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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.

However, feature parameter extraction region of duck egg image could affect the identification results of model and the speed of image processing. Therefore, the image skeleton-maximum inscribed circle algorithm and Hough gradient algorithm (2-1HT) were proposed to segment duck egg image feature paramer region. Support vector machine (SVM) and naive bayes (NB) models were established to evaluate accuracy of feature parameters extracted by different algorithms. The comparison shows that the SVM model based on feature parameters extracted by image skeleton maximum inscribed circle algorithm has the best discriminant performance, with the value of Q, SE, and SP being 98.81%, 98.44%, and 100.00%, respectively. Moreover, the process speed and prediction accuracy of image skeleton-maximum inscribed circle algorithm were much higher than that of Hough gradient algorithm. Practical ApplicationsTherefore, optimizing the image feature parameter extraction algorithm of a single duck egg in grouped duck eggs was of great significance to improve the discrimination accuracy of the model and realize the automatic recognition of infertile duck eggs.

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