Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection.
This paper proposes a methodology for cigarette classification employing Near Infrared Reflectance spectrometry and variable selection. For this purpose, the Successive Projections Algorithm (SPA) is employed to choose an appropriate subset of wavenumbers for a Linear Discriminant Analysis (LDA) model. The proposed methodology is applied to a set of 210 cigarettes of four different brands. For comparison, Soft Independent Modelling of Class Analogy (SIMCA) is also employed for full-spectrum classification. The resulting SPA-LDA model successfully classified all test samples with respect to their brands using only two wavenumbers (5058 and 4903 cm(-1)). In contrast, the SIMCA models were not able to achieve 100% of classification accuracy, regardless of the significance level adopted for the F-test. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for assessment of cigarette authenticity.
Moreira
Edilene Dantas Teles
ED
Universidade Federal da Paraíba, Departamento de Química, João Pessoa, PB, Brazil.
Pontes
Márcio José Coelho
MJ
Galvão
Roberto Kawakami Harrop
RK
Araújo
Mário César Ugulino
MC
D000465:Algorithms; D016002:Discriminant Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D014026:Nicotiana / Q000145:classification*