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A low cost smart system to analyze different types of edible Bird's nest adulteration based on colorimetric sensor array
Journal of Food and Drug Analysis ( IF 2.6 ) Pub Date : 2019-10-01 , DOI: 10.1016/j.jfda.2019.06.004 Xiaowei Huang 1 , Zhihua Li 1 , Xiaobo Zou 1 , Jiyong Shi 1 , Haroon Elrasheid Tahir 1 , Yiwei Xu 1 , Xiaodong Zhai 1 , Xuetao Hu 1
Journal of Food and Drug Analysis ( IF 2.6 ) Pub Date : 2019-10-01 , DOI: 10.1016/j.jfda.2019.06.004 Xiaowei Huang 1 , Zhihua Li 1 , Xiaobo Zou 1 , Jiyong Shi 1 , Haroon Elrasheid Tahir 1 , Yiwei Xu 1 , Xiaodong Zhai 1 , Xuetao Hu 1
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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.
中文翻译：
基于比色传感器阵列的不同类型食用燕窝掺假分析的低成本智能系统
本研究旨在开发一种低成本的智能系统，用于识别和量化掺假食用燕窝 (EBN)。智能系统由比色传感器阵列 (CSA)、智能手机和多层网络模型构成。CSA 用于收集 EBN 的气味特征，并通过智能手机系统捕获 CSA 的响应信号。主成分分析 (PCA) 和层次聚类分析 (HAC) 用于查询真实和掺假 EBN 之间的相似性。构建多层网络模型分析EBN掺假。在该模型中，使用反向传播神经网络 (BPNN) 算法实现了对真实 EBN 和掺假 EBN 的区分。然后，开发了另一个基于 BPNN 的模型来识别混合 EBN 中的掺杂物类型。最后，利用偏最小二乘法（PLS）建立了每种掺假EBN的掺假百分比预测模型。结果表明，真品EBN和掺假EBN的识别率高达90%。校准集的百分比预测模型的相关系数为0.886，预测集的相关系数为0.869。低成本的智能系统提供了一种实时、无损的工具来为客户和零售商验证 EBN。
更新日期：2019-10-01
中文翻译：
基于比色传感器阵列的不同类型食用燕窝掺假分析的低成本智能系统
本研究旨在开发一种低成本的智能系统，用于识别和量化掺假食用燕窝 (EBN)。智能系统由比色传感器阵列 (CSA)、智能手机和多层网络模型构成。CSA 用于收集 EBN 的气味特征，并通过智能手机系统捕获 CSA 的响应信号。主成分分析 (PCA) 和层次聚类分析 (HAC) 用于查询真实和掺假 EBN 之间的相似性。构建多层网络模型分析EBN掺假。在该模型中，使用反向传播神经网络 (BPNN) 算法实现了对真实 EBN 和掺假 EBN 的区分。然后，开发了另一个基于 BPNN 的模型来识别混合 EBN 中的掺杂物类型。最后，利用偏最小二乘法（PLS）建立了每种掺假EBN的掺假百分比预测模型。结果表明，真品EBN和掺假EBN的识别率高达90%。校准集的百分比预测模型的相关系数为0.886，预测集的相关系数为0.869。低成本的智能系统提供了一种实时、无损的工具来为客户和零售商验证 EBN。
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