Enhancing food safety: real-time detection of foreign materials using high-speed NIR hyperspectral imaging

Seung-Chul Yoon; Zirak Khan

doi:10.1117/12.3013745

7 June 2024 Enhancing food safety: real-time detection of foreign materials using high-speed NIR hyperspectral imaging

Seung-Chul Yoon, Zirak Khan

Proceedings Volume PC13060, Sensing for Agriculture and Food Quality and Safety XVI; PC1306002 (2024) https://doi.org/10.1117/12.3013745
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

This study focuses on the development of real-time inline sensing technology using high-speed hyperspectral imaging (HSI) and high-performance Deep Learning (DL) for the detection of Foreign Materials (FMs) on chicken breast meat. HSI and DL are useful tools for assessing agricultural and food products' safety and quality features. However, most HSI-based DL models for food quality and safety assessment lack real-time sensing capabilities critical for industrial deployment, due to the high computational demands of both HSI and DL. Therefore, we propose near-infrared hyperspectral imaging (NIR-HSI) coupled with a DL model based on a semi-supervised generative adversarial network, suitable for high-speed food production applications such as real-time inline sensing for detecting FMs during poultry processing. A line-scan NIR-HSI camera (1000-1700 nm) is used for data acquisition. For real-time imaging and DL inferencing, the software system is implemented in C++. This technology will enable the rapid and accurate detection of FMs in hyperspectral images, suggesting applications in other food products.

Conference Presentation

Citation Download Citation

Seung-Chul Yoon and Zirak Khan "Enhancing food safety: real-time detection of foreign materials using high-speed NIR hyperspectral imaging", Proc. SPIE PC13060, Sensing for Agriculture and Food Quality and Safety XVI, PC1306002 (7 June 2024); https://doi.org/10.1117/12.3013745

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