This article briefly reviews the applications of small angle X-ray scattering (SAXS) and ultra-small angle X-ray scattering (USAXS) in the study of coal pore structure. By analyzing the microscopic pore structures of coal, this study explores the advantages of SAXS and USAXS techniques in revealing the adsorption performance, gas diffusion properties, and conversion of coal. A detailed description of the basic principles, experimental methods, and application examples of these two techniques at synchrotron light sources is provided, demonstrating their irreplaceable role in coal research. Combining the characteristics of coal resources in China, it is pointed out that SAXS and USAXS have broad prospects for application in the cross-scale research of coal.

Dust explosion is a high-risk industrial accident, where microstructural changes play a critical role during the explosion process. However, conventional experimental methods struggle to capture the dynamic changes during explosions, which leads to limitations in understanding the mechanisms of dust explosions. This paper explores the advantages of applying synchrotron radiation small-angle X-ray scattering (SAXS) in dust explosion research. By leveraging its high temporal resolution (millisecond to nanosecond) and nanoscale structural probing capabilities, SAXS can provide real-time analysis of microstructural changes during dust explosions. The challenges and recent breakthroughs in using this method for dust explosion research are discussed. Furthermore, the potential applications of this method in risk assessment and explosion prevention are also envisioned.

Time-correlated Raman spectroscopy is crucial for in-situ thermometry of semiconductor materials. Industrial applications often require compact and portable time-correlated Raman spectroscopy detection equipment. The time-correlated Raman spectroscopy equipment with compact configuration and miniaturization is valuable for real-time online monitoring of industrial production. A lightweight fiber-optic probe with time-correlated Raman spectroscopy is designed for time-correlated Raman detection, steady-state non-contact in-situ thermometry, and time-correlated non-contact in-situ thermometry. A time-correlated Raman spectroscopy detection system was constructed based on this probe. The dependence of Raman spectrum characteristics of intrinsic Si wafer on temperature in the steady-state temperature range of 21.6~435.0 ℃ was investigated under non-contact conditions. By establishing the synchronous time relationship between the probe and the heating platform, the in-situ temperature of the intrinsic Si wafer under the periodic temperature change of 77.3~91.5 ℃ was successfully measured under non-contact conditions. Fiber-optic Probe with time-correlated Raman spectroscopy has potential for applications in the fields of non-contact in-situ thermometry, characterization of materials, and monitoring of chemical reaction processes.

In order to obtain the backscattering characteristics of triangular prism Au-Ge Janus nanoparticles (JNPs) in the solar spectrum, the discrete dipole approximation (DDA) method was used to simulate and calculate the backscattering efficiency of particle size, specific surface area, metal volume ratio and other factors. The results show that the position of the backscatter peak of triangular prism Au-Ge-JNPs is redshifted with the increase of triangular prism volume, and the peak intensity increases significantly. By adjusting the volume ratio of Au-Ge metal, it was found that when the ratio was 4:1, the backscattering efficiency peak reached the maximum. The relationship between the average backscattering efficiency and the equivalent radius of triangular prism Au-Ge-JNPs in the visible spectrum was analyzed, and the empirical formula was obtained, which provided a structural optimization design method for the regulation of the backscattering characteristics of Janus nanoparticles.

Transition metal dichalcogenides (TMDs) have demonstrated significant potential in nanoelectronics and optoelectronics due to their distinctive electronic, optical and mechanical properties. Strain engineering offers a potent method to tune the bandgap structure and carrier mobility of TMD materials. However, the atomic-scale thickness of these materials poses challenges in applying direct tensile strain. In this study, we utilized a rectangular-shaped blister device combined with in situ micro-Raman spectroscopy to monitor the behaviors of Raman phonon modes in a monolayer MoS2 sheet during uniaxial extension. We noted phonon softening under strain, which we attributed to the weakening of interatomic bonds in the lattice. This methodology allowed us to determine the Grneisen parameters of specific Raman modes in monolayer MoS2, yielding values (Eg2=1.35 and Ag1=0.9) that align closely with theoretical predictions. We further tested our methodology on multilayer WSe2 to confirm its broader applicability.

Black carbon (BC) aerosols significantly impact the global climate and have emerged as a key research focus in atmospheric and environmental sciences. A core-shell model of BC aerosols has been developed, featuring BC as the core and sulfate, nitrate, and organic carbon as the shells. The discrete dipole approximation (DDA) method has been employed to explore how different mixing shapes and materials influence the optical properties of BC aerosols. Results indicate that smaller particles exhibit much higher extinction and scattering efficiencies than larger ones. Ellipsoidal particles demonstrate approximately 20%~30% higher extinction efficiency and 15%~25% higher scattering efficiency compared to spherical particles. Among the core-shell configurations, nitrate-coated ellipsoids have shown the most prominent absorption performance, with an absorption efficiency factor 40%~70% higher than that of sulfate- and organic-coated ellipsoids. Organic-coated ellipsoids have displayed a significant increase in the asymmetry factor, reaching around 0.8, which is 5%~15% higher than other mixtures. Regarding size distribution, sulfate particles have been found to form smaller sizes, whereas organic particles have exhibited a more uniform and slightly larger size distribution. An increase in BC radius has been shown to substantially alter the optical properties of the particles. The extinction efficiency factor has increased with the BC radius. The absorption efficiency factor has been enhanced, while the scattering efficiency factor has decreased. These findings have provided essential parameters for more accurately simulating the radiative effects of aerosols.

Overnight waste oil has low nutritional value, poor hygiene, and poor edible quality. Illegal traders often mix it with ordinary vegetable oil to reduce production costs and improve profit margins. To protect people's food safety and health, relevant departments must strengthen the crackdown on the adulteration of overnight waste oil. Therefore, this article proposes a one-dimensional convolutional neural network algorithm to assist in the detection method of portable Raman spectroscopy quantitative analysis of overnight waste oil adulterated with sunflower seed oil. Quantitative analysis of simulated adulteration was conducted by preparing adulterated oil products with a wide range and uniform gradient variation. Raman spectral data of mixed oil products with different adulteration concentrations were collected using a portable 633 nm Raman spectrometer. The original spectral data was then preprocessed with baseline correction, noise reduction, and normalization. Finally, the training and testing sets were divided into a 4∶1 ratio, and the model was validated by leave-one-out cross-validation. The results indicate that portable Raman spectroscopy can extract spectral information of two different oils, and the spectral differences between the two oils are mainly concentrated in the two Raman spectral fingerprint regions of 450~2000 cm-1 and 2500~3100 cm-1. A quantitative analysis model for 11 types of adulterated oil products was established based on a one-dimensional convolutional neural network algorithm, achieving ideal quantitative analysis. The determination coefficient of the one-dimensional convolutional neural network model test set was 0.9922, and the root mean square error was 0.0279. In summary, the method proposed in this article can achieve a quantitative analysis of common vegetable oil adulteration, and this detection method provides a specific reference value for frontline applications and on-site non-destructive detection.

Eating and broadcasting's "new favorite" - wax bottle sugar has become a popular food for young people. Wax clothes are not easy for the human body to digest, and there may be a risk of industrial wax. In response to the potential industrial wax contamination of wax bottle sugar shells, this study conducted a quantitative analysis of illegal additives in wax bottle sugar using Raman spectroscopy technology combined with backpropagation neural network (BPNN) algorithm. The Raman spectra of natural beeswax and paraffin wax were detected, and the similarities and differences between the two Raman spectra were compared. The molecular bonds and vibration modes corresponding to the characteristic peaks of the Raman spectra were analyzed and determined. The paper simulated the research work on the natural beeswax and paraffin wax mixture and compared several linear and nonlinear correction methods. It was found that the combination of BPNN algorithm and Raman spectroscopy technology is the optimal method for detecting the mixture of natural beeswax and paraffin wax. The model also predicted the potential paraffin wax in online wax bottle sugar. This study proposes a detection technique based on Raman spectroscopy combined with BPNN algorithm to address wax bottle sugar's potential food safety issues. This technique has specific reference significance for strengthening the supervision and guidance of wax bottle sugar.

Detecting the ingredients of sports drinks has essential application value in protecting consumer health and safety and ensuring the production quality of enterprises. With the rapid expansion of the sports drink market, consumers are increasingly concerned about the safety and authenticity of beverage ingredients. Therefore, there is an urgent need for an efficient detection method to meet market demand and protect consumer rights. This study aims to develop a Raman spectroscopy technique based on the combination of Variational Mode Decomposition (VMD) and Partial Least Squares Regression (PLSR) algorithms for rapid and accurate detection of ingredients in sports drinks. Firstly, spectral data of various sports drink samples were obtained through Raman spectroscopy technology. Raman spectroscopy is often affected by noise, baseline drift, and other interference factors, leading to a decrease in the accuracy of analysis results. To solve this problem, this article introduces the VMD algorithm to decompose complex signals into multiple modalities, effectively extracting essential features from the signal and reducing noise interference in the analysis results. The PLSR algorithm was used to model the spectral features processed by VMD, and the predictive ability and stability of the model were evaluated through cross-validation and external validation. The R2 and RMSE of the test set were 0.9759 and 0.0024, respectively, and the R2 in the low-concentration application testing stage was 0.8901. The results indicate that the VMD-PLSR method has significantly better accuracy and robustness in component detection than traditional regression analysis methods. In summary, the VMD-PLSR algorithm and Raman spectroscopy-based method for detecting ingredients in sports drinks have demonstrated outstanding potential for application, providing an efficient and reliable technical means for quality control and ingredient analysis of sports drinks. Future work will focus on optimizing algorithm parameters and expanding the application of this method in detecting other food and beverage ingredients further to improve the efficiency and accuracy of food safety detection.

This study employed Fourier Transform Infrared (FTIR) spectroscopy to systematically compare the structural characteristics and relative distribution of -solanine in different parts of potatoes, eggplants, and tomatoes, aiming to explore the applicability of FTIR in rapid screening and qualitative identification of this compound. Spectral peak matching with the -solanine standard revealed the presence of -solanine-related structures in various tissue types across all three Solanaceae vegetables. Absorbance ratio analysis showed that the green skin of potatoes, the white skin of eggplants, and tomato seeds exhibited stronger infrared responses within their respective samples, suggesting a degree of tissue-specific accumulation. These findings indicate that -solanine exhibits species- and tissue-level spatial variation in plants. FTIR thus holds potential as a preliminary tool for screening toxic steroidal alkaloids in plant-derived foods. The results provide foundational spectroscopic evidence to support further exploration of distribution patterns and infrared-based identification methods for glycoalkaloids.

This study aimed to explore the application of the support vector regression (SVR) algorithm that assisted Raman spectroscopy in detecting metabolites in athletes' urine. With the development of sports science, metabolite detection of athletes has become an important means of evaluating their training effects and physical state. Metabolites such as urea and creatinine in urine are essential indicators for monitoring the metabolic function of the human kidney. In this study, artificial urine samples with different urea and creatinine contents were detected by self-developed near-infrared micro confocal Raman spectroscopy, and the spectral differences between urine samples were analyzed. The vibrational spectra of urea and creatinine molecules were predicted based on density functional theory. The Raman characteristic peak intensities of urea and creatinine and their concentrations were analyzed and compared, and the support vector regression models with different kernel functions were established. The optimal quantitative analysis model predicted the contents of urea and creatinine in human urine. The urea and creatinine determination coefficients were 0.9815 and 0.9681, respectively, and the root mean square errors were 0.0345 and 1.4591, respectively. Based on the quantitative analysis model constructed by metabolites, 800 urine samples of 40 athletes were quantitatively detected for urea and creatinine. The comparison between the detection results and the traditional test results showed that this study's method had high clinical application potential. In a word, near-infrared micro confocal Raman spectroscopy technology is a rapid, convenient, no-pretreatment, and no-damage detection analysis method with great application potential. Its repeatability and stability make it suitable for rapidly screening kidney diseases in people with special diseases.

Analyzing the light attenuation and morphological features in optical coherence tomography (OCT) images can improve the diagnostic accuracy of papillary thyroid carcinoma (PTC) tissue. We propose the PDC-DRE attenuation coefficient depth-resolved model, which effectively resolves the systemic overestimation caused by discretization in conventional approaches. The optical attenuation coefficient (OAC=(3.08±0.64) mm-1) of PTC tissues is significantly lower than that of normal thyroid tissues (OAC=(4.47±0.85) mm-1). A multi-modal image dataset was established by integrating optical attenuation features with OCT morphological imaging, and a custom ThyOCTNet model was employed to differentiate between normal and PTC tissues. Results demonstrate that the ThyOCTNet model achieves enhanced sensitivity (98.5%), specificity (98.8%), and accuracy (98.6%) with multi-modal images compared to single-modal images. This study confirms that OCT multi-modal fusion imaging combining optical attenuation and morphological features significantly improves PTC identification accuracy, providing a new technical pathway for the clinical translation of non-invasive pathological diagnostic technologies.

In order to improve the detection accuracy of wheat protein model, a method based on optimized back propagation (BP) neural network was proposed in this paper. Using the wheat spectral data, the spectral data after eliminating the abnormal samples were preprocessed differently to determine the optimal preprocessing method. Dung beetle optimizer (DBO) was used to optimize the initial weights and thresholds of BP neural network, and full-band partial least squares regression (PLSR), DBO-BP and BP prediction models of wheat protein content were established. The results showed that Standard Normal Variate Transform (SNV) pre-processing method was the best. Under the same training sample, the detection accuracy of the established DBO-BP model is the highest. Compared with PLSR and BP neural network, the prediction set root mean squared error (RMSEP) is reduced by 58.65% and 46.53%, the correlation coefficient is increased by 10.35% and 5.06%, and the residual prediction deviation (RPD) is 7.27. Therefore, the DBO-BP neural network model can quickly and accurately detect the wheat protein content, which provides a theoretical basis for the detection of wheat protein based on BP neural network.

To address the issues of high dimensionality, redundancy, and nonlinearity in near-infrared spectroscopy, which lead to low prediction accuracy and poor interference resistance in established quantitative analysis models, this paper proposes an improved Deep Residual Shrinkage Network (CBAM-DRSN) for spectral feature selection. This method is based on a deep residual network and integrates the CBAM convolutional attention module into the network. Additionally, two adjustment factors are introduced to optimize the adaptive selection of noise thresholds in the residual shrinkage modules. Finally, the Guided-GradCAM is used for the selection and visualization of effective spectral segments. Using this method, the quantitative models built after selecting features related to total sugar and nicotine in tobacco leaves achieved root mean square errors of prediction (RMSEP) of 0.620 and 0.103, respectively, and correlation coefficients (R2) of 0.965 and 0.955, respectively. These results demonstrate higher model accuracy compared to other methods. The CBAM-DRSN feature selection method effectively extracts features relevant to modeling indicators, eliminates noise and redundant information from the spectrum, and improves model accuracy while reducing the model's complexity.

Concrete moisture content testing is crucial in ensuring concrete quality, controlling the construction process, preventing cracks and deformations, and complying with industry standards. This paper proposes a near-infrared spectral concrete moisture inversion method that combines a hybrid model of Variational Mode Decomposition (VMD) and Long Short Term Memory (LSTM) to address the problem of rapid and real-time large-scale detection of concrete moisture content. Firstly, establish a standard concrete sample library with different moisture contents and complete near-infrared spectroscopy data collection under the same experimental conditions. Secondly, the raw near-infrared spectra are subjected to preprocessing such as smoothing, noise reduction, and standardization to analyze the near-infrared reflectance spectra produced by concrete with different moisture contents. Then, based on the collected near-infrared spectroscopy database, a VMD-LSTM hybrid model database is constructed, and the water content inversion performance of the hybrid model is evaluated by the root mean square error and determination coefficient of the evaluation indicators. Finally, the effectiveness of the method was verified through independent sample testing experiments. In summary, this study has achieved the advantages of rapid, non-destructive, non-invasive, and large-scale on-site detection of concrete moisture content inversion, which is of great significance for construction projects' safety and durability protection.

A rapid analysis method for Zanthoxylum bungeanum with different processing techniques based on infrared spectroscopy was established. The infrared spectra of four kinds of Zanthoxylum bungeanum treated by oven-dried, low-temperature grinding, naturally sun-dried and steam enzyme-inactivated were tested, and comparative analysis was conducted on the relative peak area ratios (A2927/A3363, A1626/A3363, and A1104/A3363) corresponding to esters, flavonoid, and saccharide absorption peaks in mid-infrared spectra of Zanthoxylum bungeanum with different processing techniques. Multivariate statistical analysis, including cluster analysis and discriminant analysis, was subsequently performed on the spectra of Zanthoxylum bungeanum treated by four different processing techniques. The results demonstrated that: different processing techniques had effect on the relative content of esters, flavonoid and saccharide in Zanthoxylum bungeanum; the relative content of esters and flavonoid in low-temperature grinding and naturally sun-dried Zanthoxylum bungeanum samples were significantly higher; the relative content of saccharide in steam enzyme-inactivated and oven-dried samples was significantly higher. The infrared spectra combined with clustering analysis and discriminant analysis, had a good distinguishing effect on 4 kinds of Zanthoxylum bungeanum samples with different processing techniques. The principal component scores extracted from spectral data were used as independent variables to establish a discriminant analysis model, which achieved both an original classification accuracy and a cross-validation classification accuracy of 100%, and it was able to accurately discriminate Zanthoxylum bungeanum samples with unknown processing techniques. Infrared spectroscopy can analyze the influence of different processing techniques on the relative content of Zanthoxylum bungeanum components. Combined with multivariate statistical analysis, it can distinguish Zanthoxylum bungeanum samples with different processing techniques, and provide technical support for the rapid evaluation of the quality of Zanthoxylum bungeanum with different processing techniques.

Olive oil is the most popular healthy oil for Mediterranean residents, and domestic people have gradually begun to accept this high-quality natural oil. However, adulteration and shoddy are the main problems the domestic olive oil consumption market faces, especially the extra virgin olive oil. This paper used the artificial intelligence algorithm combined with fluorescence spectroscopy to detect the adulterated concentration of extra virgin olive oil. UV laser-induced fluorescence spectroscopy detected the fluorescence signals of corn oil, rice oil, extra virgin olive oil, and their mixed oils. A factor analysis algorithm carried out the dimension reduction and feature extraction of high-dimensional fluorescence spectrum data, and then the concentration detection of extra virgin olive oil adulterated by corn oil and rice oil was realized by combining it with a back-propagation neural network algorithm. The determination coefficients of factor analysis combined with the back-propagation neural network algorithm model were 0.9738 and 0.9658, and the root mean square errors were 0.0031 and 0.0040. That is, the method proposed in this paper has better prediction performance. In a word, this paper verifies that UV laser-induced fluorescence spectroscopy combined with an artificial intelligence algorithm can predict the concentration of corn oil and rice oil adulterated with extra virgin olive oil, which provides a reference methodology for relevant researchers and law enforcement departments.

Grape seed oil is a new type of vegetable oil that is rich in nutrients and has antioxidant and anti-inflammatory effects. However, the phenomenon of counterfeiting not only reduces the nutritional quality of grape seed oil but may also harm consumer health. To predict the concentration of contaminated grape seed oil, this paper proposes a detection method of adulterated grape seed oil using fluorescence spectroscopy technology combined with the combination algorithm of sammon mapping and support vector machine combination algorithm. Twenty-one kinds of simulated samples of adulterated grape seed oil were prepared, the fluorescence spectra of waste oil and grape seed oil were detected, and the spectral characteristics of waste oil and grape seed oil and their corresponding material information were analyzed. We extracted features from high-dimensional fluorescence spectral data using the sammon mapping algorithm. Then, we combined it with the support vector machine algorithm to predict the concentration of contaminated grape seed oil in gutter oil. Based on the sammon mapping and support vector machine combination algorithm of fluorescence spectra, we established a quantitative analysis model for detecting the concentration of contaminated grape seed oil in gutter oil. The determination coefficient of the quantitative model test set is 0.9560, and the root mean square error is 0.0040. The research results indicate a significant difference in the fluorescence spectra of gutter oil and grape seed oil, among which biological pigments are an important factor causing spectral differences. The combination algorithm of sammon mapping and support vector machine combination algorithm performs well in processing fluorescence spectrum data. Therefore, the detection method proposed in this study, which combines fluorescence spectroscopy technology with sammon mapping and support vector machine combination algorithm, has potential application value in predicting the adulteration concentration of grape seed oil. This study has a specific reference value for preventing and eliminating the adulteration of grape seed oil and provides methodological guidance for relevant departments.

Seventy-six samples of ink pads and ink oils of different brands and types in the domestic market were selected. Under simulated actual conditions, they were evenly stamped on the same type of A4 printing paper with normal force to obtain 76 samples of ink color materials. The fluorescence images of the samples were collected using the Dahao-VF10 Pro ultra-deep field high-magnification video fluorescence microscope. The average pixel values of RGB and LCH of the fluorescence images of the samples were calculated through Matlab. The T-SNE algorithm was used to perform K-means clustering analysis on the brightness and color values of the fluorescence images of the samples, and then comprehensive quantitative inspection and identification were carried out. The research results show that 76 kinds of ink pads and ink oils can be classified into 4 categories based on the difference of LCH values for brightness clustering analysis, and into 6 categories based on the difference of RGB values for color clustering analysis. Through comprehensive analysis of fluorescence brightness and color, 76 kinds of ink pads and ink oils can be ultimately classified into 21 categories. Video fluorescence spectral image technology can widely and effectively conduct systematic analysis and effective identification of ink pads and ink oils of different brands and types, providing a new technical means with high practical value for the forensic science inspection of seal impressions.

Yellow pigment was extensively used on the mural paintings of a tomb of Tang Dynasty excavated in the southern suburbs of Xi'an City, which is relatively rare in other ancient Chinese tomb mural paintings. In this work, comprehensive analyses were conducted on the yellow pigment samples using micro-Raman spectroscopy and scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) techniques. The Raman spectroscopy, micro-morphology and elemental composition of the yellow pigment in the mural were measured and the results were discussed. The colourant of the pigment was identified as vanadinite [Pb5(VO4)3Cl], a natural mineral pigment which was rarely used in ancient Chinese paintings. The results were considered to be of essential importance for further restoration and conservation for the mural paintings. It is worth noting that the identification of yellow pigments by Raman spectra must be careful and combine with other analytical techniques to avoid misidentification.

The long-necked bottle with net patterns and double ears in hexagonal shape of the Xixia is an unearthed cultural relic collected in Ningxia Museum, which can be described as a masterpiece. Because of the long underground age, there are many rusts on the surface. In this paper, the rust of the six-edged binaural long neck vase of the Xixia was analyzed by means of micro- Raman spectroscopy. The results show that the rusts of the six-edged binaural long neck vase of the Xixia are atacamite, paratacamite, malachite and chalcanthite. Atacamite and paratacamite are harmful rusts, which repeatedly corrode the bronzes, while malachite and chalcanthite belong to harmless rusts and can be retained. The detection analysis provides a scientific basis for making a reasonable repair plan.

The remains of crossbows unearthed in Pit 1 of the Qin Terra Cotta Warriors was the only one that had been protected and repaired. The remains of crossbows provided valuable materials for the understanding of the shape of crossbows in the Qin Dynasty. Profile microscopy, scanning electron microscopy energy spectrum, raman spectroscopy Infrared spectroscopy, pyrolysis gas chromatography-mass spectrometry analysis were conducted on the red, brown, white substance of the crossbow ruins, the structure was showed as three layers, with the first layer (bottom layer) consisting of white material such as calcium carbonate, the second layer (middle layer) consisting mainly of brown material such as Chinese lacquer, animal glue, and plant glue, and the third layer (surface layer) consisting mainly of red material such as cinnabar and organic binders (Contains proteins binders), which highlighted the history of Chinese lacquer agricultural culture, mineral pigments, and ancient civilization coexisting. The process of extracting, protecting, and restoring crossbow relics was mainly involved soil relic extraction, laboratory protection and restoration, as well as exhibition and transportation safety. It was a process of referencing and continuously experimenting with relic extraction, protection and restoration techniques. The extraction, protection, and restoration techniques of crossbow relics not only inherit but also innovate, in order to provide reference for later relic protection.

As a form of scripture on palm leaf carriers, the identification of the species of palm leaves used in the preparation of the Palm leaf manuscriptures is of fundamental importance for the study of their production techniques, origins, and conservation. This study employs Near-Infrared Spectroscopy (NIRs) technology to conduct non-destructive analysis and identification of the species of leaves commonly used in artifacts of Palm leaf manuscriptures, namely the Borassus flabellifer and Corypha umbraculifera. Near-infrared fiber optic probes were used to collect spectral data from samples of Yunnan Province. The raw spectral data obtained were preprocessed using Savitzky-Golay smoothing (SG), Standard Normal Variate Transformation (SNV), and Multiplicative Scatter Correction (MSC) methods. Principal Component Analysis (PCA) was then applied to analyze the preprocessed spectral data. Through SIMCA binary qualitative analysis, a calibration model for predicting the species of Bayi scriptures using near-infrared spectroscopy was established. The application of this model to three types of Palm leaf manuscriptures demonstrated a significant linear correlation between the near-infrared spectra and the species of leaves, thereby validating the feasibility of near-infrared spectroscopy technology for the rapid and non-destructive identification of the species of leaf carriers in artifacts of Palm leaf manuscriptures.

Palm-leaf manuscripts, which use palm leaves as the recording medium, possess significant documentary and research value. Existing palm-leaf manuscripts suffer from various deteriorations, with contaminants being one of the common issues. This study selected four brown-black contaminants from the surfaces of three palm-leaf manuscript artifacts as research subjects. Comprehensive analytical techniques including optical microscopy (OM), scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), gas chromatography-mass spectrometry (GC/MS), and X-ray diffraction (XRD) were systematically employed to analyze the chemical composition of the contaminants. Experimental results indicated that all four contaminant samples primarily consist of aged and degraded oil mixtures with dust, and molds and calcium soaps were found in some of the samples. Based on the analytical findings of the brown-black contaminants, online thermal aging-headspace gas chromatography-mass spectrometry (Headspace-GC/MS) analysis of mock-up samples preliminarily revealed the impact of oil contaminants on the durability of palm-leaf materials. The research outcomes provide scientific basis for the selection of cleaning materials for palm-leaf manuscript contaminants and the formulation of cultural heritage conservation strategies.