Thetuning of latent variables and the optimization of the key parameters in machines were joint in-one so that the data dimension reduction and the selection of machine parameters can be both accomplished in one single modeling process. The calibrating-validating-testing mechanism was used for sample division. The NIR analytical models were trained based on the calibrating sample set. The model prediction results were generated and saved as a 3D box as they were determined by the simultaneous tuning of the latent variable and the machine parameter. Then the joint optimization of model parameters was selected in the way of predicting the validating samples. Further, the optimal model was evaluated by the testing samples. The optimal LV-SVM model gave the validating root mean square error as 6.797; the corresponding testing root mean square error as 8.384. The optimal LV-ELM model obtained the validating root mean square error as 6.118. The corresponding testing root means square error as 7.837. Compared with the common partial least square method, the LV-SVM and LV-ELM methods have better prediction results, which verified the application advantages of the latent variable machine learning method in near-infrared quantitative analysis. This proposed method is expected for further application in content detection of other kinds of coffee.

Surface-enhanced Raman scattering (SERS) largely compensates for the shortcoming of the weak intensity of Raman scattering and quickly becomes a research hotspot for researchers. It is widely used in food safety, environmental pollution, drug and explosive detection and other fields. Due to nanotechnology’s development, the current research on SERS mainly focuses on the preparation of metal nanoparticle substrates. The type, size, and morphology of metal nanoparticles all affect the SERS enhancement and absorption peak positions. It is necessary to optimize the process of metal nanostructures. In particular, it is necessary to combine the structure of the metal nanoparticle and its corresponding excitation light wavelength to obtain a better enhancement effect. A study of metal nanoparticles with double resonance absorption peaks was conducted to get the relationship between SERS enhancement and absorption peaks. Firstly, through FDTD Solutions, the local surface plasmon resonance peaks of gold nanoparticles with different diameters, gold nanorods with different aspect ratios and distributions were simulated. We found that when Au nanoparticles’ theoretical diameter is about 50 nm and the theoretical aspect ratio of Au nanorods is about 3.5~4.5, the absorption peaks are distributed near 532 and 785 nm, respectively, which meets the multi-band excitation light Raman enhancement conditions. For the polarization direction of the excitation light, when the light polarization direction is along the long axis direction of Au nanorods, the absorption peak is near 785 nm, and when the light polarization direction is along the short axis of Au nanorods, the absorption peak is near 532 nm. A double-absorption SERS substrate that can be used for excitation light of various wavelengths was prepared by the seed growth method. In order to control the forming rate of Au nanorods, the process parameters were optimized, including the silver nitrate(5, 10, 20, 30, 40 μL), the hydrochloric acid (0.1, 0.2 mL) and the growth time (15, 17, 21, 23 h). Double-absorption resonance peaks containing Au nanoparticles and Au nanorods were successfully obtained. Finally, using this sample as the substrate and Rhodamine 6G(R6G) as the probe molecule, the SERS characterization of the excitation light at 532, 633 and 785 nm was tested, achieving the multiple wavelength SERS detection with a concentration of 10-7 mol·L-1 of R6G, and the enhancement factor is ~105.

Inductively coupled plasma atomic emission spectrometry (ICP-AES) has become a conventional elemental analysis method, but in the ICP-AES analysis process, most of the elemental analysis lines will be interfered by background or other spectral lines overlapping. Spectral interference seriously affects the accuracy of spectral line analysis. Therefore, in element analysis, an appropriate spectral interference correction method is needed to obtain a suitable element analysis line. In this paper, according to the characteristic that the spectral intensity is superimposed, the spectral line shape is expressed as a multi-peak spectral line superposition model summed by the Voigt linear function.Moreover, the root-mean-square error of the multi-peak spectral line superposition model and the target spectral line are used to construct a multivariate function as an evaluation function of the mathematical model. The adaptive particle swarm optimization (APSO) algorithm is designed to find the optimal solution of the separated spectral lines’ characteristic parameters. Based on the standard PSO algorithm, the APSO algorithm introduces a compression factor while making the population parameter inertia weight adaptively changed according to the individual fitness value of the particle and the linear change of the learning factor. Coordinate the global search capability and local development capability within the particle population during the algorithm iteration process to ensure that the algorithm effectively and quickly converges and achieve multi-peak spectral line separation. Reduce the influence of interference spectral lines to get more accurate elemental analysis lines. The two samples of light intensity AD sampling values of the two spectral lines at the 390.844 nm characteristic wavelength of the Pr element-containing solution and the 313.183 nm characteristic wavelength of the mercury lamp returned by the ICP-AES detector are used as two sets of measured data, and the two Voigt linear approximation functions. Three superimposed composite curves with different degrees of overlap are used as three sets of simulated data. On the data curve, 50 points that can contain all the characteristic parameter information of the curve are selected as the target data points. By performing the APSO algorithm on the above five sets of target data points, the results show that the relevant parameters of the multimodal spectrum superposition model obtained by the APSO algorithm can fit the corresponding target data curve more accurately. The error is low, and the algorithm shows that it can effectively deduct the interference of spectral line overlap. Under the same set of target data, select the characteristic parameter vector corresponding to the smallest optimal fitness value as the relevant parameter of the Voigt linear function, and the multi-peak spectral line superimposed model curve fitted by this method has higher curve accuracy and smaller relative error. This algorithm has good convergence and adaptability. The algorithm can be applied to the ICP-AES in the element qualitative and quantitative analysis of the study.

Selenium is one of the 15 essential trace elements necessary for the growth of animals, plants, and humans. It has the functions of scavenging free radicals, anti-oxidation and enhancing immunity, etc., but its safe dosage range is very narrow. The pyrite’s morphology prepared by wet ball milling was characterized by scanning electron microscope (SEM) and X-ray diffraction (XRD). SEM observations were found that the pyrite after the addition of ethanol was a spherical particle agglomerate with a relatively uniform particle size, ranging from 17 to 200 nm, with an average particle size of 138 nm. The characteristic peaks in the XRD diffraction pattern are the same as the positions of the peaks in the FeS2 diffraction pattern. Therefore, it is determined that the main chemical component in pyrite is FeS2, and there are no impurity peaks in the pattern, indicating that no impurities were mixed in the process of preparation. The sample purity is high. The experimental results show that this method’s pyrite has the advantages of small particle size, large specific surface area, and high reaction activity. In the research, X-ray photoelectron spectroscopy (XPS) was used to study the mechanism of removing Se(IV) by pyrite. The research results show that (1) In a relatively wide experimental pH range (pH 2.2~11.5), pyrite can effectively remove SeO2-3 and the removal efficiency (except pH 7.8) is above 90%; (2) After the reaction of pyrite with SeO2-3, the characteristic peaks of its main constituent elements appeared the left shift indicated that a certain chemical change had taken place on the surface of the pyrite; (3) The mechanism of removal of SeO2-3 with pyrite in an acid-base environment is a little different. In an acidic environment, the removal of SeO2-3 by pyrite is a simple redox process.That is, S2-2 activated by the acid in pyrite reduces Se(IV) to Se(0), and the stronger the acidity, the better the removal effect of SeO2-3; in the alkaline environment, the oxidation-reduction and complexation reactions coexist during the removal of SeO2-3, and the surface of pyrite has complexed Fe(OH)SeO3 and elemental Se(0) exist in two forms, and the stronger the basicity, the more the content of complexed Fe(OH)SeO3. The above research results provide an important theoretical basis and application basis for removing variable valence metal anions represented by SeO2-3 from water and soil by pyrite.

Laser Raman spectroscopy is an effective method for detecting the aging state of transformer oil-paper insulation. With the expansion of sample quantity and the gradual increase of data set dimension, it is of great significance to study the evaluation method of oil-paper insulation aging in transformer suitable for high-dimensional Raman spectral data. An oil-paper insulation environment similar to the internal insulation structure of the field transformer was designed, and the accelerated thermal aging experiment was carried out and regularly sampled to obtain ten types of oil samples with increasing aging degrees, then these samples were detected using laser Raman spectroscopy. The compound sparse derivative modeling method was used to preprocess the original Raman spectral data, which can complete the noise elimination and baseline correction in one step. The differential feature selection method was introduced to screen the spectral features with significant changes under different aging degrees, and the variance of the feature point data set with different aging degrees was calculated under the same Raman shift. Furthermore, the Raman feature variable corresponding to the data sequence with a large difference was selected, and the variance threshold was set to 0.5 for feature selection, each sample selected 304 from 1 023 spectral feature points for subsequent analysis. In this paper, many different types of algorithms were introduced to process the high-dimensional sample data set of transformer oil-paper insulation aging Raman spectra. For instance, the K-means clustering algorithm, the Fisher algorithm and Random Forest algorithm were used to establish a model with the preprocessed data of the obtained samples. The evaluation accuracy, lifting degree and Kappa coefficient were introduced to evaluate the discriminant effect of each mathematical model. The results show that supervised learning Fisher algorithm and Random Forest algorithm have a better effect and discriminatory advantage compared with the unsupervised learning k-means clustering algorithm because the discrimination ability of the model is improved by 1.166 6 and 1.95, respectively; Judging from the discrimination accuracy and Kappa coefficient, the discriminant model established by the strong classifier Random Forest algorithm is better than the Fisher discriminant model, for its accuracy is improved by 10%, and the Kappa coefficient is increased by 0.111 5. Compared with a single classifier, a strong classifier composed of multiple single classifiers has better generalization evaluating of transformer oil-paper insulation aging, and the model is more stable and reliable. By comparing three different types of algorithms, the discrimination advantages of the supervised learning strong classifier Random Forest algorithm in evaluating transformer oil-paper insulation aging are determined, which lays the foundation for the effective evaluation of transformer oil-paper insulation aging.

Above ground biomass (AGB) is an important parameter that characterizes crop life activities and is particularly critical for crop growth monitoring and yield prediction. Therefore, obtaining AGB information quickly and accurately is of great significance for monitoring crop growth, guiding agricultural management and improving yield. Using UAV as a platform to carry digital camera sensors, due to the advantages of strong maneuverability, low price and high spatial resolution, and to estimate crop AGB timely and accurately has become one of the hotspots in remote sensing estimation research. As the accuracy of the AGB estimation model for digital images of different flying heights with different resolutions of UAV is different, this study tried to set up 5 types of flying heights at 10, 20, 30, 40, and 50 m during the potato tuber growth period to obtain digital images of different resolutions, and to explore its influence on the accuracy of building AGB model based on spectral information, texture features and spectral information + texture features. Firstly, based on the digital image of UAV, the spectral information and texture features are extracted separately, the vegetation index from the spectral information and texture features constructed, combined with the measured AGB obtained by ground experiments respectively for correlation analysis, and the top 10 image indexes and the top 8 texture features with larger absolute values of correlation coefficients are selected separately. Then, three variables integration variance inflation factor (VIF) are used to perform principal component analysis (PCA) dimensionality reduction processing, and the best principal components are obtained and multivariate linear regression (MLR) constructs AGB estimation model. Finally, compare the AGB estimation model precision of digital images with different resolutions with three variables and the same resolution with the same variable. The results show that: (1) When the image resolution changes between 0.43 and 2.05 cm, the correlation between texture features and potato AGB is weaker than that of vegetation index, but both reach a very significant level of correlation (p<0.01). With image resolution is reduced, its correlation is significantly different. (2) Under the same resolution image, spectral information+texture features have the best precision in estimating AGB, followed by a single texture feature model, and a single spectral model has the worst performance. (3) As digital images’ resolution increases, the accuracy of estimating AGB from spectrum information, texture information, and spectrum + texture information gradually improves.

In order to meet the needs of rapid and accurate detection of in-situ water total alkalinity, a mini online rapid tester of total alkalinity of trace in-situ water was designed by combining sequential injection analysis (SIA) and continuous spectrum detection. The system mainly applies the SIA and continuous spectrum detection method to the total alkalinity automatic titration detection process by designing the titration cell and conducting experimental research on a new method of determining the titration’s critical value by the continuous spectrum detection method. Based on the national standard of the determination on total alkalinity in industrial circulating cooling water and surface water, the detection process of in-situ water quality alkalinity was designed. Also, on the basis of the sequential injection technology as the standard of controlled titration process, under the conditions of continuous spectral scanning measurement of the solution detection process, phenolphthalein and methyl orange were used as indicators to perform a titration analysis on the total alkalinity of water quality. The phenolphthalein alkalinity and methyl orange alkalinity titration process were monitored by continuous spectral scanning. The peak value at 552 nm of the absorbance curve was used as the phenolphthalein titration threshold value. The absorbance curve’s peak value shifting from 465 to 504 nm was selected as judging conditions for the critical value of basic orange titration. The optimal indicator dosages were analyzed by the absorbance curves of the solutions obtained by adding different doses of phenolphthalein and methyl orange indicators, which were 0.01 and 0.04 mL, respectively. The system applied least-squares fitting algorithm to establish a regression model for total alkalinity determination and optimized the detection system and process. The experimental results indicate that the total alkalinity of water quality is linearly related to hydrochloric acid consumption in the range of 0.20 to 25.00 mmol·L-1 and the fitting coefficient of the working curve is no less than 0.994 2. The relative standard deviation (RSD) of the total alkalinity measurement’s repeatability is between 0.207 and 1.151%. Waste liquid volume is no larger than 16 mL. The lowest detection limit is 0.03 mmol·L-1. The recovery of spiked samples is between 97.2% and 102.3%. Also, there is no significant difference between the experimental results and the national standard method. The new method for determining the critical value of titration using continuous spectrum detection method is of great significance to improve the technical performance of the total alkalinity detector of water quality. It can be applied to many kinds of monitoring application platforms such as system grid monitoring of surface water, circulating cooling water, and breeding circulating water.

Apatite is a kind of common gem species in jewelry market. It is popular because of its rich color. Apatite with the Alexandrite effect is rare and expensive. It shows yellow green under the D65 light source and pink under the A light source. Characteristics in the visible spectrum and Alexandrite effect origin of this apatite have not been studied in detail. Based on this, the sample of this study is an apatite crystal with the Alexandrite effect. Two thin slices were cut parallel and perpendicular to the c-axis, respectively. The two samples were polished, and their visible spectra and trace elements data were collected. The results show there are many peaks the in visible spectrum of Alexandrite effect apatite. There are double absorption peaks at 583, 578 nm with strong intensity, double absorption peaks at 748, 738 nm with middle intensity, two absorption peaks at 688 and 526 nm with weak intensity and some very weak absorption peaks at 514, 483, 473 and 443 nm. The absorption peaks at 748, 738, 583, 578 nm create a transmission window in red orange region, and the absorption peaks at 583, 578, 526 nm create another transmission window in the yellow green region. Due to different relative spectral power distributions between D65 and A light sources, the transmittance through transmission windows is also different, which leads to different colors of apatite. D65 light source has the more yellow green light, passing through transmission window in yellow green region, so apatite shows yellow green color. A light source has more red light, passing through another transmission window in red orange region, so apatite shows pink. Therefore, the Alexandrite effect of apatite is caused by the absorption peaks at 748, 738, 583, 578 and 526 nm. Based on trace elements data and crystal field theory of rare earth elements, it was found that these absorption peaks were caused by neodymium (Nd). It was found that the Alexandrite effect was better in the orientation parallel to the c-axis based on visible spectra. It is suggested that the table of the gemstone should be cut parallel to the c-axis. This research combines trace elements and visible spectra to analyze the Alexandrite effect origin of apatite. It also provides guidance for cutting apatite crystal.

Hexabromocyclododecane (HBCD) is widely used in industry as a kind of brominated flame retardant. However, more and more people pay close attention to the problem of HBCD contamination in the environment due to its potential risk to human health. There are no reports focuses on the transport mechanism of HBCD in the human body. Hence, multi-spectroscopy and computer simulation methods investigated the interaction mechanisms of HBCD and bovine serum albumin (BSA)-. The solution experiments confirmed that HBCD quenched BSA’s intrinsic fluorescence through the static quench mechanism and non-radiation energy transfer. The binding constants (Ka) between them were 2.796 6×104 L·mol-1 (288 K), 2.194 1×104 L·mol-1 (293 K), and 1.174 4×104 L·mol-1 (298 K), respectively. The number of the binding site in the BSA-HBCD complex was approximately equal to 1. The thermodynamic constants were calculated to be ΔH=-61.749 kJ·mol-1 and ΔS=-128.742 J·(mol·K)-1, indicating that van der Waals or hydrogen bond play a key role in this binding process. The result of molecular docking and fluorescence spectrum indicated that the primary binding site for HBCD was located in the hydrophobic pocket of sub-domain Ⅱ A of BSA, and the binding distance was about 3.45 nm. The secondary conformation of BSA did not affect by HBCD was observed in three-dimensional fluorescence spectra and MD simulations. This research provides a theoretical basis for further understanding of toxic effects of HBCD on human toxicity.

Fading phenomenon under natural environment is complicated. Strictly, it is impossible to reproduce above phenomenon. However, it is possible to reveal a reasonable mechanism for fading ancient mural, and the rationality proposed mechanism could be verified by experimental method, which is a basic point of view of natural science. In this paper, the viewpoint that the fading of mural painting caused by degradation of binder rather than change of pigments, was proposed. To verify this viewpoint, the following experiments were conducted. To construct the simulated mural painting with stable pigments and degradable binder, thermally stable ocher and thermally degradation gelatin were used in preparation of simulated mural painting. The composition and surface of simulated painting before and after calcination were characterized by TG, IR, SEM and reflectance spectroscopy, respectively. The following results were obtained. After calcination, pore structure emerged in painting layer. From multiple-angled reflectance spectra, the reflectance of visible light increased and the absorption decreased, and the color fading of the simulated sample was observed. The ionic liquid, colorless, nonvolatile and no reaction with painting layer, was coated on the surface of faded sample. Taking the same characterization method to investigate the surface morphology and optical properties changes before and after ionic liquid treatment, the results indicate that the pore in faded painting layer was filled by ionic liquid. Furthermore, reflectance for treated painting layer decreased, the absorption improved and the color of faded painting layer changed to deep color. In fact, this experiment result could verify the conjecture that morphology changes in painting layer could cause the fading. According to this results, a real faded ancient mural painting was restored using the same method and the restoration is remarkable.

Target leaf spot is one of the main fungous diseases of cucumber. Under suitable conditions, especially under the conditions of the large temperature difference between day and night or saturated humidity, the disease develops rapidly, leads to the reduction of cucumber yield and brings economic losses. The cucumber target leaf spot segmentation can provide an effective basis for the identification and diagnosis of cucumber disease, which has great significance. In this study, a cucumber spectral image was taken as the research object, and U-net deep learning network was utilized to construct the semantic segmentation model for cucumber target leaf spot segmentation. Firstly, the regions with more prominent lesions in the visible spectrum images were selected for training and testing. We captured 135 regions out of 40 images as samples, and each region was 200×200 pixel. The Image labeler tool of Matlab was used to label the samples to mark the affected area and the healthy area. Then, the U-net network was constructed, which contains 46 layers and 48 connections. The cucumber target leaf spots’ feature extraction is completed by convolution layer, ReLU layer and max-pooling. The upsampling is completed by deep connection layer, up convolution layer and up-ReLU. The copy and crop operations and feature fusion are completed by skip connection. The U-net was used for training to get the semantic segmentation model. From 135 samples, 96 were randomly selected as training samples and the remaining 39 as test samples. Set the iterations 240, L2 regularization coefficient 0.000 1, initial learning rate 0.05, momentum parameter 0.9, gradient threshold 0.05, and then utilize the samples for training and testing. After 10 repeated training and testing, the results showed that the average execution time of the semantic segmentation model based on U-net and visible spectrum images was 46.4 s. The average memory occupation was 6 665.8 MB, and it shows that the model has a high execution efficiency. The pixel accuracy of the model was 96.23% ~ 97.98%, mean pixel accuracy was 97.28%~97.87%, mean intersection over union was 86.10%~91.59%, frequency weighted intersection over union was 93.33%~96.19%. It shows that the model has good stability and strong generalization ability. This research used less training samples to obtain a segmentation model with high accuracy, which provides a reference for small sample machine learning and provides a method basis for other vegetable disease spot segmentation, disease identification and diagnosis.

The environmental problems caused by heavy metal pollution have been particularly prominent in the regions with the rapid industrialization and urbanization development, especially the heavy metal pollution in agriculture is more concerned by the society. Therefore, it is very important to explore some fast and convenient methods on screening and monitoring heavy metal pollution. As a new technology of monitoring heavy metal pollution, the hyperspectral remote sensing has been paid attention and researched deeply by many scholars. A concept and method of inherent wavelength-scale decomposition (IWD) was proposed in the paper, and an IWD-Hankel-SVD model was established for predicting heavy metal pollution degree of vegetation combined with the Hankel matrix and the singular value decomposition (SVD), here the model was divided into single-variable model and multi-variable model. The single-variable model was mainly used to obtain the intrinsic rotation components (PRC) of spectral information of vegetation polluted by heavy metal through IWD processing and to extract the effective characteristic bands of the best PRC, then it could be realized to predict the heavy metal pollution according to the singular entropy of the model acquired by using SVD to decompose the Hankel matrix constructed based on each characteristic band. But the multi-variable model was used to realize the prediction of heavy metal pollution information by taking the relative values of vegetation chlorophyll concentration and the singular entropy acquired by the single-variable model as parameters. According to the data of leaf spectra, measured chlorophyll concentrations and Cu2+ contents in corn leaves polluted by heavy metal Cu2+ under different stress gradients, firstly the spectra of corn leaves stressed by the different Cu2+ concentrations were analyzed by IWD, the best PRC was obtained which could well retain the original spectral information, and some effective characteristic bands were extracted to be 553~680, 681~780, 1 266~1 429, 1 430~1 631, 1 836~1 913 and 1 914~2 111 nm from the PRC, then the Hankel matrix of each characteristic band was constructed and processed by SVD to obtain the singular entropy of the single-variable model, finally through the correlation analysis between the singular entropy of the model corresponding to each characteristic band and the Cu2+ contents in corn leaves, it was found that the determination coefficients R2 of the singular entropy and the Cu2+ contents in the leaves were all about 0.9 computed based on the 1 266~1 429 and 1 836~1 913 nm characteristic bands, the result shows that the two characteristic bands had more advantageous and interpretable for the IWD-Hankel-SVD model on predicting the Cu pollution degrees. At the same time, it was concluded that the multi-variable IWD-Hankel-SVD-model had stronger prediction ability of Cu pollution degrees in corn leaves by using the partial least square regression analysis based on taking the relative values of chlorophyll concentration in corn leaves and the singular entropy of the single-variable model corresponding to 1 266~1 429, 1 836~1 913 nm characteristic bands as parameters, and the determination coefficient R2 reached 0.947 6, so the multi-variable model was proved to be more robustness and steadiness.

Generally, the influence factors of the reflectance spectrum of rocks and minerals can be divided into the decisive factors related to composition and content, and the variant factors, including the particle size, roughness, observation angle and surface morphology. The study is focused on the relationship between the reflectance spectrum and the particle size of Anshan iron ore. Firstly, the reflectance spectra of the two main iron ores (hematite ore and magnetite ore) with different particle sizes are obtained by SVC HR-1024. Then, the influence of particle size on the reflectance spectra of both hematite and magnetite is analyzed. The sensitive waveband and stable waveband of the reflectance spectra related to particle size are extracted. The study indicates the following results. Firstly, the effects of particle size on the reflectance spectra of hematite and magnetite are different. For the hematite, the reflectance decreases with the particle size increase when the particle size is in the range of 0.03 to 1 mm. The effects are different in different waveband for the reflectance spectra of hematite. In the wave range of 350~550 nm, the particle size’s effect can be, neglected and this waveband can be regarded as the stable waveband. The influence is weak in the range of 550~950 nm and become obvious in the 950~1 250 nm range. In the range of 1 250~2 500 nm, the reflectance changes obviously with the particle size. Therefore, this waveband can be regarded as a sensitive waveband. However, when the particle size of the hematite is larger than 1 mm, the particle size’s effect decreased obviously without correlation. For the magnetite, the reflectance changes weakly with an amplitude of less than 3% when the particle size is in the range of 0.03 to 4 mm. There is no correlation between the reflectance and the particle size. This study revealed the quantitative relationship between the reflectance spectrum and the particle size for Anshan iron ore. The results can provide the foundation for improving the inversion accuracy of the ore grading for the Anshan iron.

As single-celled organisms, algae were small in size, easy to culture, sensitive to poisons, and able to observe toxic symptoms at the cellular level. They were ideal test organisms for rapid detection of biotoxicity in water quality. However, “algal growth inhibition test” relied on the reproductive and metabolic process of algal cells, and the measurement cycle was long, so it could not meet the needs of rapid detection of biological toxicity. The response speed and sensitivity of algae’s photosynthetic process to the toxicity of pollutants were significantly better than that of the “algae growth inhibition test”. The variable fluorescence Fv or maximum photochemical quantum yield Fv/Fm were mostly used as the endpoint of the biotoxicity reaction in the existing “photosynthesis inhibition experiments”, and the lack of comparative analysis on the sensitivity of response of multiple photosynthetic fluorescence parameters led to the low sensitivity of quantitative biotoxicity detection. Planktonic algae were ideal biotoxic test subjects, and Chlorella pyrenoidosa was taken as the subject, and photosynthetic fluorescence parameters of algae were used as toxicity evaluation indexes to study the response law of multiple photosynthetic fluorescence parameters under DCMU toxicity, to improve the speed of DCMU biological toxicity test and sensitivity, in this paper. The results showed: (1) The photosynthetic fluorescence parameters F0, Fm, σPSⅡ, τQA, Fv, Fv/Fm, Yield, rP, NPQ, α under DCMU toxicity were significantly responsive, and the responses of α, rP, Fv/Fm, Yield, NPQ in 5 minute were similar to the responses in 96 hour. (2) The inhibitory effect of α, rP, Fv/Fm, Yield, NPQ had a good dose-effect relationship with DCMU concentration. The correlation coefficient of parameter NPQ and EC50 were 0.998 5 and 2.41 μg·L-1, respectively, which are significantly better than the other four parameters and the measurement result of 96 hour. If the photosynthetic fluorescence parameter NPQ was used as the endpoint of 5 min quantitative measurement of DCMU biotoxicity, then the detection time of DCMU biotoxicity detection could be greatly shortened (from several hours to 5 min) and the detection sensitivity could be significantly improved (EC50 was 81.4% lower than the result which was calculated through the conventional parameter Fv/Fm). The experimental results provided basic data for the quantitative detection of DCMU biotoxicity based on the photosynthetic inhibition effect of algae, and the research method also provided a reference for the screening of photosynthetic fluorescence parameters of algae under the stress of other pollutants.

Rare earth doped upconversion luminescent micro-nano particles have great application prospects in anti-counterfeit identification. First of all, in the article NaYF4∶Yb3+/Eu3+ micro-nano particles prepared by hydrothermal synthesis method, The size, morphology and crystallinity of NaYF4∶Yb3+/Eu3+ micro-nano particles were investigated by X-ray diffraction (XRD)、 scanning electron microscope (SEM) and transmission electron microscope, and the luminescence properties of NaYF4∶Yb3+/Eu3+ micro-nano particles were analyzed using a 980 nm pump source; Secondly, NaYF4∶Yb3+/Eu3+ micro-nano particles and alcohol were mixed in a certain proportion to make a screen printing agent and combined with a network-customized screen template, different anti-counterfeit patterns were printed on the paper. After air drying, the words were exposed to a 980 nm laser and camera were used to study it. Finally, the printed words were divided into two parts, one was stored indoors at a constant temperature of 25 ℃, and the other was stored in the outdoor natural environment in January in winter. The storage locations are all Xi’an. After one week, in different environments the words were again tested with the same experimental instruments for imaging. The experiment and test results show that the diffraction peak of NaYF4∶Yb3+/Eu3+ micro-nano particles is a completely consistent standard card of NaYF4, and no other impurities are generated. In this experiment, The synthesized micro-nano particles are all hexagonal in shape, and the average length and cross-sectional width are 209 and 175 nm respectively. The surface of the nanocrystal is smooth, defect-free, unbent, with high crystallinity and good dispersion. The electron diffraction ring corresponds to the 312, 300, and 302 crystal planes of NaYF4∶Yb3+/Eu3+ micro-nano particles. NaYF4∶Yb3+/Eu3+ micro-nano particles are affected by doped ions, it produces four visible lights of blue, green, yellow and red by different energy level transitions. Through fluorescence spectrum analysis of NaYF4∶Yb3+/Eu3+ micro-nano particles. The asymmetry ratio of Eu3+ ion is about 1. This result shows that the magnetic dipole transition is equivalent to the electric dipole transition. Screen printing agent of NaYF4∶Yb3+/Eu3+ micro-nano particles are good in different environments, the results are clear and easy to identify. However, affected by the storage environment, The results of indoor imaging have not changed much from the original imaging results. All characters of outdoor imaging are affected by moisture in the natural environment, the brightness is slightly reduced, but they can still be recognized. The imaging results show that the prepared NaYF4∶Yb3+/Eu3+ micro-nano particles have the characteristics of stability and reliability in anti-counterfeiting identification, but they are still affected by natural environmental factors with a controllable degree. On the whole, it has great application prospects in anti-counterfeiting identification.

In recent years, some low-temperature heated corundum has occurred in the gem trade market, which characteristics are similar to nature corundum. Therefore, how to distinguish low-temperature heated corundum has become a research hotspot in gem laboratories. In this paper, nine samples were treated at 360, 610 and 650 ℃ in a weak oxidizing environment, Micro-Raman spectrometer is used to determine the mineral inclusions, the microscope is used to observe the change of inclusions, and Micro-Infrared spectrometer is used to analysis of hydrous minerals and so on, to compare the characteristics of corundum before and after low-temperature heat treatment. Heat treatment reveals that around 600 ℃ temperature and weak oxidizing environment, the blue tone decreased and the red tone increased in the corundum, which could achieve the purpose of heat treatment to improve or change the color of the corundum. Hydrous minerals such as goethite, kaolinite and boehmite exist in the open fractures of corundum, diaspore, apatite and mica exist in the corundum crystal. FTIR absorption peaks of hydroxyl (—OH) differ from different minerals. Following is before heat treatment hydrous inclusion FTIR absorption spectra: goethite shows —OH related absorption peak in 3 435 cm-1 and absorption band center in 3 185 cm-1, kaolinite shows a group of absorption peaks in 3 620, 3 648, 3 670 and 3 698 cm-1, boehmite shows absorption peak in 3 086 and 3 311 cm-1, diaspore shows a peak in 1 980 and 2 110 cm-1, apatite shows a peak around 3 550 cm-1, and muscovite shows peak around 3 624 cm-1. After 360 ℃ heat treatment, related absorption peaks of —OH in goethite disappeared. After 610 ℃ heat treatment, related absorption peaks of —OH in kaolinite, boehmite and diaspore also disappeared, but the apatite peaks still exist. After 650 ℃ heat treatment, —OH related absorption peak in muscovite still exists. After heat treatment, goethite turns from yellow to red. Although diaspore has been dehydrated and phase changed, it still presents needle-like crystal pseudomorph. The crystal shape of apatite and muscovite has no change, and the transparency of muscovite is slightly reduced. The study provides that hydrous mineral inclusions are crucial to identify low-temperature heated corundum.

Water pollution has become one of the most serious environmental problems in the world nowadays. Improving the detection sensitivity in water pollution, minimizing the limit of detection, reducing the sample pretreatment procedures, and achieving in-situ analysis have become a focus of scientific research. In this paper, a new gelatin hydrogel curing method was presented, the concentration of Cu in CuSO4 solution was analyzed by laser-induced breakdown spectroscopy (LIBS) based on this method. A Nd∶YAG laser (output wavelength: 1 064 nm, pulse width: 8 ns) was used as the laser source. The gelatin was mixed with CuSO4 solution, and then the mixture was made into a gel-like solid by heating, stirring, aging, etc. Cu Ⅰ 324.7 nm and Cu Ⅰ 327.4 nm was selected as the analytical spectral lines. The optimal experimental conditions with 2.5% mass of gelatin CuSO4 solution were obtained by analyzing the relationship between the mass ratio of CuSO4 solution and the spectral intensity. Compared with the direct analysis method, the spectral intensity of Cu Ⅰ 324.7 nm and Cu Ⅰ 327.4 nm increased by 2.26 and 2.11 times, and the signal-to-back ratio was enhanced by 190.74 and 318.77 times, respectively. Under the optimal experimental conditions, gelatin gel samples of CuSO4 standard solutions with Cu2+ concentration of 8, 12, 16, 24, 48, and 64 mg·L-1 were prepared, the LIBS spectrum was obtained and analyzed on the 6 gelatin gel samples with the energy of 60 mJ/80 mJ/100 mJ, calibration curves of analytical lines were established. At the energy of 100, 80 and 60 mJ, the linear fitting coefficient R2 of Cu Ⅰ 324.7 nm are 0.999/0.989/0.984, and the limit of detection are 0.30, 0.66 and 6.37 mg·L-1, respectively; The linear fitting coefficient R2 of Cu Ⅰ 327.4 nm were 0.997/0.973/0.956, and the limit of detection were 0.45, 0.88 and 10.20 mg·L-1, respectively. The results show that the LIBS spectral intensity of the copper element in the CuSO4 solution can be enhanced with the gelatin hydrogel curing method. The LIBS sensitivity in water pollution can be improved effectively. The limit of detection can also be reduced significantly. The linear fitting coefficient R2 and the limit of detection at Cu Ⅰ 324.7 nm are better than those at Cu Ⅰ 327.4 nm. The linear fitting coefficient R2 and limit of detection increase with the laser energy enhancement. The linear fitting coefficient R2 with the calibration curve of Cu Ⅰ 324.7 nm was 0.999, and limit of detection was 0.30 mg·L-1 at 100 mJ, which reached the detection level of the enrichment method. The gelatin hydrogel curing method with simple sample preparation procedure and without contaminating elements, provides a new method for the application of LIBS technology in the detection of water pollution.

Aphids (Aphis gossypii) are one of the main pests of cotton. The annual loss of China’s cotton yield due to aphids is as high as 5%～10%. Monitoring the grading profile of field-scale aphid damage can assist the precise application of quantified pesticide and reduce environmental pollution. The hyperspectral imaging data obtained by unmanned aerial vehicle (UAV)-mounted imaging spectrometer has the advantages of high resolution, high timeliness and low cost. The method based on derivative of ratio spectroscopy has the advantages of simple, efficient and highly accurate, which can be effectively applied to the remote sensing spectrum unmixing process, and extract more sensitive bands to the target information, providing an effective means for the establishment of pest monitoring model. Therefore, in this study, the Korla region of Xinjiang, a typical cotton production area, was selected as the experimental area to carry out the following work. (1) to use a low-altitude-unmanned-aerial-vehicle-based hyperspectral imaging instrument to acquire hyperspectral images of cotton (Gossypium) canopies. Spectral data of 76 sample points and severity of aphid damage were obtained (including 16 healthy plants, and 15 were selected from each grade of 1～4 severity of aphid damage. (2) to use the derivative of ratio spectroscopy (DRS) to select sensitive spectral bands from cotton canopy spectra to detect aphid damage at the bud stage, band 514 nm, band 566 nm band 698 nm; (3) to construct unary-linear-regression and partial-least-squares models based on the sensitive bands of reflectance spectra and derivative of ratio (DR) spectra for rating aphid damage. The rexperiments’ results revealed the following: (1) aphid damage had a significant effect on the spectral reflectance of the cotton canopy. The more seriously cotton plants were affected by the aphid, the higher the reflectivity in the visible region and the lower the reflectivity in the near-infrared band, and the “blue shift” occurred in the red envelope region. (2) sensitive bands for detecting aphid damage were effectively extracted from the DR spectra of the cotton canopy, and the three selected bands (with wavelengths of 514, 566, and 698 nm) were consistent with the sensitive bands extracted by using the correlation coefficient method; (3) )the precisions of the aphid-damage-rating models constructed using DR spectra of the sensitive bands were better than those of the models constructed using of the sensitive bands from the reflectance spectra, among which the model constructed with 698 nm band had the best accuracy（R2=0.612, RMSE=0.89）; (4) based on derivative of ratio spectroscopy method, the UAV imaging spectral monitoring model of cotton aphid infestation can obtain the spatial distribution map of different severity aphid infestation on the field scale, which is of great significance for precisely quantified pesticide.

The centuries of development of fine white porcelain in China has made the shortage of high-quality clay mineral resources increasingly serious. How to remove impurities of low-/medium-quality clay minerals and effectively utilize them have been attracted many attentions from potters, artists and material researchers. Iron, as a common impurity in minerals, plays a key harmful role in ceramic productions. One of the key technologies of improvement of low-/medium-quality clay minerals is to remove iron impurities. However, the unclear understanding of occurrence forms of iron in clay minerals limits the development of purification technology. The red clay in Guangdong province has a wide distribution and large storage. However, the high iron content restricts its further utilization as a high-quality resource. In this work, the occurrence forms of iron impurities in the red clay from Meizhou city, Guangdong province were investigated. Three types of impurities (red, yellow and gray green, respectively) in the red clay were selected and separated elaborately via washing and sieving processes. X-ray fluorescence spectrometer, scanning electron microscope equipped with energy disperse spectroscopy, transmission electron microscope equipped with selected area electron diffraction, visible-near infrared spectroscopy and first derivative spectra of visible absorption spectra were exploited to analyze the mineral assemblages, chemical composition, microstructure and spectral characteristics of the separated clay minerals. The occurrence forms of iron were identified. The results showed that iron was the key element that affected the appearance of the red clay. The clay minerals in the three samples were mainly composed of kaolinite and illite. In the red and yellow impurities, iron was absorbed or clamped on the surfaces of laminated clay minerals in the forms of hematite-goethite aggregate. In gray-green impurity, the iron was in the form of ions locked in Fe-Mg muscovite structure. The results also found that during the separation of red clay minerals, iron impurities were separated together with clay minerals from the red clay in the forms of both free and structural irons. This work is helpful for the selection of purification methods for southern red clay and for improving the processing efficiency and quality of low-grade clay.

As a carrier for inheriting human civilization and witnessing historical development, the aging mechanism and dating of traditional handmade paper are considered to be extremely valuable. The reduction of aged handmade paper’s various macro performances is ultimately due to the attenuation of physical and chemical properties on the nano/micro-scale. Therefore, the characterization and analysis of the micro-scale structure of the traditional handmade paper is the key to understanding the aging mechanism of paper and accurate assessment of aging status as well as the basis for realizing its value. Spectral analysis has attracted much attention since its excellent Spatio-temporal resolution, fast response, high signal-to-noise ratio and good sensitivity. Moreover, precious paper samples’ testingis no longer restricted because of non-destructive or micro-destructive characteristics, obtaining detailed information of aged handmade paper from nano/micro-scale. Herein, the review, which mainly focuseson non-destructive or micro-destructive spectroscopy analytical methods including IR, UV/Vis, Raman, NMR, THz, X-ray, LIFS, hyphenated spectroscopy and microscope on the most recent decade, is to provide interior and surface information of the aged paper at a micro/nano level (element, chemical structure, chemical composition and micromorphology). These analytical results are beneficial to establish a platform for multiscale detection in complicated conditions via exploring the relationship between macro and micro structure of the handmade paper, which play an important role in dealing with its state of conservation, repair work, and aging mechanism of handmade paper.

The incident light may induce surface plasmons noble-metal nanomaterials, which are the collective oscillations of the surface electrons in the metal. When the frequency of the surface plasmons matches the incident light, plasmonic resonance occurs with a special electro-magnetic and spectral property. Ultilizing this property, the spectral behavior of metallic nanomaterials can be adjusted. For example, by varying paramters including the size, shape, and dielectric constants the nanostructures’ background materials, the spectral signals can be effectively controled. So far, the surface plasmons of noble-metal nanomaterials with certain symmetries have been widely studied and applied. In addition, the spectral properties of asymmetric metallic nanostructures have also drawn great attention in the community. It has been shown that one important problem in designing plasmonic optical sensors in the visile-near infrared regime is how to achieve the effective control of key parameters of the extinction spectra, including the resonance wavelength, spectral width, and peak intensity. In this work, an asymmetric structure consisting of two silver nanorings is proposed. With the finite-difference time-domain method, the nanostructure’s extinction spectra are studied in the visible-near infrared regime by varying the parameters, including the rings’ radii, the separation, and the light’s polarization. The results indicate that two independent surface plasmons resonances are induced in the extinction spectra in the wavelength range of 0.4~3 μm. It is found from the electric fields that the resonances are correlated to two different electro-magnetic modes. It is also revealed that the two different resonance peaks in the extinction spectra can be independently adjusted by changing different parameters of the double nanorings. The shorter resonance wavelength and its spectral width can be tuned by varying the sizes of the nanorings, while the longer resonance wavelength and its spectral width can remain almost the same. Besides, The peak intensities of the two resonances can be adjusted in different trends by changing the nanorings’ separation or the polarization angle of the incident light. In this work, two plasmonic resonance peaks that can be respectively controled are revealed in the extinction spectra of the asymmetric silver double nanorings; the results shown in this work may provide us with theoretical foundations in the design of photoelectric sensors in the visible-near infrared region.

The oilfield brine in Nanyi Mountain, located in the west of Qaidam Basin China, is rich in iodine, which is valuable to exploit. The accurate determination of iodine is very difficult because of the high salinity and complicated composition of the oilfield brine. Inductively coupled plasma atomic emission spectroscopy (ICP-AES) has the advantages of fast, wide linear range, low interference. ICP-AES exhibits low sensitivity and high detection limit for direct determination of iodine due to its high ionization energy, resulting in hardly meet the analysis requirements of trace iodine in hypersaline water. A device integrating sampling, chemical reaction and gas-liquid separation was designed so that the iodide ion could be oxidized to iodine and then imported into ICP-AES for determination. The effective injection volume was increased, and the sample matrix’s influence was reduced to reduce the detection limit, and a rapid detection method for trace iodine in oilfield brine with ICP-AES was established. Better conditions of oxidation were 10 mmol·L-1 NaNO2 and 1 mol·L-1 HNO3. The detection limit at I 178.276 nm was 1.65 μg·L-1, and measurement could be completed in three minutes. Partial factor experiments were designed to investigate the interference of main coexisting ions (such as potassium, sodium, calcium, magnesium, lithium, strontium, ammonium). Results indicated that calcium had a significant effect on the determination of iodine at the 95% confidence level. This interference could be eliminated by appropriate dilution. The iodine contents in practical oilfield brine of different evaporative concentration stages were determined with a standard curve, and the recoveries were in the range of 90%～104%. This method was characterized by simple operation, low matrix interference and high accuracy. It will be a promising technique for detecting iodine in oilfield brine samples in the future, which can provide basic data for the extraction technology of iodine.

Tunable diode laser absorption spectroscopy (TDLAS) is a branch of spectrum detection technology with high sensitivity, high resolution, real-time monitoring, good portability and miniaturization. It has been widely used in environmental protection, medical treatment, meteorology and other fields. The accuracy of the TDLAS gas sensor is closely related to the calibration curve. The least square method is utilized to perform polynomial fitting on the calibration curve. However the least square method is based on the least square sum of absolute errors as the evaluation criterion. It cannot restrict the relative error. As a result, the relative error of the calibration curve of the TDLAS gas sensor at low concentration ranges is too large. This paper proposes the least square method based on relative error. The relationship between the logarithm of light intensity transmittance and gas concentration is derived as the objective function. The iteration method uses the Gauss-Newton iteration method (Gauss-Newton iteration method). In the experiment Yashilin DHS-100 constant temperature and humidity box were used to generate a large range of water vapor calibration concentrations. Vaisala HMT337 online humidity detector’s value was used as the calibration concentration. Self-developed TDLAS humidity sensor selects the water vapor absorption peak with 7 306.752 1 cm-1. The optical path of the air chamber is 50 mm. The water vapor concentration of 1%～50%VOL is calibrated. The calibration results of the least square method and least square method based on relative error are compared. The experimental results show that when using the least square method for curve fitting, the calibration curve will have a large relative error in the low concentration range. In the high concentration range the relative error gradually decreases. This cannot guarantee the measurement accuracy requirements for the entire large range. When using the relative error least square method for curve fitting, the relative error curve is relatively stable in the whole range. The maximum relative error and the relative error standard deviation are much lower than the fitting result of the least square method. When the relative error least squares method is used and the Ratio-C formula is used as the objective function for fitting, the maximum relative error is 0.049 4 and the relative error standard deviation is 0.023 7. The fitting result is far better than the fitting result of the least square method. The reliability of the calibration algorithm of relative error least squares is verified. The measurement accuracy of the TDLAS gas sensor is improved.

Underwater wet welding technology has been widely used in recent years, but there are few pieces of research on the physical nature of underwater wet welding arcing process now. In this paper, an underwater wet welding arc spectrum diagnosis platform was set up, voltage and spectrum signals in the welding process were collected simultaneously under different water depth conditions to define the arcing stage of underwater wet welding. The high-speed camera recorded the arcing process of underwater wet welding to observe the underwater dynamic changes such as arc and bubble more intuitively. On this basis, spectral signals of 5, 10, 15, 20 and 25 ms were collected by the spectrometer, the arc spectrum at a different time under different water depth conditions was obtained by changing the water depth condition. According to the principle of line selection, the Fe elements were selected as the characteristic elements to calculate the arc temperature of underwater wet welding. We selected five sets of data at a different time of arc initiation and averaged them by statistical analysis method to ensure the accuracy and reliability of the calculation results. Five suitable lines were selected from the Fe element line as the target line to calculate the arc temperature of underwater wet welding arc-initiating process. And the plasma temperature of underwater wet welding at different time under different water depth conditions was calculated by Boltzmann graphic method. The results show that the arc plasma temperature changes with the increase of arc initiation time, but its variation trend is not a simple linear increase, but a peak at different times of arc initiation. With the increase of water depth, the temperature of underwater wet welding arc plasma also increases, but the rising trend of arc temperature begins to change slowly. The increase in arc temperature at a depth of 40 m relative to a depth of 20 m is lower than the increase in arc temperature at a depth of 20 m relative to a depth of 0.3 m. With the increase of water depth, the increase of underwater environment pressure causes the arc to be further compressed, but the compression amount is limited. As the arc is compressed, the intensity of the arc light also increases. By means of spectral analysis, the physical nature of the arc-initiating process in wet-welding under water is learned from the perspective of arc physics, which provides an important reference for understanding the micro-breakdown mechanism during the arc establishment process and further improving the stability of the arc starting process in actual production.

The quality of agricultural products and food has always been one of the focuses of attention. The quality and safety of agricultural products and food are related to people’s health and related to social stability and even national security. In recent years, the safety incidents caused by the unqualified quality of agricultural products and food have attracted all social circles’ attention. The supervision of the quality of agricultural products and food has been the key point even difficulty in analysis and detection for a long time. Given a large population, the consumption of agricultural products and food is enormous in China. In the face of such a large number of the needs of non-destructive and rapid detection of agricultural products and food quality, spectroscopy analysis can provide a good solution for the non-destructive and rapid detection for agricultural products and food with the characteristics of fast, non-destructive, efficient, environmentally friendly, on-site testing. However, due to the large amount of data used in the traditional spectral analysis, it is time-consuming in developing calibration models and difficult to complete the online, high-throughput, non-destructive and rapid detection of the large number of agricultural products and food quality. On the other hand, the calculation of such a large number of data has also become one of the main bottlenecks limiting the efficiency of spectral analysis instruments, and the calculation of a large number of data also puts forward very high requirements for the hardware configuration of the instruments, which will increase the application cost of spectral analysis technology indirectly. In recent years, key variable selection has emerged and become a new hotspot of spectral analysis. According to the selection, calibration models can be developed by a few numbers of the key variables, which are of almost the same accuracy to the models developed by the full spectra. Thus it can improve the analytical instruments’ working efficiency effectively and reduce the application cost of the spectral analysis technology. It will also provide reliable technical support for the high-throughput detection of agricultural products and food quality and provide the scientific and technological support for meeting the increasing demand of the people for a better life. In this paper, the applications of spectral key variable selection in the non-destructive detection of grain and grain crops, vegetables, fruits, cash crops, meat, food quality and safety were reviewed. With summarizing others’ works in recent years, the applications of spectral key variable selection technology were summarized from the aspects of selection method, application scope, application effect, and so forth. Finally, the application of spectral key variable selection technology in non-destructive detection of agricultural products and food quality prospected from the aspects of the characteristics and trends of the variable selection methods, the stability and reliability and the practical significance of the selected variables.

In recent years, the spectral data of celestial bodies observed have achieved a dramatic increase thanks to the successful implementation of various projects of spectral sky survey. Therefore, higher requirements for the automatic classification and analysis of spectrum are proposed for large-scale projects of spectral sky survey. The classification problem is transformed into a regression one in this paper, and a method of spectral category regression based on the residual depth network is put forward to conduct a prediction of MK spectral subtype on stellar spectrum. The network is mainly composed of 25 convolution layers, 1 maximum pooling layer, 1 average pooling layer, full connection layer and 12 residual structures. The maximum pooling layer is used to filter features, the convolution layer to extract features, and the average pooling layer to reduce parameters and improve efficiency. The residual structure can prevent the degradation of the network, extract high-dimensional abstract features by deepening the network and improve training speed. Considering the non-zero probability of data with false labels and corrupted data, Log-Cosh is adopted as a loss function in this paper to reduce the negative impact of bad samples. 80 000 spectra that are randomly selected from LAMOST DR5 are used as the experimental data. The spectra are divided into the training set, verification set and test set according to the proportion of 7∶1∶2 after eliminating the bad value and normalizing the flow. The experiment includes two parts. In the first part, the network is adopted to carry out a prediction on the spectral subtype, and the maximum absolute error, the average absolute error and the standard deviation are used to compare the performance of convolution kernels with different shapes. The predicted value is taken as the abscissa and the label as the ordinate, and the second-order nonlinear fitting is used for all sample points in the test set, a straight line that is coincident with y=x is obtained, proving that the model can predict the spectral subtype well. The second part is concerning the internal analysis of the model. The main characteristics of the model in predicting four types of spectra, A, F, G, K, are mainly explored with the method of category activation mapping, thus endowing the model with interpretability. In the text data set, 91.4% of the spectral prediction errors of this method are within 0.5 spectral subtypes, and the average absolute error of the prediction is 0.3 spectral subtypes. It is shown that the method proposed in this paper can better predict spectral subtypes with faster speed and higher accuracy according to the comparison of the same data set with nonparametric regression, Adaboost regression tree and K-means.

When observing the spectral signal of an artificial space target, because of the long observation distance and the low spatial resolution of observation equipment, the spectral signatures of multiple pure materials in a certain instantaneous scene is combined in one pixel to form a “mixed spectrum”. Therefore, unmixing these mixed spectra into the collection of pure material spectra and estimating the corresponding fractional abundances have been increasingly significant in the field of spectral analysis for artificial space targets. Most existing spectral unmixing methods assume that the number of pure materials (that is, “the number of endmembers”) contained in mixed spectra of an artificial space target is known as a priori, which is unrealistic for unknown artificial space targets. Therefore, the exact estimation of the number of endmembers plays a significant role in the accuracy of subsequent spectral analysis and processing. At present, the existing methods of endmember number estimation are mostly proposed under the assumption of Gaussian white noise interference. However, when the distribution of the noise signal is a spectral correlation, poor estimation results will be provided. In this paper, based on the intrinsic dimensions of data and the theory of maximum likelihood, a Robust Eigenvalue Maximum Likelihood (REML) method is proposed. By analyzing the statistical distribution characteristics of differences between the eigenvalues of the signal covariance matrix and those of signal correlation matrix, a maximum likelihood function can be established to estimate the number of endmembers contained in mixed spectra. This method consists of two steps: first, the original spectral data is pre-processed using multiple regression and a modified minimum noise fraction method to complete the noise estimation and whitening process, thereby effectively suppressing the interference of spectrally correlated noise. Then, the number of endmembers is estimated by solving a discrete logarithmic maximum likelihood function. This method does not require any input parameters and runs fast. Simulation experiments are based on synthetic mixed spectral data generated by the visible/near-infrared spectral signatures of five varieties of artificial space target materials measured in the laboratory and U. S. Geological Survey spectral dataset. And, experimental results demonstrate that this method can effectively suppress the interference of spectrally correlated noise and Gaussian white noise, and the estimation results have good accuracy and stability.

The spectrum of lightning plasma is characterized as a rich linear spectrum of NⅡ, NⅠ, OⅠ and HⅠ superimposed on strong continuous radiation background, of which the temperature of the lightning return channel can be higher than 10 000 Kelvin and in the channel nitrogen and oxygen molecules are nearly completely dissociated. When the continuum spectrum is analyzed, the influence of each component’s molecular band spectrum on the continuum spectrum is not taken into account. The cloud-to-ground lightning discharge spectrum was recorded by a slit-free grating spectrograph with a spectral range of 400～1 000 nm, and a large number of univalent nitrogen ion spectra were observed in the low-frequency range of visible region of the spectrum, and no other evident ion spectra were observed. It is believed that the continuous radiation is mainly produced by the interaction between nitrogen ions and free electrons, including bremsstrahlung radiation and recombination radiation. In terms of bremsstrahlung radiation, when the plasma temperature is below 1×104 K, the continuous spectrum is characterized as a flat spectrum, the radiation intensity is weak, and the intensity increases in the ultraviolet section with the increase of the plasma temperature, which has no significant effect on the profile feature of a continuous spectrum of lightning plasma invisible sections. In terms of recombination radiation, assuming that the lightning plasma features the local thermodynamic equilibrium and is optically thin, and based on the classical radiation theory of hydrogen-like ions, the Gaunt Factor is introduced for quantum mechanical modification. Besides, considering that the continuous spectrum may occur in a highly excited state with large probability as ions bind free electrons during recombination, the approximate calculation method for non-hydrogen-like complexions is employed to analyze the recombination radiation of nitrogen ions, and then the functional relation between the recombination radiation coefficient and the wavelength of the continuous spectrum is derived to obtain the characteristic curve of continuous radiation spectrum of nitrogen plasma under specific parameter and to compare with profile observations of continuous lightning spectrum. Thus, it is found that the plasma electron temperature is closely related to the position of continuous radiation spectrum peak, therefore concluding that the surface electron temperature of lightning discharge channel can be reliably diagnosed by the profile fitting of the continuous lightning spectrum. Furthermore, the Z* value of the real effective nuclear charge number of nitrogen ions also has a significant effect on the continuous spectrum characteristic. In other words, if the Z* value is small, the jumping feature of the continuous spectrum will be enhanced, and if the Z* value is large, the broadening feature of the continuous spectrum will be enhanced, and the deviation from the bottom of the measured spectrum profile will be increased. By comparison, it is found that, when the Z* value is taken from 2 to 4, the theoretical curve is in good consistency with the measured spectrum profile. The range of Z* value is determined by the ion species, and the introduction of effective nuclear charge number of ions, the Z* value, may fully explain the jumping feature of the continuous spectrum of lightning plasma at a particular wavelength.

Chromium (Cr) is one of the main target elements in the evaluation of soil heavy metal pollution in the black soil area of Northeast China. With the introduction of aerial hyperspectral technology, the hyperspectral inversion of Cr content has the data basis for a wide range of applications, among which the accuracy and application range of the hyperspectral model are the key factors affecting the quality of the investigation. The commonly used modeling method is to extract spectral features and build models by various statistical means. The limitations are that the model parameters are difficult to be explained, the modeling results are greatly influenced by sample selection, and the generalization ability is poor. In this study, based on the occurrence law of Cr in soil, a new indirect inversion model based on the influencing factors and spectral characteristics of Cr was designed to improve the applicability of the model in different regions. Two research areas of Jiansanjiang and Hailun in Heilongjiang Province were selected. The hyperspectral data came from the CASI/SASI aerial hyperspectral imaging system. The band range was 380～2 450 nm. The number of ground samples in Jiansanjiang and Hailun were 225 and 121 respectively. The physical and chemical parameters of Cr, SOM, N, P, K2O, SiO2, Al2O3, Fe2O3, CaO, MgO, Na2O and pH were obtained by chemical analysis. The partial least square method was used in modeling. The analysis results of the occurrence rule of Cr show that Cr in both research areas shows a very significant positive correlation with Al2O3, Fe2O3, MgO, K2O and pH, and a very significant negative correlation with SiO2, Na2O and SOM. This feature provides a foundation for the establishment of indirect inversion models. The analysis results of the spectral characteristics of Cr in the two regions together show that the spectral reflectance has the most obvious correlation with the Cr content after standard normalized variable (SNV) transformation, and the characteristic bands are 1 520, 2 195, 2 210 and 2 225 nm. The characteristic band after SNV transformation was taken as the independent variable of the pure spectral model. The SNV characteristic band and the above-mentioned soil components closely related to Cr were taken as the independent variable of the indirect inversion model. The modeling results show that, compared with the pure spectral model, the indirect inversion model significantly improved the inversion accuracy of Cr. In the Jiansanjiang area, the modeling R2 has been improved from 0.643 to 0.751, and the verification R2 has been improved from 0.571 to 0.687. In the Hailun area, the modeling R2 has been improved from 0.537 to 0.676, and the verification R2 has been improved from 0.471 to 0.643. The root mean square error (RMSE) of the indirect model is also reduced. The experimental results of model migration between the two study areas show that the migration ability of the pure spectral model is poor, and the regression R2 of measured and predicted values after model migration is close to 0, while the migration ability of the indirect model in the two study areas is significantly improved. When indirect inversion model of Hailun was applied to Jiansanjiang, the regression R2 of measured and predicted values reaches 0.597 5, while the indirect inversion model of Jiansanjiang was applied to Hailun, the regression R2 is 0.577 3. The results can provide a new method for large-scale inversion mapping of Cr in the soil in different areas.

The soil heavy metal pollution poses a great threat to the human health, thus, it is quite important make out the contamination in the soil. There are a series of advantages in the hyperspectral remote sensing technology, such as the high spectral resolution, rapid response, non-destructive, etc., making it a well- suited in retrieving the soil’s components. In this study, the impacts of the information redundancy in the spectral and spectral transformation on the inversion of Cd content in the soil are investigated. Further, based on the hyperspectral data before and after spectral transformation, the performance comparations of hyperspectral models are carried out in this paper, as well. By so doing, the Cd contents and the corresponding lab spectrum (350～2 500 nm) of 56 soil samples are measured by the ICP-MS and ASD Fieldspec4. Then, the reciprocal and logarithm changes are performed to weaken the impacts of the light variation and soil surface roughness on the experimental results. Due to the fact that there is much redundant information in the obtained data, the Principal Component Analysis (PCA) is carried out to reduce the dimensionality of the spectral bands in the data. After this processing, only 12 principal components are selected as the input variables of the model. Regarding the hyperspectral models, the Partial Least-Squares Regression (PLSR), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) are chosen to establish the relationship between the Cd content and PCA components. Finally, for evaluating the prediction capabilities of the regression models, three precision evaluation indexes are preferred to assess the accuracy of regression models in this study, they are the correlation coefficient (R2), Root Mean Squared Error (RMSE) and Residual Predictive Deviation (RPD). Analysis results show that the cumulative contribution rate of 12 principal components of the original data after processed by the PCA can be up to 99.99%. Using principal components as the inputs, all four hyperspectral models show excellent performances in predicting the Cd content in the soil. The PCA-RF, in particular, has the most accurate prediction capability regardless of whether the spectral transformation is performed or not (whose R2 before and after spectral transformation are 0.856 and 0.855, respectively, while the RPD under both conditions are 3.39). In conclusion, the PCA is used to reduce hyperspectral data’s dimensionality, this processing can effectively reduce the redundancy of hyperspectral data and guarantee the predictive capability of hyperspectral models. Also, the principal component selected by the PCA method could be excellent input variables of the hyperspectral models. Further, the hyperspectral model based on the PCA-RF shows the most excellent performance for rapid detecting the Cd element in the soil within the study area and similar regions, which could be a new supplement for the inversion of heavy metals in the soil.

Since the State Council promulgated the “Atmospheric Pollution Prevention Action Plan” and other comprehensive atmospheric control policies in 2013, the concentration of atmospheric pollutants and other important pollutants in eastern China has been effectively controlled. Along with the changes in China’s energy policy and increasing energy development in the northwest region, air pollution also shows an increasing trend, but it has not been paid attention to in previous studies. Compared with ground observation, the monitoring method of satellite remote sensing has the advantages of not being restricted by area, lasting long observation time and monitoring many types of pollutants simultaneously. The spaceborne ultraviolet-visible hyperspectrometer OMI has been in orbit since 2005 and has been widely used in scientific applications such as the detection of the temporal and spatial changes of atmospheric pollution, the estimation of emission sources, and the assimilation and verification of models. USTC’s tropospheric NO2 column concentration product, through the secondary calibration of the OMI original measurement spectrum and the key optimization of the gas inversion algorithm, shows a good correlation in the comparison and verification with the results of ground-based observations. Analysis of air pollution in the background of sol. Combined with USTC’s OMI NO2 data product, the spatial and temporal distribution characteristics of atmospheric NO2 pollution in Xinjiang, China, can be characterized. From 2007 to 2017, NO2 pollution in Xinjiang was concentrated in northern Xinjiang. Among them, the “Urumqi-Changi-Shihezi” urban agglomeration (“Wuchangshi” area) had a strong correlation with the monthly change of the overall NO2 level in Xinjiang (correlation coefficient R=0.942, p-value<0.01). The inter-annual change of NO2 in Xinjiang has obvious phase characteristics, which is consistent with the changes in relevant policies and energy industry emissions: the changing trend is not obvious from 2007 to 2010, the overall average concentration in 2014 increased by 18.5% compared with 2010, and the “Wuchangshi” area increased by 41.3%, in 2017, the overall average concentration of NO2 decreased by 26.4% compared with 2014, and “Wuchangshi” decreased by 42.8%. Due to the dense distribution of petrochemical enterprises and economic development zones in the “Wuchangshi” area, it has become a NO2 pollution gathering area, which has a strong correlation with Urumqi and Changi NO2 changes (R=0.982, p-value<0.01; R=0.951, p-value<0.01). Controlled by the heating period’s emissions and the special meteorological conditions, the peak of NO2 change in the “Wuchangshi” area in December, and the winter pollution is particularly significant. The heating period (from October to early April each year) has a significant upward trend from 2007 to 2016 (Significance level α=0.01), which requires special attention in future atmospheric governance.

Field spectroradiometer is becoming more and more important in the remote sensing area. It is used to investigate the spectral radiometric characteristics of different geographic environment, such as spectral radiance, spectral irradiance or spectral directional reflectance factor from visible to infrared wavelengths. The accuracy of the spectral measurements plays an important role in space sensor calibration and remote sensing data inversion. It is the basis of quantitative analysis in optical remote sensing. Field spectroradiometer must be calibrated before use. On the one hand, the spectral responsivity may change during the calibration process. On the other hand, the field measurement environment may be totally different from the calibration conditions. The measurement accuracy cannot be guaranteed. The experiment is designed to investigate the influence of the environmental conditions on spectral responsivity. At constant temperature and humidity, spectral line lamp, and integrating sphere lamp are used to test whether the wavelength and spectral responsivity change or not when the silicon array detector’s temperature is changed. Results show that the wavelength is nearly unchanged when the silicon array detector’s temperature rises. However, the spectral responsivity rises as the temperature rises. When the silicon array detector’s temperature rises from 28.3 to 35.2 ℃, the spectral responsivity increases by 1.8% to 7.3% from 380 to 990 nm, 3.0% from 1 000 to 1 800 nm and 1.9% from 2 000 to 2 500 nm. When the environment humidity and temperature are changed, results show that humidity only affects the spectral responsivity near the water molecules’ absorption peaks. Also, results show that the spectral responsivity has nearly one to one correspondence with detector temperature. The influence of environment temperature can be nearly characterized according to the detector temperature change. When a group of detector temperature and corresponding spectral responsivity is measured, the relationship between the detector temperature and spectral responsivity can be obtained using the least square method. The spectral responsivity at other temperature can be calculated by interpolation. The measurement data at different environmental conditions can be corrected as long as the detector temperature is measured. The spectral responsivity difference between the calculated and measured results is less than 0.2% when the detector is 35 ℃, which show that the relationship between the detector temperature and spectral responsivity can be used to solve the environment problem.

The mammalian circadian system has a different sensitivity to various spectral components. The chronically alternating light-dark cyde (“jetlag”) has been shown to cause circadian disturbances and increase the risk of metabolic diseases. However, it remains unknown whether the spectral component affects the metabolic effects under “jetlag” light cycles. In this study, broadband white light-emitting diode (LED) and narrow-band LEDs ［blue light (BL) and red light (RL) with significantly different sensitivity to circadian system］ were used to analyze the effect of the spectral component on the metabolism under normal and aberrant light cycles in C57BL/6J mice. All the light intensities is 120 μW·cm-2. The results showed that jetlag white light (WL) mice exhibited the most body weight gain. Jetlag RL mice suffered from significant lipid metabolism disorders and impaired liver function. Jetlag WL significantly reduced glucose tolerance and insulin sensitivity, while RL and BL prevented jetlag mice from an increase in fasting serum glucose. This study shows that modulating the spectral component may improve the adverse effects of the “jetlag” light pattern on glucose and lipid metabolism.

Pipeline transportation has great advantages in long-distance transportation of oil and gas, and it is crucial to detect pipeline safety. In order to ensure the effective detection of the pipeline condition at any time, infrared imaging technology is of great significance in the field of pipeline detection because it can reflect the characteristics of the target according to the thermal radiation information of the object and ignore the influence of visible light.However, due to the diversity of the outdoor environment, infrared pipeline images have many problems, such as the uneven distribution of target features, pipeline occlusion and background target interference. These problems increase the difficulty of extracting pipeline targets, which is not conducive to the segmentation and detection of pipelines. The biological immune system exhibits excellent recognition, learning, memory, tolerance and coordination in antigen detection, extraction and elimination. These characteristics are lacking in current complex system optimization strategies. Based on the biological nervous system’s mechanism regulating immune system, a neural immune network is designed to detect and extract infrared pipeline targets in complex background. According to the regulatory mechanism of the biological neural network in the immune system, the neural network for infrared pipeline target location is constructed using the basic pipeline shape feature model. In this paper, three typical infrared pipeline images are selected, and the traditional target detection algorithm is compared with the algorithm based on neural immune network. The true positive rate of the traditional target detection algorithm is 0.405 6, and the neural immune network algorithm is 0.980 5. The Jaccard similarity coefficient of the traditional target detection algorithm is 0.271 8, and the neural immune network algorithm is 0.944 4. The absolute error rate is 0.117 5, and the neural immune network algorithm is 0.011 8. The results show that the true positive rate of the neural immune network algorithm is 0.574 9 higher than that of the traditional algorithm, and the absolute error rate is 0.105 7 lower. It proves that the proposed algorithm can extract the complete infrared pipeline target more accurately than the traditional method in the complex background. This network structure can improve the detection efficiency for pipeline safety.