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

Xiaowei Huang; Zhihua Li; Xiaobo Zou; Jiyong Shi; Haroon Elrasheid Tahir; Yiwei Xu; Xiaodong Zhai; Xuetao Hu

doi:10.1016/j.jfda.2019.06.004

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

J Food Drug Anal. 2019 Oct;27(4):876-886. doi: 10.1016/j.jfda.2019.06.004. Epub 2019 Jul 16.

Authors

Xiaowei Huang¹, Zhihua Li¹, Xiaobo Zou¹, Jiyong Shi¹, Haroon Elrasheid Tahir¹, Yiwei Xu¹, Xiaodong Zhai¹, Xuetao Hu¹

Affiliation

¹ School of Agricultural Equipment Engineering, School of Food and Biological Engineering, Jiangsu University, 301, Xuefu Rd., 212013, Zhenjiang, Jiangsu, China.

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

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Cluster Analysis
Colorimetry / economics*
Neural Networks, Computer
Principal Component Analysis

Grants and funding

This study was funded by the National Key Research and Development Program of China (2017YFC1600806); National Natural Science Foundation of China (31601543, 31801631, 31671844); Natural Science Foundation of Jiangsu Province (BK20160506, BK20180865).