Abstract
Counterfeit medicine still exists widely, which have affected our health and life. So the discrimination (classification) of medicine is becoming more and more important. Then near-infrared spectroscopy (NIRS) is a popular and effective technique used on the medicine classification with nondestructive characteristics. To solve the discrimination of medicines, the sparse signal representation model is established in the presence of spectrum crossover and overlapping. However, the sparsity of nonzero representation coefficients is low during solving the \( L_{ 2} \)-norm on representation model. To overcome this problem, in this paper a novel classification model—regularized collaborative representation classification with Gabor optimizer (RCRCG) is proposed. Gabor filter is adopted to control the \( L_{2} \)-norm for the more relevant factor vectors. Then Lasso regulation on local classification is proved to improve the accuracy on the medicine discrimination. The experiments using NIRS samples from the three datasets (active substance, Erythromycin Ethylsuccinate and Domperidone) show that the proposed method is more effective and robust than the existing ones, and it has speed-up about 1 times compared with the Sparse Representation based Classification (SRC) and Class \( L_{1} \)-optimizer classifier with the closeness rule (C_CL1C).
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 61562013), Natural Science Foundation of Guangxi Province (CN) (2017GXNSFDA198025), and Guangxi Key Lab of Trusted Software (kx201730). We thank Xi’an-Janssen Pharmaceutical Factory for useful datasets of Near Infrared spectra samples. We would like to express our appreciation to all supporters above mentioned for their strongly financial support.
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Liu, Z., Ji, H., Jiang, S. (2020). Medicine Discrimination of NIRS Based on Regularized Collaborative Representation Classification with Gabor Optimizer. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_11
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