文献类型： 外文期刊
作者： Sun, Laijun 1 ; Hui, Guangyan 1 ; Gao, Shang 1 ; Liu, Jianhai 1 ; Wang, Lekai 2 ; Dai, Changjun 2 ;
作者机构： 1.Heilongjiang Univ, Coll Heilongjiang Prov, Key Lab Elect Engn, Room 505,Bldg A8,74 Xuefu Rd, Harbin 150080, Peoples R China
2.Heilongjiang Acad Agr Sci, Agr Prod Qual & Safety Res Inst, Harbin, Peoples R China
关键词： Benzoyl peroxide;Near infrared reflectance spectroscopy;Radial basis function
期刊名称：INTERNATIONAL JOURNAL OF FOOD PROPERTIES （ 影响因子：2.727； 五年影响因子：2.938 ）
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收录情况： SCI
摘要： This research proposed to design a prediction model based on radial basis function neural network and near infrared reflectance spectroscopy in detecting concentration of benzoyl peroxide in flour. Near infrared reflectance spectra acquired from 100 different concentration samples were pre-processed by the standard normal variate method, detection of leverage, and student residual. Near infrared reflectance spectroscopy models were designed to predict benzoyl peroxide in the 36 samples by means of partial least squares, back propagation neural network, and radial basis function, respectively. The results demonstrated that the radial basis function model, with prediction correlation coefficient (R), root mean squared error of prediction, and ratio of performance to standard deviate reaching 0.9937, 15.5095, and 8.8216, respectively, had optimal prediction accuracy and feasibility providing quality evaluation and dynamic monitoring service for quality inspection department and consumers.
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