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

• Autor(a) principal: Shuangyan,Yang
• Data de Publicação: 2018
• Outros Autores: Ying,Hou, Lingchun,Yang, Jianqiang,Zhang, Weijuan,Liu, Changgui,Qiu, Ming,Li, Yanmei,Yang
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of the Brazilian Chemical Society (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532018000701480
• topic: near-infrared spectroscopy; deep learning; sparse representation classification; non-negative least squares

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.