| 王海建，洪添胜，代芬，欧阳玉平，罗瑜清，倪慧娜.基于高光谱图像技术的沙梨无损检测[J].广东农业科学,2013,40(9):185-188 |
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基于高光谱图像技术的沙梨无损检测 |
| Nondestructive testing in pyrifolia based on the hyper spectral image technology |
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| DOI： |
| 中文关键词: 高光谱技术 沙梨 多元散射校正 无信息变量消除法 BP神经网络 |
| 英文关键词: hyperspectral technology pyrifolia multiplicative scatter correction uninformative variable elimination BP neural network |
| 基金项目:国家现代农业（柑橘）产业技术体系建设专项资金（CARS-27） |
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| 摘要点击次数: 1399 |
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| 中文摘要: |
| 为探讨基于高光谱图像技术对沙梨糖度无损检测的可行性，采集80个沙梨样本在400~1 000 nm内的高光谱图像数据及其对应的糖度，采用变量标准化、多元散射校正（MSC）、平滑滤波、基线校正等方法对原始光谱数据进行预处理，发现MSC预处理效果最佳，再通过无信息变量消除法对MSC 预处理后的光谱数据进行压缩，最后分别建立BP神经网络和PLS 沙梨糖度预测模型。结果表明：无信息变量消除法将光谱变量压缩到234个，有效减少了建模的输入变量，建立的PLS预测模型和BP神经网络的预测相关系数均在0.85以上，而PLS预测模型的相关系数为0.9508，均方根误差为0.268，优于BP神经网络模型。 |
| 英文摘要: |
| In order to explore the probability of nondestructive testing based on hyperspectral image technology, the experiment collected hyperspectral image data of 80 pyrifolia samples range in 400~1 000 nm and its sugar content. Then pretreat the original spectrum data by SNV, MSC, S.Golay, Baseline methods, and found the denoising effect of MSC pretreatment was the best. Then compressed the spectrum data of MSC pretreatment through uninformative variable eliminate method. Finally respectively established the BP neural network and PLS prediction model. The experiment results showed that uninformative variable eliminate had compressed the spectrum variable to 234, which could reduce the modeling of input variable effectively. The PLS prediction model and BP neural network prediction correlation coefficient were both more than 0.85, but PLS prediction model of the correlation coefficient was 0.9508, the root mean square error was 0.268, which was higher than the BP neural network model . |
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