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Y. F. Tang, Z. Y. Zhang, G. Q. Fan, et al, (2005) Identification of official rhubarb samples based on IR spectra and neural networks, Spectroscopy and Spectral Analysis, 25(5), 715–278.

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• TITLE: Study of blood fat concentration based on serum ultraviolet absorption spectra and neural network

  AUTHORS: Wei-Hua Zhu, Zhi-Min Zhao, Xin Guo, Le-Xin Wang, Hui Chen

  KEYWORDS: Blood Fat Concentration; Ultraviolet Absorption Spectra; Neural Network (NN); Serum; Wavelet Transform

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.2 No.6, October 27, 2009

  ABSTRACT: Blood plays an important role in the clinical di-agnosis and treatment, the analysis of blood will be of very important practical significance. The experiment shows that the absorption spectra of blood are of serious noise in the wave band of 200 to 300 nm, which hides the useful spectral characteristics. The effective separation of the noise was achieved by db4 wavelet transform, and the signals of reconstruction have been obviously improved in the noise serious wave band, reflecting some useful information. The absorption peaks of different samples are displaced to some degrees. The correlation between absorbance at 278nm and blood fat concentration is no significant and random. Based on the evident correlation between serum absorption spectrum and blood fat con-centration in the wave band of 265 to 282nm, a neural network model was built to forecast the blood fat concentration, bringing a relatively good prediction. This provides a new spectral test method of blood fat concentration.