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In‐Situ Detection of Microplastic Particles on Food Using Hyperspectral Imaging With One‐Dimensional Convolutional Neural Network and Artificial Neural Network
Journal article   Open access   Peer reviewed

In‐Situ Detection of Microplastic Particles on Food Using Hyperspectral Imaging With One‐Dimensional Convolutional Neural Network and Artificial Neural Network

Nikhita Sai Nayani, Ran Yang, Yue Sun, Lihong Yang, Lifeng Zhou and Yiming Feng
Journal of chemometrics, v 40(2), e70088
Feb 2026
DOI: https://doi.org/10.1002/cem.70088
url
https://doi.org/10.1002/cem.70088View
Published, Version of Record (VoR) Open

Abstract

detection hyperspectral imaging microplastic neural network
Hyperspectral imaging (HSI) has emerged as a promising technique for microplastic detection through analysis of reflectance variations across multiple wavelengths. Traditional approaches have focused primarily on isolated microplastic particles, requiring labor‐intensive separation procedures impractical for routine monitoring. The challenge of detecting microplastics directly on food surfaces stems from spectral similarities between microplastics and food matrices, making differentiation difficult using conventional methods. Leveraging recent advances in machine learning, this study explores how artificial neural networks (ANN) and one‐dimensional convolutional neural networks (1D‐CNN) can identify subtle spectral differences to detect microplastic particles on seafood without isolation. We systematically evaluated model architectures, preprocessing techniques, and hyperparameter configurations to optimize detection performance using hyperspectral data from tilapia samples contaminated with polyethylene microspheres. Our findings demonstrate that 1D‐CNN models trained on hyperspectral data without dimensionality reduction significantly outperform other approaches, achieving object‐level detection F1 scores of 0.963 for 600‐μm particles and 0.950 for 300‐μm particles. This detection strategy represents a substantial improvement over traditional methods and highlights the potential of deep learning–based approaches for non‐destructive, efficient microplastic detection in food safety applications.

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