Geographical origin identification of dendrobium officinale based on NNRW-stacking ensembles

Abstract

Dendrobium officinale is a well-recognized functional food material. Considering its therapeutic effect and price vary among different geographical origins, this paper proposed an origin identification method based on Raman spectroscopy and NNRW (neural network with random weights)-stacking ensemble model. In a case study of dendrobium officinale samples from three different geographical origins, we compare both single estimators, i.e., KNN (k-nearest neighbors), MLP (multi-layer perceptron), DTC (decision tree classifier), and NNRW, and their stacking ensemble counterparts. The results showed that the NNRW-stacking ensemble has the best test accuracy (96.3%) and an impressive fitting speed (the fastest among all ensembles). In conclusion, the NNRW-stacking ensemble model combined with Raman spectroscopy can be a promising method for herb geographical original identification. The proposed model has demonstrated the speed advantage of NNRW (no need for gradient-based iterations) and the generalization power of stacking ensembles (reduce single-estimator bias).

Keywords

• Neural networks with random weights
• Ensemble learning
• Dendrobium officinale
• Geographical origin identification