Laboratory of Analytical Chemistry, Department of Chemistry, University of Ioannina, 45110 Ioannina, Greece.
J Hazard Mater. 2011 Jan 15;185(1):86-92. doi: 10.1016/j.jhazmat.2010.08.126. Epub 2010 Sep 15.
The application of UV-Vis spectrophotometry as an alternative or complementary approach to the classification of tobacco products is presented in this work for the first time. Two hundred fifty samples from five different cigarette brands composed of single and mixed tobacco blends were examined for that purpose on the basis of the UV-Vis spectrum of their aqueous extracts. Data transformation based on the normalization of absorbance intensities as a function of sample weight was employed in order to account for differences in the relative intensities of each sample. Principal components analysis (PCA) was used to extract outlier cases and sample classification was then pursued with the aid of discriminant analysis (DA) suggesting that a reduced number of variables (thirteen out of seven hundred initially available) could provide perfect classification (100% correct assignations) of samples containing single tobacco species or different blends and a fair classification of samples with similar composition (80% correct assignations) yielding an overall 95.7% correct classification. To this pursue, classification and regression trees were found to afford perfect classification of all samples using only a few logic rules based on appropriate split conditions at the expense of inserting 15 variables in the model.
本工作首次提出将紫外-可见分光光度法作为一种替代或补充方法应用于烟草产品的分类。为此，基于其水提物的紫外-可见光谱，对来自五个不同卷烟品牌的 250 个单烟和混合烟样品进行了检查。为了弥补各样本相对强度的差异，采用了基于吸光度强度归一化的数据分析转换。利用主成分分析（PCA）提取离群值，然后借助判别分析（DA）进行样本分类，结果表明，减少变量数量（从最初的 700 个中选择 13 个）可以实现对单种烟草或不同混合烟样本的完美分类（100%正确分配），对组成相似的样本也可以进行良好的分类（80%正确分配），总体正确分类率达到 95.7%。为此，分类回归树（CART）通过基于适当分割条件的少数逻辑规则即可实现对所有样本的完美分类，而代价是在模型中插入 15 个变量。
