Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks.
Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks.
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DOI：
10.1016/j.talanta.2006.03.008
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发表时间：
2006-12
期刊：
影响因子：
6.1
通讯作者：
Fengxia Wang;Zhuoyong Zhang;Xiujun Cui;Peter de B. Harrington
中科院分区：
文献类型：
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作者：
Fengxia Wang;Zhuoyong Zhang;Xiujun Cui;Peter de B. Harrington
基于大黄粉的近红外光谱，采用温度约束级联相关网络(TCCCNs)对其进行识别。比较了使用多个单输出网络模型(Uni-TCCCN)和多个网络多输出网络(多TCCCN)的不同网络配置。采用拉丁分割法和留一法交叉验证法进行比较研究。结果表明，多个单输出网络的预测效果普遍好于多个输出的单网络预测。与传统的反向传播神经网络(BPNN)相比，TCCCN模型获得了更好的结果。详细讨论了参数对正确辨识和参数优化的影响。在优化神经网络训练参数的基础上，利用TCCCN模型对大黄粉样品的近红外光谱进行分类，分类准确率为100%。
Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Different network configurations that used multiple network models with single output (Uni-TCCCN) and single networks with multiple outputs (Multi-TCCCN) were compared. Comparative studies were made by using Latin-partitions and leave-one-out cross-validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs. Better results with TCCCN models were obtained compared with conventional back propagation neural networks (BPNNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, NIR spectra from powdered rhubarb samples were classified by a TCCCN model with 100% accuracy.