Researching | GPXYGPFX Volume 44 Issue 12,2024

WANG An-zhi, JIN Ling-yu, WANG Jia-bin, GAN Zhao-xu, YUE Gang, YANG Hao-jie, HU Dong-sheng, CHEN Chuan-qi, YAN Qi, YAN Yun-xiang, WANG Sheng-jia, GENG Tao, and SUN Wei-min

In high-resolution astronomical spectral detection using multi-mode optical fibers for solar surface magnetic field and visual velocity measurement, there is a significant phenomenon of uneven energy distribution and speckle in the obtained spectral images. Analyzing the reasons, it can be concluded that during fiber o In high-resolution astronomical spectral detection using multi-mode optical fibers for solar surface magnetic field and visual velocity measurement, there is a significant phenomenon of uneven energy distribution and speckle in the obtained spectral images. Analyzing the reasons, it can be concluded that during fiber optic spectroscopic imaging, the imaging system receives the image of the output fiber end at different wavelengths and spreads along the dispersion direction. The high dispersion rate of the spectrometer results in a very narrow spectral range, which has excellent coherence, corresponding to each pixel in the imaging system. There are multiple transmission modes in multimode optical fibers, and the energy center of the speckle pattern formed by interference between different modes at each wavelength will deviate from the fiber’s geometric center, reducing the accuracy of spectral measurement. To address this issue, this paper proposes a multi-dimensional mechanical perturbation system to suppress speckle effects and improve the accuracy of fiber optic spectral measurements. The multi-dimensional mechanical disturbance device consists of three mechanical structures with reciprocating motion in different directions and frequencies. By adjusting three mechanical devices at different frequencies, the conduction mode in the optical fiber undergoes a nearly random phase drift, resulting in a random change in the speckle pattern at the output end of the optical fiber. After long-term exposure (corresponding to superimposing and averaging multiple speckle patterns), the influence of speckles can be eliminated. To test the mode disturbance effect of the system, the energy distribution of the outgoing speckle field of a 650 nm laser was studied using a fiber with a core diameter of 35 μm. The standard deviation of the energy center position was proposed as the evaluation function for the dispersion of the fiber mode speckle energy and its impact on the accuracy of astronomical fiber spectral measurement. Firstly, a comparative analysis was conducted on the average stacking effect of 1 000 speckle patterns under non-disturbance mode, manual disturbance mode, one-dimensional disturbance mode, and multi-dimensional disturbance mode. The results showed that manual disturbance mode and multi-dimensional mechanical disturbance mode had better effects, and the energy distribution of the average speckle pattern stacking was relatively uniform. Then, the multi-dimensional perturbation effect of the number of speckle pattern superpositions(equivalent to different exposure times) was compared. The experimental results show that after stacking and averaging 100 speckle patterns with multi-dimensional mechanical disturbance, the standard deviation is only one-thirteenth of that of a single speckle pattern. Finally, the influence of the frequency and amplitude of the scrambler device on the scrambler effect is tested. The results show that higher frequency and amplitude are beneficial to speckle suppression. For the optical fiber in this experiment, the frequency of 1.2 Hz and the amplitude of 6 cm are the most appropriate. Under these conditions, the mean centroid deviation distance of the speckle is the smallest, which is 0.21 pixels..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3422 (2024)

Get PDF

View fulltext

Study on the Spectrum and Visualization of “Trapiche” Tourmaline Based on Hyperspectral Imaging Technology

CHEN Cheng, YAN Bing, YIN Zuo-wei, CAO Wei-yu, and WANG Wen-jing

“Trapiche” tourmaline is a rare variety of tourmaline. Conventional gemstone identification instruments, infrared spectrometers, X-ray fluorescence spectrometers, and hyperspectral imaging techniques were used to test and analyze it. This paper aims to discuss the application of hyperspectral imaging technology in comp “Trapiche” tourmaline is a rare variety of tourmaline. Conventional gemstone identification instruments, infrared spectrometers, X-ray fluorescence spectrometers, and hyperspectral imaging techniques were used to test and analyze it. This paper aims to discuss the application of hyperspectral imaging technology in complex samples and enrich the multi-angle study of “trapiche” tourmaline. There are abundant parallel tubes and black solid inclusions in “trapiche” tourmaline. The visual boundary difference is due to the multidirectional distribution of parallel tubular inclusions. The parallel tubular inclusions in region a of the hexagonal column are arranged almost perpendicular to the c-axis. In the r region, the tubular inclusions of the three rhomboid regions divided by the three black arms of “trapiche” radiate outward along the c axis at an angle of 10°～30°. The appearance of the “trapiche” phenomenon comes from the fact that the black inclusions extend from the middle to the outside in three directions, and many of them are attached to the outside or wrapped in the inside of the tubular inclusions. Using the hyperspectral imaging technique, the average reflectance spectral lines and visual images of the region of interest of “trapiche” tourmaline are obtained. The hyperspectral pattern is consistent with the shading of the selection, and a wide absorption band centered at 440 nm in the purple region and 610 nm in the orange-red region appears. The content of Cr and V is high in the combination component analysis. It is considered that the d—d electron transition of Cr³⁺ causes the green color, and the D-electron spin allowed the transition of V³⁺. The visualized image shows the test state of the sample along the direction of the c-axis, the edge hexagonal column a gradually disappears, and the central three-square single cone r gradually occupies the entire plane. The pixel ratio between the green zone and the whole plane ranges from 16.81% to 49.96%. The proportion of black arms on both sides of the slice is inconsistent, and the number of black inclusions at both ends of the crystal does not show a linear relationship, but along the +c-axis, the change rate increases. The advantage of hyperspectral imaging technology lies in its ability to rapidly identify and analyze the distribution and concentration of different components in “trapiche” tourmaline and provide detailed information on the mineral’s internal structure and crystal orientation. Identifying microscopic inclusions in minerals is of significant importance for understanding the mineral’s genesis processes and environmental conditions..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3429 (2024)

Get PDF

View fulltext

Association Between Association Between DOM Composition Characteristics and CH₄ Emission of Chinese Milk Vetch Amended Under Different WaterManagement

LIU Han, MA Ting-ting, WAN Li, CHEN Xiao-fen, ZHOU Guo-peng, SUN Jin-xin, ZHU An-fan, LI Yan-li, and LIU Jia

In order to study the relationship between the composition and structure of dissolved organic matter (DOM) and methane (CH4) emissions in the paddy soil with Chinese milk vetch returning under different water managements (flooding after a short time delay), we set an incubation experiment, with no green manure and no t In order to study the relationship between the composition and structure of dissolved organic matter (DOM) and methane (CH₄) emissions in the paddy soil with Chinese milk vetch returning under different water managements (flooding after a short time delay), we set an incubation experiment, with no green manure and no tillage (DW-0) as control, different water management treatments (immediately flooding, flooding after 5 and 10 days) were set up, three-dimensional fluorescence spectroscopy (3DEEM) and parallel factor analysis (PARAFAC) were used to assess the effects of soil DOM composition and the effect of fluorescence spectra on CH₄ emissions. The results showed that both DOM content and cumulative CH₄ emissions significantly decreased under G+DW-5 treatment, and both DOM content and cumulative CH₄ emissions increased with the extension of dry tillage time. Three fluorescence components, including fulvic-like (C1), humic-like (C2), and protein-like (C3), were obtained by the 3DEEM-PARAFAC method. There were significant differences in the fluorescence components of soil DOM under different water management practices. The highest content of humus-like components (C1+C2) was found in soil DOM under the G+DW-5 treatment, which reached 79.88%, and the restricted principal component analysis indicated that soil DOM components were significantly different under different water management. The DOM Humification Index (HIX) of G+DW-5 was 5.11, significantly higher than other treatments. The correlation analysis showed that CH⁴ emissions were negatively correlated with the C1 and HIX index but positively correlated with the C2 and BIX index; the results showed that flooding after a short time delay could reduce CH₄ emissions by increasing the content of C1 and the level of soil DOM humification. Overall, the composition and structure of DOM in the paddy soil under different water managements are closely related to CH₄ emissions, which could provide a scientific basis for reducing CH₄ emissions from paddy soil..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3435 (2024)

Get PDF

View fulltext

Estimation of Potato LAI Using UAV Multispectral and Multiband Combined Textures

GUO Li-xiao, CHEN Zhi-chao, MA Yan-peng, BIAN Ming-bo, FAN Yi-guang, CHEN Ri-qiang, LIU Yang, and FENG Hai-kuan

The leaf area index (LAI) is an important indicator for characterizing crop growth, so an efficient and accurate estimation of crop LAI can guide field production management. Spectral features can provide information about the reflected and absorbed wavelengths of crops, while texture features can provide information a The leaf area index (LAI) is an important indicator for characterizing crop growth, so an efficient and accurate estimation of crop LAI can guide field production management. Spectral features can provide information about the reflected and absorbed wavelengths of crops, while texture features can provide information about the gray-scale attributes and spatial location relationships of crops. Previous studies have shown some limitations in estimating crop LAI using only spectral features, and at high LAI levels, the “saturation phenomenon” occurs, resulting in an underestimation of LAI. To fully explore the information of multispectral images from UAVs, texture information of multiple bands was combined to obtain multiband combined texture and to explore whether the fusion of spectral features with multiband combined texture can improve the accuracy of LAI estimation. Firstly, we obtained multispectral data and ground-truthed LAI data of three key fertility stages of potato; then we extracted the texture features of each fertility stage using the gray-level co-occurrence matrix (GLCM) and combined the texture features of multiple bands; then we analyzed the correlation between the vegetation index, the texture features, and the multi-band combination of textures and LAI, and synthesized the correlations and correlations with LAI, and investigated whether the fusion of spectral information and multiband combination of textures could improve the accuracy of LAI estimation. Then, we analyzed the correlation between vegetation index, texture features, and multiband combined texture and LAI and combined the correlation and variance expansion factors to select the preferred vegetation index; finally, we integrated the multiband combined texture and used partial least squares regression (PLSR), ridge regression (RR) and K-nearest neighbors regression (KNR) with parameter tuning to determine the correlation between the vegetation index and LAI, and then used KNR to estimate the correlation between the vegetation index and LAI. KNR will estimate potato LAI at each fertility stage and compare it with the model using only the vegetation index to verify the feasibility of inverting LAI using a multiband combined texture. The results showed that: (1) the correlation between single-band texture, two-band combined texture and three-band combined texture and LAI increased sequentially; (2) the preferred multiband combined texture at each fertility stage of potato showed highly significant correlation with LAI, with correlation coefficients ranging from 0.79 to 0.83; and (3) compared with the model using only the vegetation index, the addition of the multiband combined texture could significantly increase the model’s accuracy and stability. The KNR model had the highest accuracy in estimating potato LAI during the tuber formation period, with a modeling R² of 0.83，an RMSE of 0.23 m²·m^-2, and a validation R² of 0.75 and an RMSE of 0.25 m²·m^-2; the PLSR model had the highest accuracy during the tuber growth period, with a modeling R² of 0.73 and an RMSE of 0.26 m²·m^-2, and a validation R² of 0.87 and an RMSE of 0.20 m²·m^-2; and the PLSR model had the highest accuracy during the starch accumulation period, with a modeling R² of 0.73 and an RMSE of 0.26 m²·m^-2; and the PLSR model had the highest accuracy during the starch accumulation period. The PLSR model had the highest estimation accuracy, with modeling R² of 0.73 and RMSE of 0.31 m²·m^-2, and validation R² of 0.84 and RMSE of 0.25 m²·m^-2. This method can provide a reference for the UAV multispectral combination of texture features to estimate potato LAI..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3443 (2024)

Get PDF

View fulltext

Nondestructive Detection of Catalase Activity in Melon Leaves By Fluorescence Hyperspectral Imagery

WANG Jing, MA Ling, MA Si-yan, MA Yan, ZHANG Yi-yang, and WU Long-guo

To achieve timely monitoring of plant growth, rapid detection of differences in the distribution of catalase activity in melon leaves under different light intensities is essential. In this study, melon leaves were treated with different light intensities. Then the leaves were scanned using fluorescence hyperspectral i

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3455 (2024)

Get PDF

View fulltext

Spectral Recognition of Sandalwood Based on Peak and Valley Feature Extraction Technique

ZHUANG Peng-yan, NIU Jia-shun, CHENG Jun, LU Jing-yi, SUN Jian-ping, and HE Tuo

With the rapid growth of the economic level, people’s demand for mahogany products is increasing day by day. As precious mahogany, criminals replace sandalwood and counterfeit it because of its spiritual pursuit and high price. To maintain the order of the timber market and the interests of consumers and realize the ra With the rapid growth of the economic level, people’s demand for mahogany products is increasing day by day. As precious mahogany, criminals replace sandalwood and counterfeit it because of its spiritual pursuit and high price. To maintain the order of the timber market and the interests of consumers and realize the rapid non-destructive detection and identification of sandalwood, it is necessary to establish a fast and reliable intelligent identification method of sandalwood. This paper used near-infrared spectroscopy to extract the spectral information of Pterocarpus santalinus and its similar blood sandalwood. The qualitative analysis method, partial least squares discriminant analysis ( PLS-DA ), and error back propagation artificial neural network ( BPNN ) were used to establish the calibration model of spectral information. Then, Pterocarpus santalinus and its similar wood blood sandalwood were identified. By analyzing and comparing the advantages and disadvantages and recognition accuracy of these three models for these two kinds of wood recognition, the feasibility of this method in the recognition of sandalwood and sandalwood is verified. The experimental results show that the three discriminant models can quickly identify the wood spectral images, and can quickly and non-destructively classify and identify sandalwood and sandalwood. In the case of selecting different image processing methods, the results of the three discriminant models are different, and the recognition results are also different. Further analysis reveals that when the spectral data are modeled using the BPNN model, the peak and trough eigenvalues of the original spectral data within the wavelength range of 866~2 533 nm are utilized as input following preprocessing. It is observed that when the number of input layer nodes is Setto 24 eigenvalues and the hidden layer consists of 13 neurons, the model achieves the smallest root mean square error, with an accuracy rate of 96.43%. Additionally, shortening the spectral range does not result in an improvement in the model’s recognition rate. The BPNN model demonstrates the highest recognition rate across the full band range among the three models. The experimental results indicate that the combination of artificial neural network modeling and near-infrared spectral feature extraction technology yields a high recognition accuracy in identifying sandalwood wood, surpassing trevious methodologies. This study contributes to mitigating the subjectivity inherent in manual recognition processes. The utilization of computers can expedite the recognition process, while the enhanced accuracy aids in maintaining order within the wood market and safeguarding consumer rights. Simultaneously, it offers insights into realizing intelligent visual recognition of sandalwood and furnishes technical support for the sustainable development of the mahogany industry..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3463 (2024)

Get PDF

View fulltext

Silver Nanowire-Gold Plasmonic Nanostars SERS Detection of Dissolved Acetone in Oil

LUO Wen-xuan, and WAN Fu

Oil-paper insulation equipment is one of the key components in power systems, and accurately assessing the aging status of electrical equipment’s oil-paper insulation is crucial for ensuring the safety of power grids. Acetone is an important indicator of the aging process of insulation paper, and the rapid, sensitive,

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3473 (2024)

Get PDF

View fulltext

Research on Effectiveness of the Pre-Training Model in Improving the Performance of Spectral Feature Extraction

REN Ju-xiang, and LIU Zhong-bao

The development of observation technology has led to massive spectral data. How to automatically classify these data has received attention from researchers, the most important of which is feature extraction. Given the limitations of manual processing, most of the research uses machine learning algorithms to extract fe The development of observation technology has led to massive spectral data. How to automatically classify these data has received attention from researchers, the most important of which is feature extraction. Given the limitations of manual processing, most of the research uses machine learning algorithms to extract feature-based spectral data. However, these machine learning algorithms cannot handle massive spectral data due to the high spatial and temporal complexities. The pre-trained models emerging in recent years have excellent feature extraction capabilities. Still, there is little research on the effectiveness of such a model in the feature extraction of spectral data. Therefore, this paper takes the stellar spectral data as the research object separately introduces the pre-training models such as BERT, ALBERT, GTP, and Convolutional Neural Networks (CNN) for feature extraction and classification of the stellar spectral data, and tries to verify the effectiveness of these pre-training models for feature extraction of stellar spectral data by comparing the experimental results. Python programming language is used to write the spectral classification program. Based on the feature extraction of the pre-trained models, the CNN model in TensorFlow 1.14 is utilized for spectral data classification. The dataset used for the experiment is the SDSS DR10 stellar spectral dataset, including K-type, F-type, and G-type. The grid search and 5-fold cross-validation are utilized to obtain the experimental optimal parameters. The BERT model has the highest classification accuracies compared to ALBERT and GPT with the same experimental conditions. In terms of the average classification accuracies, the average classification accuracies of the BERT model are 0.025 1, 0.021 5, and 0.022 5 higher than that of ALBERT, and 0.049 7, 0.042 4, and 0.043 2 higher than that of GPT, on the K-type, F-type, and G-type stellar datasets. It is easy to draw the following conclusions by analyzing the experimental results: Firstly, the classification accuracies improve with the scale increase of training data; Secondly, the same model has the highest classification accuracies on the same training dataset of K-type stellar, followed by the F-type and the G-type; Thirdly, the BERT model has the best ability of feature extraction compared with ALBERT and GPT..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3480 (2024)

Get PDF

View fulltext

Hidden Handwriting Recognition of Calligraphy Artifact Based on Hyperspectral Technology

GAO Yu, SUN Xue-jian, LI Guang-hua, ZHANG Li-fu, QU Liang, and ZHANG Dong-hui

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3485 (2024)

Get PDF

View fulltext

Study on Inversion Method of Anshan-Type Iron Ore Grade Based on Hyperspectral Image

CAO Wang, MAO Ya-chun, WEN Jie, DING Rui-bo, XU Meng-yuan, and FU Yan-hua

Iron ore grade is an important index used to evaluate the degree of wealth and economic value of iron ore, and the verification efficiency of iron ore grade greatly influences the efficiency of iron ore mining. Because of the advantages of hyperspectral images in the fields of substance classification and content inver

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3494 (2024)

Get PDF

View fulltext

Study on Global Spatial Distribution of Surface-Atmosphere Clutter inMid-Infrared Atmospheric Strong Absorption Band

YAO Qian, LI Zheng-qiang, FAN Cheng, XU Hua, WANG Si-heng, and CHEN Zhen-ting

The mid-infrared(MIR) atmospheric strong absorption wavelength bands(near 2.7 and 4.3 m) have the characteristics of suppressing background clutter signals, which are often used on the payload of infrared early warning satellites to achieve stable detection and tracking of targets. The background clutter fluctuation le The mid-infrared(MIR) atmospheric strong absorption wavelength bands(near 2.7 and 4.3 μm) have the characteristics of suppressing background clutter signals, which are often used on the payload of infrared early warning satellites to achieve stable detection and tracking of targets. The background clutter fluctuation level has an important impact on MIR target detection, and it is of great significance to study the spatial domain fluctuation of surface-atmosphere clutter near the 2.7 and 4.3 μm bands. First, the MODTRAN radiative transfer model is used to iteratively simulate the optical depth (OD) of water vapor and CO₂ under six standard atmospheric models, screen the strong absorption bands according to the criterion of OD>1, and intersect the band calculation results under various atmospheric models. The final strong absorption bands were 2.52～2.83 and 4.18～4.47 μm. Then, global monthly averaged surface and atmosphere background products are produced based on remote sensing satellite data products and data assimilation information. Among them, combined with the monthly average surface reflectance/emissivity products (MYD09A1/ MOD11C3) of MODIS shortwave infrared and mid-infrared bands (2.13, 3.75, 3.96 and 4.05 μm), the non-negative matrix factorization (NMF) method was used to reconstruct global surface reflectance/emissivity products at strongly absorbing bands; Produce monthly-averaged global surface temperature products based on ERA5 reanalysis data; The global monthly average products of water vapor and cloud optical thickness (COT) are obtained by splicing and fusing MODIS atmospheric products (MOD05_L2 and MOD06_L2) respectively; The CO₂ global monthly average product comes from the data assimilation product of the OCO-2 satellite; In order to spatially match various data products and save computing resources, global products are limited to the 60° north and south latitude range and resampled to a spatial resolution of 1°×1°; Next, under cloudy and cloud-free conditions, the background clutter intensity of two strong absorption bands was simulated and calculated pixel by pixel, and the spatial distribution pattern of clutter was analyzed. Finally, the 11×11 square window neighborhood statistics method was used to calculate the clutter spatial domain fluctuation in the simulation results, convert the clutter fluctuation under 95% probability, and conduct histogram statistics. From the perspective of clutter suppression, a comparison of infrared target detection performance in two absorption bands is given. Study results show that under cloud conditions, the fluctuation level of background clutter in the 2.52～2.83 μm band has the characteristics of “a point-like peak, regional enhancement, and overall low values”. In contrast, the clutter fluctuation level in the 4.18～4.47 μm band shows the characteristics of “regional high values, patchy enhancement, and low values at high latitudes”. Under cloud-free conditions, the background clutter fluctuation level in the 2.52～2.83 μm band increases significantly in the low water vapor content area. In contrast, the clutter fluctuation level in the 4.18～4.47 μm band is generally low, and most of them are controlled within 2×10^-4 W/m²/sr/μm. On the global scale, the infrared target detection performance is better in the 2.52～2.83 μm band when there are clouds and in the 4.18～4.47 μm band when there are no clouds. The results of this study can provide a reference basis for detecting infrared targets in terms of spatial domain law and spectral band optimization, which is of great value for enhancing the detectability of targets..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3504 (2024)

Get PDF

View fulltext

Hyperspectral Inversion Method for Natural Grassland Canopy SPAD ValueBased on Scaling Up of Green Coverage Rate

ZHANG Ai-wu, LI Meng-nan, SHI Jian-cong, and PANG Hai-yang

Chlorophyll is a crucial indicator for assessing grasslands’ photosynthetic capacity and physiological condition.With its rich spectral information, hyperspectral remote sensing has become an important means for non-invasively estimating chlorophyll content in grasslands. However, there is a scale mismatch between the

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3513 (2024)

Get PDF

View fulltext

Research on the Inverse Model of Paper Viscosity Based on Hyperspectral Data

WANG Sa, QU Liang, ZHANG Li-fu, GAO Yu, LI Guang-hua, and CHANG Jing-jing

Paper-based cultural relics hold crucial significance to the historical and cultural heritage and the continuation of the national spirit in China. At the same time, the aging of paper seriously affects the longevity of artifacts. Paper viscosity is an important indicator reflecting the degree of paper aging. Tradition Paper-based cultural relics hold crucial significance to the historical and cultural heritage and the continuation of the national spirit in China. At the same time, the aging of paper seriously affects the longevity of artifacts. Paper viscosity is an important indicator reflecting the degree of paper aging. Traditional paper viscosity measurement methods are destructive experiments that cause inevitable secondary damage to valuable cultural relics. Hyperspectral remote sensing technology can achieve non-destructive and rapid analysis to address this issue, providing an effective approach to establishing a paper viscosity inversion model. In this study, drying and moist-heat paper aging were taken as the research subjects, and110 groups of simulated paper aging samples with measured viscosity content were obtained. Through spectral filtering, a hyperspectral database of paper aging was established. Based on this, nine data processing methods, including original spectrum first-order derivative, original spectrum second-order derivative, original spectrum reciprocal logarithm, original spectrum reciprocal logarithm first-order derivative, multiplicative scatter correction, multiplicative scatter correction spectrum first-order derivative, multiplicative scatter correction spectrum second-order derivative, multiplicative scatter correction spectrum reciprocal logarithm, and continuous wavelet transform were analyzed, as well as two feature selection methods, competitive adaptative reweighted sampling (CARS) and correlation coefficients (R), were analyzed in combination as input for the models to identify the best model through dataset partitioning validation. Research results have shown: (1) In the original spectrum, the correlation at 430 nm is the highest with viscosity, with an R-value of 0.75. After data processing, the R-value at 430 nm increases to 0.874 following the application ofthe reciprocal logarithm first-order derivative of the original spectrum method, which significantly enhances the paper viscosity information in the spectrum; (2) After the original spectrum is transformed into the second-order derivative, the correlation at 578 nm is the highest, with an R-value of 0.57, significantly lower than the highest R-value (0.75) in the original spectral data. This finding indicates that the second-order derivative is unsuitable for paper viscosity estimation. Meanwhile, the maximum correlation coefficients of other transformation methods are all higher than the original spectrum, demonstrating the effectiveness of spectral transformation; (3) For the original spectrum, as the decomposition scale increases, the paper viscosity information contained in the spectrum decreases, and the highest correlation coefficient occurs at 484nm at the decomposition scale of 2, with a value of 0.873; (4) Among different input combinations, the model with the highest accuracy uses the reciprocal logarithm first-order derivative of the original spectrum as input. Based on the CARS method, the support vector regression (SVR),random forest (RF), and AdaBoost models have R² values of 0.96, 0.93, and 0.93, respectively, and RMSE values of 14.80, 18.33, and 19.79 mL·g^-1 for the validation dataset. We recommend prioritizing using the reciprocal logarithm first-order derivative of the original spectrum as inputs and the SVR model with the CARS feature selection algorithm for paper viscosity inversion. The above research findings demonstrate the applicability of hyperspectral technology for non-destructive analysis of paper viscosity, providing a scientific basis for the restoration work of paper-based cultural relics..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3524 (2024)

Get PDF

View fulltext

A Hyperspectral Recognition Method for Camouflaged Targets Based on Background Dictionary Sparse Representation

XU Jing-yu, BAO Ni-sha, LANG Jie-shuang, LIU Shan-jun, MAO Ya-chun, and HE Li-ming

Referring to the current use of sparse representation algorithms to extract camouflaged targets from hyperspectral images, the selection of the background dictionary is affected by the “same spectrum of different objects” of the hidden targets, resulting in the inability to detect camouflaged targets accurately. In thi Referring to the current use of sparse representation algorithms to extract camouflaged targets from hyperspectral images, the selection of the background dictionary is affected by the “same spectrum of different objects” of the hidden targets, resulting in the inability to detect camouflaged targets accurately. In this paper, we take the grassland camouflage net and desert camouflage net as the research objects, collect the visible-near-infrared reflectance spectra of the camouflage net and airborne hyperspectral images respectively, and analyze the spectral characteristics of the background pixels and camouflage target pixels in the camouflage net and airborne images measured outdoors. Taking advantage of the fact that the spectra of the camouflage nets measured outdoors and the background in the airborne images are different and the possibility of neighboring image elements belonging to the same feature is high, the background dictionary selection method based on the constraints of Euclidean distances and image homogeneity features is proposed, and the sparse representation of the background dictionary is further utilized to identify the target. The results show that (1) in the wavelength range of 750~1 000 nm for grass camouflage, the reflectance of the background pixel spectrum in the image is higher than that of the camouflage net spectrum. For desert camouflage: in the range of 550～700 nm, the reflectance of the background pixel spectrum in the image is higher than that of the camouflage net spectrum. (2) By establishing spatial and spectral feature constraints with the maximum spectral Euclidean distance to the target image element and the highest homogeneity with neighboring image elements, 413 background image elements in the grass camouflage image and 507 background image elements in the desert camouflage image were selected as the background dictionary. (3) Based on the improved background dictionary selection method, the sparse representation algorithm is utilized to identify the camouflage targets, and the results can accurately discriminate the location and number of camouflage targets. The area under the curve (AUC) of the receiver operating characteristics for the detection of grass camouflage targets and desert camouflage targets reaches 0.96 and 0.98, respectively, indicating that the algorithm has good detection performance for both grass camouflage targets and desert camouflage targets..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3534 (2024)

Get PDF

View fulltext

Deep Learning-Based Monitoring of Nutrient Content in Pear Trees

HUANG Lin-feng, JIANG Xue-song, JIA Zhi-cheng, ZHOU Hong-ping, ZHOU Lei, and RONG Zi-fan

To achieve wide-scale, accurate, and timely monitoring of pear nutrients during the growth and fruiting periods of pear trees and to further provide protection and adjustment strategies for pear fertilization management and pear fruit quality improvement, we used the ASD FieldSpec3 hyperspectral spectroscopy system to To achieve wide-scale, accurate, and timely monitoring of pear nutrients during the growth and fruiting periods of pear trees and to further provide protection and adjustment strategies for pear fertilization management and pear fruit quality improvement, we used the ASD FieldSpec3 hyperspectral spectroscopy system to monitor the nutrient content of pear trees. The ASD FieldSpec3 hyperspectrometer was used to obtain leaf hyperspectral data during the fruit expansion and ripening periods of pear trees and to collect information on the leaf nitrogen, phosphorus, and potassium content. The effects of spectral preprocessing methods such as raw reflectance, first-order derivative transformation (FD), convolutional smoothing algorithm (SG), and standard normal variable transformation (SNV) on the fitting effect of the hyperspectral reflectance monitoring model were comparatively analyzed through the raw spectral curves. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and successive projection algorithm (SPA) are then used to select the characteristic bands of hyperspectral data. After that, partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), gradient boosted tree (GBDT), convolutional neural network (CNN), and deep forest (DF) algorithms were used to establish monitoring models based on the edge bands to screen the optimal hyperspectral monitoring models for the three nutrient elements, nitrogen, phosphorus, and potassium, in pear trees. The optimal modeling combination of nitrogen was the DF regression model of PCA eigenbands pre-processed by SG-SNV (R²=0.928 3, RMSE=0.238 1 g·kg^-1). The optimal modeling combination of phosphorus was the GBDT regression model of SPA eigenbands pre-processed by SG-SNV (R²=0.936 7, RMSE=0.043 1 g·kg^-1). The best modeling combination for potassium was the DF regression model (R²=0.954 4, RMSE=0.276 7 g·kg^-1) for the SG-SNV preprocessed PCA feature band. The monitoring model based on hyperspectral edge bands was well fitted (R²>0.9), which can realize the accurate monitoring of nitrogen, phosphorus, and potassium content in pear fruit during expansion and ripening..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3543 (2024)

Get PDF

View fulltext

FTIR Quantitative Analysis of Evolution and Interaction of Plastic Layer in Coking Process

TIE Wei-bo, WANG Qi, GAN Xiu-shi, WANG Xu, HUANG Jun-chen, YANG Song-tao, and ZHANG Song

The plastic layer is an important intermediate phase in the coking process. Its structure evolution and the interference between different structures in the coal blending process are of great significance for understanding coal’s properties and the blending’s interaction mechanism. This paper used self-made coking-rela The plastic layer is an important intermediate phase in the coking process. Its structure evolution and the interference between different structures in the coal blending process are of great significance for understanding coal’s properties and the blending’s interaction mechanism. This paper used self-made coking-related properties measuring device to conduct coking experiments on different blend ratios. The evolution products of the plastic layer were obtained by the interrupted cooling method, including the softening zone (SFZ), melting zone (MPBZ), flow zone (MZ), and resolidification zone (RSZ). FTIR determined the raw materials that were obtained. FTIR was divided into four bands of 3 600～3 000, 3 000～2 800, 1 800～1 000 and 900～700 cm^-1 for peak fitting analysis. I-aromatization, DOC-polycondensation degree, —CH₂— aliphatic structure, A’-hydrocarbon generation capacity, and C-oxygen-containing functional group were used to explore the changes in the coking process and the interaction among the same evolution products of blends with different mass ratios. The results show that I and DOC increased gradually in the special temperature field, and the changes were particularly significant from MZ to RSZ. With the release of volatiles and tar, the aliphatic group —CH₂ and A’ as a whole showed a downward trend. The content of C fluctuates slightly in MPBZ and MZ, but the overall trend is also downward. The condensation of aromatic carbon structure induces the decomposition of aliphatic structure and oxygen-containing functional groups. MZ to RSZ in the plastic layer is an important part of coking process. The I₁ and DOC of the same evolution products of mixed coals with different mass ratios have good additive properties in SFZ, MPBZ, and MZ, with the degree of fitting R² reaching 0.744, 0.71, 0.775 and 0.74, 0.266, 0.773 respectively. The interaction of other structures is influenced by many factors of the process of pyrolysis and bonding, so these do not have additivity. Therefore, the aromatic carbon structure at the resolidification zone and the lack of additivity of the aliphatic structure and heteroatoms are important factors affecting the different properties of coke..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3553 (2024)

Get PDF

View fulltext

Anisotropic and Heterogeneity of Coal Measures Graphite Micro-Crystalline Structure by Raman Spectroscopy

LI Huan-tong, ZOU Xiao-yan, XIA Yan, and ZHANG Wei-guo

Coal is very sensitive to geological environmental conditions such as temperature and pressure. Driven by tectonic stress, coal’s “graphite crystallite” structure grows in orientation, and the physical chemistry and structure show anisotropy. X-ray diffraction and Raman spectroscopy characterized the samples with diffe Coal is very sensitive to geological environmental conditions such as temperature and pressure. Driven by tectonic stress, coal’s “graphite crystallite” structure grows in orientation, and the physical chemistry and structure show anisotropy. X-ray diffraction and Raman spectroscopy characterized the samples with different degrees of graphitization. The results show that the coal seam slides along the layer under high temperatures and shear stress. The structural deformation makes the graphite crystallites rotate and preferentially oriented, increasing the stacking height along the c-axis direction. The anthracite stage is divided by the stacking degree L_c≤5 nm, which is a turbulent layer structure with random orientation or irregular arrangement of carbon layers, optically isotropic; when L_c≥30 nm, it is regarded as a sign of the formation of a perfect graphite structure, and the optical anisotropy is significant. It belongs to the transition state structure (semi-graphite stage) with an imperfect graphitization structure between 10~20 nm. The 1 350 cm^-1 band (D₁) and 1 620 cm^-1 band (D₂) in the Raman spectra of SXL100 and SXL130 samples in the graphite stage are obvious. Still, the full width at half maximum of the D₁ to G peak (I_D1/I_G, R₁), and the intensity ratio of D₂ to G peak (I_D2/I_G, R₃) in the Raman spectra of the graphite edge plane are significantly higher than those of the preferred orientation plane, indicating that the Raman parameters such as intensity ratio depend on the orientation of the edge plane of coal-based graphite. The intensity of the D₁ peak depends on the degree of defect or disorder of the sample. The D₂ peak of the edge plane has asymmetric characteristics, and the bimodal structure is significant. The D′₁ peak changes with the D₂ peak, which also shows the spectral behavior of the edge plane defect. The stages of anthracite (R₁≥1.0), semi-graphite (1.0>R₁≥0.5), and graphite (R₁<0.5) were divided by the defect density or order degree index R₁ of coal measures graphite, and the uniformity of coal measures graphite and the proportion and distribution of components with different graphitization degrees were evaluated. It was found that the proportion of advanced evolution to semi-graphite structure in the metamorphic anthracite CM130N sample was 3.52%, the proportion of anthracite structure in the semi-graphite BC210 sample was 46.40%, and the SXL130 sample was graphitized as a whole. However, the proportion of anthracite structure was still 3.84%, and there were still defects in the preferred orientation plane and edge plane structure. The established method has more advantages in distinguishing anthracite and semi-graphite. When the incident direction of the Raman laser is constant, R₁ and R₃ parameters can be used to explore the Raman spectral characteristics of coal measures graphite preferred orientation plane affected by tectonic stress and to evaluate the heterogeneity of graphitization and the orientation of graphite crystallites..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3560 (2024)

Get PDF

View fulltext

Characterization of the Sources and Composition of Dissolved Organic Matter in the High Occurrence Area of Algal Blooms in Northern Lake Taihu

HAN Zi-yi, ZHOU Yong-qiang, and GENG Chun-nü

With the rapid socioeconomic development in theLake Taihu basin, a large amount of industrial, agricultural, and domestic wastewater has been discharged into Lake Taihu, and nutrients have been enriched, especially in the northern lake regions of Lake Taihu, where algal blooms occur frequently. In the spring, summer, a With the rapid socioeconomic development in theLake Taihu basin, a large amount of industrial, agricultural, and domestic wastewater has been discharged into Lake Taihu, and nutrients have been enriched, especially in the northern lake regions of Lake Taihu, where algal blooms occur frequently. In the spring, summer, and fall of 2015, we conducted six field sampling campaigns at 24 sites in northern Lake Taihu’s high-incidence area of algal blooms. Absorbance coupled with three-dimensional fluorescence and parallel factor analysis (3DEEMs-PARAFAC), together with water quality parameters, were used to investigate the spatial and temporal variability of DOM and water quality in Lake Taihu under the influence of algal bloom to provide scientific support for the management and restoration of the Lake Taihu ecosystem and ensure the safety of its water supply. During the sampling period (from May 12 to October 20), the three lake regions experienced significant algal bloom and degradation. The algal blooms were closely related to the input of exogenous nutrients. During the summer, the rapid growth of algal biomass significantly consumed

N O 3 - - N

generated

N O 4 + - N

and inhibited the denitrification process of microorganisms. Parallel factor analysis identified four fluorescent DOM components: two protein-like components (tryptophan-like C1 and tyrosine-like C3) and two humic substances (microbial humic-like C2 and terrestrial humic-like C4).Terrestrial inputs from the northwest significantly affected the composition and structure of DOM in northern Lake Taihu, showing a trend of decreasing abundance and terrestrial aromaticity from west to east. In addition, the large amount of terrestrial DOM also provided an important carbon source for the growth of planktonic algae, which further influenced the composition and structure of DOM and led to the increased participation of autochthonous DOM (C2 and C3) with strong microbial activity in the lake carbon cycling process. There were differences among the three northern algal areas, and the algal bloom management in these three lakes needs to be adapted to local conditions; exogenous inputs should be reduced in the northwestern part of the lake, algal bloom monitoring and ecological restoration should be strengthened in Meiliang Bay, and ecological diversity should be preserved and restored in the northeastern part of the lake to enhance the natural purification capacity..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3568 (2024)

Get PDF

View fulltext

Determination of Two Quinolone Antibiotics in Environmental Water Samples Using Fluorescence Spectrum Coupled With Support Vector Machine Regression

WANG Yi-fei, WANG Xiao-dong, Zakhar Maletskyi, WANG Sha-sha, and MA Ji-ping

Quinolone antibiotics (QNs) are extensively utilized in treating diseases and animal husbandry owing to their potent antimicrobial properties. However, the excessive utilization of QNs and their release into environmental water sources via wastewater results in their accumulation in natural aquatic ecosystems. The cons Quinolone antibiotics (QNs) are extensively utilized in treating diseases and animal husbandry owing to their potent antimicrobial properties. However, the excessive utilization of QNs and their release into environmental water sources via wastewater results in their accumulation in natural aquatic ecosystems. The consequence of this phenomenon is the extensive propagation of bacteria that are resistant to antibiotics, as well as the proliferation of resistance genes in aquatic ecosystems. This poses a significant risk to both environmental ecology and human well-being. Traditional techniques employed in detecting QNs exhibit notable sensitivity and accuracy. However, these methods are characterized by their time-consuming nature, reliance on costly equipment, and the inherent difficulty of conducting on-site assessments. Fluorescence analysis technology necessitates a brief detection time, particularly in the case of three-dimensional fluorescence spectroscopy, which enables the acquisition of a substantial amount of target information within a limited timeframe. By integrating data statistics and machine learning models, mathematical methods can efficiently identify multiple QNs. In this study, the fluorescence spectroscopic information of QNs was extensively employed, and the support vector machine regression (SVMR) algorithm was utilized to develop prediction models for QNs, specifically ofloxacin (OFL) and norfloxacin (NOR). The fluorescence spectroscopic data of the unknown samples was subsequently fed into the developed models to obtain the determination outcomes efficiently. While constructing the model, a comparison was made between two supervised learning methods, i.e., PLS-DA and SVMR. It has been determined that SVMR exhibits a strong predictive capability. By manipulating parameters and kernel functions, it was possible to achieve a good linear range for determining OFL and NOR, spanning from 2 to 600 μg·L^-1. The resulting linear correlation coefficients exceeded 0.992 0, and the detection limits were 0.064～0.080 μg·L^-1. The validated method was proven applicable to real water samples, i.e., the recoveries were 98.62%～104.01% for seawater and 103.90%～105.89% for reservoir water. This method offers the advantage of rapid detection speed, allowing for the completion of quantitative analysis of an unknown sample within a mere 3 min. This rapid screening process identifies potential risk factors associated with QNs in the environment. This study employs a novel approach by integrating SVMR with fluorescence spectroscopy to develop a rapid detection method for QNs in real water samples. The proposed method offers a new and scientifically reliable solution for rapidly detecting QNs in environmental water..

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 3576 (2024)

Get PDF

View fulltext

[in Chinese]

Spectroscopy and Spectral Analysis

Publication Date: Jan. 16, 2025
Vol. 44, Issue 12, 1 (2024)

Get PDF

View fulltext