Journal article
In‐Situ Detection of Microplastic Particles on Food Using Hyperspectral Imaging With One‐Dimensional Convolutional Neural Network and Artificial Neural Network
Journal of chemometrics, v 40(2), e70088
Feb 2026
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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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Details
- Title
- In‐Situ Detection of Microplastic Particles on Food Using Hyperspectral Imaging With One‐Dimensional Convolutional Neural Network and Artificial Neural Network
- Creators
- Nikhita Sai Nayani - Virginia TechRan Yang - Virginia Sea GrantYue Sun - Virginia Sea GrantLihong Yang - Virginia Sea GrantLifeng Zhou (Corresponding Author) - Drexel UniversityYiming Feng - Virginia Tech
- Publication Details
- Journal of chemometrics, v 40(2), e70088
- Publisher
- Wiley
- Number of pages
- 12
- Grant note
- 4-VA partnershipUSDA National Institute of Food and Agriculture: VA-160246
This work is supported by the 4-VA grant and USDA National Institute of Food and Agriculture, Hatch project VA-160246. This research was also funded in part by 4-VA, a collaborative partnership for advancing the Commonwealth of Virginia.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001698382200003
- Scopus ID
- 2-s2.0-105028157242
- Other Identifier
- 991022160223904721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Chemistry, Analytical
- Computer Science, Artificial Intelligence
- Instruments & Instrumentation
- Mathematics, Interdisciplinary Applications
- Statistics & Probability