Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks

Fengxia Wang; Zhuoyong Zhang; Xiujun Cui; Peter de B Harrington

doi:10.1016/j.talanta.2006.03.008

Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks

Talanta. 2006 Dec 15;70(5):1170-6. doi: 10.1016/j.talanta.2006.03.008. Epub 2006 Apr 27.

Authors

Fengxia Wang¹, Zhuoyong Zhang, Xiujun Cui, Peter de B Harrington

Affiliation

¹ Department of Chemistry, Capital Normal University, Beijing 10037, PR China.

PMID: 18970896
DOI: 10.1016/j.talanta.2006.03.008

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.