基于主成分分析和人工神经网络的近红外光谱大豆产地识别

TS202.1; 为了准确、快速地识别大豆产地,通过近红外光谱技术(NIRS)结合主成分分析(PCA)和人工神经网络技术(ANN)研究不同国家大豆内含特征,建立进口大豆产地识别模型.采用箱型图校正法,剔除阿根廷、巴西、乌拉圭、美国等4个国家166组大豆样本中12组异常样本.采用多元散射校正(MSC)、标准正态变量(SNV)、Savitzky-Golay(SG)平滑滤波等方法进行光谱数据预处理,结果表明,采用SG(3)平滑结合MSC预处理效果最好.主成分分析表明,前10个主成分的累积贡献率达到99.966％.选取主成分分析得到前10个主成分为输入向量,4个产地作为目标向量,分别采用支持向量机(S...

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Published in食品工业科技 Vol. 42; no. 9; pp. 270 - 274
Main Authors 田琼, 马新华, 袁俊杰, 龙阳, 洪武兴, 卢韵宇
Format Journal Article
LanguageChinese
Published 湛江海关技术中心,广东湛江 524022 01.05.2021
Subjects
主成分分析
人工神经网络
近红外光谱
大豆产地识别
Online AccessGet full text
ISSN1002-0306
DOI10.13386/j.issn1002-0306.2020060271

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Abstract TS202.1; 为了准确、快速地识别大豆产地,通过近红外光谱技术(NIRS)结合主成分分析(PCA)和人工神经网络技术(ANN)研究不同国家大豆内含特征,建立进口大豆产地识别模型.采用箱型图校正法,剔除阿根廷、巴西、乌拉圭、美国等4个国家166组大豆样本中12组异常样本.采用多元散射校正(MSC)、标准正态变量(SNV)、Savitzky-Golay(SG)平滑滤波等方法进行光谱数据预处理,结果表明,采用SG(3)平滑结合MSC预处理效果最好.主成分分析表明,前10个主成分的累积贡献率达到99.966％.选取主成分分析得到前10个主成分为输入向量,4个产地作为目标向量,分别采用支持向量机(SVM)、邻近算法(KNN)与人工神经网络法(ANN)建立识别模型.结果表明,采用BP-ANN建模效果最好,总体测试集准确率为95.65％,其中阿根廷准确率为100％,巴西准确率为100％,乌拉圭准确率为80％,美国准确率为100％,该模型能够实现对进口大豆生产国别的识别.
AbstractList TS202.1; 为了准确、快速地识别大豆产地,通过近红外光谱技术(NIRS)结合主成分分析(PCA)和人工神经网络技术(ANN)研究不同国家大豆内含特征,建立进口大豆产地识别模型.采用箱型图校正法,剔除阿根廷、巴西、乌拉圭、美国等4个国家166组大豆样本中12组异常样本.采用多元散射校正(MSC)、标准正态变量(SNV)、Savitzky-Golay(SG)平滑滤波等方法进行光谱数据预处理,结果表明,采用SG(3)平滑结合MSC预处理效果最好.主成分分析表明,前10个主成分的累积贡献率达到99.966％.选取主成分分析得到前10个主成分为输入向量,4个产地作为目标向量,分别采用支持向量机(SVM)、邻近算法(KNN)与人工神经网络法(ANN)建立识别模型.结果表明,采用BP-ANN建模效果最好,总体测试集准确率为95.65％,其中阿根廷准确率为100％,巴西准确率为100％,乌拉圭准确率为80％,美国准确率为100％,该模型能够实现对进口大豆生产国别的识别.
Author 龙阳
田琼
马新华
袁俊杰
洪武兴
卢韵宇
AuthorAffiliation 湛江海关技术中心,广东湛江 524022
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TIAN Qiong
LU Yunyu
MA Xinhua
LONG Yang
HONG Wuxing
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DocumentTitle_FL Soybean Origin Identification Based by Near-Infrared Spectrum Based on Principal Component Analysis and Artificial Neural Network Model
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Keywords 主成分分析
人工神经网络
近红外光谱
大豆产地识别
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Snippet TS202.1; 为了准确、快速地识别大豆产地,通过近红外光谱技术(NIRS)结合主成分分析(PCA)和人工神经网络技术(ANN)研究不同国家大豆内含特征,建立进口大豆产地识别模型.采用箱...
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Title 基于主成分分析和人工神经网络的近红外光谱大豆产地识别
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