Near Infrared Spectrum Detection of Soybean Fatty Acids Based on GA and Neural Network

  • Changli Zhang
  • Kezhu Tan
  • Yuhua Chai
  • Junlong Fang
  • Shuqiang Liu
Conference paper
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 259)

This paper represented a way to build mathematical model on genetic multilevel forward neural network. Building the relationship between chemistry measurement values and near infrared spectrum datum. The near infrared spectrum data was input in this network, five kinds of content of fatty acids, which measured by chemistry method, were output. Training the weight of multilevel forward neural network by genetic algorithms, building the soybean fatty acids neural network detection model, and exploring the network model which can realize near infrared spectrum detection exactly and efficiently. The authors designed a multilevel forward neural network trained by genetic algorithms. Test showed that relative coefficient in five fatty acids of soybean can be round about 0.9, and can satisfy init detection of soybean breeding.

Keywords

near infrared multilevel forward neural network genetic algorithms soybean fatty acids 

References

  1. Mroczyk W B, Michalski K M. Analyzed elementary compositions of beet tops by NIR [J]. Computers Chem. 1995, 19(3):299-301.CrossRefGoogle Scholar
  2. Bochereau L, Beurgine Petal. Classical date analysis and neural network to NIR to predict quality of the apple [J]. Journal of Agricultural Engineering Research, 1992, 51:207-216.CrossRefGoogle Scholar
  3. Fogel D B. An introduction to simulated evolutionary optimization [J]. IEEE Trans on Neural Networks, 1994, 5(1):3-14.CrossRefPubMedGoogle Scholar
  4. Srinivas M, Patnail L M. Adaptive Probabilities of Crossover and Mutations in Gas [J]. IEEE Trans on SMC, 1994, 24(4):656-667.Google Scholar
  5. Zitzler E, Thiele L. Multi-Objective Evolutionary Algorithms: A Comparative Case Study And the Strength Pareto Approach [J]. IEEE Transactions of Evolutionary Computation, 1999, 3 (4):257-271.CrossRefGoogle Scholar
  6. Williams P C, Norris K, Gehrke C W and Bernstein K. 1983. Comparison of near-infrared methods for measuring protein and moisture in wheat. Cereal Foods Worle . 150:149-152.Google Scholar
  7. Williams P C, Stevenson S, Starkey P M and Hawtin G. 1978. The application of near-infrared reflectance spectroscopy to protein-testing in pulse breeding-programmes. Journal of Science Food Agriculture. 29: 285-292.CrossRefGoogle Scholar
  8. Morgan J E and Williams P C. 1995. Starch damage in wheat flours: A comparison of enzymic, iodometric and near-infrared reflectance techniques. Cereal Chemistry. 72 (2): 209-212.Google Scholar
  9. Murray L and Williams P C. 1987. Chemical principles of near-infrared technology. In Williams P C and Norris K ed. Near-infrared Technology. AACC St. Paul, MN.Google Scholar
  10. Norris K H, Barnes R F, Moore J E and Shenk J S. 1976. Predicting forage quality by infrared reflectance spectroscopy. Journal of Animal Science. 43: 889-897.CrossRefGoogle Scholar
  11. Norris K H. 1984. Multivariate analysis of raw material. In: Schmilt L W, ed. Chemistry and world food supplies. Manila: Reihold Publisher, 155-164.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Changli Zhang
    • 1
  • Kezhu Tan
    • 2
  • Yuhua Chai
    • 1
  • Junlong Fang
    • 1
  • Shuqiang Liu
    • 3
  1. 1.Engineering College Northeast Agricultural University HarbinChina
  2. 2.Cheng Dong College, Northeast Agricultural UniversityChina
  3. 3.Hei longjiang Engineering CollegeChina

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