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Abstract
Typical methods for the analysis of mixture components include multiple linear regression, partial linear squares, and artificial neural network. However, these methods need large amount of samples and time to improve recognition accuracy. In this paper, based on the data obtained from terahertz spectroscopy, an identification method with less sample requirements and lower calculation time but higher accuracy is proposed. Based on the wavelet transform, baseline elimination, support vector regression, and loop iteration of samples, the specific substance in the mixture can be identified effectively. For example, seven substances that exist in brain glioma are chosen as the components of a mixture, where the key substances used for glioma diagnosis are set as the target substances and the spectra of mixtures with different mix proportions serve as training data. The average correlation coefficient of identification achieves 99.135% and the root-mean-square error is 0.40%. These results have profound implications for the eventual practical application of exact qualitative and quantitative identification of components in mixtures.
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