Qualitative and Quantitative Identification of Components in Mixture by Terahertz Spectroscopy

Typical methods for the analysis of mixture components include multiple linear regression, partial linear squares, and artificia 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 identificatio 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 specifi substance in the mixture can be identifie 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 identificatio 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 identificatio of components in mixtures.