Application of Principal Components Analysis and Cluster Analysis in Evaluation of Blending Module

In order to develop a scientific and reasonable method for quality evaluation and classification of sub-blends, the factor analysis of appearance quality, routine chemical components and indexes of sensory evaluations of cured cutters of flue-cured tobacco grown in different areas in Chuxiong was carried out with principal components analysis, and the obtained principal components were fast clustered by K-mean clustering algorithm;the samples were further fast clustered with statistical analysis software SPSS11.0, thus the function of individual sample in a sub-blend was determined.The results showed that the multivariate analysis overcome the shortcomings of traditional physical, chemical and sensory index tests which failed to comprehensively evaluate and classify between numerous tobacco samples.The method is simple and fast with computer aided data processing.