Maize haploid recognition study based on nuclear magnetic resonance spectrum and manifold learning

Abstract Haploid breeding is a significant technology of maize breeding. Nondestructively, rapidly and accurately haploid kernel identification method is the basis of developing haploid breeding technology. The commonly adopted maize haploid recognition methods at present are mainly near-infrared spectroscopy (NIRS), machine vision and nuclear magnetic resonance (NMR) oil measurement. NMR spectrum analysis method based on pattern recognition was used in this paper for haploid recognition, which on the one hand could improve recognition efficiency, and on the other hand could overcome the limitation of NMR oil measurement method namely it could not be applied to maize kernels produced by conventional inducer. NMR spectrum as a kind of high-dimensional data, manifold learning could effectively maintain the nonlinear structural properties of data while reducing dimensionality and extract easily identifiable features from these structures. Most manifold learning algorithms used at present map data of different categories onto the same low-dimensional embedded manifold. In order to better reserve essential structures of different categories of data, a new multi-manifold recognition framework was proposed in this paper for haploid recognition. The new framework uses the manifold learning algorithm to conduct feature extraction of NMR spectra of haploid and diploid respectively, and two low-dimensional manifold expressions are established; new samples are discriminated using the distance measurement method after being respectively mapped to two low-dimensional manifolds. For the difficulty existing in the calculation of point-to-manifold distance, the low-dimensional manifold structure is expressed in way of manifold coverage, and then point-to-manifold distance is expressed by calculating the distance from the sample point to the covered geometry. Maize kernels generated by high-oil induction system and conventional induction system were experimented in this paper. First of all, the feasibility of NMR spectrum analysis method based on pattern recognition for haploid identification was analyzed, the experiment was carried out using single-manifold and multi-manifold identification frameworks respectively, and stability of the multi-manifold identification framework was discussed finally. Experimental results indicate that the recognition rate of maize kernels induced by high-oil inducer can reach as high as 98.33% and the recognition rate of maize kernels induced by conventional inducer can reach as high as 90%, it proved that NMR spectrum combining manifold learning algorithm is feasible for haploid recognition; in the meantime, the multi-manifold recognition framework proposed in this paper has achieved better result than single-manifold recognition framework with the recognition rate elevated by 5% or so.