Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

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http://hdl.handle.net/10138/320243

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Ayaz, H.; Ahmad, M.; Sohaib, A.; Yasir, M.N.; Zaidan, M.A.; Ali, M.; Khan, M.H.; Saleem, Z. Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging. Appl. Sci. 2020, 10, 6862.

Title: Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging
Author: Ayaz, Hamail; Ahmad, Muhammad; Sohaib, Ahmed; Yasir, Muhammad Naveed; Zaidan, Martha A.; Ali, Mohsin; Khan, Muhammad Hussain; Saleem, Zainab
Publisher: Multidisciplinary Digital Publishing Institute
Date: 2020-09-29
URI: http://hdl.handle.net/10138/320243
Abstract: Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400&ndash;1000 nm) based non-destructive <i>isos-bestic</i> myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky&ndash;Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of <inline-formula><math display="inline"><semantics><mrow><mn>88.88</mn><mo>%</mo></mrow></semantics></math></inline-formula>.


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