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Vahid Dastjerdi M, Mousavi S J, Darbani S M, Mousavi Azar A, Soltanolkotabi M. Statistical multivariate analysis in laser induced breakdown spectroscopy (LIBS) data and reduction dimensions of data by principal component analysis (PCA) with aim of polymers recognition . ICOP & ICPET _ INPC _ ICOFS 2016; 22 :404-407
URL: http://opsi.ir/article-1-1054-en.html
URL: http://opsi.ir/article-1-1054-en.html
Mohammad Vahid Dastjerdi * 
1, Seyed jabar Mousavi , Seyed mohammadreza Darbani , Ali Mousavi Azar , Mahmood Soltanolkotabi
1, Seyed jabar Mousavi , Seyed mohammadreza Darbani , Ali Mousavi Azar , Mahmood Soltanolkotabi
Abstract: (3176 Views)
In this research principal component analysis as one of statistical multivariate analysis methods with aim of reduction dimensions of data and also finding a pattern in laser induced breakdown spectroscopy data in order to classify of samples was applied.At first 5 polymer samples was prepared and then intensity corresponding to 17 wavelengths relating to atomic and ionic lines and molecular bands for each sample as input matrix in PCA method,was selected.The results show that with only 3 output variables from PCA method,analysis polymers can be recognized from each other and also dimensions of data ,was reduced.
Keywords: Laser induced breakdown spectroscopy, Principal component analysis, Polymers recognition, Reduction dimensions of data
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