30 March 2025, Volume 37 Issue 1
    

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  • Research progress in early diagnosis of periodontitis by detecting SERS in gingival crevicular fluid
    NA Miya, BAI Lina, FENG Meiling, HAN Siqingaowa, HASI Wuliji
    Chinese Journal of Light Scattering. 2025, 37(1): 1-7. https://doi.org/10.13883/j.issn1004-5929.202501001
    Abstract ( ) Download PDF ( ) Knowledge map Save
    Periodontitis is a non-specific chronic inflammatory disease caused by plaque biofilm and mediated by host immune imbalance. The prevalence of periodontitis in China is much higher than the global average, so early diagnosis and prognosis monitoring of periodontitis is of great significance. Gingival crevicular fluid (GCF) refers to the fluid that permeates into the gingival sulcus from gingival connective tissue through gingival sulcus epithelium and joint epithelium. Clinical research shows that the change of GCF can reflect the inflammation degree of periodontitis to some extent. Based on literature review and previous work, this paper summarizes the research progress of surface enhanced Raman spectroscopy (SERS) combined with machine learning in early diagnosis of periodontitis by detecting gingival crevicular fluid. This technology is expected to provide a rapid, sensitive, accurate, painless and simple new method for early diagnosis and prognosis monitoring of periodontitis.
  • A hydrophobic paper-based SERS sensor based on enzyme cleavage strategy for rapid and highly sensitive detection of miR-21 in gastric cancer
    ZHANG Dong, CHEN Miao, CAO Xiaowei, WANG Biao, BU Chiwen, XIE Feng, CHEN Yuhua, LIU Yongxia
    Chinese Journal of Light Scattering. 2025, 37(1): 8-17. https://doi.org/10.13883/j.issn1004-5929.202501002
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    Based on the AuNCs hydrophobic paper substrate and enzymatic cleavage strategy, a rapid, highly sensitive and specific method for detecting gastric cancer (GC)-associated miRNA markers was developed. First, gold nanocages (AuNCs) were assembled on the hydrophobic release paper surface to form a Array style AuNCs substrate with rich "hotspots". Then, miR-21 complementary single-stranded nucleotide ssDNA-21 is modified on the surface of the arrayed AuNCs substrate, followed by ligation of the 4-MBA-labled Au–Ag nano-shuttles (Au-AgNSs) to form a complex structure of the Au-AgNSs@4-MBA@ssDNA-21@AuNCs substrate. Finally, after hybridization between ssDNA-21 and miR-21, DNA phosphodiester bonds in ssDNA-21 miR-21 double stranded DNA were selectively cleaved using double stranded specific cleavage enzyme (DSN) for SERS detection. The linear range of the SERS microfluidic chip for detecting miR-21 is 1 fM~1 nM, with a detection limit (LOD) of 0.34 fM. In addition, the SERS sensor is easy to operate, with a testing process of only 30 minutes, and exhibits good specificity and reproducibility. By using this SERS sensor, we successfully detected and distinguished the difference of miR-21 expression levels in serum of healthy people and GC patients. The accuracy of SERS was validated by real-time quantitative polymerase chain reaction (qRT PCR).
  • DFT and experimental study on Raman spectra of two types fentanyl-like substances
    WANG Shudong, TIAN Renkui, WANG Lixiong, ZHENG Xuan, ZHANG Yan
    Chinese Journal of Light Scattering. 2025, 37(1): 18-22. https://doi.org/10.13883/j.issn1004-5929.202501003
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    Based on the density functional theory, the structure optimization of furanylfentanyl and isofentanyl were performed at the B3LYP/6-311++G(d,p) level, and the Raman spectra were calculated at the same level. Using standard samples, accurate Raman spectra of furanylfentanyl and isofentanyl were obtained experimentally, and the results are in good agreement with the theoretical calculation. The Raman peaks were assigned by PED (potential energy distribution) analysis, the comparison between experimental and theoretical studies shows that the Raman characteristic peaks of furanyfentanyl are 1472, 1003, and 1642 cm-1, the strongest Raman peak is 1472 cm-1, while the Raman characteristic peaks of isofentanyl are 1004, 1451, and 1608 cm-1, the strongest Raman peak is 1004 cm-1. From the results, it can be seen that the substituent groups of fentanyl have a significant impact on their Raman spectra, which is also the key to distinguishing fentanyl and its analogs. This study provides accurate experimental data and reliable theoretical support for the rapid detection of furanyfentanyl and isofentanyl by Raman spectroscopy, PED analysis provide important reference for the study of the characteristics of Raman spectra of fentanyl and its analogs, which will contribute to the development of Raman detection standards for fentanyl New Psychoactive Substances and the establishment of drug Raman spectroscopy databases.
  • In situ detection of dyed Croci Stigma using MIL-101(Cr) modified thin-layer chromatography surface enhanced Raman spectroscopy
    ZHANG Binbin, XU Yunlong, WANG Pu, PANG Jilei
    Chinese Journal of Light Scattering. 2025, 37(1): 23-29. https://doi.org/10.13883/j.issn1004-5929.202501004
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    To overcome the problem of relying on metal sol substrates in traditional thin-layer chromatography surface enhanced Raman spectroscopy, this study mixed AuNPs/MIL-101(Cr) composite with excellent SERS performance and chromatographic silica gel to form a homogenate, which was then spread on a glass slide to prepare MIL-101(Cr) modified thin layer chromatography plates with both intrinsic SER activity and good stability. Establish a MIL-101(Cr) modified thin layer chromatography-surface enhanced Raman spectroscopy method by combining a portable Raman spectrometer with a MIL-101(Cr) modified thin-layer chromatography plate. The innovation of this method lies in that before performing SERS detection on the sample, only 0.01mol/L hydrochloric acid solution needs to be added to wet the sample spots, and strong SERS signals can be collected without adding any SERS substrate. The results showed that the MIL-101 (Cr) modified thin layer chromatography-surface enhanced Raman spectroscopy method successfully detected dyed Croci Stigma with four common dyes through SERS in-situ detection, which can be used for simple, rapid, and sensitive identification of adulteration in traditional Chinese medicine staining. Compared with the traditional thin layer chromatography-surface enhanced Raman spectroscopy method, it can better meet the needs of rapid and sensitive on-site detection of traditional Chinese medicinal materials.
  • Phonon anharmonic effects in semiconductor material CuAlO2
    LANG Yuhang, HU Qiwei, ZHANG Yin, XIE Yinghua, LI Shengnan, LIN Boyu, LEI Li, YUAN Yuquan
    Chinese Journal of Light Scattering. 2025, 37(1): 30-38. https://doi.org/10.13883/j.issn1004-5929.202501005
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    As a rare transparent conducting oxide material with p-type conductivity, CuAlO2 can be widely used in fields such as transparent electronic devices and solar cells, but its low conductivity limits its practical applications. In semiconductor materials, there is a direct relationship between the anharmonic effect of phonons and conductivity, which allows for the influence of electrical conductivity by adjusting phonons. However, the anharmonic behavior of phonons in CuAlO2 is still unclear.In thispaper, we prepared CuAl1-xCrxO2 (x=0~0.4) material using a solid-state reaction method and combines X-ray diffraction, Raman scattering spectroscopy, and first principles calculations to study the phonon anharmonic effects of CuAl1-xCrxO2 (x=0~0.4) samples under different pressures and temperatures. The refinement of crystal structure by X-ray diffraction shows that with the increase of Cr doping concentration, the thermal vibration factor of Cu atoms first increases and then decreases, with the maximum thermal vibration factor at x=0.1; Raman scattering spectroscopy and first principles analysis indicate that the Eg and A1g modes are mainly three phonon processes, but the four phonon process of the A1g mode increases with increasing doping concentration. Our research results have important guiding significance for further improving the conductivity of CuAlO2.
  • Research on Pathogen Classification Using Raman Spectroscopy Based on Wavelet Transform and Transformer Model
    YAO Qi, YANG Jingjing, HUANG Ming
    Chinese Journal of Light Scattering. 2025, 37(1): 39-46. https://doi.org/10.13883/j.issn1004-5929.202501006
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    Quantitative analysis of pathogens is crucial for the prevention and treatment of infectious diseases. Compared to traditional microbiological identification methods, Raman spectroscopy offers advantages such as speed, non-destructiveness, and high sensitivity. However, it faces limitations including long analysis times and the requirement for specialized expertise. To address these challenges, this paper proposes a method combining wavelet transform and Transformer models for the precise detection of pathogens. The proposed method is validated on a publicly available Raman spectroscopy dataset of pathogens, with comparative analysis against Random Forest, VGG19, ResNet, and AlexNet algorithms. The results demonstrate that the spectral data processed with wavelet transform achieves a 3% accuracy improvement on the Transformer model compared to the raw data. Specifically, the accuracy reached 95.21% for the classification of 30 types of pathogens and 99.2% for the classification of 8 types of antibiotics. The method outperforms the comparative algorithms in terms of classification accuracy, and also exhibits high recall and F1 scores. This study enhances the efficiency and accuracy of rapid bacterial infection diagnostics, providing a novel tool for biomedical detection research.
  • Research on online detection of milk powder components based on partial least squares regression and in-situ Raman spectroscopy technology
    WANG Lixiang, GUO Xiangwei, LANG Qinzheng, Zhao Song
    Chinese Journal of Light Scattering. 2025, 37(1): 47-53. https://doi.org/10.13883/j.issn1004-5929.202501007
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    The online non-contact in-situ spectral detection technology of milk powder composition is very beneficial to dairy production enterprises, which is very important for processors to evaluate the quality of products in real-time. Raman spectroscopy is a very commercial potential in-situ spectral detection technology. This paper analyzes the feasibility of in-situ Raman spectroscopy in detecting the main components of milk powder (fat, protein, lactose, etc.). The Raman spectrum experiments of skimmed milk powder and whole milk powder with different mixing ratios were carried out, and the statistical laws of Raman spectrum characteristic peaks and fat content were studied. The prediction model based on the partial least squares regression method was established, and the fat content was predicted based on the regression model. The determination coefficient of the regression model was more significant than 0.9886, and the root mean square error was less than 0.6248. In conclusion, in situ, Raman spectroscopy has potential commercial application value for the online evaluation of milk powder quality.
  • Theoretical and experimental studies on Raman and Ultraviolet spectra ofrosiglitazone
    CHEN Yufeng, LI Yutong, HAN Jinling
    Chinese Journal of Light Scattering. 2025, 37(1): 54-59. https://doi.org/10.13883/j.issn1004-5929.202501008
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    Rosiglitazone (RSG), C18H19N3O3S, is a thiazolidinedione insulin sensitizer. In this paper, the structure optimization of RSG and its Ag complexes was carried out at the level of LanL2d pseudopotential group of B3LYP/6-31+G(d,p)(C,H,N,O,S) and Ag atoms based on density functional theory (DFT). The Raman spectrum of RSG molecule and their silver complexes were obtained by frequency calculation, and the characteristic peaks of Raman spectra in the band 400-1800cm-1 were identified. The surface electrostatic potential and frontier orbit of RSG molecule was calculated, and the possible sites of chemical reaction were analyzed. Using time-dependent density functional theory (TDDFT), the molecular internal excited state of RSG was calculated and analyzed, using the charge transfer spectra of RSG molecular internal relationship between the charge transfer was studied, The corresponding analysis was made with the ultraviolet spectrum measured by experiment. This provides important information for understanding the mechanism of RSG molecules.
  • Research on ElasticNet quantitative inversion algorithm and Ramanspectroscopy detection for fish oil adulteration
    LU Yujun, XU Gang, LIU Huadong
    Chinese Journal of Light Scattering. 2025, 37(1): 60-68. https://doi.org/10.13883/j.issn1004-5929.202501009
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    Fish oil is one of the health products pursued by the public at present. The volume of the domestic fish oil consumption market is also growing, and the adulteration of fish oil is also becoming increasingly prominent. To realize the quantitative analysis of fish oil adulteration, this paper proposes a research work based on near-infrared micro-Raman spectroscopy combined with an elastic net quantitative inversion algorithm to realize the adulteration of fish oil with different animal oils. The Raman spectrum characteristic peaks of different animal oils were analyzed in detail. The Raman spectrum databases of adulterated fish oils and the corresponding ElasticNet quantitative inversion models were established. The results showed that the R2 of the four quantitative models for the test set were 0.9848, 0.9876, 0.9886, and 0.9880, and the RMSE was 0.0389, 0.0352, 0.0339, and 0.0347, respectively. Therefore, for the problem of fish oil adulteration, the technical method of combining ElasticNet quantitative inversion algorithm and Raman spectroscopy proposed in this paper has essential reference value and research significance for the field of fish oil quality detection, and this method can guide the application of in-situ rapid detection of fish oil detection in the future.
  • Research onIdentification Method of Microplastics in Urban Rivers Based on GA-GRNN Algorithm and Micro Raman Spectroscopy
    LI Jing, ZHANG Yuan, ZHANG Ying, LIU Jiawei
    Chinese Journal of Light Scattering. 2025, 37(1): 69-76. https://doi.org/10.13883/j.issn1004-5929.202501010
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    Microplastic pollution has become a global environmental problem. Strengthening the supervision of microplastic pollution in urban waters is the key to solving the problem. Therefore, this paper carried out research on a rapid and real-time microplastic identification method in urban rivers. In this paper, a genetic algorithm optimized generalized regression neural network (GA-GRNN) algorithm combined with micro Raman spectroscopy was proposed. The experimental detection and theoretical calculation of microplastic powder were carried out, and the fitting interpretation of Raman spectrum characteristic peaks and hidden peaks of microplastic powder were analyzed. The Raman spectra of microplastic suspensions with different concentrations were evaluated. The GA-GRNN algorithm established the microplastic recognition and classification model. The model’s classification accuracy was 100%, which realized the accurate recognition of microplastic particles separated from the river. This paper presents a very practical technical method for combining the GA-GRNN algorithm with micro Raman spectroscopy, which has a good reference significance in the supervision and guidance of micro plastic pollution in urban waters in the future.
  • Prediction ofSeabuckthorn Seed Oil Content by Raman Spectroscopy based on FA-XGBoost Algorithm
    WANG Zhao, LIU Bin, WU jun
    Chinese Journal of Light Scattering. 2025, 37(1): 77-85. https://doi.org/10.13883/j.issn1004-5929.202501011
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    Seabuckthorn seed oil has high nutritional and medicinal value, and high profits drive illegal businesses to pass shoddy goods off as good, confusing the false with the true. More methodological research is needed to identify seabuckthorn seed oil adulteration. Therefore, this paper proposes a method to detect the adulteration of seabuckthorn seed oil based on the FA-XGBoost algorithm, which enables Raman spectroscopy. Seabuckthorn seed oil was adulterated with sunflower seed oil in different volume fractions. The Raman spectra of all samples were measured by a near-infrared micro Raman spectrometer. The Raman spectra of seabuckthorn seed oil and sunflower seed oil were qualitatively analyzed. A regression model for adulterating sunflower seed oil with seabuckthorn seed oil was constructed using the FA-XGBoost algorithm. The predictive performance of the FA-XGBoost model was evaluated using a test set with a determination coefficient of 0.9959 and a mean squared error of 0.0031. This paper proposes a quantitative detection method for seabuckthorn seed oil adulteration. The concentration prediction of sunflower seed oil adulterated with seabuckthorn seed oil is realized through the combination of the FA-XGBoost algorithm and Raman spectroscopy. This method has potential application value and practical significance for regulating the domestic seabuckthorn seed oil consumption market.
  • Rapid identification of adulteration behavior of Gastrodia elata Blume by MDS-SVM algorithm and Raman fluorescence spectroscopy
    ZHANG Cuiping, ZHANG Junxing, LIU Yewei, HE Xuefeng, ZHOU Minghui
    Chinese Journal of Light Scattering. 2025, 37(1): 86-93. https://doi.org/10.13883/j.issn1004-5929.202501012
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    Gastrodia elata is an essential traditional Chinese medicine. Its powder is often used in traditional Chinese medicine preparations, health products, food additives, and other fields. However, illegal traders will add other ingredients to the powder to reduce costs or increase weight. To achieve a rapid, non-destructive, and susceptible laser spectral detection technology for detecting the adulteration behavior of Gastrodia elata powder, this paper conducted an experimental study on the Raman fluorescence spectrum of Gastrodia elata powder adulteration. Through the spectral research of pure natural Gastrodia elata powder and starch, the characteristics of the starch Raman spectrum were analyzed, and a quantitative model based on multidimensional scale transformation combined with a support vector machine (MDS-SVM) was proposed. By measuring the Raman fluorescence spectrum of simulated adulterated powder, the mds-svm quantitative model for Gastrodia elata powder adulteration prediction was established. The prediction effect of the model was good; the determination coefficient of the prediction results of the model on the test set was 0.8933, and the root mean square error was 0.0131. The results show that Raman fluorescence spectroscopy has the performance of characterizing the composition information of Gastrodia elata powder and combining it with the MDS-SVM algorithm, which can quickly identify the adulteration behavior of Gastrodia elata powder. This paper provides a rapid and non-invasive method for the rapid identification of Gastrodia elata powder and a reference for the law enforcement application of relevant departments.
  • Quantifying sugar adulteration behavior in natural honey using Raman spectroscopy using random forest regression
    LUO Fuxia, ZHOU Dong, MA Songsu
    Chinese Journal of Light Scattering. 2025, 37(1): 94-100. https://doi.org/10.13883/j.issn1004-5929.202501013
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    Although the methods of honey adulteration may vary between regions and markets, honey adulteration is a global issue. In response to the issue of adulterated honey with white sugar, this article developed a non-invasive, non-invasive, and efficient laser spectroscopic detection technology for quantitative detection of honey adulteration by detecting white sugar in honey using micro Raman spectroscopy and random forest regression algorithm. Twenty-one honey samples with different adulteration concentrations were experimentally measured, and the characteristic spectral peaks and their corresponding vibration assignments of white sugar and honey Raman spectra were analyzed. A quantitative white sugar adulterated honey analysis model was constructed based on the random forest algorithm. It was found that the Raman spectral feature information near 425, 680, 854, and 2910 cm-1 was the most influential feature peak position on the accuracy of the random forest model. The prediction accuracy of the random forest model was verified through the test set, with a determination coefficient of 0.9998 and a root mean square error of 0.0026. Based on the above results, the Raman spectroscopy technique using random forest regression proposed in this article can quantify the behavior of sugar adulteration in natural honey. This study has a positive impact on protecting consumers’ legitimate rights and promoting the honey market’s healthy development.
  • Quantitative analysis of atropine sulfate in eye drops based on surface-enhanced Raman spectroscopy and machine learning
    BAO Lin, XU Zixuan, FANG Guoqiang, HAN Siqingaowa, HASI Wuliji
    Chinese Journal of Light Scattering. 2025, 37(1): 101-108. https://doi.org/10.13883/j.issn1004-5929.202501014
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    This study utilizes surface-enhanced Raman spectroscopy (SERS) in combination with a light gradient boosting machine (LGB) algorithm to establish a quantitative analysis model for atropine sulfate injection, enabling rapid and precise detection of atropine sulfate concentrations in eye drops. Initially, uniformly sized silver nanoparticles (Ag NPs) were synthesized, with their morphology and extinction spectra characterized experimentally, and their electric field distribution in silver colloidal dimers was calculated using Comsol simulation software. Using atropine sulfate injection as the research focus, we examined the impact of coagulants on Ag NPs’ sensitivity in detecting atropine sulfate and optimized the type and concentration of coagulants, thereby establishing a foundation for its quantitative analysis. Using the LGB algorithm with spectral data from varying concentrations of atropine sulfate injections, we developed a quantitative analysis model. Finally, this quantitative model was applied to SERS spectral analysis of atropine sulfate in eye drops, yielding a correlation coefficient (R2) of 0.998, indicating high model accuracy. These findings may serve as a reference for the production and quality control of atropine sulfate eye drops in the marketplace.
  • Flame characteristic spectroscopic analysis of hydrocarbon fuels
    LI Yu , TANG Hongchang, SUN Wenmei, WANG Xianbin, TANG Yi, SHI Lifen
    Chinese Journal of Light Scattering. 2025, 37(1): 109-115. https://doi.org/10.13883/j.issn1004-5929.202501015
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    Using a heated shock tube and an spectroscopic detector ICCD,transient emission spectra of n-heptane, n-decane, xylene and isooctanein the combustion reaction weremeasured in the range of ultraviolet to near infrared.Experiments were conducted at apressure of 1.5 atm and a temperature of 1500 K.Results show that the main emission bands are attributed to CH、OHand C2 radicals produced during the combustion process,and the small radicals CH、OHand C2 are the important reaction products in the combustion process of n-heptane, n-decane, xylene and isooctane;The results are consistent with the combustion kinetics mechanism;Determined the electron level transitions corresponding to the characteristic spectra of CH、OHand C2 radicals. Analyzed the production mechanism of small free radicals of CH，OHand C2from the perspective of chemical reaction with the theory of combustion kinetics.The results provide experimental basis for understanding the microscopic process of hydrocarbon fuels combustion reaction and verifying the mechanism of hydrocarbon fuels combustion reaction.
  • Research on Non-Contact Heart Rate Detection under Low Illumination Based on Multi-Spectral Imaging Technology
    WANG Yu, REN Yu, LI Dongliang, WANG Tingting, ZHAO Yizhuo, LI Teng
    Chinese Journal of Light Scattering. 2025, 37(1): 116-122. https://doi.org/10.13883/j.issn1004-5929.202501016
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    In order to solve the problem of low accuracy of the existing non-contact heart rate detection and realize the monitoring of cardiovascular diseases in low illumination environment, this paper proposes a heart rate detection method combining multi-spectral imaging technology with imaging photoelectric plethysmography (IPPG), which uses the characteristics of hummingbird V1 multi-spectral camera, such as wide spectral band range (350 nm~950 nm) and high energy utilization rate, to realize non-contact heart rate detection and improve the sensitivity and accuracy of the detection results. In this paper, Hummingbird V1 and the same CMOS RGB camera are used to collect video images of human faces at the same time in low illumination, and the heart rate is detected by spatial pixel averaging and independent component analysis, and the standard heart rate parameters of volunteers are compared and analyzed. The experimental results show that the multispectral camera can detect the heart rate under the illumination of 6 Lux, which is 10 Lux lower than that of RGB camera, and its error is ≤5 bpm under the condition of 18.5 Lux, which is 5.84 bmp lower than that of RGB camera. Therefore, multi-spectral imaging technology can effectively improve the accuracy of non-contact heart rate detection in low illumination.
  • Diffusion mapping combined with K-nearest neighbor algorithm helps rapididentification of Colla corii asini by space offset visible near-infrared spectroscopy
    XUE Xiaorui, ZHANG Kaiping
    Chinese Journal of Light Scattering. 2025, 37(1): 123-128. https://doi.org/10.13883/j.issn1004-5929.202501017
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    Colla corii asini is a kind of traditional and precious Chinese medicine. The main raw materials are donkey skin, cow skin, etc. Different raw materials determine the medicinal properties and efficacy of ass hide glue. In order to realize the nondestructive testing of colla corii asini raw materials, this paper uses the space offset visible near-infrared spectroscopy technology combined with diffusion mapping and K-nearest neighbor algorithm to carry out the identification and classification of colla corii asini raw materials. This research realized a space offset visible near-infrared spectroscopy technology based on the spatial offset theory, carried out the spatial offset spectral experiments of donkey hide, cow hide and their mixtures of different concentrations, realized the dimensionality reduction of high-dimensional spectral data through the diffusion mapping algorithm, and then completed the identification of donkey hide and cow hide colla corii asini based on the K nearest neighbor algorithm. The recognition effect of the model was evaluated through the comprehensive evaluation index of the classification algorithm confusion matrix and accuracy. The results show that the diffusion mapping combined with K nearest neighbor algorithm proposed in this study has high feasibility and reliability in assisting space offset visible near-infrared spectroscopy to identify colla corii asini raw materials. This paper proposes and verifies a new method for rapid identification of colla corii asini raw materials, which provides a new methodological reference for relevant technicians.
  • LED fluorescence spectroscopy combined with convolutional neural network algorithm for predicting the mixed concentration of walnut oil and sunflower oil
    CUI Liang, ZHAN Jungui, HE Changtao
    Chinese Journal of Light Scattering. 2025, 37(1): 129-135. https://doi.org/10.13883/j.issn1004-5929.202501018
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    Walnut oil is a kind of nutty vegetable oil with rich nutrition and high prices. Using cheaper oil mixed with walnut oil is one of the primary means of adulteration. To achieve a rapid and efficient quantitative analysis method and detection technology of walnut oil adulteration, this paper proposes to predict the mixed concentration of walnut oil and sunflower oil using the UV LED fluorescence spectrum combined with a convolutional neural network algorithm. Firstly, a series of mixed samples of walnut oil and sunflower oil were prepared, and the fluorescence spectra of the mixed oil samples were excited by UV LED. The noise information in the fluorescence spectra was removed by using the EMD-PSO optimization threshold algorithm. The superposition spectral peaks of the fluorescence spectra were calculated and analyzed theoretically, and the fluorescence spectra database of the mixed oil samples was established. Then, a convolutional neural network model was constructed based on the fluorescence spectrum database to predict the concentration of walnut oil in mixed oil samples. The experimental results show that the detection technology proposed in this paper not only characterizes the difference in the fluorescence spectra of the two vegetable oils but also has good accuracy and stability in predicting the mixed concentration based on a convolutional neural network. The R2 and ME predicted by the model for the test set are 0.9853 and 0.0783, respectively. In summary, this study provides a new approach for rapid and non-destructive detection of the adulteration concentration of walnut oil and sunflower oil, which is expected to be widely applied in the food and oil industry.
  • Study on Rayleigh scattering intensity in silver nanoparticle dispersions
    MA Jianli, YANG Bo, YANG Guangwei, ZHANG Meng, LI Can, LIU Ke, ZHU Jianqi
    Chinese Journal of Light Scattering. 2025, 37(1): 136-143. https://doi.org/10.13883/j.issn1004-5929.202501019
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    Rayleigh scattering is a specific type of light scattering phenomenon that occurs when light, which is significantly smaller than the scatterer, interacts with an inhomogeneous medium. The examination of the intensity distribution features of Rayleigh scattering is crucial for assessing the interior properties of particles. This work presents the design and construction of a cost-effective, user-friendly Rayleigh scattering experimental apparatus with a broad variety of applications. The change in Rayleigh scattering intensity of silver nanoparticle dispersion was rigorously examined across various concentrations, incoming light intensities, and photometric distances. The experimental results indicate that the scattered light produced by the silver nanoparticle dispersion, with a particle size of 2-10 nm and irradiated by red light at a wavelength of 650 nm, exhibits Rayleigh scattering. When the concentration of silver nanoparticle dispersion is below 60 ppm, the intensity of scattered light exhibits a linear correlation with the concentration. When the concentration exceeds 100 ppm, the intensity of scattered light exhibits no significant correlation with the concentration. The intensity of dispersed light is directly proportional to the incoming light intensity and inversely proportional to the square of the observed distance. The findings are highly significant for the investigation of Rayleigh scattering and the examination of particle characteristics.
  • Analysics of Xixia Painted Wooden Chair Pigments by X-ray Diffractometer and X-ray Fluorescence Analyzer
    TANG Wenjuan, Wang Yanling
    Chinese Journal of Light Scattering. 2025, 37(1): 144-148. https://doi.org/10.13883/j.issn1004-5929.202404020
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    X-ray diffraction spectrometer and X-ray fluorescence were used to analyze the pigments of Xixia painted wooden chair unearthed inBaisikouWestTower. The results showed that the red, orange and yellow pigments used in Xixia painted wooden chair was composed of vermilion, lead and gold. The test results provide an accurate basis for the use of pigments un the protection and restoration
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