近红外光谱结合化学计量学方法检测蜂蜜产地
为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析。kennard-Stone法划分训练集和预测集。光谱用一阶导数加自归一化预处理后,再用小波变换（WT）进行压缩和滤噪。结合滤波后光谱信息,分别用径向基神经网络（RBFNN）和偏最小二乘-线性判别分析（PLS-LDA）建立了苹果蜜产地和油菜蜜产地的判别模型。对不同小波基和分解尺度进行了讨论。对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度较好,都为96.2%。对油菜蜜：WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度较好,为85.7%;WT-PL...
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| Published in | 农业工程学报 Vol. 27; no. 8; pp. 350 - 354 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
中南林业科技大学理学院,长沙,410004%湖南省食品测试分析中心,长沙,410025%中南林业科技大学食品学院,长沙,410004
2011
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.3969/j.issn.1002-6819.2011.08.061 |
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| Abstract | 为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析。kennard-Stone法划分训练集和预测集。光谱用一阶导数加自归一化预处理后,再用小波变换（WT）进行压缩和滤噪。结合滤波后光谱信息,分别用径向基神经网络（RBFNN）和偏最小二乘-线性判别分析（PLS-LDA）建立了苹果蜜产地和油菜蜜产地的判别模型。对不同小波基和分解尺度进行了讨论。对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度较好,都为96.2%。对油菜蜜：WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度较好,为85.7%;WT-PLS-LDA模型在小波基为db9、分解尺度也为1时,预测精度较好,为90.5%。研究表明：WT结合线性的PLS-LDA建模比WT结合非线性的RBFNN建模更适于蜂蜜产地判别;近红外光谱技术具有快速判别蜂蜜产地的潜力。 |
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| AbstractList | 为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析。kennard-Stone法划分训练集和预测集。光谱用一阶导数加自归一化预处理后,再用小波变换（WT）进行压缩和滤噪。结合滤波后光谱信息,分别用径向基神经网络（RBFNN）和偏最小二乘-线性判别分析（PLS-LDA）建立了苹果蜜产地和油菜蜜产地的判别模型。对不同小波基和分解尺度进行了讨论。对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度较好,都为96.2%。对油菜蜜：WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度较好,为85.7%;WT-PLS-LDA模型在小波基为db9、分解尺度也为1时,预测精度较好,为90.5%。研究表明：WT结合线性的PLS-LDA建模比WT结合非线性的RBFNN建模更适于蜂蜜产地判别;近红外光谱技术具有快速判别蜂蜜产地的潜力。 O43%TP3%S896.1; 为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析.kennard-Stone法划分训练集和预测集.光谱用一阶导数加自归一化预处理后,再用小波变换(WT)进行压缩和滤噪.结合滤波后光谱信息,分别用径向基神经网络(RBFNN)和偏最小二乘—线性判别分析(PLS-LDA)建立了苹果蜜产地和油菜蜜产地的判别模型.对不同小波基和分解尺度进行了讨论.对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度较好,都为96.2％.对油菜蜜:WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度较好,为85.7％;WT-PLS-LDA模型在小波基为db9、分解尺度也为1时,预测精度较好,为90.5％.研究表明:WT结合线性的PLS-LDA建模比WT结合非线性的RBFNN建模更适于蜂蜜产地判别；近红外光谱技术具有快速判别蜂蜜产地的潜力. |
| Author | 李水芳 单杨 朱向荣 李忠海 |
| AuthorAffiliation | 中南林业科技大学理学院,长沙410004 湖南省食品测试分析中心,长沙410025 中南林业科技大学食品学院,长沙410004 |
| AuthorAffiliation_xml | – name: 中南林业科技大学理学院,长沙,410004%湖南省食品测试分析中心,长沙,410025%中南林业科技大学食品学院,长沙,410004 |
| Author_FL | Li Shuifang Zhu Xiangrong Li Zhonghai Shan Yang |
| Author_FL_xml | – sequence: 1 fullname: Li Shuifang – sequence: 2 fullname: Shan Yang – sequence: 3 fullname: Zhu Xiangrong – sequence: 4 fullname: Li Zhonghai |
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| ClassificationCodes | O43%TP3%S896.1 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.3969/j.issn.1002-6819.2011.08.061 |
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| DocumentTitleAlternate | Detection of geographical origin of honey using near-infrared spectroscopy and chemometrics |
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| EndPage | 354 |
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| Keywords | 径向基函数神经网络 小波变换 产地判别 蜂蜜 近红外光谱 偏最小二乘-线性判别分析 |
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| Notes | Near infrared spectroscopy combined with chemometrics methods has been used to detect the geographical origin of honey samples. The samples were divided into the training set and the test set by kennard-Stone algorithm. After being pre-treated with first derivative and autoscaling, the spectral data were compressed and de-noised using wavelet transform （WT）. The radical basis function neural networks （RBFNN） and partial least squares-line discriminant analysis （PLS-LDA） were applied to develop classification models, respectively. The performances of different wavelet functions and decomposition levels were evaluated in relation to the total prediction accuracy for the test set. For apple honey samples, when wavelet function was db1 and decomposition level was 2, both WT-RBFNN model and WT-PLS-LDA model produced the largest total prediction accuracy of 96.2%. For rape honey samples, when wavelet function was db4 and decomposition level was 1, WT-RBFNN model made the largest total prediction accuracy of 85.7%; |
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| PublicationTitle | 农业工程学报 |
| PublicationTitleAlternate | Transactions of the Chinese Society of Agricultural Engineering |
| PublicationTitle_FL | Transactions of the Chinese Society of Agricultural Engineering |
| PublicationYear | 2011 |
| Publisher | 中南林业科技大学理学院,长沙,410004%湖南省食品测试分析中心,长沙,410025%中南林业科技大学食品学院,长沙,410004 |
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| Snippet | 为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析。kennard-Stone法划分训练集和预测集。光谱用一阶导数加自归一化预处理后,再用小波... O43%TP3%S896.1; 为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析.kennard-Stone法划分训练集和预测集.光谱用一阶导数加自归一化... |
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| StartPage | 350 |
| SubjectTerms | 产地判别 偏最小二乘-线性判别分析 小波变换 径向基函数神经网络 蜂蜜 近红外光谱 |
| Title | 近红外光谱结合化学计量学方法检测蜂蜜产地 |
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