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Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks |
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| Authors: | Wang Fengxia Zhang Zhuoyong Cui Xiujun de B Harrington Peter |
| Institution: | aDepartment of Chemistry, Capital Normal University, Beijing 10037, PR China bFaculty of Chemistry, Northeast Normal University, Changchun 130024, PR China cCenter for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701-2979, USA |
| Abstract: | 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. |
| Keywords: | Temperature-constrained cascade correlation networks Rhubarb Near-infrared spectra Identification Artificial neural network Latin-partitions |
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