Pattern recognition analysis on nutritional profile and chemical composition of edible bird’s nest for its origin and authentication

Abstract

Authenticity of food is of great importance to ensure food safety and quality, and to protect consumer rights. A rapid and accurate method for authentication of edible bird’s nest (EBN) was proposed by using nutritional profile and chemical composition, and pattern recognition analysis. The authentication of EBN includes identification and classification of EBN by production origin (houses or caves), species origin (Aerodramus fuciphagus or Aerodramus maximus) and geographical origin (Peninsular Malaysia or East Malaysia) based on their active compositional content. Three pattern recognition methods, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA), were employed to develop classification models for authentication of EBN origins. Compared to PCA and HCA, LDA is more accurate and efficient in distinguishing EBN by different production, species, and geographical origins, having classification ability of 100% and prediction ability of 92% as validated by cross-validation method. The key chemical markers for production origin differentiation are total phenolic content, zinc, valine, and calcium, while for species origin discrimination are sialic acid, serine, phenylalanine and valine, and for geographical origin differentiation are arsenic and mercury. The findings suggest that nutritional and chemical profiles combined with pattern recognition analysis are promising strategy for rapid authentication of EBN and its products.

Keywords

• Chemical marker
• Classification model
• Principal component analysis
• Hierarchical cluster analysis
• Linear discriminant analysis