Optical polarimetry has been used to characterize muscle tissue samples of chicken, beef and mutton, exhibiting statistically significant (p < 0.01) differences in total depolarization and retardance of three tissue groups. Herein, the total depolarization and retardance were utilized to differentiate and classify the three tissue groups. Specifically, the Bagging classification algorithm was employed for this multi-class differentiation. The performance of the optical polarimetry in tandem with the Bagging model for machine-assisted classification of the three tissue groups was assessed in terms of a comprehensive set of evaluation metrics. The Bagging model correctly classified 47/48, 19/20 and 15/18, whereas the sensitivity (Sn = 97.9 %, 82.6 %, 100 %), specificity (Sp = 97.4 %, 98.4 %, 95.8 %), positive predictive values (PPV = 0.97, 0.95, 0.83) and negative predictive values (NPV = 0.97, 0.94, 1.0) were calculated for the chicken, beef and mutton tissue samples, respectively. This automatic classification of the three tissue samples indicates a novel application of the optical polarimetry in the meat industry.
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