Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm

AUTOR(ES)

Shuangyan, Yang, Ying, Hou, Lingchun, Yang, Jianqiang, Zhang, Weijuan, Liu, Changgui, Qiu, Ming, Li, Yanmei, Yang

FONTE

J. Braz. Chem. Soc.

DATA DE PUBLICAÇÃO

2018-07

RESUMO

A cigarette brand automatic classification method using near-infrared (NIR) spectroscopy and sparse representation classification (SRC) algorithm is put forward by the paper. Comparing with the traditional methods, it is more robust to redundancy because it uses non-negative least squares (NNLS) sparse coding instead of principal component analysis (PCA) for dimensionality reduction of the spectral data. The effectiveness of SRC algorithm is compared with PCA-linear discriminant analysis (LDA) and PCA-particle swarm optimization-support vector machine (PSO-SVM) algorithms. The results show that the classification accuracy of the proposed method is higher and is much more efficient.

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