食品工业科技2013，Vol.34Issue(15)：284-288,5.
基于近红外光谱和共轭梯度神经网络的板栗褐变检测
Chinese chestnut browning detection by near infrared spectroscopy and scaled conjugate gradient back propagation neural network
摘要
Abstract
In order to realize the non-destructive detection of Chinese chestnut browning,near infrared spectroscopy(NIR)with the brand of 12000～4000cm-1 was used to acquire the spectra of shelled and unshelled chestnuts of "Mao Ban Hong" with the different browning grade.The original near infrared spectra data were processed by Savitzky-Golay smoothing and standard normal variate (SNV) transforming.Then,principal component analysis(PCA) was applied to extract the characteristic information of the spectrum,and the back propagation neural network based on the scaled conjugate gradient algorithm(SBP)was set up by the principal components as the input.The results showed that the recognition of browning level for unshelled chestnuts was the best when the principal components number was 8 for SBP,and the accuracy for the training and testing samples were 100％ and 98.7％,respectively.For the shelled chestnuts,10 was the best number of principal components for SBP neural network,and the recognition for the training and testing samples were 65.3％ and 64.4％ respectively.Finally,the comparison between the traditional back propagation neural network based on gradient descent algorithm(GBP) and Radial basis function neural network(RBF) was proceeded.The results from the validation samples showed that the recognition of SBP for discriminating unshelled and shelled chestnuts was 100％ and 66.7％,respectively.SBP was better than GBP and RBF for the discrimination of browning for Chinese chestnut.关键词
近红外光谱/板栗/褐变/主成分分析/BP神经网络Key words
near infrared spectroscopy/ chestnuts/ browning/ principal component analysis/ back propagation neural network分类
轻工纺织引用本文复制引用
潘磊庆,郑剑,史娇智,袁建,屠康..基于近红外光谱和共轭梯度神经网络的板栗褐变检测[J].食品工业科技,2013,34(15):284-288,5.基金项目
浙江省自然科学基金(Y3110450) （Y3110450）
国家自然科学基金(31101282) （31101282）
浙江农林大学自然科学基金预研项目(2044010004) （2044010004）
中央高校基本科研业务费专项资金(KYZ201120) （KYZ201120）
江苏省高校优势学科建设工程. （）
