[Rapid and nondestructive discrimination of hybrid maize seed purity using near infrared spectroscopy].

Near infrared spectroscopy technology was applied to study rapid and nondestructive discrimination method of hybrid maize seed purity. With NongDa108 hybrid seeds and mother 178 seeds, a discrimination model for the purity of maize single seed was built by near infrared reflectance spectroscopy with distinguished partial least squares (DPLS). A total of 200 seeds including 100 hybrid seeds and 100 mother seeds were divided into two groups: calibration set (150 samples) and validation set (50 samples), and each group had same number of hybrid and mother seeds. To eliminate human errors as much as possible we used two sample cups with transmission hole diameter of 3.0 and 4.5 mm, respectively, at the bottom for spectrum acquisition. The location of sample cups and seeds were fixed during spectrum acquisition process. The result showed that the average identification rate with 3 mm transmission hole diameter was 99.82%, significantly higher than that of 4.5 mm whose average identification rate was just 90.96%. There was no significant difference among the identification rates of one replicate and two replicates spectrum on endosperm face, two replicates spectrum on embryo face and four replicates. The rates of validation set reached about 99%, slightly more than that of one replicate on embryo face. The identification rates of one spectrum and two replicates spectrum on endosperm face in calibration and validation set were 100%, with the spectral region between 4000 and 8000 cm(-1). With 3.0 mm transmission hole diameter and 4000-8000 cm(-1) spectral region, the seed purity identification rates in calibration and validation sets built up by one spectrum on endosperm face were 100%. With the increase in principal components, the identification rates in calibration set and validation set gradually increased, and when principal components reached 9, the rate in both of sets were 100%. The results have important value for rapid and nondestructive testing of hybrid maize seed purity.

[Quantitative analysis of hybrid maize seed purity using near infrared spectroscopy].

A quantitative identification model for testing the purity of hybrid maize seeds was built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). The NIR spectra of 123 seeds powder samples (Nongda108 and mother178) with the purity of 600-100% were collected using MPA spectrometer. All samples were divided into two groups: calibration set (82 samples) and validation set (41 samples). Synergy interval partial least squares (SiPLSu) was used for selecting effective spectral regions and building models. The influences of different spectral regions and different calibration samples on the prediction results and different main components were compared. The result showed that the spectral regions 6 000 8 000, 6 000-9 000 and 6 000-10 000 cm(-1) all had better prediction results (R2 over 95%). Spectral region 6 000-10 000 cm(-1) was regarded the optimum spectral region for building the model with less main components(8), and the determination coefficient (R2) of calibration and validation sets were 96.61% and 97.67% respectively, SEC (standard error of calibration) and SEP (standard error of prediction) were 2.15% and 1.78% respectively, RSDs (relative standard deviation) were 2.04% and 1.94% respectively. Even with different calibration samples, the average determination coefficients (R2) of calibration and validation sets were 96.21% and 95.75%, SEC (standard error of calibration) and SEP (standard error of prediction) were 2.29% and 2.23% respectively, RSDs (relative standard deviation) were 2.81% and 2.73% respectively, which further proved the model's stability. With the increase in the number of main components, the identification rates in calibration set and validation set gradually increased, when the number of main components reached 8, the model determination coefficients reached the best (96.61% and 97.67%), and related coefficients of true value and predicted value were 98.29% and 98.87% respectively. The results have important value for rapid and accurate testing of hybrid maize seed purity.

[Determination of hard rate of licorice (Glycyrrhiza uralensis F.) seeds using near infrared reflectance spectroscopy].

With 112 licorice seed samples with different hard rates ranging from 0.3% to 99.3%, harvested in different years from 2002 to 2007 and from different locations of China including Xinjiang municipality, Ningxia province, Inner-Mongolia municipality, Gansu province, Shanxi province and Heilongjiang province, a model for determining hard rate of licorice seeds was tried to be built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). All the seeds samples were divided into two groups: calibration set (including 84 samples) and validation set (including 28 samples). The influences of different spectral regions, different main components and different calibration samples on the prediction results were compared. The result indicated that the spectral regions of 4000-8000, 5000-9000, 5000-8000, 5000-7000 and 5000-6000 cm(-1) all had satisfied and similar prediction results, then 5000-6000 cm(-1) was regarded as the optimum spectral region for building the model because of its faster operation speed. The model with 6 main components had better relative high determination coefficient (R2) and low standard errors and absolute errors. With the spectral range from 5000 to 6000 cm(-1) and 6 main components, there was a better fitting between the predictive value and true value. Determination coefficients (R2) of calibration and validation sets are 90.23% and 91.24%, the coefficients of correlation are 0.9532 and 0.9579, the standard errors are 10.31 and 9.72, and the average absolute errors are 8.01% and 7.45% respectively. Even with different calibration samples, the models have high determination coefficient (R2 over 90%), low standard errors (about 10.00) and low absolute errors (about 7.90%). The building of NIR model for determining hard rate of licorice seeds could promote the application of hard seeds in cultivation.

[Identification of hardness of licorice single seed using near infrared spectroscopy].

To break the dilemma on judging hard seeds and soft seeds of licorice and other legume families nondestructively, a distinguishing model for the hardness of licorice single seed was tried to be built by near infrared reflectance spectroscopy with distinguished partial least squares(DPLS). A total of 244 licorice seeds were divided into three groups: calibration set (120 samples), validation set (60 samples) and prediction set (64 samples), and each group has the same number of hard seeds and soft seeds. To eliminate the human error as far as possible, a specially made sample cup was designed for spectrum acquisition. Then the locations of the seed and the fiber-optic probe were fixed during each spectrum acquisition process. The influences of different replicate time, different spectral region and different calibration samples on the identification rate were compared. The result indicated that four replicates could increase the identification rate of the model significantly, the identification rates of the model of four replicates in calibration, validation and prediction set samples were 95.83%, 95.00% and 96.88% respectively, while that of one replicate were 93.33%, 91.67% and 82.81% respectively. The model of the spectral region between 4,000 and 80,000 cm(-1) was better than that of other regions, and the identification rate in calibration, validation and prediction set samples were 95.53%, 95.94% and 94.53% respectively. Even with different samples, the predication rates were all more than 90%. The identification rates of hard seed and soft seed in prediction set samples were 92.50% and 96.56% respectively. The prediction for seeds with different size and different color showed that this model was not suitable for bigger and smaller seeds, especially not for black seeds. NIR offered a new way to distinguish the hardness of licorice singe seed quickly, precisely and nondestructively, which will advance the study on the mechanism of hardness of crop seeds.

[Research of seed quality grading of Glycyrrhiza uralensis].

OBJECTIVE: To formulate the seed quality grading standard of Glycyrrhiza uralensis. METHOD: Thousand-grain weight, seed moisture, germination rate, purity of G. uralensis seed samples from 24 regions were tested. Through statistical analysis, the key indicator and the reference indicators for seed quality grading were defined. RESULT: Germination percentage was the primary indicator of seed quality grading, thousand-grain weight, cleanliness and moisture content were important reference indicators. CONCLUSION: The seed quality of each grade should reach the following requirements: for first grade seeds, germination percentage > or = 85% , purity > or = 92%, thousand-grain weight > or = 13 g, seed moisture < or = 11%; for second grade seeds, germination percentage 75%-85%, purity 83%-92%, thousand-grain weight 11-13 g, seed moisture < or = 11%; for third grade seeds, germination percentage 65%-75%, purity 74%-83%, thousand-grain weight 9-11 g, seed moisture < or = 11%.

[Survey of study on the extraction, purification and determination methods of glycyrrhizic acid in Licorice].

Extraction, purification and determination methods of glycyrrhizic acid in Licorice are surveyed in this paper. The extracting efficiency of dilute ethanol solvent for glycyrrhizic acid is higher than water extraction, and ammonia appended to ethanol solution can increase the effect of extraction. Ultrasound method, microwave-assisted method and supercritical CO2 fluid extraction are more effective than the conventional techniques, due to the short extraction time, low consumption of solution and energy. The modem analytical methods such as TLCs, HPLC and CE can determine the content of glycyrrhizic acid rapidly and exactly.

[The genetic control of heading time in wheat].

Responses to vernalization,photoperiod and earliness per se are three important factors affecting heading time in wheat. Their actions and interactions may adjust the phasic development of wheat to avoid environmental stress. The genetic control of heading time in wheat is very complex, and this paper summarizes its research progress in details.