Multimode optical imaging for identification of fish fillet substitution and mislabeling (Conference Presentation)

Fartash Vasefi; Rachel Isaacs; Stas Sokolov; Li Kang; Rosalee Hellberg; Daniel L. Farkas; Jianwei Qin; Diane E. Chan; Moon  S. Kim

doi:10.1117/12.2523224

14 May 2019 Multimode optical imaging for identification of fish fillet substitution and mislabeling (Conference Presentation)

Fartash Vasefi, Rachel Isaacs , Stas Sokolov, Li Kang, Rosalee Hellberg , Daniel L. Farkas , Jianwei Qin , Diane E. Chan , Moon S. Kim

Author Affiliations +

Proceedings Volume 11016, Sensing for Agriculture and Food Quality and Safety XI; 1101606 (2019) https://doi.org/10.1117/12.2523224
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Our goal is to analyze spectral imaging data using multiple optical imaging instruments available in USDA-ARS and SafetySpect laboratories to provide analysis along three axes of classification of fish fillets: 1. farm-raised vs. wild-caught species; 2. fresh vs. frozen fillets; 3. Species A vs. Species B targeting most mislabeled fish types in the US market. We are collecting spectral signatures using four imaging systems: (1) Reflectance spectral imaging in the visible and NIR (400-1000 nm), (2) Reflectance spectral imaging in the short wave infra-red (SWIR) (1000-2500 nm), (3) Fluorescence spectral imaging with UVA and violet illumination, (4) Raman imaging with a 785 nm laser excitation. The fish fillet samples were purchased from online vendors. We image with each of the modalities and then freeze, thaw and reimage each fillet (2 cycles of freeze/thaw) to demonstrate effect of freeze/thaw process in the multimode spectral signatures. All fish fillet samples are DNA tested to ensure the species marketed are not mislabeled. We use feature extraction/selection strategy for different modes of measurements based on the measurement physics and biological/chemical characteristics. We analyze different combinations of feature extraction and selection techniques and operate an exhaustive search, optimization, and fusion to find out the most important features using different imaging modes. This process helps identify which imaging mode (or combination) will have the highest impact and yield 95%+ classification accuracy. This optimization procedure will be based on cost function (sensitivity, specificity, area under the curve) from receiver operating characteristics (ROC) curve.

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Fartash Vasefi, Rachel Isaacs , Stas Sokolov, Li Kang, Rosalee Hellberg , Daniel L. Farkas , Jianwei Qin , Diane E. Chan , and Moon S. Kim "Multimode optical imaging for identification of fish fillet substitution and mislabeling (Conference Presentation)", Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 1101606 (14 May 2019); https://doi.org/10.1117/12.2523224

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KEYWORDS

Optical imaging

Imaging spectroscopy

Image processing

Imaging systems

Reflectivity

Luminescence

Near infrared

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