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14The high nitrite content in edible bird's nests is a major concern to the local swiftlet industry. It 15 lowers the price of the edible bird's nests and it brings severe health hazards to consumers and farmers. 16 This research investigated the nitrite and nitrate contents of eight types of local edible bird's nests by 17 using ion chromatography system and evaluating its colour using the CIE system in L*a*b* 18 parameters. The nitrite content obtained ranged from 5.7 µg/g for the house nests to 843.8 µg/g for the 19 cave nests. The nitrate content for the house and cave nests was 98.2 µg/g and 36999.4 µg/g, 20 respectively. The cave nests with darker and redder colour had higher nitrite and nitrate contents than 21 the brighter and more yellow house nests. This likely suggests that the nitrite and nitrate contents have 22 correlations with edible bird's nests colour. Correlations studies suggested that the nitrite content had 23 high correlations with colour parameters, L*a*b* of edible bird's nests at significant level of P < 0.10. 24 These findings suggest that edible bird's nests' colour may be a useful indicator for measuring nitrite 25 and nitrate contaminations. 26

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.

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