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 wavenurnber 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.

A New Discrimination Method of Maize Seed Varieties Based on Near-Infrared Spectroscopy

A new discrimination method for the maize seed varieties based on the near-infrared spectroscopy was proposed. The reflectance spectra of maize seeds were obtained by a FT-NIR spectrometer (12 000-4 000 cm(-1)). The original spectra data were preprocessed by first derivative method. Then the principal component analysis (PCA) was used to compress the spectra data. The principal components with the cumulate reliabilities more than 80% were used to build the discrimination models. The model was established by Psi-3 neuron based on biomimetic pattern recognition (BPR). Especially, the parameter of the covering index was proposed to assist to discriminating the variety of a seed sample. The authors tested the discrimination capability of the model through four groups of experiments. There were 10, 18, 26 and 34 varieties training the discrimination models in these experiments, respectively. Additionally, another seven maize varieties and nine wheat varieties were used to test the capability of the models to reject the varieties not participating in training the models. Each group of the experiment was repeated three times by selecting different training samples at random. The correct classification rates of the models in the four-group experiments were above 91. 8%. The correct rejection rates for the varieties not participating in training the models all attained above 95%. Furthermore, the performance of the discrimination models did not change obviously when using the different training samples. The results showed that this discrimination method can not only effectively recognize the maize seed varieties, but also reject the varieties not participating in training the model. It may be practical in the discrimination of maize seed varieties.

A Photovoltaic InAs Quantum-Dot Infrared Photodetector

A photovoltaic quantum dot infrared photodetector with InAs/GaAs/AlGaAs structures is reported. The detector is sensitive to normal incident light. At zero bias and 78 K, a clear spectral response in the range of 2 -7 mu m has been obtained with peaks at 3.1, 4.8 and 5.7 mu m. The bandgap energies of GaAs and Al0.2Ga0.8As at 78K are calculated and the energy diagram of the transitions in the Quantum-Dot Infrared Photodetector (QDIP) is given out. The photocurrent signals can be detected up to 110 K, which is state-of-the-art for photovoltaic QDIP. The photovoltaic effect in our detector is a result of the enhanced band asymmetry as we design in the structure.

Preparation and luminescence properties of Eu2+ in SiO2 xerogels

Al-doped and B, Al co-doped SiO2 xerogels with Eu2+ ions were prepared only by sol-gel reaction in air without reducing heat-treatment or post-doping. The luminescence characteristics and mechanism of europium doping SiO2 xerogels were studied as a function of the concentration of Al, B, the europium concentration and the host composition. The emission spectra of the Al-doped and B, Al codoped samples all show an efficient emission broad band in the blue violet range. The blue emission of the Al-doped sample was centered at 437 nm, whereas the B, Al co-doped xerogel emission maximum shifted to 423 nm and the intensity became weaker. Concentration quenching effect occurred in both the Al-doped and B, Al co-doped samples, which probably is the result of the transfer of the excitation energy from Eu2+ ions to defects. The highest Eu2+ emission intensity was observed for samples with the Si(OC2H5)(4):C2H5OH:H2O molar ratio of 1:2:4. (c) 2006 Elsevier B.V. All rights reserved.

Solid-core tellurite glass fiber for infrared and nonlinear applications

By optimizing glass composition and using a multistage dehydration process, a ternary 80TeO(2)-10ZnO-10Na(2)O glass is obtained that shows excellent transparency in the wavelength range from 0.38 mu m up to 6.10 mu m. Based on this optimized composition, we report on the fabrication of a single-mode solid-core tellurite glass fiber with large mode area of 103 mu m(2) and low loss of 0.24 similar to 0.7 dB/m at 1550 nm. By using the continuous-wave self-phase modulation method, the non-resonant nonlinear refractive index n(2) and the effective nonlinear parameter gamma of this made tellurite glass fiber were estimated to be 3.8x10(-1)9 m(2)/W and 10.6 W-1.m(-1) at 1550 nm, respectively. (C) 2009 Optical Society of America

Single-Mode Solid-Core Tellurite Glass Fiber with Large Mode Area and Low Loss for Infrared Applications

低损耗实芯碲酸盐光纤的非线性研究