A rapid, low-cost method based on the combination of impedance and physicochemical properties was established to distinguish between fresh and frozen thawed chicken breast muscles. The method involved determining the influence of freeze thaw cycles on chicken breast. Freeze thaw cycles were grouped into four (0, 1, 2, and 3), and impedance and physicochemical properties were investigated. Results showed that most quality traits such as pH, cooking loss, expressible loss, shear force, modulus, and textural properties were affected by freeze thaw cycles (P < 0.05). Cooking loss, pH, hardness, chewiness, elasticity, and resilience decreased with increased freeze thaw cycle (P < 0.05). The microstructure of samples and zeta potential of the exudates of the four groups were also examined, and the impedance changes resulted from the ionic charge loss that occurred in the exudates because of the freeze thaw process. Chewiness and expressible loss were positively correlated with impedance parameters, whereas hardness. was negatively correlated with impedance. A much higher prediction accuracy was obtained from the learning vector quantization neural network, showing that physicochemical properties could be added to the prediction to improve classification accuracy. Therefore, the impedance system combined with other physicochemical properties could be applied to classify chicken breasts with different freeze thaw cycles. (C) 2016 Elsevier Ltd. All rights reserved.