基于光谱技术的桔子汁品种鉴别方法的研究
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摘要
为了实现桔子汁不同品种的快速光谱鉴别,首先采用主成分分析法对光谱数据进行聚类分析,从定性分析的角度得到四种不同品种桔子汁的特征差异。同时将小波变换用于对大量光谱数据的压缩,并结合RBF神经网络建立桔子汁品种鉴别的定量分析模型。该模型将小波压缩后的数据作为神经网络的输入向量,建立径向基函数RBF神经网络。4个品种共240个样本用来建立RBF神经网络的训练模型,剩余的60个样本用于预测。预测结果表明,小波变换结合RBF神经网络的桔子汁品种鉴别的准确率达到100%。说明文章提出的基于光谱技术的鉴别方法具有很好的分类能力,它为桔子汁品种的快速鉴别提供了一种新方法。
In order to quickly analyze varieties of orange juice with near infrared spectra,firstly,principal component analysis(PCA) was used to analysze the clustering of orange juice samples,and the characteristic differentia of four orange juice varieties was obtained through qualitative analysis.Then plentiful spectral data were compressed by wavelet transform(WT) and the model was built with radial basis function neural network(RBF-NN),which offered a quantitative analysis of orange juice varieties discrimination.The model regarded the compressed data as the input of RBF-NN input vectors and built a RBF-NN model.Two hundred forty samples from four varieties were selected randomly to build the training model,which in turn was used to predict the varieties of 60 unknown samples.The discrimination rate of 100% was achieved by WT-RBFNN method.It was indicated that wavelet transform combined with RBF-NN is an available method for variety discrimination based on the near infrared reflectance spectroscopy technology.It offered a new approach to the fast discrimination of varieties of orange juice .
In order to quickly analyze varieties of orange juice with near infrared spectra,firstly,principal component analysis(PCA) was used to analysze the clustering of orange juice samples,and the characteristic differentia of four orange juice varieties was obtained through qualitative analysis.Then plentiful spectral data were compressed by wavelet transform(WT) and the model was built with radial basis function neural network(RBF-NN),which offered a quantitative analysis of orange juice varieties discrimination.The model regarded the compressed data as the input of RBF-NN input vectors and built a RBF-NN model.Two hundred forty samples from four varieties were selected randomly to build the training model,which in turn was used to predict the varieties of 60 unknown samples.The discrimination rate of 100% was achieved by WT-RBFNN method.It was indicated that wavelet transform combined with RBF-NN is an available method for variety discrimination based on the near infrared reflectance spectroscopy technology.It offered a new approach to the fast discrimination of varieties of orange juice .
引文
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[10]SUN Su-qin,TANG Jun-ming,YUAN Zi-min,et al(孙素琴,汤俊明,袁子民,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2003,23(2):258.
[11]YU Wei,YONG Ke-lan(俞蔚,雍克岚).Food Science(食品科学),2003,24(11):105.
[12]WANG Si-hong,YIN Qi-fan,FAN Yan-ling,et al(王思宏,尹起范,范艳玲,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(8):957.
[13]LU Shuang,HOU Yue-qian(陆爽,候跃谦).Machine Tool and Hydraulic Pressure(机床与液压),2005,3:185.
[14]WANG Wei,WU Geng-feng,ZHANG Bo-feng,et al(王炜,吴耿锋,张博锋,等).Earthquake(地震),2005,25(2):19.
[15]YU Qiu-yang,ZHU Bin,GUO Wei(俞秋阳,朱斌,郭伟).Relay(继电器),2004,32(17):34.
[16]Shao X G,Leung A K,Chau F T.Account Chem.Res.,2003,36(4):276.
[17]SHAO Xue-guang,ZHONG Hong-bo,ZHANG Mao-sen,et al(邵学广,仲红波,张懋森,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),1997,17(6):85.
[18]SHAO Xue-guang,GU Hua,CAI Wen-sheng,et al(邵学广,顾华,蔡文生,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),1999,19(2):139.
[19]WANG Fang,CHEN Da,SHAO Xue-guang(王芳,陈达,邵学广).Tobacco Science&Technology/Tobacco Chemistry(烟草科技/烟草化学),2004,(3):31.
[20]HAN Ying-zhe,ZHANG Yan-xin,CHANG Sheng-jiang,et al(韩应哲,张延,常胜江,等).Journal of Optoelectronics/Laser(光电子/激光),2005,16(6):718.
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