Shisong Tang
Huazhong University of Science and Technology
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Featured researches published by Shisong Tang.
Optics Express | 2017
Jiaming Li; Zhongqi Hao; Nan Zhao; Ran Zhou; Rongxing Yi; Shisong Tang; Lianbo Guo; Xiangyou Li; Xiaoyan Zeng; Yongfeng Lu
Spatially selective excitation was proposed to improve excitation efficiency in laser-induced breakdown spectroscopy combined with laser-induced fluorescence (LIBS-LIF). Taking chromium (Cr) and nickel (Ni) elements in steels as examples, it was discovered that the optimal excitation locations were the center of the plasmas for the matrix of the iron (Fe) element but the periphery for Cr and Ni elements. By focusing an excitation laser at the optimal locations, not only excitation efficiency but also the analytical accuracy and sensitivity of quantitative LIBS-LIF were better than those with excitation at the plasma center in conventional LIBS-LIF. This study provides an effective way to improve LIBS-LIF analytical performance.
Food Chemistry | 2019
Ping Yang; Ran Zhou; Wen Zhang; Rongxing Yi; Shisong Tang; Lianbo Guo; Zhongqi Hao; Xiangyou Li; Yongfeng Lu; Xiaoyan Zeng
Stability and sensitivity of toxic elements determination is still unsatisfactory in agricultural product using laser-induced breakdown spectroscopy (LIBS). A simple and low cost sample pretreatment method named solid-liquid-solid transformation method was proposed in this work. The target analytes of cadmium (Cd) and lead (Pb) from rice samples were prepared through ultrasound assisted extraction in hydrochloric acid solution. The solution was dropped on the glass slide after centrifuging process and was further dried on a heater. Finally, the glass slide contained the analytes was carried out for LIBS determination. Compare with conventional pellet method, the spectral intensity of Cd and Pb element were enhanced significantly using LIBS. The limits of detection were 2.8 and 43.7u202fμg/kg, respectively. The limits of quantification were 9.3 and 145.7u202fμg/kg, respectively. The results demonstrated that LIBS coupled with ultrasound assisted extraction should be a promising tool to detect toxic elements in rice.
Journal of Analytical Atomic Spectrometry | 2017
W. T. Li; Xinyan Yang; Xiangyou Li; Shisong Tang; Junyu Li; Rongxing Yi; Ping Yang; Zhongqi Hao; Lianbo Guo; Xiaolei Li; Xiaoyan Zeng; Yongfeng Lu
Stand-off laser-induced breakdown spectroscopy (ST-LIBS) has attracted increasing attention as a valuable method for the remote analysis of materials. In this work, a multi-collector (MC) system combining low cost small lenses imitating the structure and shape of an artificial optical compound eye (AOCE) is presented to enhance the spectral intensity of ST-LIBS. The volume of the MC system is one eighteenth smaller than that of a telescope system and the number of collectors can be flexibly changed according to the requirements. The mechanisms of spectral enhancement are also discussed. In order to illustrate the performance of the MC system, the spectral intensities and the limits of detection (LoDs) of Mn and Cr elements acquired by the telescope system and the MC system were compared at a distance of 2 meters. The results showed that the spectral intensity acquired from the MC system was enhanced by 2.2 times, and the LoDs of Mn and Cr were decreased to 294 μg g−1 and 49 μg g−1. These results indicate that the MC system has great potential in providing a portable, flexible and effective collection method for remote detection.
Journal of Analytical Atomic Spectrometry | 2018
Yun Tang; Lianbo Guo; Jiaming Li; Shisong Tang; Zhihao Zhu; Shixiang Ma; Xiangyou Li; Xiaoyan Zeng; Jun Duan; Yongfeng Lu
The method of spatially selective laser-stimulated absorption (SS-LSA) was proposed to reduce self-absorption in laser-induced breakdown spectroscopy (LIBS). Through scanning intensity maps of analytes, it was discovered that elemental distribution was inhomogeneous in the formed plasma. Taking copper (Cu) and chromium (Cr) elements in steel as examples, the distribution of Cu and Cr has its own characteristics. By focusing the laser beam on the optimal locations of the plasma with an optical parametric oscillator (OPO) wavelength-tunable laser, the linear determination coefficient (R2 factor) for Cu and Cr elements can reach 0.993 and 0.999, respectively. For Cu determination, compared with LIBS and LSA-LIBS, the root-mean-square error of cross-validation (RMSECV) of SS-LSA-LIBS decreased by 81 and 52%, respectively. The analytical accuracy of the quantitative analysis was much higher than those of conventional LIBS and LSA-LIBS, which indicates that the proposed method can effectively reduce self-absorption and improve LIBS analytical performance.
Analytical Methods | 2018
Yunxin Yu; Hanyue Yu; Lianbo Guo; Jun Li; Yanwu Chu; Yun Tang; Shisong Tang; Fan Wang
The adulteration of rice in the food industry is a very serious problem nowadays. To realize the rapid and stable identification of adulterated Wuchang rice, a hyperspectral imaging system (380–1000 nm) has been introduced in this study. Piece-wise multiplicative scatter correction (PMSC) was first used to correct the non-linear additive and multiplicative scatter effects. Then, the adulterated rice samples were identified via support vector machines (SVM). The PMSC-SVM model was attained over the whole spectral range, with the correct classification rate (CCR) increased from 95.47% to 99.20%, the kappa coefficient increased from 0.95 to 0.99, and the prediction CV (coefficient of variation) decreased from 3.04% to 1.56%. Furthermore, a simplified PMSC-SVM model was established, where 13 principal components were selected using 5-fold cross-validation. The CCR was increased from 95.40% to 99.08%, the kappa coefficient was increased from 0.94 to 0.99, and the prediction CV was decreased from 3.02% to 1.72%. The results demonstrated that the accuracy and stability for identifying adulterated rice has been improved by PMSC in the hyperspectral imaging system.
Journal of Analytical Atomic Spectrometry | 2018
Yangmin Guo; Lianbo Guo; Zhongqi Hao; Yun Tang; Shixiang Ma; Qingdong Zeng; Shisong Tang; Xiaolei Li; Yongfeng Lu; Xiaoyan Zeng
The quantitative analysis of iron ore by laser-induced breakdown spectroscopy (LIBS) is usually complicated due to nonlinear self-absorption and matrix effects. To overcome this challenge, a hybrid sparse partial least squares (SPLS) and least-squares support vector machine (LS-SVM) model was proposed to analyze the content of total iron (TFe) and oxides SiO2, Al2O3, CaO, and MgO in iron ore. In this study, 24 samples were used for calibration and 12 for prediction. Sparse partial least squares was used for variable selection and establishing the multilinear regression model between spectral data and concentrations; LS-SVM was used to fit the residual errors of the SPLS regression model to compensate for the nonlinear effects. The model parameters were determined by using the tenfold cross-validation (CV) method. With the hybrid model, the root-mean-square-error of prediction (RMSEP) values of TFe, SiO2, Al2O3, CaO, and MgO were 0.6242, 0.3569, 0.0456, 0.0962, and 0.2157 wt%, respectively. The results showed that the hybrid model yielded better performance than only the conventional SPLS or LS-SVM model. This study demonstrated that the hybrid model is a competitive data processing method for iron ore analysis using LIBS.
Journal of Analytical Atomic Spectrometry | 2018
Chu YanWu; Tong Chen; Feng Chen; Yun Tang; Shisong Tang; Honglin Jin; Lianbo Guo; Yongfeng Lu; Xiaoyan Zeng
The early diagnosis of malignant solid tumours remains a challenge. Here, we propose an efficient way to discriminate between nasopharyngeal carcinoma (NPC) serum and healthy control serum by using laser-induced breakdown spectroscopy (LIBS). Serum was dripped onto a boric acid substrate for LIBS spectrum acquisition. The focus elements (Na, K, Zn, Mg, etc.) were selected for diagnosing NPC using LIBS. With the random forest (RF), characteristic spectral lines were selected based on the variable importance. The spectral lines with variable importance greater than the average were selected. The selected spectral lines are the input of the extreme learning machine (ELM) classifier. Using the RF combined with the ELM classifier, the accuracy rate, sensitivity, and specificity of NPC serum and healthy controls reached 98.330%, 99.0222% and 97.751%, respectively. This demonstrates that LIBS combined with a RF-ELM model can be used to identify NPC with a high rate of accuracy.
Journal of Analytical Atomic Spectrometry | 2018
Jingjun Lin; Xiaomei Lin; Lianbo Guo; Yangmin Guo; Yun Tang; Yanwu Chu; Shisong Tang; Changjin Che
The classification of discarded alloy steel for recycling purposes and the identification of the alloy steels used in spare parts are very important in modern society. In this work, two typical classification methods, partial least squares discriminant analysis (PLS-DA) and a support vector machine (SVM), were used to study the classification of steels with similar constituents. Forty (40) steel species were detected using laser-induced breakdown spectroscopy (LIBS), and the identification by the PLS-DA and SVM models was 96.25% and 95% accurate, respectively. Based on these two classification algorithms, the least squares support vector machine (LSSVM) algorithm was used to further improve the identification accuracy. The kernel function parameter and error penalty factor of the LSSVM model were 0.4353 and 6.9644, respectively. The results showed that the identification accuracy reached 100%; therefore, combining LIBS with the LSSVM algorithm proved to be an effective approach for accurately identifying steel species with similar constituents.
Journal of Cereal Science | 2018
Ping Yang; Yining Zhu; Xinyan Yang; Jiaming Li; Shisong Tang; Zhongqi Hao; Lianbo Guo; Xiangyou Li; Xiaoyan Zeng; Yongfeng Lu
Applied Optics | 2018
Ping Yang; Yining Zhu; Shisong Tang; Zhongqi Hao; Lianbo Guo; Xiangyou Li; Yongfeng Lu; Xiaoyan Zeng