A review is presented on different analytical techniques used for qualitative and quantitative analysis of serratiopeptidase, a proteolytic enzyme, which has recently gained importance as an anti-inflammatory agent.Ef...A review is presented on different analytical techniques used for qualitative and quantitative analysis of serratiopeptidase, a proteolytic enzyme, which has recently gained importance as an anti-inflammatory agent.Efforts have been made to collate all the relevant references to the extent possible. The review discusses the advantages and disadvantages of the cited analytical techniques, which will help to give insights into the methods used for estimation of serratiopeptidase as such, from clinical isolates and from its dosage forms.The review highlights the basic as well as advanced techniques performed for estimating serratiopeptidase. The techniques illustrated here have been demonstrated to be useful for qualitative and quantitative determination of serratiopeptidase and may find application in analyzing other related proteases.展开更多
Laser-induced breakdown spectroscopy(LIBS)is a spectroscopic analytic technique with great application potential because of its unique advantages for online/in-situ detection.However,due to the spatially inhomogeneity...Laser-induced breakdown spectroscopy(LIBS)is a spectroscopic analytic technique with great application potential because of its unique advantages for online/in-situ detection.However,due to the spatially inhomogeneity and drastically temporal varying nature of its emission source,the laser-induced plasma,it is difficult to find or hard to generate an appropriate spatiotemporal window for high repeatable signal collection with lower matrix effects.The quantification results of traditional physical principle based calibration model are unsatisfactory since these models were not able to compensate for complicate matrix effects as well as signal fluctuation.Machine learning is an emerging approach,which can intelligently correlated the complex LIBS spectral data with its qualitative or/and quantitative composition by establishing multivariate regression models with greater potential to reduce the impacts of signal fluctuation and matrix effects,therefore achieving rela-tively better qualitative and quantitative performance.In this review,the progress of machine learning application in LIBS is summarized from two main aspects:(i)Pre-processing data for machine learning model,including spectral selection,variable reconstruction,and denoising to improve qualitative/quantitative performance;(ii)Machine learning methods for better quantification performance with reduction of the impact of matrix effect as well as LIBS spectra fluctuations.The review also points out the issues that researchers need to address in their future research on improving the performance of LIBS analysis using machine learning algorithms,such as restrictions on training data,the disconnect between physical principles and algorithms,the low generalization ability and massive data processing ability of the model.展开更多
基金Ipca Laboratories Limited and its management for providing support
文摘A review is presented on different analytical techniques used for qualitative and quantitative analysis of serratiopeptidase, a proteolytic enzyme, which has recently gained importance as an anti-inflammatory agent.Efforts have been made to collate all the relevant references to the extent possible. The review discusses the advantages and disadvantages of the cited analytical techniques, which will help to give insights into the methods used for estimation of serratiopeptidase as such, from clinical isolates and from its dosage forms.The review highlights the basic as well as advanced techniques performed for estimating serratiopeptidase. The techniques illustrated here have been demonstrated to be useful for qualitative and quantitative determination of serratiopeptidase and may find application in analyzing other related proteases.
基金supported by the project funded by the National Natural Science Foundation of China(Nos.62305123,32301694,and 12064029)the National Key R&D Program of China(No.2023YFE0204600).
文摘Laser-induced breakdown spectroscopy(LIBS)is a spectroscopic analytic technique with great application potential because of its unique advantages for online/in-situ detection.However,due to the spatially inhomogeneity and drastically temporal varying nature of its emission source,the laser-induced plasma,it is difficult to find or hard to generate an appropriate spatiotemporal window for high repeatable signal collection with lower matrix effects.The quantification results of traditional physical principle based calibration model are unsatisfactory since these models were not able to compensate for complicate matrix effects as well as signal fluctuation.Machine learning is an emerging approach,which can intelligently correlated the complex LIBS spectral data with its qualitative or/and quantitative composition by establishing multivariate regression models with greater potential to reduce the impacts of signal fluctuation and matrix effects,therefore achieving rela-tively better qualitative and quantitative performance.In this review,the progress of machine learning application in LIBS is summarized from two main aspects:(i)Pre-processing data for machine learning model,including spectral selection,variable reconstruction,and denoising to improve qualitative/quantitative performance;(ii)Machine learning methods for better quantification performance with reduction of the impact of matrix effect as well as LIBS spectra fluctuations.The review also points out the issues that researchers need to address in their future research on improving the performance of LIBS analysis using machine learning algorithms,such as restrictions on training data,the disconnect between physical principles and algorithms,the low generalization ability and massive data processing ability of the model.
