Bacteria Strain Identification with Fluorescence Spectra

Abstract:

From the research of fluorescence spectra of lactobacillus acidophilus, streptococcus mutans and lactobacillus bulgaricus, it was indicated that under the stimulation of UV light, the prbiotic solution would emit fluorescence. Under the stimulation of optimal excitation wavelength of 290nm, the fluorescence peak fell in the range of 300-650nm. 150 groups of spectral data measured by wavelet transform were used for compression, after which each group of data reduced from 1341 points originally to 168 points, which not only retained the features of the original spectrum, but also increased the processing speed of neural networks. Radial basis function neural network method was used for compressed data research, 40 groups of experimental data were trained for each strain, based on which 30 groups of unknown data were identified. Results showed that after training, the radial basis function neural network could accurately predict unknown strains.

• Periodical: Applied Mechanics and Materials (Volume 865)
• Pages: 630-635
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.865.630
• Online since: June 2017
• Authors: Hui Yang*, Xue Xiao, Xue Song Zhao, Yan Wu, Jun Jun Zong
• Keywords: Data Compression, Fluorescence Spectrum, Radial Basis Function Neural Network

References

[1] J. Yang, X. H. Pan, Y. Y. Zheng, etc. Evaluation of health care function of probiotic preparation, Shanghai J. Prev. Med. 2 (2000) 73-75.

[2] Q. Li, S. C. Zhang, X. Y. Zeng, etc. Probiotics nutrition and its health care function, Food Res. Dev. 25(02) (2004) 106-109.

[3] H. Wei, C. M. Liu, B. C. Cheng, etc. Development of Beneficial Health Effects of Viable and Non-viable Probiotics Cells, Food Sci. 28(11) (2007) 618-623.

[4] X. Z. Hu. Probiotics, prebiotics and health care function and safety, Shanghai Med. Pham. 22(8) (2001) 356-358.

[5] X. H. Guo. Foundation and Application of Probiotics. 2002, Beijing: Beijing Scientific and Technical Publishers.

[6] G. Zhang. Lactic Acid Bacteria-Basis, Technology and Application. 2007, Beijing: Chemical Industry Press.

[7] L. Zhang, B. P. Tan, K. S. Mai, etc. Screening and Identification of a Potential Probiotic in Shrimp, Fenneropenaeus chinensis, Period. Ocean Univ. Chin. 38(02) (2008) 225-231.

[8] H. B. Li, X. L. Song, S. Wei, etc. Screening and identification of four probiotic bacteria isolated from intestine of shrimp, Ocean Limin Sin. 39(04) (2008) 374-380.

[9] Y. Liu, C. Y. Song, W. L. He, etc. Characterization of the orientation of ethanol-water clusters by polarized fluorescence spectrum, Acta. Phys. Sin. 56(05) (2007) 2962-2967.

[10] Y. Liu, X. W. Ni. The fluorescence spectral characteristics and the emission mechanism of ethanol excimer in water solution, Acta. Phys. Sin. 58(05) (2009) 3572-3577.

[11] J. F. Zhang, S. S. Hu. Chaotic time series prediction based on RBF neural networks with a new clustering algorithm, Acta. Phys. Sin. 56(02) (2007) 713-719.

DOI: 10.7498/aps.56.713

[12] L. Cheng, X. L. Ding, Z. G. Liu, etc. A newly micro-X-ray fluorescence spectrometer and applications for non-destructive analysis of archaeological objects, Acta. Phys. Sin. 56(12) (2007) 6894-6898.

DOI: 10.7498/aps.56.6894

[13] R. C. Chin, M. W. Mayo, W. Hallidy, etc. Compact phase resolved fluorescence spectrometer for detection and discrimination of airborne and waterborne bacteria, SPIE, 2836 (1996) 158-169.

[14] D. Stephan, M. L. Jörg, R. Rainer, R. Jürgen. Ultraviolet fluorescence excitation and emission spectroscopy of marine algae and bacteria, Marine Chem. 62(1-2) (1998) 137-156.

DOI: 10.1016/s0304-4203(98)00026-7

[15] E. G. Héctor, S. J. Landulfo, A. Z. Renato, T. Marcos. Rapid Identification of Bacterial Species by Fluorescence Spectroscopy and Classification Through Principal Components Analysis, J. Fluorensc. 13(3) (2003) 489-493.

DOI: 10.1023/b:jofl.0000008059.74052.3c

[16] S. Elliott, J. R. Lead, A. Baker. Thermal quenching of fluorescence of freshwater, planktonic bacteriaOriginal Research Article, Anal. Chim. Acta. 564(2) (2006) 219-225.

DOI: 10.1016/j.aca.2006.01.087

[17] Research and Development Center of Feisi Science and Technology Products 2005 Neural Networks and Realized by Matlab7 . Beijing: Publishing House of Electronics Industry, (2005).

[18] C. H. Dong. Matlab Neural Networks and Application (2nd Ed. ), Beijing: National Defense Industry Press, (2007).

[19] S. M. Ma, S. D. Liu, Z. Y. Zhang, etc. Radial Basis Function Networks and IR Spectrometry Applied for Identification of Official Rhubarb Sample, Spectrosc. Spectr. Anal. 25(06) (2005) 874-877.

[20] Y. B. Li, X. Y. Huang, M. Sha, etc. Radial basis function neural network for overlapping chromatographic peaks, Chromat. 19(02) (2001) 112-115.

[21] X. H. Yu, Z. Y. Zhang, Q. Ma, etc. Quantitative Prediction of Active Constituents in Rhubarb by Near Infrared Spectroscopy and Radial Basis Function Neural Networks, Spectrosc. Spectr. Anal. 27(03) (2007) 481-485.

[22] K. Wang, H. Zhang, S. J. Chang, etc. Adaptive wavelet algorithm for complicated spectra data compression, Acta. Phys. Sin. 56(06) (2007) 3613-3618.

[23] H. P. Zhang, J. B. Zhao, Z. L. Yang. Wavelet Transform and Its Application in Data Compression, Comput. Modern. 11 (2003) 105-106.