Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can a...Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.展开更多
Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy(LIBS).Based on the understanding that the superp...Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy(LIBS).Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance,a data selection method based on plasma temperature matching(DSPTM)was proposed to reduce both matrix effects and signal uncertainty.By selecting spectra with smaller plasma temperature differences for all samples,the proposed method was able to build up the quantification model to rely more on spectra with smaller matrix effects and signal uncertainty,therefore improving final quantification performance.When applied to quantitative analysis of the zinc content in brass alloys,it was found that both accuracy and precision were improved using either a univariate model or multiple linear regression(MLR).More specifically,for the univariate model,the root-mean-square error of prediction(RMSEP),the determination coefficients(R^(2))and relative standard derivation(RSD)were improved from 3.30%,0.864 and 18.8%to 1.06%,0.986 and 13.5%,respectively;while for MLR,RMSEP,R^(2)and RSD were improved from 3.22%,0.871 and 26.2%to 1.07%,0.986 and 17.4%,respectively.These results prove that DSPTM can be used as an effective method to reduce matrix effects and improve repeatability by selecting reliable data.展开更多
Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) method...Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error(ARE),relative standard deviation(RSD) and root mean squared error of prediction(RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%,RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.展开更多
Laser-in duced breakdown spectroscopy(LIBS),firstly proposed in 1962 as Brech and Cross[I]successfully detected the plasma emission induced by a ruby laser,has attracted more and more attention in both academia and in...Laser-in duced breakdown spectroscopy(LIBS),firstly proposed in 1962 as Brech and Cross[I]successfully detected the plasma emission induced by a ruby laser,has attracted more and more attention in both academia and industry due to its unique analytical features such as little or no sample preparation,simultaneous multi-elemental analysis,and remote sensing etc[2-4].Restrained from the highcost and poor reliability of instruments back then,the research popularity of LIBS declined quickly after a few years of initial mania of LIBS study.Since the 1990s,benefiting from the significant development of the hardware setups including laser,spectrometer,and ICCD,the'LIBS fever,re-emerged with continuous progress achieved in various applications as well as fundamental studies for the past two decades.In 2004,James D Winefordner,a prestigious an alytical scientist,crowned LIBS as a'future superstar5 for chemical analysis[5],marking the great potential of LIBS.However,on the way of fully commercialization and industrialization,LIBS is facing three big challenges:(1)to improve the quantitative analysis performance,particularly the repeatability and reproducibility performance;(2)to reduce the instrumental cost;(3)to improve the long-term stability and robustness for industrial applications.To finally transform LIBS from'future superstar,to'superstar5,joint effort of worldwide LIBS community is needed[6].展开更多
Ever since its creation in 1963 [1], laser-induced breakdown spectroscopy(LIBS)has gained considerable attention due to its unique capability for real-time, in situ or online analysis [2, 3]. The future world is headi...Ever since its creation in 1963 [1], laser-induced breakdown spectroscopy(LIBS)has gained considerable attention due to its unique capability for real-time, in situ or online analysis [2, 3]. The future world is heading into the age of artificial intelligence(AI), and data would be the most valuable asset for human society [4].展开更多
Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique gaining much attention since it was created in 1962[1].In 2021,the 4th Asian Symposium on LIBS (ASLIBS) and the ten-year anniver...Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique gaining much attention since it was created in 1962[1].In 2021,the 4th Asian Symposium on LIBS (ASLIBS) and the ten-year anniversary of Chinese Symposium on LIBS (CSLIBS) were jointly held in Qingdao,symbolizing the development of the Asian and Chinese LIBS communities into a new stage.Since the initiation of CSLIBS in Qingdao (2011) and ASLIBS in Wuhan[2](2015).展开更多
Laser-induced breakdown spectroscopy(LIBS)has been widely studied due to its unique advantages such as remote sensing,real-time multi-elemental detection and none-to-little damage.With the efforts of researchers aroun...Laser-induced breakdown spectroscopy(LIBS)has been widely studied due to its unique advantages such as remote sensing,real-time multi-elemental detection and none-to-little damage.With the efforts of researchers around the world,LIBS has been developed by leaps and bounds.Moreover,in recent years,more and more Chinese LIBS researchers have put tremendous energy in promoting LIBS applications.It is worth mentioning that the application of LIBS in a specific field has its special application background and technical difficulties,therefore it may develop in different stages.A review summarizing the current development status of LIBS in various fields would be helpful for the development of LIBS technology as well as its applications especially for Chinese LIBS community since most of the researchers in this field work in application.In the present work,we summarized the research status and latest progress of main research groups in coal,metallurgy,and water,etc.Based on the current research status,the challenges and opportunities of LIBS were evaluated,and suggestions were made to further promote LIBS applications.展开更多
Nowadays, there are about 415 million adults suffering from diabetesN. At present, the treatment for diabetes patients is monitoring their blood glucose level frequently and then taking the appropriate amount of oral ...Nowadays, there are about 415 million adults suffering from diabetesN. At present, the treatment for diabetes patients is monitoring their blood glucose level frequently and then taking the appropriate amount of oral hypogly- cemic drugs and insulin to control their blood glucose level.展开更多
基金financial supports from National Natural Science Foundation of China(No.62205172)Huaneng Group Science and Technology Research Project(No.HNKJ22-H105)Tsinghua University Initiative Scientific Research Program and the International Joint Mission on Climate Change and Carbon Neutrality。
文摘Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.
基金financial support from the Scientific Research Program for Young Talents of China National Nuclear Corporation(2020)National Natural Science Foundation of China(Nos.51906124 and 62205172)+1 种基金Shanxi Province Science and Technology Department(No.20201101013)Guoneng Bengbu Power Generation Co.,Ltd(No.20212000001)。
文摘Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy(LIBS).Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance,a data selection method based on plasma temperature matching(DSPTM)was proposed to reduce both matrix effects and signal uncertainty.By selecting spectra with smaller plasma temperature differences for all samples,the proposed method was able to build up the quantification model to rely more on spectra with smaller matrix effects and signal uncertainty,therefore improving final quantification performance.When applied to quantitative analysis of the zinc content in brass alloys,it was found that both accuracy and precision were improved using either a univariate model or multiple linear regression(MLR).More specifically,for the univariate model,the root-mean-square error of prediction(RMSEP),the determination coefficients(R^(2))and relative standard derivation(RSD)were improved from 3.30%,0.864 and 18.8%to 1.06%,0.986 and 13.5%,respectively;while for MLR,RMSEP,R^(2)and RSD were improved from 3.22%,0.871 and 26.2%to 1.07%,0.986 and 17.4%,respectively.These results prove that DSPTM can be used as an effective method to reduce matrix effects and improve repeatability by selecting reliable data.
基金supported by National Natural Science Foundation of China (Grant Nos. 61505223, 41775128)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. Y03RC21124)+1 种基金the External Cooperation Program of Chinese Academy of Sciences (Grant No. GJHZ1726)the project of China State Key Lab. of Power System (Grant Nos. SKLD18KM11, SKLD18M12)
文摘Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error(ARE),relative standard deviation(RSD) and root mean squared error of prediction(RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%,RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.
文摘Laser-in duced breakdown spectroscopy(LIBS),firstly proposed in 1962 as Brech and Cross[I]successfully detected the plasma emission induced by a ruby laser,has attracted more and more attention in both academia and industry due to its unique analytical features such as little or no sample preparation,simultaneous multi-elemental analysis,and remote sensing etc[2-4].Restrained from the highcost and poor reliability of instruments back then,the research popularity of LIBS declined quickly after a few years of initial mania of LIBS study.Since the 1990s,benefiting from the significant development of the hardware setups including laser,spectrometer,and ICCD,the'LIBS fever,re-emerged with continuous progress achieved in various applications as well as fundamental studies for the past two decades.In 2004,James D Winefordner,a prestigious an alytical scientist,crowned LIBS as a'future superstar5 for chemical analysis[5],marking the great potential of LIBS.However,on the way of fully commercialization and industrialization,LIBS is facing three big challenges:(1)to improve the quantitative analysis performance,particularly the repeatability and reproducibility performance;(2)to reduce the instrumental cost;(3)to improve the long-term stability and robustness for industrial applications.To finally transform LIBS from'future superstar,to'superstar5,joint effort of worldwide LIBS community is needed[6].
文摘Ever since its creation in 1963 [1], laser-induced breakdown spectroscopy(LIBS)has gained considerable attention due to its unique capability for real-time, in situ or online analysis [2, 3]. The future world is heading into the age of artificial intelligence(AI), and data would be the most valuable asset for human society [4].
文摘Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique gaining much attention since it was created in 1962[1].In 2021,the 4th Asian Symposium on LIBS (ASLIBS) and the ten-year anniversary of Chinese Symposium on LIBS (CSLIBS) were jointly held in Qingdao,symbolizing the development of the Asian and Chinese LIBS communities into a new stage.Since the initiation of CSLIBS in Qingdao (2011) and ASLIBS in Wuhan[2](2015).
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.61575073)Huazhong University of Science and Technology(No.2020kfyXGYJ105).
文摘Laser-induced breakdown spectroscopy(LIBS)has been widely studied due to its unique advantages such as remote sensing,real-time multi-elemental detection and none-to-little damage.With the efforts of researchers around the world,LIBS has been developed by leaps and bounds.Moreover,in recent years,more and more Chinese LIBS researchers have put tremendous energy in promoting LIBS applications.It is worth mentioning that the application of LIBS in a specific field has its special application background and technical difficulties,therefore it may develop in different stages.A review summarizing the current development status of LIBS in various fields would be helpful for the development of LIBS technology as well as its applications especially for Chinese LIBS community since most of the researchers in this field work in application.In the present work,we summarized the research status and latest progress of main research groups in coal,metallurgy,and water,etc.Based on the current research status,the challenges and opportunities of LIBS were evaluated,and suggestions were made to further promote LIBS applications.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant No.61308086
文摘Nowadays, there are about 415 million adults suffering from diabetesN. At present, the treatment for diabetes patients is monitoring their blood glucose level frequently and then taking the appropriate amount of oral hypogly- cemic drugs and insulin to control their blood glucose level.
