Identification of Haploid Maize Kernel Using NIR Spectroscopy in Reflectance and Transmittance Modes: A Comparative Study.

The spectra measurements mode that suitable for haploid maize kernel identification was explored using MicroNIR-1700 series of miniature near infrared spectrometer by JDSU company. Based on Near Infrared Spectroscopy (NIRS) qualitative analysis techniques, we conducted a comparative study using reflectance and transmittance spectra to identify haploid maize kernels. Partial least squares-discriminant analysis (PLS-OLDA) was used to compress the pretreated spectral data, and then the identification models were built based on Support Vector Machine (SVM). The measured data were recorded in reflectance and transmittance modes and the recognition correct rates were calculated. For measurements taken in reflectance mode, the average recognition rate was less than 60% regardless of embryo side positions. In transmittance mode, however, the average recognition rate reached 93.2%. The experiment results show that diffuse reflection spectrum could only obtain corn grain surface information, so embryo side positions severely affect haploid maize kernel identification effect when reflectance measurements mode have been employed, but they have far less impact on transmittance mode. The near infrared diffuse transmittance spectra analyzes non-uniform samples can achieve the analysis of optical path depth information accumulation, all information of the sample interior can be obtained, so transmittance spectra could identify haploid maize effectively and be desensitized to kernel positions. NIRS qualitative analysis techniques with features of rapid, nondestructive could identify the haploid and Micro-NIR spectrometer scan fast and cost less, which have utility for automatically selecting haploid maize kernels from hybrid kernels.

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.

[Fast discrimination of commerical corn varieties based on near infrared spectra].

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenumber range from 4 000 to 12 000 cm(-1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.

[Nondestructive identification of different oil content maize kernels by near-infrared spectra].

Using 220 maize single kernels, containing 75 common maize single kernels, 72 high-oil maize single kernels and 73 super high-oil maize single kernels as study materials, BPANN identification model was set up for maize single kernel with different oil content based on principal components of near infrared (NIR) spectra. Four fifths of the samples were randomly selected as training set and the other samples as prediction set. Fourteen principal components from the second to the fifteenth were selected as nets input and -1, 0, 1 as nets output. Ten models were set up like this and the accurate identification rate of all the training sets can reach 100%. For prediction sets, fifteen common corn grain samples had an average accurate identification rate of 99.33%, fourteen high-oil corn grain samples had an average accurate identification rate of 97.88%, fourteen super high-oil corn grain samples had an average accurate identification rate of 91.43%, and total maize grains in prediction set had an average accurate identification rate of over 95%. Results showed that NIR spectroscopy combined with BP-ANN technology could identify maize kernels fast and nondestructively according to oil content, which offered a very useful classification method for maize seed breeding. The effect of different principal component on BPANN models was also studied. Results told us that the first principal component with over 99% of variance contribution had negative effect on the identification model. The predictive ability of identification models set up by different principal component was discriminatory, although the learning accurate identification rates were all 100%. So it is necessary to choose correlative principal component to set up identification model.

[Prediction of IVDMD with near infrared reflectance spectroscopy (NIRS) in maize stalk].

The in vitro dry matter digestion (IVDMD) in maize stalk was analyzed with 161 samples selected from 600 samples of different eco-environments, hybrids and inbred lines, development stages, and various parts of the plants in two years. The technique of near infrared reflectance spectroscopy (NIRS) and partial least square regression (PLS) were used to establish the models by comparing several preprocessing procedures and wavelength ranges. The optimal models could be obtained in the range of 6 101. 7-5 773. 8 cm(-1) and 4 601. 3-4 246. 5 cm(-1) by the spectral data preprocessing of the Max-Min normalization. The model is suitable for measuring various sample IVDMD. The determination coefficients of the modes were 0.907 3 and 0.906 6 for cross and external validation, respectively. The root mean square error of prediction was 2.08%, and the coefficient of correlation(r) was 0.956 between NIRS predicted and actual IVDMD in these materials. The results showed that NIRS is a simple effective means for measuring IVDMD in maize stalk. The results are of great value of application in screening and evaluating quality constituents of silage maize.

[Prediction of NDF and ADF concentrations with near infrared reflectance spectroscopy (NIRS)].

The NDF (Neutral Detergent Fiber) and ADF (Acid Detergent Fiber) concentrations in maize stalk were analyzed with 147 samples selected from 600 samples of different eco-environments, hybrids and inbred lines, development stages, and various parts of the plants in two years. The technique of near infrared reflectance spectroscopy (NIRS) and partial least square (PLS) regression were used to establish the models. The results showed that the calibration models developed by the spectral data pretreatment of the first derivative + vector normalization, and the first derivative + multivariate scattering correction were the best for NDF and ADF with the same spectral regions (7501.7-5449.8 cm(-1) and 4601.3-4246.5 cm(-1)). All these models yielded coefficients of determination of calibration (R2(cal)) for NDF and ADF that are higher than 0.94, while R2(cv) and R2(val) ranged from 0.92 to 0.96 for cross and external validation. The root mean square error of estimation, root mean square error of cross validation, and root mean square error of prediction (RMSEE, RMSECV and RMSEP) for NDF and ADF ranged from 1.49% to 1.81%. The models can be used to measure various samples in screening and evaluating quality constituents of silage maize.