Nowadays, the drug situation is becoming increasingly severe. The continuous emergence of new drugs and more hidden ways of drug transport have brought new challenges to drug detection. The traditional drug detection method has some problems, such as complicated sample pretreatment process, expensive detecting equipment, difficulty to carry,and so on, which cannot meet the needs of on-site rapid detection. With the advantages of fast detection speed, no sample pretreatment, and small equipment size, spectral detection technology is gradually becoming an important technical means in drug detection. Recently, based on the progress of research on spectral detection technology, the application of several spectral detection techniques in drug detection has been reviewed. For heroin, methamphetamine, and other common solid drugs, terahertz time-domain spectroscopy and energy-dispersive X-ray diffraction technology with high penetration, visualization, and other advantages can achieve the characterization of paper, wood, rubber, and other common hidden drugs, suitable for rapid screening of drugs in public safety detection. Aiming to detect drug components in drug solutions and body fluids of drug users (urine, saliva, blood, etc.), this paper reviews the latest progress in infrared spectroscopy detection technology and surface-enhanced Raman scattering technology. Due to its highly specific molecular fingerprint, surface-enhanced Raman scattering technology has become a research hotspot in liquid drug identification and detection. Its advantages include being immune to aqueous solution. The method is expected to be used for portable rapid detection of liquid opioid drugs. Because of the abuse of gaseous drugs such as nitrous oxide, a variety of trace gas detection methods based on spectral detection technology are summarized in this paper. Because of the abuse of gaseous drugs such as nitrous oxide, this paper summarizes a variety of trace gas detection methods based on spectral detection technology. It reviews the progress of exhaled nitrous oxide detection using a quantum cascade laser. This technology has a good potential for application in the field of gaseous drug detection.

Hydrogen-fueled gas turbines are the most promising research direction at present. Although hydrogen fuel, especially green hydrogen energy, can lower greenhouse gas emissions and enhance energy efficiency, the thermoacoustic oscillation phenomena are easily generated by hydrogen energy’s high combustion rate and quick chemical reaction rate during combustion. The coupling of an acoustic wave and heat release at a phase difference of less than 90° results in thermoacoustic oscillation, which can wear down a gas turbine and even harm its components. Flame heat release variation, acoustic pressure pulsation, flow field fluctuation in the combustion chamber, etc. are some of the sources of thermoacoustic oscillation. The coupling analysis of these variables can increase our comprehension of thermoacoustic oscillation and give us a theoretical foundation for forecasting it. The parameters used in the research of thermoacoustic oscillations include dynamic pressure, flame heat release, temperature, equivalent ratio, and velocity, and the related test techniques should be capable of high-frequency measurements of them. The measurement means of dynamic pressure mainly include pressure sensors and microphones. Due to the fast time domain response and obvious dynamic characteristics of pressure, it is the key parameter of the thermoacoustic coupling effect and is most widely studied. OH, chemiluminescence, or fluorescence signals are mostly used to characterize the flame heat release, and the measurement methods include Intensified CCD(ICCD), a photomultiplier tube(PMT), planar-laser-induced fluorescence(PLIF), etc. Tunable diode laser absorption spectroscopy(TDLAS), PLIF, Raman spectroscopy (RS), and other optical diagnostic techniques for temperature measurement are available in addition to the widely used thermocouples. Since the equivalence ratio directly impacts combustion parameters, it is challenging to measure dynamic changes in the equivalence ratio using conventional methods like flue gas analyzers. TDLAS, PLIF, laser-induced breakdown spectroscopy(LIBS), and other optical measurement techniques were later developed, and they are all capable of obtaining dynamic changes in the equivalence ratio. Velocity pulsation is a parametric quantity that acts directly on thermoacoustic oscillations, and measurement techniques include one-dimensional dual microphone velocimetry, hot-wire anemometry, laser Dopplervelocimetry (LDV), and multi-dimensional particle image velocimetry measurement (PIV), schlieren, etc. So far, most of the techniques on thermoacoustic oscillation measurement are relatively well developed. This paper lists the principles of these combustion diagnostic techniques and their applications to thermoacoustic oscillation or unstable combustion phenomena, and summarizes the development and prospects of thermoacoustic oscillation measurement.

In recent years, non-invasive detection based on near-infrared spectroscopy and artificial intelligence algorithms has received much attention in medicine and biology due to its safety, non-invasiveness, and high efficiency. One key issue is selecting effective input features for intelligent regression models from wide-band near-infrared spectroscopy. This paper establishes a non-invasive near-infrared blood glucose concentration intelligent prediction model by combining near-infrared spectroscopy, genetic algorithm, and support vector regression (GA-SVR) using blood glucose concentration detection as an example. Firstly, according to the OGTT experimental rules, non-invasive dynamic blood near-infrared spectroscopy and corresponding blood glucose concentrations of volunteers were collected. The optimal near-infrared feature wavelength combination was further determined based on a genetic algorithm. Finally, the support vector machine regression model was established to achieve blood glucose concentration prediction. In this paper, comparative experiments were designed to compare the proposed method with the genetic algorithm and multi-layer perceptron regression (GA-MLPR), partial least squares regression (GA-PLSR), and random forest regression (GA-RFR). The experimental results show that the proposed GA-SVR model has the best prediction performance, and the correlation coefficient of the test set is increased by 44% compared with GA-PLSR, the correlation coefficient reaches 99.97%, and the mean square error is 0.000 97. The study shows that the proposed GA-SVR can achieve effective feature selection of near-infrared spectroscopy data, verifying the feasibility of intelligent algorithms for feature selection. The excellent performance of this feature selection model provides a new approach to non-invasive detection.

Methane (CH4) currently stands as a significant clean energy source, constituting a primary component of natural gas. However, due to its highly flammable and explosive properties, monitoring CH4 concentrations in the atmosphere and critical locations is paramount. Laser absorption spectroscopy, with its advantages of high sensitivity, rapid detection, excellent selectivity, and non-contact capabilities, has found extensive applications in gas measurements and related fields. Optical multi-pass cells (MPCs) are often employed to increase the optical path length (OPL) to achieve higher measurement accuracy. Real-time and precise calibration of the optical path length is of utmost significance. The concentration of the measured gas can be directly inverted by using Lambert Beer’s law through the accurate value of optical path length and other parameters, avoiding the complex step of standard gas calibration in traditional methods. Due to the complex structure and high computational complexity of methods such as Frequency Modulated Continuous Wave (FMCW) and Optical Frequency Domain Reflectometer (OFDR) proposed by previous researchers, we propose a method for multi-pass cell internal optical path length measurement based on Amplitude Modulated Continuous Wave(AMCW) technology in this study, which has the advantages of simple structure and fast measurement speed. This method is integrated with laser absorption spectroscopy to measure the optical path length and CH4 absorption spectrum simultaneously. The laser beams, one with a center wavelength of 650 nm for measuring the optical path length and another from a Distributed Feedback (DFB) laser with a center wavelength of 1 654 nm for measuring the absorption spectrum, are simultaneously coupled into a multi-pass cell with a physical base length of 12 cm using fiber couplers. At the exit end, the amplitude modulation phase of the laser for the optical path length measurement and the optical intensity of the laser for CH4 absorption spectrum measurement are measured to obtain both optical path length and absorption spectrum information simultaneously. Measurements were conducted using a standard CH4 gas with a volume fraction of 297×10-6 and absorption lines of CH4 near 6 057.1 cm-1. First, the output wavenumber of the DFB laser at different operating currents was calibrated, which allowed the transformation of the absorption spectrum’s x-axis from point numbers to wavenumber. Next, the incident angle of light entering the multi-pass cell was adjusted, and data for 4 sets of different optical path lengths and absorption spectra were measured. The internal optical path lengths of the multi-pass cell and the corresponding absorption peak values were 1.606 m and 0.021 2, 3.326 m and 0.044 5, 5.050 m and 0.067 8, and 6.762 m and 0.089 9, respectively. Linear fitting was applied to the measured optical path lengths and the ones estimated from the number of reflections, yielding a high correlation coefficient r≈1. Additionally, linear fitting was conducted between the measured optical path lengths and the absorption peak values, demonstrating excellent linearity with r≈0.999 87. These results validate the feasibility and accuracy of the AMCW technology for real-time measurement of internal optical path lengths within the multi-pass cell, providing a novel method and approach for determining the optical path length and measuring concentration in laser absorption spectroscopy.

Hyperspectral imaging is a non-contact, non-destructive detection method to analyze substances’ chemical composition, physical properties, and morphology.Limited by the response speed and inherent noise of the detector,it is difficult for traditional hyperspectral imaging techniques to achieve high-speed and high signal-to-noise detection of molecular fingerprint spectra in the mid-infrared band. With the advantages of high measurement speed, high spectral resolution, and wide spectral coverage,the spectroscopy technology based on time-stretch frequency upconversion provides a reliable method for rapidlyanalyzing the type and morphology of the samples when combined with hyperspectral imaging technology. In thispaper, a mid-infrared time-stretch frequency upconversion hyperspectral imaging system was constructed. The average power of the 1 047 nm pump pulse and the 1 550 nm signal pulse generated by the same laser source is 2 W and 100 mW, respectively. Using synchronous pump technology, mid-infrared pulses were generated in one periodically poled lithium niobate crystal, and frequency upconverted into near-infrared pulses in another. This process transferred the mid-infrared molecular fingerprint spectra to the near-infrared band, which can effectively address the problem of lacking high-speed and low-noise detectors in the mid-infrared band. By tuning the operating temperature and working channels of the crystal,the detection range of the system can cover 2 700~3 900 nm, enabling the measurement of multiple samples. Combining the time-stretch method with hyperspectral imaging technology, the benzene solution’s absorption spectra and spatial distribution information in a colorimetric dish were measured through point-by-point scanning. The spectral data obtained highly matched the results from a Fourier transform infrared spectrometer. Moreover, the system could perform hyperspectral imaging of a 600 m×1 200 m spatial region within 8 s. The acquisition time for a single pixel was 12.9 ns, and a spectral measurement speed of 77.6 MSpectra·s-1 and spectral resolution of 5.8 cm-1 was achieved. These results verified the systemhas the potential to measure the spectra and spatial distributionof liquid molecules within the spectral coverage range with highspeed and highresolution. This paper solves the problems of slow response speed, long integration time, and low signal-to-noise ratio of traditional hyperspectral methods in the mid-infrared band. It enables the spectraldetection and morphological measurement of multi-component samples with a spectral refresh rate of 107 frames per second. It could provide a new approach for imaging analysis in material and biological fields.

Compared with conventional narrow-band-scanning absorption spectroscopy, hyperspectral absorption spectroscopy shows great technical advantages thanks to more abundant absorption information covering a wider spectral range. In practical hyperspectral absorption applications, the laser source can usually be tuned within a wide spectral span (>20 nm). However, full-span tuning puts very high data acquisition, storage, and processing requirements, leading to a high system cost and mammoth workload. Consequently, scanning band selection with proper bandwidth becomes crucial for hyperspectral absorption applications. Here, a numerical method for scanning band selection is developed based on the frequency-dependent lower-state energy. And favourable scanning bands around 1.3 and 2 m wavebands are selected. For the 1.3 m waveband, the selected bands are consistent with those exploited in publications, validating the feasibility and correctness of the method. For the 2 m waveband, the superiority of the selected bands in temperature measurement is verified with experimental data recorded at given operation conditions. Advantageous scanning bands around 2 m are provided concerning the wide operation condition in practical applications. Calculations demonstrate that proper scanning bands exist at the 1.8 m short and 1.9 m long waveband, with the optimum at 1.86 m. A larger scanning bandwidth may not result in the best temperature measurement accuracy, but generally, it brings about a smaller and more controllable uncertainty over the whole spectrum. These findings are instructive for field applications of hyperspectral absorption spectroscopy in engine combustion field diagnosis.

The tunable diode laser absorption spectroscopytechnology is widely used in flow field detection. However, the harsh environment of on-site application puts higher requirements on the test system’s environmental adaptability and anti-noise interference ability. Direct absorption spectroscopy technology can obtain all the information on the absorption spectrum and is suitable for many types of test environments, such as wind tunnels and engine flow fields. However, due to the influence of a harsh on-site test environment, the signal-to-noise ratio of the obtained signals is usually poor, and it isn’t easy to achieve high-precision detection. This article analyzes the data processing and analysis algorithm of direct absorption spectroscopy technology and proposes a high-precision temperature analysis method based on transmission curve fitting for this problem. A temperature detection system based on direct absorption spectroscopy technology was developed using the absorption spectra of water vapor near 7 185.6 and 7 444.3 cm-1. The processing and analysis of test signals in the temperature range of 573～1 173 K were carried out. The analysis results of the baseline-absorption line fitting algorithm(BALF) and the transmission curve fitting(TCF) algorithm were compared. When the detection temperature was 773 K, compared with the BALF algorithm, the peak-to-peak fitting error near 7 444.3 cm-1 of the TCF algorithm can be reduced by about 30%, and the peak-to-peak fitting error near 7 185.6 cm-1 can be reduced by about 16%. In the temperature range of 573～1 173 K, the maximum error of the TCF algorithm for temperature inversion is 13 K, which is reduced by 23 K compared with the BALF algorithm. When random noise signals with different amplitudes were added to the test signals, the standard deviation of test results using these two algorithms increased with noise amplitude. Moreover, the higher the temperature, the greater the standard deviation of test results. When the peak-to-peak noise value was 20, 60, and 100 mV respectively, for different temperatures, the minimum standard deviation of test results using the BALF algorithm was 18 K, and the maximum was 313 K. The minimum standard deviation of test results using the TCF algorithm is 4 K, and the maximum is 44 K. Experimental analysis results show that compared with the BALF algorithm, the TCF algorithm can correct the baseline fitting error of laser power, improve the fitting accuracy of transmission signals, and reduce temperature analysis errors. Comparing the temperature analysis results of test signals under different noise levels shows that the TCF algorithm can achieve higher detection accuracy and precision under noise interference and has stronger anti-noise interference ability.

In this paper, photoluminescence spectra with an excitation wavelength of 473 nm are used to study and compare the photoluminescence features of pearls of different colors. The results show that there are obvious differences in the photoluminescence spectra of pearls of different color types obtained under the wavelength of excitation light source, which has important indication significance for the origin of the color of pearls: (1) Most of the gray and yellow pearls with untreated color range from 570 to 600 nm, which is consistent with the main luminescent peaks of aragonite. It is inferred that the color origin is mainly related to the arrangement structure of aragonite. (2) The strongest luminescence peaks of untreated purple and orange pearls are also located in the range of 570~600 nm, related to aragonite. Still, a series of small sharp luminescence peaks,which have no relationship with aragonite, also appear in the range of 500~570 nm. It is inferred that the color is related to the arrangement structure of aragonite and influenced by organic matter in such pearls. (3) The main photoluminescence peak of untreated black pearls occurs from 800 to 900 nm, and the origin of its color may be mainly related to the organic matter in the pearls. (4) The strongest photoluminescence peak of most dyed pearls is located in the range of 650~750 nm, and the photoluminescence peak is mainly related to the dye. The similar photoluminescence characteristics of dyed pearls of different colors indicate that the main chromic components of the dyes currently used in the market have similar group compositions. The related research results can provide a more scientific basis for detecting and identifying pearls and studying color origin.

Oil spills at sea are one of the important forms of Marine pollution. In weathering and migration, oil spills will form emulsions such as oil-in-water, water-in-oil, water-in-oil-in-water, and other emulsions. Among them, water molecules greatly affect oil-in-water emulsions, and their fluorescence characteristics are not prominent, making it difficult to classify and identify light oil emulsions. It has important significance for pollution control in the future. Several common light oils were selected to mix with seawater and emulsifiers in different proportions to prepare the light oil emulsion of the oil-in-water type. A convenient laser-induced fluorescence (LIF) system built in the laboratory was used to detect the fluorescence spectra of light oil emulsions. In this paper, the classification model of the sparrow search algorithm (SSA) optimized support vector machine (SVM) (from now on referred to as SSA-SVM) is constructed to realize the classification and identification of oil spill in the emulsion stage. Firstly, principal component analysis (PCA) was used to reduce the dimension of the fluorescence spectrum, and the first three principal components with a cumulative contribution rate of 99% were selected as inputs, and the type of light oil was taken as the output; after that, SSA is used to obtain the optimal parameters of SVM iteratively. Then, the SSA-SVM classification model was constructed. Finally, samples from the test set are substituted into the model for the classification identification, and the identification accuracy is 100%. In this study, the particle swarm optimization (PSO) support vector machine model (from now on referred to as PSO-SVM) and genetic algorithm optimization support vector machine model (from now on referred to as GA-SVM) were constructed at the same time as a comparison. From the experimental results, compared with the PSO algorithm and GA algorithm, the SSA algorithm improved the classification and recognition accuracy of the test set’s lightweight oil emulsions by 1.77% and 3.04% year-on-year. The fitness curve reached the highest in the 2nd generation, which is better than the 4th generation of PSO and the 36th generation of GA, and the convergence speed is faster. In this study, the laser-induced fluorescence technique is used to realize the classification and identification of light oil emulsions of oil-in-water type, which promotes the development of the classification and detection mechanism of oil spill area on the sea surface, and the proposed SSA-SVM model provides a new way of classification and identification of light oil emulsions.

The development of detection techniques and equipment for aircraft surface damage has significant practical significance for flight safety and operational efficiency. Spectral matching technology is a crucial technology that must be addressed in the hyperspectral detection of aircraft surface damage. The recognition accuracy of different spectral matching algorithms often varies depending on the research object. To use a spectral matching algorithm to achieve damage identification of aircraft samples, this article first built an indoor near-infrared hyperspectral system for aircraft surface damage detection and collected hyperspectral data of reference samples and skin samples. It produced standard spectra of two types of pixels using damaged pixel spectra and non-destructive pixel spectra. Subsequently, based on the matching method for calculating the similarity between the measured pixel spectrum and the standard spectrum, four types of single spectrum matching algorithms, namely spectral angle (SA), Mahalanobis distance (MD), spectral information divergence (SID), and spectral correlation coefficient (SCC), and six types of combined spectrum matching algorithms, were used for damage identification of two types of aircraft samples. The accuracy of multiple spectral matching algorithms’ damage identification results was evaluated using the overall classification accuracy Pa and Kappa coefficient. A reasonable threshold group that can better meet the detection requirements is provided by optimizing the threshold parameters of single algorithms, such as SA, MD, SID, and SCC. Furthermore, based on the above four types of single matching algorithms, six types of combined matching algorithms were designed and used for sample damage identification, like SA-MD, SA-SID, SA-SCC, MD-SID, MD-SCC, and SID-SCC. The results show that the identification accuracy of those combined algorithms is relatively higher than that of any matching algorithms. Finally, this article presents the optimal single matching algorithm and combination matching algorithm for damage identification of aircraft samples, with the SCC algorithm and MD-SCC algorithm achieving damage identification rates of over 95% and 97.5% for both types of samples, respectively. This can provide technical support for hyperspectral detection of aircraft surface damage in the outfield.

Aronia melanocarpa (Michx.) Elliott. It is a berry from the Rosaceae Family rich in polyphenols, known as its main chemical components, including anthocyanins, flavonoid glycosides, tannins, etc., of A. melanocarpa. It has shown antioxidant, bacteriostatic, anti-tumor, anti-inflammatory, weight loss, glucose regulation, lipids, and other pharmacological activities. It has now been added to the list of new raw food materials. The polyphenol content of A. melanocarpa is closely related to its efficacy value. Therefore, improving their detection method is crucial to standardizing the raw material and product market from A. melanocarpa. However, the current detection method is cumbersome and time-consuming, and it is difficult to meet the industrial development needs of A. melanocarpa after it enters the list of new food raw materials. Thus, It is urgent to develop a method for rapidly determining polyphenol content. Mid-infrared spectroscopy established a rapid and quantitative determination method of polyphenol content in A. melanocarpa. The infrared spectral data of 750 samples from A. melanocarpa in 15 regions were collected for the spectral analysis, and the content of polyphenols in each sample was measured. The K-S sample division method was used to divide the sample into a correction set and verification set in the proportion of 4∶1. The grouped spectral information was pretreated by multiple scattering correction (MSC), standard normalization (SNV), smoothing (SG), first derivative (FD), second derivative (SD) and other spectral preprocessing methods. Compared with the original spectrum by random forest regression (RFR) modeling and prediction, the best spectral preprocessing method was determined as MSC. The competitive adaptive reweighting algorithm (CARS) and continuous projection algorithm (SPA) were used to select the optimal characteristic spectral wavelength of the polyphenols of A. melanocarpa. The spectral data selected by the two methods were combined with random forest regression (RFR), partial least squares regression (PLSR), limit learning machine (ELM), and support vector machine regression (SVR) for modeling and comparison to determine the optimal algorithm model. The results showed that the CARS algorithm can effectively reduce the redundancy of infrared spectral data and improve the accuracy and stability of model prediction. The CARS-RFR model had the best prediction performance. Its correction set Rc, RMSEC, verification set Rp, RMSEP, and RPD were 0.986 5, 0.073 2, 0.974 3, 0.100 6, and 6.235 6, respectively. The above results revealed that the combination of mid-infrared spectroscopy and chemometrics, especially the CARS-RFR model, can effectively, rapidly, and accurately detect the polyphenol content of A. melanocarpa. The research results can thus provide technical support for rapidly determining the polyphenol content of A. melanocarpa.

Fluorescence spectroscopy is a fast, accurate, and non-destructive detection method widely used to detect and analyze pesticide residues. This article applies the three-dimensional fluorescence spectroscopy method combined with the parallel factor (PARAFAC) algorithm to achieve qualitative and quantitative analysis of mixed organic pesticides. Firstly, in different proportions, two kinds of mixed solutions are prepared, including spinosad-lambda-cyfluthrin and spinosad-ningnanmycin. The three-dimensional fluorescence spectra are obtained by scanning with an LS55 fluorescence spectrophotometer; the emission wavelength range is 200~600 nm, and the excitation wavelength range is 250~322 and 260~370 nm, respectively. Then, the parallel factor algorithm is applied to model and analyze the preprocessed spectral data. The predicted spectra and score values of each component in the mixture are obtained through trilinear decomposition, and the predicted spectra are visually matched with the actual spectra to identify pesticide categories. Finally, a linear fitting analysis is conducted between the score value and the concentration of pesticide component, and the mean squared error, coefficient of determination, and recovery rate parameters are calculated. The results showed that the predicted spectra of the pesticide components corresponding to the two mixtures are consistent with their true spectra, especially with high overlap at the fluorescence characteristic peaks. The prediction means that the squared error of each component in the spinosad-lambda-cyhalothrin mixture is 1.985 6×10-8 and 4.480 0×10-7, respectively. The corresponding prediction mean squared error of the spinosad-ningnanmycin mixture is 2.155 2×10-7 and 5.572 2×10-5, respectively. The model’s coefficient of determination exceeds 0.99, and the average recovery is close to 100%. The test results indicate that three-dimensional fluorescence spectroscopy combined with a parallel factors algorithm can analyze pesticide mixtures with high analytical accuracy. The research content of this article provides a certain methodological basis for the qualitative and quantitative analysis of mixed organic pesticides, and this method can also be extended to the detection and analysis of mixed systems of other types of samples.

Reflectance spectrum, as a significant characteristic of the object surface, is widely used in various fields such as remote sensing target identification, content detection of material components, agricultural crop maturity detection, and disease diagnosis in medical imaging. However, while the reflectance spectrum enriches target information, it also brings data redundancy, causing great difficulties in acquiring, processing, and transmitting spectral data. To settle these difficulties, our team has focused on spectral data analysis and processing utilizing compressed sensing technology. It was found that sparse representation of global spectral data was achieved, and spectral reconstruction accuracy was improved. Various sparsities of data in each spectral band constrain different sampling rates in spectral compressed sensing reconstruction methods. This paper proposes an entropy density segment compressed sensing method for reflectance spectrum reconstruction. Specifically, entropy average density is defined as the segmenting reference in the search for breakpoints. Based on the reference, the decision on whether the entropy density of each segmented spectrum is high or low can be given. After that, the sampling rates of each segmented spectrum are reassigned according to the limited equidistant constraint condition. The measurement and sparse matrices are generated for sparsity sensing of segmented reflectance spectrum. The optimal solution is obtained using the orthogonal matching pursuit algorithm. Iteration times of each segmented spectrum are reassigned. Each segmented reflectance spectrum is iteratively matched and reconstructed using the columns in the sensing matrix and sparse signals. Finally, the reconstructed segmented reflectance spectrums are stitched. A comparative experiment was conducted on the reflectance spectrum of the standard color block (24 Munsell ColorChecker) using the global spectral compressed sensing method and our proposed method. The experimental results show that compared with the global spectral compressed sensing method, the proposed method has higher reconstruction accuracy in high entropy density segments and higher compressed efficiency in low entropy density segments. RMSE and MAPE are improved under the same total compressed sampling rate, which enhances the overall curve reconstruction effect.

Apples have a unique flavor, crisp and delicious, and are widely loved by consumers worldwide. Soluble solid content (SSC) is an important internal quality indicator of apples. Hyperspectral imaging (HSI) has been widely used as a non-destructive tool to predict SSC in apples because it can simultaneously acquire spatial and spectral information. However, the widespread application of HSI is hindered due to expensive equipment and time-consuming operations. Spectral super-resolution (SSR) is an efficient way to acquire HSI images by establishing a mapping relationship from low spectral resolution images to corresponding high spectral resolution images. Hence, this study aims to adopt SSR to obtain HSI images from apples RGB images and use the hyperspectral data to predict the SSC of apples. Firstly, the apples of uniform size are selected as samples. Each apple is marked using the black grid matte paper to label the region of interest (ROI), and RGB and HSI images of apples are measured. Then, the global thresholding method generates 220 ROI image pairs of RGB and HSI. Secondly, a dense connection network, a multi-scale hierarchical regression network, and a Transformer network are used to achieve SSR of Apple RGB images to gain HSI images. Finally, the reflectance spectra of HSI images were extracted, and a competitive adaptive reweighted sampling algorithm was applied to obtain the spectra of effective wavelengths (EWs). Partial least squares regression (PLSR), random forest (RF), and extreme learning machine (ELM) are used to predict the SSC of apples by using the full spectra and spectra of EWs. The results show that the Transformer network achieves the best SSR with the mean relative absolute error (MRAESP) of 0.135 9 and the root mean square error (RMSESP) of 0.026 2 in the SSR prediction set, and the spectra obtained after SSR are most consistent with the ground truth. As for the full spectra, ELM provides the best prediction performance for SSC analysis with the coefficient of determination (Rp2) of 0.925 5 and root mean square error (RMSEP) of 0.003 in the prediction set. The prediction results of PLSR were relatively poor, and RF performed the worst. When the spectra of EWs are used, tELM obtains the optimal performance Rp2 and RMSEP=0.002 2. In contrast, PLSR obtains a slightly poor result and the worst result of estimating SSC is acquired by RF. In conclusion, based on the Transformer image SSR, this article has accomplished the accurate detection of sugar content in apples, offering a low-cost and efficient method for obtaining HSI images. It has realized a rapid and convenient new sugar content detection method, expanding the imaging application scenarios in fruit quality analysis. This provides a theoretical basis for promoting the development of smart agriculture and the food industry.

Gong pear is prone to mechanical damage during harvesting, transportation, and sales, accelerating fruit decay and reducing its quality. Different treatment measures were taken to quickly distinguish the different degrees of damage to the gong pear and reduce economic loss. In the past, hyperspectral technology was used to study the damage degree of fruits. Usually, only the reflectance spectrum was used for thestudy. In this study, the reflectance (R), absorbance (A), and Kubelka-Munk (K-M) spectra of Gong pears were obtained by hyperspectral technology and combined with three deep learning algorithms to distinguish healthy and different damage degrees of Gong pears. Firstly, 60 fresh and undamaged Gong pears were selected as healthy samples, and 60 samples of Ⅰ, Ⅱ, and Ⅲ damaged Gong pears were prepared by free fall collision device. The spectral data of these 240 Gong pear samples were collected by hyperspectral imaging system, and the acquired spectra were corrected in black and white to obtain the original spectra of reflectance (R), absorbance (A), and Kubelka-Munk (K-M) of Gong pear. Then, three kinds of original spectral data were preprocessed by Baseline calibration, De-Trending, moving average (MA-S), multiple-scattering correction (MSC), convolution smoothing (SG-S), and standard normal variable transformation (SNV). BP neural network (BP), Limit gradient lift (XGBoost), and random forest (RF) discriminant analysis models were established to distinguish different damage degrees of Gongli. According to the discrimination results of the model on the damage degree of Gong pear, the accuracy of the BP model based on reflectance, absorbance, and K-M spectrum is better, with the overall accuracy reaching 85% or more. Itwas found that the BP model established by the baseline reflectance spectrum after pretreatment showed a greater improvement than that established by the unpretreated reflectance spectrum. The accuracy of discrimination reached 93.33%. To improve the accuracy and operation efficiency of the BP model, competitive adaptive reweighting (CARS) and no-information variable elimination (UVE) methods were used to screen out the spectral information of characteristic bands for the 3 kinds of original spectra and Baseline pre-treated spectra, and the BP model was established with the filtered characteristic spectral data. The discrimination results show that the A-RAW-CARS-BP model has the best discrimination accuracy, and the overall accuracy reaches 96.66%. The results show that it is feasible to discriminate the damage degree of Gong pear by using three kinds of original spectra, which provides a theoretical basis for detecting different damage degrees of Gong pear by hyperspectral technology.

The accurate detection of chlorine leakage in an open environment has been an urgent problem for chlor-alkali manufacturers. Differential optical absorption spectroscopy (DOAS) can realize long-distance measurements of trace polluting gases in the atmosphere. Due to the flat characteristic of the UV absorption spectrum of chlorine, it is impossible to differentiate the absorption characteristics from the noise signal by normal methods. A new algorithm based on a one-dimensional convolutional neural network (1D-CNN) is proposed to solve the problem of poor accuracy caused by noise interference, which can fully use spectral information and extract chlorine absorption characteristics layer by layer. Compared with commonly used models such as least squares (LS), multilayer perceptron (MLP), support vector machine (SVR), and k-nearest neighbor (KNN), the inversion result of this algorithm has the highest accuracy (R2=0.996, RMSE=4.40, MAE=2.64, SMAPE=8.51%). Due to the inevitable random noise in -the system, the preprocessing effects of the S-G filter, Fourier transform, singular value decomposition, and wavelet transform decomposition algorithms are compared. The results show that S-G filtering and wavelet decomposition algorithms can retain the characteristic information of chlorine while removing noise and further improving the model’s performance. The concentration inversion model based on 1D-CNN provides a new feasible method for long-distance quantitative detection of chlorine leakage in the open environment.

The composition and structure analysis is an important way to understand and study matters. X-ray fluorescence analysis (XRF) is one of the most universal nondestructive analysis methods for the composition and structure of substances, which can be used for qualitative analysis and quantitative detection of elements in substances. During the detection of light elements by wavelength dispersive X-ray fluorescence spectrometer, multilayer analyzer crystals are key optics. Using artificial multilayer crystals instead of natural crystals as analyzer crystals can effectively improve the ability of spectrometer to detect light elements. This paper, aiming at the actual application demands of domestic wavelength dispersive X-ray fluorescence spectrometer to analyze light elements, Mo/B4C, Cr/C, Cr/Sc, W/B4C (period thickness d=3.63 nm) and W/B4C (period thickness d=2.85 nm) multilayer analyzer crystals are designed, which are suitable for the fluorescence analysis of light elements B, C, N, O, and F, respectively. Five kinds of multilayer crystals have been fabricated using the direct-current (DC) magnetron sputtering technique on super-polished silicon substrates with the size of 50 mm×30 mm by magnetron sputtering. Interfacial microstructures of as-deposited multilayer crystals were characterized by grazing incidence X-ray reflectometry (GIXR) on a high-resolution X-ray diffractometer. The measured results indicate that all five kinds of multilayer crystals have high-quality periodic layer structures and smooth interfaces, and the thickness deviation of period thickness for the five multilayer crystals is less than 1%. Atomic force microscopy (AFM) was used to characterize the surface morphology of each multilayer crystal. The results reveal that all five multilayer crystals have smooth surface morphology and small surface roughness. Finally, the reflectivity of the five crystals at grazing incidence geometry was obtained by simulation: Mo/B4C (30.1% @B-K line), Cr/C (29.3% @C-K line), Cr/Sc (35.4% @N-K line), W/B4C (d=3.63 nm, 8.6% @O-K line), W/B4C (d=2.85 nm, 10.7% @F-K line). Based on these investigations, the five kinds of multilayer analyzer crystals meet the practical application requirements of wavelength dispersive X-ray fluorescence spectrometer. They can be applied to detect light elements B, C, N, O, F.

The development of highly efficient, photostable, and eco-friendly fluorescent dyes is currently a prominent focus in scientific research, attracting significant attention. Recent studies have emphasized Twisted Intramolecular Charge Transfer (TICT) ’s crucial role in determining fluorescent dyes’ luminescence efficiency. Consequently, effectively suppressing the TICT process is imperative for progressing fluorescent markers and probes. Among the various fluorescent dyes, coumarin and its derivatives, particularly 7-amino coumarin dyes, have gained significant recognition as extensively utilized constituents in diverse systems owing to their robust fluorescence and prolonged fluorescence lifetimes. Nevertheless, previous examinations have predominantly concentrated on the effect of different solvents on the excited-state behavior of coumarin dye molecules, overlooking the significant influence of dye molecule structure on the TICT process. This study utilized a combination of femtosecond time-resolved transient absorption (TA) spectroscopy and density functional theory (DFT) calculations to gain deeper insights into the excited-state dynamics of coumarin dyes C460 and C481 in various solvents. The results revealed that C481 predominantly undergoes the TICT process in highly polar methanol solvents with strong hydrogen-bonding capabilities. Furthermore, quantum chemical calculations indicated that introducing fluorine substitutions reduces the molecules’ internal torsional barriers, leading to enhanced non-radiative deactivation processes and, consequently, more pronounced fluorescence quenching phenomena. This investigation provides insights into the selection of suitable solvents for optimizing the performance of fluorescent dyes and offers valuable guidance for their design.

To enhance plasma applications’ effect, plasma’s discharge kinetic process must be explored in depth. The rotation temperature is one of the important parameters reflecting the energy transport in the plasma discharge process, which is mainly calculated by the Boltzmann slope method, which is commonly used to calculate the rotation temperature of the plasma in the thermodynamic equilibrium state and has a large error in calculating the rotation temperature of the plasma in the non-thermal equilibrium state. To address this problem, this thesis proposes an analytical method to calculate the rotation temperature using diatomic molecular spectral bands. A needle-ring electrode plasma jet device is used for the experiment, and a plasma jet is formed by discharging a mixture of Ar/Air/CH4 gas into the device with an operating voltage of 10~14 kV. A spectrometer collects the spectral data of the plasma jet at different discharge voltages to calculate the rotation temperature. The OH (A—X), CH (A—X) and N2+(B-X) emission spectra were selected to investigate the rotation temperature of the Ar/Air/CH4 plasma jet, using the property that the spectral lines in the wavelength interval of the three diatomic molecules, are not affected by the change of vibration temperature. The rotation temperature of the DBD-excited Ar/Air/CH4 plasma jet was obtained by selecting one spectral line every 20 K in the range of ±100 K calculated by the Boltzmann slope method, fitting it to the experimental spectral line, calculating the root-mean-square error obtained from the fit of the spectral line, and analyzing the accuracy of the fit. The root-mean-square error of the rotation temperature, calculated by the Boltzmann slope method obtained in the fit, is higher than the minimum root-mean-square error, and the root-mean-square error can be reduced by 38%. The minimum root-mean-square errors obtained by fitting the OH, CH, and N2+ spectral bands were 4.6, 2.9, and 2.1, respectively, and the results of fitting the N2+ band were 61% lower than the results of fitting the OH band. The results show that calculating the root-mean-square error of spectral line fitting can effectively improve the accuracy of the calculation results, compared with the Boltzmann slope method when calculating the rotation temperature of the plasma in the non-thermal equilibrium state. The accuracy of the calculation results can be further improved by choosing the emission spectrum of the N2+(B-X) band for fitting to the experimental spectral line.

Diiodothyronine (formula C15NO4H13I2), triiodothyronine (formula C15NO4H12I3), and tetraiodothyronine (formula C15NO4H11I4) are the major hormones secreted by the thyroid gland, which contribute to the development of the human brain, the synthesis of neurotransmitters, the regulation of metabolism and the normal functioning of thyroid function playing a crucial role. Three compounds of C15NO4HnIm(n=11, 12, 13, m=4, 3, 2, n+m=15) have been studied systematically by theoretical calculations in this paper, which provides a detailed theoretical basis for future experimental research. In this research, combining the Gaussian software package and GaussView software to carry out theoretical calculation, the geometrical and electronic structures of C15NO4HnIm(n=11, 12, 13, m=4, 3, 2, n+m=15) clusters are optimized by using density functional theory (DFT) at the B3LYP/Lanl2mb level. Then, based on these clusters’ stable ground state structure, the excited state absorption and emission spectra are studied at the same basis set level using the polarized continuum model (PCM) with time-dependent density functional theory (TDDFT). The results show that the geometrical structure symmetry of the optimized C15NO4HnIm (n=11, 12, 13, m=4, 3, 2, n+m=15) clustersare C1; based on the stable structure of the ground state for C15NO4HnIm (n=11, 12, 13, m=4, 3, 2, n+m=15) clusters, the transport properties are obtained, C15NO4H13I2 has neither p-type transport property nor n-type transport property, the C15NO4H12I3 and C15NO4H11I4 clustersare p-type transport material; and then, based on the theory of time-dependent density functional, the solvent effect is calculated based on the optimized ground state structures. Meanwhile, the absorption spectra characteristics of the molecules in water solventare further obtained, and the chiral spectra of the C15NO4HnIm(n=11, 12, 13, m=4, 3, 2, n+m=15) clusters are also studied by ECD. The theoretical research can provide a comparable theoretical value for the experimental research and a feasible reference value for future experimental research.

Amino acids play an important role in organismsas the basic building blocks of proteins. The functions of amino acids with different group compositions and chiral structures are different, showing an urgent requirement to identify the basic chemical structure and molecular vibration information. This will provide an important theoretical basis for constructing basic biomolecule spectra and structural correlation models. NIR spectrum mainly shows the first overtone and binary combination vibration information of various hydrogen-containing groups (such as O—H, N—H, C—H, etc.). The vibration information is relatively complex, coupled with the resolution limitations of conventional infrared spectroscopy instruments and other reasons, resulting in a spectrum of experimental results. The band becomes wider, and its vibration mode cannot be accurately identified, making analysis more difficult. The theoretical calculation can independently calculate each vibration mode, providing clearer spectral information, and then analyze the wide absorption band obtained experimentally. This makes it easier to identify and analyze the structural vibration information of various groups in different molecular systems. In this work, the DFT calculation method carried out the structure optimization and anharmonic vibration analysis of three amino acids (glutamic acid, cysteine, glycine) and polypeptide (glutathione) composed of these three amino acids. The high-precision NIR spectrum of the four molecules in the band of 7 500～4 500 cm-1 was calculated, and detailed band assignments were made, dividing the entire near-infrared band into three spectral regions dependent on the vibration intensity. Furthermore, the influence of structure and constituent groups on the spectral characteristics was explored, and the corresponding relationships between the spectra and the structure were established. Our research would provide ideas for a deeper understanding of the structural properties of amino acids and peptides.

To reveal the difference in material composition, color origin, and scientific identification characteristics of natural pink Dushan jade and dyed pink Dushan jade from Nanyang, Henan Province, conventional gemological characteristics, infrared spectrum, Raman spectrum, UV-visible absorption spectrum, electron paramagnetic resonance spectrum, and photoluminescence spectrum were tested. The results show that the refractive index and relative density of Dushan jade are concentrated, which is close to that of plagioclase. The natural red-only jade shows two refractive index values of plagioclase and zoisite in different positions. Infrared spectra combined with Raman spectra show that the pink part of the natural red monozoite is consistent with the characteristic spectrum peak of zoisite, indicating that the zoisite has a high degree of fossilization. In contrast, the white and green parts are dominated by basic plagioclase. The chromaticity of Dushan jade has a low degree of zoisite mineralization, so the conventional gemological parameters are close to that of plagioclase, which often requires dyeing treatment and shows abnormal yellow-white long-wave ultraviolet fluorescence. The UV spectrum of the natural samples showed a wide and slow absorption band near 522 nm, which revealed that Mn was the main cause of the natural red-jade pink color. The electron paramagnetic spectrum further reveals the six-fold hyperfine structure of Mn2+, which is the 6A1→4T1(4G) spin-forbidden transition of Mn2+ in the octahedral field. The staining of Dushan jade shows the dye absorption near 549 nm, corresponding to the intense luminescence center at 578~589 nm in the photoluminescence spectrum. UV fluorescent lamp, UV-VIS spectrum, and photoluminescence spectrum can be effective nondestructive testing methods for natural red and colored pink Dushan jade.

The nephrite deposits in China are mainly distributed in Qinghai provinces, among which Yeniugou County is one of the most important nephrite deposits. In this paper, the yellow-green nephrite samples of Yeniugou were tested by conventional gemmology, infrared spectroscopy (FTIR), Raman spectroscopy, electron probe microanalysis (EPMA), and thin section observation, and their mineralogical and spectral characteristics were analyzed. The results show that the refractive index of nephrite in this area is 1.61, the relative density is about 2.97~2.99, the Mohs hardness is 6~6.5, its luster is glass-oil, and the crystallinity is good. The matrix mineral of the sample was tremolite, and the minor minerals were diopside, epidote,sphene, apatite, etc. The contents of oxides in the matrix were w(SiO2)=58.92%~59.40%, with an average value of 59.19%, w(MgO)=24.70%~25.56%, with an average value of 24.98%, w(CaO)=13.15%~13.43%, with an average value of 13.29%. The particle size of the mineral is about 0.01~1.00 mm. The mineral has a typical felt-like interwoven texture and microscopic dermatoplastic texture, and some micro radial dermatoplastic texture-broom texture can be seen. All mineralogical characteristics are in line with the national identification standard of nephrite.The infrared and Raman spectra of nephrite samples in this area are consistent with the spectral peaks of tremolite. However, the infrared spectral intensity of the sample at 468 cm-1 is higher than that at 509 cm-1, and certain peaks in both the infrared and Raman spectra display red or blue shifts. Combined with the chemical composition analysis, it is considered that the tremolite in nephrite is calcareous hornblende, and the shift of spectral peak is related to the Fe, Mn elements or crystal structure. Based on mineralogical characteristics, electron probe test, and field investigation, it is concluded that the nephrite ore body in Yeniugou was formed under the joint action of magnesian marble, basic diabase, and late intermediate-acid magmatic hydrothermalism. Meanwhile, shearing played an important role in the quality of nephrite. In this study, the mineralogical and spectral characteristics of the Yeniugou mining area in Qinghai were determined, and its metallogenic environment was also discussed, which provided a basis for prospecting and deep metallogenic prediction in this area.

Nitrite is a common water quality pollutant andis the main source of wastewater, fertilizer, and sewage treatment plants. The size of nitrite concentration in water quality is an important indicator to assess the health of water bodies. Still, the traditional method of nitrite concentration detection is complicated to operate. It easily interferes with the detection environment, which can not intuitively and accurately reflect the health of water quality. To explore a new way to assess the health of water bodies, this paper uses the IPSO-BPNN model to predict the concentration of nitrite transmission spectral data. Ten concentrations of nitrite standard solutions (0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18, and 0.20 mg·L-1) are first selected, and the ten concentrations of nitrite solutions are scanned at the same time intervals by using the OCEAN-HDX-XR micro spectrometer, The spectral transmittance of the spectral data is obtained by white board calibration to obtain spectral transmittance values for the spectral data. Two preprocessing methods, maximum-minimum normalization, and mean-centering, are used to unify the spectral data into uniform dimensions and centroids, making the spectral data comparable and interpretable among different samples. Due to the high dimensionality of the original spectral data, kernel principal component analysis is used for data dimensionality reduction, and six principal components representing 97.94% of the original data information are selected for the training of the IPSO-BPNN model. When predicting nitrite concentration, the original particle swarm optimization algorithm is improved by introducing adaptive learning factor and inertia weight updating formulae and particle population diversity guiding strategy, and learning rate adaptive formulae are introduced based on the BP neural network to improve the algorithm’s performance. By comparing the change curves of function fitness values for iterations performed under different particles, 30 iterations using 100 particles are chosen to find the optimal weight and bias combinations. The results show that the coefficient of determination of the IPSO-BPNN prediction model is 0.983 760, the root-mean-square error is 0.007 320, and the average absolute error is 0.004 705, which is a better fit compared with the current Random Forest, Linear Regression, BP-ANN, PSO-BPNN, and PSO-SVR models that have better prediction performance and higher accuracy. Based on these results, a hyperspectral water quality nitrite concentration prediction method based on the IPSO-BPNN model is proposed, providing a new idea for assessing water body health.

The adulteration of edible oil seriously threatens consumers’ physical health and disrupts the social market order. Therefore, developing effective methods for identifying adulterated edible oil is crucial to establishing a safe and reliable food supply chain and enhancing consumer welfare. This article studies a method to identify adulterated edible oil using sesame oil as a case study. The study first formulated three types of contaminated sesame oil by adding sesame flavor, corn oil, soybean oil, and rapeseed oil. The FLS920 steady-state fluorescence spectrometer was then employed to collect 3D fluorescence spectrum data from 45 experimental samples, including these three types of contaminated sesame oil and different brands of pure sesame oil. Subsequently, two-dimensional features were extracted from the experimental samples using the 2D-LDA method. The principle of nearest-neighbor classification was applied to identify adulterated edible oils accurately. Moreover, the proposed method was compared with the PARAFAC-QDA and NPLS-DA methods. The results demonstrated that the 2D-LDA method effectively extracted two-dimensional features characterizing adulterated sesame oil. These features facilitated maximum separation of different classes of experimental samples in the projection subspace. Simultaneously, they allowed experimental samples of the same class to cluster closely in the subspace. The distinct characteristics of these features enhanced sample separability in the low-dimensional subspace, resulting in 100% identification accuracy. In contrast, the PARAFAC-QDA and NPLS-DA methods achieved 85% and 95% discrimination accuracies, respectively. Hence, the 2D-LDA method outperformed these two methods in identifying edible oil adulteration, offering a simpler and more accurate identification process and results. This study provides an efficient and feasible new solution for on-site food safety supervision.

Returning crop straw to the field with livestock manure is an important technique for comprehensively utilizing solid waste resources. Exogenous organic matter returning primarily involves infiltrating dissolved organic matter (DOM) into the soil. Investigating the evolution characteristics of DOM can effectively predict the turnover of unstable carbon pools. However, the evolution patterns of soil DOM when combining straw returning with adding chicken manure are still unclear. This study conducted a field trial during the maize growth period in the North China Plain, with four treatments: wheat straw returning, wheat straw combined with chicken manure application, chicken manure returning, and no fertilizer application. Using three-dimensional fluorescence spectroscopy combined with parallel factor analysis, the temporal evolution of DOM content and composition during wheat straw returning was elucidated, and the influence of DOM composition evolution on straw decomposition was investigated. The results showed that adding chicken manure significantly increased the straw mass loss rate, carbon and nitrogen release rates, soil microbial carbon, and microbial nitrogen by 4.25%, 5.56%, 1.56%, 12.44%, and 56.98%, respectively. The humification index of soil DOM increased significantly by 11.97%. Variance decomposition analysis revealed that the combination of humic-like and protein-like substances positively impacted microbial carbon and nitrogen. The combination of humic substances and protein substances, as well as microbial activity, had a positive impact on straw carbon release. Humic-like substances and microbial biomass carbon positively affected straw nitrogen release. In summary, the supplementation of chicken manure has demonstrated a remarkable enhancement in the efficacy of straw incorporation, as evidenced by the notable augmentation in soil microbial biomass and expedited processing of labile carbon pools. Consequently, this synergistic effect facilitates a more efficient turnover of soil nutrients while effectively mitigating any potential environmental hazards attributed to straw residue. The findings contribute significantly to our comprehension of the turnover dynamics of labile carbon pools in soil after adding external organic materials. Moreover, the study furnishes a solid theoretical framework and empirical evidence to support the optimization of wheat straw incorporation through the application of chicken manure.

The flavor characteristics of cigars are closely related to their origin. Using near-infrared spectroscopy to analyze the similarity of cigar tobacco leaves from different origins provides a basis for zoning and product design for domestic cigar planting. This study analyzed 526 tobacco leaf samples collected in Yunnan, Dominica, Brazil, and Indonesia from 2021 to 2023 using principal component analysis and Fisher’s distance method to determine the similarity of tobacco leaves from different origins. The results showed that Dominican and Brazilian tobacco leaves had high similarity, while Yunnan and Dominican tobacco leaves had some similarity; within the Yunnan region, there was high similarity between Yuxi, Wenshan, and Pu’er, and high similarity between Dehong and Lincang; there was some similarity between Dehong and Dominica, and some similarity between Lincang and Indonesia. The similarity results obtained within large production areas were consistent with sensory evaluations, and more detailed similarity analysis results can provide more detailed technical support for domestic cigar tobacco leaf planting zoning, product design, etc.

This study proposes a remote LIBS baseline correction preprocessing method based on genetic algorithm (GA) optimized nonweighted penalty least squares (arPLS) to ensure public safety and prevent terrorist attacks. It combines this method with an ANN classification model to accurately identify four types of explosives (TNT, RDX, HMX, and CL-20) at a distance of 6 m. The GA-arPLS algorithm’s foundation is adding a fitness function to arPLS, which allows it to assess the fitting baseline and choose the best option in the candidate parameter space for fitting the LIBS baseline. On the one hand, it is primarily caused by the instrument’s inherent dark current noise, bremsstrahlung, or environmental factors. This is because LIBS spectral signals typically include noise signals such as continuous radiation and atomic and molecular emission lines, which cover a wide range of light bands in LIBS spectra. Therefore, in long-distance environments, it is necessary to improve the ability to identify characteristic spectral lines through GA-arPLS preprocessing; on the other hand, it is difficult to capture small differences between the characteristic spectra of similar explosives for classification when qualitatively analyzing organic compounds of similar elements directly through LIBS spectroscopy. As a result, spectral analysis accuracy needs to be raised. This study used the LIBS dataset as input for closest neighbor classification (KNN) and support vector machine (SVM) before and after GA-arPLS correction. SVM’s classification accuracy increased by 8.4%, whereas the KNN model’s accuracy increased by 8.7%. The classification accuracy demonstrates that the GA-arPLS baseline correction preprocessing method can effectively reduce the continuous background of remote LIBS spectra. Meanwhile, the artificial neural network (ANN) constructedclassification model achieves the optimal classification recognition effect by improving the recognition accuracy of similar explosives from 89.2% to 100%. Studies have demonstrated that this baseline correction preprocessing technique successfully lowers the noise interference and continuous background radiation of remote LIBS and enhances the robustness and predictive power of the remote LIBS classification model. The research findings are anticipated to increase the precision and effectiveness of remote LIBS in explosive detection to better respond to possible explosive threats.

Wheat is a primary staple crop in China and is pivotal in the nation’s economic development. Seeds form the foundation of all agricultural activities, with seed vigor being one of the most crucial evaluation indicators. Seeds with high vigor exhibit superior field performance and storage resilience. Thus, accurately identifying wheat seeds’ vigor is paramount to China’s agricultural production. Traditional seed vigor detection techniques are time-consuming, demand expertise, and can irreversibly damage the seeds. Previous attempts to detect seed vigor using hyperspectral imaging technology typically focused on batch testing of seeds, utilizing either image data or spectral data, but rarely combining both for single seed vigor detection. This study explores the potential of hyperspectral imaging technology for rapid, non-destructive detection of individual wheat seeds. A total of 210 manually aged wheat seeds (105 viable, 105 non-viable) were studied. Hyperspectral data within the seeds’ 400~1 050 nm band were collected, followed by a standard germination test to ensure a one-to-one correspondence between the hyperspectral data and germination results. The dataset was divided into training, testing, and real datasets in a 4∶2∶1 ratio. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to select feature bands, resulting in 30 feature bands corresponding to seed nutrients like proteins, starch, and lipids influencing seed vigor. To identify the optimal classification model, prediction models for wheat seed vigor were established using support vector machine (SVM), k-nearestneighbor (KNN), one-dimensional convolutional neural network(1DCNN), and the improved ECA-CNN machine learning algorithms, based on both full-band and feature-band spectral data from the training and testing sets. The results indicated that models built using feature-band data outperformed those using full-band data. The ECA-CNN model, constructed with feature band data, exhibited the best performance, achieving an overall accuracy of 99.17% for the training and 80% for the testing sets. The overall method and pixel method classification strategies were compared using the real dataset to negate the influence of modeling processes on comparison strategies. The findings revealed that the pixel method surpassed the overall method in detection efficacy, with an overall accuracy of 86.67%, a precision of 92.31%, and a recall rate of 80%. This research offers theoretical support for the rapid, non-destructive detection of individual wheat seed vigor.

There are differences in spectral data acquisition equipment and environmental conditions. In near-infrared spectroscopy quantitative analysis, low prediction accuracy was found in the models established. To enhance the universality and generalizability of near-infrared spectroscopy quantitative analysis models and improve their predictive accuracy, a universal model strategy is proposed based on the transfer component analysis method improved by the transfer matrix (TM-TCA). The TM-TCA method adopts a two-step correction strategy to correct the slave spectral data, reducing the spectral differences caused by instrument offsets, drifts, or instabilities. It can make the characteristics of the corrected slave spectral data similar to the master’s to the maximum extent, eliminate the deviation caused by different instruments or external conditions, and enhance the prediction ability of the model to the slave spectral data. Firstly, the spectral transfer matrix between the master and the slave is obtained. The transfer matrix converts the master-slave spectral data matrix, which is then used as the input for the transfer component analysis method. Subsequently, the kernel function and the number of eigenvalues in transfer learning are chosen using iterative optimization of multiple indicators. The RBF kernel function is selected, and the number of eigenvalues is 52. Comparative experiments are conducted with other methods to verify the effectiveness of TM-TCA. The experimental results show that the spectral correction rate based on TM-TCA reaches 97.1%, with a reduction of 82.9% in the average relative mean squared (ARMS). The ARMS value surpasses that achieved by the transfer matrix and TCA methods, 46.5% and 30.2%, respectively. To validate the effectiveness of the model construction strategy, a universality quantitative analysis model is established based on TM-TCA and partial least squares regression (PLSR) under different device conditions. Compared to the prediction performance, the TCA-PLSR model’s coefficient of determination of the TM-TCA-PLSR model reaches 0.872 9, which is improved by 41%. The root-mean-square error of prediction (RMSEP) and the mean absolute error (MAE) are 0.154 3 and 0.115 9, respectively, reduced by more than 90%. Furthermore, the relative prediction determination (RPD) of the TM-TCA-PLSR model exceeds 2.5, indicating that the model has practical application value. The experimental results demonstrate that the TM-TCA transfer method reduces the difference between the master and slave spectra. The master model established based on TM-TCA exhibits a certain degree of universality capability.

For the industrial application scenario of waste copper alloy recycling and classification, two machine learning algorithms based on microjoule high-frequency laser-induced breakdown spectroscopy (MH-LIBS) combined with artificial neural network (ANN) and support vector machine (SVM) are used. Seven copper alloy samples (H59, H62, H70, H85, H96, HPb59-1, HPb62) collected in point and motion modes were classified and recognized, respectively. The results show that ANN and SVM can achieve 100% accuracy in classifying the copper alloys collected in point mode. The classification accuracy for the copper alloys collected in motion mode is 100% and 99.86%, respectively. It can be seen that the microfocus high-frequency laser-induced breakdown spectroscopy system combined with machine learning algorithms can realize the fine classification of copper alloys, which is suitable for the rapid analysis of waste copper alloys on site.

Measuring surface reflectance is crucial for studying the spectral characteristics of ground objects and inspecting satellite surface products. This paper uses a ground spectrometer to measure the sand surface reflectance in the Ali region of the Qinghai-Tibet Plateau, and studies and analyzes its typical spectral characteristics. Conduct authenticity testing and error impact analysis on the surface reflectance products (MOD09/MYD09) and surface albedo products (MCD43 A4) generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. The results indicate that the Gobi in the Ngari region exhibits typical sand spectral characteristics. The surface reflectance values in each band range from 0.08 to 0.35. The surface reflectance in the 300～700 nm spectral range increases with the wavelength, while in the 750～1 750 nm spectral range, the surface reflectance also increases with the wavelength. The surface reflectance changes less in the nm spectral range. Compared to my country’s sand reflectance of the Dunhuang radiation correction field, the spectral curve is very similar, and the spectral angle is 2.18°. The observation geometry and atmospheric lighting conditions influence the results of the star-ground comparison. The validation accuracy of the afternoon star Aqua product is superior to that of the morning star Terra, with an average error of approximately 5%, aligning with the official nominal accuracy of MODIS. Research shows that the spectral characteristics of the sandy land in the Ngari area are stable and representative, making it an ideal site for on-orbit radiation calibration and inspection of our country’s optical satellites.

The composition and origin of “pocking mark” and “grass flower” in Guizhou Luodian tremolite jade (Luodian nephrite) were studied using scanning electron microscopy, electron probe, and laser Raman spectroscopy. The test results show that large clumps of “pocking mark” are found at some of the junctions between calcite and tremolite or the crystal boundary of tremolite, and some small pores of calcite or tremolite are distributed with a small flake“pocking mark”. Scanning electron microscopy (SEM) and electron probe (EPM) analysis showed that the “pocking mark” was composed of iron oxide, and the Raman spectral peak of the “pocking mark” was hematite. The “grass flower” is the globular or amorphous shape of the “desert rose” and exists at the junction of the two structural tremolites or the edge of the columnar and lamellar tremolites. The “grass flower” in Luodian nephrite was identified as manganese oxide by SEM energy spectrum and EPM surface scanning, and the characteristic peaks of Raman spectra showed that it was calcium manganese (640, 356 and 287 cm-1) or hydrohydroxyl manganese(636, 582 and 506 cm-1). According to the composition and structural characteristics of Luodian nephrite with “pocking mark” and “grass flower” patterns, it can be inferred that the large clumps of “pocking mark” in Luodian nephrite are caused by the mixing of Fe-rich hydrothermal and tremolite ore-forming hydrothermal. In contrast, the small flake “pocking mark” is formed by Fe-rich hydrothermal filling. The disseminated lamellate or columnar tremolite around the “grass flower” was formed by the mixed crystallization of Mn-rich hydrothermal solution and tremolite jade ore-forming hydrothermal solution. In the later stage, the Mn-rich hydrothermal fluid migrated to the edges or cracks of the columnar or lamellate tremolite and precipitated to form calcimanganite or birnessite.

Diamond inclusions retain a lot of information when the diamond is formed. Different types of inclusions correspond to different growth environments. There are few studies about diamond inclusion in diamonds, and researchers have heatedly discussed its forming mechanism. However, the study on diamond inclusions usually requires destructive sample preparation, and this research method is unsuitable for faceted diamond research. This paper used Micro-Raman spectroscopy mapping technology to receive high-resolution Raman mapping images. They use infrared, photoluminescence, and three-dimensional fluorescence spectroscopy to study non-destructively on a faceted diamond sample with diamond inclusion and symmetrical cloud inclusions this brown sample with a green hue. The table shows the dark brown ribbon around the pavilion and the inclusion of symmetrical radiate clouds. A crystal can be seen in the middle with the naked eye, and microscopic characteristics show its octahedral habit. Obvious green fluorescence can be seen under longwave ultraviolet. According to the infrared spectrum, these diamond types and dark brown radiate clouds are related to hydrogen-rich. Its brown body color is related to non-deformation-related defects and deformation-related defects. The low-temperature photoluminescence spectrum shows that this sample has an H4 center, and it is inferred that the diamond may undergo radiated irradiation and high-temperature annealing. Peaks at 545 and 563 nm of PL are related to hydrogen defects, peak at 637 nm (ZPL of NV-center), and peak at 741 nm GR1 defects, which caused the green hue of diamond. This implies that this diamond has been naturally irradiated and has not experienced high temperatures during residence. Three-dimensional fluorescence spectroscopy verifies that the sample emitted strong green fluorescence under LWUV, related to the broadened peak at em 520 nm. The optimal excitation wavelength is located at ex 420 nm. The fluorescence center is related to H3, verifying that the diamond has undergone irradiation and annealing. Raman spectroscopy confirmed the octahedral crystal inclusion in the sample as a diamond. According to Raman mapping results, the frequency of Raman peaks at the rim of inclusion shows quite a difference, so there is a certain stress in the diamond inclusion. The maximum residual stress is estimated to be about 0.49 GPa. The crystallization rate is faster than that in the edge. Changing temperature and pressure conditions during crystallization may generate residual stress between the inclusion and the host. In addition, there are many impurities in diamonds, which can also cause certain lattice distortion.

Blue coral is a rare coral species. Previous studies have confirmed that the color of blue coral is related to biliverin from a biological point of view. Still, there is no relevant spectral evidence and a lack of microstructure studies, so the explanation for the color of blue coral is murky. Based on this, according to the absorption band of the infrared transmission spectrum of blue coral from 4 000 to 2 000 cm-1, this paper shows that blue coral contains certain organic matter, and the organic matter content is positively correlated with the color depth. The characteristic Raman peaks of biliverdin were found at 1 616, 1 542, 1 459, 1 356, 1 314, 1 264, 1 167 and 970 cm-1 in the blue region of blue coral. The absorption bands in the UV-VIS spectra of 286, 357, and 590 nm are also consistent with the absorption bands of biliverdin. The above spectral characteristics indicate that the blue color of blue coral is related to biliverdin. Secondly, the micromorphologic characteristics and composition analysis of blue coral showed that the pores formed by blue coral polyps were approximately parallel longitudinally, and the holes were superimposed in segments. From the inner surface of the pores (spiniform aragonite) to the periphery of the pores (lamellar aragonite) to between the pores (columnar aragonite), the content of organic matter decreases significantly as the color changes from blue to white. Combined with the previous research results, it can be inferred that the exudation of keratin and calcareous substance from the ectoderm of polyps forms the white aragonite between the pores. After the polyps die, the endoderm and the nematocytes on the surface of the endoderm become fossilized, preserving the organic matter in the polyps, which contains biliverdin, causing the blue color of the blue coral.

Hyperspectral data collects essential and detailed spectral responses from ground objects through hundreds of contiguous narrow spectral wavelength bands and is widely used for vegetation fine classification. However, classification accuracy is not often satisfactory in a cost-effective way when using all original hyperspectral information (HSI) for practical applications because of its strong correlation and redundantness. Therefore, feature wavelength/band selection is crucial and difficult for HSI applications. Previous band selection methods have some drawbacks, such as low computation efficiency, lack of interpretability, being trapped in local optimization, and so on. Our study focuses on the hyperspectral feature band selection for the vegetation species fine classification of Poyang Lake wetland in continuous extreme drought conditions. Hyperspectral reflectance data of 10 plant species, such as Green polygonum, Artemisia Selengensis, Astragalus sinicus, Rorippa globose, Rumex trisetifer Stokes, Sonoma alopecurus, Phalaris arundinacea, Carexcinerascens, Miscanthus sacchariflorus and Phragmites australis collected by SVC spectrometer (SVC HR1024) is used in this work. We introduce the Quantum Genetic Algorithm (QGA), which is combined with Spectral Angle Mapper-based k-Nearest Neighbors classifier (KNN-SAM), and propose a new feature band selection algorithm, i.e., QGA-KNN-SAM, to select feature wavelength. Then, we use the K-Medoide clustering algorithm to determine the feature band interval. In our experiment, the classification performance of the proposed QGA-KNN-SAM is compared with the traditional GA-KNN-SAM algorithm. QGA-KNN-SAM generates an average classification accuracy value of 95%, higher than GA-KNN-SAM (90%). Moreover, QGA-KNN-SAM generates the feature bands range between 589~634.4 nm, which is relatively more concentrated than achieved by GA-KNN-SAM (1 107.6~1 205 nm). A wavelength band that reflects the surface hydrological characteristics and vegetation should be considered in the fine classification of wetland vegetation, which is different from the fine classification of traditional vegetation. Compared with the band distribution of commonly used multispectral and hyperspectral satellite images, it is found that the QGA-KNN-SAM algorithm selects feature bands with better directionality and interpretability. This algorithm improves the computational efficiency and interpretability of band selection and compensates for the lack of the QGA method in band selection research, providing methodological and theoretical support for similar studies.

Cobalt (Co) was classified as a group 2B carcinogen by the International Agency for Research on Cancer. It is potentially harmful to the safety of the entire urban ecosystem, and it is particularly important to quickly and accurately detect soil Co content. Hyperspectral techniques have great potential for inversion of soil Co content. 88 surface (0~20 cm) soil samples were collected from Urumqi, Xinjiang, to determine the Co content and original spectral reflectance. The original spectral reflectance was preprocessed and applied with 17 types of transformation, which include the root-mean-square (RMS), the logarithm of the logarithm (LT), the inverse of the logarithm (RL), the inverse of the logarithm (RT), the logarithm of the inverse (AT), the first-order differentiation (FD), the second-order differentiation (SD), the inverse first-order differentiation (RTFD) (RTSD), logarithmic first-order differentiation (LTFD), logarithmic second-order differentiation (LTSD), root-mean-square first-order differentiation (RMSFD), root-mean-square second-order differentiation (RMSSD), logarithmic first-order differentiation of the inverse (ATFD), logarithmic second-order differentiation of the inverse (ATSD), logarithmic first-order differentiation of the inverse (RLFD) and logarithmic second-order differentiation ( RLSD). Then, the Co content and 18 types of soil spectral data were subjected to Pearson correlation analysis (PCC) and CARS to screen the spectral signature variables for modeling. The soil Co content was taken as the dependent variable, and the screened spectral feature variables were taken as independent variables. Based on three algorithms, namely partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVMR), the hyperspectral inversion models of urban soil Co content were constructed, and the coefficient of determination (R2), the root-mean-square error (RMSE) and the mean absolute error (MAE) were used as the evaluation indexes. Some conclusions can be drawn: The hyperspectral models’ estimation accuracy and stability for urban soil’s Co content are in descending order of the RFR, PLSR, and SVMR models. The best estimation model for Co content is the ATFD-RFR model (R2=0.871，RMSE=0.124，MAE=0.273) which the RPD is 7.90; in this model, compared with the R-RFR model, the R2 improved from 0.536 to 0.871, RMSE and MAE reduced by 0.32 and 0.243, respectively. Spectral transform can effectively enhance the spectral features; enhancement of spectral features is most significant with first-order differential transform, among which the RTFD can not only effectively enhance the spectral features of Co but also improve the estimation accuracy of the model very well. The RFR model can be extended in oasis urban soil hyperspectral inversion estimation when the spatial heterogeneity of sample sites is insignificant, and the measured values are low and homogeneous.

The accurate inversion of soil heavy metal pollution in hyperspectral analysis relies on carefully selecting characteristic band extraction methods and inversion models. Finding the optimal combination of these two factors to achieve the highest system inversion accuracy remains an urgent and essential problem in this field. The present study involved the collection of 92 sets of soil samples from a typical Chromium (Cr) contaminated area in South China. The Cr content was quantified using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Additionally, the ASD Field Spec4 Spectrometer was employed to gather hyperspectral information in the laboratory. The spectral information preprocessing employed the combined SG+SNV+SD method. Here, SG refers to the Savitzky-Golay smoothing filter, SNV stands for Standard Normal Variate normalization, and SD represents second-order derivative transformation. This combined methodology was employed on the unprocessed spectral data to diminish the impact of soil scattering and noise. Consequently, it enhanced both the quality of spectral data and the precision of feature analysis. Four algorithms, namely Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), Uninformative Variable Elimination (UVE), and Genetic Algorithm (GA) were employed to extract Characteristic bands. Subsequently, the relationships between the extracted Characteristic bands and Cr content were established by using four inversion models: Multivariate Linear Regression (MLR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Artificial Neural Network (ANN). A comparative analysis of various Characteristic band extraction methods and combinations of inversion models regarding their impact on the accuracy of soil Cr content inversion determined that the SG+SNV+SD preprocessing enhances the spectral data’s capability to represent characteristic information. CARS and UVE Characteristic band extraction methods can significantly enhance the predictive performance of PLSR, MLR, and SVR models. In contrast, the SPA method improves the predictive effectiveness of the ANN model. Through the combination approach of SG+SNV+SD+CARS+PLSR, a total of 98 characteristic bands located within the ranges of 800～1 000, 1 400～1 700, and 2 100～2 450 nm were extracted. Model validation yielded an R2 value of 0.97, RMSE of 5.25 mg·kg-1, MAE of 4.35 mg·kg-1, and RPD of 3.94. These evaluation metrics demonstrate the exceptional predictive capability of the model for soil Chromium Cr. In this research, soil Cr pollution was selected as a case study for hyperspectral inversion. A comparative analysis of various combinations of characteristic band selections and inversion model methods identified the optimal approach for modeling the inversion of heavy metal pollution in representative soils characterized by limited sample size and high contaminant concentrations.

To quickly and accurately classify the maturityof blueberries, this study established a discriminant model for blueberry maturity based on near-infrared spectroscopy detection technology and deep forest algorithms. A LabSpec 5000 spectrometer was used to collect three different maturity levels of blueberry standard samples, and a total of 150 spectral samples were obtained. To determine the optimal number of input model features, the original spectral data was subjected to SavitzkyGolay convolution smoothing, and then principal component analysis was used to reduce the smoothed data to 4 principal components. The polynomial feature derivation method derived 2nd, 3rd, 4th, and 5th order features for each principal component. The optimal feature derivation order in the deep forest was considered 4th order. To test the maturity discrimination effect of the deep forest, it was compared with random forest, extreme gradient boosting tree algorithm (xgboost), and stacking fusion model. In the comparison, the optimal hyperparameter combination for each model was determined. The deep forest and stacking fusion model used manual parameter tuning, while random forest and xgboost used a Bayesian optimization algorithm for hyperparameter optimization. The model evaluation indicators were accuracy, confusion matrix, receiver operating characteristic (ROC) curve, AUC measurement, and anti-noise ability. The results showed that on the test set, the accuracy of the deep forest and stacking fusion model was 95.56%, while that of random forest and xgboost was 93.33%. The AUC value of deep forest was 1, while that of random forest, stacking fusion model, and boost were 0.99, 0.98 and 0.96, respectively. The anti-noise ability of deep forest and stacking fusion model was better than that of random forest and xgboost. Overall, the deep forest model in this study had a better discrimination effect than the other three models and provided technical support for blueberry maturity discrimination.

Hyperspectral technology has unique advantages in the inversion of soil heavy metal content. Still, there is a large amount of redundant information in the hyperspectral data, and corresponding methods are needed to reduce the influence of redundant information on the inversion accuracy to realize the accurate prediction of soil Zn content. In this study, we used the soil Zn content and hyperspectral data collected from the farmland of Mojiang Hani Autonomous County, Yunnan Province, as the data source, and Savitzky-Golay smoothed the acquired hyperspectral data, and then four different mathematical transformations, R′，(1／R)′，（R）′ and (logR)′ were used to process the spectra. Five indexes are constructed, namely normalized index (NDI), difference index (DI), ratio index (RI), sum index (SI), and inverse difference index (IDI). The spectral index with the largest absolute value of the correlation coefficient with the soil Zn content was selected as the input to the model and combined with the partial least squares method (PLSR) and multiple regression (MLR) to establish an optimal inversion model for soil Zn content. The results show that (1) the optimized spectral indices exhibit high correlations with soil zinc content under various mathematical transformations. These indices effectively enhance the sensitivity of spectral measurements to variations in zinc levels, with correlation coefficients achieving absolute values of 0.7 or higher. (2) The best prediction model (1/R)′ PLSR based on the optimized spectral index has a validation set of R2 of 0.77, RMSE of 5.07 mg·kg-1, and RPD of 2.09. Compared with the MLR model with the same variable, the R2 increased by 0.04, the RMSE decreased by 0.47, and the RPD increased by 0.18, which has better predictive ability and can be used as an optimal estimation model for soil Zn content in the study area. (3) the spatial distribution map of soil Zn content in the study area was drawn based on the optimal estimation model combined with the spatial interpolation method. It can be seen that the spatial distribution of soil Zn content is higher in the middle of the map and decreases with the increase of terrain elevation. It is feasible to estimate soil Zn content based on an optimized spectral index combined with the PLSR modeling method, which can provide a reference for estimating Zn content in farmland soil.

Long-term consistent records of Total Column Ozone (TCO) are of great significance for assessing ozone layer changes and continuous observation. Although there is abundant satellite monitoring data for ozone, the consistency between different datasets is poor. Differences in satellite payloads, design calibration of spectrometers, and inversion algorithms lead to significant cross-payload biases in TCO observations in the same region. To obtain consistent TCO records, homogenization at the raw data and algorithms level is more physically meaningful, but it requires complete sharing of all instrument parameters, raw data, and all inversion algorithms between different satellite payload teams, which is very difficult. This paper introduces a method to eliminate cross-payload systematic bias based on statistics. In this paper, a quantile-quantile (Q-Q) bias correction method is proposed to eliminate the cross-payload TCO systematic bias between the Environmental Trace Gases Monitoring Instrument 2 (EMI-2) and the TROPO spheric Monitoring Instrument (TROPOMI). Using the overlapping observations in November 2021, this study characterizes the systematic bias between EMI-2 and TROPOMI through the Q-Q bias correction method. Then, it homogenizes the TCO observations of EMI-2 in December 2021 to the TROPOMI level. This Q-Q bias correction method significantly improves the overall consistency of cross-payload TCO observations, increasing the correlation coefficient R between EMI-2 and TROPOMI from 0.96 to 0.98, providing a basis for continuous ozone observation. Bias analysis of the data before and after homogenization of EMI-2 with ground station data shows that the Q-Q bias correction method improves the accuracy and consistency of EMI-2 observations, reducing the error with ground-based data from 5% to 3%. Ground station data indicate that the accuracy of EMI-2 data is higher in temperate and polar regions, but the error is higher than 5% in tropical regions. It is preliminarily speculated that this is because the cloud height is higher. The cloud fraction is larger in tropical regions, and the accuracy of cloud pressure and cloud fraction in cloud data is insufficient. The effect of compensating for the ozone below the clouds with “ghost columns” is poor, but the bias is reduced after homogenization. The study shows that the Q-Q bias correction method introduced in this paper is crucial for global long-term TCO records and can be applied to future assessments of global ozone recovery.