Original Article
A low cost smart system to analyze different types of edible Bird's nest adulteration based on colorimetric sensor array

https://doi.org/10.1016/j.jfda.2019.06.004Get rights and content
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Highlights

  • A low-cost system for EBN adulteration constitutes by CSA and smart cellphone.

  • A multi-layered model was developed to distinguish and quantify adulterate EBN with four types by BPNN and PLS.

  • This system promotes the development and practical application of CAS.

Abstract

This study was performed to develop a low-cost smart system for identification and quantification of adulterated edible bird's nest (EBN). The smart system was constructed with a colorimetric sensor array (CSA), a smartphone and a multi-layered network model. The CSA were used to collect the odor character of EBN and the response signals of CSA were captured by the smartphone systems. The principal component analysis (PCA) and hierarchical cluster analysis (HAC) were used to inquiry the similarity among authentic and adulterated EBNs. The multi-layered network model was constructed to analyze EBN adulteration. In this model, discrimination of authentic EBN and adulterated EBN was realized using back-propagation neural networks (BPNN) algorithm. Then, another BPNN-based model was developed to identify the type of adulterant in the mixed EBN. Finally, adulterated percentage prediction model for each kind of adulterate EBN was built using partial least square (PLS) method. Results showed that recognition rates of the authentic EBN and adulterated EBN was as high as 90%. The correlation coefficient of percentage prediction model for calibration set was 0.886, and 0.869 for prediction set. The low-cost smart system provides a real-time, nondestructive tool to authenticate EBN for customers and retailers.

Keywords

Colorimetric sensor array
Edible bird's nest
Adulteration
Multi-layered network model
Smart system

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