Moreira Edilene Dantas Teles, Pontes Márcio José Coelho, Galvão Roberto Kawakami Harrop, Araújo Mário César Ugulino
Universidade Federal da Paraíba, Departamento de Química, João Pessoa, PB, Brazil.
Talanta. 2009 Oct 15;79(5):1260-4. doi: 10.1016/j.talanta.2009.05.031. Epub 2009 May 27.
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.
本文提出了一种利用近红外反射光谱法和变量选择进行香烟分类的方法。为此，采用连续投影算法（SPA）为线性判别分析（LDA）模型选择合适的波数子集。所提出的方法应用于一组210支来自四个不同品牌的香烟。为作比较，类类比软独立建模法（SIMCA）也用于全光谱分类。所得的SPA-LDA模型仅使用两个波数（5058和4903 cm(-1)）就成功地根据品牌对所有测试样品进行了分类。相比之下，无论F检验采用何种显著性水平，SIMCA模型都无法达到100%的分类准确率。本研究获得的结果表明，所提出的方法是评估香烟真伪的一种有前景的替代方法。
