Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model
Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model
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DOI：
10.1016/j.jfoodeng.2006.04.042
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发表时间：
2007-04-01
影响因子：
5.5
通讯作者：
Deng, Xunfei
中科院分区：
文献类型：
--
作者：
He, Yong;Li, Xiaoli;Deng, Xunfei
可见/近红外光谱法（NIRS）具有速度快、无损、精度高、检测数据可靠等特点，是一种无污染、快速、定量定性的分析方法。本文建立了一种新的茶叶品种的可见/近红外光谱（325 ~ 1075 nm）鉴别方法。建立了反射率光谱与茶叶品种之间的关系。利用小波变换对光谱数据进行压缩。小波变换的特征可以在主成分（PC）空间中可视化，从而发现与不同类型光谱样本相关的结构。它似乎提供了一个合理的茶叶品种集群。将主成分分析法计算得到的前8个主成分的分数作为输入，应用于一个具有一个隐藏层的反向传播神经网络。随机选取8个品种200个样本，建立BP-ANN模型。利用该模型对40个未知样品的品种进行了预测。识别率达到100%。该模型是可靠的、实用的。(c) 2006 Elsevier Ltd.版权所有。
Visible/near-infrared spectroscopy (NIRS), with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc., is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for discrimination of varieties of tea by means of vis/NIR spectroscopy (325-1075 nm) was developed in this work. The relationship between the reflectance spectra and tea varieties was established. The spectral data was compressed by the wavelet transform (WT). The features from WT can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectra samples. It appeared to provide a reasonable clustering of the varieties of tea. The scores of the first eight principal components computed by PCA had been applied as inputs to a back propagation neural network with one hidden layer. The 200 samples of eight varieties were selected randomly to build BP-ANN model. This model was used to predict the varieties of 40 unknown samples. The recognition rate of 100% was achieved. This model comes to be reliable and practicable. (c) 2006 Elsevier Ltd. All rights reserved.