Apple Grading Using Principal Component Analysis and Kernel Fisher Discriminant Analysis Combined with NIR Spectroscopy

摘要：

　　Principal component analysis (PCA) and kernel Fisher discriminant analysis (KFDA) were applied to grade Fuji apples combined with near infrared reflectance (NIR) spectroscopy.Firstly,(7)used to reduce the dimensionality of NIR spectra acquired by the Antaris Ⅱ FT-NIR speetrophotometer on apples.Secondly,nonlinear discriminant information was extracted by kernel Fisher discriminant analysis (KFDA).Finally,the k-nearest neighbors algorithm with leave one out strategy was utilized to classify apple samples into two grades.LDA can only solve linearly separable problems,and it is not suitable in solving some nonlinear problems.But unlike LDA,KFDA can solye nonlinearly separable problems,and it projects data onto a high-dimensional feature space by the nonlinear mapping.Experimental results showed that KFDA achieved higher classification rate compared with LDA.

关键词： Apple NIR Principal component analysis Kernel Fisher discriminant analysis

作者: WU Xiaohong XU Wenjie WU Bin QIU Shengwei

作者单位: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China Jingjiang College, Jiangsu University, Zhenjiang 212013, China Department of information Engineering, ChuZhou Vocational Technology College, Chuzhou 239000, China

会议类型: 国际会议

会议名称: 2nd International Conference on Polymer Composities and Polymer Testing (2013第二届高分子复合材料与高分子测试国际会议暨先进工业技术与方案国际会议)(ISPCPT2013)

会议地点: 太原

会议语种:英文

页码: 658-662

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）