Abstract: Shelf life is an important indicator for evaluating the freshness of mutton, which is directly related to its quality. To explore the application prospects of bioimpedance technology for shelf-life detection of food, a nondestructive and efficient shelf-life detection method was proposed for ready-to-prepare mutton. Combining the key factors affecting the change of freshness of ready-to-prepare mutton and the measurement principle of bioimpedance, the electrodes were designed independently for measuring bioimpedance according to the different testing conditions such as the number of electrodes, electrode materials, and electrode arrangement. The changes of impedance and TVB-N content of ready-to-prepare mutton at three storage temperatures of 0℃, 4℃ and 8℃ and the correlation of impedance with TVB-N content and shelf life were revealed; a shelf-life prediction model and evaluation method of ready-to-prepare mutton based on BP neural network was established with TVB-N content as the key indicator, and it was compared with SVM（support vector machine） model and decision tree model. The F1-score of the BP neural network model was up to 95.9%. Based on the BP neural network model established above, a shelf-life detection system of ready-to-prepare mutton was developed by using Java language, which realized user-friendly data visualization and real-time detection of the shelf life of ready-to-prepare mutton. The research result can provide theoretical basis and software tool for the rapid and nondestructive detection of the shelf life of ready-to-prepare mutton, which can ensure the quality and safety of ready-to-prepare mutton and promote the sustainable and healthy development of the food industry.