Discrimination of cigarette based on near-infrared spectroscopy technology and firefly algorithm optimized support vector machine parameters

Abstract

Objective: In order to accurately and quickly discriminate cigarettes. Methods: After collecting the near-infrared spectra of different brands and reducing the interference factors through the spectral preprocessing method, the spectral pretreatment method, the population number of firefly algorithm （FA） and the number of iterations on the correct rate of cigarette classification were investigated by using firefly algorithm to optimize support vector machine （SVM） parameters. Results: The standard normal variable transformation （SNV） combined with the first derivative method （1D） was used for near-infrared spectroscopy preprocessing. Under the condition that the number of firefly populations was 20 and the number of iterations was 20, optimized support vector parameters could achieve better recognition. As a result, the classification accuracy rate of the training set was 100%, and the classification accuracy rate of the test set was between 96.67% and 100.00%. Conclusion: It shows that using near-infrared spectroscopy technology combined with FA algorithm to optimize SVM can achieve accurate identification of cigarette brands.

Keywords

• near infrared spectroscopy
• cigarette
• discrimination
• firefly algorithm
• svm