[Maize Hybrid Seed Purity Identification Based on Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectra].

This article explore the feasibility of using Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectroscopy (908.1-1677.2 nm wavelength range) to identify maize hybrid purity, and compare the performance of NIR and NIT spectroscopy. Principle Component Analysis (PCA) and Orthogonal Linear Discriminant Analysis (OLDA) were used to reduce the dimension of spectra which have been pretreated by first derivative and vector normalization. The hybrid purity identification model of Nonghua101 and Jingyu16 were built by SVM. Models based on NIR spectra obtained correct identification rate as 100% and 90% for Nonghua101 and Jingyu16 respectively. But NIR spectra were greatly influenced by the placement of seeds, and there existed significant difference between NIR spectra of embryo and non-embryo side. Models based on NIT spectroscopy yielded correct identification rate as 98% both for Nonghua101 and Jingyu16. NIT spectra of embryo and non-embryo side were highly similar. The results indicate that it is feasible to identify maize hybrid purity based on NIR and NIT spectroscopy, and NIT spectroscopy is more suitable to analyze single seed kernel than NIR spectroscopy.

Discrimination of Maize Haploid Seeds from Hybrid Seeds Using Vis Spectroscopy and Support Vector Machine Method.

Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0. 05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels.

[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.

[Study on method of maize hybrid purity identification based on hyperspectral image technology].

The feasibility of employing hyperspectral image technology to identify maize hybrid purity was studied by analyzing the spectral information of maize hyperspectral image. The hyperspectral images of hybrid and female parent of maize variety NH101 in the range of 871-1 699 nm including 308 wavelengths were collected by hyperspectral imaging system. We extracted average spectral information of interested region on maize seed and built identification models of hybrid and female parent of maize variety NH101 based on processed spectral data. The influences of different sample laying modes (seed embryo facing the light source, seed embryo backward light source, and seed put in different locations on sample stage) and experimental environments on the performance of identification models were discussed. Spectral collected under different sample laying modes and experimental environments were used to test the robustness of identification models. The average correct acceptance rates and average correct rejection rates are more than 90%. The feature spectral bands (1 195-1 246 nm) with which the differences between hybrid and female parent are the largest were extracted by a wavelength selection method based on standard deviations, called Qs. The performance of identification models built based on spectral data in feature spectral bands reached the same level of models built based on spectral data in the full range of 925-1 597 nm. The results demonstrated the feasibility of using hyperspectral image technology as an objective and rapid method for the identification of maize hybrid purity.

[Study on spectral measurement methods in identification of maize variety authenticity based on near infrared spectra of single kernels].

In order to establish the better maize variety identification models based on single kernel samples, the near-infrared spectral measurement methods were studied by comparing the direction of the maize seed's embryo, diffuse reflectance and transmission mode, devices of holding the sample according to their impacts on the performance of variety identification models. Partial least squares-discriminant analysis (PLSDA) was used to compress the pretreated spectral data into 9 variables, and then the identification models were built based on biomimetic pattern recognition (BPR). The results show that with the maize grain's embryo facing the light source the models can be made perform better than with embryo backing toward the light source, diffuse reflectance mode is better than transmission mode, and small sample pool performs better than the small aperture. The measurement method of acquiring the diffuse reflectance near infrared spectra of maize by small pool with the seed embryo facing the light source can make models have the best performance. The average correct identification rate of the models is 94.6%, and the average correct rejection rates for the varieties not belonging to the models reached 96.5%.

[The utility research on NIR diffuse reflectance and transmittance measurements mode in authenticity identification of maize population samples].

In the present paper, the spectral measurements of maize population samples were researched so as to identify their authenticity. Diffuse reflectance and transmittance measure modes were used to collect spectral data of 8 maize varieties. DPLS-DA was used to compress pretreated data. The recognition models of eight maize varieties were built based on biomimetic pattern recognition (BPR). The average correct recognition rate and average correct rejection rate of identification models built by two modes were calculated. The average recognition rate and rejection rate of diffuse reflectance method reached 94.50% and 96.71%, and those of transmittance method reached 98.5% and 98.00%, respectively. Both of them met the requirements of maize preliminary screening, and the recognition rate and rejection rate of transmittance method are higher than diffuse reflectance method by 4% and 1.3% respectively.

[Study of feature extraction methods for maize's near infrared spectra in biomimetic pattern recognition].

Near infrared spectrum is an important step in near infrared spectrum qualitative analysis, which influences the qualitative analysis results directly. Diffuse transmittance measurements mode was used to collect spectral data of eight maize varieties. PCA, ICA, PLS-DA and wavelet transformation were used to extract features of pretreated data. Finally, we used the test set data to test the recognition models of eight maize varieties which were built based on biomimetic pattern recognition (BPR). We draw a conclusion that PLS-DA can make models get higher average correct recognition rate than PCA, ICA and Wavelet transformation.