[Investigation of spectroscopy of ZnCuInS/ZnSe/ZnS quantum dots].

ZnCuInS/ZnSe/ZnS quantum dots were non-toxic and heavy-metal free semiconductor nanocrystals. In the present paper, ZnCuInS/ZnSe/ZnS core/shell/shell quantum dots were prepared with the particle size of 3.3, 2.

[Investigation of the temperature-dependent spectral characteristics of colloidal PbSe nanocrystals].

In the present paper colloidal PbSe nanocrystals were prepared with the particle size of 3.8 and 5.8 nm, and the temperature- dependent optical properties of colloidal PbSe nanocrystals were investigated.

[Investigation of spectroscopy of ZnCuInS/ZnS quantum dots and poly-TPD].

In the present paper ZnCuInS/ZnS core/shell quantum dots were prepared with the particle size of 3.2 nm. Its radiation is based on the donoracceptor pair transitions, not the band edge emission.

[A method of recognizing biology surface spectrum using cascade-connection artificial neural nets].

A method of recognizing the visible spectrum of micro-areas on the biological surface with cascade-connection artificial neural nets is presented in the present paper. The visible spectra of spots on apples' pericarp, ranging from 500 to 730 nm, were obtained with a fiber-probe spectrometer, and a new spectrum recognition system consisting of three-level cascade-connection neural nets was set up. The experiments show that the spectra of rotten, scar and bumped spot on an apple's pericarp can be recognized by the spectrum recognition system, and the recognition accuracy is higher than 85% even when noise level is 15%.

[A new linear neural network multi-component analysis method and its application in the analysis of VC yinqiao tablets quantitative analysis].

We measured NIR spectrum of VC yinqiao tablets with spectral instrument, analyzed the contents of acetaminophen and vitamin C in the VC yinqiao tablets with principal component analysis (PCA) and Linear Neural Network, and discussed the choice of principal component number and ANN's parameters affecting the network. To compare arithmetic performance, the authors also processed the spectral data with partial least squares and PCA-BP neural network. Compared with other two data process methods, the experiment and the result of data process showed that the PCA-linear neural network possess the best forecasting precision.

[Study on the method to adjust optical fiber's spectrum attenuation with the Fourier transform].

A method based on Fourier transform to compensate the non-linear attenuation of optical fiber used as a probe in a spectrum-collecting system was proposed. First the output electric currents of photoelectric tube with and without fiber were transformed to the frequency field. So an adjustable function in frequency field was obtained, and the adjustable function was transformed to the spectrum field, so the final adjustable function was obtained.

[Influence of smooth, 1st derivative and baseline correction on the near-infrared spectrum analysis with PLS].

This paper studied the influence of using pre-procession such as smooth, 1st derivative and baseline correction on the analysis of near-infrared spectrum. Comparing the analysis results by the pre-procession methods, and using PLS arithmetic, the best pre-procession was determined. In smooth pre-procession method, the best smooth points were proposed for regression using PLS.

[Research on model and wavelength selection of near infrared spectral information].

Based on stepwise linear regression, and according to the theory of near infrared absorbption, spectrum (1000-2500 nm) obtained by detector was divided into three ranges, which were I (1000-1400 nm) and II (1400-1860 nm) and III (1860-2500 nm). In each range the regression wavelengths of different wavelength gaps were picked up stepwise. Regression coefficients and parameters were calculated by Matlab5.

[The PLS analysis with short wave near-infrared of whole wheat for protein].

Short wave near-infrared spectrum of whole wheat was obtained by diffusion reflection. PLS method was used to analyze protein content of whole wheat. Different wavelength ranges were chosen for regression and information abstraction.

[The effect of adjusting the ANN's parameters on the identifying of biology spectrum].

A method to identify the visible spectrum of micro areas on the biological surface with artificial neural net (BP-ANN) was introduced in this paper. The visible spectra (from 500 nm to 730 nm) of the micro areas with some rotten or scars on the surface of the apples were measured with fiber sensor spectrometer. A kind of ANN with a single hidden layer was created to identify the characters on the surface automatically.