[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.