Kapitel/Beitrag aus dem Buch: Beyerer, J et al. 2023. OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings.
This study explored the possibility of detecting different types of meat in a miniaturized patty by applying a random forest classifier on the spectral dimension followed by neighborhood majority voting on the spatial dimension to improve the random forest prediction. Hyperspectral images of patties made of 100% beef, 100% pork, and 100% horse meat were acquired with a short-wave infrared (SWIR) hyperspectral camera. The pixel-wise meat type prediction by random forest multi-class classifier was accurate to 97.5%. After the majority voting of the neighboring pixels, the prediction accuracy increased to 100%. As next, synthetic hyperspectral images of adulterated patties were generated for validating the model. The prediction accuracy of the model on the synthetic images were bigger than 98%. The findings of the proposed workflow support the development of rapid analysis tools in tandem with machine-learning to detect adulteration in minced meat.
Djaw, E et al. 2023. Method Development for Spatially Resolved Detection of Adulterated Minced Meat. In: Beyerer, J et al (eds.), OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000155014-6
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Veröffentlicht am 25. Mai 2023