Abstract:

For the fourth degree of soybean oil, a BP neural network was introduced to construct model for its near-infrared spectral data. The structure and parameters of the neural network were analyzed, and the structure design of samples training aggregation and primary factor of network input were thoroughly investigated. With regard to other multivariable modeling methods, extraction effects of useful information about near infrared spectrum were analyzed. For acid value prediction standard deviation of the fourth degree of soybean oil, the results of multivariable linear regression, primary components regression and partial least square are 0.1472%, 0.1801% and 0.1576% respectively, whereas the result of BP neural network is 0.1387%.

Key words: acid value of oil, near infrared spectrum, BP neural network, standard deviation, least square method