Since 2000, various novel nanomaterials (NMs) have been successively applied in latent fingerprint (LP) development, resulting in the emergence of NM-based development of LPs. Due to the innovation of developing materials and methods, the NM-based development of LPs has mushroomed. In the past decade, more and more research on LP development using rare earth (RE) luminescent NMs has been reported, and this has gradually become one of the hot topics in this research field. The development of LPs based on RE luminescent NMs possesses prominent advantages such as strong contrast, high sensitivity, good selectivity, and low toxicity. Therefore, it has important theoretical research values and broad practical application prospects. Recently, most papers reported in this field were research articles, while the review articles were very limited. In this review, the application progress of LF development based on RE luminescent NMs was systematically summarized from two aspects: the renewal of developing materials and the innovation of developing methods. On the renewal of developing materials, this review emphasizes the LF development based on RE down-conversion (DC) luminescent NMs, RE up-conversion (UC) luminescent NMs, and RE multi-functional nanocomposites. On the innovation of developing methods, this review focuses on LF development based on electrostatic adsorption, hydrophobic effect, chemical bonding, and aptamer recognition strategies. The trends of LP development based on RE luminescent NMs can be summarized as follows: in terms of the developing materials, they involve in the transition from DC luminescent NMs to UC luminescent NMs, from single luminescent NMs to multiple luminescent NMs, from photoluminescent NMs to multi-functional nanocomposites; in terms of the developing methods, they involve in the transition from non-specific development to high targeting development, from qualitative development of trace evidence to quantitative detection of residual substances, from physical development to chemical development. In addition, developing results are involved in the transition from effective development of trace evidence to nondestructive detection of biological evidence; developing procedures are involved in the transition from potentially toxic operation to environmentally friendly operation; developing assessments are involved in the transition from subjective, uncomprehensive and qualitative description to objective, comprehensive and quantitative analysis. At the end of this review, we also put forward some prospects for LF development based on RE luminescent NMs, which should be further studied.

Genes are carriers of genetic information and are important for understanding biological phenomena at the molecular level. In recent years, the efficient detection of genes and the research in gene-related fields have attracted great attention. The common molecular biological method adopted for genetic testing is a laboratory test. Surface-enhanced Raman scattering (SERS) real-time characterization technology has gradually played a significant advantage in gene detection, identification, and analysis. Raman spectroscopy is a progressive bioactive molecular sensor based on photochemical technology. This paper reviewed the progress of research on surface-enhanced Raman scattering (SERS) of gene analysis in recent years. The potential application of gene analysis in the biological field is speculated. In the 1970s, the discovery and confirmation of the SERS phenomenon rekindled the research of Raman spectroscopy. Since the SERS technique is not interfered with by water, it possesses advantages such as high sensitivity、high selectivity、a large amount of information, and the ability to study the structure of aqueous solution substances, SERS can provide abundant structural information for organisms quickly and accurately, which is the "fingerprint" information of molecules. It has been widely employed in the in-situ non-destructive detection of target samples, amongst others. It improves the efficiency of R&D and production． SERS has developed rapidly in gene analysis and has become a hot topic at home and abroad, which shows unique value and role． In this paper, the principle and classification of Raman spectroscopy are briefly introduced by analyzing the characteristics of Raman spectroscopy combined with the structural characteristics of genes, Focusing on the principle of SERS scattering, the enhancement mode of Raman spectral signal amplification and the enhancement of substrate materials and beacons, ways to solve the problems of low content detection difficulty, low sensitivity, complex signal, poor selectivity and specificity, and poor signal reproducibility and stability is analyzed; In recent years, SERS technology has rapidly developed in the field of gene analysis, displaying a unique potential application prospect and becoming a subject of academic scrutiny worldwide. This paper reviews the development and application of surface-enhanced Raman scattering (SERS) in biosensors and bioimaging. The exploration of SERS in molecular structure and mechanism of action is elaborated. The application of SERS technology combined with the latest biotechnology in gene analysis is simultaneously classified and discussed. The latest progress of SERS gene analysis technology in disease diagnosis, pathogen detection, drug analysis, and transgenic identification has been discussed and reviewed through the investigation and analysis of related literature. The article also summarizes the opportunities and challenges SERS technology faces in its practical application, briefly analyzes its existing problems, and explores surface-enhanced technologys development direction and potential in biotechnology. The paper concludes with speculation about the development of SERS technology.

The Citrus Huanglongbing (HLB), caused by the Asian citrus psyllid, represents a severe disease with no current cure. Its control is of significant importance and economic value. Current diagnostic approaches utilizing the spectral differences between healthy and diseased leaves show promising applications. Diseased leaves exhibit notable differences from healthy ones in the chlorophyll reflection zone and the O—H stretching vibration region of the visible spectrum. With its low cost and simplicity in data collection and processing, the visible spectrum detection scheme presents a feasible and significant approach for the rapid detection of HLB. To reduce spectral data redundancy and computational load, achieving precise early identification of HLB and minimizing misdiagnosis of similar symptoms, this study collected 160 leaf samples from HLB-affected areas. These samples were classified into four categories—healthy, mild disease, severe disease, and magnesium deficiency-using qPCR determination. Reflecting on the characteristics of leaf samples in the visible light band (450~800 nm), the study involved preprocessing spectral data through S-G smoothing and down sampling. To select feature wavelengths that encapsulate maximum spectral information, Genetic Algorithm (GA), Successive Projections Algorithm (SPA), and Competitive Adaptive Reweighted Sampling (CARS) were employed for feature wavelength extraction and dimensionality reduction, further simplifying model complexity and enhancing prediction accuracy. Considering the generalization ability and detection speed, the study used the Least Squares Support Vector Machine (LS-SVM) and Random Forest (RF) to classify and discriminate the dimensionally-reduced data from the two variable selection algorithms. The best rapid detection scheme was selected by validating and optimising different models. -In comparison with others, the SPA-RF model achieved a discrimination accuracy of 100% and 97.5% for the training and test sets, respectively. The results demonstrate that the combination of SPA and RF in the classification model effectively accomplishes early pathological identification of HLB and distinguishes HLB-diseased leaves from similar symptoms, providing a basis for rapid detection and control of Citrus Huanglongbing.

Screening for BRCA2 gene mutations is clinically important for breast cancer risk assessment, morbidity detection, early diagnosis and gene therapy. Most existing breast cancer gene sensing assays require complex processing, high cost and weak recognition of single-base mutations. To simplify the process of gene-sensor preparation and improve its application possibility, the article realized the detection of BRCA2 in one step through the design of DNA molecular hairpin structure. The molecular hairpin stem contains BRCA2 complementary sequences, and the stem-loop junction is labeled with a tetramethylrhodamine fluorescent group. The target gene can be measured in one step. The optimized experimental results showed that the sensors detection range was different when the concentration of molecular hairpin was different. The linear detection range of BRCA2 was 1~50 nmol·L-1 at 150 nmol·L-1, 1~100 and 1~200 nmol·L-1 at 300 and 600 nmol·L-1, respectively, and the detection limits for the three concentrations were 0.54, 0.72, and 1.81 nmol·L-1, respectively. In addition, the specificity of the sensor for detecting BRCA2 was outstanding, especially for identifying single-base mutations. The method is simple and specific and can be used not only for early screening of breast cancer in high-risk groups but also for other types of genetic tests, providing a new method with simple preparation and convenient operation for applying genetic sensors.

Magnetic fluid is a colloidal liquid magnetic material formed by uniformly distributing ultrafine magnetic particles in the carrier liquid. It breaks the traditional form of solid magnetic materials and is a new type of material with both solid magnetism and liquid fluidity, with an extensive range of applications. At present, magnetic fluids have been applied in the medical field, and research has shown that magnetic fluids can be used to treat cancer cells, separate blood vessels and cells, targeted drug delivery, etc. In addition, magnetic fluids can also be applied in sealing, lubrication, and other aspects. Terahertz waves refer to frequencies ranging from 0.1 to 10 THz and wavelengths ranging from 30 to 3 000 μm electromagnetic radiation. Because the vibration and rotation modes of many biomolecules are in the terahertz frequency band, and the energy of terahertz waves is low, they will not damage the tested object. Therefore, terahertz waves can be used for non-destructive testing and are a safe and reliable measurement method. Microfluidic technology can be used to measure tiny amounts of liquid samples, with advantages such as simple operation, fast detection speed, and saving of measured samples. This study innovatively combines terahertz technology with microfluidic chip technology to study the terahertz characteristics of magnetic fluids at different magnetic fields and temperatures at different times. It was found that when a magnetic field was applied at different times, as time increased, the terahertz time-domain spectrum of the magnetic fluid shifted to the right. The intensity of the frequency-domain spectrum decreased. When different temperatures are applied, as the temperature increases, the time-domain spectrum also shifts to the right, and the transmission intensity decreases. It is preliminarily believed that the magnetic particles in the magnetic fluid undergo aggregation and directional arrangement under both external magnetic field and electric field conditions. With the extension of time, the particle spacing decreases, and it is approximately believed that the radius of the nanoparticles increases, making it difficult for terahertz waves to pass through and reduce their intensity. As the temperature increases, molecules thermal motion intensifies, and molecules vibration and rotation strengthen, making it difficult for terahertz waves to penetrate and thus reducing their intensity. Due to the lack of relevant reports on using terahertz to study the characteristics of magnetic fluids, this discovery provides a new method for exploring magnetic fluids. The study of terahertz characteristics of magnetic fluids under external magnetic fields can be applied to the medical field. These findings provide a new approach to applying terahertz technology in biomedicine and provide technical support for the in-depth application and research of magnetic fluids.

In order to meet the needs of the prevention and control of pollutants emitted from ships in China, China issued the General Requirements for the Monitoring of Air Pollutant Emission from ships in 2021, and monitoring air pollutants from ships has become an urgent task. Currently, most ships in inland rivers in China use low-end diesel engines as the main power plant, and diesel engines will continue to be used as the main power plant for a long time in the future. The oil products used by ships usually have many impurities and poor quality. Coupled with the fact that most diesel engines are relatively old, they will continue to emit air pollutants such as sulfur oxides and nitrogen oxides when sailing and entering and leaving the port, which cause serious harm to human health and atmospheric, ecological environment, so the monitoring and research of pollutants in ship exhaust are particularly important. In this paper, based on the imaging differential absorption spectroscopy technology, a ground-based fast imaging differential absorption spectroscopy systems is developed, which can detect the spatial distribution of pollutants. The time resolution of the system can reach seconds, and the field of view angle of the system is 6°×6°. The data inversion is based on the QDOAS software, and the solar scattering spectrum of the zenith at noon is selected as the reference spectrum to retrieve the inclined column concentration of the polluted gas in the observation area. According to the image information of the observation area captured by the camera in the system, the two-dimensional spatial distribution image of pollutants can be obtained by matching the pollutant concentration information with the spatial information. In order to verify the reliability of the system, a comparative verification experiment aimed at SO2 in the smoke plume of a power plant was carried out in Tongling in September 2022, and the results were compared with the results of the sweep ground-based imaging differential absorption spectrometer. The results show that the correlation of SO2 inclined column concentration is 0.935. In December 2022, the pollutant emission from ships along the Yangtze River was observed by using the ground-based fast imaging differential absorption spectroscopy system, and the continuous spatial two-dimensional distribution information of SO2 with a time resolution of 5 s was obtained. by matching with the image information obtained by the camera, the high concentration emissions can be traced back to specific ships. This study provides an effective real-time monitoring means for ships and other fast-moving pollution sources and provides reliable data support for treating ship emissions.

Mid-infrared absorption spectroscopy is one of the most promising non-invasive blood glucose measurement techniques. The accuracy of blood glucose concentration measurement results of the mid-infrared absorption spectrum is closely related to the reliability of spectral signals. However, collecting mid-infrared spectral signals is susceptible to environmental or human factors, and an anomaly spectrum containing a large amount of interference information will be generated. The existence of an anomaly spectrum will reduce the effectiveness and reliability of the prediction model, so the detection and removal of abnormal samples are crucial. This study proposes that the twin check abnormal sample detection method can accurately screen and eliminate abnormal samples. This algorithm is divided into two stages. Firstly, the Monte Carlo cross-validation abnormal sample detection method is used to preliminarily screen abnormal samples and improve the stability of the spectral sample set. Secondly, based on the theory that Mahalanobis distance square approximately obeys chi-square distribution, the optimal threshold is adaptively determined, and the remaining data sets are re-identified with abnormal samples. 64 samples of the glucose-mixed imitated solution containing glucose, albumin, urea, lactic acid, fructose and cholesterol were studied. The twin check method first uses the characteristic that the sum of squared prediction errors is sensitive to abnormal samples to make a preliminary judgment on the abnormal samples in the spectral data set, and a total of 3 abnormal samples are detected. The PLS correction model is established after removing the abnormal samples from the spectral data set. The correlation coefficient of this model is 0.91, and RMSECV is 60.17 mg·dL-1. Secondly, the twin check method is based on the theory of Mahalanobis distance square approximately conforming to chi-square distribution, which realizes the adaptive identification of abnormal samples. A total of 12 abnormal samples were detected. The performance of the PLS model constructed after removing all abnormal samples was improved, with the correlation coefficient reaching 0.99 and RMSECV reaching 57.77 mg·dL-1. By comparing the results of the twin check method with the non-abnormal sample removal, PCA-MD method and Monte Carlo method, the superiority of this algorithm in abnormal sample detection is proved. Compared with the PLS model without removing abnormal samples, the correlation coefficient increased from 0.86 to 0.99, and RMSECV decreased from 67.51 to 57.77 mg·dL-1, increasing by 15.12% and 14.42%, respectively. This study provides a good solution strategy for the problem of false detection of normal samples or missing detection of abnormal samples due to the easy influence of threshold of existing abnormal sample detection methods, which is conducive to the methods accurate detection and elimination of abnormal samples, thus improving the accuracy and prediction performance of the prediction model. This method provides a way to eliminate the abnormal samples of mid-infrared absorption spectrum accurately.

Rice is the primary grain crop in China, and the quality of rice is closely related to the external environment, such as soil characteristics, climate, sunshine time, and irrigation water. The high-quality rice-origin area has certain regional limitations. Therefore,the rice can be seen as an apparent geographical marker. There are often some counterfeits or branded famous high-quality rice in the market, which can damage the rice brand, reduce the rice quality guarantee of consumers, and disturb the market stability, so rapid identification technology of rice origin is needed. The rice origin identification models of five sources in Jilin Province (Daan, Gongzhuling, Qianguo, Songyuan and Taoerhe) are done by laser-induced breakdown spectroscopy and machine learning algorithms. The principal component analysis (PCA) algorithm, combined with four machine learning algorithms, Bagged Trees, Weighted KNN, Quadratic SVM, and Coaster Gaussian SVM, has been established. A total of 450 groups of LIBS data are selected. The spectral data of rice LIBS are pretreated with Savitzky-Golay smoothing (S-G smoothing) for noise reduction and normalisation. The principal component analysis uses the rice LIBS data, which shows that the rice origins had an excellent cluster distribution of clustering spaces. Still, there is spatial overlap in some rice origins. Utilising5x cross-validation, the identification accuracy of rice origins can reachmore than 91.8% by adopting PCA-Bagged Trees, PCA-Weighted KNN, PCA-Quadratic SVM and PCA-Coarse Gaussian SVM, and the recognition accuracy of PCA-Quadratic SVM model is as high as 97.3%. The results show that the combination of LIBS technology and machine learning algorithms can identify rice origin with high precision and high efficiency.

The steel industry has become a mainstay of the Chinese national economy. Due to the limitation of production technology, Chinese steel products are mainly concentrated in the middle and low-end products of uneven quality. It could result in the severe waste of steel resources and the pollutionofmetal garbage wastes. Therefore, the rapid identification and classification method of steel products is significant for environmental protection and for improving steel resources recycling rate. This work utilised laser-induced breakdown spectroscopy (LIBS) to quickly collect the spectral data of 10 kinds of special steels. Then, a support vector machine (SVM) learned and modelled the spectral data to obtain the rapid steel classification model. However, due to the element composition of different special steels being complex and similar, the performance of classification results may be directly and significantly affected by SVM model parameters. To realise the rapid classification and detection of different grades of steel alloys, the two different methods of particle swarm optimisation (PSO) and grid search optimization were used to optimize the model parameters and speed up the training efficiency. Then, the spectral intensity of 17 characteristic lines of 6 major trace elements (Mn, Cr, Cu, V, Mo and Ti) in samples and 17 feature information variables extracted from the LIBS spectrum data with full variables by principal component analysis (PCA) were chosen as the input to establish the PSO-SVM, PSO-PCA-SVM, PCA-SVM and SVM models for steel classification respectively. The experimental results show that compared with the SVM models optimization time of 115.64 s, the shortest optimization time of PSO-SVM is 11.5 s, and its classification accuracy (96.67%) is not significantly inferior to the accuracy of the PCA-SVM model (97.5%). The results show that LIBS combined with the PSO-SVM algorithm can achieve rapid and high-precision steel classification, which provides a new solution to detect and classifythedifferent steel products rapidly and precisely.

Cyromazine is a white crystalline powder insecticide used in the agricultural production of various melons and fruits, solanaceous fruits, beans, and leafy vegetables. It is widely used in agriculture and other fields. However, the massive use of Cyromazine will be extremely destructive to the ecological environment and will endanger human health. Therefore, there is an urgent need for rapid detection technology of Cyromazine pesticide residues in the process of vegetable agricultural production. Surface-enhanced Raman spectroscopy (SERS) has the advantages of high sensitivity, high accuracy and simple sample preparation and has become a hot research technology in the field of pesticide residue detection. Density function theory (DFT) can be used for theoretical simulation of molecular structure and properties and calculation of Raman spectra. The surface-enhanced Raman spectra of Cyromazine molecule introduced into gold nanoclusters are calculated based on the density functional theory; the software of Multiwfn and VMD are used to explore the surface electrostatic potential distribution of herbicidal strong molecules. Based on the B3LYP/6-31++G（d，p）basis set, the structure optimization and Surface Enhanced Raman spectroscopy calculation of the Cyromazine-gold nanoclusters complex formed by combining 4Au atomic clusters are studied. The 6-31++G（d，p）basis set is used for the atoms in the Cyromazine molecule that may coordinate with the Au nanoclusters, and the LANL2DZ pseudo basis set is used for the Au Atomic Clusters. The Raman spectra and the surface-enhanced Raman Spectra of Cyromazine are obtained, and the characteristic peaks are identified and compared. According to the molecular electrostatic potential distribution, Au nanoclusters may form coordination with N1, N3 and N5 atoms in the C6H10N6 molecule and form C6H10N6-4Au nanoclusters. The Raman spectra of C6H10N6-4Au coordinated by N1, N3 and N5 atoms are calculated and analyzed, the maximum Raman spectral enhancement of C6H10N6-4Au molecules coordinated by Au clusters and N1, N3 and N5 is 4.0 times, 1.4 times and 3.2 times, respectively, and the position of the spectral peak has a certain degree of red shift or blue shift. The research results lay a theoretical foundation for the rapid detection of pesticide residues on the surface of vegetables by SERS technology.

To investigate the influence of gas pressure on the process of gliding arc discharge, the spectral characteristics of gliding arc discharge were studied on a high-pressure discharge experimental platform. The excited state substances in gliding arc discharge are diagnosed by emission spectroscopy,and the electron density, vibration temperature and rotation temperature of gliding arc discharge under different gas pressures are calculated. The emission lines of the second positive system of nitrogen N2(C3Πu→B3Πg) and the first negative system of nitrogen N+2(B2Σ+u→X2Σ+g) are observed in the emission spectra of the gliding arc discharge. With the increase of gas pressure, the emission intensity of the emission line of the second positive system of nitrogen increases, whereas the emission intensity of the first negative system of nitrogen changes slightly. The Stark broadening method calculates the electron density in gliding arc discharge. It is found that the electron density is 1023 orders of magnitude, and the electron density increases linearly with the increase of gas pressure. The vibration temperature of sliding arc discharge is calculated using the Boltzmann diagram method for the five spectral lines of the second positive system of nitrogen molecules. The change of vibration temperature with gas pressure is not monotone. The range of vibration temperature changes is small before 0.3 MPa and increases rapidly after the gas pressure exceeds 0.3 MPa. The rotational temperature of the discharge from the gliding arc was calculated by fitting the emission lines at 390~391.6 nm in the first negative nitrogen molecules system. The rotational temperature changed significantly with the increase of gas pressure before 0.24 MPa, and the addition of rotational temperature decreased when the gas pressure exceeded 0.24 MPa.

Fluorine rubber (FKM) material has excellent heat resistance, corrosion resistance, oil resistance and more, making it a popular material in aviation, aerospace, petroleum, machinery and other industries. However, the traditional service life testing method for FKM materials has disadvantages, such as damage to the material to be tested, complicated operation steps, and low testing efficiency. Therefore, in practical applications, FKM is often replaced directly and regularly, but it has a higher possibility of harming the environment by pollution. A new method for predicting the service life of FKM materials using terahertz time-domain spectroscopy (THz-TDS) was proposed. At firstly，Perform THz-TDS detection on the FKM material that has been subjected to different temperatures and different periods in a hot air aging box, obtain its corresponding time-domain spectrum, and extract the corresponding peak-to-peak value, that is, the difference between the maximum detection current value and the minor detection current value in a transmission sampling cycle. Fitting the peak-to-peak value of the THz time-domain waveform of the FKM material with different aging temperatures and the aging time, it could be seen that the peak-to-peak value of the time-domain spectrum of the FKM sample decreases with the aging time, and the higher the aging temperature, the downward trend was more prominent. The correlation coefficients of the fitted straight lines at the three aging temperatures of 200, 300 and 360 ℃ were as high as 0.973 4, 0.982 1 and 0.993 5, respectively. Secondly, according to the basic idea of the Arrhenius formula, a mathematical model of FKM material life prediction was established with the peak-to-peak value of the time domain spectrum as the reference standard. The correlation coefficient of the fitted straight line of the relationship between the logarithm of the aging reaction rate constant and the reciprocal of the aging temperature obtained using the time-domain spectral peak-to-peak service life estimation method was 0.996 9. Finally, the critical value of the THz-TDS peak-to-peak failure in the THz-TDS-based service life prediction mathematical model was verified by measuring the elongation at the break of the FKM material by mechanical stretching. The FKM based on -the constructed THz-TDS could be obtained, and the material life estimation model was established with higher correct efficiency. The research results show that applying the THz-TDS detection method to FKM material life detection has high accuracy. In addition, THz-TDS detection has the advantages of non-destructiveness, high efficiency, simple operation-wide applicability, etc., which can provide online detection and life prediction for FKM material products. It also references studying life prediction methods for other non-polar and dielectric materials.

As one of the most widely planted crops in China, the yield of corn is of great significance to Chinas food security. Since different varieties have different characteristics, scientific seed selection according to planting conditions can significantly improve the yield and reduce the cost of production. Still, the appearance of different corn seeds is extremely similar, which leads to a certain degree of difficulty in scientific seed selection. In this study, based on near-infrared spectroscopy combined with the Kernel Extreme Learning Machine (KELM) to construct a discrimination model for the classification of corn varieties, the use of sweet glutinous yellow corn, sweet princess, Chang sweet, golden superman, sweet No.5 five kinds of maize seeds, each kind of 6 grains as a sample, a total of 126 samples as the object of the study, the near-infrared spectroscopy data collected by the standard normal variate transformation (SNV) treatment. Competitive Adaptive Re-weighted Sampling (CARS) was used to downscale the dataset. The samples were randomly divided into training and test sets according to the ratio of 5∶1 to explore the effect of the Northern Goshawk Optimization Algorithm (NGO) on the performance of the KELM model. The two important parametric regularization parameters C and Gaussian kernel function γ of the KELM model was optimized using the NGO algorithm, particle swarm algorithm (PSO), and gray wolf algorithm (GWO), respectively, and the C and γ corresponding to the highest accuracy rate of the 50-50 cross-validation recognition were selected as the modeling parameters to build the KELM classification model. The KELM models performance is established after each algorithms optimisation is compared. It is found that the performance of the KELM model established after optimization by the NGO algorithm is higher than that of the KELM model optimized by the other two algorithms, and the recognition accuracy of the test set can reach 100%. The CARS-NGO-KELM, CARS-PSO-KELM and CARS-GWO-KELM models are built based on CARS dimensionality reduction, and the results show that the NGO algorithm still performs better in the face of dimensionality reduction data, and its test set accuracy and F1 value both reach 100%. To verify the effect of sample size on the model, the KELM model was retrained using a total of 90 samples after synchronizing the sample size of each species. The results showed that after synchronizing the number of samples of each species, the performance of each model was improved on both the training and test sets. In this study, a variety of optimization algorithms are introduced to construct a machine learning model based on near-infrared spectroscopy and the recognition accuracy is increased to 100%, which realizes a fast, non-destructive, and accurate variety identification of maize seeds. The results of the study provide a new method for the rapid identification of maize varieties and also have certain significance as a guide for the supervisory authorities.

The synthesis of the required target spectrum with monochromatic light-emitting diodes (LED) is of great significance in reality. When multiple components of the LEDs are needed, and the accuracy required for the target spectrum synthesis is high, solving the problem of the proportion of the components becomes a combination optimization problem with a non-negative solution of the over-determined set of linear equations. Generally speaking, the approximately global solutions can be found for the heuristic-based methods. However, the convergence speed to the worldwide optimizer is low partly because the objective functions analysis properties, such as gradient information, are not used. Gradient-based algorithms converge to local solutions fast and with high accuracy, but the requirement for non-negative solutions in the problem limits their global convergence. Meanwhile, the least squares information of the objective function was not fully utilized in previous research. In this paper, a two-stage optimization algorithm, named LLR~~LBFGS, is proposed based on the mathematical analytic properties, the quadratic nonlinear format of the objective function, and the non-negative requirements for the final solution. In the first stage, unconstrained linear fitting is carried out to obtain the unique solution of the least squares theory of the expressions. In the second stage, the non-negative global optimal solution of the problem is further obtained with the help of the constrained quasi-Newtonian method LBFGS. Taking the fitting of standard target spectra CIE-A, CIE-D65, CIE-D50, CIE-D55 and CIE-D75 as the research object, the new method is compared with the Lasso Regression Algorithm (LASSO), Ridge Regression Algorithm (RIDGE), Differential Evolution Algorithm (DE), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in solving the same problem in terms of accuracy, running speed, and the decision coefficient R2. The numerical results based on actual industrial cases show that the LLR~~LBFGS converge faster, and the solution accuracy is higher because the information on the objective function is utilized more efficiently. Its universality indicates excellent potential to be applied more generally to solve the problem of LED spectral fitting. A more flexible solution to solve the spectral matching problem can be set up according to the design ideas of this paper. This also has significant implications for improving the effectiveness of intelligent optimization methods for finding the optimal solution for LED spectral matching.

This article synthesized two carbazole pyridine N-oxide internal salts, 4- (9H carbazole 9-yl) pyridine 1-oxide (CPNO) and 4-(4-(9H carbazole 9-yl) phenyl) pyridine 1-oxide (CPPNO). It measured their UV visible absorption and fluorescence spectra in different solvents, showing good sensitivity to solvent polarity. The calculation shows that both compounds have a sizeable excited state dipole moment, which is the reason for the solvent polarity sensitivity of the compound. The research results provide a new approach for developing novel fluorescent polarity probes.

Acid phosphatase (ACP) is an important biomarker for several diseases, such as prostate cancer, Gaucher disease and kidney disease. Therefore, developing sensitive and highly selective assays for ACP is of great significance. To date, several assays for ACP have been reported, including immunoassay, spectrophotometry, chromatography and electrochemistry. Among these methods, fluorescence-based assays have gained prominence due to their high sensitivity, good selectivity, efficiency, and precision. In this study, a novel “turn-on” fluorescent substrate for ACP, named APBA-PLP (ABPL), was synthesized through the Schiff base reaction between 2-aminophenyl boronic acid (2-APBA) dimer and pyridoxal phosphate (PLP). After the reaction, the characteristic FTIR peaks associated with —CHO groups of PLP and —NH2 groups of 2-APBA dimer disappear, and a new FTIR band, originating from CN vibrations of ABPL, appears. In addition, the1H and1H-1H COSY NMR spectra display all the ABPL signals. The FTIR and NMR results indicate the successful synthesis of ABPL. Furthermore, ABPL is applied to the detection of ACP. We observe an increase in fluorescence intensity of the ABPL solution upon adding ACP. This phenomenon is attributed to the aggregation-induced enhanced emission property of the 2-APBA dimer, which arises from its highly ordered stacking. The highly ordered stacking structure is disrupted upon the introduction of PLP onto the 2-APBA dimer (i.e., the synthesis of ABPL). Subsequent addition of ACP leads to the hydrolysis of PLP into pyridoxal, causing the detachment of PLP from the 2-APBA dimer molecule. The 2-APBA dimer molecules can then reassemble into a highly ordered structure, increasing the fluorescence intensity. The relative fluorescence intensity at 376.5 nm exhibits an excellent linear relationship (R2=0.99) with ACP activity in the 0～5 U·L-1 range. Moreover, when ACP, pectinase, papain, and lipase activity were 4, 50 000, 80 000, and 10 000 U·L-1, respectively, the corresponding relative fluorescence intensities were 0.2, -0.006, 0.03, and 0.05. This result confirms the high selectivity of ABPL towards ACP. ABPL can be easily synthesized and exhibits a linear and highly selective response to ACP, presenting a new strategy for the design and synthesis of efficient fluorescent substrates for ACP detection.

Heat stress can inhibit the growth of tomato seedlings and lead to yield loss. Temperature is often used as an indicator to evaluate the impact of plant heat stress. However, due to the difference between individual plant heat tolerance and their health status, plants under the same temperature may show different heat stress symptoms, which could lead to misclassification. Therefore, combined with chlorophyll fluorescence technology and visible near-infrared spectroscopy, this paper proposes a classification method for tomato seedlings heat stress severity. The chlorophyll fluorescence parameters and visible near-infrared (Vis-NIR) spectrum data of the controlled and heat-stressed plants were collected. Using multiple chlorophyll fluorescence parameters as indicators, a clustering model based on the k-means++ algorithm was established to obtain the classification labels on the severity of heat stress. The reasonableness of the clustering result was verified by analyzing the difference between the chlorophyll fluorescence parameters and the biochemical indicators among the three samples. Then, the spectral data were labelled based on the output of the clustering model; six characteristic bands highly related to the samples heat-stress severity were extracted using three preprocessing methods and their combinations, combined with three characteristic wavelength selection algorithms. With six characteristic bands as input and the heat-stress-severity as output, classification models are established based on four machine learning algorithms to classify the heat-stress-severity. The results showed that The chlorophyll fluorescence parameters Fv/Fm, Fv/Fo, NPQ, Y(NPQ) and Y(NO) showed significant moderate to high correlation with their heat stress status, and the samples were labelled as non-heat-stressed samples, mild heat-stressed samples and severe heat-stressed samples based on the five parameters. After feature extraction, more than 99% of redundant features are eliminated, and only six characteristic wavelengths remain. Characteristic wavelengths that can be used to establish classification models are obtained. The LDA model performs best among the four models, with a classification accuracy of 92.45%, an F1 score of 0.929 1, and an AUC of 0.978 0. The results indicate that using chlorophyll fluorescence technology combined with Vis-NIR technology to detect heat stress is feasible. This study provides an effective method for rapidly detecting heat stress, rapid screening of heat tolerance in plants and early warning of heat stress.

Humification is a carbon sequestration process where organic residues are transformed into humic substances (HS) through microbial conversion or chemical oxidation and polymerization. The active functional groups present in the resulting humic acids (HA) significantly impact the environmental chemical behavior of pollutants. Natural minerals containing Mn/Fe/Al/Si oxides can facilitate the oxidative polymerization of organic monomeric small molecules (e.g., polyphenols, reducing sugars, amino acids) into HA. Oxygen, as a natural oxidant, can synergistically enhance non-biological humification in the presence of metal oxides and improve the quality of the resulting HA. However, the mechanisms of abiotic humification enhancement by metal oxides under varying oxygen atmospheres and the impact on the evolution of organic matter remain unclear. This study used catechol and glycine as representative precursors for polyphenol and protein degradation, respectively, with iron oxide enhancing humification reactions. Solution and extracted HA samples were analyzed by ultraviolet-visible spectroscopy (UV-Vis), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) during the reaction process. The results indicated that the aromatization of organic matter, as indicated by UV254, followed the order of 21%>40%>0% oxygen concentration. HA yields mirrored the UV254 findings, with an approximately 25% increase in HA production observed at 21% oxygen concentration compared to 40% and 0% oxygen concentrations. XPS spectroscopic analysis revealed that 21% oxygen concentration promoted the conversion of amino group N to pyrrole N in HA. In comparison, 40% oxygen concentration facilitated the conversion of amino group N to amide N, and under 0% oxygen concentration, a substantial amount of amino group N remained in HA. FTIR analysis indicated that ·OH generated from the aromatic compound ring cleavage contributed to oxygen-containing functional groups(CO, COOH) in HA. FTIR combined with hyperspectral 2D maps from UV-Vis suggested that oxygens involvement promoted the conversion of amino acids into CO in HA and the transformation of amides into aromatic and aliphatic moieties in HA. Structural equation modeling (SEM) indicated that releasing iron ions was a key factor in promoting HA yield, and 21% oxygen concentration accelerated organic matter polymerization by enhancing iron ion release. Furthermore, the divalent iron/trivalent iron ratio was negatively correlated with HA yield, implying that oxygen indirectly affected humification by promoting iron ion conversion. In conclusion, changes in oxygen concentration affect the transformation of humification products, subsequently influencing HA yield and functional group composition. This study reveals the impact of oxygen concentration on humification products, offering references for refining humification theory.

Near-infrared spectroscopy has become an indispensable analysis method in industrial and agricultural production process quality monitoring. It has been widely used in the qualitative and quantitative analysis of food, agriculture, medicine and others.-A near-infrared spectroscopy prediction model with high prediction accuracy, high-speed running,and strong generalization ability plays an essential role in the qualitative and quantitative analysis of different substances. However, due to the increase innear-infrared spectroscopy data, the disadvantages of traditional near-infrared spectroscopy modeling methods are obvious. With the development of artificial intelligence technology, deep learning algorithms have been widely used in the field of near-infrared spectroscopy. The quantitative analysis model of near-infrared spectroscopy based on a parallel convolution neural network (PaBATunNet) was proposed. PaBATunNet comprisedone1-D convolutional layer, one parallel convolution module (Module), one flattening layer, four fully connected layers and one parameter regulator (PR).The Module included five submodules and one Concatenate function, which was used to extract the linear and nonlinear multidimensional features of the spectral data, respectively and concatenate them. The prediction accuracy of PaBATunNet was improved by PR, which optimized the model parameters. The high contribution characteristic wavelengths of PaBATunNet were given based on Gard-CAM, which improved the interpretability of PaBATunNet. By taking public near-infrared spectroscopy datasets of grain, diesel fuel, beer and milk as examples, the prediction results of PaBATunNet were compared with partial least squares (PLS),principal component regression (PCR), support vector machine (SVM) and back propagation neural network (BP). The results showed that the prediction accuracies of PaBATunNet tograin, diesel fuel, beer and milk datasets were respectively increased by 30.0%, 40.7%, 43.0% and 52.8% in comparison with PLS, 28.8%, 35.9%, 40.8% and 52.2% in comparison with PCR, 45.5%, 37.4%, 45.3% and 54.7% in comparison with SVM, and 7.9%, 32.4%, 90.1% and 62.0% in comparison with BP. Compared with the traditional near-infrared spectroscopy modeling methods, the PaBATunNet based on the parallel convolutional neural network, has solved the problems of low prediction accuracy, long running time, poor generalization ability and poor interpretability. It can be effectively applied to quantitative analysis in industrial and agricultural production. It provides a theoretical basis for establishing the rapid, nondestructive and high-precision near-infrared spectroscopy quantitative analysis model.

Clobetasol propionate (CP) is a kind of glucocorticoid, which can be used to treat eczema, psoriasis and other skin diseases. Long-term and frequent use of CP-contained skin care products by infants and young children may lead to serious health hazards. CP is a prohibited ingredient in cosmetics, and it is necessary to develop a rapid and simple method to detect the banned hormone CP in skin care products. In this work, there is a halogen exchange reaction between orange luminescent all-inorganic perovskite quantum dots (CsPbBr1.5I1.5 QDs) and CP, and the fluorescence peaks occur blue shift. There is an excellent linear relationship between the shift wavelength of CsPbBr1.5I1.5 QDs emission peak and the concentration of CP, which realizes the rapid analysis of the content of CP in infant skin care products. The detection limit is 0.2 mmol·L-1, and the detection time is 10 min. A luminescent color card for CP detection in infant skin care products was made, which could be used to quickly, accurately, and simply evaluate CP content in infant skin care products.

Glow discharge atomic emission spectroscopic analysis technology can analyze and characterize the surface of metal samples layer by layer along the depth direction, and the glow discharge emission source has the advantages of a fast sputtering rate, high analysis efficiency, and large-area sputtering. In addition, the glow discharge plasma energy is low. The layer-by-layer sputtering excitation process of the material will not cause changes in the structure of the material itself. The sample preparation can be achieved layer by layer along the sample depth direction. Combining a glow discharge sputtering source with a scanning electron microscope, spectral analysis, and detection instrument can be used as an effective means for high-through put quantitative characterization of metal materials. It is necessary to perform glow discharge plasma sputtering on the material surface under multi-size and large-area sputtering conditions. Therefore, based on the traditional glow sputtering source, this paper improves the structure of the anode cylinder, designs four kinds of large-diameter anodes with diameters of 15, 20, 30, and 40 mm respectively, and carries out COMSOL numerical simulation and actual study on sputtering effect. Large-size sputtering surface can obtain richer information on the surface of the material. However, under the same sputtering conditions, the increase of the anode diameter will also lead to a decrease in the sputtering rate, a decrease in the ionization rate of the central area of the sputtering surface, and affect the sputtering uniformity, pit flatness, and other issues. To solve these problems, an auxiliary anode structure that can be applied to a large-aperture DC glow discharge sputtering source is designed in this paper. Changing the electric field distribution in the discharge space regulates the plasma distribution in the discharge space, and the electron ionization rate in the central area of the anode is enhanced. In this paper, the design principle of the auxiliary anode structure is explained in detail, and the numerical simulation research and the actual sputtering effect comparison experiment are carried out on the traditional anode cylinder and the auxiliary anode. The results show that the addition of auxiliary anodes has a significant effect on the sputtering rate of large-diameter sputtering sources. The sputtering rate of sources with an anode diameter of 30mm is increased by 33%~48%, and the sputtering rate of sputtering sources with an anode diameter of 40mm is increased by 34%~57%. The sputtering excitation of copper samples was carried out using a large-aperture auxiliary anode sputtering source, and the sputtering crater morphology was tested by optical coherence tomography (OCT). The results show that adding an auxiliary anode can effectively improve the sputtering uniformity and flatness of the sputtering crater. The actual measurement data are given in this paper.

According to a 2021 China CDC Weekly Express report, mushroom poisoning was one of Chinas most serious food safety issues. In 2021, a total of 327 mushroom poisoning incidents involving 923 patients and 20 deaths were investigated, and the overall mortality was 2.17%. About 74 poisonous mushrooms have been successfully identified. Boletuses are one of the peoples favourite wild mushrooms because of their delicious taste. However, boletuses poisoning incidents can occur due to improper cooking or mixing with toxic boletuses. Quickly identifying variety and the main ingredients of boletus has become a problem that needs to be resolved immediately. Thus, tenfresh boletuses samples were purchased from the Chuxiong Hongfu market. The pileus was prepared by free-hand section, then the silver colloid area was prepared by using the silver colloid prepared by microwave. Under the DXR Laser confocal micro Raman spectrometer, the surface-enhanced Raman spectroscopy (SERS) spectra of 10 samples were achieved.In these results, the main ingredients of samples 7, 8 and 10 were the same, while samples 6 and 9 also shared the same main ingredients. However, the other samples had different ingredients compared to each other. As an example, in sample 1, the main ingredients were L-phenylalanine (1 583 cm-1 ring C—C stretching vibration and 1 199 cm-1 NH2 rocking), L-histidine(1 572 cm-1 CC stretching vibration COO- asymmetric stretching and 1 229 cm-1 in-plane ring deformation vibration ), isoleucine (1 342 cm-1 C—H, N—H deformation vibration, 486 cm-1 COOH rocking vibration and 353 cm-1 lattice vibration), L-aspartic acid (1 136 cm-1 C—N stretching vibration), glycine (1 386 cm-1 COO- deformation vibration and 889 cm-1 C—C stretching vibration), methionine (681 cm-1 C—S antisymmetric stretching vibration) and pyranose (973 cm-1 symmetric ring vibration) By applying the spectrum software OMNIC Specta and randomly selecting 10 lines to build the database of the sample spectra, the species of fresh boletuses could be identified quickly by measuring their spectra and matching them with the standard spectra in the database. In all samples, only samples 2 and 10 had relatively low matching rates, which were less than 60%. Furthermore, samples7 and 8 shared similar matching rates and cross terms with each other, which indicated they were of the same species. Samples 6 and 9 had similar matching rates but fewer cross terms, which indicated they were of the same or similar species.DNA test results showed that samples 7, 8 and 10 were Boletus baingan, while samples 6 and 9 were Baorangia pseudocalopus. This experiment provides a simple and reliable method to detect the analysis of the main ingredients and rapidly identify Boletuses. Also, this approach has great potential for species identification of wild mushrooms. This experiment has great application value in quickly determining the poisonous wild mushroom species and gaining time to rescue the patient in the case of wild mushroom poisoning. To our knowledge, it is the first time SERS has been used on wild mushroom fruiting bodies.

Agate, also known as hematite, is an important part of ancient Chinese jade. The Khitan people are particularly fond of the string decorations made of agate. Because agate accounted for a large proportion of the jade unearthed from the tombs of the Liao Dynasty, about one-third were agate, which is of great significance for studying the processing and aesthetic orientation of jade in the Liao Dynasty. However, there is little scientific and technological analysis and research on Khitan agate. Four agate beads were unearthed in Zhangjiayao Forest Farm No.1 tomb, of which No.1 bead is a necklace, and No.2—4 beads are arm ornaments. To study the characteristics and processing technology of agate unearthed in No.1 Liao Tomb in Zhangjiayao, in this paper, laser micro Raman spectroscopy, super depth of field microscopy, X-ray fluorescence spectroscopy, X-ray flaw detection and other techniques are used to observe and analyze the agate string decoration unearthed from Liao tomb in Zhangjiayao forest farm, and the source of raw materials, processing technology and preservation status are discussed. The results show that the Raman spectra of the four samples are the same, showing the scattering peaks at 130, 203, 399, 413, 462, 501, 550, 637, 688, 796, 990, 1 160 and 1 345 cm-1, the main composition of the sample is: α- Quartz and clinopyroxene, the coloration minerals are goethite, hematite and hexagonal fibrotite, and the processing technology is opposite drilling. To know the content of plagioclase in the sample, Lorentz fitting was carried out for the characteristic peaks at 462 and 501 cm-1 of the Raman spectrum of the model, and the Raman spectral band integral ratio analysis was carried out for them. The results showed that the integral ratio of the Raman spectral band of the sample was 4.18. According to the function curve, the relative content of the modelis estimated to be about 20%, calculated that the ratio of moganite and α- quartz characteristic peak areaA501/A462) is about 0.22~0.3. According to the content of moganite, it is inferred that the agate maybe North Red Agate, and the source may be the local source of LiaoDynasty. According to the X-ray flaw detection results of agate, the processing technology of tube beads is opposite drilling, and the processing tools may be solid drilling tools with a small amount of jade sand. There are a lot of soil erosion pits, crackles, whitening and other phenomena on the surface of the sample, and the overall weathering condition is relatively severe. After studying the acidity and alkalinity of the soil in the No. 1 coffin of Zhangjiayao, it is found that the soil in the wooden coffin is neutral soil, which has little impact on the tube beads. On this basis, combined with the current research on jade weathering, it is speculated that the weathering of samples is related to the quality of raw materials and the weathering of samples.

Xuzhou Tushan Tomb No.2 is an important tomb of princes in the early Eastern Han Dynasty excavated in recent years, and it has been selected as one of the top ten new archaeological discoveries in the country in 2020. The tomb belongs to a brick-room tomb, and the extensive use of lime in many places is rare in the same period，representing the highly developed technology level of lime production and application at that time. The lime material is one of the most widely used cementitious materials in ancient China, but there is still a significant gap in scientific research. In this paper, the use of lime in the tomb was investigated. Various techniques such as X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric-differential scanning calorimetry, scanning electron microscopy, Iodine starch experiment and density, porous material density, porosity, water absorption test and X-Ray fluorescence were used to analyse and test various types of lime samples. The research results show that all the lime samples in the No.2 Tomb of Tushan were produced by manual calcination of natural lime, and no aggregate component was added, which reflects that the ancient people in the Han Dynasty were very skilled in lime burning; the raw materials of lime came from lime fired from bluestone-type high-calcium natural materials with calcium carbonate content of more than 85%, indicating that there was already a consciousness of selecting raw materials when calcining lime; lime has a wide variety of uses in the tomb, including masonry mortar for the masonry walls of the tomb, decorative plastering for the masonry walls and top coupons in the tomb, lining for anti-corrosion and moisture absorption under the body of the tomb owner in the coffin, showing that the ancients had a relatively deep understanding of the properties of lime and were able to make good use of it; due to the contact with the external natural environment, the sample has undergone obvious erosion and weathering, so it is urgent to carry out certain protection and reinforcement treatment. This study is of great significance for understanding the development of scientific and technological civilisation, such as the properties of lime, its production status, and its application in production and life among Chinese ancestors during the Eastern Han Dynasty. It also provides a scientific reference for the subsequent protection of related cultural relics.

Stellar spectral classification is a significant research direction in astronomy. With the rapid development of technology, the stellar spectral data collected by large survey telescopes have reached terabytes or even petabytes, and the traditional classification methods can no longer meet the processing needs of such a vast amount of data. CNNs learn the local features of the data by convolution operations, remove redundant information, and compress the features by maximum pooling operations. However, since the fully-connected layer of the original CNN model lacks the function of long-range dependency mining, this problem can be solved by adding LSTM networks, which can extract important features and detect small differences in features through their unique three “gates” of long-range dependency mining capability. Therefore, this paper proposes a deep model based on the composite of CNN and LSTM for classifying stellar spectra in LAMOST DR8. This model can better learn the features of stellar spectra, which provides an important help for stellar evolution studies. To improve the convergence speed of the model, the common Z-Score normalization method is used to process the data. The model proposed in this paper achieved a classification accuracy of 94.56% in the F, G, and K classification experiments. Meanwhile, compared with the previously used RBM, PILDNN, PILDNN*, DBN, Inception v3, 1D-SSCNN, and LSTM methods, the results show that the method in this paper has a higher classification accuracy. In the ten-class experiments, the method in this paper achieves 97.35% accuracy. The results are better than the experimental results using only LSTM and 1D-SSCNN methods, and the training time is reduced by nearly ten times. The F1 score is used to illustrate the classification accuracy of each class of stellar spectra, and the F1 value of each type is above 0.9 in both the three-classification and ten-class experiments. Compared with the results of previous experiments in the literature, the results of this papers model are better. With the confusion matrix results, it can be concluded that the models accuracy in this paper is higher in the experiments with more spectral categories, and it can even reach 100%. In summary, the model based on the combination of CNN and LSTM proposed in this paper can effectively classify large-scale stellar spectral data and achieve excellent classification results.

In recent years, a novel variety of jade known as “African Dulong Jade” has emerged in the jewelry market of Western Yunnan. It possesses a distinctive, vibrant green hue adorned with intricate veins and feathery patterns, juxtaposed against a colorless greyish-white matrix, occasionally tinged with reddish-brown tones. Notably, its quality surpasses that of the “Dulong Jade” found in the Nujiang Prefecture Yunnan Province, exhibiting promising market expansion prospects.However, the gemological characteristics and mineral species of this jade remain mysterious.To shed light on this subject, this research explores the mineral composition, structural characteristics, chemical components, spectral attributes, and the origin of its captivating color. Employing advanced analytical instruments such as the Infrared spectrometer, Raman spectrometer, X-ray powder diffractometer, Scanning electron microscope, Energy dispersive spectroscopy, X-ray fluorescence spectrometer, and Ultraviolet-visible spectrometer, the study delves into a comprehensive investigation.The infrared spectrum examination reveals that the green portion of the jade exhibits mixed infrared spectrum characteristics akin to both muscovite and quartz. Notably, infrared absorption peaks at 3 623, 1 080, 694 and 1 619 cm-1 signify the presence of muscovite. Raman spectra demonstrate that the matrix component of the jade corresponds to α-quartz, as evidenced by characteristic peaks observed at 204, 262, 355, 395 and 463 cm-1. Conversely, the green section of the jade displays muscovite characteristics with peaks highlighting at 261, 395, 694 and 3 623 cm-1. Furthermore, the metallic luster observed on the surface of the mineral signifies the presence of pyrite, with characteristic peaks at 343, 379, and 437 cm-1. Locally disseminated orange-yellow to reddish-brown minerals are identified as hematite, exhibiting characteristic peaks at 224, 295, 409, 493 and 610 cm-1. X-ray powder diffraction analysis confirms the existence of 2M1 muscovite with discernible diffraction peaks observed at 3.489, 2.981 and 2.563 . Insights obtained from scanning electron microscope and energy spectrum scanning elucidate that muscovite manifests as columnar and scale-like aggregates, featuring individual particle lengths ranging from 25 to 40 μm and widths of 2 to 4 μm. Additionally, gersdorffite, an impurity mineral, exhibits minimal content and particle sizes of approximately 10 to 15 μm. Within gersdorffite, iron (Fe) and cobalt (Co) elements partially replace nickel (Ni) through isomorphism. Moreover, the greater concentration of arsenic (As) compared to sulfur (S) suggests a high-temperature hydrothermal deposition origin.Through chemical composition analysis and ultraviolet-visible spectra examination, it is ascertained that the chromogenic mineral muscovite contains barium (Ba) and chromium (Cr), while iron (Fe) is lacking in the quartz matrix. Furthermore, the content of Ba exceeds that of Cr, which acts as the chromic element within muscovite. Beyond color characteristics, this study reveals distinguishing chromogenic elements, chemical composition, and impurity minerals between “African Dulong Jade” and “Dulong Jade” from NujiangPrefecture Yunnan Province using various spectral techniques.These findings serve as compelling evidence for differentiating the two “Dulong Jade” types and contribute to an enhanced understanding of the component information and spectral characteristics of naturally occurring green quartzite jades. Consequently, it establishes a foundation for future inquiries into the metallogenesis geological background and geographical origin tractability of “African Dulong Jade”.

Freshwater nucleated pearls have gained a significant share in the pearl market, due to their large size, good roundness, rich color, and strong luster. However, there is still no explicit conclusion on color-causing mechanism in colored freshwater nucleated pearls, which is attributed to the fact that there are very few studies currently focused on the mechanism research. Herein, we filtrate multiple colors of freshwater cultured pearls to study the mechanism via laser Raman spectroscopy and colorimetry. The Raman spectroscopy revealed these samples possess three different C—C and CC peaks, exhibiting interesting corresponding relationship, which means the concurrence of C—C (1 123～1 125 cm-1) corresponding to the CC (1 506～1 509 cm-1), the C—C (1 132～1 134 cm-1) corresponding to the CC (1 522～1 523 cm-1) or the CC (1 525 cm-1), the C—C (1 128～1 131 cm-1) corresponding to the CC double peak (1 506～1 509 and 1 520～1 523 or 1 525 cm-1). Owing to the differences of CC peaks in the Raman spectra, the samples were classified into P, T, G, P+T, T+G, and P+G. Combining with the chromatic parameters, we explored the relationship between the lightness (L) or saturation (S) and the CO3/C—C peak area ratio in the same type, displaying the positive correlation between the lightness (L) and the CO3/C—C peak area ratio for P-type pearls. In contrast, the negative correlation was found between the saturation (S) and the CO3/C—C peak area ratio for P, T, and G types, which confirmed that the three different C—C and CC peaks were caused by different color-causing substances. Moreover, we attributed color-causing organic compounds to linear polyenes by Raman spectroscopy. The Raman peaks of linear polyenes show redshift with the increase of orderliness. Linear polyene molecules with peaks closer to the infrared region have more extended and straighter carbon skeletons, which leads to the difference between the peaks of C—C and CC in the Raman spectra of different samples. We also categorised the colored freshwater nucleated pearls via chromatic parameters. The pearls with hue angle (h)∈(0, 15), (15, 45), (335, 360) are classified as the red series, yellow series, and purple series, respectively.

Scheelite is a rare gemstone with a massive granular structure, exhibiting a white to light yellow coloration, greasy luster, and obvious fluorescence. The scheelite deposit at Xuebaoding in the Pingwu region of Sichuan Province is a vein-like hydrothermal-type deposit with weak alteration of host rocks. Scheelite produced has a high color saturation, perfect crystal form, and pure color, associated with beryl, cassite and muscovite. The nearly colorless to orange tone scheelite from Xuebaoding was taken as the research object in this study. Comprehensive analysis was conducted using X-ray powder diffraction (XRD), Fourier infrared spectrum analysis, ultraviolet-visible spectrophotometer, laser Raman spectrometer, electron probe, and laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS). The mineral composition, crystal structure, characteristic identification spectral bands, color-causing ions, as well as the contents of the main and trace elements, rare earth and other chemical components of the nearly colorless to orange tone scheelite were determined by combining mineralogy, spectroscopy and main and trace elements characteristics. The relationship between rare earth element content and color genesis was also discussed. All have to provide the diagnostic basis for identification of nearly colorless to orange tone scheelite from Xuebaoding. The research results demonstrate that scheelite exhibits good crystallinity, displaying a uniform chemical composition without obvious discontinuity. The mineral composition is relatively concentrated, and the accessory minerals are mostly muscovite and illite. Significantly, scheelites typical infrared characteristic peaks were observed at 440, 809 and 870 cm-1, along with a peak at 448 cm-1 related to Ca2+. The spectral peak exhibits higher absorption for deeper color tones. In the same way, the Raman characteristic peak is at 909 cm-1, and the Ca—O lattice vibration peak is at 207 cm-1, with increasing intensity as the color tone deepens. Additionally, the ultraviolet absorption peaks show strong absorption in the orange-yellow region, with peaks around 383, 570, 584 and 804 nm. The nearly colorles scheelite samples only exhibit a peak at 383 nm, while the scheelite sample with a lighter yellow tone demonstrates weak absorption of Fe3+ in the near ultraviolet region. Conversely, the scheelite samples with a deeper yellow tone display strong absorption of Fe3+ in the blue-purple region and the orange tone due to Nd3+. Moreover, the chemical components of scheelite reveal a WO3/CaO mass ratio that approaches or exceeds the ideal value, while the content of the nearly colorless scheelite sample is relatively concentrated. The trace element Fe demonstrates a positive correlation with color tone, whereby the content increases as the yellow tone becomes darker. Nevertheless, the total amount of rare earths varies widely, with an enrichment of light rare earths, significant negative Eu anomalies, and insignificant Ce anomalies. The light yellow tone is influenced by trace elements Si and Fe and the d-d electronic transition of Fe3+. Similarly, the yellow tone is affected by trace elements Mn and Fe, along with the electronic transition of Fe3+. In contrast, the orange tone is significantly impacted by rare earth elements Nd and Sm.

Gongchangling iron mine is the largest BIF-hosted high-grademagnetite ore deposit in China, various scholars have carried out many researches, however few studies are related to Raman spectra of magnetite. This paper measured the Raman spectra of magnetite in typical BIF ore and high-grade iron ore of Gongchangling by HORIBA XploRAPLUS micro-Raman spectrometer. The results show that the peaks of magnetite in BIF at 300, 550 and 670 cm-1 shift to high wavenumber, which should be related to the increase of the average oxidation state of magnetite in BIF and the decrease of miner allattice size caused by the isomorphic substitution of trace elements. The Raman peak intensities of magnetite in high-grade iron ore increase obviously, probably due to the decrease of the overall content of trace elements and, correspondingly, the relative increase of iron ion content in magnetite. Some magnetites in BIF ore show weak Raman spectral features of hematite, which may be related to the epigenetic oxidation after the formation of magnetite. X-ray diffraction (XRD) analysis shows that the main iron mineral in the high-grade ore is magnetite, whereas the magnetite in the BIF ore is more similar to magnesioferrite. The results of SEM-EDS suggest that the contents of trace elements in the magnetite from high-grade iron ore are lower than those from BIF ore, i.e., experiencing the process of “purification”, which is consistent with the abovementioned conclusion. The research indicates that Raman spectra measurement is proven to be a fast, simple, reproducible and non-destructive method and can be used to estimate the overall content of trace elements of magnetite and further distinguish the magnetite both in BIF and high-grade iron ores of Anshan-Benxi area.

Given the time-consuming and labor-consuming problem of traditional maize stalk strength destructive detection methods, this study used hyperspectral imaging data combined with statistical learning methods to detect the puncture strength and breaking force of the stalks of 19 maize varieties in the filling stage and wax maturity stage. Moreover, the feature extraction and modeling methods suitable for detecting corn stalk strength are given. In the experiment, 19 corn varieties were planted at a planting density of 5 000 plants·mu-1. The hyperspectral images of the base of the stalks at the filling stage and wax maturity stage were collected, and the target area segmentation method was used to automatically perform spectral image reflectance correction and target spectral curve extraction. Principal Component Analysis (PCA) and wrapped feature extraction were used to extract spectral features from the collected sample data, and principal component regression (PCR) and partial least squares regression (PLSR) were developed for the prediction of stalk strength. By comparing each feature extraction method and the cross-validation prediction results of each model, we found suitable feature extraction and modeling methods for maize stalk strength detection. The experimental results showed that the PCA method extracted spectral features had obvious dimensionality reduction effect. However, the PCR model built with PCA method extracted features had average prediction effect on maize stalk strength, and the PLSR model built with wrapped feature extraction method had better prediction effect than the PCR model at both the filling and waxing stages. The residual predictive deviation (RPD) of the PLSR model was higher than that of the PCR model. The RPD of the PLSR model ranged from 2.90 to 3.93, which could be used for quantitative analysis to predict stalk strength.

Sugar content is a crucial parameter for assessing watermelon quality, influencing watermelons marketability and commercial value. However, the natural biological characteristics of large volume and thick skin pose challenges for rapid and non-destructive evaluation of the sugar content of watermelon. In this study, 230 watermelons were selected for investigation. A custom-designed full-transmission visible-near-infrared detection system was developed. Spectral data of all samples were acquired online. Each sample spectral data comes from the equatorial part of the watermelon. The overall watermelon sugar content and the central sugar content were measured separately to provide reference values for the assessment of sugar content. In the data processing phase, the spectral data of each sample was averaged, and spectral data in the 690～1 100 nm was selected. The Monte Carlo method was implemented to remove abnormal samples, and preprocessing, such as Standard Normal Variate correction and Savitzky-Golay smoothing, was applied to optimize the spectral data. The SPXY algorithm was used to divide the calibration and prediction sets. Utilizing the optimized spectral data, linear Partial Least Squares Regression (PLSR) and non-linear Least Squares Support Vector Machine (LS-SVM) models were developed to forecast each samples center sugar content and overall sugar content. The results revealed that, Combined with standard normal variate correction and Savitzky-golay smoothing, the LS-SVM model yielded the most favorable results in predicting the overall watermelon sugar content. The calibration correlation coefficient (RC) of 0.92 and root mean square error of calibration (RMSEC) of 0.37°Brix were obtained for the calibration set. Correspondingly, the prediction correlation coefficient (RP) of 0.88 and root mean square error of prediction (RMSEP) of 0.40°Brix were obtained for the prediction set. Furthermore, feature wavelength selection algorithms (e.g., Competitive Adaptive Reweighted Sampling, Uninformative Variable Elimination, Successive Projections Algorithm) were used for variable selection. Study found that the LS-SVM model combined with Competitive Adaptive Reweighted Sampling and Uninformative Variable Elimination methods has the optimal performance in predicting the overall watermelon sugar content with a calibration correlation coefficient (RC) of 0.94 and a calibration root mean square error of 0.31°Brix. Correspondingly, the prediction correlation coefficient (RP) and the root mean square error of prediction (RMSEP) were 0.91 and 0.37 °Brix, respectively. Additionally, the number of variables was significantly reduced from 1 524 to 39. This study provides a reference for the practical application of rapid and non-destructive testing of sugar content in watermelon.

At present, the varieties of cabbage on the market are complex; the quality and germination rate of different cabbage seeds are different, but the appearance of cabbage seeds is not very different, so it has become a big problem to distinguish the types of cabbage seeds. This paper explores the feasibility of analyzing cabbage seed categories based on visible/short-wave near-infrared spectroscopy to achieve rapid differentiation of cabbage seed categories. The experiment purchased three varieties of cabbage seeds of Hong Kong species Sijiu, October red and September fresh from the Nanchang Seed Trading Place. The seeds with good appearance and moderate size were selected, and each kind of cabbage seed was evenly divided into three categories. 30 copies, divided into modeling and prediction sets according to 2∶1, totalling 90 copies of all samples. The near-infrared spectrometer was used to obtain the spectral reflectance of cabbage seeds with a sampling interval of 1 nm, and the wavelength coverage was 325~1 075 nm. The original spectral data were corrected by multivariate scattering (MSC), convolution smoothing (S-G) and standard normal transformation (SNV). )Three preprocessing methods were used for preprocessing. A partial least squares regression (PLSR) model was established for the spectral variables after preprocessing, and SNV was determined to be the best preprocessing method. In addition, principal component analysis (PCA) was used to conduct cluster analysis on cabbage seeds. The scores of the first three principal component factors (PCs) show that the three kinds of cabbage seeds have differences in spectral characteristics. Finally, the original spectral variables, the first three PCs (with a cumulative contribution of 97.15%) and 13 characteristic wavelengths selected based on the random frog (RF) algorithm were used as partial least squares discriminant (PLS-DA) and least squares support vector machines ( The input variables of the LS-SVM) model, from the model results, we can see that among the three input variables when the RF screening characteristic wavelength is used as the model input variable, the model prediction effect is the best, and the model established by PCs is the worst. The characteristic wavelengths screened by RF can better reflect the original spectral information. Judging from the prediction effects of different models, the LS-SVM model has better prediction accuracy than the PLS-DA model. The RF-LS-SVM model is the best prediction model among all models, and the modeling set and prediction set are both 100%. In conclusion, using visible/short-wave near-infrared spectroscopy to study the types of cabbage seeds is feasible. It can achieve a good prediction effect, which provides a theoretical basis for the rapid differentiation of cabbage seeds.

Originating in China, mulberry is one of the fruits of the homology of medicine and food and has a long history. However, the industrialization of mulberry fruit has been limited by its characteristics of short maturity period and the tendency for thin skin to decay. Total soluble solid (TSS) is an important component of determining the mulberry flavor and qualityandis one of the most basic quality characteristics for its postharvest-commercialization. This study aims to optimize a prediction model for monitoring the TSS content in postharvest mulberry fruits using near-infrared hyperspectral imaging and deep learning methods and to evaluate the impact of common postharvest storage temperature on the quantitative models, thus providing support for rapid quality assessment of mulberry fruits. Mulberry fruits with consistent commercial maturity were selected for storage at room temperature (25 ℃) and low temperature (4 ℃). Samples from different storage stages were selected for spectral data collection and TSS content determination until mulberry fruits became unfit for consumption. Based on the spatial information provided by the corrected hyperspectral images, regions of interest were extracted to obtain representative spectra without background accurately. Then, standard normal variate (SNV), multiplicative scatter correction (MSC), and Savizkg-Golag (SG) smoothing were used for spectra preprocessing to improve the spectral signal-to-noise ratio. Prediction models for TSS content measurement in postharvest mulberry fruits were established using deep learning. For mulberry samples stored at room temperature and low temperature, the optimal CNN models obtained the residual prediction deviation values of 5.828 and 5.429, with the root mean square error of prediction (RMSEP) values of 1.082 and 1.099°Brix, respectively, indicating that the prediction performance of the CNN model was degraded due to the low-temperature storage. The classical machine learning methods of partial least squares (PLS) and least square support vector machine (LS-SVM) were used to establish models for TSS prediction further to verify the effectiveness of the constructed CNN models. Results showed that the nonlinear LS-SVM model was more suitable for predicting TSS content in mulberry fruits than the linear PLS model. For mulberry fruits stored at two different temperatures, the optimal LS-SVM models achieved RPD values of 4.221 and 4.423 for TSS prediction, respectively, indicating that the CNN performed better than the classical machine learning methods. In conclusion, hyperspectral imaging combined with deep learning CNN has excellent potential in predicting TSS content inpostharvest mulberry fruits, which provides technical support for rapid assessment of mulberry quality.

Using different wavelength spectra to identify different types of foreign fibers can effectively eliminate foreign fibers and increase the detection rate. Given the problem of a single evaluation index existing in traditional band division methods, the interaction of attribute indexes of different fibers is studied, combined with the advantages of the multi-attribute group decision-making (MAGDM) method, this paper proposes to use the MAGDM method to realize the selection of the optimal detection band for foreign fibers in cotton. According to the relation of attribute indexes of different fibers, the inter-class separability, correlation and ABS index are determined as attribute evaluation indexes. Firstly, to solve the problem of inaccurate evaluation criteria in the MAGDM method, a system of evaluation criteria function linear equations is constructed so that the rank of the augmented matrix is equal to the number of unknowns, ensuring that the equation system has a unique solution, thereby improving the accuracy of the decision result. Next, the power mean (PA) operator is used to eliminate the adverse effects of unreasonable evaluation information values on decision results, combined with the Maclaurin symmetric mean (MSM) operator to comprehensively consider the relationship between input arguments, deriving the weighted interval-valued intuitionistic fuzzy power Maclaurin symmetric mean (WIVIFPMSM) aggregation operator.Then, the TOPSIS method is used to determine the weight information of foreign fibers, the evaluation information of various attributes of different foreign fibers is aggregated, and the decision results are chosenaccording to the established evaluation criteria. Thus, a MAGDM method based on interval-valued intuitionistic fuzzy sets (IVIFSs) is constructed to realize the optimal band selection of various attributes of foreign fibers. Moreover, the WIVIFPMSM aggregation operator is compared with the inter-class separability band selection (ISBC) method and adaptive band selection (ABS) method, the influence of different band division methods on the results are analyzed, and the existing problems and deficiencies in existing research are summarised. To improve the decision accuracy of the MAGDM method, parameterks influence on decision results is analysed, and it is proved that the IVIFPMSM aggregation method has better stability, which provides a new idea for the study of band division of foreign fibers in complex environments. Finally, it is verified through experiments that the near-infrared bandW3: 780～1 100 nm is the optimal detection band. In addition, this paper has a specificguiding significance for the theoretical extension of band selection and the application of MAGDM methods.

Soil heavy metal pollution caused by mining in mining areas seriously affects crop yield and causes human diseases. It is necessary to prevent soil heavy metal pollution from damaging health. Hyperspectral remote sensing can rapidly and dynamically acquire continuous spectra signals of ground objects, which provides a new idea for developing soil heavy metal content monitoring based on remote sensing. Aiming at the typical lead-zinc mining area in Laiyuan County, Hebei Province, soil samples from the mining area and surrounding areas are collected on-site, and the reflectance spectra of soil were obtained using SVC HR-1024i spectrometer (350~2 500 nm). Through the spectral data smoothing, first derivative (FD), multivariate scattering correction (MSC), standard normal variate transform (SNV), first derivative after multivariate scattering correction (MSC+FD), and first derivative after standard normal variatetransform (SNV+FD), six kinds of spectral transformations were performed. The difference index (DI), ratioindex (RI), and normalizeddifference index (NDI) methods were used to extract the spectral indices from the six pretreated data. The contents of heavy metals cadmium (Cd), lead (Pb) and zinc (Zn) in soil were obtained through laboratory chemical testing and analysis. Different spectral transformation methods pretreated different heavy metals. The optimal spectral transformation methods for heavy metal elements were obtained. The difference index, ratio index, and normalized vegetation index were used to extract the optimal band combination under different spectral indices to get the optimal independent variables for modeling different heavy metals. The inversion models of heavy metal elements were constructed based on random forest and partial least square method. The research indicated that the noise could be effectively reduced, and the spectral characteristics were enhanced by pretreatment of spectral data. The results showed that the correlation between the spectral data and the heavy metal content was improved after the pretreatment. The optimal independent variables for different heavy metal elements were selected to increase the practical features of inversion modeling. Random forest algorithm and partial least squares regression method were used to establish prediction models for three heavy metals: cadmium (Cd), lead (Pb), and zinc (Zn). The R2 of the optimal models reached 0.90, 0.91, and 0.84, respectively, which confirmed the validity of this research method. This study can provide a basis for the inversion modeling of soil heavy metal content in lead-zinc mining areas and a method reference for detecting soil heavy metal content in mining areas.

Due to global biodiversity loss, the estimation of biodiversity using spectral technology has become a hot topic for ecologists and remote sensing scientists. There are many studies on alpha diversity but few studies on beta diversity. There are still some problems worth exploring. To explore the best spectral index and image spatial resolution for estimating plant beta diversity using remote sensing technology, this paper took meadow grassland as the research area. It calculated six beta diversity estimation indices from three aspects: spectral distance, spectral angle and biodiversity concept based on UAV hyperspectral remote sensing images. We developed four indices, and two are existing indices. Mantel tests and correlation coefficients were used to select the best spectral index. Then, the selected index was applied to images with different spatial resolutions to obtain the best observation scale. In addition, to improve the estimation ability of the index, this paper compared two spectral transformation methods, the first derivative transform and Savitzky-Golay filter, and three feature band selection methods: correlation coefficient, successive projections algorithm and the competitive adaptive reweighted sampling. The results showed that in both subscale observation (pixel size<quadrat size) and equal scale observation (pixel size=quadrat size), the best spectral index was the spectral distance index, and the spectral distance index performed well under different image spatial resolutions. The best estimation result can be obtained in the grassland area when the image spatial resolution is about 0.25 m. The spectral distance index constructed after the first derivative transformation and extraction of characteristic bands by the correlation coefficient method has the strongest correlation with beta diversity. In the future, this index can be used to build estimation models or directly indicate beta diversity. This paper has guiding significance for scientifically selecting spectral index and image spatial resolution to estimate plant beta diversity.

Plant polyphenols, known as “Category 7th nutrients” for human health, have received widespread attention in many fields, including medicine, food and nutrition. However, red-fleshed kiwifruit peel (RKP), which contains many polyphenols and is known as an excellent raw material for extracting plant polyphenols, is often discarded as a by-product. First, the polyphenols were extracted by pulsed ultrasound (PU) assisted with natural deep eutectic solvent (NADES) from RKP. Subsequently, protocatechuic acid (PCA), the critical polyphenolin RKP, was selected to investigate the inhibition effect on low-density lipoprotein (LDL) oxidation and to investigate the interaction mechanism with bovine serum albumin (BSA) by fluorescence spectroscopy and UV-Vis spectroscopy. The results were as follows: The extraction rate of polyphenols by NADES was significantly higher than that of conventional solvents. Among the 6 kinds of NADES solvents screened, the highest extraction rate of polyphenols from RKP (29.84 mg GAE/g DW) was obtained with choline chloride-ethylene glycol under the conditions of ultrasonic power of 400 W, material-to-liquid ratio of 1∶40 (g·mL-1), temperature of 70℃, extraction time of 20 min and water content of 20% (ω/ω). PCA had a strong scavenging ability on DPPH radicals, with the highest scavenging rate of 94.39% in the results of the spectroscopic experiments. PCA could significantly prolong the delay time of conjugated diene (CD) production and peak value during LDL oxidation, effectively inhibit the production of lipofuscin and total fluorescent products during lipid oxidation and reduce the oxidation of tryptophan (Trp) residues and oxidative modification of lysine (Lys) residues during LDL oxidation. These spectroscopic experiments showed a strong inhibition effect on LDL oxidation. The interaction between PCA and BSA was studied, and the following results were obtained using multispectral techniques. The interaction and the strong affinity between PCA and BSA occurred; there was only one binding site, and the hydrophobic force played a major role in the interaction process. The microenvironment around Tyrosine (Tyr) was almost unchanged after the interaction, while a reduction of polarity and enhanced hydrophobicity around Trp residues occurred. The interaction between these two molecules was further verified in the three-dimensional fluorescence spectra, and the protein structure of BSA was changed after this interaction occurred. The quenching mechanism of interaction between PCA and BSA was static quenching according to these fluorescence and UV-Vis spectra experiments. This study provides new insights for the development of RKP and PCA.

The analysis of pollution source composition of composite polluted water bodies is currently a challenge in water pollution source identification. The aqueous fluorescence fingerprint technology based on the fluorescence excitation-emission matrix is the key method to solve this problem. This study applied aqueous fluorescence fingerprint technology to perform the pollution source identification analysis of Y River in southern Jiangsu region. The fluorescence signal of textile printing and dyeing wastewater in the Y River derived from allochthonous upstream inputs, which was effectively identified by parallel factor analysis and aqueous fluorescence fingerprint comparison.Heavily contaminated regions were divided by ammonia nitrogen concentration. 109 outlets in the Y River Basin were screened by aqueous fluorescence fingerprint. 35 main responsible units were accurately located through the pollution path tracking method. After implementing the rectification requirements, the water quality of Y River was rapidly improved from below Class Ⅴ to Class Ⅲ and steadily met the standards. The Y River regulation project was cancelled, significantly reducing costs and improving efficiency. In this work, a new model of precise pollution source identification and minimally invasive control was proposed for composite polluted rivers based on aqueous fluorescence fingerprint technology, which is of great significance for realizing pollution control in a precise, science-based, and lawful way.

Formaldehyde (HCHO) is one of the most important trace gases in the atmosphere.Its closely related to human health and the environment and plays an extremely important role in tropospheric photochemical reactions. In recent years, the autumn tropospheric ozone and HCHO pollution problems in the Pearl River Delta (PRD) region of China have become more serious, and the tropospheric HCHO is one of the key indicators for analyzing the formation mechanism of the boundary ozone. Therefore, it is of great importance for us to carry out the HCHO observation experiments in the PRD region. This study retrieved the differential slant column density (DSCD) of O4 and HCHO at HeShan Observatory from September 20 to October 3, 2019 using the multi-axis differential optical absorption spectroscopy (MAX-DOAS) technique. The air mass factor (AMF) of HCHO was calculated using the geometric approximation method, thus obtaining the tropospheric HCHO vertical column density (VCD). The results showed that the tropospheric formaldehyde VCD fluctuated between 4.99×1013 and 6.48×1016 molec·cm-2 during the observation period, with an average value of 2.18×1016 molec·cm-2. The retrieved tropospheric HCHO VCD was almost consistent with that from TROPOspheric Monitoring Instrument (TROPOMI), with a correlation coefficient (R) of 0.80. However, the tropospheric HCHO VCD from TROPOMI on 25 and 28 September was lower (about 25%) than that from MAX-DOAS, which may be caused by the different observation methods. In addition, the retrieved O4 and HCHO DSCD and HEIdelberg PROfile (HEIPRO) algorithms based on the optimal estimation method were used to retrieve the tropospheric aerosol and HCHO profiles during the observation period. The results showed that the HCHO pollution was mainly concentrated near the surface (0～800 m), and the HCHO pollution during the observation period mainly came from the local industrial and motor vehicle emissions. The retrieved surface HCHO from MAX-DOAS was compared with that from 2,4-dinitrophenylhydrazine chromatography technique. The surface HCHO from MAX-DOAS showed similar trends with that from the 2,4-dinitrophenylhydrazine chromatography technique. The high values (up to 14.31 μg·m-3) of surface HCHO from 27 to 29 in September were simultaneously monitored, with the correlation coefficient (R) and slope of 0.88 and 0.98, respectively, which validates the reliability of surface HCHO results retrieved from MAX-DOAS technique.The results of this study show that the MAX-DOAS technique can realize the real-time monitoring of tropospheric HCHO VCD, which can be used as an important means to verify satellite-based measurements. The tropospheric HCHO profiles can be retrieved using the MAX-DOAS technique.

In order to improve the sensitivity of the algal chlorophyll fluorescence method for the detection of trace herbicides, the optimal response time of microalgae to the toxicity of three triazine herbicides (Atrazine, Prometryn and Terbumeton) was investigated, as well as the sensitization effect of methyl-ethyl vegetableoil on the biological toxicity of herbicidesdetected by algal fluorescence method were further studied. In addition, four chlorophyll fluorescence kinetic parameters of Fv/Fm, Y(Ⅱ), NPQ and qP were measured by WATER-PAM-WALZ as the toxicity evaluation index, and Chlorella proteinacea, Microcystis aeruginosa and Scenedesmus obliquus were adopted as the test algae. Therefore, the results were as follows: ①The optimal biotoxic response times of Chlorella proteinosa, Microcystis aeruginosa, and Scenedesmus obliquus were to the three herbicides 20, 10 and 10 min, respectively. Moreover, the most effective concentrations of methyl-ethyl vegetable oil were 0.25%, 0.5% and 0.25%, respectively. ②Scenedesmus obliquus had the highest sensitivity to the three herbicides, while Microcystis aeruginosa was more adaptable and resistant to herbicides. Under 20 μg·L-1 Prometryn exposure, the Fv/Fm inhibition rates of both Scenedesmus obliquus and Chlorella proteinosa were up to 23%, while that of Microcystis aeruginosa only reached 8%. ③Y(Ⅱ) and NPQ showed the highest sensitivity to herbicide toxicity under the treatment of methyl-ethyl vegetable oil. Besides, the inhibitory effect of 40 μg·L-1 Prometryn on Y(Ⅱ) of Scenedesmus obliquus was 56%, and the inhibition rate of Fv/Fm was up to 41%, which was 2.5 times that of the control group. However, when exposed to 200 μg·L-1 herbicide, the NPQ values of the three algae increased by more than 1.5 times, and the qP was less affected by methyl-ethyl vegetable oil. Hence, the above results demonstrated that the contact area between herbicide and algae cells was enlargedby methyl-ethyl vegetable oil, and the penetration of herbicide was enhanced, which improved the toxic response of algae fluorescence to herbicide as well as reduced the detection limit of herbicide. In this study, we first utilized methyl-ethylated vegetable oil to detect triazine herbicides using the algal fluorescence method. The results demonstrated that the algal fluorescence method exhibited a highly effective detection capability when combined with methyl-ethylated vegetable oil. This approach successfully addressed the issue of insensitive responses observed in chlorophyll parameters in certain cases. As a result, a novel and rapid method for sensitively detecting trace triazine herbicides biotoxicity in the environment has been established.