Quantitative analysis of wheat maltose by combined terahertz spectroscopy and imaging based on Boosting ensemble learning

To improve the prediction accuracy of existing data modeling that is based on either spectral data or image data alone, we herein propose a method for the quantitative analysis of wheat maltose contents based on the fusion of terahertz spectroscopy and terahertz imaging, which allows features and balance fusion information to be extracted from the data, and fusion modeling of the feature information to be conducted. Moreover, a Boosting-based, novel multivariate data fusion method and a Boosting iteration termination index based on the structural risk minimization theory are proposed to achieve automatic optimization of the basic model parameters of least squares support vector machines (LS-SVMs). The best results were obtained with data fusion combining spectroscopy and image feature data, with classification performances better than those obtained on single analytical sources, thereby indicating that the multivariate data fusion method proposed is an effective method for the quantitative detection of maltose content in wheat. Furthermore, four unknown maltose concentration wheat samples are analyzed quantitatively using proposed model.