title: [Building artificial neural networks model on portable NIR integrity wheat component measuring apparatus].
abstract: The quantitative analysis model of protein in integrity wheat was built by three layers back propagation artificial neural networks for portable near infrared (NIR) integrity wheat component measuring apparatus. The structure diagram of integrity wheat component measuring apparatus, light route structure of apparatus and the spectrum of integrity wheat were given in the present paper. The theory of artificial neural network was briefly introduced and the results of quantitative analysis model of protein were given. For calibration set and prediction set, the correlation coefficient was 0.90 and 0.96 respectively; the relative standard deviation is 3.77% and 4.46% respectively. Because of the influence of light route structure, electrical circuit, and integrity sample forms on the measuring apparatus, some nonlinearity exists between the spectral parameters and chemical values. The results of artificial neural networks nonlinear model were superior to linear model.
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100094, China.
authors.forename: Hai-yan
authors.initials: HY
authors.lastname: Ji
authors.forename: Ming
authors.initials: M
authors.lastname: Wen
authors.forename: Bin
authors.initials: B
authors.lastname: Hao
mesh_terms: D016571:Neural Networks, Computer; D010940:Plant Proteins / Q000032:analysis; D019265:Spectroscopy, Near-Infrared / Q000295:instrumentation* / Q000379:methods; D014908:Triticum / Q000737:chemistry*
chemical_list: D010940:Plant Proteins