加入收藏  设为首页
 
Home | 中文  
   Home   |   About Journal   |   Editorial Board   |   Instruction   |   Rewarded   |   Indexed-in   |   Impect Factor   |   Subscriptions   |   Contacts Us
News

ScholarOne Manuscripts Log In

User ID:

Password:

Forgot your password?

Enter your e-mail address to receive an
e-mail with your account information.

  Journal Online
    Current Issue
    Advanced Search
    Archive
    Read Articles
    Download Articles
    Email Alert
    
Links  
22 CAST
22 CNCOS
22 CNKI
22 WanfangDATA
22 CHEMSOC
22 sinospectroscopy
22 CPS
Quick Search  
  Adv Search
2021 Vol. 41, No. 06
Published: 2021-06-01

 
1661 Research Progress in Identification and Detection of Fungi Based on SERS Spectroscopy
LI Ling1,2, HE Xin-yu1,2, LI Shi-fang1,2, GE Chuang3*, XU Yi1,2,4*
DOI: 10.3964/j.issn.1000-0593(2021)06-1661-08
Fungi are a group of pathogenic microorganisms with nuclei and cell walls. They are widespread and cause a variety of diseases in animals, plants, and humans. Fungal infection is one of the most common clinical infectious diseases, making the efficient detection method and related research of fungi attract more attention in recent years. At present, fungi traditional detection methods mainly include culture, microscopy and molecular biology detection, which have the disadvantages of complex operation and time-consuming. Surface-enhanced Raman scattering (SERS) technology has gradually played an important role in fungal detection and identification due to its advantages such as no interference from water, providing molecular fingerprint information and rapid detection. In this paper, based on the brief introduction of the structural characteristics and the common detection methods of fungi, Raman/SERS technology in the identification and detection of fungi was investigated and discussed. Firstly, through the analysis of the characteristics of Raman/SERS and the structure of fungi, according to the related references, the feasibility of SERS technology for fungal detection was analyzed. It was found that there are some problems in the detection of fungi by SERS, such as low sensitivity, complex signal, poor selectivity and specificity, poor signal reproducibility and stability. To solve these problems, the enhancement mechanism of SERS was analyzed. In particular, the review and prospect of the new advances in SERS analysis focused on the nano-enhanced medium materials of SERS, the signal amplification effect of SERS tag and the combination of SERS spectral microfluidic chip analysis technology. The selection of nanomaterials and the construction of nanostructures showed that the SERS enhancement effect produced by the SERS enhancement substrate has great potential in fungal identification and rapid diagnosis of clinical disease. Based on the SERS tag’s signal amplification mechanism, the sensitivity, specificity and reproducibility of fungal SERS detection can be greatly improved. More importantly, the design and integration of SERS nano-enhanced substrates on microfluidic chips and the construction of signal amplification strategy based on SERS tags are more likely to achieve high-throughput and high-content SERS detection of fungal samples, which shows great research value and application prospects in the identification and detection of fungi.
2021 Vol. 41 (06): 1661-1668 [Abstract] ( 233 ) RICH HTML PDF (3800 KB)  ( 169 )
1669 Research Progress of Terahertz Metamaterial Biosensors
YANG Jun1,QI Li-mei1*,WU Li-qin2,LAN Feng3,LAN Chu-wen4,5,TAO Xiang1,LIU Zi-yu1
DOI: 10.3964/j.issn.1000-0593(2021)06-1669-09
Terahertz (THz) wave, refers to the frequency range of 0.1~10 THz electromagnetic wave located in the electromagnetic spectrum between infrared and microwave. The photon energy of the Terahertz wave is lower than that of visible light, and the corresponding energy of 1 THz is only about 4.14 meV, which means the damage caused by radiation to tissues and organs in living organisms would be reduced greatly and will not ionize the biological molecules. Therefore, this band has important potential application value in basic science, human security, dangerous goods detection, high-speed communication and medical imaging. In the application of medical and biological detection, it is usually necessary to detect trace amounts of analytes, which requires higher sensitivity and detection accuracy. However, the existing detection methods are affected by the low reliability of terahertz wave intensity detection. Metamaterial-based biosensing can achieve sub-wavelength resolution by enhancing local electromagnetic resonance, which greatly improves the sensor’s resolution and sensitivity and has attracted widespread attention.The metamaterial is a kind of artificial periodic structure that can enhance the local electromagnetic resonance response, realize sub-wavelength resolution and improve the sensor’s resolution and sensitivity. The terahertz metamaterial sensor provides a new detection method for the field of biosensing, which has the advantages of high sensitivity, fast response speed, and label-free detection. With the rapid development of micro-nano processing technology, the cost of making metamaterial terahertz sensors continues to decrease, which has great potential application value in the field of biomedicine. Research on terahertz sensors based on metamaterials has become a very popular international frontier direction. However, there are no reports on the latest research progress of terahertz metamaterial sensors. Therefore, this paper has collected and sorted out relevant information and reviewed the latest applications of the terahertz metamaterial sensor in various biological detection scenarios, including medical diagnosis, food safety, pesticide detection and so on. Finally, the development and application prospect of Terahertz metamaterials in biosensors are summarized and prospected. This paper will provide an important reference for people to grasp the latest application progress of Terahertz metamaterial biosensors and guide the development and application of Terahertz metamaterial biosensors.
2021 Vol. 41 (06): 1669-1677 [Abstract] ( 507 ) RICH HTML PDF (7453 KB)  ( 281 )
1678 Study on Terahertz Absorption Characteristics of Water Based on Microfluidic Technology
WANG Guo-yang, BAI Zhi-chen, WANG Jia-hui, SU Bo*, ZHANG Cun-lin
DOI: 10.3964/j.issn.1000-0593(2021)06-1678-05
The characteristic vibration mode and rotation mode of many biomacromolecules are located in the terahertz band, and the low electron energy characteristic of the terahertz wave makes it impossible to destroy the samples to be measured in the experimental process, so terahertz technology can be used to identify biological samples. In many studies, biological samples are in a solution state. The interaction between water and other molecules in solution involves many biological phenomena, so it is very important to study the terahertz characteristics of water.As we all know, water molecules are very common polar molecules, and hydrogen bond between molecules will have strong interaction with terahertz wave, which makes water have strong absorption of terahertz wave, so it is very difficult to use terahertz technology to study the dynamic characteristics of biological samples in aqueous solution.In order to solve this problem, microfluidic technology can be introduced. Microfluidic technology is famous for its ability to control microscale fluid precisely, and its channel depth can reach 50 μm or even less. Because microfluidic technology reduces the propagation distance of terahertz waves in the fluid, the absorption of terahertz wave by water is greatly reduced. In this study, a microfluidic sandwich chip was made of Zeonor1420R with high transmittance to the terahertz wave. The length, width and depth of microchannels on the chip are 3 cm, 4 mm and 50 μm, respectively, and the diameter of the terahertz detection area is 3 mm.In the fabrication of a microfluidic chip, a strong adhesive double-sided adhesive with a thickness of 50 μm is used to replace the polydimethylsiloxane (PDMS) film in the traditional sandwich microfluidic chip, so that there is no leakage phenomenon in the heating process of the microfluidic chip. In addition, we design a temperature control system, which is composed of a heating plate, a temperature sensor and a temperature controller. The temperature control system can control the temperature with the precision of 0.1 ℃. The deionized water in the microfluidic chip was heated by this system, and the terahertz transmission was measured every 5 ℃ from 20 to 90 ℃. Through the analysis of the experimental data, it was found that the terahertz transmittance of water decreased with the increase of temperature, indicating that the absorption of water to terahertz waves increased with the increase of temperature.The results provide a precondition for the study of THz absorption characteristics of liquid samples by microfluidic technology at different ambient temperatures in the future and provide technical support for the application and development of THz in the future.
2021 Vol. 41 (06): 1678-1682 [Abstract] ( 251 ) RICH HTML PDF (2032 KB)  ( 175 )
1683 Terahertz Transmission Characteristics of Water Induced by Electric Field
CAI Yan, WANG Jia-hui, BAI Zhi-chen, SU Bo*, WU Rui, CUI Hai-lin, ZHANG Cun-lin
DOI: 10.3964/j.issn.1000-0593(2021)06-1683-05
Many biomolecules’ rotation, vibration or the whole vibration mode of the molecular group are located in the terahertz band, so we can use terahertz spectrum technology to detect biomolecules. At the same time, because the photon energy of the terahertz wave is only millivolts, it will not damage the internal structure of molecules, so terahertz time-domain spectroscopy technology has a good application prospect in biological detection. As we all know, most biomolecules can only play their biological activities in the liquid environment, so it is necessary to study the interaction between biomolecules in the liquid environment. However, the rotation mode, vibration mode and the energy related to hydrogen bond of water molecules are all in the terahertz band, so they have strong absorption; in addition, water molecules are polar molecules, and polar molecules have strong resonance absorption for terahertz wave, which makes it difficult to use terahertz technology to characterize the activity of biomolecules dynamically. Therefore, in the study of the interaction between biomolecules and terahertz wave in solution, it has become a research hotspot in recent years to minimize water molecules’ absorption to terahertz wave. At present, the main methods to reduce the absorption of THz wave by water are: adding ions that inhibit hydrogen bond association in solution samples to reduce the absorption of THz wave by water; adjusting the absorption of THz wave by water by changing the temperature of the solution; reducing the absorption of THz wave by water by reducing the distance between the sample and THz wave by using microfluidic chip technology. In addition, the excitation of laser, the treatment of electric field or magnetic field can also change the absorption of THz wave by water. In this paper, a microfluidic chip containing deionized water is put into the electric field to study the influence of deionized water treated by electric field for different time on THz wave’s absorption intensity. The results show that THz wave’s transmission intensity increases with the increase of the standing time of deionized water in the electric field. When standing in the electric field for 60 minutes, the spectrum intensity of the THz wave reaches the maximum, close to that of air. It can be concluded that the applied electric field changes the dipole moment of water molecules, which affects the vibration and rotation of water molecules as a whole, and changes the hydrogen bond structure in water, resulting in the enhancement of the THz transmission spectrum.
2021 Vol. 41 (06): 1683-1687 [Abstract] ( 202 ) RICH HTML PDF (2230 KB)  ( 75 )
1688 Polarizability Measurements for Salicylic Acid Embedded in Polymer Matrix Using Terahertz Time-Domain Spectroscopy
ZHANG Tian-yao1,2, ZHANG Zhao-hui1*, ZHAO Xiao-yan1, WEI Qing-yang1, CAO Can1, YU Yang1, LI Ying1, LI Xing-yue1
DOI: 10.3964/j.issn.1000-0593(2021)06-1688-07
Polarizability acts as an essential property in the design of novel materials. It is of great significance to obtain the dynamic polarizability over terahertz frequencies for substance containing complex intermolecular interactions. The experimental access of the polarizability is based on the accurate determination of its intrinsic dielectric constant. Meanwhile, thecoherent detection scheme of terahertz time-domain spectroscopy (THz-TDS) makes it an ideal alternative for dielectric constants measurements. However, most organic crystalline materials occupy strong absorption features over terahertz frequencies, and samples for THz characterization are commonly prepared by homogeneously dispersing the analyte within the THz transparent polymer matrix. The application of the dilution matrix makes the dielectric constants of mixture samples measured by the THz-TDS cannot be directly utilized to calculate the polarizability. We proposed in this work a ternary model based on the Landau, Lifshitz and Looyenga (LLL) theory to extract the intrinsic dielectric constants of analyte by removing the dielectric contribution of trapped air and polymer background. The extracted intrinsic dielectric constants can be further used to calculate polarizability combining with the Clausius-Mossotti equation. The proposed methodology is exemplified in detail with the polarizability determination of crystalline salicylic acid (SA) embedded in twopolymer matrixpolyethylene (PE) and polytetrafluoroethylene (PTFE), respectively. Mixture pellets at three concentrations are designed for THz-TDS measurement for each polymer matrix. The application of the proposed method on the original dielectric spectra of mixture pellets provides intrinsic dielectric constants and polarizability values of salicylic acid with promising consistency. The results reported in this work also represent the first experimentally measured polarizability values for crystalline salicylic acid, which is (17.2±0.2) and (17.6±1.0) Å3 for analyte embedded in PE and PTFE respectively.
2021 Vol. 41 (06): 1688-1694 [Abstract] ( 170 ) RICH HTML PDF (4109 KB)  ( 64 )
1695 Ultrafast Dynamics Investigation on Alizarin by Transient Absorption Spectroscopy
QIN Chao-chao1,2, LIU Hua1,2, ZHOU Zhong-po1,2*
DOI: 10.3964/j.issn.1000-0593(2021)06-1695-06
Excited proton transfer is one of the most basic chemical reactions in photo-physics, photochemistry and photobiology. Excited state intramolecular proton transfer (ESIPT) is usually defined as the process of proton transfer from the proton-donor group to the proton-acceptor group on the excited state energy surface, forming intramolecular bond-rings containing hydrogens. When the organic molecules is excited to the excited state, the proton transfer process takes place in a very short time, usually on the sub-picosecond scale. The effect of the proton transfer can be used in organic light-emitting diodes and fluorescent probes. Alizarin, namely 1,2-dihydroxyanthraquinone, extracted from the roots of madder originally, has a similar structure to quinone derivatives and is often used as dyes, dyestuffs and pharmaceuticals. In recent years, alizarin molecules have been found to have proton transfer properties, which can be used to prepare new green dye-sensitized batteries. In this paper, the proton transfer process of alizarin molecules dissolved in ethanol solution is investigated and analyzed by using the steady-state absorption, steady-state fluorescence, femtosecond transient absorption spectroscopy and first-principles calculation. The results of steady-state absorption and steady-state fluorescence show that the normal configuration of alizarin molecule 9,10-ketone is in a stable state in the ground state, which is prone for the energy transition. However, in the excited state, the tautomer 1,10-ketone of alizarin molecule is in a stable state, easy to produce fluorescence emission. Femtosecond transient absorption spectroscopy employs a laser with the excitation wavelength of 370 nm. The measured transient absorption spectra show that the ground state bleaching signal of the alizarin locates at 430 nm wavelength. The global fitting method is used to analyze the transient absorption spectra, and the results show that the proton-transfer time in the excited state of the normal configuration of the alizarin 9,10-ketone is 110.5 femtoseconds, the vibration-relaxation time in alizarin 1,10-ketone tautomer is 30.7 picoseconds, and the fluorescence life of alizarin 1,10-ketone tautomer is 131.7 picoseconds. By using the method of single-wavelength dynamic fitting, the transient absorption spectra are analyzed, and the results for the time scale of proton-transfer are basically consistent with that of the global fitting method. The normal configuration of alizarin molecule 9,10-ketone molecule is in a trend of the rapid decrease in 110.5 femtoseconds, while the tautomeric configuration 1,10-ketone molecule is in a tendency of the fast increase in this time scale. When the delay time increases, it decays slowly for 1,10-ketone tautomeric configuration of the alizarin molecule.
2021 Vol. 41 (06): 1695-1700 [Abstract] ( 242 ) RICH HTML PDF (2629 KB)  ( 267 )
1701 Spectroscopic Studies on the Interaction Between Salvianolic Acid B and Bovine Serum Albumin
ZHANG Chuan-ying1, PENG Xin1*, RAO Heng-jun2, QI Wei2, SU Rong-xin2, HE Zhi-min2
DOI: 10.3964/j.issn.1000-0593(2021)06-1701-07
As the main water-soluble active ingredient from Salvia miltiorrhiza, salvianolic acid B (SAB) has a wide range of biological activities. Serum albumin is the most abundant protein in plasma (about 60%) and can bind with various endogenous and exogenous compounds, which can play a role in the storage and transport of compounds. When SAB enters into the human body, it must be combined with the protein in the blood system and then transported to its receptor site and played its pharmacological effects. Thus, in order to better understand the distribution, transport and metabolism of SAB in vivo, the interaction of SAB with bovine serum albumin (BSA) has been investigated using fluorescence spectra, circular dichroism (CD) and nuclear magnetic resonance spectra (NMR) under simulated physiological conditions. The results showed that the binding of SAB to BSA could quench the intrinsic fluorescence of BSA through the combined quenching mechanism (static and dynamic), but the static quenching was the primary one. The binding constant was 7.51×105 L·mol-1 (288 K), 7.40×105 L·mol-1 (298 K) and 5.57×105 L·mol-1 (308 K), respectively. It was found to be in the order of 105 L·mol-1 and decreased with the increasing temperature. The results of site marker competitive experiments indicated that SAB specifically bound to Site I of BSA in the hydrophobic pocket of sub-domain IIA. The stoichiometric ratio between BSA and SAB was calculated by using the Scatchard equation, and the result suggested that the SAB can form a 1∶1 type non-covalent complex with BSA. The three-dimensional fluorescence and CD studies indicated that SAB induced some microenvironmental changes of tryptophan and tyrosine in BSA. That was, the binding of SAB to BSA brought the tryptophan and tyrosine residues to a more hydrophilic environment, while the changes of secondary and tertiary structures of BSA were relatively small. Furthermore, the chemical shift of SAB at various concentrations of BSA was studied using NMR spectra, and the results showed that the benzene ring of H5” and H6” in SAB played a vital role in the binding process during the BSA-SAB complexation. This research will be helpful for understanding the mechanism of SAB in vivo and the influence of the binding of SAB to the conformation and function of serum albumin in biological processes, and can provide some theoretical basis for the development of SAB related new drugs.
2021 Vol. 41 (06): 1701-1707 [Abstract] ( 199 ) RICH HTML PDF (4081 KB)  ( 62 )
1708 Reconstruction of Stack Plume Based on Imaging Differential Absorption Spectroscopy and Compressed Sensing
ZHONG Ming-yu1, 2, 3, ZHOU Hai-jin2, SI Fu-qi2*, WANG Yu2, DOU Ke2, SU Jing-ming1, 2, 3
DOI: 10.3964/j.issn.1000-0593(2021)06-1708-05
The quality of computed tomography image of stack plume has two limits. Oneof the limitations isthe temporal resolution of data acquisition of remote sensing instrument. The conventional remote sensing instrument used for tomography is an multi-axis differential optical absorption spectrometer. Limited by its’ speed of data acquisition, the temporal resolution of the reconstructed image is low. The other limitation is the insufficient data acquired from remote sensing instruments. Algebraic reconstruction algorithms and iterative statistical algorithms are usually used in the reconstruction, but the reconstructed images have a low resolution and plenty of artifacts. To overcome the first limitation, data acquisition system in this paper is composed of imaging differential optical absorption spectrum technique. Compared with the system composed of the multi-axis differential optical absorption spectrum, the system’s temporal resolution increases above 160 times. An algorithm based on compressed sensing and a low third derivative model is introduced to overcome the second limitation. The algorithm is called projection on convex sets-low the third derivative method, which is POCS-LTD in short. The proposed algorithm belongs to gradient projection for sparse reconstruction algorithms, which is divide into two steps: projection and total variation iteration. In the process of projection, the algebraic reconstruction algorithm is used to make the reconstructed image conform to the projection equation, and the optimization algorithm is used in the process of total variation iteration. In the process of the total variation, the normalized value of the low third order derivative model is used as the iterative direction of the optimization algorithm, and the module of the difference between the result of the previous iteration and the present projection operation is used as the iterative step. According to nearness, the reconstructed images are evaluated, the relative difference of maximum and concordance correlation factor by numerical simulation. The numerical simulation shows that the proposed algorithm has good error resist,and reduces nearness by 80% compared with the traditional low third derivative method. The method is used to reconstruct the plume by the data gets from the field campaign. A clear plume and suppression of artifacts can be seen from the reconstructed image. The data acquisition system and algorithm introduced in this paper promote temporal resolution and reduce artifacts of the reconstructed images, and improve the technique’s practicability.
2021 Vol. 41 (06): 1708-1712 [Abstract] ( 165 ) RICH HTML PDF (2985 KB)  ( 61 )
1713 Study on Simultaneous Classification of Hardwood and Softwood Species Based on Spectral and Image Characteristics
WANG Cheng-kun1, ZHAO Peng1,2*
DOI: 10.3964/j.issn.1000-0593(2021)06-1713-09
Wood is an indispensable renewable resource in people’s lives, and it also plays a vital role in architecture, craft, furniture, structural material and so on. The common wood species in the market are various, and the quality and price of different wood species also differ very much.Therefore, the use of intelligent technology to undertake correct wood classification can prevent illegal trader’s shoddy product and reduce the workload of wood classification personnel greatly. Though accurate wood classification results can be obtained through the genetic and anatomical information of the wood sample, the identification process of these two methods is relatively complex, not easy for non-professionals. With the help of image information or spectral information of wood surface, wood species can be classified and conveniently. However, due to the similarity among different wood species, the classification accuracy of these two methods is often not high or only suitable for some specific wood species. Therefore, we propose a multi-feature wood classification algorithm based on the image information and spectral information of wood cross-section. First, spectral reflectance curve and image information of wood cross-section are collected, respectively. Then, the Segnet image segmentation method is used to divide the wood samples into two groups: wood with and without pores. The characteristics of pores, spectral features and textural features are extracted from wood species with pores, and the textural features and spectral features are extracted from wood species without pores. Next, according to these characteristics, a support vector machine (SVM) is used to classify wood and record the classification results. Finally, the similarity criterion is used to judge the best classification results for the samples with inconsistent classification results. In order to verify the effectiveness of the method described in this paper, the mixed sample set of 20 common hardwood and softwood species is used and classified. Experimental results show that these three wood features can be used for classification, and the highest wood recognition rate is 93.00%, 89.33% and 69.23% for spectral, textural and pore features, respectively. By similarity measurement, the three wood features can complement each other so as to improve further the wood species classification accuracy with the highest recognition accuracy of 98%. To sum up, the method described in this paper can be used to classify a mixed wood sample set that includes hardwood and softwood. The spectral features, textural features and pore features of the wood cross-section can complement each other, thus improving classification accuracy. In addition, in this paper,we also compareour method with the state-of-the-art wood species identification methods and find that the classification rate of this algorithm is higher than other algorithms.
2021 Vol. 41 (06): 1713-1721 [Abstract] ( 236 ) RICH HTML PDF (5605 KB)  ( 91 )
1722 Sensitive Bands Selection and Nitrogen Content Monitoring of Rice Based on Gaussian Regression Analysis
WANG Jiao-jiao1, 2, SONG Xiao-yu1*, MEI Xin2, YANG Gui-jun1*, LI Zhen-hai1, LI He-li1, MENG Yang1
DOI: 10.3964/j.issn.1000-0593(2021)06-1722-08
Accurate detection of rice nitrogen content is an important aspect of precision fertilization in rice fields. Nitrogen content variation of rice leaves will cause changes in emissivity of leaves and canopy. Hyperspectral remote sensing is one of the key technologies for non-destructive monitoring of crop nitrogen. This study focuses on the study of nitrogen content monitoring and the sensitive band’s selection through machine learning methods based on 2-year rice nitrogen fertilization experiments carried out in Jianli Hubei during 2018—2019. Hyperspectral reflectance spectral data at the leaf and canopy level and the corresponding leaf nitrogen content data were collected at rice tillering, jointing, booting, flowering and filling stage, respectively. Correlation analysis and Gaussian process regression (GPR) were used to select nitrogen sensitive bands for raw spectra and first-order derivative reflectance (FDR) spectra in rice leaves and canopy level. The nitrogen content estimation models were then constructed through single-band regression analysis, Random Forest (RF) and Support Vector Regression (SVR) method for rice raw spectra data. The Gaussian Process Regression-Random Forest (GPR-RF), Gaussian Process Regression- Support Vector Regression (GPR-SVR), and GPR method were also used to construct the nitrogen estimation model for the nitrogen-sensitive selection bands. The results showed that the GPR method’s sensitive bands were consistent with variation rule of the nitrogen content and spectral changes in rice. The leaf-level model’s over all accuracy was higher than that of the canopy-level model under the same conditions while using FDR spectra was more accurate at canopy level for it could attenuate the effect of background noise in the rice field. R2 of calibration datasets and validation sets are 0.79 and 0.84 at the leaf level, while 0.80 and 0.77 at canopy level. Compared with the correlation regression model, the machine learning methods were less affected by rice growth stages (R2>0.80, NRMSE<10%). RF was more suitable than SVR for modeling GPR-selection nitrogen sensitive bands, and the GPR-RF model can use about 1.5% of the bands to reach the accuracy of the RF model using all the bands. The GPR model works well on nitrogen estimation through nitrogen -sensitive bands at leaf and canopy level, not only for the full-growth stage but also for the single-growth stage(R2>0.94, NRMSE<6%). Besides, compared with the other four machine learning methods, the GPR model can improve the accuracy and stability of the estimation of nitrogen content at the canopy level that R2 increased by 0.02 and NRMSE decreased by 1.2%. GPR method provides a methodological reference for selecting crop nitrogen hyper-spectrally sensitive bands and inversion of the nitrogen content of leaves and canopy level during rice different growth period.
2021 Vol. 41 (06): 1722-1729 [Abstract] ( 261 ) RICH HTML PDF (3841 KB)  ( 107 )
1730 Chlorophyll Content Estimation of Jujube Leaves Based on GWLS-SVR Model
Nigela Tuerxun1, Sulei Naibi2, GAO Jian3, SHEN Jiang-long1, ZHENG Jiang-hua1*, YU Dan-lin4
DOI: 10.3964/j.issn.1000-0593(2021)06-1730-07
Chlorophyll Contentis an indicator of the photosynthetic capacity, growth and nutritional status of jujube trees. The distribution of chlorophyll content is different in jujube trees planted in different geographical locations under the influence of natural and human-made factors. The Hyperspectral reflectance of jujube leaves and the SPAD value of jujube leaves that representing chlorophyll content in Ruoqiang county were measured on the spots. To estimate the SPAD value of jujube leaves efficiently and losslessly, the global Moran’s I of jujube SPAD value was calculated, The statistics was calculated based on the correlation between SPAD value and Hyperspectral bands to choose the most important characteristic bands. The GWLS-SVR(Geographically Weighted Least Squares-Support Vector Regression)model was used to predict the SPAD value and compared with multiple linear regression (MLR) and support vector regression (SVR) models, and explored the ability of the model to estimate the SPAD value of the jujube leaves. The results show that: (1) the First derivative of the spectrum can effectively remove the noise and highlight the spectral information, especially in the range of 492~510, 542~543, 642~652, 657~670 and 682~692 nm, and significantly improve the correlation of the spectrum with SPAD value. (2) statistics method can effectively select the feature bands of the sensitive range, thus improves the model estimation accuracy. The two variables with the highest importance of the original spectrum were 595 and 696 nm, and the feature band of the first derivative of the spectrum was 688 nm. Among them, the statistics of a single band were always lower than those of multiple band combinations of the same sensitive band interval, which may be caused by the strong collinearity between the adjacent bands. (3) There was significant spatial aggregation on the SPAD value of jujube leaves in Ruoqiang county, the global Moran’s I was 0.125 8 (p<0.1), which is suitable for the establishment of GWLS-SVR model that considers the spatial location. (4) By combining Bootstrap resampling and t-test, the GWLS-SVR model that combined with geographic location information was generally better than the support vector regression and multiple linear regression model, and the results were highly significant (p<0.001). Among the models, the GWLS-SVR model based on the First derivative of the spectrum was the optimal estimation of SPAD value for jujube leaves (R2=0.975, MSE=1.082), which can provide a certain reference for the Hyperspectral quantitative inversion of the SPAD value of jujube and the rapid and non-destructive monitoring of jujube growth.
2021 Vol. 41 (06): 1730-1736 [Abstract] ( 210 ) RICH HTML PDF (3317 KB)  ( 73 )
1737 A Comparative Study on Roujean and Ross Li Models of Winter Jujube in South Xinjiang Under Different Outdoor Light
SUO Yu-ting1,2, LUO Hua-ping1,2*, LIU Jin-xiu1,2, LI Wei1,2, CHEN Chong3, XU Jia-yi1,2, WANG Chang-xu1,2
DOI: 10.3964/j.issn.1000-0593(2021)06-1737-08
How to eliminate or reduce the difference of inversion data and improve the detection accuracy under different illumination is a big problem in the outdoor detection of winter jujube in South Xinjiang. Therefore, this paper obtains the bidirectional reflection distribution function of winter jujube in South Xinjiang by using a hyperspectral camera The least square method is used to fit the parameters of the roujean model and Ross Li model. Finally, the inversion results of the roujean model and Ross Li model are compared, and the viewpoint of which weather, which wavelength and which model are the best is put forward. The experimental results show that: (1) in cloudy weather, in an inversion of Dolp of winter jujube in South Xinjiang,The R-square of Ross Li model is 0.974 8 and that of roujean model is 0.969 9; the R-square of Ross Li model is 0.972 3 and that of roujean model is 0.974 9 when the intensity component of winter jujube is retrieved. In cloudy weather, in an inversion of Dolp of winter jujube in South Xinjiang,the R-square of Ross Li model is 0.965 1, and that of roujean model is 0.977 8; in an inversion of winter jujube intensity component, the R-square of Ross Li model is 0.942 0, and that of roujean model is 0.968 8. In sunny weather, in an inversion of Dolp of winter jujube in South Xinjiang,R-square of Ross Li model is 0.965 5, R-square of roujean model is 0.926 2; in an inversion of winter jujube intensity component, R-square of Ross Li model is 0.928 5, R-square of roujean model is 0.833 1. The best scheme of the whole inversion is to use the Ross Li model for the inversion of winter jujube DOLP in cloudy weather, roujean model for the inversion of intensity component, Ross Li model for the inversion of winter jujube DOLP and intensity component in sunny weather, and roujean model for the inversion of winter jujube DOLP and intensity component in cloudy weather. (2) The best scheme of multi band inversion is: in cloudy weather, when the intensity component of winter jujube in South Xinjiang is retrieved, the wavelength is 1 000~1 100 nm, Ross Li model is needed, the wavelength is 1 450~1 600 nm, roujean model is needed, and the other two models can be used; when the DOLP of winter jujube in South Xinjiang is retrieved, the wavelength is near 1 300 nm, Ross Li model is needed, and the other two models can be used. In cloudy weather, when the intensity component of winter jujube in South Xinjiang is retrieved, the wavelength is 1 000~1 350 nm, roujean model is needed, the wavelength is near 1 600 nm, Ross Li model is needed; when the Dolp of winter jujube in South Xinjiang is retrieved, in the range of 1 000~1 350 nm, roujean model is needed, and in the range of 1 600 nm, Ross Li model is needed. On sunny days, when retrieving the intensity component of winter jujube in South Xinjiang, the wave-length is in the range of 1 000~1 350 nm and around 1 600 nm, the Ross Li model is needed, and there is no special requirement for other wave-bands; when retrieving DOLP of winter jujube in South Xinjiang, the wavelength is near 1 000 nm, the roujean model is needed, and the wavelength is near 1 600 nm the Ross Li model is needed. Thus, the method of eliminating or reducing the difference of inversion data is explored, which lays a foundation for improving the accuracy of outdoor detection of winter jujube in South Xinjiang.
2021 Vol. 41 (06): 1737-1744 [Abstract] ( 200 ) RICH HTML PDF (4863 KB)  ( 60 )
1745 Velocity Measurement Technology of Supersonic Flow Field Based on Spontaneous Emission Spectrum
QI Xin-hua, CHEN Li*, YAN Bo, MU Jin-he, CHEN Shuang, ZHOU Jiang-ning
DOI: 10.3964/j.issn.1000-0593(2021)06-1745-06
The measurement of plasma state parameters is an important basis for studying plasma characteristics, including plasma simulated reentry environment, plasma stealth, plasma drag reduction, and boundary layer control. Based on the spontaneous emission spectrum of the plasma jet, a new method of plasma supersonic jet velocity is proposed in this paper. Firstly, the spontaneous emission spectrum generated by argon atoms in the plasma was measured, and the characteristic spectral line of 696.54 nm was selected as the moving light source for the speed measurement experiment of the plasma generator; secondly, the optical path of speed measurement was designed by using a spectrometer, energy transmission fiber, Electron-Multiplying CCD (EMCCD) camera and high spectral resolution Fabry-Perot (F-P) interferometer for high temperature plasma; finally, the velocity measurement experiment was carried out of supersonic jet on an argon wall stabilized arc plasma generator. In this experiment, the spontaneous emission spectrum of argon atom at the same measuring point was collected into a spectrometer by the collecting lens, which wasan angle of 49°and 90°between with the plasma jet motion direction, respectively. After being split by the spectrometer, only the characteristic line of 696.54 nm was retained into the energy transmitting optical fiber, to eliminate the influence of the spontaneous emission spectrum of other wavelengths; the characteristic emission spectrum from the spectrometer, which was transmitted by optical fiber and shaped into parallel light by the lens, irradiated the F-P interferometer with a fineness of 30 and a free spectral range of 6.6 GHz, then a multi-beam interference ring was formed and collected by an EMCCD camera, so as to realize the ultra-high precision resolution of characteristic spectral lines. According to the Doppler principle, the frequency shift of Ar 696.54 nm at the same measurement point collected at different angles was different, and the radius of the interference ring collected by EMCCD was also different. By measuring the radius of the interference ring formed by the characteristic spectral lines for the same level and different collection directions, the flow velocity of the high temperature plasma jet can be computed. The comparative experiments of two vehicles were carried out for the same nozzle, and the axial velocities of the two vehicles were 791 and 783 m·s-1, respectively, which had good repeatability. Based on the Doppler principle, the results show that, using the spontaneous emission spectrum of high temperature gas, combined with the high spectral resolution F-P interferometer, the high temperature plasma jet velocity can be accurately measured. This method belongs to non-contact measurement and does not interfere with the flow field and is especially suitable for the measurement of high temperature flow field, which is difficult to be applied by traditional sensors.
2021 Vol. 41 (06): 1745-1750 [Abstract] ( 187 ) RICH HTML PDF (4923 KB)  ( 58 )
1751 Vehicle Exhaust Detection Method Based on Portable FTIR
QU Li-guo1,2,3, LIU Jian-guo1, XU Liang1*, XU Han-yang1, JIN Ling1, DENG Ya-song1,2, SHEN Xian-chun1, SHU Sheng-quan1,2
DOI: 10.3964/j.issn.1000-0593(2021)06-1751-07
With the improvement of vehicle emission standards, The relevant VOC standards changed from total hydrocarbon detection to non-methane hydrocarbon (NMHC) detection. With the increase of oxygen-containing fuels, non-methane organic gas (NMOG) measurement was increased. The vehicle exhaust detection method based on portable FTIR is proposed aiming at the problems such as single component analysis, limited accuracy and complex VOC detection process of domestic automobile exhaust analyzer. The structure of the FTIR optical system is optimized based on a corner cube to meet the requirements of anti-vibration. The scanning speed of the moving mirror is improved to meet the requirements of a portable and fast FTIR spectrometer. The output band range of the FTIR infrared light source is 2~20 μm, with a resolution of 0.5 cm-1, a scanning speed of 1 Hz, and a gas pool optical range of 10 m. Stirling detector is used, with spectral responses ranging from 600 to 6 000 cm-1. Typical HC compounds such as CH4, C2H2, C2H4, C2H6, C3H6, n-C5H12, i-C5H12, C7H8, HCHO, C2H5OH, CH3CHO are selected as alternatives to VOC gases. The test bands of vehicle exhaust composition determined by the standard spectrum are 900~1 100 and 2 700~3 100 cm-1, covering all the gas absorption bands to be measured. Based on the AVL bench test, NEDC and WLTC working condition experimental test are carried out. The test vehicle is Toyota VIOS, and the test oil product was No. 92 State 5. The portable FTIR adopts an extraction method for tail gas measurement. The original exhaust sample is from a porous probe installed in the extension part of the exhaust pipe. The front end is equipped with a sample gas sampling device, which mainly includes particulate filtration and moisture removal to prevent pollution of the FTIR optical system. The experiment shows that FTIR can effectively and rapidly measure CO, CH4, NO and main HC compounds in automobile exhaust. When the gas concentration is lower than the FTIR detection limit of mole fraction 0.5 μmol·mol-1, noise signals will be introduced and the reliability will be reduced. It can be seen from the analysis that the average concentration of output gas is downgraded in descending order: CO, C2H4, CH4, NO, i-C5H12, C2H6, C7H8, n-C5H12, C2H5OH, CH3CHO. It can be seen from the three cycles of NEDC working conditions that each gas emission presents a consistent and regular change. The time series comparison of SEMTECH-DS and FTIR measurement data for CO shows a good consistency of laws. However, due to the difference in measurement technology and sampling dilution system of FTIR and SEMTECH-DS, the concentration difference between them is large. Compared with the traditional exhaust detection technology, the portable FTIR measurement system has a good response to the transient events, and can measure the multi-component concentration in real time to obtain the instantaneous emission data of motor vehicles, which can meet the requirements of the new regulation test and also provide reliable data support for the later emission characteristic analysis and simulation of motor vehicles on the actual road.
2021 Vol. 41 (06): 1751-1757 [Abstract] ( 229 ) RICH HTML PDF (6520 KB)  ( 140 )
1758 A Neural Network Recognition Method for Garnets Subclass Based on Hyper Spectroscopy
LIU Ting-yue1, DAI Jing-jing2*, TIAN Shu-fang1
DOI: 10.3964/j.issn.1000-0593(2021)06-1758-06
Hyperspectral technology is a rapid, nondestructive and accurate means of mineral detection, which can clearly reflect mineral chemical composition change. Garnets have the characteristic of three diagnostic peaks in the thermal infrared wavebands, and the wavelength positions of the reflection peaks are closely related to the chemical composition, so the subclass classification of garnets can be studied according to the thermal infrared wave spectrum characteristics. Reflection peak wavelength positions of uvarovite and spessartine are easy to distinguish with other types. However, that of almandine and pyrope, andradite and grossular have a large overlap and are difficult to distinguish with each other. Therefore, a fast and accurate classification method based on the thermal infrared spectrum is urgently needed. In this paper, the information about wavelength position and difference between wavelengths of the three reflection peaks of 85 different types of garnet samples were obtained from the thermal infrared spectroscopy library. Three nonlinear BP neural network methods, cluster analysis and multiple linear discrimination analysis were used to carry out garnet subclass recognition experiments, and the accuracy rate, recall rate and F1 value were used to evaluate the classification accuracy. The experimental results showed that the accuracy rate, recall rate and F1 value of BP neural network algorithm after classification could all reach 100%, and all types of garnets got a good distinction; the accuracy rate, recall rate and F1 value of clustering analysis and multivariate linear discriminant analysis were 86.1%, 80%, 79.2% and 84.2%, 80%, 79.5% separately, the four types of garnets with overlapping reflection peaks could not be well differentiated. According to the results, the nonlinear BP neural network is more suitable for the subclassification of garnets. Our study used powerful automatic nonlinear mapping ability of the BP neural network, has found the complex mapping relationship between the wavelength positions of the reflection peak in the thermal infrared spectrum of the garnets and the subclass types, and proved the feasibility and superiority of BP neural network method combined with thermal infrared spectrum characteristics. The identification of garnet subclass provided is fast and effective, and it can give good technical enlightenment for the rapid and effective identification of other minerals.
2021 Vol. 41 (06): 1758-1763 [Abstract] ( 190 ) RICH HTML PDF (2288 KB)  ( 117 )
1764 Thermal-Infrared Spectroscopy of Garnet Minerals
DAI Jing-jing1, ZHAO Long-xian2, WANG Hai-yu2
DOI: 10.3964/j.issn.1000-0593(2021)06-1764-05
Hyperspectral technology is a new non-damaging method for mineral detection and identification. In recent years, short-wave infrared (SWIR) technology with a wavelength of 1.1~2.5 μm had been successfully applied in alteration minerals and correlated ore deposits studies. However, SWIR technology was more suitable for hydrated and hydroxyl-bearing minerals, and was not sensitive for minerals containing SinOk, SO4, CO3, PO4. Then thermal-infrared technology with the wavelength of 3~14 μm could remedy the technical defect, and was more detectable for minerals without hydro and hydroxyl. Garnet is an important neosilicate mineral containing SinOk, divided into many groups according to the chemical components including grossular, andradite, pyrope, almandine, spessartine et al., and garnet is indicative for temperature, pressure and other mineral forming conditions and ore prospecting. Recently, little research on thermal-infrared spectroscopy of garnet minerals has been conducted. In this paper, sixteen garnet minerals with different components and colors were collected, and thermal-infrared spectroscopy of these garnet minerals was firstly studied using Agilent 4300 thermal-infrared spectrometer. What’s more, the content of principal elements including SiO2,MgO,Al2O3,K2O,CaO,Fe2O3 of these garnet minerals was measured using Niton XRF analyzer at the same position, and the relationship between the content of principal elements and absorption wavelength were analyzed. The result indicated that garnet minerals had diagnostic features with twin peaks in the thermal-infrared band region of 10~13 μm, which presented the main absorption at about 11.5 μm and a secondary absorption at about 12 μm. The wavelength at these two absorption was well correlated with the Al2O3 and Fe2O3 content of garnet minerals, which was linear negative correlated with the content of Al2O3, and was linear positive correlated with the content of Fe2O3. The results can help quickly identify the chemical components of garnet minerals and add alteration zoning of skarn ore deposits in the field that indicate ore prospecting. The study started an application of Agilent4300 thermal-infrared spectrometer in the mineral study and gave a good example of thermal-infrared spectroscopy of garnet minerals study, which could provide the fundamental basis for thermal-infrared spectroscopy of other tectosilicates, neosilicates, single-chain silicates, carbonates and sulphates.
2021 Vol. 41 (06): 1764-1768 [Abstract] ( 163 ) RICH HTML PDF (2633 KB)  ( 109 )
1769 Experimental Study on the Effect of Observation Angle on Thermal Infrared Spectral Unmixing of Rock
LI Tian-zi2, LIU Shan-jun1*, SONG Liang3, WANG Dong1, HUANG Jian-wei4, YU Mo-li1
DOI: 10.3964/j.issn.1000-0593(2021)06-1769-06
Rock quantitative remote sensing has gradually become the main means of mineral resources exploration and geological environment monitoring, and spectral unmixing is an important method for rock quantitative remote sensing. However, in practical application, because the satellite’s observation of the earth is influenced by topographic fluctuation, the observation has a certain angle, resulting in the variation of the measured emissivity spectrum. However, in the present study, the mineral endmember spectrum used for unmixing is obtained by vertical observation on the surface of the sample in the laboratory, ignoring the observation angle’s effect on the emissivity spectrum and reducing the unmixing spectral accuracy. Therefore, in this work, the observation angle is taken as the consideration factor to influence the unmixing spectral accuracy of rock. First, the common quartz, orthoclase and plagioclase surfaces are fabricated into general roughness, and a total of nine observation angles of 0°~77° are designed to observe the emissivity spectrum, and the effects on the thermal infrared spectral characteristics of the observation angles are analyzed. Secondly, using the mineral endmember with the observation angle of 13°~77°, the corresponding angle virtual rock spectrum is constructed, and the rock spectrum of 9 observation angles is unmixed with the mineral endmember of 0° spectrum, and the effect of the observation angle on the thermal infrared spectrum unmixing of rock is analyzed. The results show that: (1) in the range of 0°~20°, the observation angle has a weak effect on the spectrum, and the influence is significant from starting at 30°. Basic law: As the angle increases, the spectral absorption depth increases, but the situation at each band is different. The CF moves obviously to the short band direction after the observation angle is more than 50°. The absorption valley of RF is significantly deeper than 20°, and the valley bottom moves in short band direction. The emissivity of TF decreases significantly after the observation angles are greater than 40°. Therefore, the change of observation angle, will cause the obvious change of spectral characteristics. (2) In the range of 0°~20°, the effect of observation angle on spectral unmixing is not obvious, and the error of unmixing is less than 5%. When the observation angle is greater than 20°, the observation angle significantly affects spectral unmixing, the error of unmixing at 30°~77° is more than 5%, the average unmixing error reaches 17.2%, and the unmixing accuracy is low. This indicates that when the quantitative inversion of rock mineral components based on the spectral unmixing method is carried out, the influence of observation angle is considered, which is of great significance to improve the inversion accuracy and accurately determine the rock type.
2021 Vol. 41 (06): 1769-1774 [Abstract] ( 171 ) RICH HTML PDF (2809 KB)  ( 49 )
1775 Inversion Method for Cellulose Content of Rice Stem in Northeast Cold Region Based on Near Infrared Spectroscopy
XU Bo1, XU Tong-yu1, 2*, YU Feng-hua1, 2, ZHANG Guo-sheng1, FENG Shuai1, GUO Zhong-hui1, ZHOU Chang-xian1
DOI: 10.3964/j.issn.1000-0593(2021)06-1775-07
In lodging resistance breeding of rice, the cellulose content of rice stem, as an important phenotypic data of crop traits, is constrained by human and time costs, which makes the size of the collecting population limited. The rapid and non-destructive detection of crop traits information can be achieved by using hyperspectral technology. In lodging resistance breeding of rice, rice stem cellulose content is one of the important character information. In order to explore the near-infrared spectral inversion model of cellulose content in rice stem, the bottom 2 and 3 segments of rice stem base formfilling stage to maturity stage were collected as experimental samples by field plot experiment, and the stem near-infrared reflectance spectrum data were measured by NIRQuest512 hyper-spectrometer in the laboratory. Standard normal variate (SNV), continuous wavelet transform (CWT), and the combination of the two methods (SNV-CWT) were used to pretreat the original near-infrared reflectance spectrum. Through comparative analysis, it was found that the original spectrum was optimized when it is firstly processed by SNV and then decomposed by CWT at 6 scales, and then the spectral characteristic variables are screened by the synergy interval PLS (SiPLS) method and iteratively retaining informative variables (IRIV) method for the characteristic spectral curve obtained by the optimal pretreatment (SNV-CWT). 64 and 16 characteristic variables were extracted, respectively. To optimize the model and improve its accuracy, the IRIV algorithm was used to conduct secondary screening of the characteristic variables selected by SiPLS, and 6 characteristic variables were obtained with the characteristic wavelengths of 1 200, 1 207, 1 325, 1 470, 1 482 and 1 492 nm. Finally, the support vector machine regression (ε-support vector machine regression, εSVR) and the kernel-based extreme learning machine (KELM) prediction model were established based on the selected characteristic variables. The model parameters (penalty coefficient C, kernel function coefficient γ and insensitive parameter ε) use grey wolf optimizer (GWO), differential evolution grey wolf optimizer (DEGWO) and self-adaptive differential evolution grey wolf optimizer (SaDEGWO) adaptive proposed in this paper for optimal selection. The results show that the SaDEGWO optimized εSVR model constructed by the characteristic variables selected by the SiPLS-IRIV method after spectral pretreatment with SNV-CWT method has the highest accuracy. The model parameters C, γ, ε are 302.838 2, 0.087 7, 0.070 8, respectively, and the coefficient of determination (R2p) of the test set is 0.880. The root-mean-square error (RMSEP) of the test set is 15.22 mg·g-1, residual predictive deviation (RPD) is 2.91. It indicates that the model has the good predictive ability, and this method can provide a reference for the prediction of cellulose content in rice stems.
2021 Vol. 41 (06): 1775-1781 [Abstract] ( 178 ) RICH HTML PDF (4590 KB)  ( 118 )
1782 Least Angle Regression Combined With Competitive Adaptive Re-Weighted Sampling for NIR Spectral Wavelength Selection
LU Hao-xiang1, ZHANG Jing2, LI Ling-qiao1*, LIU Zhen-bing1, YANG Hui-hua1,3, FENG Yan-chun4, YIN Li-hui4
DOI: 10.3964/j.issn.1000-0593(2021)06-1782-07
Near-infrared spectroscopy is widely used in drug detection, petrochemical industry, etc., because it has no damage to the samples, and the detection speed is fast, and the accuracy is high. In particular, it has more accurate detection performance with the in-depth application of machine learning and deep learning modeling methods in recent years makes. However, the NIR spectral data of the sample has relatively high dimensions and has problems such as spectral overlap, collinearity and noise, which will negatively impact the performance of the NIR spectral model. In this case, the selection of effective characteristic wavelength points of the sample is extremely important. In order to improve the accuracy and reliability of the quantitative and qualitative analysis models of NIR spectra, a variable selection method for NIR spectra is proposed, which combines the advantages of Least Angle Regression and Competitive Adaptive Re-weighted Sampling, and has better performance. In this method, LAR was used to preliminarily screen the characteristic wavelengths in the whole spectrum of the sample, and then CARS was used to further select the selected characteristic wavelengths to effectively remove the irrelevant characteristic wavelengths. In order to verify the effectiveness of the method, the method was evaluated from two aspects of quantitative and qualitative analysis. In the quantitative analysis experiment, PLS regression analysis model was established using FULL, LAR, CARS, SPA and UVE as comparison methods and drug sample data set as example. PLS model established by variables screened by LAR-CARS showed higher predictive determination coefficient and lower predictive standard deviation in drug data set. In the qualitative analysis experiment, the classification model was established with SVM, ELM, SWELM and BP as comparison methods and drug data sets with different proportions of training sets. The accuracy of the SVM classification model established by the variables screened by LAR-CARS reached the highest 100%. From the experimental results, it can be seen that LAR-CARS can effectively select the wavelength points that the characteristics of the sample, and the quantitative and qualitative analysis model established by using the selected wavelength points has better robustness and can be used for the characteristic wavelength screening of the sample spectrum.
2021 Vol. 41 (06): 1782-1788 [Abstract] ( 257 ) RICH HTML PDF (2102 KB)  ( 87 )
1789 Iterative Interval Backward Selection Algorithm and Its Application in Calibration Transfer of Near Infrared Spectra
ZHENG Kai-yi, FENG Yu-hang, ZHANG Wen, HUANG Xiao-wei, LI Zhi-hua, ZHANG Di, SHI Ji-yong, ZOU Xiao-bo*
DOI: 10.3964/j.issn.1000-0593(2021)06-1789-06
The near-infrared spectra (NIR) with advantages of fastness, non-destructiveness and easy operation have been widely used in food analysis. As an indirect analysis method, NIR should calibrate the model between spectra and concentrations for analysis. Thus, the maintenance of the model can ensure high accuracy. The changes of external conditions, including the changes of samples characters, the variations of functions between physical and chemical indicators and the changes of the environment such as humidity and temperature, can diverge the spectra of the same samples and then decrease the prediction accuracy of the original model. To solve this problem, recalibration can eliminate the chances of spectra butcost huge laborious and economic expense. Thus, calibration transfer can correct the spectral divergence and improve model prediction accuracy without the expense of recalibration. In previous work, the calibration transfer algorithms usually use full spectra variables to transfer, which increase computation burden and not find spectra intervals with chemical information. Thus, this paper proposed a variable selection method called iterative interval backward selection (IIBS) for calibration transfer. IIBS firstly calculates the importance vectors of variable intervals in spectra, including regression coefficients (β), residual errors (Res) and VIP (VIP) vectors. Then set the geometric mean of the important values of variables in each interval as the corresponding interval’s importance. Moreover, based on the importance values of intervals, remove the smallest one. After that, repeat the above procedure iteratively for both primary and secondary spectra, including computing the importance and values of variables and intervals and remove the intervals with minimal importance value. Finally, compute the root mean squared error of validation (RMSEV) for each interval subsets combination of both primary and secondary spectra and choose the intervals combination with minimal RMSEV as the best one. Two datasets, including corn and wheat datasets, were executed to test this algorithm. The results show that compared with the spectra with full intervals, the β, Res and VIP can select fewer but more important variable intervals from whole spectra to improve the calibration transfer accuracy. In contrast with different variable importance vectors, the β can select variables intervals with low prediction errors.
2021 Vol. 41 (06): 1789-1794 [Abstract] ( 147 ) RICH HTML PDF (3599 KB)  ( 51 )
1795 A Mid-Infrared Wavelength Selection Method Based on the Impact Value of Variables and Population Analysis
ZHANG Feng1, TANG Xiao-jun1*, TONG Ang-xin1, WANG Bin1, TANG Chun-rui2, WANG Jie2
DOI: 10.3964/j.issn.1000-0593(2021)06-1795-05
The Fourier transform infrared spectra absorption peaks of alkane gases are overlapping seriously in the mid-infrared region. A wavelength selection method based on the impact value of variables and population analysis (IVPA) is proposed to select the wavelength of five alkane gases infrared spectra composed with methane, eth, propane, iso-butane and n-butane. IVPA algorithm will go through a number of iterations to select variables. In each iteration, the variables are divided into sample space and variable space. The impact value of variables is calculated in the sample space. According to the impact value, the variables are divided into elite variables and normal variables by using the weighted bootstrap sampling technology. Meanwhile, in the variable space, the frequency of each variable in the optimal model is counted. Finally, the variables with a lower frequency of normal variables are eliminated by the exponential decay function, and the root means squared error (RMSE) value obtained during each iteration is recorded. The variable subset corresponding to the minimum RMSE as the final selected variable. The proposed algorithm is tested by alkane dataset, and the results are compared with stability competitive adaptive reweighted sampling (SCARS) and iteratively variable subset optimization (IVSO) variable selection method proposed in recent years. Taking iso-butane analysis results as an example, the minimum cross-sensitivity of IVSO, IVPA and IVPA to the other four gases was 0.67%, 0.56% and 0.11%, respectively. The maximum cross sensitivity was 1.69%, 1.49% and 1.02%, respectively. The relative errors of iso-butane prediction were 1.94%, 1.65% and 0.51%, respectively. The number of selected variables by the above three methods is 52, 17 and 13, respectively. The results show that the IVPA method selected the least variables, only 0.36% of the original spectral data, obtained the lowest cross sensitivity for the other four gases, and got the most accurate prediction for iso-butane, which shows that the proposed wavelength selection method can be applied to the absorption overlapping spectra, and can improve the prediction accuracy and efficiency of the analytical model.
2021 Vol. 41 (06): 1795-1799 [Abstract] ( 162 ) RICH HTML PDF (2193 KB)  ( 41 )
1800 Non-Destructive Detection of Male and Female Information of Early Duck Embryos Based on Visible/Near Infrared Spectroscopy and Deep Learning
LI Qing-xu1, WANG Qiao-hua1, 2*, MA Mei-hu3, XIAO Shi-jie1, SHI Hang1
DOI: 10.3964/j.issn.1000-0593(2021)06-1800-06
Gender identification of embryonated eggs in China has always been a key issue in poultry industry development. In poultry meat production, males tend to be bred, while the egg production industry tends to breed females. If the male and female eggs can be identified in the early hatching process, it will reduce the cost of poultry hatching industry improve the economic benefits of poultry egg and meat production industry. This paper takes duck eggs as the research object. To realize the gender identification of duck eggs at the early hatching stage, a visible/near-infrared transmission spectrum acquisition system was constructed, which can collect the Spectral data of 345 duck eggs hatching from 0 to 8 days with the wavelength range of 200~1 100 nm. A 6-layer Convolutional Neural Network (CNN) for duck eggs’ spectral information was established, including input layer, 3 convolutional layers, 1 fully connection layer and output classification layer. The convolutional layer is used for extraction for the effective information in the spectrum. The full connected layer can integrate the local features extracted by the convolution layer for the classification decision of the output layer. In addition, the introduction of local response normalization and dropout operations in the convolutional neural network can accelerate the convergence speed of the neural network. The convolutional neural network was used to construct a duck embryo male and female information recognition network. By comparing and analyzing the recognition effects of different incubation days, it was found that the recognition effect was the best after 7 days of incubation. Subsequently, the duck eggs’ original spectral data hatched for 7 days were removed for noise, and the 500~900 nm band was selected for subsequent characteristic wavelength selection and modeling. Competitive adaptive reweighting algorithm (CARS), successive projections algorithm (SPA) and genetic algorithm (GA) were used to select the characteristic wavelengths that can distinguish the sex of duck embryos, and the selected characteristic wavelengths are converted into a two-dimensional spectral information matrix. The two-dimensional spectral information matrix retains the effective information of the one-dimensional spectrum and greatly facilitates the combination with the convolutional neural network. They were using a two-dimensional spectral information matrix combined with a convolutional neural network to achieve male and female identification of early hatching duck embryos. After testing, the model based on the SPA algorithm and the CNN network has a better effect, among the accuracy of the training set, development set, and test set are 93.36%, 93.12%, and 93.83%, respectively; the model based on the GA algorithm and CNN network was followed. In other words, the accuracy of the training set, development set and test set are 90.87%, 93.12%, and 86.42%, respectively; the accuracy of the training set, development set and a test set of the model based on the CARS algorithm and CNN network is 84.65%, 83.75%, 77.78%. The research results show that the visible/near-infrared spectroscopy technology and convolutional neural network can realize non-destructive identification of male and female duck embryos in early hatching, which provides technical support for developing subsequent related automated detection devices.
2021 Vol. 41 (06): 1800-1805 [Abstract] ( 263 ) RICH HTML PDF (3474 KB)  ( 153 )
1806 Fast Identification of Hazardous Liquids Based on Raman Spectroscopy
NAN Di-na, DONG Li-qiang, FU Wen-xiang, LIU Wei-wei, KONG Jing-lin*
DOI: 10.3964/j.issn.1000-0593(2021)06-1806-05
Fast and accurate identification of unknown hazardous fluids are of pivotal interest in public security and safety. Raman spectroscopy is a fast and sensitive non-contacting measurement technology. Its virtues have it has become one of the important technologies in the public security field in recent years. In this study, the Raman spectra of forty-two dangerous and common liquids were measured: five chemical warfare agents (including sarin, soman, tabun, VX, and mustard gas), and their fifteen precursors, hydrolysates (including dimethyl hydrogen phosphite, trimethyl phosphite, triethyl phosphite, ethyl methylphosphonochloridoate, methylphosphonic dichloride, methylphosphonic difluoride, chlorosarin, bis(diethylamino)phosphoryl chloride, 2-(diethylamino)ethanethiol, thiodiglycol, isopropyl alcohol, 3,3-dimethyl-2-butanol, methylphosphonic acid, isopropyl methylphosphonate, and pinacolyl methylphosphonate), five chemical warfare agents simulants (including trimethyl phosphate, triethyl phosphate, tributyl phosphate, dimethyl methylphosphonate, and diisopropyl methylphosphonate), fifteen toxic industrial compounds (including o-xylene, m-xylene, anisole, chlorobenzene, ethyl acetate, vinyl acetate, benzyl acetate, methanol, ethanol, 1-butanol, acetonitrile, acetone, hexane, 1,1,1-trichloroethane, and carbon tetrachloride), gasoline, and water. Raman spectroscopy detection method for these compounds using a portable Raman spectrometer equipped with a 785 nm excitation laser was developed to obtain high SNR scattering spectrum data. Structural assignments to Raman bands observed in the spectrum were also proposed. Six pattern recognition algorithms, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), naive bayesian (NB), classification tree (CT), and support vector machine (SVM) were studied to identify and classify Raman spectrum data. The results show that support vector machine and linear discriminant analysis model could realize the fast identification with a high recognition accuracy rate of 100%. However, considering the influence of non-standard spectrum, instrument conditions, and changes in the external environment on support vector machine recognition results, the linear discriminant analysis model seemed superior in identifying the aforementioned dangerous liquids. Meanwhile, all the testing process can be completed within 1~2 minutes without loss of samples. It can be concluded from this study that the combination of Raman spectroscopy with fingerprint characteristics and pattern recognition algorithm can be used for rapid screening of unknown compounds. Moreover, this method provides solutions for timely detection of customs clearance, guarantee of logistics security, and emergency response to chemical terrorist incidents.
2021 Vol. 41 (06): 1806-1810 [Abstract] ( 251 ) RICH HTML PDF (2729 KB)  ( 139 )
1811 Study on Erythrocyte Sublethal Damage Under Different Shear Stress Based on Raman Spectroscopy
WANG Jin-shuang1, FU Ying-ying1, FU Min-rui1, GAO Bin1*, ZHENG Da-wei1, CHANG Yu2
DOI: 10.3964/j.issn.1000-0593(2021)06-1811-05
In this study, confocal Raman spectroscopy was used to study the Sublethal damage erythrocytes of artificial heart pump under different shear stress to verify the ability of Raman spectroscopy to evaluate the degree of erythrocyte Sublethal damage. It provides a new idea for the evaluation of blood injury. The standard Raman spectra of hemoglobin and red blood cells were collected and compared in order to determine the peak intensity of erythrocytes. The sheer force of 0, 50, 100, 150, 200, 250 and 300 pa was applied to the tested blood samples for 1 s on the blood shear test platform. Using confocal Raman instrument, the Raman spectra of erythrocytes under shear stress were collected under 10 times long focal lens, 532 nm laser light source wavelength, integration time 10 s, twice, and 2.5 mW. Through the normalization method to compare the changes of the Raman spectrum of red blood cells, evaluate the degree of red blood cell Sublethal damage, and use the curve fitting method to fit the characteristic peak and shear stress to verify the ability of Raman spectrum to evaluate the sub-injury of red blood cells. Comparing the standard Raman spectra of hemoglobin and red blood cells, it is found that the erythrocyte spectra can reflect the internal structure of hemoglobin. The results show that Raman spectroscopy can be used to distinguish the Sublethal damage erythrocytes under different shear stress, and it is inferred that the shear stress can pass through the cell membrane and affect the internal hemoglobin structure. With the increase of shear force, the left spectral line of 1 376 cm-1 increased. Obviously, the peak intensity of 1 549 and 1 604 cm-1 increased, and the vibration band of oxygen concentration marker band 10 at 1 639 cm-1 decreased. Among them, the peak strength of 1 549 cm-1 position is the high spin band of ferrous ion. Under the action of different shear forces, the difference of peak strength is the most obvious, which has an obvious positive linear relationship with the shear stress, and the fitting effect is good. Raman spectroscopy has the advantages of simple treatment, short time, simple operation and good reproducibility can accurately detect the subtle changes in the internal structure of cells, and evaluate the degree of sub-injury of red blood cells. it makes up for the deficiency of the traditional evaluation of hemolysis, provides a new technical means for the evaluation of blood injury caused by artificial heart pump, and broadens the application field of the Raman detection method.
2021 Vol. 41 (06): 1811-1815 [Abstract] ( 194 ) RICH HTML PDF (3223 KB)  ( 47 )
1816 Connection of Absorption and Raman Enhancement Characteristics of Different Types of Ag Nanoparticles
ZHANG Can, ZHANG Jie*, DOU Xin-yi, ZHU Yong
DOI: 10.3964/j.issn.1000-0593(2021)06-1816-05
When we use nano-structured materials for surface-enhanced Raman scattering (SERS), we will first test the absorption spectrum because original researchers believe that the reason why nano-structured materials generate SERS is that the absorption of incident light by nano-structured materials causes the localized surface plasmon resonance (LSPR), so we equate the curve of SERS enhancement factor with wavelength to the absorption spectrum curve. In recent years, some scholars believe that the connection between them can be very indirect and can be misleading in many cases. AgNPs are famous for their ability to significantly improve Raman scattering due to their local surface plasmon resonance, so AgNPs are the ideal nanomaterial for the substrate. In order to clarify the specific relationship, we studied the enhancement factor (EF) of surface-enhanced Raman scattering, absorption spectra and spatial electric field distribution of silver nanoparticles (AgNPs) in three different states, experimentally and theoretically. Experimentally, we prepared Ag-sol by chemical reduction method. They were characterized by a transmission electron microscope (TEM), ultraviolet-visible spectrophotometer (UV-Vis) and Raman’s measurements and statistics and calculations of the EF and absorption spectra of silver sols were carried on. Theoretically, we used the simulation software COMSOL Multiphysics to establish different aggregation types of AgNPs models, and simulated the EF curve with wavelength and absorption spectra corresponding to the experiments. The results show that the spatial distribution of surface plasmon resonance plays an important role in absorption and maximum EF value. The resonance absorption peak with a fixed position(first peak position) is mainly affected by the “single particle type” effect, and the absorption peak at the maximum EF (the second peak position) is dominated by the blue-shifted resonance peak caused by the “coupling gap type” effect, the maximum EF value and the position of the second absorption peak will be influenced by the particle gap, polarization angle and other factors. Studies have shown that the absorption spectrum of the AgNps sample is partially related to the maximum EF curve.
2021 Vol. 41 (06): 1816-1820 [Abstract] ( 272 ) RICH HTML PDF (4046 KB)  ( 123 )
1821 The Forming Mechanism of Surface Morphology of Nanostructures and Its Effect on Graphene Raman Spectra
SUN Ning, CHEN Jun-fan, ZHANG Jie*, ZHU Yong
DOI: 10.3964/j.issn.1000-0593(2021)06-1821-07
All the time, SERS based metal nanoparticles structure prepared by vacuum thermal evaporation and high-temperature annealing has been widely used in various detection fields due to its good sensitivity, stability and uniformity. Because of its excellent optical properties, chemical inertness and fluorescence quenching effect, graphene has been a hot material in optical micro-nano devices since its discovery. Graphene can also effectively separate probe molecules and substrate to optimize Raman spectral quality, so it has been widely used in the SERS research field. Meanwhile, graphene can effectively isolate the direct contact between the metal nanostructure and the air to prevent the metal nanostructure from being oxidized and become ineffective. It can also catalyze the deoxidation reaction of silver oxide to improve the stability of SERS substrate. Affected by the types and thickness of the metal film, annealing time, temperature and pressure, and the type of gas during the preparation process of the graphene/metal nanostructure SERS substrate, the influence on metal nanostructure morphology is quite different. Besides, the Raman spectra of graphene will be enhanced, frequency-shifted and broaden because of the stress and doping characteristics of the Raman peak. (1) In this paper, the SERS substrate of graphene/silver nanocomposite structure was prepared by vacuum thermal evaporation and high-temperature annealing, the forming mechanism model of metal nanoparticles was established, and the forming process of metal nanoparticles was analyzed from the three stages of hole formation, hole growth and metal island formation. The silver films at 5, 10, 15 and 20 nm were deposited, and the coverage rates of silver nanoparticles after annealing were ~35.1%, ~24.4%, ~30% and ~96.0%, respectively. Graphene was transferred on the silver film samples, after annealing treatment, it was found that graphene prevented the formation of silver nanoislands. (2) The influence of the thickness of the silver film, the effect of covered graphene cover on the geometric morphology, and Raman enhancement characteristics of the composite structure was theoretically analyzed. Due to its high Young’s modulus and surface tension, graphene could effectively inhibit the transformation of the silver film to nanoparticles in the annealing process so as to realize the regulation of the surface morphology of the composite structure. (3)The effect of silver nanoparticle structure on Raman spectrum of graphene was studied experimentally, and the reasons for the effect of different silver film thickness on Raman spectrum enhancement, shift and broaden of graphene were analyzed theoretically.
2021 Vol. 41 (06): 1821-1827 [Abstract] ( 152 ) RICH HTML PDF (4212 KB)  ( 52 )
1828 Investigation of a High-Pressure Pressed Powder Pellet Covered With Polyester Film Technique for the Determination of Chlorine in Soil and Sediment by X-Ray Fluorescence Spectroscopy
BO Wei, LI Xiao-li*, DU Xue-miao, LIU Bin, ZHANG Qin, BAI Jin-feng
DOI: 10.3964/j.issn.1000-0593(2021)06-1828-06
Chlorine is an important analyte in geo-chemical exploration analysis. X-ray fluorescence spectrometry is a key technical method for the determination of halogen elements. Many studies have shown that chlorine’s measured value in the same pellet increases or decreases with the increase of repeated determination times when chlorine is determined by X-ray fluorescence spectrometry. In this paper, new sample preparation technique-high pressure pressed pellet covered with polyester film,and chlorine determination in soil and sediment is proposed. Cl’s correlation coefficient was markedly improved, and the RMS was reduced from 0.009 63 (without film) to 0.001 98 (with film). The limit of detection of chlorine was improved from 30 μg·g-1 without film-coating to 21 μg·g-1 with film-coating. And with the new sample preparation, the chlorine remained constant or showed only minor changes in 10 subsequent analysis or 10 subsequent daily irradiation. The reason for the measured value of chlorine increases or moderate decreases with the increase of repeated measurements is explained from the point of view of pre- vacuum time and X-ray tube application power, drying sample and non-drying sample, forms of chlorine. A high-pressure pressed powder pellet covered with polyester film can circumvent chlorine diffusion with moisture to the surface of the sample or decomposition loss. The vacuum degree decreases obviously with the increase of pre-vacuum time and desorption, and the intensity of chlorine changes obviously. With the introduction of the high-pressure film-coating sampling, the change of vacuum degree was not obvious, and the value of chlorine was remained constant or showed only minor changes. The high-pressure sample preparation (1 600 kN) solves the difficulty of forming different types of geological samples. The high-pressure film-coated sample preparation technology completely eliminates the dust effect and is of great significance to the X-ray fluorescence spectrometer under irradiation. Chlorine could be better identified with the used pellet, and the standard pellet could be preserved for a long time.Also it avoided misspending of the reference materials.This method can accurately determine 32 components in soil and water sediment. The sample preparation method is also suitable for the elemental analysis of other difficult to form samples and their fluorescence intensity changes due to vacuum and long-time irradiation.
2021 Vol. 41 (06): 1828-1833 [Abstract] ( 171 ) RICH HTML PDF (1359 KB)  ( 103 )
1834 μ-XRF Analysis and Data Mining of Deep-Sea Co-Rich Ferromanganese Nodules in Western Pacific
REN Jiang-bo1,2, WANG Fen-lian1,2*, HE Gao-wen1, 2, ZHANG Xin3, DENG Xi-guang1,2, YU Hong-xia4
DOI: 10.3964/j.issn.1000-0593(2021)06-1834-07
Co-rich ferromanganese nodules in the Western Pacific Ocean are rich in Mn, Fe, Co, Ni, Cu and other metal elements, which are newly discovered seabed solid mineral resources in recent years. They are heterogeneous geochemical and mineralogical aggregates. Co-rich ferromanganese nodules with a particle size of 6 cm record tens of millions of years of marine sedimentary history during their growth. Therefore, high-resolution analysis technology is urgently needed to reveal the paleo marine environmental information. In this study, we used a microscopic X-ray fluorescence spectrometer (μ-XRF) to scan the surface of C3BC1704 Co-rich ferromanganese nodule, and obtained in situ high-resolution multi-element signal intensity data, and evaluated the application quality of μ-XRF technology in Co-rich ferromanganese nodules for the first time. The results show that Mn, Fe, Ti, Co, Ca, Ni elements have sensitive signal intensity changes, and the data show relatively good normal distribution characteristics, which can be used for quantitative or semi quantitative analysis. The signals of Si, Cu, Al and other elements are weak, and their data show a left skewed normal distribution. It is suggested that the relevant data should be used for reference only. The amount of data obtained is huge and independent of each other. In this study, different elements are connected into the multi-dimensional matrix to realize the mathematical operation and screening between the location information and feature elements of the data. The distribution and variation characteristics of metal elements are understood, and the environmental changes in the growth process of cobalt rich nodules are revealed. The results show that Mn, Fe and other elements fluctuate violently in the growth layer, and the distribution of metal elements in the Co-rich ferromanganese nodules is extremely uneven, showing multiple genetic types of alternating micro layers and seven large growth cycles. The main body of the C3BC1704 Co-rich ferromanganese nodule is exposed to seawater, and its metal elements mainly come from seawater, which belongs to the typical hydrogenic types. Further quantitative analysis showed that the contents of Mn, Cu and Ni decreased synchronously from the inside to the outside, while the contents of Fe, Ti and co increased synchronously. It is indicated that the Co-rich ferromanganese nodules relatively tend to be enriched in diagenesis in the early stage and hydrogenises mainly in the late stage. The distribution and variation characteristics of metal elements clearly show the growth structure, reveal the environmental changes, and promote the construction of the metallogenic model of Co-rich ferromanganese nodules.
2021 Vol. 41 (06): 1834-1840 [Abstract] ( 171 ) RICH HTML PDF (5477 KB)  ( 99 )
1841 Study on Alumina/Cerium Oxide X-Ray Diffraction and Raman Spectroscopy
WANG Yi1, 2, LI Chang-rong1, 2*, ZENG Ze-yun1, 2,XI Zuo-bing1, 2, ZHUANG Chang-ling1, 2
DOI: 10.3964/j.issn.1000-0593(2021)06-1841-05
The size of alumina inclusions in steel seriously affects its performance. Refinement or removal of these inclusions is highly valued. Because the size of inclusions in molten steel is relatively small and errors occur in the analysis process, the idea of amplifying the inclusion reaction was implemented to study the effect of different proportions of rare earth cerium oxide and alumina powder at a high temperature of 1 600 ℃ on the inclusion phase change and size. A high-temperature box-type furnace was heated, the temperature was maintained, and the furnace was subsequently cool. According to the test results, the specific change process of cerium aluminum oxide was examined. An energy spectrum analyzer, X-ray diffractometer, X-ray fluorescence spectroscopy instrument, and Raman spectrometer were employed to target inclusion changes specifically. The results show that with an increasing amount of alumina added, the phase of the product during the powder sintering process changed from +4 valence rare earth oxide to +3 valence rare earth oxide. According to the XRD pattern, the CeAlO3 phase was first generated, followed by the characteristic peak strength of the CeAlO3 phase gradually diminishing and disappearing, which was replaced by the CeAl11O18 phase. The XRD peak broadened, the characteristic peak weakened, the half-height width increased, the grain size decreased, and the crystallinity was reduced. Combined with three mathematical models of the average grain size, namely, D-S, W-H and H-W, R2 was calculated to be 0.761 78, 0.971 01, 0.920 81, and 0.961 87; and 0.989 65, 0.988 01, 0.978 42, and 0.981 28. The comparison reveals that the H-W method results exhibit a better fit, and the grain size of samples 4# to 1# gradually decreased to 7.63, 6.27, 5.99 and 3.97 μm, indicating that the increase in rare earth cerium could promote nucleation and reduce the grain size. By analysing the Raman spectra, as the phase fraction of Al2O3 increased, the Raman peak intensity from 464~465 cm-1 gradually weakened until it disappeared, and it was deduced that the phase was a ceria phase. The Raman intensity at a displacement from 4 351~4 399 cm-1 gradually increased, and combined with the XRD pattern. It could be determined that the substance was CeAl11O18. The obtained change rule is consistent with that determined by XRD. By enhancing the substances in steel requiring targeted research, the evolution process of alumina powder products after the addition of ceria powder is analyzed. The research results provide a reference to resolve the problem of alumina inclusion modification in steel.
2021 Vol. 41 (06): 1841-1845 [Abstract] ( 440 ) RICH HTML PDF (1848 KB)  ( 145 )
1846 Study on Measurement of Mercury Ion in Water by Thiamine-Fluorescence Excitation-Emission Matrix
JULDEZ Nurlan1,2,3, SHEN Jian1,2,3, LENG Xiao-ting3, CHAI Yi-di1,2,3, WANG Shi-feng3, HU Yuan3, CUI Hao-yue3, WU Jing1,2,3*
DOI: 10.3964/j.issn.1000-0593(2021)06-1846-06
Mercury is a heavy metal element with significant accumulation effect and genotoxicity, which is extremely harmful to human health and the ecological environment. In China, the water environment is facing serious problems of mercury pollution. Developing rapid, efficient, and economical method for mercury ion detection can effectively promote the source control of mercury pollution in the aquatic environment. This study innovatively proposed a method, namely thiamine-fluorescence excitation-emission matrix (EEM), to monitor mercury ion in the water environment. Results showed that the position and number of fluorescence peaks of thiamine significantly changed after it happed redox reaction with mercury ion, which could be used as a characteristic signal for detecting mercury ions in water. In addition, when using this method to detect mercury ions in water, it was suggested that the concentration of thiamine should not be too high, and the reaction system should be kept in the alkaline environment. The reaction temperature and reaction time could be further optimized by the first-order kinetic model to reduce the detection cost and improve the detection effectiveness. Under the specific detection conditions (thiamine concentration 10 μmol·L-1, pH 9.7, reaction time 120 min, temperature 20 ℃),the linear detection range of mercury ion concentration was suggested to 4~15 μmol·L-1. The thiamine-EEM method owns outstanding advantages and good practical application values compared with the traditional method of mercury ion monitoring in water, which can effectively help the pollution source supervision of mercury in water environment and greatly improve the efficiency of environmental law enforcement.
2021 Vol. 41 (06): 1846-1851 [Abstract] ( 182 ) RICH HTML PDF (3599 KB)  ( 37 )
1852 Research on Estimation of Oil-Water Ratio of Light Oil Emulsion Based on Fluorescence Spectroscopy
YUAN Li1,2, WANG Li-bin1, JIAO Hui-hui1
DOI: 10.3964/j.issn.1000-0593(2021)06-1852-06
During the weathering and migration of sea surface oil, different oil spill emulsions will be formed, which will cause great harm to the marine environment. Scientific quantification of oil spilt emulsions is helpful for oil spill emergency treatment and disaster damage assessment. Due to the lack of systematic experimental data, physical and chemical and optical parameters, the fine spectral response characteristics and variation rules of different types of oil-water emulsions are not clear, and the data relationship between the spectra of different types of oil spilt emulsions, and the surface oil-water ratio of seawater can not be given. In this paper, through the laboratory experiment of light oil emulsion, using laser-induced fluorescence technology, starting with the difference and change law of fluorescence spectrum response of different types and different surface oil-water ratio, the relevant data of emulsified diesel oil is used as the modeling sample, and the relevant data of emulsified kerosene as the verification sample, the statistical analysis is carried out, and the prediction models of surface oil-water ratio under two types of water in oil and oil in water were designed respectively. In the process of data processing, in order to eliminate the influence of the LIF system on the intensity of the fluorescence signal received, the Raman scattering signal of water is used to normalize the fluorescence signal of emulsion and the ratio of the two is used as the subsequent analysis data. The specific data research shows that the non-linear regression model can be established between the logarithm of fluorescence peak value and the logarithm of the surface water content of oil in oil emulsion spiltoil; the non-linear regression model can also be established between the fluorescence peak value and surface water content of oil in water emulsion oil spill. The correlation coefficients of non-linear fitting were above zero points nine.That is, the model has high quality. The coefficients in the model depend on different oil types and different characteristic fluorescence peaks.It can be seen that the fluorescence peaks of different emulsified oils have the same change trend with the surface oil-water ratio, but the degree of change is different. On this basis, a parameter look-up table is used to estimate the oil-water ratio of light oil emulsions. The surface oil-water ratio can be inverted according to the fluorescence relative intensity.To a certain extent, this method can effectively quantify the oil emulsion on the sea surface, and provide a theoretical basis and basis for the real-time and accurate quantitative analysis of oil spill emulsion in the future, and also provide technical reference for the emergency treatment of oil spill pollution on the sea surface, so it has important research significance and practical value.
2021 Vol. 41 (06): 1852-1857 [Abstract] ( 174 ) RICH HTML PDF (2658 KB)  ( 48 )
1858 Impacts Analysis of Typical Spectral Absorption Models on Geostationary Millimeter Wave Atmospheric Radiation Simulation
CHEN Hao1,2, WANG Hao3*, HAN Wei3, GU Song-yan4, ZHANG Peng4, KANG Zhi-ming1
DOI: 10.3964/j.issn.1000-0593(2021)06-1858-05
To analyze atmospheric radiation characteristics of geostationary millimeter wave remote sensing, three different typical spectral absorption line database based line-by-line millimeter wave atmospheric absorption models, Millimeter-wave Propagation Model(MPM), ROSenkranz Model (ROS), High-resolution TRAN smission molecular absorption Model(HITRAN), were applied. Under different atmospheric temperature, pressure and water vapor conditions, the differences between the three models were analyzed on 424 GHz, which are planned to add to a geostationary satellite. A multi-layer millimeter wave radiative transfer model was presented. By utilizing intensively observing radiosonde data of Shanghai, simulated millimeter wave radiations of Fengyun 3B(FY3B) microwave humidity sounder (MWHS) were calculated under the three atmospheric absorption models. The simulations were compared with the real observations of FY3B MWHS, with temporal-spatial matching. The accuracy of the three models was analyzed. Simulations show that, for 424 GHz channels, the trends of the three models’ results are the same over atmospheric temperature, pressure and water vapor. The values of MPM and ROS are much closer to each other than HITRAN. The performance of MPM is better than ROS and HITRAN. Simulation errors in channel 5 are less than channel 3 and 4 of FY3B/MWHS.
2021 Vol. 41 (06): 1858-1862 [Abstract] ( 197 ) RICH HTML PDF (3416 KB)  ( 46 )
1863 Photoluminescence and Radioluminescence of Tb3+ Ion-Doped Lithium Aluminosilicate Glasses
CHEN Yan-ping1, LUO De-li2*, HUANG Bin1, CHENG Hao1, TANG Xian-chen1, LI Qiang1, LEI Hong-bo1, CHEN Dan-ping3
DOI: 10.3964/j.issn.1000-0593(2021)06-1863-06
The lithium aluminosilicate scintillation glasses doped with different concentrations of Tb3+ ions were prepared by the melt quenching method. Photoluminescence properties were characterized by UV excitation, emission and fluorescence lifetime, and radio luminescence properties were measured by X-ray and cathode-ray excitation. The results show that the cross relaxation between Tb3+ ions increased with increasing the doping concentration of Tb3+ ions in the case of low concentration of Tb3+, which resulted from the energy of 5D37Fj transition transfers to 5D47Fj transition, so it leads to the fluorescence lifetime and emission intensity of 5D3 excited state decreasing. The emission intensity of 5D47Fj increasing. When the concentration of Tb3+ ions continued to increase in higher concentration of Tb3+ ions, the nonradiative transition would be increased, which was the main reason for the decrease of fluorescence lifetime and emission intensity of 5D4 excited state. It can be found that the energy transfer from 5D3 energy level to 5D4 energy level was increased with the increasing energy of excitation source by comparing the photoluminescence and radioluminescence, which is due to that the density of the glass was low.
2021 Vol. 41 (06): 1863-1868 [Abstract] ( 206 ) RICH HTML PDF (3165 KB)  ( 58 )
1869 Optimization of IBBCEAS Spectral Retrieval Range Based on Machine Learning and Genetic Algorithm
LING Liu-yi1,3*, HUANG You-rui1,2*, WANG Chen-jun1, HU Ren-zhi3, LI Ang3, XIE Pin-hua3
DOI: 10.3964/j.issn.1000-0593(2021)06-1869-05
Incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) can highly sensitively detect trace gases by using an optical resonator to enhance the absorption path. At present, IBBCEAS mainly uses light-emitting diode (LED) as its incoherent light source. The fitting result of the measured gas concentration with improper spectral retrieval range may have a large deviation if the reflectivity curve of the resonator’s mirror does not match well with the LED radiation spectrum with limited bandwidth. Taking the case of quantitative detection for atmospheric NO2, the influence of retrieval range on NO2 fitting results is analyzed. It is found that the relative fitting error will increase rapidly when the retrieval range is narrowed to a certain extent. In this paper, a method for optimizing retrieval range based on machine learning using RBF neural network and genetic algorithm is proposed in order to minimize the error. 435 sample data are obtained by retrieving NO2 concentrations with various spectral subranges, which are members of 430~480 nm and have different widths and center wavelengths. 80% of the sample data are used to train the RBF neural network, and the rest for the testing network. The nonlinear mapping relationship between input parameters, starting and ending wavelengths of retrieval range, and output parameter, relative fitting error, is obtained by the trained network. The optimal retrieval range is searched using a genetic algorithm, in which starting and ending wavelength of the retrieval range are encoded into an individual, and a population is generated with a number of random individuals. After the evolution of multi-generation populations, the optimal retrieval range is obtained by the genetic algorithm, which uses the output of the RBF neural network, i.e. relative fitting error, as individual fitness. Every population has 100 individuals, and the maximum evolution generation is set to 100. When the populations evolve in the 61st generation, the optimal individual, corresponding to 445.78~479.44 nm of the optimal retrieval range, appears, and the optimal fitness is 3.584%. The NO2 fitting results with the other four typical and the optimal retrieval ranges with the same width are compared. The results show that fitting error, relative fitting error and standard deviation of fitting residual with the optimal retrieval range are lower than those with the other four retrieval ranges. The results demonstrate the feasibility of using machine learning to determine the optimal retrieval range of an IBBCEAS system.
2021 Vol. 41 (06): 1869-1873 [Abstract] ( 211 ) RICH HTML PDF (3867 KB)  ( 89 )
1874 Diagnosis of Atmospheric Pressure Helium Cryogenic Plasma Jet
SONG Peng1,3, LI Zheng-kai2, CHEN Lei2*, WANG Xiao-fang1, LONG Wu-qiang1, ZENG Wen2
DOI: 10.3964/j.issn.1000-0593(2021)06-1874-06
In order to accelerate the process of helium plasma jet’s engineering,a stable helium plasma jet was produced in the atmosphere through a self-designed coaxial Dielectric Barrier Discharge structure with a discharge frequency of 10 kHz. By analyzing the voltage and current waveform under different working conditions, it can be found that simply increasing the volume flow of helium gas can only increase the current pulse slightly, but has little effect on the discharge time and the number of current pulses. However, when the peak discharge voltage is increased, the current pulse amplitude increases significantly. The types of active particles, electron excitation temperature and electron density of atmospheric pressure helium plasma jet were diagnosed by emission spectroscopy. The results show that the main active particles of helium plasma jet are He I atom, N2 second positive band system, N+2 first negative band system, hydroxyl (OH), H atom Balmer line system (Hα, Hβ) and O atom. It shows that although the purity of the helium gas used in this test has reached 99.99%, there is still a small amount of air remaining. At the same time, the air in the atmosphere will be sucked into the discharge space and be ionized. It can be found that the relative spectral intensity of the main active particles showed an upward trend with the increase of the volume flow of helium gas and the increase of the peak discharge voltage. The electronic excitation temperature under different test conditions was calculated by the Boltzmann slope method between 3 500 to 6 300 K. With the increase of the discharge peak voltage and helium gas’s volume flow rate, the electron excitation temperature basically shows a rising trend. However, due to the presence of a reverse electric field, the electronic excitation temperature may show a downward trend at some certain peak voltages; According to the Stark broadening principle, the electron density of the atmospheric pressure helium plasma jet was calculated, and it is found that the electron density can reach the order of 1015 cm-3 while increasing the peak voltage and helium volume flow can effectively increase the electron density in the plasma jet. The study of these parameters is of great significance for the application of helium plasma jets.
2021 Vol. 41 (06): 1874-1879 [Abstract] ( 209 ) RICH HTML PDF (3021 KB)  ( 142 )
1880 Spectral Feature Construction and Sensitivity Analysis of Water Quality Parameters Remote Sensing Inversion
WANG Xin-hui1, 2, GONG Cai-lan1, 2*, HU Yong1, 2, LI Lan1, 2, HE Zhi-jie1, 2
DOI: 10.3964/j.issn.1000-0593(2021)06-1880-06
Water quality remote sensing monitoring is one of the important application directions of remote sensing. As an auxiliary mean of traditional water sampling and testing, remote sensing has the advantages of rapid, large-area and contactless. However, most remote sensing sensors commonly used in inland water monitoring are designed for land observation or ocean watercolor observation. The design and setting of sensor performance indicators do not consider the characteristics of inland water, limiting the application of water quality remote sensing monitoring. This study proposes a method for constructing the spectral characteristics of water quality parameter based on variation coefficient and noise ratio index, and study the influence of the spectral resolution, signal-to-noise ratio (SNR) and radiation resolution on typical water quality parameters inversion models through the spectral simulation experiments. Firstly, aimed at three main water quality parameters in Shanghai, we construct the spectral characteristics of dissolved oxygen(DO), total phosphorus(TP) and ammonia nitrogen (NH3-N) respectively and establish remote sensing inversion models. Then, we carry out the spectral simulation experiment and calculate the sensitivity (S) and water quality sensitive differential index (CI) for sensitivity analysis. Finally, we evaluate the spectral resolution’s influence, SNR and radiation resolution on water quality parameters inversion models from two aspects of accuracy and stability. The results show that this method can effectively determine the bands of water quality parameters inversion models. Spectral resolution has little effect on the contrast-type inversion models, while SNR and radiation resolution greatly influence the models. With the increase of SNR and radiation resolution, the water quality inversion models’ accuracy and stability are improved to some extent. According to Comprehensive sensitivity analysis of sensors parameters, when the SNR is better than 56 dB, the radiation resolution is not less than 9 bit, and the spectral resolution is appropriate. It can be better applied to inland water quality remote sensing monitoring. This research can provide a reference for the development of sensors for inland water quality monitoring and provide technical support for water resources supervision departments to carry out remote sensing monitoring of water quality, which is conducive to accelerating the construction of an intelligent monitoring system for the water environment.
2021 Vol. 41 (06): 1880-1885 [Abstract] ( 231 ) RICH HTML PDF (4064 KB)  ( 95 )
1886 Evaluation Method for Damage Degree of Light Sources Used to Lighting Colorful Cultural Relics Based on Spectrum Analysis
ZHAO Kai-qing, DANG Rui*
DOI: 10.3964/j.issn.1000-0593(2021)06-1886-05
The spectral radiation in the light source is an essential cause of color damage, such as fading and discoloration of painted cultural relics. However, the spectral power distribution of different light sources is different, and the absorption and reflection characteristics of different materials to the radiation energy of each waveband are different, which leads to a great difference in the degree of damage caused to colorful cultural relics under the same exposure. Especially with the extensive application of LED with flexible spectral composition in cultural relic lighting, how to evaluate the lighting damage degree of the light source is the critical problem that needs to be solved. In this study, a 1440 hours experiment was used to irradiate 17 kinds of typical pigments of polychrome cultural relics with 10 kinds of narrow band spectrum under the constant temperature and humidity. By measuring the CIE L*a*b*and calculating the color difference of pigments under different light sources every 240 hours, the curve of color difference changing with exposure was drawn. Based on the curve analysis, the change law and color damage response function of pigments to different wavebands is obtained, and the formula to evaluate the damage degree of lighting source on polychrome cultural relics is established. The results show that: firstly, no matter under any kind of narrow band spectrum, the average color difference of pigments increases with the increase of exposure, but the increased range is smaller and smaller; Secondly, under the same exposure, the shorter the wavelength, the greater the damage to the pigments. The influence ratio of different peak wavelengths to the pigments is 447 nm∶475 nm∶500 nm∶519 nm∶555 nm∶595 nm∶624 nm∶635 nm∶658 nm∶733 nm=1.000∶1.096∶0.816∶0.921∶0.853∶0.777∶0.814∶0.796∶0.706∶0.674; thirdly, the formula for damage evaluation of the light source based on the difference of spectrumis, D=∫780380S(λ)〈0.468exp{-[(λ-462.9)/17.75]2}+0.627 9exp{-[(λ-535.1)/12.13]2}+0.813 5exp{-[(λ-527.7)/463]2}〉,When the relative spectral power distribution function S(λ) of any light source is measured by the spectrometer, the color damage value D of the light source to be measured can be calculated by substituting the measured data into the formula.
2021 Vol. 41 (06): 1886-1890 [Abstract] ( 188 ) RICH HTML PDF (2481 KB)  ( 48 )
1891 Retrieving Corn Canopy Leaf Area Index Based on Sentinel-2 Image and PROSAIL Model Parameter Calibration
SU Wei1,2, WU Jia-yu1,2, WANG Xin-sheng1,2, XIE Zi-xuan1,2, ZHANG Ying1,2, TAO Wan-cheng1,2, JIN Tian1,2
DOI: 10.3964/j.issn.1000-0593(2021)06-1891-07
Leaf area index (LAI) is related to photosynthesis, transpiration and biomass accumulating processes of vegetation closely. It is one of the important parameters of corn growth monitoring, disaster stress monitoring and yield prediction, as well as an important parameter of the radiative transfer model and crop growth model. Sentinel-2satelliteis the second satellite of the Global Monitoring for Environment and Security (GMES) plan. It has high spatial and temporal resolution, and visible and near-infrared bands resolution is 10 m, so sentinel-2 satellite is an ideal data source for agricultural remote sensing applications. The PROSAIL radiative transfer model is an effective way to retrievecorn canopy LAI using remote sensing images. However, there are some problems for LAI retrieval currently, including uncertainty of model inputs, difficulty in parameter adjustment, ill-posed inversion and low speed etc. Model inputs calibration can be used to acquire the exact value of model inputs in the uncertainty range of the observed reflectivity. Rich and accurate parameter information is provided to reduce the errors in parameter retrieval. In this paper, a sentinel-2A satellite image was used as the data source, and Markov Chain Monte Carlo (MCMC) method was used to calibrate model inputs. The spectral reflectance uncertainty of 5% was added to obtain the posterior value probability distribution of each parameter, to optimize the parameter setting in the retrieval process and improve the accuracy of LAI retrieval. The results showed that: (1) The sensitive model inputs of the PROSAIL model were LAI, chlorophyll content of leaves and leaf structure coefficient within visible and near-infrared bands. Taking these three parameters as variables in the look-up table could effectively retrieve LAI, and the determination coefficient of retrieval accuracy reached 0.7. (2) MCMC method could be used to calibrate the PROSAIL model input and acquire each model input distribution in the study area. The posterior parameter distribution was close to the actual situation, indicating the feasibility and effectiveness of using the MCMC method for parameter calibration. (3) Input calibration could effectively improve the LAI retrieving accuracy, especially in reducing retrieval deviation and outliers. After inputs calibration, the average error of LAI retrieval decreased from 20% to 8%, while the estimation accuracy increased from 76% to 90%. These results showed that the model inputs calibration of the PROSAIL model by MCMC could improve the LAI retrieval accuracy and reduce the retrieval deviation. It provided a reference for improving the retrieval accuracy of crop canopy parameters by the PROSAIL radiative transfer model.
2021 Vol. 41 (06): 1891-1897 [Abstract] ( 199 ) RICH HTML PDF (3412 KB)  ( 65 )
1898 Application of Hyperspectral Imaging in the Diagnosis of Acanthopanax Senticosus Black Spot Disease
ZHAO Sen, FU Yun*, CUI Jiang-nan, LU Ye, DU Xu-dong, LI Yong-liang
DOI: 10.3964/j.issn.1000-0593(2021)06-1898-07
Taking the leaves of Acanthopanax acanthopanax infected with black spot disease as an example, the study of plant disease detection by spectral technology provides a research basis for early screening and precise treatment of medicinal plant diseases. The experiment aimed to realize the supervised classification and identification of plant diseases by hyperspectral imaging technology. The experimental procedure is as follows: First, the leaf samples of Acanthopanax japonicus were collected using the hyperspectral imaging technique. After the spectral data were preprocessed by removing light and dark noise and smoothing, the data dimension was reduced using principal component analysis. Then, a support vector machine (SVM) based on different kernel functions was used to establish a classification model separately. Finally, the overall classification accuracy, Kappa coefficient and other factors were used to evaluate different kernel functions’ influence on the classifier performance. According to the leaf’s surface characteristics, the leaf was divided into four kinds of samples: healthy bright part, healthy dark part, mild disease and severe disease. It can be seen that the healthy sample of Acanthopanax senticosus had a significant peak at 540 nm, and the spectral curve rose sharply at 620~680 nm; while the spectral reflectance of disease samples showed a slow and steady rising trend. The above features could completely distinguish healthy samples with close reflection intensity from serious disease samples on the image. After comparison, it was found that the first four principal components (PC1, PC2, PC3, PC4) have certain differences in the classification results. The main differences were that PC1 contains much information and can better distinguish various samples; PC2 showed a cross-confusion between bright healthy samples and seriously diseased samples; PC3 was a supplement to PC2, which can find mild diseased areas; PC4 contribution rate was only 0.19%, and it could still accurately identify serious diseased areas. The differences of principal component components in showing various sample characteristics can be used to reference complex sample classification. Compared with the classification accuracy of SVM modeling based on different kernel functions, the results showed that the linear kernel function’s recognition process was greatly affected by light intensity reflection. The training accuracy of the Sigmoid kernel function was easily affected by the size of the data set, and there were certain errors in recognition of healthy light or dark and minor diseases. The effect of polynomial kernel function and radial basis kernel function was good, and the accuracy of the polynomial kernel was higher, which was 92.77%. Research showed that the hyperspectral imaging technology could accurately identify the healthy and diseased leaves of Acanthopanax senticosus and provided a new method for the automatic diagnosis of diseases of medicinal plant leaves.
2021 Vol. 41 (06): 1898-1904 [Abstract] ( 165 ) RICH HTML PDF (4355 KB)  ( 115 )
1905 Hyperspectral Estimation Model of Soil Organic Matter Content Using Generative Adversarial Networks
HE Shao-fang1, SHEN Lu-ming1, XIE Hong-xia2*
DOI: 10.3964/j.issn.1000-0593(2021)06-1905-07
In the previous study of the estimation model of soil organic matter content, most models were based on the feature bands, linear and non-linear empirical models rarely explored the ability promotion using an extended modeling dataset. To further improve the performance of the estimation model, it proposed a dynamic estimation model of soil organic matter content using generative adversarial networks (GAN) to generate the pseudo hyperspectral and organic matter content. Paddy soil samples and hyperspectral data (350~2 500 nm) were collected from Changsha and its surrounding areas of Hunan Province, and the organic matter content was monitored chemically. Based on these data, equivalent new samples were generated by GAN and combined with the modeling set to form anenhanced modeling set. After completing each epochformal training of GAN, the prediction models of soil organic matter content were dynamically constructed using cross-validation ridge regression (RCV), partial least squares regression(PLSR) and BP neural network (BPNN) on four observation points (corresponding 50, 100, 150 and 239 generated samples in enhanced modeling set) (the abbreviation of models were GAN-RCV, GAN-PLSR and GAN-BPNN). The experimental results showed that: (1) Among the estimation models fitted on modeling set of the origin data, RCV was the best-performing model, whose determination coefficient (R2) and root square error (RMSE) were 0.831 1 and 0.189 6; (2) In the 150 epochs formal training of GAN, the performance of GAN-RCV, GAN-PLSR and GAN-BPNN dynamically constructed on the enhanced modeling set were significantly improved, specific performances: R2 of GAN-RCV obtained the maximum 0.890 9 (RMSE 0.153 7), minimum 0.850 5 (RMSE 0.18) and mean 0.868 7 (RMSE 0.168 6), the maximum R2 increased by 7.2% (RMSE decreased by 18.9%) compared with RCV fitted on the modeling dataset, R2 of GAN-PLSR had the maximum 0.855 4 (RMSE 0.176 9), minimum 0.727 0 (RMSE 0.243 2) and mean 0.780 1 (RMSE 0.217 7), the maximum R2 increased by 20.6% (RMSE decreased by 29.5%) than PLSR constructed on the modeling dataset, GAN-BPNN performed best, whose R2 had the maximum 0.905 2(RMSE 0.143 3), minimum 0.801 7(RMSE 0.207 3) and mean 0.868 1(RMSE 0.168 6), the maximum R2 increased by 30.8%(RMSE decreased by 44.5%) comparing BPNN fitted on the modeling set; (3) With the increase of the number of generated samples in the enhanced modeling dataset, the improvement effect of model accuracy showed a trend of increasing first and then decreasing, and among the four observation points, the model constructed on the third had the most significant performance improvement. Sufficient experiments showed that the dynamic estimation model based on GAN improved the performance effectively. According to the evaluation results on the test set, the optimum model could be used to predict the soil organic matter content in the follow-up application.
2021 Vol. 41 (06): 1905-1911 [Abstract] ( 231 ) RICH HTML PDF (1251 KB)  ( 78 )
1912 Study on Inversion Model of Chlorophyll Content in Soybean Leaf Based on Optimal Spectral Indices
LIU Shuang, YU Hai-ye, ZHANG Jun-he, ZHOU Hai-gen, KONG Li-juan, ZHANG Lei, DANG Jing-min, SUI Yuan-yuan*
DOI: 10.3964/j.issn.1000-0593(2021)06-1912-08
The accurate acquisition and prediction of chlorophyll content can provide a theoretical basis for precise management of crop planting. Optimal spectral index was used to establish the soybean chlorophyll content inversion model in this paper. The hyperspectral and chlorophyll content data of soybean flower bud differentiation were obtained. Firstly, seven typical spectral indices related to chlorophyll content were constructed, namely ratio index (RI), difference index (DI), normalized difference vegetation index (NDVI), modified simple ratio index (mSR), modified normalized difference index (mNDI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), respectively. First derivative (FD) processing was performed on the original hyper spectrum, and then the original and first derivative hyper spectrum are combined with all wavelengths in the full spectrum wavelength range to calculate 14 spectral indices. Then use the correlation matrix method to select the optimal spectral index. The correlation analysis was conducted between the spectral index calculated by all wavelength combinations and chlorophyll content. The maximum value of the correlation coefficient was taken as the index to extract the 14 optimal wavelength combinations, and the corresponding spectral index value was calculated as the optimal spectral index. Finally, the optimal spectral indices were divided into three groups as model input variables combined with the three methods of Partial least squares regression (PLS), Least squares support vector machine regression (LSSVM), and LASSO regression to model, then compare and analyze the results. The coefficients of determination R2c, R2p and the root mean square error RMSEC and RMSEP as model evaluation indicators, then soybean chlorophyll content inversion model with the highest accuracy, were finally selected. The results show that the 14 optimal spectral index wavelength combinations are RI (728, 727), DI (735, 732), NDVI (728, 727), mSR (728, 727), mNDI (728, 727), SAVI (728, 727), TVI (1 007, 708), FDRI (727, 708), FDDI (727, 788), FDNDVI (726, 705), FDmSR (726, 705), FDmNDI (726, 705), FDSAVI (727, 788) and FDTVI (760, 698), the maximum correlation coefficient with chlorophyll content are all greater than 0.8. The method to establish the optimal chlorophyll inversion model was the LSSVM modeling method combined with the first derivative spectral index (combination 2). The R2c=0.751 8, R2p=0.836 0, RMSEC=1.361 2, RMSEP=1.220 4, indicating that the model had high accuracy and could provide a reference for monitoring the growth status of soybean in a large area.
2021 Vol. 41 (06): 1912-1919 [Abstract] ( 256 ) RICH HTML PDF (7635 KB)  ( 90 )
1920 Gemmological and Spectroscopic Studies of the Jade Looked Like “Frozen Matrix” Chicken-Blood Stone
CHEN Qian, CHEN Tao*, XU Xing, KANG Bin-yan, ZHENG Jin-yu, LI Meng-yang
DOI: 10.3964/j.issn.1000-0593(2021)06-1920-05
The object of this study is a kind of jade that is similar to “Frozen Matrix” Chicken-Blood Stone. There are orange-red minerals in its semitransparent “Matrix”. Using X-ray powder diffraction spectrometer (XRD), scanning electron microscopy (SEM), infrared spectrometer (IR), and laser Raman spectroscopy(LRM) to analyze the gemological and spectroscopic characteristics of the jade. The results are as follow: the “Matrix” of the jade is mainly composed of ordered dickite, and the part in orange-red is realgar. The crystals are self-shaped hexagonal-plate. The length diameter of dickite is 15~20 μm, and the thickness is 2~4 μm. There is no obvious directionality in the aggregation of dickite. Some samples contain minerals like pyrite, fluorite, quartz, and calcite. The fingerprint region of FTIR spectra of “Matrix” has the main characteristic peaks of kaolinite minerals, which are located at 430, 470, 540, 698, 755, 795, 913, 937, 1 002, 1 034 and 1 118 cm-1; the functional areais characterized by the absorption peak at 3 622, 3 652 and 3 706 cm-1. The absorption peak of 3 622 cm-1 is caused by the in-plane stretching vibration of the inner hydroxyl OH1, and 3 653 cm-1 is attributed to the in-phase stretching vibration of the inner surface hydroxyl OH2 and OH4. The high-frequency peak is weak, while the low-frequency peak is strong. The absorption peak caused by the stretching vibration of hydroxyl OH3 on the inner surface is located at 3 706 cm-1. The result of FTIR spectra shows that the “Matrix” is a highly ordered dickite. Raman spectra showed that the “Blood” of the jade is realgar with the characteristic Raman shift at 186, 222, 235, 273, 346 and 355 cm-1. Among them, 186 and 222 cm-1 is attributed to the bending vibration of S—As—S, while the stretching vibration of As—S causes 346 and 355 cm-1. The Raman spectra of “Matrix” shows that the low-frequency region has the characteristic shift of kaolinite minerals of 133, 241, 266, 336, 436, 463, 747, 792 and 914 cm-1. In the high-frequency region, three stepped spectral peaks similar to the infrared spectra can be seen. The maximum strength of 3 624 cm-1 is attributed to the stretching vibration of OH1. The sub-strong peak 3 646 cm-1 is caused by the in-phase stretching vibration of OH2 and OH4. The peak strength of stretching vibration attributed to OH3 is the lowest and located at 3 706 cm-1. The characteristics of Raman shift in the high-frequency region indicate that“Matrix” is highly ordered dickite, which is consistent with the conclusion of FTIR spectra. Although the “Matrix” of the jade share the same composition of “Frozen Matrix” Chicken-blood stone, its’ “blood” is not cinnabar but realgar. Therefore, this study’s object is not Chicken-blood stone, and it should be called “Clay minerals jade”.
2021 Vol. 41 (06): 1920-1924 [Abstract] ( 177 ) RICH HTML PDF (4111 KB)  ( 66 )
1925 Determination of Available Boron in Soil by ICP-OES With Boiling Water Extraction
ZHANG Peng-peng1, 2, HU Meng-ying1, 2, XU Jin-li1, 2*, CHEN Wei-ming1, 2, GU Xue1, 2, ZHANG Ling-huo1, 2, BAI Jin-feng1, 2, ZHANG Qin1, 2
DOI: 10.3964/j.issn.1000-0593(2021)06-1925-05
The determination of available boron in soil is of great significance to evaluate the supply level of available boron in the soil. The content of available boron in the soil directly affects the growth process of plants, so how to extract and determine the content of available boron is very important. However, the traditional methods of boiling water extraction curcumin colorimetry and boiling water extraction methylene amine colorimetry have a long process and slow speed, which can not meet the requirements of large-scale and rapid determination of soil samples. In this study, the available boron in soil was extracted by boiling water, and the extraction solution was determined by inductively coupled plasma atomic emission spectrometry. The main purpose of this study is to compare the extraction in a closed and open environment, the best extraction time, the interference of spectral lines in the determination process and different soil types, and to optimize the conditions suitable for analyzing the available boron in soil. The results showed that the available boron in different types of soil was the closest to the certified value of reference material in the boiling water extraction for 10 minutes under the open environment. In the determination process, the content of iron raised by the boiling water extraction was relatively low, which had no effect on the content of available boron. The detection limit of this method is 0.004 9 μg·g-1, and the RSD of the results is less than 9%. The accuracy of the method is verified by 12 national standard materials for the analysis of effective soil components, and the results are consistent with the recommended values. Boiling water extraction inductively coupled plasma atomic emission spectrometry (ICP-OES) has the advantages of simple operation, short process, fast detection, accurate and reliable analysis results, avoiding boron pollution in the process of sample treatment. It can extract dozens of soil samples at a time, greatly improving the analysis efficiency, and is suitable for the determination of effective boron content in the soil.
2021 Vol. 41 (06): 1925-1929 [Abstract] ( 301 ) RICH HTML PDF (926 KB)  ( 342 )
1930 SVD-ANFIS Model for Predicting the Content of Heavy Metal Lead in Corn Leaves Using Hyperspectral Data
HAN Qian-qian, YANG Ke-ming*, LI Yan-ru, GAO Wei, ZHANG Jian-hong
DOI: 10.3964/j.issn.1000-0593(2021)06-1930-06
Heavy metals can enter the human body through the food chain after the crops had been polluted by them and can seriously harm the body health. Therefore, how to quickly and accurately monitor the content of heavy metals in crops has become important research in the fields of ecology and food security. The conventional biochemical monitoring methods have the disadvantages of cumbersome operation, long implementation process and destructiveness, while the hyperspectral remote sensing has the advantages of high spectral resolution, a large amount of information, strong biochemical inversion ability, convenience and fast, and no damage to the monitored object, so using hyperspectral remote sensing to monitor of heavy metal content in crops has become one of the hotspots in the field of remote sensing research. The potted corn plants stressed by different concentrations of Pb(NO3)2 solution were used as the research object in the paper, based on the data of the reflectance spectra of corn leaves under different lead ion (Pb2+) stress gradients and the measured Pb2+ contents in the leaves and combined with the Singular Value Decomposition (SVD) theory and Adaptive Network-based Fuzzy Inference System (ANFIS) structure, an SVD-ANFIS model was established for predicting the Pb2+ content in corn leaf. Firstly, SVD was used to process the reflectance spectra of Old leaves (O), Middle leaves (M), New leaves (N) under different stress gradients so that the singular values of the original spectral information were obtained. Then, the singular values corresponding to O, M, N leaves were selected to seek the optimal input combination of the ANFIS structure. Finally, the singular values of the spectra of the O-M (double-input) combination were selected as the input quantity of the ANFIS structure. After obtaining the optimal fuzzy rule base through training and learning, the output quantity of ANFIS structure was the content of Pb2+ in the leaves. Thus the SVD-ANFIS model achieved its predictive performance. The results showed that the model’s output error value was small and the prediction accuracy was high, and the prediction effect was best when the membership function was chosen as bell function in the fuzzy training process. When the multi-parameter Back Propagation (BP) neural network prediction model was used to verify the superiority of the prediction of the SVD-ANFIS model, the determination coefficient (R2) of the BP model and SVD-ANFIS model were 0.977 6 and 0.988 7, and the root means square error (RMSE) were 2.455 9 and 0.601 3 respectively, so the SVD-ANFIS model was shown to has a higher fit degree and better prediction effect. At the same time, spectral data of the corn leaves polluted by Pb2+ in different years were selected to test the feasibility of the SVD-ANFIS model, and its R2 and RMSE were 0.986 4 and 0.887 4, respectively, it indicated that the SVD-ANFIS model could be better used to predict the content of Pb2+ in corn leaves with high robustness and could be used as a method to predict the content of heavy metals in corn leaves.
2021 Vol. 41 (06): 1930-1935 [Abstract] ( 190 ) RICH HTML PDF (2221 KB)  ( 55 )
1936 Study on the Mineral Composition and Spectral Characteristics of “Bumblebee Stone”
LIU Jia-jun1, LUO Jie1*, YUE Su-wei1, XU Ya-lan2
DOI: 10.3964/j.issn.1000-0593(2021)06-1936-06
Recently, a kind of jade with yellow and black stripes appeared in the Liwan jewelry market in Guangzhou. It is called “Bumblebee stone” because of its patterns. The stripe structure of “Bumblebee stone” is similar to that of Sardonyx, which is easy to be confused. In this paper, the “Bumblebee stone” was determined by a petrographic microscope, X-ray diffraction, electron probe micro analysis, Fourier transform infrared spectroscopy and Raman spectroscopy, and the basic Basic physical properties, mineral composition and spectral characteristics were studied. The results showed that the color of “Bumblebee stone” was mainly gray, yellow, orange and black, with a hardness of 3~5 and a relative density of 2.58~2.73. It has weak yellow fluorescence under long wave ultraviolet light and releases gas by reacting with dilute hydrochloric acid. Microscopic petrographic analysis showed that the matrix mineral of “Bumblebee stone” was calcite an irregular and granular fibrous, with a particle size of 0.02~0.3 mm. CaO, FeO and MgO content in “Bumblebee stone” was 53.64%~56.66%, 2.23%~3.62% and 1.05%~1.79%, respectively. As and S element were found in some test points. The mol percentage of Mg/Ca in the sample was 2.59%~4.68%, which was low magnesium calcite. Fourier transform infrared spectroscopy analysis showed that the main characteristic peak of “Bumblebee stone” coincides with the theoretical value of carbonate minerals, namely 1 514, 1 427, 881, 710 cm-1, which caused by asymmetric stretching vibration, in-plane bending vibration and out-of-plane bending vibration of CO2-3. There are characteristic peaks of pyrite in the black part of “Bumblebee stone”, of which 1 123, 1 050 cm-1 is S-S stretching vibration and 423 cm-1 is Fe2+-[S2]2- stretching vibration. Raman analysis showed that the yellow part of the “Bumblebee stone” sample have both the calcite Raman shifts of 1 083, 713, 282 and 157 cm-1, and pararealgar Raman peaks of 346, 233, 184 cm-1. Besides, the orange-red part of the “Bumblebee stone” sample showed the Raman characteristic peaks of realgar at 338, 221 and 184 cm-1, which were caused by S-AS-S stretching vibration, S-As-S bending vibration combined with As-S stretching vibration, and As-As stretching vibration, respectively. The results of X-ray powder diffraction are consistent with those of infrared absorption spectrometry analysis and Raman spectrometry analysis, showing that the main mineral of “Bumblebee stone” is calcite, and the secondary minerals are pyrite, realgar and pararealgar. According to the national standards, it can be named “Carbonate jade”.
2021 Vol. 41 (06): 1936-1941 [Abstract] ( 339 ) RICH HTML PDF (3091 KB)  ( 89 )
1942 Coal and Rock Identification Method Based on Hyper Spectral Feature Absorption Peak
WEI Ren1, XU Liang-ji2*, MENG Xue-ying1, WU Jian-fei1, ZHANG Kun1
DOI: 10.3964/j.issn.1000-0593(2021)06-1942-07
Coal is an important natural resource in our country and plays an important role in the development of industry and the national economy. In the process of underground mining, the traditional manual identification of coal-rock interface to cut coal and rock by the shearer is relatively inefficient, the recognition accuracy is poor, and there are many uncertain factors. In the future underground mining, “unmanned” has gradually become the technological development trend of underground mining. The realization of unmanned mining first needs to accurately and efficiently determine the coal-rock interface, and the coal-rock recognition algorithm will become the “brain” of unmanned equipment. Hyperspectral is an emerging technology that has developed rapidly in recent years and has a wide range of substance identification and classification applications. In this paper, hyperspectral is used as the technical means of coal and rock identification, collecting coal and rock hyperspectral data, and designing algorithms to realize coal and rock identification by extracting the characteristic bands of hyperspectral. Coal and rock identification are based on the difference between coal and rock composition. Coal and rock have different forms of aluminum in elemental components. The aluminum in coal samples is alumina, while the aluminum in rock samples is aluminum hydroxide. The vibration of the crystal lattice of AL-OH causes it to produce a strong absorption peak in the 2 130~2 250 nm band. Alumina does not have a strong absorption peak in this band, so 2 130~2 250 nm is used as the characteristic band design algorithm. Taking the mining area of Huainan area as the research area, sampling was conducted in multiple mining areas to obtain 23 sets of coal samples such as coking coal, gas coal, and lean coal; and 25 sets of rock samples such as floor mudstone, sandstone, and shale were obtained. After grinding the sample, use the FieldSpec4 spectrometer produced by the American ASD company to collect the reflectance spectra of coal and rock samples between 350 and 2 500 nm. After pretreatment, use continuum removal method, first-order differential method, second-order differential method and SCA- The SID model method extracts features from the 2 130~2 250 nm band of coal and rock, trains the extracted feature vectors with random forest and SVM algorithms, and applies the model to the test set for classification. In the end, the performance on the test set is good, the overall recognition rate is high, the recognition of the first-order differential and continuum removal methods is 83.3%, and the Kappa coefficients are 0.66 and 0.68, respectively. The recognition rates of the second-order differential method and SCA-SID model method are both above 90%, and the Kappa coefficient is 0.83. From the model’s time complexity and space complexity, the second-order differentiation method is more efficient and reliable than the SCA-SID model method. These identification methods provide an application reference for the automatic coal and rock identification technology underground in actual engineering.
2021 Vol. 41 (06): 1942-1948 [Abstract] ( 218 ) RICH HTML PDF (3748 KB)  ( 95 )
1949 Study on the Production Technology of Baihuimian From Suyang Site
SUN Tian-qiang1, WEI Guo-feng1*, CHENG Bao-zeng2, REN Guang2
DOI: 10.3964/j.issn.1000-0593(2021)06-1949-06
The Suyang site is located in Yiyang County, Luoyang City, Henan Province is one of the important sites in the prehistoric period in the Central Plains. The ground and walls of the unearthed houses are painted with “Baihuimian”(Lime layer). The raw materials and production methods are important for the study of prehistoric building materials significance. In this work, an X-ray diffraction analyzer (XRD) and X-ray fluorescence spectrometer (XRF) was used to analyze the Baihuimian collected from the Suyang site, and several natural calcareous raw materials (loess-doll, limestone and oysters) were collected from the surrounding areas of the site to explore the raw materials of Baihuimian from the Suyang site. In addition, Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) were used to analyze and detect the Baihuimian from the Suyang site, natural loess-doll and simulated Baihuimian. The ν2/ν4 value of calcite in the infrared spectrum can be used to reflect the disorder degree of its crystals, draw the characteristic trend line of ν2/ν4, and finally combine the observation of the microscopic appearance to explore whether the ancestors of Suyang used artificially burned raw materials into lime when making Baihuimian. The results show that the main mineral phases of the Baihuimian samples from the Suyang site are quartz, calcite and a small amount of feldspar. The chemical composition has a higher calcium and silicon content, and its chemical composition and substance are similar to those of natural loess-doll, but different from limestone and oysters; according to the result displayed by the infrared spectrum, the average ν2/ν4 value of the Baihuimian from Suyang site is about 3.6, which is much lower than the ν2/ν4 value of artificially fired lime, and its ν2/ν4 characteristic trend line is also similar to that of natural loess-doll; combined with the results of scanning electron microscopy, it can be preliminarily believed that the ancestors of Suyang used natural loess-doll as raw material when making Baihuimian, and the loess-doll was not artificially fired. It was directly crushed and mixed with water in proportion to form a slurry, and smeared on the wall, the ground and the pit wall. This paper provides a relatively easy research method for distinguishing the nature loess-doll and the carbonized products of artificially fired loess-doll and helps to understand further the raw materials and production processes of prehistoric Baihuimian. It also provides a reference for archaeologists to study the craftsmanship of prehistoric residents to make building materials and the awareness of prehistoric residents to improve their living environment.
2021 Vol. 41 (06): 1949-1954 [Abstract] ( 211 ) RICH HTML PDF (3191 KB)  ( 37 )
1955 Rapid Classification and Identification of Plastic Using Laser-Induced Breakdown Spectroscopy With Principal Component Analysis and Support Vector Machine
LIU Jun-an, LI Jia-ming*, ZHAO Nan, MA Qiong-xiong, GUO Liang, ZHANG Qing-mao
DOI: 10.3964/j.issn.1000-0593(2021)06-1955-06
A large number of discarded plastic products cause serious damage to the ecological environment. It is urgent to recycle plastic by classification. The traditional classification method can not meet the needs of industrial production due to its high cost, low efficiency and complex operation. Laser-induced breakdown spectroscopy (LIBS) has been widely used in the field of substance identification with many advantages, such as simplicity, flexibility, speed and sensitivity. In this paper, 20 kinds of plastics were classified and identified by LIBS combined with principal component analysis (PCA) and support vector machine (SVM). Since few papers have studied the classification and recognition rate of plastic at present, the experiment further studies and analyzes the time spent in the experimental process on the premise of ensuring the accuracy of identification, so as to meet the requirements of rapid classification in industrial production. During the study, 100 groups of spectral data were collected for each plastic, 50 groups of data were randomly selected as the training set to establish the model, and the remaining 50 groups were used as a test set to validate model. Therefore, the training set and the test set each had 1 000 groups of spectral data. The data of the training set was input into SVM for training without any processing, and the best model was established by using the five-fold cross validation. At this time, the recognition accuracy of the test set was 99.90%, the modeling time was 1 hour, 58 minutes, 41.13 seconds, and the prediction time was 11.96 seconds. Thus, it can be seen that the SVM algorithm can be used simply to achieve high accuracy, but it needs a lot of time. In order to improve the experimental efficiency, a principal component analysis algorithm is introduced to process the data, transform the original high-dimensional data into low-dimensional data, and train the model with the data after dimension reduction. For different principal component numbers, the experimental values were obtained by random training ten times and taking the mean value. Experiments show that when the number of principal components is 13, the corresponding recognition accuracy is 99.80%, while PCA processing time is 1.44 seconds, modeling time is 12.16 seconds, and prediction time is only 0.02 seconds. Although the PCA algorithm combined with the SVM algorithm has a slight decrease in the accuracy of classification and recognition for 20 kinds of plastics, it greatly reduces the time of model training and greatly improves the experimental efficiency. The results show that the two algorithms can be used to classify and identify plastic quickly and accurately.
2021 Vol. 41 (06): 1955-1960 [Abstract] ( 215 ) RICH HTML PDF (2972 KB)  ( 179 )
1961 Application Prospect of Laser Induced Breakdown Spectroscopy in Disease Diagnosis
ZHANG Kun1, XU Zong-wei1*, CHEN Chuan-song2, FANG Feng-zhou1
DOI: 10.3964/j.issn.1000-0593(2021)06-1961-05
Trace elements are closely related to human health. For example, the homeostasis of metal elements may influence cells’ metabolic function, which may result in the production of certain diseases. Laser-induced breakdown spectroscopy (LIBS), a factor-free analysis technique that can be employed for molecularly complex biological materials or clinical specimens, has the capability to obtain elemental signals from a single laser pulse and a small amount of material (Nanogram order).This article mainly reviews the related research of LIBS process in disease diagnosis since 2015, including several common diseases (calculi, hair loss, eye disease, etc.) and malignant tumor diagnosis (skin cancer, liver cancer, stomach cancer, breast cancer, ovarian cancer, cervical cancer, etc.). The samples studied include in-vitrotissue, calculus, tissue sections, serum, plasma, whole blood and other biological materials. These biological samples contain or accumulate metal substances and metal-doping compounds that can be detected, quantified, and imaged. LIBS can perform microanalysis, location analysis, and quantitative analysis of the endogenous and exogenous chemical elements in the sample with sensitivity and microscopic resolution of several parts per million. Finally, the medical development trend of the LIBS technique prospects. It is hoped that a simple review can attract more scientists to pay attention to the application of LIBS technology in the field of disease diagnosis to promote the improvement of the LIBS method and play a greater role in disease diagnosis and treatment.
2021 Vol. 41 (06): 1961-1965 [Abstract] ( 247 ) RICH HTML PDF (1060 KB)  ( 91 )
1966 Dark-Field Confocal Brillouin Spectrum Detection System
NING Ying1, WU Han-xu1, XU Meng2, QIU Li-rong1, ZHAO Wei-qian1, NI He1*
DOI: 10.3964/j.issn.1000-0593(2021)06-1966-05
Confocal Brillouin spectroscopy is widely used in physical chemistry, materials science, and mineralogy due to its advantages of non-invasive, label-free, high spatial resolution. Since the spontaneous Brillouin scattering intensity is weak, the signal spectrum easily overlaps with the elastic background or be obliterated when the extinction ratio of the system is insufficient, so accurately measuring the brillouin frequency shift is unachievable. The increasing demand for viscoelastic detection of turbid media in biomedicine also put forward higher requirements on the extinction ratio of the Brillouin detection system. In order to improve the extinction ratio of the confocal Brillouin system, a dark-field confocal Brillouin detection system is constructed in this paper. The non-intersecting optical path configuration avoids the collection of reflected light so it weakens the elastic background light to improve the extinction ratio while ensuring the excitation intensity. Experiments show that compared to the traditional bright field configuration, the extinction ratio of the dark field system is increased by 20 dB; The background light of an intralipid solution at a concentration of 0.001% is obviously suppressed in the dark illumination configuration, the signal spectrum is revealed, thereby attains the accurate measurement of the turbid medium’s Brillouin frequency shift data; Three standard samples of distilled water, PMMA, and SiO2 glass were selected to verify the non-strict backscatter angle. The experimental result is consistent with the theoretical analysis, which ensures that subsequent calculations of axial sound velocity and longitudinal elastic modulus are accurate and effective. The dark field confocal Brillouin spectrum detection system combines the advantages of dark field illumination and confocal detection. It not only has a high resolution of confocal detection, but also improves the system’s anti-elastic background light performance and achieves high extinction detection. The dark field confocal Brillouin spectrum detection system provides a new idea for real-time non-destructive detection of the mechanical properties of substances in biomedicine and materials science.
2021 Vol. 41 (06): 1966-1970 [Abstract] ( 181 ) RICH HTML PDF (1663 KB)  ( 42 )
1971 Spectroscopic Characterizations of Metal-Complexes of 4-Hydroxybenzoic Acid With the Ni(Ⅱ), Mn(Ⅱ), and Cu(Ⅱ) Ions
Samar O. Aljazzar
DOI: 10.3964/j.issn.1000-0593(2021)06-1971-05
One of the phenolic acids is 4-hydroxybenzoic acid (HBA) which takes the form of a white crystalline solid with a molecular formula of C7H6O3, a melting point of 214.5 ℃ and a molecular weight of 138.12 g·mol-1. It soluble in polar organic solvents like acetone and alcohols, and slightly soluble in chloroform and water. The reactions between the metal ions and the HBA were carried out under specific conditions like (molar reaction was 2∶2 (ligand to metal), reaction temperature was 60 ℃, media was neutral (pH 7), and solvent was H2O ∶MeOH (1∶1). Under these conditions, the HBA was deprotonated to form (HOC6H4CO-2; L-). The ligand L- was coordinated to the metal ions forming the metal complexation. The reaction of 4-hydroxybenzoic acid (HOC6H4CO2H; HL) with the Ni(Ⅱ), Mn(Ⅱ) and Cu(Ⅱ) ions afford metal-complexes with gross formula of [Ni2L2(NO3)2(H2O)4], [Mn2L2(NO3)2(H2O)4] and [Cu2L2(NO3)2(H2O)4], respectively. These complexes were characterized by elemental analysis (CHN), magnetic susceptibility, UV-Vis spectra, infrared (IR), and X-ray powder diffraction (XRD) techniques. The complexes of HBA are insoluble in common solvents and hence molar conductance could not be measured, but this very insolubility indicates that the complexes are neutral. Data has demonstrated that the ligand (L-) was coordinated to the metal ion by bidentate bridging carboxylate group (COO-), with an octahedral geometry. Thus, HBA is expected to act as bidentate uninegative ions and the coordination number of the metal ions is six. XRD results showed that the complexes possess uniform and organized microstructures in the nanometer range with a main diameter in the range of 11~28 nm.
2021 Vol. 41 (06): 1971-1975 [Abstract] ( 153 ) RICH HTML PDF (1661 KB)  ( 50 )
1976 Spectroscopic Investigations for the Six New Synthesized Complexes of Fluoroquinolones and Quinolones Drugs With Nickel(Ⅱ) Metal Ion: Infrared and Electronic Spectroscopy
Samar O. Aljazzar
DOI: 10.3964/j.issn.1000-0593(2021)06-1976-06
Quinolone has a broad spectrum of synthetic antibiotics with a strong therapeutic effect and quinolone is a term used for chemical treatments used to treat a powerful bacteria. Quinolones are divided into 4 generations according to the bacterial spectrum, the majority of quinolones used clinically belong to the sub fluoroquinolones group, which has a fluorine atom linked to the central ring system, usually on its carbon atom 6 or 7. Herein in this article, six new nickel(Ⅱ) complexes (Ⅰ—Ⅵ) have been synthesized in aqueous alkaline media at pH ranged 8-9, the chemical reactions take place between levofloxacin (HLEV), lomefloxacin (HLOM), nalidixic acid (HNLA), oxolonic acid (HOXO), pipemidic acid (HPIP), and pefloxacin mesylate (HPEF) with nickel(Ⅱ) nitrate hexahydrate. The microanalytical (percentage of carbon, hydrogen and nitrogen), molar conductance (Λm), Infrared (FTIR) spectra, electronic (UV-Vis) spectra, and effective magnetic moment instrumentals were used to identify the suggested structures and their surface morphology. According the analytical and spectroscopic analyses, the stoichiometry between nickel(Ⅱ) metal ion and drug ligands was found to be 1∶2 with general formula as [Ni(L)2(H2O)2xH2O (L=LEV (Ⅰ), LOM (Ⅱ), NAL (Ⅲ), OXO (Ⅳ), PIP (V), and PEF (Ⅵ); x=2 or 4). By the comparison between FTIR spectra of quinolone drugs and their complexes, it can be deduced that all the drug ligands act as a bidentate chelates through oxygen atoms of pyridine ring and carboxylate group. The electronic configuration of all synthesized nickel(Ⅱ) complexes were octahedral geometry which confirmed based on the values of magnetic susceptibility and the electronic transition bands.
2021 Vol. 41 (06): 1976-1981 [Abstract] ( 138 ) RICH HTML PDF (1866 KB)  ( 44 )
1982 Synthesis, Structural, Spectroscopic Characterization and Biological Properties of the Zn(Ⅱ), Cu(Ⅱ), Ni(Ⅱ), Co(Ⅱ), and Mn(Ⅱ) Complexes With the Widely Used Herbicide 2,4-Dichlorophenoxyacetic Acid
Lamia A. Albedair
DOI: 10.3964/j.issn.1000-0593(2021)06-1982-06
2,4-Dichlorophenoxyacetic acid (2,4-D) is a board-leaf selective herbicide and globally used in agricultural activities. Complexation mode, spectroscopic investigations and biological properties of complexes formed between 2,4-D (C6H3Cl2OCH2·COOH; HL) with Zn(Ⅱ), Cu(Ⅱ), Ni(Ⅱ), Co(Ⅱ), and Mn(Ⅱ) metal ions were investigated. To characterize the binding mode between 2,4-D and the metal ions, many physicochemical approaches were employed. The complexes obtained are characterized quantitatively and qualitatively by using micro elemental analysis, FTIR spectroscopy, UV-Vis spectroscopy, 1H-NMR, and magnetic susceptibility measurements. Results of these approaches suggested that the gross formula of the complexes obtained with the metal ions were [ZnL2](2H2O (1), [CuL2(H2O)2] (2), [NiL2](3H2O (3), [CoL2(H2O)2] (4), and [MnL2(H2O)2] (5). In all complexes, two L- anion were coordinated the metal ion by their bidentate carboxylate groups. From the spectral study, all the complexes obtained as monomeric structure and the metals center moieties are six-coordinated with octahedral geometry except Ni(Ⅱ) and Zn(Ⅱ) complexes which existed as a tetrahedral and square pyramidal geometry respectively. The complexes were screened in vitro against several microbes (fungi and bacteria) using Kirby-Bauer disc diffusion method, and data has demonstrated that complex 3 showed excellent antifungal activity.
2021 Vol. 41 (06): 1982-1987 [Abstract] ( 145 ) RICH HTML PDF (784 KB)  ( 48 )
1988 Synthesis and Structural Characterizations of Ternary Iron(Ⅱ) Mixed Ligand Complex: Low Cost Materialsas a Precursor for Preparation of Nanometric Fe2O3 Oxide
Khaled Althubeiti
DOI: 10.3964/j.issn.1000-0593(2021)06-1988-05
The reaction of the ligands, ethylenediaminetetraacetic acid terasodium salt (Na4EDTA) and N—N heterocyclic diamines like2,2’-bipyridine (bipy) with iron(Ⅱ) sulfate with 1∶2∶2 stoichiometric ratios form the mononuclear ternary complex of formulae, [Fe2(EDTA)(bipy)2] at pH~7. The FTIR and Raman laser spectra of the iron(Ⅱ) complex show that 2,2’-bipyridine is present asa bidentate ligand and the ethylenediaminetetraacetic acid terasodium salt as monodentate carboxylate anion. The electronic spectra and magnetic moments data suggest the six coordination number. It has two iron(Ⅱ) centers in octahedral environments, which are interlinked by carboxylato-O atoms of ethylenediaminetetraacetate and by nitrogen atoms of the two 2,2-bipyridine ligands in a chelating mode. Thermal analysis study show thatiron(Ⅱ) complex containing EDTA and 2,2’-bipyridine on its thermalde composition form the corresponding Fe2O3 oxide in nano size at the temperature range ~475 ℃. The iron(Ⅱ) complex was performed as a convenient low cost precursor for the preparation of Fe2O3 nanoparticles by the the thermal decomposition method. The iron(Ⅲ) oxide composition has been discussedusing FTIR, X-ray diffraction (XRD), transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDX).
2021 Vol. 41 (06): 1988-1992 [Abstract] ( 158 ) RICH HTML PDF (1846 KB)  ( 79 )