All-inorganic perovskite quantum dots CsPbX3 (QDs) (X=Cl, Br, I) have a luminescence that can cover the entire visible light region (400~700 nm). A single luminescence peak is narrow and has a high quantum efficiency. Their emission wavelength and band gap can be regulated by regulating the halogen atom X in CsPbX3. CsPbX3 is widely used in the display field due to its excellent optical properties. However, it hinders its further applications. Metallic-organic framing (MOFs) are porous frame structures that serve as a matrix carrier to improve material stability due to their unique porous structure and permanent porosity. MOFs restricting QDs inside the subject can not only protect them from the external environment, isolating them from each other without reunion, but also realize various new features and applications. Composites (CsPbX3 QDs @ MOFs) that restrict QDs to MOFs have better optical properties than CsPbX3 QDs and better stability to the surrounding environment. Composites (CsPbX3 QDs@MOFs) have many applications in optoelectronic devices, sensors, encryption, and anti-counterfeiting. The review begins with the CsPbX3 QDs correlation structures, preparation, optical properties, applications, and existing problems. For example, in toxicity, stability, anion exchange and the corresponding solution, the structure, preparation, characteristics, and application of MOFs and further, the application of CsPbX3 QDs@MOFs, such as white light diodes (WLEDs), security and encryption, used as catalyst, remote white light transmitter, and realize the wide gamut application, verify these new composite applications in backlight display. This paper presents some problems and needs for improvement in the composite CsPbX3 QDs@MOFs, provides some ideas on the next direction of the composite materials, and makes prospects for the research prospect.

The surface spectral reflectance of the object is regarded as the fingerprint of its color, and at the same time, it is also an important feature to characterize the physical and chemical properties of substances. Multispectral imaging technology that is based on spectral reconstruction can overcome the dependence of RGB images on imaging conditions. Meanwhile, it can effectively improve the spatial resolution and acquisition efficiency of multispectral images and reduce equipment costs. Different from the principle of multispectral cameras, multispectral imaging based on spectral reconstruction first capture the digital images of the object using a digital imaging system, and then the corresponding multispectral images are reconstructed using spectral reconstruction methods. However, due to the mechanism of current spectral reconstruction methods, for both machine learning and deep learning methods, they are sensitive to exposure change of the image in practice. This means the spectral reconstruction model established at one exposure level cannot be directly used at another exposure level, or the curve shape of the reconstructed spectral reflectance will deviate from the ground truth. The sensitivity to exposure changes of current spectral reconstruction methods has limited their application in open environments with variable illumination intensity and inhomogeneity. To deal with the problems of current methods, an adaptive weighted spectral reconstruction method based on polynomial root expansion is proposed in this paper. In the proposed method, the raw RGB response of samples is firstly expanded by the root polynomial, and then the spectral reconstruction model is established by the pseudo-inverse algorithm. It will ensure the proposed method will be against the exposure changes. After that, an adaptive weighting matrix is constructed in the spectral invariant feature space to improve the spectral reconstruction accuracy further. The proposed method is verified and compared with the existing method through theoretical experiments and three sample sets. Results show that the existing spectral reconstruction methods are all sensitive to exposure change, and the proposed method can effectively adapt to the exposure change. The spectral root-mean-square error (RMSE) and the color difference (ΔE*ab) are significantly lower than existing methods. In addition, results indicate that constructing the adaptive weighting matrix in spectrally invariant feature space is crucial to improve the spectral reconstruction accuracy of the proposed method. The research results are important for high-precision multispectral image acquisition in the open environment.

The narrow emission spectrum of light emitting diode(LED) limits differential optical absorption spectroscopy(DOAS)retrieval range, and it is not easy to realize simultaneous measurement of various gases. In this paper, two kinds of ultraviolet LEDs are combined to form a combined LED broadband light source, which is applied to DOAS to simultaneously detect atmospheric SO2 and O3. The spectral analysis shows that their spectrum is superimposed in 280~295 nm and has an obvious lamp structure in 275~301 nm. The structure enhances and drifts to the shortwave direction with the increase of the dual-peak light intensity ratio. During actual measurement, the LED spectrum will change independentlydue to the environmental conditions. Moreover, atmospheric extinction is different in their emission spectral bands. Therefore, the dual-peak light intensity ratio ofthe atmospheric spectrum will change continuously and is inconsistent with the lamp spectrum. It is not easy to offset the lamp structure by dividing the two.The spectrum retrieval results show that the combined lamp structure as a reference spectrum can not fit well with the interference structure. In order to remove the influence of independent LED spectrum changes on spectrum retrieval during measurement, it is proposed to use each LED lamp structure as a reference spectrum to participate in the fitting. The fitting residuals of SO2 and O3 are reduced from 1% and 6‰ to about 4‰, respectively, and the interference structure is well removed.Compared with evading interference structure, the retrieval range of SO2 and O3 is broadened, and the number of SO2 and O3 absorption peaks in the retrieval range is increased by 1.75 and 1 time, respectively. The average fit errors of SO2 and O3 are reduced by 67.5% and 37.3% respectively. The measurement accuracy is significantly improved. The measuremens are compared with the SO2 and O3 levels measured by the traditional xenon lamp long path DOAS system. The comparison shows excellent agreements with Pearson correlation coefficients (R) of SO2 and O3 measurements above 95%. The results demonstrate that the lamp structure of a combined LED broadband light source can be fitted by the independent lamp structure of each LED in DOAS retrieval.

Multispectral images can carry more data information to represent color than common three channel images, which causes problems in storage space and communication. In order to solve the above problems, researchers propose to use an interim connection space (ICS). Multispectral data is compressed into the ICS before storage and transmission, spectral data is reconstructed from the ICS when needed, and the interim connection space determines the effect of the transition. Derhak et al. ［JIST, 50: 53-63, 2006］ proposed a 6 dimension ICS called LabPQR. First three dimensions of this space for a given spectral reflectance r are the tristimulus values XYZ (denoted by a column vector t) under a specified viewing condition (represented by a weighting table matrix H). The rest three dimensions is the combination coefficients, denoted by a column vector tPQR, for the metameric black rb under the first three main unit and orthogonal basis vectors, denoted as a matrix B, for the metameric black space, funded using principal component analysis. Here, the spectral decomposition gives the metameric black rb based on the compressed tristimulus value vector t, i. e., rb=r-Mt, where the mapping matrix M is the well-known “R-matrix”. The metameric black space consists of all metameric black rb from the spectral image or an independent training reflectance dataset. The reconstructed reflectance rp is simply given by Mt+BtPQR。 In this paper, a new ICS is proposed and is named MLabPQR. The difference between MLabPQR and LabPQR is the choice of the mapping matrix M. For the proposed MLabPQR, the matrix M was chosen as the “Wiener estimation matrix”. The “Wiener estimation matrix” does not only depend on the viewing condition matrix H but also depends on the training reflectance dataset. Therefore, the choice of the Wiener estimation matrix can keep the main spectral information for the spectral image, which, we hope, can improve the spectral and colorimetric accuracies for the reconstruction. The proposed ICS was tested using the NCS reflectance dataset and a spectral image, and compared with other ICSs such as LabPQR, LabRGB, XYZLMS and LabW2P in terms of spectral accuracy measures (root mean square error (RMSE) and goodness of fit coefficient (GFC)) and colorimetric accuracy measure (CIELAB colour difference). All ICSs were trained using an independent Munsell reflectance and test datasets. Comparison results showed that our proposed ICS out performed all other ICSs in terms of both spectral and colorimetric accuracy measures. Hence, the proposed ICS is expected to find applications in spectral image compression and cross media reproduction.

Benzene is an important component of volatile organic compounds (VOCs), and its pollution of the atmosphere has attracted increasing attention. The mid-infrared band is usually the fundamental frequency fingerprint absorption region of molecules, so it has become an important band for detecting trace gas molecules. Moreover, the differential absorption lidar is an important means of detecting atmospheric trace gases. Therefore, aiming at the problem of real-time remote sensing of regional benzene concentration, an integral path differential absorption (IPDA) lidar for detecting atmospheric benzene concentration based on inter-band cascade lasers (ICLs) is proposed. Firstly, we construct the retrieval algorithm of IPDA lidar and its error analysis model based on analyzing the detection principle of IPDA lidar. Secondly, the absorption spectra of benzene and major interfering gases (such as HCl, CH4 and H2O) near the mid-infrared vicinity region of 3 100 cm-1 from the HITRAN database are analyzed in detail. By considering comprehensively the influence of HCl, CH4 and H2O on the detection results, the measurement wavelength and reference wavelength of the IPDA lidar are selected to be 3 090.89 and 3 137.74 cm-1 respectively. Thirdly, we designed an IPDA lidar for detecting atmospheric benzene concentration based on two continuous-wave ICLs. The output wavelengths of these ICLs can be tuned by controlling the temperature and driving curren, so that their wavelengths can be stabilized in the strong absorption spectrum region and the weak absorption spectrum region respectively. And then, a spectroscopic system with a mid-infrared diffraction grating as the core is designed to realize synchronous detection of dual-wavelength receiving signals. Finally, combined with the mid-latitude standard atmospheric model, the performance of lidar under the conditions of different visibilities, path lengths, and water vapor concentrations is analyzed and discussed. And then, we carry out test experiments by building a mid-infrared band detection gas cell to verify the feasibility of the IPDA lidar. These results from simulations and experiments show that the relative error of benzene concentration is less than 10% within the concentration-path length product (CL) range of 0.1～24 mg·m-3·km, and the relative error of detection is better than 1%, while the CL of benzene is 5 mg·m-3·km, under the condition of atmospheric visibility of 5 km, and the water vapor concentration of less than 0.4%; and that the linear correlation coefficient R2 of differential absorption lidar detection in the mid-infrared band is about 98.7% by preliminary experiments.

The ratio of the pore volume in the drug tablet to the total volume of the tablet in the natural state is called porosity. In the production process of drug tablets, due to the physical and chemical properties of raw materials, human factors and environmental factors, the formation of pores is inevitable. Porosity is an important characteristic of drug tablets. The porosity size will affect the disintegration, dissolution and bioavailability of tablets. At present, the common methods for measuring the porosity of tablets, such as mercury intrusion method, helium specific gravity method, infrared spectroscopy, etc., can not achieve nondestructive and rapid detection of the porosity of tablets. For this reason, this paper proposes a method to detect the porosity of a single drug tablet by using continuous terahertz waves. Two standard planar drug tablets are used as research objects respectively. The signal transmitted through each tablet is measured in the frequency range of 500~750 GHz using vector network analyzer, and the packaging phase value of each tablet is extracted from the measured S parameters. Then phase unwrapping and correction are carried out to obtain the true phase value of the tablet, and the effective refractive index of the tablet is obtained by calculating the phase difference between the tablet and the air. At the same time, the theoretical model of zero porosity approximation (ZPA) is used to link tablets effective refractive index and porosity. The relative errors between the calculated porosity of the two tablets measured by the vector network analyzer and the standard porosity measured by the gas displacement method are 7.3% and 5.3% respectively. The experimental results show that measuring the tablets porosity using continuous terahertz wave is feasible. The THz wave method for measuring the porosity of tablets is simple, practical, non-destructive and fast, which lays a foundation for rapid, sensitive and non-destructive porosity measurement in the future pharmaceutical tablet manufacturing and production.

Fluorescent carbon dots (CDs) with long wavelength emission have attracted increasing attention due to their promising application prospects in biological fields. CDs with long wavelength emission have been synthesized mainly with high temperature and high pressure, while their synthesis at room temperature is relatively rarely studied. In this paper, blue-green luminescent tunable CDs were prepared by alkaline catalysis, and only two steps were required. The fructose and sodium hydroxide solutions were mixed firstly, followed by dialyzing without any additional energy input or external heating. The synthesized CDs were studied by transmission electron microscopy, steady-state fluorescence, and UV-Vis absorption spectroscopy. Moreover, circular dichroic spectrophotometer and picosecond time-correlated single photon counting system was used to analyze the interaction mechanism between CDs and bovine hemoglobin (BHb). Although there have been some studies about the detection of BHb using carbon dots, previous studies mainly focus on the detection of BHb rather than the fluorescence quenching mechanism of carbon dots. Stern-Volmer imagesof the interaction and the influence of BHb on the fluorescence lifetime of CDs have been measured, implying the contribution of static fluorescence quenching. In this process, the content of the α-helical structure of BHb has decreasedby about 3%, demonstrating that the secondary structures of BHb were changed after interacting with CDs. With the addition of BHb, the complexes of BHb and CDs were formed so the fluorescence of CDs decreased. Moreover, the addition of interference samples in the experiment of BHb detection confirmed that the proposed CDs were highly selective, and the variation of reaction times further revealed that the CDs had high stability. It is noticed that, when mixed with BHb, the fluorescence intensity of CDs decreased gradually, and the proportion of decline was linearly related to the concentrations of BHb. Therefore, the biosensors based on CD fluorescence quenching could be established for the specific detection of trace BHb with a linear concentration range from 0 to 5 μmol·L-1 and a detection limit of 243 nmol·L-1 (S/N=3). The CDs were also excited with different wavelengths at 370 and 425 nm, and the results proved that the fluorescence intensities of CDs had a good linear relationship with the concentrations of BHb for both excitation wavelengths, which might provide more application values for the detection of BHb. Due to its convenient synthesis, simple operation and easy availability, the proposed CD probe is of great significance in life sciences and criminal investigation.

The illegal logging of valuable tree species is mainly motivated by the global market that consumes logs, lumber, veneers, and furniture. Rapid and reliable identification of the country of origin of protected timbers is one of the measures for combating illegal logging. There is a global need to create a wood origin identification system to ensure the integrity of wood supply and control the trade, exploitation, and smuggling of these products. Near-infrared spectroscopy (NIRS) is a promising technique for calibration-based and rapid species identification. In the present work, Near-Infrared Spectroscopy combined with machine learning techniques were used to discriminate six wood species (Pinus massoniana, Paulownia fortunei, Zelkova schneideriana, Tectona grandis, Tilia amurensis, Ailanthus altissima) originating from two regions. The initial step was to create a spectral dataset of tree origins by collecting spectral data on these six wood species from two distinct origins, each constituting a dataset. Then, reduce feature dimensionality to two dimensions to investigate the data distribution across datasets. Secondly, the high-dimensional spectral data were dimensionally reduced using principal component analysis and linear discriminant analysis, respectively, to improve the models generalization and to compare the effects of the two techniques on the models accuracy. Finally, six different machine learning, namely, Support vector machine, Logistic regression, K-Nearest neighbors, Nave Bayes, Random Forest, and Artificial neural network, were used to train these wood samples spectra and assess their discrimination performance. The results showed that the highest accuracies of Pinus massoniana, Paulownia fortunei, Zelkova schneideriana, Tectona grandis, Tilia amurensis, Ailanthus altissimaare 98.3%, 100%, 100%, 100%, 100%, 98.3%, and the fastest operation speed are 0.183, 0.182, 0.181, 0.182, 11.424 and 12.969 s respectively. We evaluated and compared the performance of six models based on different machine learning algorithms to predict the geographic origin of the wood. Compared to the other five models, the best results were obtained by the Artificial neural network approach, but its running time is more than other algorithms, and requires a higher number of tuned and optimized parameters. Moreover, both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying NIRS assisted by machine learning technique is suitable for the rapid identification and discrimination of wood origin and can be an essential tool for tracing the origins of wood, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

With the rapid development of high-supersonic aircraft, non-contact diagnosis technology, etc, more molecular bands are excited, and the demand for rovibrational spectrum data under high temperature and high pressure has increased dramatically. In addition, with the rapid improvement of the cavity ring-down spectroscopy (CRDS), and the tunable semiconductor laser absorption spectrum technology (TDLAS) with high sensitivity, the research on the corresponding spectrum are promoted further accordingly. CO, an important product of high-temperature combustion, is the first to be investigated. In this paper, firstly, the local discrete potential energy points near the molecular equilibrium internuclear separation were obtained employing the Rydberg-Klein-Rees (RKR) method, with the use of spectral parameters of the 12C16O on the ground state (vibrational quantum number ν<41) determined by the experimental. Then it is fitted to more than ten common analytical potential functions, and it is shown that the SPF and Morse functions have good fitting accuracy, but they are still unreasonable on the long-range part. Because of this, the dissociation energy from the experiment is adopted for revising the long-range part, and a new, semi-empirical, global potential function named Revised-Morse was constructed, which not only could accurately reproduce the known vibrational levels but reasonably predict the unknown high vibrational levels with accurate dissociation limit. The multi-reference configuration interaction method (MRCI) was used confirmed its rationality. The levels with high ν calculated in this paper agree with the result from the literature. Secondly, the electronic dipole moment surfaces (DMs) of 12C16O on the ground state at vibrational quantum number ν<63 under three kinds of electric fields were obtained using the multi-reference averaged coupled-pair functional (ACPF) theory combined with differential technology. Based on the above Revised-Morse potential function and DMs, the vibrational and transition levels up to the dissociation limit, and the transition moment, line strength, Einstein coefficient and intensity at room temperature with ν<63 were obtained by solving the one-dimensional Schrodinger equation, meanwhile, the spectral constants such as radiation lifetime and centrifugal distortion were also obtained. The calculated values are almost completely consistent with the results from the HITRAN. This paper not only reproduces the known spectral bands perfectly but also predicts hundreds of thousands of new spectral lines and some new spectral parameters, which can provide a reference for spectral detection. To establish the temperature measurement model based on 12C16O, the partition function of temperature below 9 000 K was further investigated, and the rovibrational spectra at different temperatures were simulated. The variation of the spectral line with temperature was illustrated by the line spectrogram below 20 000 cm-1 (logarithmic coordinate) and ν0-1 band (linear coordinate). Several possible temperature measurement model schemes are proposed. Finally, the influence of pressure on the rovibrational line is discussed.

The quality of jasmine flowers in terms of flavour, medicinal and nutritional uses is influenced by the factors of its origin. Hence, the origin traceability of jasmine flowers is of great significance in protecting the rights and interests of consumers and promoting the healthy development of the jasmine industry. In order to discriminate the geographical origin of Jasmine, a hundred Jasmine samples from four main producing districts, including Hengzhou of Guangxi, Qianwei of Sichuan, Fuzhou of Fujian and Yuanjiang of Yunnan, were collected. Near-infrared spectra, (900~1 700 nm) of those samples were acquired using integrating sphere and fibre-optics probes. Savitzky-Golay (SG) spectral smoothing and multivariate scatter Correction (MSC) were used for spectral pre-processing. After the spectral pre-processing, a jasmine origin discriminant model was developed using PCA combined with linear discriminant analysis (LDA) and k-nearest neighbor (KNN). In the modelling process, 68 samples were used as the training set and 32 samples were used as the test set, and the model parameters were optimised by interaction tests. The results show that the discriminant models based on both PCA-LDA and PCA-KNN have good prediction ability, in which the prediction accuracy of both methods reaches 100% for the spectral data obtained by integrating sphere sampling, and the prediction accuracy of PCA-LDA and PCA-KNN for the spectral data obtained by fiber optic probe sampling is 100% and 93.75% respectively. Finally, a comparative analysis of the chromatographic fingerprint profiles of jasmine flowers from different origins further elucidated the material basis for identifying jasmine origins based on NIR spectroscopy. Thus, this work provides a fast, environmentally friendly, and accurate method to trace the geographical origin of Jasmine, which is meant for the protection of the place of origin for Jasmine.

Citrus is the largest kind of fruit in China. Nitrogen is very important for the growth and development of citrus. Real-time and non-destructive monitoring of the nitrogen status of citrus is of great significance for accurate management of nitrogen nutrients. Nitrogen in plants can be divided into assimilable nitrogen, structural nitrogen and functional nitrogen. The content of each component of different forms of nitrogen in citrus leaves has a certain indicative effect on the physiological and biochemical reactions of leaves. Among them, the content of functional nitrogen is an important indicator of nitrogen nutrition status in citrus. “Chunjian” orange was used as the experimental material in this study. The reflectance spectra of citrus leaves under different nitrogen treatments were measured by the visible-near infrared spectrometer at the fruit swelling period and fruit coloring period, and the functional nitrogen content in leaves was determined by chemical analysis. The correlation between the original spectrum, first-order differential spectrum and the functional nitrogen content of leaves at the fruit swelling and fruit coloring periods of citrus was analyzed, and the sensitive bands were selected. The non-destructive monitoring model of the functional nitrogen content of leaves at the fruit swelling period and fruit coloring period of citrus was constructed by using the full-band and sensitive bands, combined with the spectral vegetation index method, spectral chemical measurement method and machine learning method, and the effects of various spectral variants and spectral preprocessing methods on the accuracy of the model were compared and analyzed. The results showed that the non-destructive monitoring model of functional nitrogen content in citrus leaves constructed by standard normal variate transformation pretreatment of the full-band original spectrum combined with the backpropagation neural network had high accuracy during thefruit swelling period. The calibration set determination coefficient R2c and validation set determination coefficient R2v were all 0.78, and the RMSEC and RMSEV of the modeling set were all 0.82 g·kg-1. The model accuracy based on the original spectrum of the sensitive band combined with the random forest was also high, with R2c and RMSEC were 0.84 and 0.67 g·kg-1, R2v and RMSEV were 0.74 and 0.83 g·kg-1, respectively. In the fruit coloring period of citrus, the full-band original spectrum was preprocessed by standard normal variate transformation. The accuracy of the non-destructive monitoring model of functional nitrogen content in citrus leaves constructed by BPNN was high, with R2c and RMSEC was 0.77 and 1.04 g·kg-1, R2v and RMSEV were 0.76 and 1.13 g·kg-1, respectively. The study has shown that visible-near infrared spectroscopy can achieve non-destructive monitoring of functional nitrogen content in citrus leaves.

Leaf C/N ratio is an important indicator reflecting the individual nutrient utilization efficiency of Camellia oleifera. Estimating C/N ratio based on canopy hyperspectral characteristics can provide important theoretical basis for nutrient monitoring and precise fertilization of Camellia oleifera. There are very limited studies on non-timber product forests physical and chemical properties -using hyperspectral data, especially for Camellia oleifera with the synchronous biological characteristics of flowers and fruits. In addition to the collinearity problem, its complex physical and chemical properties pose great challenges to the response of sensitive spectral characteristics and the construction of estimation models. In this study, the Changlin series of Camellia oleifera in the Huanagshan area of Anhui Province was taken as the research objects. The canopy spectra of 120 Camellia oleifera plants were collected in the field, and the hyperspectral characteristics of the 400~1 000 nm wavelength range in the visible and near-infrared spectral regions were selected for analysis. Original hyperspectral data were processed by using multiplicative scatter corrections (MSC) and first derivative (FD) transformations, and three types of two-band indices (i.e., difference index-DI, ratio index-RI, and normalized difference index-NDI) were constructed respectively. Correlation analysis was used to observe the changes inspectral response feature regions under different processing methods. Response variables were extracted by variable combination population analysis (VCPA), and an optimal feature variable subset was obtained by removing collinearity to construct three machine learning models (i.e., random forest-RF, support vector machine-SVM and BP neural network-BPNN). Finally, the effects of spectral parameters on model estimation accuracy under different treatments were compared, and the optimal estimation model of the C/N ratio of Camellia oleifera leaves was identified according to model evaluation indices. Results showed that: (1) The original spectrum after MSC or FD feature transformation combined with VCPA can mine more potential variables. (2) The combination of a two-band spectral index expands the response region of sensitive bands and further enhances the ability of VCPA to select characteristic variables. FD-RI and FD-NDI are with the best treatment effect. (3) The overall accuracy of the three machine learning models ranked indescending order were BPNN>RF>SVM. Among all models, the BPNN model constructed by FD-NDI spectral parameters has the best prediction ability performance. The determination coefficient (R2) of the training and test sets are 0.71 and 0.66, respectively, and the relative percent difference (RPD) is 1.74. This study established an optimal BPNN estimation model for the C/N ratio of Camellia oleifera leaves in the harvest period, which expands the application range of hyperspectral of Camellia oleifera leaves.

Jingdezhen porcelain plays an important role in the development history of Chinese ceramics. According to the research, Jingdezhen ceramics were made of porcelain stone as raw material before the Song and Yuan Dynasties, namely the “single-unit” formula. After the Yuan Dynasty, a “binary” formula by adding kaolin to the “single-unit” formula was created. From the Five Dynasties to the Southern Song Dynasty, the “single-unit” formula has been widely used, and the technology level has made the quality of Jingdezhen white porcelain reach the standard of modern fine porcelain. Since this is the case, why was kaolin introduced into the blank formula in the Yuan Dynasty? Besides, it would lead to an increase in technical difficulty and cost. Is the reason for the emergence of the “binary” formula inevitable or accidental? In this paper, by combing a large number of literature, simulating experiments and modern test methods such as wavelength dispersive X-ray fluorescence spectrometer (XRF), X-ray diffraction (XRD) and scanning electron microscope (SEM), the advantages of “binary” formula and the historical necessity of introducing kaolin were discussed from the social, technical and economic vision at that time. The results were as follows: Socially, due to the Yuan Dynasty governments political diplomacy, military war, foreign trade management system, and living customs and religious beliefs of the Mongolian nationality and Muslims different from Han nationality, there was a new demand for big porcelain. However, the increasing size was easy for deformation and cracking, creating the conditions for finding a better raw material-kaolin. Technically, first of all, kaolin can be directly used by panning, which has great advantages as a raw material compared to the porcelain stone that needed to be pounded and panned many times. Secondly, the body of the “binary” formula can overcome the disadvantages of large temperature difference between the front and back positions of the kiln high and uncontrollable firing temperature. Finally, compared with the body of the “single-unit” formula, introducing kaolin into the porcelain body had some obvious merits including higher temperature and wider firing range, higher strength, less deformation because of the increase of aluminum content, the complexity of composition, fine particle size and fully developed mullite quantity. Those were beneficial for kiln workers controlling and mastering the production of ceramics. Economically, with the emergence of the “binary” formula, the production of Jingdezhen porcelain no longer used single porcelain stone, which not only broadened the range of raw materials but also extended the ceramic industry chain, thus increasing the market competitiveness of the products. To sum up, it can be concluded that the emergence of “binary” formula technology is due to the above three factors, and its emergence is inevitable in history.

Camellia oleifera fruit is widely planted in hilly and mountainous areas in southern China. The harvest time of Camellia oleifera fruit is currently decided by solar terms and experience, and the prematurity or too late picking will bring economic losses. This study aimed to explore the feasibility of hyperspectral imaging (HSI) technology to identify the maturity stages of Camellia oleifera fruit accurately. The HSI system with a spectral range of 400～1 000 nm was applied to collect hyperspectral images of 480 Camellia oleifera fruit samples at different maturity stages. PLS-DA and PSO-SVM models were individually developed based on spectra preprocessed with five different pretreatments including SNV, SNV-detrend, SG, first-order derivative and second-order derivative. The optimal preprocessing method was selected and further used in feature wavelength screening. Consequently, it was found that the simplified model built by feature wavelengths selected using CARS gave better performance compared to SPA. The classification accuracies of CARS-PLS-DA and CARS-PSO-SVM models in the prediction set were 92.5% and 89.2%, respectively, and the kappa coefficients were above 0.86. Furthermore, color features were extracted from the hyperspectral images by color moment approach, and PLS-DA and PSO-SVM models were built based on the combination of color features and feature wavelengths. Then, the performance of the models built by feature wavelengths screened by CARS was still found to be the best with classification accuracies of 94.2% and 93.3% for CARS+color-PLS-DA and CARS+color-PSO-SVM models in the prediction set, respectively. The models developed by combination features showed better classification results than models based on wavelengths alone, and the classification accuracies were improved by 1.7% and 4.1% in the prediction set, respectively. The optimal CARS+color-PLS-DA model gave the best predicted performance with its Kappa coefficient of 0.923 1. As a result, our work indicates that the application of HSI technology combined with chemometric methods can be used to identify the maturity stages of Camellia oleifera fruit, which provides a rapid, nondestructive and accurate way in Camellia oleifera fruit maturity detection.

In order to significantly increase the signal-to-noise ratio of fingerprint development, in this paper, the background-free development of latent fingerprints based on NaYbF4∶Ho upconversion luminescent powders (UCLPs) and SrAl2O4∶Eu, Dy afterglow luminescent powders (AGLPs) were proposed, and the contrast between developed fingerprint (signal) and background substrate (noise) was also quantified using spectral analysis. First, NaYbF4∶Ho UCLPs and SrAl2O4∶Eu, Dy AGLPs were synthesized via solvothermal and combustion approaches , respectively. Then, the morphologies, crystal structures, absorption spectra, and luminescent properties of the above two powders were characterized. NaYbF4∶Ho UCLPs were cylindrical shaped nanomaterials with hexagonal crystal structure, had a maximum near-infrared (NIR) absorption wavelength of 976 nm, and could emit green upconversion luminescence at the wavelength of 539 nm under 980 nm NIR excitation. While, SrAl2O4∶Eu, Dy AGLPs were polyhedral shaped micromaterials with monoclinic crystal structure, had a maximum ultraviolet (UV) absorption wavelength of 331 nm, and could emit green afterglow luminescence at the wavelength of 515 nm under 365 nm UV excitation. Finally, latent fingerprints on various fluorescent substrates were stained using NaYbF4∶Ho and SrAl2O4∶Eu, Dy dry powders, followed by fluorescently enhanced via upconversion luminescence mode and afterglow luminescence mode, respectively, and thus the background-free development of fingerprints was achieved. In addition, the signal-to-noise ratio in fingerprint development was subjectively evaluated and objectively analyzed by vision effect and spectral analysis, respectively. Fingerprint development results showed that developed fingerprints could emit bright green luminescence in the dark field, producing enough color contrast between the fingerprint and the substrate. The aid of upconversion luminescence mode or afterglow luminescence mode could avoid the interference of background noise. Spectral analysis showed that the intensity contrast between the developing signal and the background noise was significant, resulting a high signal-to-noise ratio. Compared with the normal fluorescence mode based on traditional fluorescent powders, the upconversion luminescence mode and afterglow luminescence mode had an outstanding advantage of ultra-high signal-to-noise ratio in latent fingerprint development. Our proposed UCLP-based upconversion luminescence mode and AGLP-based afterglow luminescence mode have achieved the background-free development of latent fingerprints, which will not only expand the applications of rare earth luminescent powders but also broaden the innovative ideas for fingerprint development.

Lithium-ion batteries play an important role in developing and applying new energy. The composition ratio of ternary cathode materials has a major impact on the performance and quality of lithium-ion battery products, and production control requires timely and accurate control of composition changes in the mix. Energy dispersive X-ray fluorescence (EDXRF) technology has a good prospect for rapid analysis in this area, but the analytical accuracy of commercial instruments cannot currently meet production requirements. To solve the technical problem of high-precision EDXRF analysis of ternary cathode material composition Mn, Co, Ni, a self-calibrating formal EDXRF analysis technology based on homologous excitation is proposed in this paper, which uses a tungsten target X-ray tube (25 kV/400 μA) and two electrically cooled SDD detectors with the energy resolution of 135 eV (@5.9 keV) to form a two-channel synchronous X-ray fluorescence excitation and detection device. After splitting the primary X-rays emitted by the X-ray tube by the dual channel collimator, the calibration sample and the sample to be measured are excited. The two detectors simultaneously measure the fluorescence counts of the two samples and use the energy spectrum data of the standard sample to perform “normalization” processing to achieve synchronous correction of the energy spectrum data of the sample to be tested, thus reducing the influence of X-ray tube instability in the analytical instrument. The stability instrument was analyzed regarding the count decay rate, count variation, and overall effect through 140 repeatability tests in 8 hours and compared with that of the single optical path. The relative standard deviation and maximum relative deviation were used as evaluation indices to assess the stability instrument. The counting attenuation rate decreased from -0.049 3%·h-1 of the single optical path to 0.001 0%. For 11 data points with large fluctuations, the relative standard deviation decreased from 0.151 4% to 0.032 6% of the single optical path, indicating that the self-calibrating formal EDXRF analysis technology of homologous excitation can effectively reduce the effects of counting attenuation and primary X-ray energy spectrum fluctuations. From the perspective of comprehensive effect, the relative standard deviation and maximum relative deviation of Mn, Co, and Ni are 0.076% and 0.170%, respectively, after the correction of synchronous data, which is twice as stable as that of the single optical path. The paper establishes a mathematical model for quantitative analysis based on dual optical path EDXRF analysis. Through experimental verification, the absolute errors of Mn (17.361%~20.016%), Co (12.991%~14.965%), Ni (29.653%~33.065%) in powder compacted samples are not more than -0.072%, -0.061%, 0.098%, respectively, and the analysis time of single sample is 200 s, indicating that the self-calibration formal EDXRF analysis technology with homologous excitation can effectively improve the instrument analysis accuracy, and achieve fast accurate testing requirements.

Chloramphenicol (CAP) is a synthetic antibiotic that inhibits protein synthesis by binding to the ribosomes of bacteria to achieve the purpose of antibacterial. Long-term intake of residual CAP animal-derived food can lead to anemia and leukemia in the human body and can also cause the body to develop drug resistance, which will seriously endanger human health. Many countries have regulations prohibiting the detection of CAP in livestock products. Therefore, designing a more rapid, simple and highly specific CAP detection method is of great significance. In this paper, the thiolated aptamer of CAP (SH-Apt) was used to modify the silver nanorod array chip(chip)as the SERS substrate, and the DNA hybridization indicator methylene blue (MB) as the Raman reporter, A novel high-specificity CAP-SERS aptamer sensor was constructed by utilizing the competitive binding relationship between CAP, CAP aptamer complementary strand (cDNA) and CAP aptamer (Apt). The chip and CAP-SERS aptamer sensor were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and EDS spectroscopy. The results showed that a large amount of silver elements were uniformly distributed on the surface of the chip and the CAP-SERS aptamer sensor was successfully prepared. Detection of CAP standards at room temperature, the results of the sensor-related performance analysis suggest that, with an increase of CAP concentration (0.001～10 ng·mL-1) caused the decreased SERS signal at 1 624 cm-1(ISERS) intensity (ISERS=-971logc+1 983). A good negative correlation (R2=0.991) was achieved, and the limit of detection (LOD) was as low as 0.2 pg·mL-1 (S/N=3), the Raman enhancement factor EF=1.01×107. Further indicating that the substrate has good Raman enhancement. The sensor is used to detect individual CAP tablets and CAP in human and pig sera, spik experiments were performed, and the results were satisfactory. The recovery and relative standard deviations(RSD) were 91.2%～120.5% and 0.97%～8.1%, which proved that the sensor had good accuracy. The sensor is intended to be used for the rapid quantitative detection of CAP in real samples because of its simple manufacture, high sensitivity, strong selectivity, good reproducibility, good stability, and fast detection speed. It provides a new idea for detecting CAP.

In order to realize the qualitative identification and quantitative detection of multi-component organophosphorus pesticides in mixed systems, this paper combines ultraviolet-visible absorption spectra with Parallel Factor Analysis (PARAFAC) to analyze the mixed solution of multi-component organophosphorus pesticides in water rapidly. The absorption spectra of experimental samples of single-component, two-component and three-component pesticide solutions composed of chlorpyrifos, methyl-parathion and profenofos in pure water were obtained by UV-Vis spectrometer. These pure water-organophosphorus pesticide absorption spectrum data were constructed into different three-dimensional data matrices. Then the PARAFAC algorithm was used to decompose the three-dimensional data after the factor number was determined by the nuclear consensus diagnosis method. It was found that the spectrum obtained by the decomposition of two-component and three-component pesticides was very similar to the actual single-component spectrum, which shows the algorithm can realize the qualitative analysis of multi-component organophosphorus pesticides in water. A linear regression model was constructed using the score matrix obtained by the algorithm decomposition and the true concentration of each component to predict different data sets (including a spectral data set with farmland water as dilution background). The prediction results of the model show that the PARAFAC algorithm has a significant second-order advantage. Even when the spectral overlap is serious, and there is interference information in the prediction set that does not exist in the calibration set, the algorithm can still effectively detect the mixed system. Qualitative analysis and quantitative detection were achieved for all the two-component mixed solutions, with the model evaluation coefficient of R2 greater than 0.9 and the RPD greater than 3. The qualitative analysis was achieved for chlorpyrifos, methyl-parathion, and propamocarb in the three-component mixed solutions, in which chlorpyrifos and methyl-parathion met the quantitative detection requirements, and only profenofos showed unsatisfactory quantitative detection results. It may be that the overall spectral intensity level of profenofos solution is significantly lower than that of chlorpyrifos and methyl-parathion solutions of the same concentration, and its spectral contribution is the smallest, so the algorithm has poor resolution of profenofos in its mixed system. PARAFAC algorithm achieves the effect of “mathematical separation” instead of “chemical separation” that can qualitatively identify and quantitatively detect multi-component organophosphorus pesticide mixtures with serious spectral overlap without complicated preprocessing. The method provides a theoretical basis for rapidly detecting and analysing organophosphorus pesticide residues in water.

Plastic film is a bulk type accounting for one-fifth of plastic products in China. One of the most important indicators in manufacturers production is the thickness of plastic film. How to accurately, quickly and conveniently measure the thickness of plastic film is a research topic with great economic value. In this paper, in order to verify the feasibility of measuring the thickness of the plastic film by X-ray absorption spectroscopy, experimental samples of polyethylene plastic film with different thicknesses are made, and the 30 kV pipe voltage and 1 μA. The tube current of A excites X-rays, irradiates plastic film samples of different thicknesses, collects blank spectral data and original X-ray absorption spectral data of different samples with X-ray detector, and obtains photon intensity of each spectrum in 256 channels. In the process of data analysis, in order to achieve the effect of data dimension reduction, principal component analysis is selected to process the collected data; The new dataset with reduced dimension is analyzed two times, one for machine learning directly and the other for machine learning after normalization. In machine learning, 70% are used as training sets, and the remaining 30% are used as test sets. The input data is the X-ray absorption spectra of each group of samples, and the output data is the plastic film thickness predicted by the model. At the same time, to reduce the error caused by randomness, multiple trainings were conducted to evaluate the effect of thickness estimation with average accuracy. Finally, comparative analysis of experimental data concludes that when the error tolerance is set to 50 μm, the accuracy of measuring the thickness of the plastic film by using the machine-learned X-ray absorption spectroscopy after normalization can reach 98.4%. At the same time, as long as the number of samples of the original spectral data is increased and the sampling distribution of different thicknesses is effectively planned, the accuracy of this method can be greatly improved in theory and can be extended to the thickness measurement task of other materials. Compared with other thickness measurement methods on the market, X-ray absorption spectroscopy has the advantages of nondestructive testing, rapid testing and a wide application range, which has a good application prospect for enriching the plastic film thickness measurement technology of manufacturers production lines and relevant regulatory departments, improving the thickness measurement efficiency and improving the measurement accuracy. It has a good application prospect to enrich the thickness measuring technology of plastic film of the production line and related supervision department, improve the thickness measuring efficiency and accuracy.

The tomb of the Pingyuan Princess is located in Fuxin County, Fuxin City, Liaoning Province. As an aristocratic tomb of the Liao dynasty, its murals are rich in content, which is of great value to the study of politics, economy, social culture and funeral customs of The Khitan nationality in the Liao Dynasty, as well as the development of the tomb murals in Liao Dynasty. There are abundant research materials on the shape, theme, artistic value and other aspects of the Liao dynasty murals, and a clear review of their development. However, there are few reports on analysing the materials and techniques of the Liao dynasty tomb murals and the scientific and technological exchanges between the Khidan people and the Central Plains. Due to the need for an exhibition in 2020, it is urgent to carry out conservation and restoration. A super depth of field microscopy, SEM-EDS, laser Raman spectroscopy, XRD, Py-GC/MS were employed to research the pigment, the base layer and the cementation from the mural of The Princess of Pingyuan. The analysis results show that the pigments used in the tomb murals of Princess Pingyuan are all mineral pigments, among which the red pigments are lead, black pigments are carbon black, yellow pigments are iron yellow, and animal glue is used as pigment cementing material. Calcite is the main component of the limestone lithophore, quartz, plagioclase, and albite are the main components of the grass-mud ground layer. In terms of production technology, the Khitan people not only learned from and absorbed the culture of the Han people but also fully learned the advanced science, technology and technology of the Central Plains and practiced in the production of the murals of the Liao Tomb. The research on the material and technological characteristics of the tomb murals of the pingyuan Princess can provide a reference for the later protection and restoration work and enrich the material materials of the tomb murals of Liao and Song dynasties, which have a certain academic significance.

As the only energy source in the solar system, the sun is a rich treasure of spectral information for having a very wide continuous spectrum and tens of thousands of absorption and emission lines. The energy of solar electromagnetic radiation is mainly concentrated in the visible and infrared regions, among which the solar infrared spectrum with Doppler redshift characteristics can be used as the information source for astronomical velocity measurement and navigation. As an important part of astronomical velocity measurement navigation, Solar spectral Doppler redshift velocity measurement can deduce the relative radial velocity between spacecraft and the sun by calculating the Doppler redshift of the received solar spectrum relative to the standard solar spectrum. However, the spectral distortion caused by such solar activities as sunspots, corona, or flares will lead to the instability of the solar spectrum, which will affect the velocity measurement accuracy of the solar spectrum and in turn, the navigation accuracy. In order to improve the navigation performance of solar spectral velocity measurement, based on the principle of solar spectral velocity measurement, the signal processing method of solar spectral Doppler redshift velocity measurement is explored in this paper. This paper proposes an adaptive EMD-NDFT Doppler redshift velocity measurement method for solar spectral velocity measurement navigation. By this method, the redshift is calculated according to the Doppler effect of the solar spectrum and the radial velocity of the spacecraft relative to the light source is derived. The method consists of EMD processing, NDFT and correlation matching. First, the non-stationary received solar spectral signals are stratified adaptively by using the EMD algorithm, and the adaptive threshold filtering and noise reduction are carried out according to each layer of intrinsic mode signal to obtain a stable reconstructed signal. Second, according to the characteristics of non-uniform sampling of the solar spectrum, the standard solar spectrum and the received spectrum respectively are transformed by NDFT to convert the spectrum from the time domain to the frequency domain. Thirdly, Taylor matching is performed on the low-frequency characteristic spectral lines of the two spectra and the phase difference to obtain the radial velocity of spacecraft relative to the Sun. This method combines time-domain denoising and frequency-domain sparsity to obtain radial velocity more quickly and accurately. This paper analyses the spectral changes of sunspot activity in different years within a cycle, and their doppler redshift velocities are calculated and analyzed. The simulation results show that the adaptive EMD-NDFT method can effectively improve the accuracy of velocity measurement and greatly reduce the computational complexity for the solar spectral data in different periods and under different noises.

Landsat satellite images have become the most widely used data source in large-scale ecological monitoring studies worldwide. In remote sensing application studies of large and medium scale areas, due to seasonal, lighting and climatic conditions and different satellite re-entry cycles and sensors, patchy effects and chromatic unevenness may exist after stitching the mosaic of multi-scene remote sensing images. With the rapid development of remote sensing cloud computing technology, exploring a fast and efficient method to repair Landsat chromatic stripes based on cloud platform is important. In this paper, we propose a histogram image homogenization method based on a random forest algorithm implemented on the Google Earth Engine (GEE) cloud platform, which homogenizes the Landsat Top of Atmosphere (TOA) and Surface Reflectance (SR) of Shanxi Province from 1986 to 2020 (Landsat 5 TM/7 ETM+/8 OLI) normalized vegetation index (NDVI) images after inversion were used as the study data, and MOD13Q1 (250 m resolution), MOD13A1 (500 m resolution) and MOD13A2 (1 km resolution) MODIS datasets were used as the validation data after 2000. The NDVI images of Shanxi Province from 1986 to 2020 before and after image restoration were compared separately, and the results of the study showed that (1) 20 years of the 35-year image analysis had strip color difference problems, and in 1994, for example, the restored Landsat TOA and Landsat SR images compared with those before restoration, the mean NDVI values of the restored areas increased by 32.6% and 29.03% respectively, and the profile analysis showed that the fit increased by 0.162 3 and 0.118 0 respectively; (2) The results of the trend analysis of the 1986—2020 one-dimensional linear regression showed that the fit of the restored images was high and the fluctuation of the year-by-year images was smaller after the long time series analysis. Among them, the slopes of the restored Landsat TOA and SR images decreased by 0.006 2 and 0.006 7, and theR2 improved by 0.024 8 and 0.008 4 respectively; (3) Pearson correlation analysis of Landsat and MODIS images found that the correlation coefficients of the restored Landsat SR and TOA images improved by an average of 0.049 and 0.061 (p<0.05), where the correlation coefficients of restored Landsat SR and TOA images and MOD13Q1, MOD13A1, and MOD13A2 images increased by 0.050, 0.047, 0.049, 0.066, 0.060, and 0.059, respectively; (4) 2000—2020 Landsat and MODIS image time series analysis results show that the overall trend of the restored Landsat images is more similar to MODIS images, and the fit of the restored Landsat TOA and SR images is improved by 0.058 6 and 0.031 9, respectively. The proposed GEE cloud platform-based stochastic The proposed fast image restoration method based on the GEE cloud platform random forest algorithm achieves the accurate evaluation of NDVI inversion results of long time series remote sensing images, and the application of this method can quickly and efficiently solve the chromatic patch and banding effects caused by image mosaic.

2 200 nm Al—OH group vibration is important for the exploration of the deposit in the porphyry metallogenic system. ASD portable spectrometer was used to measure 15 core samples form three different deposit types: Tiegelongnan High- sulfide epithermal deposit, Jiama porphyry deposit and Sinongduo low- sulfide epithermal deposit in Tibet. The results show that: (1) the overall spectral reflectance is 45%~70% in Tiegelongnan, 38%~58% in Jiama and 27%~56% in Sinongduo, the spectral difference is 5~15%; (2) high- sulfide epithermal deposit show a double peak at 2 200 nm, and the other two types show single peak; (3) the spectrum of low- sulfide epithermal deposit enhanced by the second derivative is negative at 2 200 nm, the spectral symmetry and absorption index are also lower than the others; (4) The gaussamp function takes excellent fitting on the single peak spectrum at 2 200 nm (R2=1), which can completely simulate white mica group minerals. The above analysis shows that the 2 200 nm Al—OH group vibration can obviously distinguish different deposits in the porphyry metallogenic system. On the one hand, the presence of illite-smectite in low-sulfide epithermal deposit lead to low reflectance, and the water in minerals result in low values of spectral symmetry and absorption index; On the other hand, the average spectral curve of high-sulfide epithermal deposit takes double peaks because of the kaolinite and dickite minerals, which is the mark for the identification of different epithermal deposits. The study can be applied to aerospace hyperspectral remote sensing exploration by using a 2 200 nm single band.

The Mussel organisms from the sulfide hydrothermal field of the mid-ocean ridge can virtually record the ecological environment information around the region. However, the distribution characteristics, ultrastructure and genesis of the minerals of the shells are not well studied. A mount of mussels were first collected from the Wocan-1 hydrothermal field in the Northwest Indian Ocean, by the Manned deep-sea submersible (JIAOLONG) in 2017, which were ideal samples for investigating this scientific issue. The mussel, deep-sea Bathymodiolus of the Indian Ocean (Bathymodiolus marisindicus), is analysed by the Scanning electron microscope, Laser Raman spectroscopy, and Fourier transform infrared spectrum for their natural cross-section morphology, and mineral component. The results show that the longitudinal growth of the Bathymodiolus shell includes periostracum, prism layer,transition layer, aragonite slate layer and myostracum from the outer to the inner. In the fibrous prismatic prism layer of the shell, the c-axis cross-section of the prism is irregular, and the width of the calcite prism perpendicular to a-axis is about 818~960 nm, and it is nearly 45° oblique with the aragonite layer, and there are interlaced calcite prisms. The shape of the transition layer of the shell is extremely irregular, and it continues the growth orientation of the prismatic layer showing a trend of transition from the prismatic to the slate of aragonite. The aragonite layer has a lamellar structure, and its about 205~1 260 nm in thickness. In the aragonite layer of the Bathymodiolus shell of the Wocan-1 hydrothermal vent, the thickness of the aragonite tablets of the same region is the same, but the thickness of the tablet of different regions is different. The myostracum has a simple prismatic ultrastructure, which is overlaid by both prismatic and nacre layers (aragonite lamellar layer). Spectral analysis shows that the minerals of the nacre and prism layers of the Bathymodiolus shell are inorganic aragonite with relatively high crystallinity and biogenic calcite,respectively. The morphology characteristics, mineral components, and genesis of the Bathymodiolus shell analyzed in the study can provide a potential example for studying the hydrothermal fields mollusk shell formation mechanism and bioinduced mineralization process.