Rhubarb Identification by Using Temperature-Constrained Cascade Correlation Networks

Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. 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, and that the Multi-TCCCN models with Latin-partitions gave slightly better performance than those with leave-one-out cross validation. Better results with TCCCN models were obtained compared with conventional back-propagation neural networks (BP-NNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, rhubarb powdered samples investigated were classified by a TCCCN model based on their NIR spectra with 100% accuracy.