Prediction of tenderness of chicken by using viscoelasticity based on airflow and optical technique

Tenderness is an index for evaluating meat quality. A prediction model of tenderness was established based on the chicken deformation, which was determined by a viscoelasticity system combined with airflow and optical technique. Different preprocessing methods were used to preprocess the deformation. The interval variables that represent the viscoelasticity of the chicken in deformation, were screen by synergy interval partial least squares algorithm (Si-PLS) and moving window partial least squares algorithm (Mw-PLS). The prediction model was established by principal component regression (PCR) and partial least squares regression (PLSR). The optimum PLSR prediction model was established when Mw-PLS was used to screen the interval variables of Savitzy-Golay (S-G) smoothing data. The correlation coefficient and the root mean square error of the calibration set were 0.965 and 0.874 kg, respectively. The corresponding value of the prediction set was 0.943 and 1.005 kg. This research provides a new method to assess the quality of poultry meat that conducts on airflow and optical techniques.