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Machine learning combined with electrochemical sensor for rapid detection of Sudan Red I in food

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Journal of Food Measurement and Characterization Aims and scope Submit manuscript

Abstract

Ensuring food safety necessitates rapid and reliable food contaminant detection methods. Here, we present a novel approach that integrates electrochemical sensors with machine learning to detect Sudan Red I in food. Our study showcases a modified stainless steel needle (SSN) electrode adorned with silver nanoparticles (AgNPs) and investigates three distinct convolutional neural network (CNN) architectures: Inception V1, ResNet-50, and SqueezeNet V1.1. This combination aims to predict the sensor response accurately. The AgNPs/SSN electrode displays heightened electrochemical activity towards Sudan Red I, offering a linear detection range of 0.1–20 μM and an exceptional 10 nM detection limit. Notably, the Inception V1 and ResNet-50 networks exhibit superior predictive capability compared to SqueezeNet V1.1. Our interdisciplinary approach establishes a rapid, sensitive, and dependable detection system for food contaminants. By fusing electrochemical sensors with machine learning, we propel the development of intelligent detection systems with significant implications for food safety enhancement.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by Natural Science Foundation of Henan (222300420250); Municipal Science and Technology Plan Project of Nanyang City (JCQY009); Interdisciplinary Sciences Project, Nanyang Institute of Technology; Doctoral Research Start-up Fund of Nanyang institute of Technology (NGBJ-2020-11); Cooperative Scientific Research Project of Chunhui Plan of the Ministry of Education of China (202200696).

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  1. School of Information Engineering, Nanyang Institute of Technology, Nanyang, 473004, China

    Xi Sun, Feng Liu & Xiao Xue

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  1. Xi Sun
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  2. Feng Liu
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Correspondence to Xiao Xue.

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Sun, X., Liu, F. & Xue, X. Machine learning combined with electrochemical sensor for rapid detection of Sudan Red I in food. Food Measure 18, 95–104 (2024). https://doi.org/10.1007/s11694-023-02150-w

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