%0 Journal Article
%T Automatic classification of tobacco leaves based on near infrared spectroscopy and nonnegative least squares
%A Hou Ying
%A Li Changyu
%A Li Ming
%A Liu Weijuan
%A Yang Panpan
%A Yang Yanmei
%A Zhang Huaihui
%A Zhang Jianqiang
%J Journal of Near Infrared Spectroscopy
%@ 1751-6552
%D 2018
%R 10.1177/0967033518762617
%X A nonnegative least squares classifier was proposed in this paper to classify near infrared spectral data. The method used near infrared spectral data of training samples to make up a data dictionary of the sparse representation. By adopting the nonnegative least squares sparse coding algorithm, the near infrared spectral data of test samples would be expressed via the sparsest linear combinations of the dictionary. The regression residual of the test sample of each class was computed, and finally it was assigned to the class with the minimum residual. The method was compared with the other classifying approaches, including the well-performing principal component analysis¨Clinear discriminant analysis and principal component analysis¨Cparticle swarm optimization¨Csupport vector machine. Experimental results showed that the approach was faster and generally achieved a better prediction performance over compared methods. The method can accurately recognize different classes of tobacco leaves and it provides a new technology for quality evaluation of tobacco leaf in its purchasing activities
%K Near infrared spectroscopy
%K sparse representation classification
%K nonnegative least squares
%K deep learning
%U https://journals.sagepub.com/doi/full/10.1177/0967033518762617