Amino acid determination by HPLC combined with multivariate approach for geographical classification of Malaysian Edible Bird’s Nest

• Published in: Journal of food composition and analysis 2022-04, Vol.107, p.104399, Article 104399
• Main Authors: Lee, Ting Hun, Lee, Chia Hau, Azmi, Nurul Alia, Liew, Rock Keey, Hamdan, Norfadilah, Wong, Syie Luing, Ong, Pei Ying
• Format: Article
• Language: English
• Subjects: Amino acids; Classification; Edible Bird’s Nest; Geographical; Multivariate analysis
• ISSN: 0889-1575; 1096-0481
• DOI: 10.1016/j.jfca.2022.104399

[Display omitted] •18 amino acids were successfully quantified in Edible Bird’s Nest (EBN).•EBN from West and East Malaysia could be classified based on amino acid contents.•Multivariate analysis showed excellent classification and prediction ability.•The classification accuracy of the proposed models was 100 %.•4 amino acids were assessed as potential markers between West and East Malaysia. Edible Bird’s Nest (EBN) is mainly used as a functional food where its quality is affected by many factors including geographical region. This study aims to differentiate the EBN from West Malaysia (WM) and East Malaysia (EM) based on amino acid profiles by high-performance liquid chromatography (HPLC) combined with multivariate approach. A total of 33 authentic EBN samples were collected from WM (n = 23) and EM (n = 10) for classification. The data obtained was used to identify the reliable potential markers between WM and EM via serial multivariate analysis including hierarchical clustering analysis (HCA), principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). EBN samples from WM and EM were clearly distinguished by the developed OPLS-DA model with high prediction ability (Q2) of 62.7 %. The model’s robustness was validated and blind test samples were 100 % properly allocated to their respective groups. Glycine, cysteine, tryptophan and aspartic acid were proposed as potential markers to classify the EBN from WM and EM. Overall, the predictive model shows high accuracy for EBN classification.