[A Study of the Relationship Among Genetic Distances, NIR Spectra Distances, and NIR-Based Identification Model Performance of the Seeds of Maize Iinbred Lines].

This paper explored the relationship among genetic distances, NIR spectra distances and NIR-based identification model performance of the seeds of maize inbred lines. Using 3 groups (total 15 pairs) of maize inbred lines whose genetic distaches are different as experimental materials, we calculates the genetic distance between these seeds with SSR markers and uses Euclidean distance between distributed center points of maize NIR spectrum in the PCA space as the distances of NIR spectrum. BPR method is used to build identification model of inbred lines and the identification accuracy is used as a measure of model identification performance. The results showed that, the correlation of genetic distance and spectra distancesis 0.9868, and it has a correlation of 0.9110 with the identification accuracy, which is highly correlated. This means near-Infrared spectrum of seedscan reflect genetic relationship of maize inbred lines. The smaller the genetic distance, the smaller the distance of spectrum, the poorer ability of model to identify. In practical application, near infrared spectrum analysis technology has the potential to be used to analyze maize inbred genetic relations, contributing much to genetic breeding, identification of species, purity sorting and so on. What's more, when creating a NIR-based identification model, the impact of the maize inbred lines which have closer genetic relationship should be fully considered.

[Feasibility study on an approach for identifying corn kernel varieties with seed coating agents via near infrared spectroscopy].

It is generally accepted that near infrared reflectance spectroscopy (NIRS) can be used to identify variety authenticity of bare maize seeds. In practical, maize seeds are covered with seed coating agents. Therefore it's of huge significance to investigate the feasibility of identifying coated maize seeds by NIRS. This study employed NIRS to quickly determine the variety of coated maize seeds. Influence of seed coating agent on NIR spectra was discussed. The NIR spectra of coated maize seeds were obtained using an innovative method to avoid the impact of the seed coating agent. Coated seeds were cut open, and the sections were scanned by the spectrometer, so as to acquire the information of the seed itself. Then, support vector machine (SVM), soft independent modeling of class analogy (SIMCA), and biomimetic pattern recognition (BPR) was employed to establish the identification model for four maize varieties, and yield 93%, 95.8%, 98% average correct rate respectively. BPR model showed better performance than SVM and SIMCA models. The robustness of identification model was tested by seeds harvested from four regions and model showed good performance.

[Near infrared spectroscopy analysis method of maize hybrid seed purity discrimination].

Near infrared spectroscopy analysis method of discrimination of maize hybrid seed purity was studied with the sample of Nong Hua 101 (NH101) from different origins and years. Spectral acquisition time lasted for 10 months. Using Fourier transform (FT) near infrared spectroscopy instruments, including 23 days in different seasons (divided into five time periods), a total of 920 near infrared diffuse reflectance spectra of single corn grain of those samples were collected. Moving window average, first derivative and vector normalization were used to pretreat all original spectra, principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to reduce data dimensionality, and the discrimination model was established based on biomimetic pattern recognition (BPR) method. Spectral distortion was calibrated by spectra pretreatment, which makes characteristics spatial distribution range of sample spectra set contract. The relative distance between hybrid and female parent increased by nearly 70-fold, and the discrimination model achieved the identification of hybrid and female parent seeds. Through the choice of representative samples, the model's response capacity to the changes in spectral acquisition time, place and environment, etc. was improved. Besides, the model's response capacity to the changes in time and site of seed production was also improved, and the robustness of the model was enhanced. The average correct acceptance rate (CAR) of the test set reached more than 95% while the average correct rejection rate (CRR) of the test set also reached 85%.