基于近红外光谱和共轭梯度神经网络的板栗褐变检测

doi:10.13386/j.issn1002-0306.2013.15.025

基于近红外光谱和共轭梯度神经网络的板栗褐变检测

基金项目:

浙江省自然科学基金(Y3110450)；国家自然科学基金(31101282)；浙江农林大学自然科学基金预研项目(2044010004)；中央高校基本科研业务费专项资金(KYZ201120)；江苏省高校优势学科建设工程；

详细信息

中图分类号: S664.2
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出版历程
- 收稿日期: 2013-01-09

Chinese chestnut browning detection by near infrared spectroscopy and scaled conjugate gradient back propagation neural network

摘要

摘要: 为了实现板栗褐变的无损检测,本实验以"毛板红"板栗为样品,在12000～4000cm-1范围内采集带壳和去壳板栗4个褐变等级的近红外光谱,用Savitzky-Golay平滑和标准正态变量变换（SNV）方法对光谱原始数据进行预处理,采用主成分分析法提取光谱的特征信息,建立基于共轭梯度调整算法的BP神经网络（SBP）识别板栗褐变模型。结果表明,对去壳板栗,最佳主成分因子数为8时,网络训练集和测试集对板栗褐变识别准确率最好,分别为100%和98.7%;对带壳板栗,最佳主成分因子数为10,网络训练集和测试集对板栗褐变识别准确率最好,分别为65.3%和64.4%。最后比较了所建网络与传统的基于梯度下降算法的BP神经网络（GBP）与径向基函数（RBF）网络的性能,验证集结果表明,构建的基于共轭梯度调整算法神经网络模型（SBP）效果好于GBP和RBF,对去壳板栗和带壳板栗褐变识别准确率分别为100%和66.7%。
- 近红外光谱; /
- 板栗; /
- 褐变; /
- 主成分分析; /
- BP神经网络;
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 component's 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.
- near infrared spectroscopy; chestnuts; browning; principal component analysis; back propagation neural network; /
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