Geographical origin discrimination of edible bird’s nests using smart handheld device based on colorimetric sensor array

Abstract

A smart handheld device based on colorimetric sensor array and smart cellphone was constructed to discriminate geographical origins of edible bird’s nest (EBN). Three hundred and twenty EBN samples were collected from Malaysia and Indonesia. The colorimetric sensor array consists of chemo-response dyes and was used to capture the odor molecule. The smart cellphone was used to obtain images and to extract red, green and blue colors using in-house software before and after contact with each sample. The differences in the nutritional (carbohydrate, protein and Sialic acid) and volatile components (VCs) between Malaysian and Indonesian EBNs were measured by conventional chemical methods. The colorimetric sensor arrays showed a unique pattern of color changes upon its exposure to EBN from different geographical origins. Data analysis was performed using pattern recognition algorithms including principal component analysis (PCA), Hierarchical cluster analysis (HCA), and partial least square regression (PLSR). The PCA and HAC were applied to investigate the similarity between sample groups. The PLS model was developed to demonstrate the relation between colorimetric responses and characteristic VCs of EBN. For the PLS model, the value of correlation coefficient is higher than 0.86 in calibration and prediction set. Results demonstrated that the smart handheld device was capable for geographical origin discrimination of EBN.