Apple Internal Quality Classification Based on Electronic Signal Analysis Using Sparse Principal Component Analysis

摘要：

Apple internal quality classification is fairly important in fruit sales, packing and storage for producers and sellers. But internal properties like firmness, ascorbic acid content, and Ethylene production rate are difficult to measure without damaging the tested apples. Therefore, we propose a damage-free apple internal quality approach using such electronic signals as impedance, admittance, conductance, etc. With these multidimensional electronic signals, we adopt Principal Component Analysis (PCA), Sparse Principal Component Analysis (SPCA), and Gray Relational Analysis (GRA) to classify apple internal quality. According to three sets of our experiments, it is verified that PCA, SPCA and GRA all work well, and that SPCA outperforms the other two in terms of requirement of electronic signals and classification accuracy.

作者: Mengbo You Cheng Cai Huiling Ma

作者单位: College of Computer and Information Engineering,Northwest A&F University, Yangling, Shaanxi, 712100 College of Life ScienceNorthwest A&F University, Yangling, Shaanxi, 712100

会议类型: 国际会议

会议名称: 2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)

会议地点: 西安

会议语种:英文

页码: 1-10

在线出版日期: 2011-10-18（万方平台首次上网日期，不代表论文的发表时间）