30 December 2025, Volume 37 Issue 4
    

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  • Research Progress on Freeform Surface Optical See-Through Head-Mounted Display Systems
    SONG Rui, LIU Hongli
    Chinese Journal of Light Scattering. 2025, 37(4): 515-526. https://doi.org/doi:10.13883/j.issn1004-5929.202504001
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    As augmented reality (AR) head-mounted displays rapidly advance, freeform optical systems have become one of the main solutions in optical design. This paper first analyzes the key issues and challenges faced by optical see-through head-mounted display systems, focusing on the balance between parameters such as field of view, exit pupil diameter, angular resolution, and system size, as well as the challenges posed by vergence-accommodation conflict. Solutions are proposed to optimize these parameters and enhance the user experience. The article then summarizes several structural types of freeform optical see-through head-mounted displays and recent research developments, providing an in-depth analysis of the advantages and disadvantages of various approaches. Finally, it explores trends in the field, particularly in achieving virtual reality interaction and addressing the vergence-accommodation conflict. The paper highlights the potential of technologies such as multi-focal displays, gesture recognition, and adaptive optics, noting that these innovations will further drive the application of freeform optical see-through head-mounted displays in both augmented and virtual reality. This work lays the theoretical foundation and research direction for designing more efficient, lightweight, and user-friendly head-mounted display optical systems.
  • A Review of the Application of Compton Backscattering in Wall Defect Detection Based on Compton Backscattering
    CHEN Siyu, YANG Jianbo, SUN Wanyi, HUANG Xin, ZHANG Jiang, MO Site
    Chinese Journal of Light Scattering. 2025, 37(4): 527-533. https://doi.org/doi:10.13883/j.issn1004-5929.202504002
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    Compton backscattering imaging is an important technique in the ray detection method. By detecting the Compton scattering photons emitted by the irradiated object, the irradiated object is imaged to obtain the defect information. This paper will review the Compton backscattering detection method, introduce the basic principle of Compton backscattering, and then discuss the research status of Compton backscattering detection technology in wall defects at home and abroad. Finally, the future development prospects of Compton backscattering imaging detection method are summarized and prospected.
  • A Review of Underwater Image De-blurring Methods Based on Polarization Imaging
    CAI Yaxin, XU Jing, ZHU Jiayue, FANG Ming, SONG Yansong
    Chinese Journal of Light Scattering. 2025, 37(4): 534-548. https://doi.org/doi:10.13883/j.issn1004-5929.202504003
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    Clear underwater imaging can effectively enhance the ability of visual underwater operations. Affected by backscattering, underwater imaging is prone to image blurring. The main reason is that small particles in the water scatter light beyond the reflection of the real target into the reflected light path, resulting in a decrease in image quality. Due to the effective utilization of polarization technology in underwater image backscattering and target reflection polarization characteristics, combined with underwater imaging models to achieve image clarity, polarization technology has been developed as a branch of underwater image deblurring in recent years. This article mainly introduces the basic principle, development path, and research status at home and abroad of underwater image clarity algorithms based on polarization technology, and analyzes and prospects its development trend.
  • Advances in the Application of Surface-Enhanced Raman Spectroscopy in Drug Detection
    YANG Liang
    Chinese Journal of Light Scattering. 2025, 37(4): 549-559. https://doi.org/doi:10.13883/j.issn1004-5929.202504004
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    In recent years, there has been a steady increase in new drug substitutes and a greater variety of drug disguises, which has posed a challenge to the investigation and handling work of anti-drug departments. In the face of so many types of drugs and the increasing trend of new drugs, the establishment of rapid and effective detection methods is of great significance for effectively combating drug crimes and carrying out anti-drug rehabilitation work. Surface-enhanced Raman Spectroscopy (SERS) is a very promising technique for trace analysis, and many research results have been achieved in the field of drug detection. In this paper, the concept and characteristics of surface-enhanced Raman spectroscopy and its application in drug detection are summarized. This paper focuses on the analysis of SERS methods for drug detection in simple systems such as drug powders and solutions, and SERS detection methods developed in samples containing complex matrices such as saliva, urine, blood, hair and sewage. Finally, an outlook on the development trend of SERS in drug detection is made, with a view to providing a reference for the in-depth application of this technology in the field of anti-drugs.
  • Research Progress of Hyperspectral Combined with Chemometrics in the Analysis of Document Materials
    LIU Yingxia, MO Yan, JIA Shuoguo
    Chinese Journal of Light Scattering. 2025, 37(4): 560-570. https://doi.org/doi:10.13883/j.issn1004-5929.202504005
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    In recent years, hyperspectral analysis technology combined with chemometrics has been widely used in the field of forensic science, which has achieved accurate identification and analysis of physical evidence. This study briefly introduces the concept of hyperspectral imaging technology and commonly used chemometrics methods, compares their applicability, and focuses on the research and application of hyperspectral imaging combined with chemometrics in the inspection of document materials. The problems existing in the research are prospected in order to promote the further research and application of hyperspectral imaging technology in document inspection.
  • Data Summary and Application Analysis of Raman Spectroscopy of Pigments in Painted Cultural Relics
    XUE Weishan, SUN Feng, , , MA Zijun, DU Yue
    Chinese Journal of Light Scattering. 2025, 37(4): 571-581. https://doi.org/doi:10.13883/j.issn1004-5929.202504006
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    Painted cultural relics reflect the philosophy and aesthetics of the ancients. They have strong artistic value and research value, with rich historical information contained therein can be obtained through scientific and technological methods. Raman spectroscopy offers several advantages, including non-destructive analysis, accuracy, sensitivity, speed, and sensitive, fast and simple. It is very suitable for the analysis and detection of pigments in painted cultural relics. This work introduces the fundamental principles and evolution of Raman spectroscopy technology. Basing on research group’s comprehensive pigment analysis of painted cultural relics, a Raman spectral database of common pigments has been established to facilitate rapid preliminary identification. This study demonstrates various applications of this technique in heritage conservation, including investigation of discoloration mechanisms, determination of pigment properties and provenance, and reconstruction of painting techniques. Advanced applications are also be summarized such as quantitative analysis and spatial resolution imaging. Spatial resolution imaging enables both surface examination and subsurface analysis to support research on pigment degradation and complex multi-layered artworks. The findings provide valuable technical references for the scientific study of cultural heritage materials. Besides, for in-situ detection of immovable cultural relics and the analysis of organic dyes and other practical difficulties, breakthroughs can also be realized with the enhancement of Raman spectroscopy technology. In fact, Raman spectroscopy technology is also used in the analysis and detection of other types of cultural relics, and plays a very potential and irreplaceable role in the analysis, research and protection of cultural relics.
  • Research on Optical Probe System for Thermal Runaway Gasdetection of Lithium-ion Batteries Based on Raman Spectroscopy
    CHEN Da, ZHENG Xiao, GUO Xiang , PEI Linlin
    Chinese Journal of Light Scattering. 2025, 37(4): 582-592. https://doi.org/doi:10.13883/j.issn1004-5929.202504007
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    Due to their high density, long life and low self-discharge rate, lithium-ion batteries are becoming more and more widely used and have become an indispensable energy choice in the aviation field. However, the risk of thermal runaway poses a significant challenge to aviation safety. Raman spectroscopy provides a method for the safety assessment of lithium ions due to its non-destructive and non-contact characteristics. In this paper, a Raman probe system that can be used in gas detection system is optimized, and Zemax is used to design the optical path, and the results show that the radius of the system point diagram is less than 19 μm, and the numerical aperture of the scattered light collection optical path reaches 0.36, which can effectively collect Raman scattered light. The simulation results show that the stray light PST of the improved optical system reaches less than 10-6, which effectively suppresses the stray light. A Raman library was established with standard gas as a sample, and the designed probe was tested, and the experimental data showed that the performance of the probe optical system was good. The real-time monitoring of the thermal runaway gas of lithium-ion batteries can accurately identify the Raman spectral characteristics before and after thermal runaway, which verifies the effectiveness of the Raman probe design and provides an effective means for the detection of thermal runaway gas in lithium-ion batteries.
  • Multi-point Focusing of Scattering Light Field under Weight Control
    NIU Yuhao, GAO Zhan, FU Zhongqiu, ZHANG Zhenhua
    Chinese Journal of Light Scattering. 2025, 37(4): 593-598. https://doi.org/doi:10.13883/j.issn1004-5929.202504008
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    In order to realize focusing light at multiple points and flexibly control the spot intensity of each point after passing through the disordered medium, we delve into algorithm associated with wavefront shaping technique in this paper. Firstly, we introduced the fundamental principle of the traditional wavefront shaping algorithm for achieving focusing of the scattering light field based on the transmission process of coherent light waves within disordered media. Then, we illustrate the challenges of uneven spot brightness distribution and uncontrollable intensity inherent in the traditional approach by analyzing the focusing of scattered light fields at two-point positions as a case study. Subsequently, we proposed a multi-point focusing strategy for the scattering light field based on weight control. This strategy optimizes the phase modulation scheme of the scattering sub-waves by introducing a normalization process and a weights coefficient methodology, thereby enabling flexible control of the spot intensity at the focal point. Finally, the performance of the traditional algorithm and the weight control algorithm in multipoint focusing is compared by simulation experiments. The experimental results demonstrate that, in comparison with the conventional wavefront shaping algorithm, the weights control algorithm is capable of effectively focusing the scattering light field at multiple points, and the spot intensity can be flexibly controlled by modifying the value of the weights coefficients. It is proved that the method has evident advantages in the application of multi-target complex scattering light field regulation.
  • Research on In-Situ Measurement of Solution Concentration Based on Fiber Optic Sensors
    SHEN Hanyang, FAN Li, CHEN Yi, SHU Qinghao, YANG Jing, HU Qiwei, YUAN Yuquan
    Chinese Journal of Light Scattering. 2025, 37(4): 599-608. https://doi.org/doi:10.13883/j.issn1004-5929.202504009
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    Based on the principle of intermodal interference in optical fibers, a fully fiber optic sensor with a single-mode-coreless-single-mode structure was designed and simulated. An in-situ measurement device was developed using this sensor to measure the concentration of saturated sodium chloride (NaCl) solution at different temperatures. Finite element simulation was employed to optimize the geometrical dimensions of the sensor, and a quantitative relationship between the interference intensity and the NaCl solution concentration was established. Using the in-situ measurement system based on the fiber-optic sensor, the concentrations of saturated NaCl solution at 25 ℃, 30 ℃, 35 ℃, 40 ℃, and 50 ℃ were measured, yielding values of 25.62%, 26.11%, 26.49%, 26.92%, and 27.38%, respectively. These result-s show good agreement with those obtained via the evaporation method, with a maximum absolute error of 0.86%. The fiber-optic sensor enabled simultaneous in-situ measurement of concentration and temperature while maintaining high measurement accuracy, indicating its potential applications in fields such as chemical engineering, pharmaceuticals, battery health monitoring, and extreme condition detection.
  • Grad-CBAM Deep Learning based SERS Classification for Serum Biopsy Diagnosis
    CAI Yuyang, #, MI Yanlin#, ZENG Xingyue, SUN Xiaolin, ZHANG Huijuan, SUN Haoran, ZHAO Yan, LI Xue, YAN Yinzhou
    Chinese Journal of Light Scattering. 2025, 37(4): 609-620. https://doi.org/doi:10.13883/j.issn1004-5929.202504010
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    Here we propose a Convolutional Neural Network with a Spatial-Channel Attention Mechanism combined with Gradient-Weighted Class Activation Mapping (Grad-CBAM-CNN) for the classification of serum SERS spectra from patients with systemic lupus erythematosus (SLE) and neuropsychiatric systemic lupus erythematosus (NPSLE). The classification model can capture major SERS spectral features effectively and demonstrate an accuracy of 96.7% with a small dataset of 300 samples for NPSLE diagnosis by optimizing network architecture and incorporating an attention mechanism. The classification performances from various CNN models are analyzed, by which the model configuration is optimized to significantly reduce the requirement of dataset volume and computing power of CNNs. Four SERS characteristic peaks for NPSLE are identified and visualized by the collaboration of the developed four high-performance models. This work not only advances the application of deep learning in the classification of limited biomedical datasets, but also provides a robust framework for the identification of potential specific biomarkers in serum for early diagnosis of systemic autoimmune disease for clinical applications.
  • Research on Detection Method of Testosterone Propionate in Athletes’ Blood Based on SERS
    JIANG Dan, ZHOU Weizhuo
    Chinese Journal of Light Scattering. 2025, 37(4): 621-628. https://doi.org/doi:10.13883/j.issn1004-5929.202504011
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    Doping testing is a core component in ensuring fairness in sports competitions. Testosterone Propionate (TP), as a representative of synthetic metabolic steroids, faces severe challenges in trace residue detection. The aim of this study is to establish a support vector machine (SVM) model based on surface-enhanced Raman spectroscopy (SERS) technology, combined with a new intelligent optimization algorithm-Hippopotamus Optimization Algorithm (HOA), to achieve highly sensitive, specific and rapid detection of trace residual testosterone propionate in athlete blood samples. Firstly, the simulated blood samples of athletes containing TP were preprocessed, and SERS signal enhancement was performed using silver nanosol to obtain the characteristic Raman spectral fingerprint information of TP. In response to the problems of high dimensionality, complex background, and difficult feature extraction in SERS spectral data, this study innovatively introduces the Hippopotamus Optimization Algorithm (HOA). By utilizing the powerful global search and optimization capabilities of HOA, the key parameters of the SVM model are automatically optimized to construct the optimal HO-SVM classification model. Through HOA optimization, the generalization ability and classification accuracy of SVM models are effectively improved. The results indicate that SERS exhibits excellent detection sensitivity for TP in blood. In addition, the SVM model optimized by HOA (HO-SVM) performs well in TP quantification, with a test set determination coefficient of 0.9610 and a root mean square error of 0.0597. The HO-SVM model can effectively process the complex information of SERS spectra and achieve efficient recognition and classification of TP characteristic peaks. This study successfully developed a novel detection method based on the combination of SERS technology and Hippopotamus Optimization Support Vector Machine (HO-SVM) algorithm. This method fully utilizes the high sensitivity and fingerprint recognition characteristics of SERS, as well as the advantages of HOA in optimizing SVM hyperparameters, significantly improving the detection performance of testosterone propionate in blood. This method provides a powerful technical means for precise and efficient screening of prohibited substances in athletes’ biological samples, and has important application prospects in the field of anti doping.
  • Study on Classification and Identification of Fluorite and Its Processed Products Based on Raman Spectroscopy Combined with PCA-SVM Algorithm
    LI Zengming, HAN Siqingaowa, FENG Lan, BAI Lina, HASI Wuliji
    Chinese Journal of Light Scattering. 2025, 37(4): 629-635. https://doi.org/doi:10.13883/j.issn1004-5929.202504012
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    In order to rapidly classify and identify fluorite and its processed products, the Raman spectra of fluorite, calcite, and quartz, which have very similar appearances, were first detected. Then, the Raman spectra of fluorite from different origins, as well as the raw and processed products of fluorite, were analyzed. A classification and identification model combining Raman spectroscopy with Principal Component Analysis (PCA)-Support Vector Machine (SVM) algorithm was established, and the Raman spectral data were classified and identified. The results show that for the similar appearance of fluorite, calcite, and quartz, there are obvious differences in Raman spectra, so these different kinds of mineral medicines can be accurately classified and identified by the naked eye observing or the PCA-SVM algorithm identifying the obvious differences in the Raman spectra. Although the Raman spectra of the fluorite from different origins, the Raman spectra of fluorite and the processed fluorite are so similar that it is almost impossible to differentiate them with the naked eye, the PCA-SVM algorithm is also able to accurately classify and identify based on small differences in the Raman spectral data. Meanwhile, the accuracy rate can reach 100%. This method has the advantages of being fast, accurate, non-destructive, convenient, portable, and low-cost, which is of great value for the classification and identification of mineral medicines and their quality control.
  • Material Detection Method of Fast-Drying Clothes Based on Micro Raman Spectroscopy
    LIU Ganlin, ZHU Zhixue, HE Xiaolin
    Chinese Journal of Light Scattering. 2025, 37(4): 636-642. https://doi.org/doi:10.13883/j.issn1004-5929.202504013
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    With people’s attention to sports health as an essential part of sports equipment, the performance of fast-drying clothes material significantly impacts sports effect and comfort. The traditional material testing methods for quick-drying clothes often rely on physical and chemical properties testing, making it challenging to fully reflect the microstructure and composition characteristics of materials. This paper proposes a micro Raman spectroscopy-based material detection method for fast-drying clothes. The purpose is to explore fast-drying clothes’ chemical composition and molecular structure characteristics through high-resolution spectral analysis. Firstly, the Raman spectra of common materials of quick-drying clothes were tested, and the characteristic peaks were extracted and analyzed. Secondly, the classification model based on a generalized regression neural network is established, and its classification accuracy is evaluated using a confusion matrix. Finally, different brands of quick-drying clothes on the market are selected to test the model’s classification effect. The results show that micro Raman spectroscopy combined with the GRNN algorithm can identify the main components of fast-drying clothing materials. The results show that micro Raman spectroscopy technology, as an efficient, accurate, and non-destructive detection method, combined with the GRNN algorithm, provides a new idea for the material analysis of sports equipment, which has important theoretical significance and practical application value and provides an efficient, accurate and nondestructive analysis method for the material identification of sports fast drying clothes.
  • Strong CouplingInteraction and Strain Regulation of Au nanorod-WS2-Hydrogel Composite
    FANG Shihao, ZHANG Xushuo, LUO Junsheng, LAN Peisheng, WEN Jinxiu, ZENG Wei
    Chinese Journal of Light Scattering. 2025, 37(4): 643-651. https://doi.org/doi:10.13883/j.issn1004-5929.202504014
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    The strong coupling of light and matter is a key research direction in the field of modern optics, which is of great significance to the development of cutting-edge technologies such as quantum information processing, high sensitivity sensing and optoelectronic devices. Focusing on monolayer tungsten disulfide (WS2) in 2D transition metal sulfur compounds, this study demonstrates strong coupling with the composite structure formed by Au nanorods on the hydrogel substrate. The Rabi splitting energy of the Au nanorods-WS2-hydrogel composite can reach up to 103 meV, meeting the strong coupling criterion. By utilizing the reversible deformation properties of the double crosslinked network hydrogel, the peak position and intensity of WS2 were adjusted through mechanical stretching, ultimately achieving the strong coupling effect of the composite structure under strain regulation. This research has proven that a robust coupling system with significant Rabi splitting energy and dynamic control can be achieved at room temperature. This offers new possibilities for accurately managing strong coupling systems and has the potential for broad application in flexible electronics.
  • Research on Gasoline Raman Spectroscopy Measurement and Machine Learning Detection Model
    ZHAO Hairu, ZHENG Yuanyuan, YANG Bo, YAN Chunrong, TUO Pandeng, HUANG Ming
    Chinese Journal of Light Scattering. 2025, 37(4): 652-658. https://doi.org/doi:10.13883/j.issn1004-5929.202504015
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    The quality of gasoline directly affects the combustion efficiency of gasoline, the lifespan of engines and the emissions of exhaust gases, etc. Therefore, it is very important to test the quality of gasoline. This paper adopts Raman spectroscopy technology combined with machine learning algorithms to construct a rapid identification model for gasoline quality. Firstly, mix No. 92, No. 95, and No. 98 gasoline in pairs with a 10% gradient to create fuel samples of different proportions. Based on the collection of Raman spectral data of all types of gasoline samples, a Raman spectral dataset of gasoline was established. Then, the Raman spectral data are preprocessed by using the asymmetric least square method and mean normalization to extract spectral features. Finally, four machine learning algorithms including Gradient Boosting Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest were respectively used to train and predict the preprocessed data and evaluate the model. The experimental results show that Gradient Boosting Decision Tree has the best recognition effect, with classification accuracy, precision, recall rate and the harmonic mean of precision and recall for all types of gasoline samples reaching 98.18%, 98.32%, 98.18%, and 98.18% respectively. This paper provides a rapid method for identifying the quality of gasoline, and offers a rapid detection solution for the supervision of the gasoline market in the circulation link and the protection of consumers' legitimate rights and interests.
  • Quantitative Study on the Microstructure of High-Temperature Melts and Glasses of Ternary CaO-SiO2-B2O3 System
    XU Feiyan, YOU Jinglin, ZHAO Yufan, SHENG Meiqin, LIU Guopeng, XIA Xiang
    Chinese Journal of Light Scattering. 2025, 37(4): 659-668. https://doi.org/doi:10.13883/j.issn1004-5929.202504016
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    In this study, glass samples with varying B2O3 contents (0~12 mol%) and basicity ratios CaO/SiO2=1.0 were prepared using aerodynamic levitation technology. The microstructural evolution of the CaO-SiO2-B2O3 ternary system in both molten and glassy states was investigated through in situ high-temperature Raman spectroscopy, quantum chemistry ab initio calculations, and molecular dynamics (MD) simulations. The deconvolution of Raman spectra revealed that B2O3 incorporation promotes the degree of network polymerization by increasing Q3 and Q4 species while reducing Q2,Q1, and Q0 species, this trend remains consistent across both molten and glassy states, with the molten state exhibiting a higher proportion of Q1 and Q0 species, while the glassy state displays a greater prevalence of Q3 and Q4. MD results are consistent with these results, showing that B2O3 promotes the conversion of non-bridging oxygen to bridging oxygen, thereby enhancing network connectivity. The combination of experiment and simulation verifies the accuracy and reliability of the conclusions. It provides critical insights into the structure modifications induced by B2O3, offering a theoretical foundation for considering and developing fluoride-free mold fluxes for continuous casting processes.
  • Non-Invasive Detection Method for Spatial Bone Loss Markers Based on Laser-Induced Breakdown Spectroscopy
    HE Jiye, WANG Hongpeng, WAN Xiong,
    Chinese Journal of Light Scattering. 2025, 37(4): 669-675. https://doi.org/doi:10.13883/j.issn1004-5929.202504017
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    Bone loss in space is a worldwide problem that has not yet been completely solved. Early rapid diagnosis and treatment are equally important. Due to the space station’s particular application scenario, the realization of noninvasive evaluation of clinical diagnosis is the key to engineering application. Calcium ion is a typical marker of bone loss, and it is mainly excreted through urine. Determining the urinary calcium value in urine is helpful in indirectly evaluating spatial bone loss. Therefore, this paper proposes a non-invasive detection method of bone loss markers based on laser-induced breakdown spectroscopy combined with a machine learning algorithm. Firstly, a biochemical analysis of urinary calcium value was performed on the urine samples collected clinically. Then, the LIBS system was used to measure the urine samples adsorbed by the test strip, and the collected LIBS spectra were preprocessed. Secondly, the plasma emission spectra of urine samples were interpreted to analyze the correlation between the characteristic peak intensity of calcium and urinary calcium value. Finally, the partial least squares regression algorithm was used to establish the quantitative urinary calcium value prediction model. The R2 of the training and validation sets were 0.9362 and 0.9328, respectively, and the RMSE were 0.5142 and 0.5091, respectively. In conclusion, this paper proposes and verifies a new method for the noninvasive detection of bone loss markers, which has a specific reference value for the early diagnosis of spatial bone loss.
  • Identification of Coagulated Blood in Vitro Based on PKO Optimized SVC Assisted Raman Spectroscopy
    LIU Chunwei, ZHU Hongsheng, LI Jiatong
    Chinese Journal of Light Scattering. 2025, 37(4): 676-681. https://doi.org/doi:10.13883/j.issn1004-5929.202504018
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    The non-invasive identification technology for coagulated blood clots is of great significance for forensic testing, as it can provide critical scientific support and evidence for case investigation. The non-destructive Raman spectroscopy detection provides a foundation for physical evidence protection and multi-technology verification. This paper proposes a Raman spectroscopy detection method for in vitro coagulation of blood, studies the chemical information represented by the Raman spectral peaks of cats, dogs, and humans blood, compares the Raman spectral differences of the three types of blood, and combines Pied Kingfisher Optimizer (PKO)-Support Vector Classification (SVC) algorithm to achieve the identification of cats, dogs, and humans coagulation of blood. The identification effectiveness of this method was demonstrated through the confusion matrix. This article provides a practical and feasible detection scheme for rapid and non-invasive identification of coagulated blood at the crime scene.
  • Support Vector Machine Algorithm Empowers Microscopic Raman Spectroscopy Technology to Quickly Identify Categories of Biodegradable Plastics
    LI Pei, GOU Jianan, JIANG Bin
    Chinese Journal of Light Scattering. 2025, 37(4): 682-690. https://doi.org/doi:10.13883/j.issn1004-5929.202504019
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    Petrochemical plastic pollution has become a global environmental pollution issue, and replacing petrochemical plastics with biodegradable plastics is a significant trend for sustainable human development and technological civilization in the future. However, the degradation conditions of different biodegradable plastics vary. Classifying and identifying them is necessary to improve the degradation rate and shorten the degradation cycle of biodegradable plastics. This paper proposes a support vector machine algorithm combined with micro Raman spectroscopy technology to identify different types of biodegradable plastics rapidly. Obtain standard vibration spectra of different types of biodegradable plastics through a micro Raman spectrometer, analyze the Raman spectral characteristic peaks and corresponding molecular vibration bonds of various biodegradable plastics, interpret the spectral characteristics of different biodegradable plastics, and establish a corresponding database of biodegradable plastic standard samples. Based on Raman spectroscopy database, the influence of different kernel functions on support vector machine classification models was compared, and the classification performance of the models was evaluated through confusion matrix and macro-average method. The accuracy of SVC for the optimal classification result of the test set was 0.996, and the recall rate was 0.996. The research results indicate that the combination of support vector machine algorithm and micro Raman spectroscopy technology can achieve the classification and recognition of different types of biodegradable plastics. This technology provides methodological references for addressing the related waste disposal issues after the large-scale promotion of biodegradable plastics in future human society.
  • RFE-GBDT Algorithm Combined with Raman Spectroscopy for Identifying the Components of Sports Nutrient Supplements
    LIANG Jie, GONG Jian
    Chinese Journal of Light Scattering. 2025, 37(4): 691-698. https://doi.org/doi:10.13883/j.issn1004-5929.202504020
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    Accurately identifying their ingredients with the widespread application of sports nutrient supplements in fitness and exercise has become particularly important. Traditional detection methods are often time-consuming and costly and require highly professional skills from operators. This study aims to explore an efficient and accurate method for identifying the components of sports nutrient supplements by combining a recursive feature elimination-gradient boosting decision tree (RFE-GBDT) algorithm with Raman spectroscopy technology. Firstly, the collected Raman spectroscopy data is preprocessed, including steps such as denoising, normalization, and feature extraction, to improve its quality and usability. Secondly, after feature selection, the RFE-GBDT algorithm is used to construct quantitative models, and their accuracy and robustness in component discrimination are evaluated through coefficient of determination and root mean square error. Finally, after experimental verification, the predicted parameters R2 and RMSE of the RFE-GBDT algorithm were 0.9632 and 3.6713, respectively. The model combining the RFE-GBDT algorithm with Raman spectroscopy performed well in identifying the components of sports nutrient supplements. The experimental results show that this method improves the accuracy of component identification and significantly shortens the time required for analysis. Compared with traditional detection methods, the proposed method has significant advantages in efficiency and economy. This study demonstrates that combining the RFE-GBDT algorithm and Raman spectroscopy provides a novel and effective solution for identifying the components of sports nutrient supplements with broad application prospects. Future research can further explore combining other machine learning algorithms and spectroscopic techniques to promote the development of sports nutrient analysis technology and provide consumers with safer and more effective product protection.
  • Raman Spectral Analysis of Four New Amphetamine-Type Drugs
    WANG Shudong, OUYANG Jiahui, WANG Ye, ZHANG Yan, ZHENG Xuan
    Chinese Journal of Light Scattering. 2025, 37(4): 699-704. https://doi.org/doi:10.13883/j.issn1004-5929.202504021
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    Amphetamine-type drugs are the most common types of drugs. To investigate the application of Raman spectroscopy in the rapid detection of amphetamine-type drugs, this study combines Raman spectroscopy experiments, density functional theory(DFT) calculations, and potential energy distribution (PED) analysis to examine the Raman spectra of four new amphetamine-type drugs-DOB, TMA, 3-FA, and 4-FA, and assigns their main characteristic spectral bands. The results show that for amphetamine-type substances, the breathing and deformation vibrations of the benzene ring exhibit the strongest Raman activity. However, due to differences in substituent groups, their characteristic peaks show significant variations. Para- and meta-substitution on the benzene ring lead to distinct differences in the Ramanproperties of amphetamines. By analyzing the Raman spectra of these four amphetamine-type substances in relation to their chemical structures, this study provides important insights for the rapid detection of amphetamine-type drugs using Raman spectroscopy.

  • Research on Ethanol Concentration Detection Method Based on Raman Spectroscopy and PCA-SVM Algorithm

    HUANG Xiaoyu, YANG Bo, CHEN Mengxue, ZHOU Weibo, CAO Jing, HUANG Ming
    Chinese Journal of Light Scattering. 2025, 37(4): 705-709. https://doi.org/doi:10.13883/j.issn1004-5929.202504022
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    As a vital organic solvent,Ethanol is widely used in various production processes including pharmaceuticals, fuel, and chemicals. Methods for the rapid detection of its concentration holds practical significance. Raman spectral data of ethanol solutions with different concentrations (10% to 100% at 10% intervals) were measured to establish a dataset. The data were divided into a training set and a testing set. Principal Component Analysis (PCA) was employed to reduce dimensionality by integrating it with Support Vector Machine (SVM) classification. The experimental results indicate that the classification accuracy of the test set data reached 100% by adopting the PCA-SVM algorithm. The study demonstrates the effectiveness of the proposed method.
  • Structural Changes of Cefuroxime Axetil by Variable Temperature Infrared Spectroscopy
    ZHANG Qianzhi, GUAN Yanyan, CHEN Xiaohong, YANG Muzi, CHEN Jian
    Chinese Journal of Light Scattering. 2025, 37(4): 710-715. https://doi.org/doi:10.13883/j.issn1004-5929.202504023
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    Infrared spectroscopy technology was applied to analyze the spectra of cefuroxime axetil during the heating process from 20 ℃ to 180 ℃, and fails to find significant changes in the characteristic infrared absorption peaks of cefuroxime axetil within 180 ℃. After conducting two-dimensional correlation spectroscopy analysis on the series of infrared spectra collected during the heating process, it was found that the absorption peaks of double bond C=C (1666 cm-1) and C=O (1716 cm-1) were very sensitive to temperature changes and interaction between them were confirmed. Combined with the moving two-dimensional window correlation spectrum, it was further confirmed that the C=C and C=O of cefuroxime axetil started to change from 85 ℃. In this article, the process that cefuroxime axetil would isomerize into δ-isomer of Cefuroxime axetil when heated to 85 ℃ could be characterized by variable temperature Infrared combining two-dimensional correlation spectroscopy analysis. Rapid and non-destructive testing techniques and analysis methods for the study of thermal stability of drug structures were provided.
  • Investigation of the Effects of Different Treatment Methods on the Molecular Structure of Konjac Glucomannan Using FTIR Spectroscopy
    LIU Yan, WANG Dongdong, LIU Jin, SI Minzhen
    Chinese Journal of Light Scattering. 2025, 37(4): 716-724. https://doi.org/doi:10.13883/j.issn1004-5929.202504024
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    This study investigates the molecular structural variations of konjac glucomannan (KGM) under different processing conditions, including heating (thermal treatment), soaking (aqueous treatment), and drying (dehydration treatment). Fourier Transform Infrared Spectroscopy (FTIR) and second-derivative spectroscopy were used to analyze key structural changes, focusing on β-glycosidic bonds (896 cm-1), C—O—C bonds (1156 cm-1, 1078 cm-1), and C—H bonds (2944 cm-1, 2882 cm-1). The results show that thermal treatment (95 ℃, 3 h) significantly destabilized C—O—C bonds, as indicated by a 15.4% decrease in the 1156 cm-1 peak area and a 41.2% decrease in the 1078 cm-1 peak area (P<0.05). In contrast, the β-glycosidic bond peak area increased by 300.0% (P<0.01), suggesting molecular rearrangement or crosslinking at high temperatures. Aqueous treatment (20 ℃, 24 h) had a stabilizing effect on the C—O—C backbone, with peak areas increasing by 15.4% (1156 cm-1) and 25.5% (1078 cm-1). Hydration also improved β-glycosidic bond integrity, increasing its peak area by 50.0%, which may enhance KGM stability in food applications. Dehydration treatment (50 ℃, 48 h) had minimal effects on the primary KGM backbone, but reduced the β-glycosidic bond peak area by 50.0% (P<0.05). This decline may negatively impact KGM’s gelling and functional properties. Second-derivative spectroscopy improved peak resolution for 896 cm-1, 1156 cm-1, and 1078 cm-1, further validating the accuracy of FTIR analysis. These findings provide insights into the stability of KGM under different processing conditions, offering a scientific basis for optimizing its functional applications in food processing and storage. Additionally, this study highlights FTIR combined with second-derivative spectroscopy as a robust tool for exploring the structure-function relationships of polysaccharides.
  • Identification and Expression Estimation of Colchicine-Related Structures in Fresh Hemerocallis citrina Using FTIR Spectroscopy
    LIU Yan, WANG Dongdong, ZOU Yafeng, SI Minzhen
    Chinese Journal of Light Scattering. 2025, 37(4): 725-735. https://doi.org/doi:10.13883/j.issn1004-5929.202504025
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    Hemerocallis citrina is widely consumed for its nutritional and medicinal benefits. However, whether it contains colchicine or colchicine-like toxic structures remains a subject of scientific debate. In this study, Fourier transform infrared spectroscopy (FTIR) was employed to investigate the presence and relative expression levels of colchicine-related structures in different parts of H. citrina (root, stem, and flower). A spectral reference system was established using a colchicine standard and Colchicum autumnale corm (a known colchicine-rich plant) to identify functional group-specific absorption peaks, including amide C=O, methoxy C—O, and ether C—O—C. Six characteristic peaks were selected to perform normalized absorbance and peak area ratio analyses. Furthermore, a composite metric—Colchicine-related Expression Index (CEI)—was developed to semi-quantitatively estimate the structural expression intensity across tissues. The results showed that the flower exhibited the strongest absorption features and the highest CEI values, suggesting it as a potential accumulation site of colchicine-like structures. This work introduces a spectral strategy integrating functional group recognition, multi-peak ratio modeling, and index-based evaluation, offering a rapid and non-destructive tool for trace-level toxic structure screening in complex plant matrices.
  • Raman Spectroscopy Analysis of the Rust on the Surface of the Gold andSsilver-Painted Grand wine Bottle and Bearing Plate in Handan
    WANG Jing, LI Man
    Chinese Journal of Light Scattering. 2025, 37(4): 736-743. https://doi.org/doi:10.13883/j.issn1004-5929.202504026
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    The gold and silver-painted grand wine bottle and bearing plate, as the inscribed bronze relics unearthed from the tombs of the marquises of the Eastern Han Dynasty, It was of great significance to study the establishment of the official and the casting of bronze ware in western Sichuan at that time, and the casting of bronze vessels. These were analyzed and tested before protected, the Raman spectroscopy and polarized light microscopic analysis were adopted for the scientific detection and analysis of them, and it was found that the rusts included cuprite, malachite, azurite, lanarkite and atacamite, among which, atacamite was a harmful rust, which must be cleared.
  • Spectroscopy Characteristics of Emerald from Afghanistan
    WANG Li, LUO Meng, BAI Ying, ZHANG Qiuchun
    Chinese Journal of Light Scattering. 2025, 37(4): 744-750. https://doi.org/doi:10.13883/j.issn1004-5929.202504027
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    With the increasing status of Afghan emeralds in the emeralds market, Effectively Iidentifying the Characteristics of Afghan emeralds Has become the Main Research Focus at Present. This Article Uses Large-scale Instruments Such as The Stereomicroscope, Fourier Ttransform Infrared Spectrometer, Ultraviolet Spectrometer, and Micro Area Laser Raman Spectrometer to Test and Analyze the Gemological and Spectral Characteristics of Afghan Emerald Samples. The UV Spectrum Shows that the Green of Afghan Emerald due to The Combined Action of Cr3+and V3+, The Absorption of Fe2+ Result in a Blue Tone in the Grandmother green band. The Infrared Spectrum Reveals that Afghan Emerald Belongs to the Type II Water Dominated Emerald. The Laser Raman Spectrum Shows that the Overall Vibration Intensity of the Groups in Afghan Emerald is Greater in the Vertical c-axis Direction than in the Parallel c-axis Direction of the Crystal.
  • Gemological and Spectroscopic Characteristics of Guatemala Blueish-violet Jadeite
    ZHANG Lian, LU Taijin
    Chinese Journal of Light Scattering. 2025, 37(4): 751-756. https://doi.org/doi:10.13883/j.issn1004-5929.202504028
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    In recent years, Guatemalan jadeite has received more attention in China’s gemstone trading market, with green and gray-blue varieties being the most commonly encountered. This study focuses on blueish-violet Guatemala jadeite as the research subject. Building on conventional gemological testing, infrared spectroscopy, X-ray fluorescence spectroscopy (XRF)and Raman spectroscopy were employed to conduct an in-depth investigation into its gemological characteristics, spectroscopic properties, and color origin. The results indicate that the primary mineral composition of blueish-violet Guatemala jadeite, with characteristic peaks of jadeite identified in both infrared and Raman spectroscopy. DiamondViewTM samples have yellow-green fluorescence. Combined with the fluorescence characteristics and XRF test results, it shows that the Fe and Ti contents in the sample are relatively high, while the Mn content is relatively low. Ultraviolet-Visible absorption spectroscopy (UV-Vis) revealed absorption bands at 614 nm and 538 nm, attributed to Fe2+-Ti4+ and Fe2+-Fe3+ charge transfers, respectively, suggesting that the coloration is associated with Fe2+, Fe3+, and Ti4+.
  • FTIR Spectral Differences and Formation Mechanisms Between Native Surfaces and Cross-Sections of Conch Pearl and Queen Conch Nacre
    HUANG Xuebing, YAN Xuejun, LU Qianyun, KONG Ye, GAO Jiayi, YAN Jun
    Chinese Journal of Light Scattering. 2025, 37(4): 757-764. https://doi.org/doi:10.13883/j.issn1004-5929.202504029
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    Fourier-transform infrared (FTIR) diffuse reflectance spectroscopy was employed for the first time to characterize the spectral differences between the native surfaces and corresponding fracture planes of conch pearls and queen conch shells. These observations were further correlated with scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses to elucidate the underlying mechanisms. The results were shown that: the intensity ratio of ν3 to ν2 bands of biogenic aragonite on native surfaces significantly exceeded that of aragonite on fracture planes.The ν2 band of surface aragonite exhibited a notable frequency-shift compared to fractured aragonite, accompanied by reduced full-width at half-maximum (FWHM).A characteristic peak at 842 nm was identified in fracture-plane aragonite but absent on native surfaces.SEM and XRD analyses revealed that crystallographic anisotropy of aragonite crystals between surface and fracture orientations directly accounts for these spectroscopic and diffraction singularities. This work provides critical insights for understanding biomineralization processes and offers practical guidance for distinguishing conch pearls from polished queen conch shell.
  • A Study on the Detection and Analysis of Thangka in Sakyamuni Buddha
    WANG Yanling, ZHANG Yuling, Ma Li, WEI Yali, ZHOU Yuan
    Chinese Journal of Light Scattering. 2025, 37(4): 765-770. https://doi.org/doi:10.13883/j.issn1004-5929.202504030
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    In this paper, the fibers, tissue structure and surface colored pigment components of the "Sakyamuni Buddha" Tangka of the XiXia were detected and analyzed by using micro-morphological observation, scanning electron microscope, near-infrared imaging technology, X-ray fluorescence spectrometry and laser Raman spectrometry. These detection means determined that the silk thread material of the Tangka painting core is natural mulberry silk and the silk thread weaving method is plain weave, which provides important basis for understanding material and production technology of the Tangka. In addition, the pigment components of the Tangka color painting were clarified, such as red is painted with a mixed pigment of lead and cinnabar; white is painted with lead white pigment; the main green is painted with a green pigment containing Cu element, which helps to deeply understand thement usage of the Tangka. It is of great significance for the research on the painting process, age identification and cultural connotation of the Tangka.
  • Research on Realistic Reproduction of Texture and Color in Digital Chinese Painting Based on Res-CAE
    ZHU Shihao, FENG Jie, SUN Licun, LI Xinting, YUAN Ping, DENG Hongyang
    Chinese Journal of Light Scattering. 2025, 37(4): 771-780. https://doi.org/doi:10.13883/j.issn1004-5929.202504031
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    This study proposes a staged optimization framework to address challenges in color and detail reproduction in the digital preservation of traditional Chinese paintings. In the foundational research stage, to overcome the limitations of traditional spectral reflectance methods—namely the lack of spatial information and subjective dependency—a novel Residual-Convolutional Autoencoder (Res-CAE) deep learning model is developed. By jointly learning spatial and spectral features from hyperspectral data cubes, the Res-CAE model demonstrates significant advantages across the CAVE dataset and multiple test scenarios, including standard color charts and Chinese painting-specific palettes. The model achieves promising results, with CIE ΔE00<0.7 and spectral reflectance RMSE>0.0995.To address the limitations of Res-CAE in spatial resolution and texture detail representation, this study further proposes an enhanced framework integrating Gaussian-Laplacian transformations. Through a detail enhancement module, it is observed that while Gaussian enhancement improves texture details (with a 27.93% increase in gradient magnitude), it causes unacceptable color deviations (ΔE00>3) in 79% of the color patches in the 24-color chart test (only patches 3, 9, and 19 are within acceptable limits). In contrast, Laplacian enhancement maintains detail improvement while achieving significantly better color accuracy (ΔE00<3.076) compared to the Res-CAE reconstructions. By combining objective metrics with subjective evaluations, the conflict between quantitative indicators and visual perception is effectively resolved, confirming the effectiveness of the scenario-specific enhancement strategy. The results suggest that Laplacian transformation is more suitable for scenarios requiring joint optimization of “color and detail” in high-saturation regions, whereas Gaussian enhancement is recommended only for non-color-sensitive areas where texture complexity is prioritized. This work provides a scalable technical approach for the coordinated optimization of color and detail in the digital reproduction of traditional Chinese paintings.
  • Scientific Analysis and Plastic Restoration Research of Han Dynasty Copper Basins in the Collection of the Capital Museum
    ZHANG Xinyuan, ZHOU Hua, NI Yan
    Chinese Journal of Light Scattering. 2025, 37(4): 781-788. https://doi.org/doi:10.13883/j.issn1004-5929.202504032
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    The bronze basin of the Han Dynasty studied in this paper is a third-class cultural relic in the collection of the Capital Museum, unearthed in Yanqing, Beijing. In this paper, metallographic, XRD, XRF and other methods were used to analyze the manufacturing process and diseases of copper basins, and the production process was determined to be casting , chloride ions are present and desalting is required. Due to the serious deformation of this artifact, shaping is the focus of this repair, through the hammering method, the traditional molding method and the hydraulic jack molding method three methods are tested, according to the shaping effect, the use of hydraulic jack to repair the large area of deformation of the abdomen; After the transformation of the existing shaping tools, the damage to the artifacts is effectively reduced, and the shaping needs of large-diameter bronzes can be met, and the expected protection effect is achieved after restoration.
  • Scientific and Technological Analysis of the Colored Pottery of the Hongshan Culture Excavated from the Baqi Site in Xiawa Town, Aohan Banner County
    LI Minghua, FU Qianli, XIA Yin, HUI Na , ZHANG Shangxin, FU Fei, MA Tian
    Chinese Journal of Light Scattering. 2025, 37(4): 789-797. https://doi.org/doi:10.13883/j.issn1004-5929.202504033
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    Located in Xiawa Town, Aohan Banner, Chifeng City, Inner Mongolia, the Eight Banners site was discovered during a joint archaeological survey in 2016, in which 17 Red Mountain Culture remains were found, all of which were habitation sites, except for one that was a ceremonial site, and a large number of pottery sherds from the Red Mountain Culture period were collected.In view of the lack of scientific and technological analysis of the excavated painted pottery of the Hongshan Culture, this study selected nine pieces of painted pottery barrel shaped vessel fragments, five pieces of painted pottery bowl fragments and one converging mouth painted pottery jar fragments for scientific and technological testing, the analysis includes the structure and composition of the painted pottery pigments, the petrographic composition of the pottery tires, and the pottery tires’ firing temperatures.Analysis results show that the color pottery black color pigment is based on black manganese ore, and the red color is based on hematite; the texture of the pottery tire is tight, the admixtures are uniform, which is due to artificial screening; the firing temperature of the tube-shaped ware is high, reaching up to 1100 ℃.Through the analysis of the pottery production process of the Hongshan culture, this paper initially reveals that a certain degree of standardized production existed in the pottery production of the Hongshan culture period.
  • A Method for Extracting Feature Wavelengths in Water Quality Detection Based on IPSO-GA Algorithm
    BAO Han, CAO Hongkui, SHEN Jia
    Chinese Journal of Light Scattering. 2025, 37(4): 798-804. https://doi.org/doi:10.13883/j.issn1004-5929.202504034
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    In response to the issue of low detection accuracy caused by the insufficient generalization ability of single feature wavelength extraction methods in water quality detection processes, an improved Particle Swarm Optimization-Genetic Algorithm (IPSO-GA) for feature wavelength extraction in water quality detection is proposed in this paper. The Inertia Weight Particle Swarm Optimization (IPSO) algorithm is enhanced to dynamically adjust the inertia weight and learning factors, thereby strengthening the algorithm’s global and local search capabilities, accelerating convergence speed, and incorporating the Genetic Algorithm (GA) to enhance the global search ability and prevent the algorithm from falling into local optima. The feature wavelengths extracted by the IPSO-GA algorithm are combined with Partial Least Squares (PLS) to construct the IPSO-GA-PLS water quality detection prediction model, which is compared with other prediction models. Experimental results demonstrate that the IPSO-GA algorithm proposed in this paper effectively handles noise interference and selects the most representative wavelengths for feature wavelength extraction compared to traditional methods. The Root Mean Square Error (RMSE) of the IPSO-GA-PLS water quality detection prediction model based on this algorithm is reduced to 0.0345, the Mean Absolute Error (MAE) reaches 0.0308, and the correlation coefficient is improved to 99.86%, enabling faster and more accurate water quality detection and providing an efficient prediction model for water quality detection.
  • Identification Study of Zanthoxylum bungeanum Using Fourier Transform Infrared Spectroscopy Analysis under the Framework of Deep Learning
    MA Dianxu, CAI Yan, LI Xiaopan, CHEN Lijun, DU Guofang, SHAN Changji
    Chinese Journal of Light Scattering. 2025, 37(4): 805-812. https://doi.org/doi:10.13883/j.issn1004-5929.202504035
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    Fourier Transform Infrared Spectroscopy (FTIR) technology is extensively utilized in the analytical research of various agricultural crops, including rice, Gastrodia elata, tobacco, and walnuts, due to its rapid, non-destructive, and precise attributes. Fourier transform infrared spectroscopy (FTIR), two-dimensional correlation infrared spectroscopy (2D-IR) and convolutional neural network analysis (CNN) in deep learning were used to analyse and identify seven species of Zanthoxylum bungeanum from different species and regions.In the Fourier Transform Infrared Spectroscopy (FTIR), the absorption spectra of the seven kinds of Zanthoxylum bungeanum showed absorption peaks in the ranges of 3500~2850 cm-1,1650~1400 cm-1 and 1250~1000 cm-1, this indicates that Zanthoxylum bungeanum is rich in volatile oils, alkaloids, proteins, sugars, and cellulose; In the spectrum of Zanthoxylum bungeanum pepper, only Sichuan Hanyuan rattan pepper had strong absorption at 2930 cm-1, 2860 cm-1 and 1750 cm-1, which showed obvious differences compared with other Zanthoxylum bungeanum spectra, and the other Zanthoxylum bungeanum spectra were similar, so it was difficult to identify the seven kinds of Zanthoxylum bungeanum by Fourier transform infrared spectroscopy only. With the perturbation of temperature, two-dimensional correlation infrared spectroscopy (2D-IR) analysis was performed. In the synchronous spectrum, the 2D-IR showed relatively strong automatic fronts at 1000 cm-1, 1420 cm-1 and 1650 cm-1, indicating that the esters, phenols and proteins in Zanthoxylum bungeanum decomposed to a certain extent. In addition, among the 7 samples, only the position and intensity of the automatic front were very similar, and the rest of the Zanthoxylum bungeanum showed different automatic fronts, and most of the samples could be distinguished according to their differences.Furthermore, CNN analysis was carried out on 183 spectra of 7 kinds of Zanthoxylum bungeanum in deep learning, and 129 samples were randomly selected for model training, and the classification accuracy of the model on the training set reached the optimal state of 100%, and the spectra of 54 samples were predicted, and there was only one wrong score of the predicted spectral samples, with an accuracy of 98.15%. Therefore, FTIR, 2D-IR and CNN in deep learning can accurately classify Zanthoxylum bungeanum pepper, and this method can be applied to the classification and identification analysis of other substances.
  • Study on Classification Detection and Recognition of Tomato Virnus Diseases Based on Hyperspectral Images
    YUAN Ping, FENG Jie, ZHANG Jiangkun, DENG Hongyang, ZHU Shihao, CHEN Jie
    Chinese Journal of Light Scattering. 2025, 37(4): 813-821. https://doi.org/doi:10.13883/j.issn1004-5929.202504036
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    Viral disease is an important factor affecting tomato yield, which spreads widely and can easily cause plant death, fruit yield reduction and even harvest failure. In this paper, a spectrometer and a color camera were used to collect the spectral data and high-definition pictures of the leaves of the four disease stages of tomato, and the diseases were divided into four levels according to the color difference calculation software Color Tell and the lesion identification program. The spectral data of four grades of disease will be collected and preprocessed by convolutional smoothing, multivariate scattering correction, standard normal transformation, etc. The results show that the average recognition accuracy of the two models is higher than 87.5%, and the average detection rate of the whole band modeling is 93.3% 、92.4%、87.5%、98.0.%；The average values of principal component modeling at all levels were 96.6%, 92.7%, 91.3% and 98.9%.

  • The Detection and Identification of Tomato Diseases Through Multi-dimensional Data Fusion
    YUAN Ping , FENG Jie, WANG Shaobo, ZHANG Jiangkun, CHEN Jie, LI Xinting
    Chinese Journal of Light Scattering. 2025, 37(4): 822-831. https://doi.org/doi:10.13883/j.issn1004-5929.202504037
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    Crop diseases are one of the key factors affecting crop yields, and precise identification of diseases is paramount in crop disease prevention efforts. This paper proposes a classification method that utilizes color, image, and spectral data combined with machine learning or deep learning networks for tomato disease leaves. Specifically, RGB images are first captured under a standard D65 light source using a camera. The images are then segmented to isolate the diseased areas of the leaves, from which the average RGB values are extracted. The RGB color threshold intervals for each type of disease are determined. After visualizing the clustering of RGB values for each disease, the AlexNet deep learning network is employed to classify a custom dataset of tomato disease leaf images, achieving a maximum accuracy of 87.95% on the test set. Additionally, a hyperspectral sorter is used to collect spectral curves of leaves with different diseases, revealing significant differences in their average spectral curves. Following the extraction of characteristic bands using the successive projections algorithm, the support vector machine algorithm is applied to classify the feature spectral data of the diseased leaves. The results indicate that the average classification accuracy on the test set reaches 95.28%.
  • Identification of High and Low-Value Wood Species Using ResNet Combined with Near-Infrared Spectroscopy
    DUAN Mingkang, XIN Yingjian, LI Kang, WANG Hongpeng, WAN Xiong,
    Chinese Journal of Light Scattering. 2025, 37(4): 832-837. https://doi.org/doi:10.13883/j.issn1004-5929.202504038
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    The domestic high-value furniture market is currently marred by confusion and chaos, with the frequent occurrence of substituting inferior goods for high-quality ones and peddling counterfeits, posing numerous challenges for market supervision. Consequently, the rapid and non-destructive differentiation of high-value and low-value wood is an urgent issue that needs to be addressed. This paper presents a method based on the residual network algorithm that aids in the identification of high-value and low-value wood species using near-infrared diffuse reflectance spectroscopy. Initially, with the assistance of the Shanghai Institute of Quality Supervision, Inspection and Technology, eleven different types of high-value and low-value wood were selected, and their surface coatings were uniformly polished. Subsequently, considering the non-uniform characteristics of wood texture morphology and component distribution, a random multi-point detection approach was employed to acquire the near-infrared spectra of diffuse reflectance from the wood surfaces, and a multi-point averaging strategy was utilized to obtain statistically significant near-infrared diffuse reflectance spectra. Ultimately, utilizing the residual network, which introduces shortcut connections to resolve the issues of vanishing or exploding gradients in convolutional neural networks, a classification model for the eleven wood types was constructed. The research findings indicate that after random multi-point detection and averaging of the spectra, the resultant data exhibits statistical characteristics capable of representing the attributes of wood species, with the residual network classification model achieving an accuracy rate of 99.6%. By integrating near-infrared diffuse reflectance spectroscopy with the residual network algorithm, this paper successfully achieved the differentiation of eleven high-value and low-value wood species. Moreover, the random multi-point detection method proved to be an effective approach to eliminate or mitigate local variations. In summary, the detection method proposed in this paper, which combines near-infrared diffuse reflectance spectroscopy with a residual network, holds significant importance and serves as a valuable reference for the rapid online monitoring of wood species in the market.
  • Qualitative Discriminative Analysis of Different Artifacts of Ophiopogon Japonicus (L.f.) Ker-Gawl based on Infrared Spectroscopy Combined with Intelligent Network Algorithm
    SANG Hongyang , TIAN Hanju , QIAO Junhao , XUE Yangjie, LI Yunfa, ZHAO Xinmei, YANG Chunjing , YANG Ling
    Chinese Journal of Light Scattering. 2025, 37(4): 838-848. https://doi.org/doi:10.13883/j.issn1004-5929.202504039
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    To analyze the qualitative discrimination of different artifacts of Ophiopogonjaponicus (L.f.) Ker-Gawl using infrared spectroscopy combined with intelligent network algorithm.The infrared spectral data of different artifacts of Ophiopogon japonicus (L.f.) Ker-Gawl were collected and analyzed using Decisiontree (DT) and Convolutional Neural Network (CNN) intelligent network algorithms, and the confusion matrices were compared with those of different algorithms and modeling bands, and the accuracy (Accuracy), precision (Precision) and accuracy (Precision) were calculated. Confusionmatrix) under different algorithms and modeling bands, and calculate its Accuracy, Precision and Recall. In the mid-infrared DT model, the discrimination model of fresh Ophiopogon japonicus (L.f.) Ker-Gawl was less accurate than that of other samples, and in the near-infrared DT model, the performance of the discrimination model was better; in the CNN discrimination model, the discrimination models built in the mid-infrared and near-infrared bands both achieved better discrimination accuracy. The qualitative discrimination model constructed by infrared spectroscopy can be used as an effective means to identify different artifacts of Ophiopogon japonicus (L.f.) Ker-Gawl, and the intelligent discrimination model of near-infrared spectroscopy combined with convolutional neural network has the overall advantage, which not only realizes the rapid and nondestructive detection process, but also improves the reliability of the discrimination through advanced data analysis algorithms, which provides an innovative technological solution for the accurate identification of different artifacts of Ophiopogon japonicus (L.f.) Ker-Gawl and is important reference to promote the standardization of quality control of Ophiopogon japonicus (L.f.) Ker-Gawl. It has important reference value for promoting the standardization of quality control.
  • Classification of Correction Fluids in Thermal Paper Using Infrared Spectroscopy Combined with Chemometrics
    ZHAO Yibo, WANG Xiaobin, XU Jinfeng, ZHANG Lingshen
    Chinese Journal of Light Scattering. 2025, 37(4): 849-855. https://doi.org/doi:10.13883/j.issn1004-5929.202504040
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    To achieve rapid and non-destructive identification of thermal paper correction fluids, Fourier transform infrared spectroscopy (FTIR) combined with chemometrics was employed to classify and differentiate 32 commercially available thermal paper correction fluids. The infrared spectra of the correction fluid samples were collected and preprocessed with baseline correction, smoothing, and vertical coordinate normalization. Principal component analysis (PCA) was then applied to extract seven principal components (cumulative variance contribution rate of 95.92 %), effectively reducing data dimensionality. Validation via K-means clustering (K=3) and hierarchical clustering demonstrated that the samples were classified into three distinct categories, with three-dimensional PCA score plots revealing significant intra-class aggregation and inter-class separation. Further predictive models—Naive Bayes, Random Forest, and Fisher discriminant analysis—were constructed. Leave-one-out cross-validation indicated classification accuracies of 90.62 % for Naive Bayes, and 93.75 % for both Random Forest and Fisher discriminant models. The experiment confirmed that FTIR coupled with chemometrics can efficiently differentiate the types and compositional variations of thermal paper correction fluids, providing reliable technical support for forensic identification and criminal traceability.
  • Predicting of the Visible Spectrum of Mixed Pigmentsusing Kubelka-Munk Theory and DBN Neural Network
    CAO Minyu, QIU Chengcong, PAN Guoxiang, WANG yuanfei, XIAO Chunming, XU minhong, LI jinhua
    Chinese Journal of Light Scattering. 2025, 37(4): 856-863. https://doi.org/doi:10.13883/j.issn1004-5929.202504041
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    The intelligent color matching and spectral prediction of inorganic pigments are significant issues in the pigment industry. In this paper, two yellow and blue pigments, namely Ca-FeOOH and ZnAl-LDHs-AG, were prepared by the precipitation method. The phases of the two pigments were confirmed by XRD characterization, and the spectra of green pigments obtained by physically mixing Ca-FeOOH and ZnAl-LDHs-AG at different ratios were tested. A color-mixing mechanism modeling method based on the hybrid K-M + DBN neural network was proposed to predict the reflectance values of the color-mixing system, and the reliability of the model was verified. The research results show that the prediction accuracy of the model is above 97%, demonstrating excellent spectral prediction capabilities.
  • Application of Infrared Spectroscopy and Simpson’s Integration Algorithm to Study Adenosine Content in Wild Cordyceps sinensis
    LIU Ziheng, TANG Yanlin, ZHANG Zhilong, LIU Hancheng, ZONG Jin, WU Xianzhong
    Chinese Journal of Light Scattering. 2025, 37(4): 864-870. https://doi.org/doi:10.13883/j.issn1004-5929.202504042
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    The correlation between adenosine content and infrared spectrum of wild Cordyceps sinensis was investigated. Five key wavenumber ranges were identified: 418.48~703.90 cm-1,732.83~817.68 cm-1,1039.46~1299.81 cm-1,1928.50~2967.96 cm-1,and 3598.58~3752.86 cm-1. Simpson's integration method was applied to these key regions, yielding three indices: area (Area), full width at half maximum (FWHM), and the ratio of area to the peak height (A/H). A multivariate linear regression (MLR) model was established using these indices and the average values (Mean) of the wavenumber ranges. The results indicated that the models based on Area and A/H performed best, with determination coefficients (R2) of 0.815 and 0.832, and root mean square errors (RMSE) of 0.072 and 0.066, respectively. In contrast, the FWHM model exhibited relatively poor performance, with an R2 of only 0.685. The validation results further demonstrated that the Area and A/H models retained superior performance, with R2 values of 0.842 and 0.826, respectively. These findings provide a theoretical foundation for the rapid determination of adenosine content in Cordyceps sinensis.
  • Light passes through scattering medium to realize fringe projection
    DUAN Meigang, ZHANG Chenlong, WANG Jianmin, WANG Jing
    Chinese Journal of Light Scattering. 2025, 37(4): 871-878. https://doi.org/doi:10.13883/j.issn1004-5929.202504043
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    Structured light illumination and fringe projection are important parts of optical three-dimensional measurement technology and play a very important role in many applications. However, this technology will be limited by light scattering when it is in a complex environment. In order to reduce the influence of scattering effect on the transmission of projection information, this paper proposes to use the Nutcracker optimization algorithm to modulate the front phase of incident light wave, and use this method to realize fringe image projection in scattered light field, which can project fringe images with different numbers, sizes and angles through scattering media. This result may broaden the scope of fringe-based imaging technology, and also provide a new scheme for the development of scattered light field regulation based on feedback wavefront shaping.
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E-mail: cjls@scu.edu.cn （备用：cjls@qq.com）
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