Support Vector Machine Algorithm Is Applied To The Biological Activity Of The Mixed System Level Classification Of The Spectrum Of Quantitative Analysis Of Heavy Elements

Chemometrics was designed to optimize the process of chemical measurements and get useful chemical information from the data of chemical measurements.Support vector machine (SVM) has solid theoretical foundation and can deal with small dataset, nonlinear optimization, high-dimensional feature space, local minimization and other realistic problems. Along with the development of SVM, some derived algorithms have been put forward and the application of SVM has gradually been the hot point for researchers in the world. Today, SVM has been successfully applied in face recognition, voice identification, handwritten digit recognition, text classification, risk assessment, protein structure recognition, gene recognition and other pattern recognition domains and achieves equivalent or superior results compared to those obtained by some other methods. It is very exciting that their capability to generalize input-output mapping from a limited set of training examples is great. In this paper, we use SVM to solve the problems of determining mixture and to classify the spectrums of heavy metal atom:Using SVM to determing mixtures, such as amino acid, catecholamines(CATs) by informations from the mix spectrograms of DPV and Raman without pre-separation. Study shows that SVM can well deal with such mixture, relative to BPN , it gains more accuracy information.Using SVM to classify the unknown energy levels of heavy metal-U II, which can not be classified by experiment. Although some people have tried to use traditional chemometric techniques to predict the unknown energy levels, there still have some samples which can not be predicted. So we use SVM to deal with such heavy metal to gain the energy levels. The results show that SVM predict more accuracy and completely than traditional methods of chemometrics.