Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Jiajie Li; Changlong Dai; Shuyu Li; Zhuqian Xiao; Wei Wang; Huilin Zheng; Hongpeng Wang; Jun Huang

doi:10.1016/j.foodchem.2026.150340

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Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Jiajie Li·Changlong Dai·Shuyu Li·Zhuqian Xiao·Wei Wang·Huilin Zheng·Hongpeng Wang·Jun Huang

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Abstract

Accurate detection of biogenic amines (BAs) in meat remains challenging due to their high structural similarity and co-occurrence. Herein, an Mn-N-C nanozyme was synthesized via a metal-organic framework confined pyrolysis strategy, possessing excellent oxidase (OXD)- and peroxidase (POD)-like activities. The dual enzyme-like activity showed Km values of 0.1584 mM (OXD) and 0.1498 mM (POD), respectively, in detection system. Leveraging these properties, a colorimetric sensor array was constructed, enabling the detection of four representative BAs within a concentration range of 2-10 ppm with 100% classification accuracy. In addition, a concentration independent recognition model based on an artificial neural network was developed to address signal nonlinearity interference in meat. The integrated system achieved accurate trace-level identification of BAs in perishable fish, pork, and chicken, demonstrating its applicability for early-stage BAs monitoring and quality deterioration warning during storage and transportation.

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Biogenic Amines Colorimetry Animals Meat Machine Learning Fishes Swine Chickens Biosensing Techniques Manganese Food Contamination Biogenic amines Colorimetric sensor arrays Machine learning Mn-N-C nanozyme Journal Article Evaluation Study

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Jiajie Li, Changlong Dai, Shuyu Li, Zhuqian Xiao, Wei Wang, Huilin Zheng, Hongpeng Wang, Jun Huang. 2026-07-09. Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.. https://doi.org/10.1016/j.foodchem.2026.150340

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Animals subject Biosensing Techniques subject Colorimetry subject Fishes subject Food Contamination subject Journal Article subject Machine Learning subject Meat subject

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