Research On LIBS Spectrum Recognition Technology Based On PLS Algorithm

LIBS(Laser Induced-breakdown Spectroscopy)spectrum detection technology is also known as laser induced breakdown technology.It is a new technology based on laser induced plasma radiation for qualitative classification and quantitative detection of material elements.It has a wide range of applications in the fields of military counter-terrorism,precious metal measurement,geological environmental protection and space exploration.It has many excellent characteristics,such as long distance non contact,small damage to measurement material,no sample preparation,and almost all material elements.But the research of LIBS spectrum detection technology mainly focuses on the physical optics theory,the improvement of instrument accuracy and the control of delay technology.Compared with other mature spectral analysis systems,such as the near infrared spectral analysis system,the accuracy of the identification of material elements in the LIBS spectrum detection technology still needs to be improved,and the spectral signal data is de-noised and the partial least squares(PLS)algorithm is used to model the data,which can improve the accuracy of the recognition.So that the LIBS spectrum detection technology will be more widely applied.This paper mainly introduces the following aspects:The paper first introduces the laser induced breakdown technology and the instrument needed in the experiment.Then,a remote LIBS spectrum experimental system platform is built,and a large number of spectral signal data are obtained on the basis of this experiment.The LIBS spectral signal is de-noised by means of mean centralization,multiple scattering correction,standard normal variable transformation and wavelet transform,and the best wavelet transform with DB wavelet base,sqtwolog threshold method and 5 decomposition layer number is the best,and the denoised spectral signal is used in the noise de-noising basis.The small second multiplication algorithm is modeled.Finally,the partial least squares algorithm is used to identify the common metal elements,copper,aluminum,lead and iron.For metal copper,on the basis of 12 point smoothing,the RMSECV and RMSEP values of the PLS algorithm are the smallest,respectively,0.914 and 1.29,and the correction set correlation coefficient is 0.9844.The number of relations is 0.9878.For lead,the smooth window size is 10,the main fraction is 8,the correction set correlation coefficient is 0.9624,the prediction set correlation coefficient is 0.9748;the RMSECV value is 1.97,and the RMSEP value is 1.71.For aluminum,with a smooth window of 10 and a main fraction of 8,the recognition result is the best.At this time,the RMSECV of the model is 1.77,the RMSEP is 1.56,the correlation coefficient of the correction set and the correlation coefficient of the prediction set are the largest,which are 0.9323 and 0.9489 respectively.Finally,the target recognition rate of common metal elements reaches 80%,which proves the feasibility and effectiveness of partial least squares algorithm in improving the spectral recognition rate.