Frost-resistant hydrogel colorimetric sensing array for accurate detection of foodborne pathogens in cold chain systems

Yu Zhang; Meng-Xian Liu; Yong-Liang Yu; Shuai Chen

doi:10.1016/j.bios.2024.116990

Frost-resistant hydrogel colorimetric sensing array for accurate detection of foodborne pathogens in cold chain systems

Biosens Bioelectron. 2024 Nov 26:271:116990. doi: 10.1016/j.bios.2024.116990. Online ahead of print.

Authors

Yu Zhang¹, Meng-Xian Liu², Yong-Liang Yu¹, Shuai Chen³

Affiliations

¹ Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang, 110819, China.
² Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Miyagi, Japan.
³ Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang, 110819, China; Foshan Graduate School of Innovation, Northeastern University, Foshan, 528311, China. Electronic address: chenshuai@mail.neu.edu.cn.

PMID: 39616899
DOI: 10.1016/j.bios.2024.116990

Abstract

The rapid and precise identification of foodborne pathogens in low-temperature environments is critically important yet challenging, particularly within the cold chain system. This study introduces a frost-resistant colorimetric sensing array (FR-CSA), based on polyvinyl alcohol/polyacrylamide/lithium chloride (PVA/PAM/LiCl) double network (DN) hydrogels, designed for the detecting and classifying foodborne pathogens at 4 °C and -20 °C. The integration of LiCl into the PVA/PAM DN hydrogels results in a dense 3D nano-network that significantly lowers the freezing point, enhancing the sensing functionality at subzero temperatures, addressing a critical gap where conventional CSAs fail to perform. The FR-CSA demonstrates high performance, accurately responding to twelve common volatile organic compounds (VOCs) emitted by pathogens and generating distinctive color response patterns. Employing principal component analysis (PCA) and linear discriminant analysis (LDA), the FR-CSA effectively identifies four prevalent low-temperature foodborne pathogens: Staphylococcus aureus, Listeria monocytogenes, E. coli O157:H7, and Salmonella. Additionally, the FR-CSA has been successfully applied to a chicken breast meat model, confirming its efficacy across the tested temperature range. This work presents an innovative approach for pathogen detection in critical low-temperature settings of cold storage, offering significant potential contributions to food preservation within the cold chain system.

Keywords: Colorimetric sensor array; Foodborne pathogen; Frost-resistant hydrogel; Low-temperature detection.