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7 June 2024 Multimode spectroscopy for food quality, adulteration, and traceability (QAT) applications
Hossein Kashani Zadeh, Mitchell Sueker, Sayed Asaduzzaman, Nicholas MacKinnon, Gregory Bearman, Jianwei Qin, Rosalee S. Hellberg, Fartash Vasefi, Kaylee Husarik, Moon S. Kim, Kouhyar Tavakolian
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Proceedings Volume PC13060, Sensing for Agriculture and Food Quality and Safety XVI; PC130600C (2024) https://doi.org/10.1117/12.3014191
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States
Abstract
Food deception is a worldwide concern. Incorrect labeling of fish, costing the US $13B with 40% inaccurately tagged, underscores this concern's magnitude. Additionally, 53M tons of wasted meat and poultry each year exacerbate the situation. The FDA's proactive steps, with rigorous data and traceability rules, target this problem. Still, a straightforward authentication process for the supply chain remains elusive. The repercussions, surpassing the annual $40 Billion financial burden, also pose health dangers, erode consumer confidence, and devalue brands. Responding to this need, we present an innovative portable multi-spectroscopy tool for Quality Adulteration and Traceability (QAT). Utilizing fluorescence at 365 and 405 nm, VisNIR, and SWIR, this tool aids in identifying fish types and evaluating freshness. Various machine learning techniques were employed on this data. The promising outcomes in distinguishing fish types and gauging freshness hint at the potential of this spectroscopy approach to replace traditional, expensive lab procedures.
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Hossein Kashani Zadeh, Mitchell Sueker, Sayed Asaduzzaman, Nicholas MacKinnon, Gregory Bearman, Jianwei Qin, Rosalee S. Hellberg, Fartash Vasefi, Kaylee Husarik, Moon S. Kim, and Kouhyar Tavakolian "Multimode spectroscopy for food quality, adulteration, and traceability (QAT) applications", Proc. SPIE PC13060, Sensing for Agriculture and Food Quality and Safety XVI, PC130600C (7 June 2024); https://doi.org/10.1117/12.3014191
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KEYWORDS
Spectroscopy
Data modeling
Machine learning
Performance modeling
Quality systems
Reflectivity
Safety
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