期刊
JOURNAL OF FOOD ENGINEERING
卷 96, 期 3, 页码 469-479出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2009.08.028
关键词
Rheometer; Honey; Principal Component Analysis; Cluster Analysis; Partial Least Squares; Support Vector Machines
资金
- Chinese National Foundation of Nature and Science [30771246]
- National High Technology Research and Development Program of China [2006AA10Z212]
- Chinese National Higher Education [20060335060]
A rheometer was used to classify commercial honeys. Five kinds of Yichun honeys from different floral origins and five kinds of Acacia honeys from different geographical origins were classified based on a rheometer by four pattern recognition techniques: Principal Component Analysis (PCA), Cluster Analysis (CA), Partial Least Squares (PLS), and Support Vector Machines (SVM). All the samples for different floral origins or different geographical origins were demarcated clearly by PCA, PLS. The samples from different floral origins could be classified by SVM, and the samples from different geographical origins also have a high correct classification rate (97.5%). The classification rates for different floral origins and geographical origins were 95% and 97.50% by CA, respectively. Three regression models: Principal Component Regression Analysis (PCR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) were used for category forecast. The regression analysis showed that SVR with radial basis function kernel worked most effective. (C) 2009 Elsevier Ltd. All rights reserved.
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