Researching | GPXYGPFX Volume 44 Issue 7,2024

MA Huan-zhen, YAN Xin-ru, XIN Ying-jian, FANG Pei-pei..., WANG Hong-peng, WANG Yi-an, DUAN Ming-kang, JIA Jian-jun, HE Ji-ye and WAN Xiong|Show fewer author(s)

Blood is a regulated exceptional genetic biological resource. In response to the issue of easy oxidation and deterioration in traditional blood spectral detection, dynamic confocal Raman fluorescence spectroscopy technology based on biomimetic blood vessels was used to conduct blood species identification research on s

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1877 (2024)

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Microscopic Spectral Characteristics and Redox Properties of Nanosized Ce1-4x(FeAlCoLa)xO2-δ Solid Solutions

SUN Shi-long, ZHANG Guo-fang, SHU Jun, GUO Rui-hua..., LI Yi-ming, LIU Zhuo-cheng, XU Jian-yi and GE Qi-lu|Show fewer author(s)

Hydrothermal method was used to synthesize nanosized Fe3+, Al3+, Co2+, and La3+ co-doped Ce1-4x(FeAlCoLa)xO2-δ(x=0.00~0.05) solid solutions. The solid solutions microstructure,morphology,spectral characteristics,and redox activities were systematically characterized and analyzed by XRD, TEM, SEM, UV, PL, Raman, and te Hydrothermal method was used to synthesize nanosized Fe3+, Al3+, Co2+, and La3+ co-doped Ce1-4x(FeAlCoLa)xO2-δ(x=0.00~0.05) solid solutions. The solid solutions microstructure,morphology,spectral characteristics,and redox activities were systematically characterized and analyzed by XRD, TEM, SEM, UV, PL, Raman, and temperature-programmed reduction (TPR) with H2. XRD results showed the Ce1-4x(FeAlCoLa)xO2-δ solid solutionsexhibited the CeO2 cubic fluorite structure. A tiny diffraction peak corresponding to the Co3O4 impurity phase at 36.6° was observed when the doped content reached x=0.04, indicating that x=0.04 was the solid solubility of doped ions in the CeO2 lattice. The positions of the (111) diffraction peaks were shifted towards a higher angle, which proved the doped ionsinduced the distortion of the lattice. The TEM and SEM images showed the samples were spherical with high crystallinity, and doping caused lattice contraction. The UV absorption spectra revealed that the doped samples absorption edges were gradually red-shifted compared to pure CeO2. Extra absorption peaks corresponding to the doped ions were found in the region of 560~780 nm. The band gap energies decreased from 2.84 eV (pure CeO2) to 2.1 eV (x=0.05). The reasoncould be that the doped ions formed new impurity energy levels between the valence and conduction bands, which allowed the electrons to transition from the valence band to the lower impurity energy levels and then lowered the band gap energies. In addition, the distortion of the lattice and increased concentration of oxygen vacancies prevented the electrons from transferring to higher energies, which can also result in the reduction of band gap energies. PL spectra showed that doping significantly reduced the emission peak intensities. Raman spectra demonstrated that the dopingresulted in the shift of the F2g peak, the decrease of peak intensities, and the widening of peaks. Meanwhile, the relative intensities of the peak corresponding to the oxygen vacancies were also observed to be enhanced. Thus, both the PL and Raman spectra proved that doping increased the degree of lattice distortion and the concentration of oxygen vacancies. The H2-TPR test results showed that doping can effectively reduce the redox reaction temperatures and improve the redox activities. The sample doped with x=0.03 possess the lowest surface reduction temperature and the largest peak areas, which meansthis sample exhibited the best redox activities. It can be concluded that the redox performances of the samples were closely related to the grain sizes, lattice defects, and oxygen vacancy concentrations. This study showed that the four ions co-doped with CeO2 could effectively modify the microstructure and improve the samples catalytic activities at a low doping concentration..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1883 (2024)

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Analysis and Research on the Pigments of Mural Painting Layer in the SanHuang Temple

YAN Hai-hong, ZHANG Xin-rui, ZHANG Qun-xi, SUN Feng, and DONG Wen-qiang

SanHuang Temple is located in Xian City,Shaanxi Province,and houses three ancient emperors:Fuxi,Shennong,and Huangdi. The walls were originally painted with exquisite murals. It is the product of integrating ancient Chinese traditional medicine and Confucianism,which has great art and academic research value. However,

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1889 (2024)

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Research on Almond Plumpness Detection Method Based on Terahertz Imaging Technology and Feature Extraction Method

HU Jun, L Hao-hao, QIAO Peng, HE Yong, and LIU Yan-de

As a kind of nutrient-rich nut,it is of great economic value and practical significance to test the quality of almonds. Because of the almond hard shell,it is difficult for traditional detection methods to realize internal detection.In this paper,the emerging terahertz transmission imaging detection technology is used As a kind of nutrient-rich nut,it is of great economic value and practical significance to test the quality of almonds. Because of the almond hard shell,it is difficult for traditional detection methods to realize internal detection.In this paper,the emerging terahertz transmission imaging detection technology is used to study almond plumpness detection. Firstly,the terahertz spectral images of almonds with different fullness are acquired. Secondly,the terahertz spectra of sample free region,empty shell region and full almond region are extracted,respectively. To improve the accuracy of the model and reduce the computational effort,Competitive Adaptive Reweighting Sampling (CARS),Uninformative Variable Elimination (UVE),Successive Projections Algorithm (SPA),Monte Carlo Uninformative Variable Elimination (MCUVE) and Genetic Algorithm (GA) for feature extraction of terahertz spectral information. The corresponding Least squares support vector machine (LS-SVM),Random forest (RF) and K-nearest neighbor (KNN) qualitative discriminant models are established to detect and identify the full and empty regions of almonds.In addition,the terahertz feature image was to jpg format and then to RGB format,the shell image and kernel image were separated by G-channel extraction and image binarization,and the ratio of shell kernel pixels in the terahertz feature image was detected. The image of shell and kernel were separated by contour extraction and image binarization. The actual plumpness was the ratio of shell kernel pixels in the original image. The terahertz transmission imaging techniques feasibility for detecting the almonds plumpness was proved by calculating the error between the detection plumpness and the actual plumpness. The established KS-GA-RF model had the best identification effect,with an accuracy of 98.21%. According to the ratio of shell and kernel pixels,the corresponding detection and actual fullness were calculated,respectively,with an error of 16%. This study verified that combining terahertz graph and spectrum could well realize the visual detection of inner kernel plumpness of P. chinensis,providing a new idea for the accurate classification of almonds. It also delivers a theoretical reference for terahertz imaging to detect the plumpness of other nuts and has significant application value..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1896 (2024)

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Prediction of Soluble Solid Contents in Apples Using Vis-NIRS and Functional Linear Regression Model

HUANG Hua, LIU Ya, MA Yi-hang, XIANG Si-han..., HE Jia-ning, WANG Shi-ting and GUO Jun-xian|Show fewer author(s)

Soluble solid contents (SSC) are an important indicator of apple quality and maturation and can be used for quality analysis and ripeness prediction. In this paper, 552 samples of Sugar Core Red Fuji apples from Aksu of Xinjiang Province were picked at equal intervals of three days from the fruit swelling and setting s Soluble solid contents (SSC) are an important indicator of apple quality and maturation and can be used for quality analysis and ripeness prediction. In this paper, 552 samples of Sugar Core Red Fuji apples from Aksu of Xinjiang Province were picked at equal intervals of three days from the fruit swelling and setting stage to the complete mature stage, the visible near-infrared spectroscopy (vis-NIRS) of the samples at 380 to 1 110 nm were collected respectively, and the SSC were measured. Then, the collected discrete data of vis-NIRS were transformed into spectral curves using the basis function smoothing method, i. e., function-type data, and respectively, the functional linear regression model was established with Vis-NIRS curves, first-order derivative curves, and second-order derivative curves as functional explanatory variables and SSC as scalar response variables. To confirm and analyze the performance of the model, partial least squares regression (PLSR), kernel support vector machine (KSVM), random forest (RF), gradient boosting tree (GBM) and deep neural network (DeepNN) were established by the original spectral discrete data after moving smooth, first-order derivative and second-order derivative pre-processing. The results show that among the 18 models, for the training set, the PLSR-dNIRmodel, KSVM-dNIR model, RF-dNIR model, GBM-dNIR model, and Deep NN-d2NIR model were outperformed the FunLR-NIR model, FunLR-dNIR model and FunLR-d2NIR model, and the Deep NN-d2NIR model was optimal (rc=0.999 6, R2c=0.998 6, RMSEC=0.074 0, RPDC=27.436 6). For the test set, the FunLR-NIR model, FunLR-dNIR model, and FunLR-d2NIR model outperformed all other models, and the FunLR-NIR model was optimal (rv=0.953 4, R2v=0.907, RMSEV=0.585 6, RPDV=3.301 7). The results of the training sets and test sets show that the kernel support vector machine model, random forest model, gradient boosting tree model, and deep neural network model are prone to overfitting. In contrast, the functional linear regression model has better generalizability. Besides, the prediction results of the three functional linear regression models (FunLR-NIR model, FunLR-dNIR model, and FunLR-d2NIR model) showed that all the models have good robustness and high prediction accuracy. The experimental results showed that the functional linear regression models combined with vis-NIR spectroscopy and functional data analysis could successfully and effectively achieve the prediction of soluble solid contents of apples at the ripening stage..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1905 (2024)

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Design,Synthesis and Performance of Fluorescent Probe for Detection of Hg2+ With 1,3-Oxathiolane as Receptor

LIANG Qing-xiang, ZHOU Wu, WU Ai-bin, SHU Wen-ming, and YU Wei-chu

Hg2+ is one of the most toxic heavy metal ions,which can cause air,soil,and water pollution,seriously damaging human health. Therefore,developing effective analytical methods to detect Hg2+ in environmental systems is particularly important. Fluorescent probes have been widely used to detect Hg2+ due to their advantage

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1913 (2024)

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Hyperspectral Imaging Technology Combined With Ensemble Learning for Nitrogen Detection in Dendrobium nobile

KUANG Run, LONG Teng, LIU Hai-lin, WU Ji-hui..., L Jin-sheng, XIE Zi-ran, LIU Wen-tao, LAN Yu-bin, LONG Yong-bing, WANG Zai-hua and ZHAO Jing|Show fewer author(s)

Dendrobium nobile leaf blades nitrogen content is a decisive factor for precise fertilization. Traditional nitrogen content detection methods are time-consuming and can completely deplete samples. Efficiently detecting the nitrogen content of D.nobile leaf blades has become a growing concern for herbal medicine cultiv Dendrobium nobile leaf blades nitrogen content is a decisive factor for precise fertilization. Traditional nitrogen content detection methods are time-consuming and can completely deplete samples. Efficiently detecting the nitrogen content of D.nobile leaf blades has become a growing concern for herbal medicine cultivation enterprises. To quickly and non-destructively obtain the nitrogen content of D.nobile leaf blades,this study used fresh D.nobile leaf blades as experimental samples. After obtaining their hyperspectral images in the range of 402.6~1 005.5 nm and nitrogen chemical detection values,the images underwent the extraction of regions of interest(ROI),followed by preprocessing of the spectral information within those regions learner algorithms including Partial Least-Squares Regression (PLSR),Kernel Ridge Regression (KRR),and Support Vector Regression (SVR),as well as ensemble learning algorithms including Random Forest (RF),Bagging,and Adaboost,were utilized to model the nitrogen content of D.nobile. Regression prediction models were constructed based on the full-band spectral information of fresh D. nobile leaf blades and feature bands of spectral information extracted through CARS,and the prediction accuracy was compared. The results showed that when constructing the monitoring model based on the full-band spectral information,the RF model built with spectral data preprocessed by the Savitzky-Golay filtering （SG） method had the best prediction result (R2CV=0.961 4, RMSECV=0.081 8, R2P=0.972 6, RMSEP=0.063 3),and all models achieved R2 values over 0.90. When constructing the regression prediction model based on feature bands extracted through CARS,the Bagging model had the highest accuracy and stability,with the best prediction result observed in the SG-CARS-Bagging model (R2CV=0.938 7, RMSECV=0.100 0, R2P=0.953 5, RMSEP=0.082 6),while the accuracy of the individual learner models KRR and SVR was significantly lower. The CARS algorithm feature extraction removed some important bands,improving modeling efficiency but reducing model accuracy. Therefore,when optimizing the feature parameters of the regression model,it is necessary always to consider the balance between accuracy and efficiency. The research results indicate that ensemble algorithms such as RF,Bagging,and Adaboost have higher stability and prediction accuracy than individual learner algorithms such as PLSR,KRR,and SVR. They are more suitable for analyzing and processing hyperspectral data and have obvious advantages in the nitrogen nutrition monitoring of D.nobile..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1918 (2024)

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Effects of Vacuum Heat Treatment on Morphology and Structure of Larch Wood Cellulose

SUN Bai-ling, WANG Xiao-qing, and CHAI Yu-bo

In this study,larch wood,the main timber species in Northeast China,was improved by vacuum heat treatment to explore its cellulose morphology and structural thermal response mechanism. The wet chemical method was used to extract the cellulose of untreated and heat-treated larch wood under different vacuum heat treatmen

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1928 (2024)

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Preparation and Spectroscopic Properties of Zn3(As1-xPx)2 Nanostructures

WANG Hao, SUN Nai-kun, PANG Chao, WANG Zhi-shuai..., CHEN Shang-feng, LI Wu, TIAN Hui and DAI Qin|Show fewer author(s)

Zn3As2 and Zn3P2 have the same pseudo-cubic lattice structure and present a wide range of application prospects in the field of optoelectronic devices because of their high electron mobility,narrow direct band gaps,and good air stability. At present,there is relatively little research on the nanostructure of Zn3As2-Zn3 Zn3As2 and Zn3P2 have the same pseudo-cubic lattice structure and present a wide range of application prospects in the field of optoelectronic devices because of their high electron mobility,narrow direct band gaps,and good air stability. At present,there is relatively little research on the nanostructure of Zn3As2-Zn3P2 solid solution,and Zn3(As1-xPx)2 (x=0, 0.05, 0.1) master alloys were obtained by high-pressure sintering technology,and then a variety of Zn3(As1-xPx)2 nanostructures are synthesized by chemical vapor deposition,including macro-sized nanoribbons (Length 3~10 mm; Width 1~4 mm; Thickness ~20 μm),nano sails,nanorods and nano silver hairpins. The effect of P doping on phase composition,element content,microstructure,and spectral characteristics was systematically investigated. XRD results showed that the main phase of Zn3(As1-xPx)2 macroscopic nanoribbon samples was α′ phase. With the increase of P doping contents,the (224) diffraction peak shifted to the right,indicating a decrease in the lattice constant. Electron spectroscopy analysis showed that the actual content of P in these nanoribbons corresponding to x=0.05 and x=0.1 Zn3(As1-xPx)2 master alloys was x=0.026 and x=0.062,respectively. The microstructure analysis showed that the growth mode of Zn3As2 macroscopic nanoribbons was along the 〈221〉 crystal face rhombus-shaped layer-like growth and that P doping led to a reduction in the macroscopic size of the nanoribbons,accompanied by growth mode change from rhombus-shaped layered growth to nanoparticle stacked layered growth. Raman spectra of the nanoribbon samples showed characteristic peaks at 79, 97, 198, 320, 428 and 1 107 cm-1. P doping led to a blue shift of 1 107 cm-1 characteristic peaks in Raman spectra,and 1 101 and 1 599 cm-1 characteristic peaks in Fourier infrared spectroscopy (FTIR),and 300,422,and 635 nm characteristic peaks in PL spectra were also blue-shifted. The linear relationship between photocurrent and voltage of Zn3As2 and Zn3(As0.974P0.026)2 nanoribbons indicate good ohmic characteristics,and the photoresponse of Zn3(As0.974P0.026)2 nanoribbons after P doping shows the highest sensitivity under 900 nm conditions..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1934 (2024)

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Quantitative Analysis of Coal Properties Using Laser-Induced Breakdown Spectroscopy and Semi-Supervised Learning

WANG An, CUI Jia-cheng, SONG Wei-ran, HOU Zong-yu..., CHEN Xiang and CHEN Fei|Show fewer author(s)

Laser-induced breakdown spectroscopy (LIBS) is an emerging atomic spectroscopy technique that has the advantages of low sample pre-treatment and rapid, in situ, and simultaneous multi-element measurements. LIBS demonstrates good prospects in the field of coal analysis. In recent years, chemometric and machine learning Laser-induced breakdown spectroscopy (LIBS) is an emerging atomic spectroscopy technique that has the advantages of low sample pre-treatment and rapid, in situ, and simultaneous multi-element measurements. LIBS demonstrates good prospects in the field of coal analysis. In recent years, chemometric and machine learning models have been widely used to improve the quantitative accuracy of LIBS in coal analysis. Generally, these models rely on a certain number of training samples to ensure the reliability of the prediction results. However, obtaining the certified content (label information) of coal samples used for model training requires traditional chemical analysis, which is complex and time-consuming. This may lead to insufficient training samples and poor model performance. To tackle the small sample problem in LIBS-based coal analysis, this work proposes a semi-supervised learning method based on the ensemble of multiple models. 5 baseline models are first established based on the initial training set, including multiple linear regression (MLR), partial least squares regression (PLSR), locally weighted partial least squares regression (LW-PLSR), support vector regression (SVR), and kernel extreme learning machine (K-ELM). The unlabelled data are processed using the 5 models, and 5 prediction values are obtained. For each unlabelled sample, the standard deviation of the 5 prediction values is calculated, and the unlabelled sample corresponding to the smallest standard deviation is added to the training set. Its pseudo label is the average of the 5 prediction values. As the training set is iteratively expanded, its corresponding training model is updated. The final training model is optimized and used to analyse the test samples. The proposed method is tested on a coal dataset containing 20 training samples, 39 test samples and 280 unlabelled samples. The results show that the proposed method improves the coefficient of determination (R2) for content prediction of fixed carbon, ash，and volatile by 0.033,0.102 and 0.118, respectively. Therefore, if the number of training samples is insufficient, semi-supervised learning can effectively improve the accuracy and reliability of LIBS quantification..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1940 (2024)

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Study on the Interaction and Stability of Mixed Proteinaceous Binders in Polychrome Relics

LI Yu-heng, YANG Lu, and GE Ruo-chen

Animal glue,eggs,and milk were often mixed in ancient China as cementitious materials in painted cultural relics. To explore the interaction of mixed binders and the stability under light aging conditions,to understand the deterioration mechanism of polychrome relics,the stability and protein secondary structure of egg Animal glue,eggs,and milk were often mixed in ancient China as cementitious materials in painted cultural relics. To explore the interaction of mixed binders and the stability under light aging conditions,to understand the deterioration mechanism of polychrome relics,the stability and protein secondary structure of egg-animal glue,milk-animal glue,and milk-egg mixed binders were characterized by Scanning electron microscopy,FTIR and TG analysis. The results showed that after mixing,the above binders thermal stability and light stability were significantly increased. The infrared spectrum results show that spectral peak broadening after aging of the sample,and the amide Ⅰ band of the protein characteristic spectrum with a wavenumber range between 1 600~1 700 cm-1 shifts towards the wavenumber direction. The Gaussian fitting results of the amide Ⅰ band show that with the change of mixing ratio,the content of ordered structure in the mixed binders shows a trend of first increasing and then decreasing. In the samples with the highest ordered structure content,the random curl structure content also reaches a lower level. At the same time,when the mixing ratio of egg and animal glue is between 1∶1 and 1∶2,the content of the ordered structure is higher than any single binders,with a maximum content of 64.87%,indicating its strongest stability. The content of the secondary structure of protein in the aged binders has changed. The α-helix structure shows a downward trend,while the random curl structure shows a significant increase. The α-helix structure in several mixed binders is still higher than that of a single binder material,indicating that the mixed binders have a certain degree of stability. The results of Thermogravimetric analysis also showed that the thermal stability of the mixed binders before and after aging was higher than that of the single binders,presumably because the protein in the mixed system combined with the lipid in eggs and milk and the interaction between protein and lipid further promoted the stretching and rearrangement of protein molecular structure,which improved the stability. From the stability perspective,the reason for the mixed-use of adhesive materials in painted cultural relics was explained,providing a reference for studying ancient adhesive materials aging mechanism and protection methods..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1946 (2024)

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Study on Dynamic Causes of Cracks in Ancient Architectural Murals by Passive Infrared Thermography

HE Yuan, and LIU Cheng

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1952 (2024)

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The “Unknown” Spectral Classification Study of LAMOST: ODS-YOLOv7 Model

WANG Xiao-min, GAO Jun-ping, PU Yuan, QIU Bo..., ZHANG Jian-nan, YAN Jing and LI Rong|Show fewer author(s)

Identifying celestial spectra is essential for making new astronomical discoveries and conducting detailed studies of celestial objects. The LAMOST DR8 v1.0 release of low-resolution spectral data contains approximately 530 000 spectra named “Unknown”. The reason is that they have no category labels. And 88.56% of thes Identifying celestial spectra is essential for making new astronomical discoveries and conducting detailed studies of celestial objects. The LAMOST DR8 v1.0 release of low-resolution spectral data contains approximately 530 000 spectra named “Unknown”. The reason is that they have no category labels. And 88.56% of these spectra have signal-to-noise ratios between 0 and 10. Therefore,the effective output of LAMOST will increase if we analyze these spectra. In this paper,we propose an ODS-YOLOv7 model to deal with the problem of the “Unknown” spectral classification. It is an end-to-end category prediction model and is suitable for one-dimensional spectra. We also add a one-dimensional convolutional attention module to improve the accuracy of spectra recognition. After training on a set of known category spectra with signal-to-noise ratios between 0 and 10, the ODS-YOLOv7 model can learn the effective features of the low signal-to-noise spectra. Thus,it can enable us to predict “Unknown” spectra. Experiments show that the model has an F1-score of 0.98,0.95,and 0.95 for the spectral identification of low signal-to-noise stars,galaxies,and quasars spectra with known labels. In the meantime,ODS-YOLOv7 obtains the best results in comparison experiments with traditional algorithms KNN,RF,DT,SVM,and deep learning algorithms 1D CNN,1DSSCNN,ResNet,DenseNet,and VIT. The experimental results also give confidence in the predictions of the ODS-YOLOv7 model for the “Unknown” spectra in DR8 v1.0,with 92% of the confidence levels above 60%. To ensure the quality of the model output,only spectral categories with a prediction confidence level greater than 99% are selected as output in this paper. Ultimately,37.19% and 47.03% of the “Unknown” spectra released in DR8 v1.0 and DR9 v0,respectively,are predicted by this model.In addition,the paper tests the accuracy of the models predictions using manual authentication. To improve the interpretability of the model,the paper takes the Grad-CAM method for two-dimensional image visualisation. It improves it into an algorithm suitable for visualising one-dimensional spectral data to predict output features. Experiments show that the model focuses on different features in the visualisation of different classes of astronomical features and that the model is good at predicting low signal-to-noise “unknown” spectral classes..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1960 (2024)

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TDSC-Net: A Two-Dimensional Stellar Spectra Classification Model Based on Attention Mechanism and Feature Fusion

LI Rong, CAO Guan-long, PU Yuan, QIU Bo..., WANG Xiao-min, YAN Jing and WANG Kun|Show fewer author(s)

Traditional one-dimensional spectra perform poorly when classifying celestial objects with a low signal-to-noise ratio (SNR). Therefore,the paper uses two-dimensional spectra and proposes a feature fusion model called TDSC-Net (Two-Dimensional Spectra Classification Network),incorporating an attention mechanism for ste Traditional one-dimensional spectra perform poorly when classifying celestial objects with a low signal-to-noise ratio (SNR). Therefore,the paper uses two-dimensional spectra and proposes a feature fusion model called TDSC-Net (Two-Dimensional Spectra Classification Network),incorporating an attention mechanism for stellar classification. TDSC-Net employs identical feature extraction layers to get features from the two-dimensional spectra of stars,specifically from the blue and red ends. The extracted features are fused and employed for the classification task. The stellar spectral data in this experiment is selected from the LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) database. They are using Z-score normalization on the spectra to reduce convergence difficulties caused by significant variations in spectral flux values and evaluating the model performance by four metrics:Precision,Recall,F1-score,and Accuracy. The experiments consist of two parts:In the first part,TDSC-Net is employed to classify A,F,G,K,and M-type stars to validate the reliability of using two-dimensional spectra for multi-class stellar classification. In the second part,the two-dimensional spectra are classified based on different SNRs to investigate the impact of SNRs on classification accuracy. The first parts results show that the five-class classification accuracy reaches 84.3%. The classification accuracies of A,F,G,K,and M types are 87.0%,84.6%,81.2%,87.4%,and 89.7%,respectively. These accuracies are higher than the results obtained from one-dimensional spectra classification after spectra extraction. The results of the second part indicate that even in the low SNR (SNR<30),the accuracy of two-dimensional spectra classification can still reach 78.9%. Once the SNR surpasses 30,the impact of SNR on spectra classification becomes less significant. These provide evidence for the importance of using two-dimensional spectra classification in low SNR and demonstrate the effectiveness of TDSC-Net in stellar spectra classification..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1968 (2024)

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Micro X-Ray Fluorescence Technology Reveals Macrofossil Bones and Surrounding Matrix Element Characteristics——A Case Study of the Middle Triassic Mixosaurus panxianensis

WANG Yi-nuo, FU Wan-lu, ZHOU Min, LU Hao..., SUN Zuo-yu, YAO Ming-tao and JIANG Da-yong|Show fewer author(s)

We apply the Micro-XRF,which can be used to in situ non-destructively study the element distribution of the skeleton and surrounding matrix in vertebrate fossils to scan the holotype and paratype of the Middle Triassicmarine reptile Mixosauruspanxianensis (~244 Ma),visualizing the overall element distribution of the sp

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1974 (2024)

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Establishment of High-Throughput Model of Peanut Protein Components and Subunits by Near-Infrared Spectroscopy

CUI Hao-fan, LIU Hong-zhi, GUO Qin, GU Feng-ying..., ZHANG Yu and WANG Qiang|Show fewer author(s)

Peanut is a high-quality plant protein resource. The content of peanut protein components and subunits significantly affects its functional characteristics and determines its application range in the food field. Peanut proteins mainly include arachin and arachin. Among them, arachin contains four subunits (40.5, 37.5, Peanut is a high-quality plant protein resource. The content of peanut protein components and subunits significantly affects its functional characteristics and determines its application range in the food field. Peanut proteins mainly include arachin and arachin. Among them, arachin contains four subunits (40.5, 37.5, 35.5, 23.5 kDa), arachin I contains three subunits (15.5, 17, 18 kDa), and arachin II contains only one 61 kDa subunit. To realize the rapid, non-destructive, high-throughput and high-sensitivity detection of peanut proteins main components and subunits, 145 high-quality peanut samples in China were used as the research object. Firstly, the portable near-infrared peanut quality rapid tester was used to collect the spectra of different peanut samples in the wavelength range of 900~1 700 nm. Then,the peanut protein components and subunits were determined by polyacrylamide gel electrophoresis. The subunit content of arachin was between 44.3% and 67.3%. The content of arachin subunits was between 35.2% and 55.7%. The 61 kDa subunit content ranged from 13.5% to 25.3%. The content of 40.5 kDa subunit was between 6.8% and 16.0%. The content of 37.5 kDa subunit was between 6.9% and 17.4%. The content of 35.5 kDa subunit was between 5.7% and 19.2%. The 23.5 kDa subunit content was between 18.7% and 27.4%. The content of the 18 kDa subunit was between 5.9% and 11.7%. The content of the 17 kDa subunit was between 6.9% and 13.6%. The content of the 15.5 kDa subunit was 4.5%~11.9%. The near-infrared spectral models of two protein components and two subunits (arachin, arachin, 37.5, 23.5 kDa) were optimized by comparing seven spectral pretreatment methods, including normalization, first derivative (FD) and second derivative (SD), baseline calibration, detrend, multiple scattering corrections (MSC) and data element resolution (Deresolve), combined with principal component analysis (PCA) and partial least squares (PLSR). The near-infrared spectroscopy models of six subunits (61, 40.5, 35.5, 18, 17, 15.5 kDa) were constructed, and the simultaneous detection of the above 10 indicators was realized. The results showed that the calibration sets correlation coefficient (Rcal) 0.90～0.96, and the corrected root mean square error (SEC) was 0.25%～1.27%. The prediction sets correlation coefficient (Rcp) was 0.76～0.96, and the root mean square error of prediction (SEP) was 0.50%～1.81%. It has good predictive ability and can be used for rapid detection of protein components and subunit content in peanut varieties, which provides a new method for fast evaluation of peanut protein quality..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1982 (2024)

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Optimization of Polished Rice Varieties Discrimination Based on Near Infrared Spectroscopy

YANG Sen, WANG Zhen-min, SONG Wen-long, XING Jian, and DAI Jing-min

Due to the enormous market value of geographically iconic,adulteration or fraud often occurs. Therefore,to ensure the brand benefits from geographically iconic rice and consumer rights,it is important to identify polished rice varieties accurately. Near-infrared spectroscopy is a common method to distinguish polished r Due to the enormous market value of geographically iconic,adulteration or fraud often occurs. Therefore,to ensure the brand benefits from geographically iconic rice and consumer rights,it is important to identify polished rice varieties accurately. Near-infrared spectroscopy is a common method to distinguish polished rice varieties. The varieties can be classified by extracting the different features of different types in near-infrared spectroscopy. However,there are some problems in the existing studies,such as insufficient characteristic wavelength selection performance and insufficient discrimination accuracy for specific varieties,which limit the improvement of the discrimination accuracy of polished rice varieties based on the near-infrared spectroscopy method. In response to the above problems,this paper studies the optimisation of milled rice variety identification based on near-infrared spectroscopy from the two aspects of characteristic wavelength selection and variety identification strategies for four types of rice,Wuchang,Xiangshui,Koshihikari,and Yinshui in Northeast China. First,permutation entropy (PE) and adaptive sliding window (ASW) were combined to improve the feature wavelength selection performance. An adaptive sliding permutation entropy (ASW-PE) based method for selecting the characteristic wavelength of polished rice spectrum was proposed and compared with the traditional algorithm. Secondly,a discriminant strategy based on the discriminant objective was proposed to improve the discriminant accuracy of different specified varieties. By studying the matching optimisation of the spectral preprocessing algorithm and classification modelling algorithm,a discriminant process of “specified cultivation-selected model-selected algorithm” was established. Experimental results show that using the adaptive sliding permutation entropy algorithm proposed in this article to select characteristic wavelengths can reduce the milled rice variety discrimination error by at least 50% compared with the traditional algorithm; using the milled rice variety judge strategy based on the discrimination target proposed in this article. Compared with the conventional judge strategy based on fixed models,the discrimination accuracy can be improved by at least 2.5%..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1988 (2024)

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Qualitative and Quantitative Analyses of Cooked Donkey Meat Adulteration Based on NIR Spectroscopy

NIU Xiao-ying, MU Xiao-qing, SUN Jie, ZHAO Zhi-lei, and ZHANG Chun-jiang

Donkey meat has excellent flavor and rich nutrition and is in high price and low supply. The problem of cooked donkey meat adulterated with other meat,such as horse and mule meat,needs to be solved urgently. To realize the qualitative and quantitative analysis of cooked donkey meat samples of different adulteration rat Donkey meat has excellent flavor and rich nutrition and is in high price and low supply. The problem of cooked donkey meat adulterated with other meat,such as horse and mule meat,needs to be solved urgently. To realize the qualitative and quantitative analysis of cooked donkey meat samples of different adulteration ratios,horse and mule meat samples were used to degrade donkey meat. The gradient was 10%,and the donkey meat contents were 0%~100%. Spectra of samples were collected in the range of 4 000~12 500 cm-1. The methods of linear discriminant analysis,support vector machine,and generalized regression neural network combined with smoothing algorithm (5 points,15 points,25 points),multiplicative scattering correction (MSC),standard normal variable (SNV),Baseline correction,normalization,and Detrend were used to establish the NIR discriminant models of adulterated cooked donkey meat samples. Partial least squares regression (PLSR) and backpropagation (BP) were used to establish quantitative models to determine the content of donkey meat in adulterated samples. For minced after cooked meat samples,the results of SNV pretreatment combined with a support vector machine were optimal,and the discriminant accuracy of the calibration set and prediction set was 98.70% and 94.78%. The results of Detrend pretreatment combined with linear discriminant analysis were optimal for minced before cooked meat samples. The discriminant accuracy of the calibration and prediction sets reached 98.47% and 96.23%,respectively. Compared with the PLSR model,the BP model obtained better results,with a higher coefficient of determination (R2),relative percent deviation (RPD),and lower root mean square error (RMSE). For the adulterated samples of minced after cooked meat samples,the BP model of the donkey and mule adulterated samples was better after Detrend pretreatment. R2,RMSE,and RPD of the cross-validation set and prediction set were 0.971,0.067,5.844,0.980,0.086,6.984,respectively. After normalized treatment,the results of BP model of donkey and horse adulterated samples were optimal,and the parameters were 0.997,0.032,18.026,0.982,0.089,7.454,respectively. For the adulterated samples of minced before cooked meat samples,the results of the BP model with Detrend pretreatment were better,and the optimal quantitative model parameters of donkey and mule adulterated samples were 0.982,0.041,7.470,0.986,0.103,8.452,respectively. The best model parameters of donkey and horse adulteration were 0.986,0.036,8.348,0.961,0.101,and 5.044,respectively. The results show that the NIR spectroscopy combined with different modeling algorithms can realize the rapid,nondestructive detection of different donkey meat contents. The methodology can be used for future qualitative and quantitative analysis of cooked donkey meat adulteration..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1993 (2024)

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Correlation Analysis Between the Size and Spectral Characteristics of Titanium Alloy Arc Additive Deposition Layer Based on ReliefF Algorithm

XIAO Xiao, WANG Xue-qing, ZHANG Chi, GE Xue-yuan, and LI Fang

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2002 (2024)

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Research on Chromatic Aberration Correction Method for High-Resolution Roland Circle Optical System

CHEN Ji-wen, and CHEN Zuo-er

The Roland circle optical system has many advantages. Still,due to using a planoconvex lens in the system,the imaging points that have undergone dispersion also generate a certain amount of offset distance near the Roland circle,resulting in the imaging points of each characteristic wavelength arranged sequentially on The Roland circle optical system has many advantages. Still,due to using a planoconvex lens in the system,the imaging points that have undergone dispersion also generate a certain amount of offset distance near the Roland circle,resulting in the imaging points of each characteristic wavelength arranged sequentially on a curved surface. Due to the current use of new collection devices such as linear array CMOS or CCD,where the photosensitive area of these sensors is a flat surface,serious chromatic aberration is caused in the results. This article proposes a chromatic aberration correction method using the weighted least squares method to fit the image plane and calculate the two correction parameters of the center offset distance and offset tilt angle of the system image plane. Based on the original Roland circle optical system,these two correction parameters are introduced for chromatic aberration correction,thereby determining the optimal position of the linear array photoelectric sensor image plane and achieving high resolution. In addition,elements such as C,P,and S are very important in the VUV spectrum. The chromatic aberration correction method based on weighted least squares proposed in this article can achieve optimal resolution by increasing the weight proportion of characteristic wavelengths of such elements. Finally,optical simulation was conducted using Zemax software to complete the simulation and optimization of the Roland circle optical system. The imaging width simulation of spot diagrams in the 170~410 nm wavelength range and the spectral images of each elements characteristic wavelengths collected on CMOS sensors were provided. The resolution in practical applications was calculated,and the illuminance spectral images showed that their resolution could be maintained at 0.02 nm,close to the optimal theoretical resolution of the system. The research results of this article indicate that using the weighted least squares method to fit the image plane can achieve chromatic aberration correction for Roland circle optical systems and achieve high resolution..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2011 (2024)

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Novel System Development for Film Thickness Measurement of Oil on Water

HUANG Wei, SUN Hao, LIU Zhi-yuan, WANG Kun..., SU Ming-xu and YANG Hui-nan|Show fewer author(s)

Oil slick pollution seriously influences the ecological environment. Many countries have invested a lot of money and workforce in investigating oil slick on water. Thickness measurement of oil film on water with high precision is important for the prevention and treatment of oil slicks. Here,diesel was taken as the res Oil slick pollution seriously influences the ecological environment. Many countries have invested a lot of money and workforce in investigating oil slick on water. Thickness measurement of oil film on water with high precision is important for the prevention and treatment of oil slicks. Here,diesel was taken as the research object. Fourier Transform Infrared Spectrometer obtained theabsorption spectra of diesel at different temperatures (293/298/303/308/313/318 K) and water at room temperature (298 K). It was found that diesel and water were absorbed in the near-infrared region (6 000.0~11 000.0 cm-1),and the absorption spectra of diesel did not shift with temperatures. The wavenumberν1 (8 381.6 cm-1) with the maximum absorption coefficient of diesel was selected to establish the inversion model of oil film thickness on water based on a single-wavelength. ν1 and the wavenumber ν2 (8 918.7 cm-1) with an absorption coefficient around zero were selected to establish an inversion model of oil film thickness on water based on dual-wavelength. Furthermore,the absorption coefficients of water at these two wavenumbers were very small and had little influence on the measurement results. A calibration tool with known oil film thicknesses (0~1 000 μm) was employed to validate the measurement precision of the inversion models. It was found that the average relative deviations between the film thicknesses obtained by the two models and the known values were 36.4% and 2.5%,respectively. The maximum relative deviations were 44.7% and 3.7%,respectively. The maximum standard deviations were 7.0 and 5.6 μm,respectively. It revealed that the dual-wavelength model was superior to the single-wavelength model. On this basis,a novel film thickness measurement system for oil on water with dual-wavelength absorption spectroscopy was developed. The temporal resolution of the developed system was 0.03 s. The variations of oil film thicknesses after dropping a certain volume (1 mL) of diesel on the water surface were investigated,and the ultrasonic pulse echo-method was simultaneously employed to compare with the developed system. Each drop of diesel was measured 10 times by the two methods. A total of 20 groups of oil film thickness data (16~35 mL) were measured,and the average values of the results measured by the two methods were compared. It showed that the average relative deviation of the oil film thicknesses measured by the two methods was 2.5%. Moreover,the maximum relative deviation was 3.7%. The maximum standard deviation of oil film thicknesses obtained by 10 repeated measurements of dual-wavelength absorption spectroscopy was 6.4 μm. In summary,the measurements of the thickness of oil film on water could be achieved by the developed high-precision system. It had the advantages of interference-free,fast-response,compact-structures,etc.,and could be applied to different types of oil slick measurement. The developed system was expected to provide new ideas and scientific guidance for the monitoring,preventing and treating oil spills..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2018 (2024)

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Quantitative Study of Borax Filler in Heat-Treated Rubies

XIANG Zi-han, YIN Zuo-wei, ZHANG Zhi-qing, and WANG Wen-wei

The heat treatment of ruby belongs to the optimization treatment,and borax is commonly used as the flux. In the market,heat-treated rubies are sold naturally and have a high value,but the amount of borax in the crack will affect the price. Therefore,in this paper,the borax in this kind of ruby was quantitatively studie

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2024 (2024)

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SATS: A Stellar Spectral Classification Algorithm Based on Multiple Feature Extraction

TU Liang-ping, LI Shuang-chuan, TU Dong-xin, LI Jian-xi, and DING Zhi-chao

An in-depth study of a stellars spectrum provides insight into its chemical composition and physical properties. Stellar spectrum classification is an important direction in stellar spectrum research. With the emergence of massive stellar spectrum data,artificial classification cannot meet scientific research needs. B

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2029 (2024)

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Study on the Multi-Parameter Detection Method With Variable Optical Path Length for Water Quality Based on Ultrasound and Micro-Nano Bubble

LI Wen, LI De-jian, MA Yong-yue, TIAN Wang..., CHEN Yin-yin, WANG Li-min, L He, LI Jie and LUO Zi-yun|Show fewer author(s)

A new method for multi-parameter water quality detection with variable optical range has been proposed, addressing scientific and technical challenges in the current national standard analytical methods. This method utilized ultrasound and micro-nano bubble (US-MNB), continuous spectrum analysis, and sequential injecti A new method for multi-parameter water quality detection with variable optical range has been proposed, addressing scientific and technical challenges in the current national standard analytical methods. This method utilized ultrasound and micro-nano bubble (US-MNB), continuous spectrum analysis, and sequential injection analysis (SIA). A water quality multi-parameter detection system was designed and validated for the analysis of total phosphorus (TP), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and hexavalent chromium (Cr6+). The feasibility of the new method was verified. The systems core consists of a digestion chamber integrating ultrasound and micro-nano bubbles and a detection chamber with an adjustable optical range. This structural design enabled rapid digestion and stable detection. The multi-parameter detection process was optimized based on national water quality testing standards. Spectrophotometry and sequential injection analysis techniques were employed for continuous spectral detection of the four water quality parameters. Firstly, the TP was digested by a US-MNB with strong oxidants at normal temperature and pressure. At the same time, the complexes were directly determined by spectral scanning after the color reaction of NH3-N in the detection chamber, and thenthe TP was determined after digesting. Similarly, the compounds in the detection chamber after the chromogenic reaction of the Cr6+ were directly determined by spectral scanning. At the same time, the COD was digested, and then the COD was measured after digestion. The time used for the whole detection process was greatly reduced, and the determination of multi-parameter of water quality can be completed automatically in a short time, significantly improving the efficiency of detection. In this system, four water quality parameters(TP, COD, NH3-N, and Cr6+)were measured, combined with the least squares method to establish a regression model, fit the regression equation and calculate the correlation coefficient, and plot the concentration-absorbance standard working curve of each parameter. The results showed that the TP standard working curve had a fitting coefficient ≥0.984 5, with a positive correlation between concentration and absorbance. The repeatability (RSD) ranged from 3.05% to 3.62%, and the spike recovery rate was 97.8% to 103.6%. The COD standard working curve had a fitting coefficient ≥0.998 7, with a negative correlation between concentration and absorbance. The repeatability (RSD) ranged from 2.12% to 2.74%, and the spike recovery rate was 98.7% to 104.7%. The NH3-N standard working curve had a fitting coefficient ≥0.995 3, with a positive correlation between concentration and absorbance. The repeatability (RSD) ranged from 3.41% to 3.59%, and the spike recovery rate was 99.2% to 102.4%. The Cr6+ standard working curve had a fitting coefficient ≥0.993 8, with a positive correlation between concentration and absorbance. The repeatability (RSD) ranged from 3.51% to 3.92%, and the spike recovery rate was 98.9% to 109.3%. The system accurately determined the content of TP, COD, NH3-N, and Cr6+ in water samples and demonstrated excellent stability and reliability. The studyon the multi-parameter detection method with variable optical range in water quality based on US-MNB is of great significance in broadening the application of spectroscopy in the field of rapid detection of water quality multi-parameters and improving detection efficiency..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2037 (2024)

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Estimation and Correction Method of Long-Term Radiometric Degradation of FY-3D/MERSI-Ⅱ Reflective Solar Bands

WANG Ling, HU Xiu-qing, ZHANG Bei, XU Han-lie..., HE Xing-wei, QI Cheng-li, ZHANG Xiao-han, SI Yi-dan, XU Na and CHEN Lin|Show fewer author(s)

The estimation and correction of long-term radiometric degradation in remote sensing instruments are crucial for improving the stability and accuracy of satellite remote sensing applications. This study used long-term observation data of FY-3D/MERSI-Ⅱ over stable targets in deserts and DCC (Deep Convective Clouds) to e

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2045 (2024)

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Target Detection Algorithm for Land-Based Hyperspectral Images Associated With Geospatial Data

ZHAO Jia-le, WANG Guang-long, ZHOU Bing, YING Jia-ju..., LIU Jie, LIN Chao, CHEN Qi and ZHAO Run-ze|Show fewer author(s)

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2056 (2024)

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Retrieval Model for Water Quality Parameters of Miyun Reservoir Based on UAV Hyperspectral Remote Sensing Data and Deep Neural Network Algorithm

QIAO Zhi, JIANG Qun-ou, L Ke-xin, and GAO Feng

With the rapid development of industrialization and social economy,water pollution and deterioration of water sources are increasingly aggravated,and effective water quality monitoring is an important prerequisite for water source protection. Miyun Reservoir is an important surface water source in Beijing,which plays a With the rapid development of industrialization and social economy,water pollution and deterioration of water sources are increasingly aggravated,and effective water quality monitoring is an important prerequisite for water source protection. Miyun Reservoir is an important surface water source in Beijing,which plays an important role in protecting water safety in the capital. In order to monitor the water quality parameters and pollution degree of Miyun Reservoir more accurately,this study used four phases of UAV hyperspectral remote sensing data to construct a water quality parameter retrieval model based on a deep neural network algorithm. Total nitrogen (TN) and total phosphorus (TP) water quality parameters in Miyun Reservoir were retrieved. Firstly,the hyperspectral image dimensionality reduction processing based on the recursive feature elimination method was used,and the spectral data and groundwater quality monitoring data were superimposed. The network structure parameters,such as the number of hidden layers and the number of ganglion points,were determined by minimizing the error in the training process. Then,the migration method gradually expanded the network from knowledge source domain to network,and the water quality parameters of TN and TP concentration in Miyun reservoir were trained and verified. Finally,the water quality parameters of Chaohe Dam and Baihe Dam in Miyun Reservoir were retrieved to reveal the spatio-temporal evolution of the main water quality parameters. The results show that ① the R2 of the TN and TP concentration retrieval models constructed in this study are 0.835 5 and 0.770 3,and the MSE is 0.015 3 and 0.000 8. The Ensemble Deep Belief Network (EDBN) model based on random subspace has a better retrieval effect on water quality parameters. ②TN concentration in Miyun Reservoir fluctuates with seasons,with a low concentration in summer and a relatively high concentration in autumn. The change in TP concentration is relatively stable,indicating that the control effect of phosphorus pollution in the surrounding area of Miyun Reservoir is good.③The water quality of the Baihe Dam was better than that of the Chaohe Dam. The seasons obviously affected the changes of the former,while the latter was significantly affected by human activities. The TN concentration of Miyun reservoir was in Class III,and the TP was generally in Class II. The water quality can meet the standards of drinking water sources,but it is still necessary to strengthen the supervision of nitrogen and phosphorus pollution. These results will provide an important scientific basis for efficiently monitoring water quality and water resources protection in the Miyun reservoir..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2066 (2024)

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Visible Near-Infrared Reflection Spectrum Characteristics and Angular Effects of Sea Ice Contaminated by Ship Oil Spills

XU Jian-kang, LIU Bing-xin, DU Yu-long, LI Ying..., LIU Peng and CHEN Peng|Show fewer author(s)

With the opening of the Arctic shipping route,the number of vessels traveling to and from polar ice regions has been increasing yearly,leading to an increased risk of oil spills in the ice zone. Difficulties in cleanup and long-lasting pollution characterize oil spills in icy areas. Therefore,the development of fast an With the opening of the Arctic shipping route,the number of vessels traveling to and from polar ice regions has been increasing yearly,leading to an increased risk of oil spills in the ice zone. Difficulties in cleanup and long-lasting pollution characterize oil spills in icy areas. Therefore,the development of fast and accurate monitoring methods has become an important approach to improving cleanup efficiency and reducing pollution hazards. Remote sensing technology has been widely applied in monitoring oil spills in open waters,but there is relatively less research on monitoring oil spills in ice-covered seas. In particular,there are few reports on the reflection spectral characteristics of oil-contaminated sea ice and their variations with viewing angles.In this study,through simulated experiments of oil spills on sea ice,visible-near-infrared reflection spectra of oil-contaminated sea ice were measured at different observation zenith angles and relative azimuth angles. Measurements were taken at intervals of 10° for zenith angles ranging from -50° to 50°,and relative azimuth angles included 0°,90°,180°,and 270°. Analyzing the spectral standard deviation before and after ice pollution,the wavelength of 560 nm with the most significantdifference was selected as the characteristic wavelength for identifying oil-contaminated sea ice. This study constructed a kernel-driven model to explore the relationship between the reflectance difference of the characteristic wavelength and the geometric variations of observations. This Ross Thick-Roujean-r-RPV model considered the forward scattering characteristics of sea ice. The model was tested using measured data,and the fitting errors in the principal plane and vertical principal plane were 0.004 62 and 0.004 16,respectively,showing better fitting performance than commonly used kernel-driven models such as Ross Thick-Li sparse,Ross Thick-Li sparse R,Ross Thick (QU)-Roujean,and Ross Thick (QU)-Li sparse R-r-RPV. Using this model,the study further simulated the angular effects of the reflectance difference in the characteristic wavelength of sea ice before and after oil pollution under different observation geometries. The results showed that under the same observation geometry,there were differences in the reflectance spectra of sea ice before and after pollution,with polluted sea ice having lower reflectance than clean sea ice. Additionally,clean sea ice peaked in the wavelength range of 1 013~1 196 nm,disappearing after pollution. When the azimuth angles of observation were different,there were also differences in the reflectance of sea ice,characterized by an increase in the forward direction with increasing observation angles and a decrease in the backward direction with increasing observation angles. In the principal plane direction,the reflectance increased initially and then decreased with increasing observation angles. The largest spectral difference was observed at a zenith angle of 50° and a relative azimuth angle range of 250°~290°,which is most favorable for extracting oil spills in sea ice. The research findings of this study can provide a reference for the selection of bands and observation geometries for monitoring sensors of oil spills in icy regions for ships..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2075 (2024)

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Exploring the Capability of Airborne Hyperspectral LiDAR Based on Radiative Transfer Models for Detecting the Vertical Distribution of Vegetation Biochemical Components

HAO Yi-shuo, NIU Yi-fang, WANG Li, and BI Kai-yi

In natural environments,vegetation communities often demonstrate vertical structure due to competition and natural selection among plant groups and between populations and their environment. Different growth stages of the same plant also exhibit various structural and biochemical parameters in the vertical dimension. D In natural environments,vegetation communities often demonstrate vertical structure due to competition and natural selection among plant groups and between populations and their environment. Different growth stages of the same plant also exhibit various structural and biochemical parameters in the vertical dimension. Detecting these three-dimensional spatial distribution features enables quantitative assessment of ecological environments in three dimensions,crucial for estimating forest carbon reserves and biodiversity conservation. Traditional passive hyperspectral remote sensing and lidar techniques face significant limitations in vertical vegetation profiling. However,the emergence of Hyperspectral LiDAR (HSL),a new type of sensing instrument,offers a fresh approach to studying the vertical distribution of physiological and biochemical parameters in vegetation. Yet,due to hardware constraints,the suitability of HSL under unmanned aerial platforms for complex three-dimensional forest scenes remains insufficiently explored. This paper begins with laboratory experiments using a prototype HSL to conduct small-scale indoor measurements with torch flower plants as the target to verify its integrated capability in extracting spatial and spectral information. Subsequently,the three-dimensional radiative transfer model LESS was used to simulate forest scenes with vertical heterogeneity. The model simulated airborne HSL devices for extracting hyperspectral three-dimensional point clouds of forests. Vegetation indices and a random forest model are utilized to invert chlorophyll and carotenoid concentrations in forest canopy layers. The results show that in indoor experiments,the HSL echo information effectively discriminates the spectral differences at different heights of plant structures. The NDVI values in the upper red leaf area and lower green leaf area of torch flowers are respectively less than and greater than 0.5. The LESS model successfully constructed high-resolution hyperspectral three-dimensional point clouds of forest scenes. Out of 24 groups of vegetation indices,17 groups exhibit good detection accuracy (MAPE<13%). The chlorophyll concentration inversion model demonstrates an R2 of 0.93 with MAE values of 6.26,3.40,and 2.81 for the upper,middle,and lower layers,respectively. The carotenoid concentration inversion model shows an R2 of 0.91 with MAE values of 1.59,2.58,and 0.39 for the upper,middle,and lower layers,respectively. This study indicates that HSL is an effective device for extracting vegetation spectral information in three dimensions and possesses immense potential for investigating the vertical distribution of biochemical components in complex vegetation scenes such as forests when deployed on aerial platforms..

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2083 (2024)

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Effect of Soil Particle Size on Fluorescence Characteristics of Petroleum Hydrocarbons and Correction Methods

YANG Jin-qiang, YANG Rui-fang, ZHAO Nan-jing, YIN Gao-fang..., FANG Li, SHI Gao-yong, LIU Liang-chen, HUANG Peng and LIU Wen-qing|Show fewer author(s)

Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 2093 (2024)

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Spectroscopy and Spectral Analysis

Publication Date: Aug. 28, 2024
Vol. 44, Issue 7, 1 (2024)

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