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| Research on the Method of Identifying Maize Haploid Based on KPCA and Near Infrared Spectrum |
| LIU Wen-jie, LI Wei-jun*, LI Hao-guang, QIN Hong, NING Xin |
| Laboratory of High-Speed Circuit & Artificial Neural Network, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China |
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Abstract The method of maize haploid identification plays a significant role in advancing Maize Haploid Breeding Technology. Given to its advantages of cost-effectiveness, high performance and being easy to operate, near-infrared spectroscopy (NIRS) has drawn great attention in the field of agricultural research. At the beginning of the experiment, NIRS data of both haploid and polyploidy maize seeds that are provided by National Maize Improvement Center of China are collected via US JDSU’s near-infrared spectrometer. After pre-processing that the original data were processed by smoothing, first derivative, and vector normalization in order to eliminate the influence of noise, the NIRS data is subsequently mapped in a high-dimensional space, where nonlinear feature extraction take place through Kernel Principal Components Analysis (KPCA) whose kernel function is Gaussian kernel. Then Vector Machines Support (SVM) was used to establish the classification model for it. Finally, based on the result of classified identification test, the experiment makes a prediction of whether maize seeds are haploid. In particular, this paper designs two sets of comparative experiments with average recognition rate being 95% and 93.57%. The result indicates that the method based on KPCA is both feasible and valid. The above experiment proves that the process of “high-dimensional spatial mapping—nonlinear feature extraction—modeling classification analysis” is more suitable for studying maize seeds data collected via NIRS. Therefore, this paper may provide some new idea and method for Maize Haploid Identification technology.
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Received: 2016-01-28
Accepted: 2016-05-05
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Corresponding Authors:
LI Wei-jun
E-mail: wjli@semi.ac.cn
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