Prediction of texture in different beef cuts applying image analysis technique

Abstract

Purpose: Measuring texture parameters are time consuming and expensive; it is necessary to develop an efficient and rapid method to evaluate them. Image analysis can be a useful tool. The purpose of this paper is to predict texture parameters in different beef cuts applying image analysis techniques. Design/methodology/approach: Samples were analyzed by scanning electron microscopy. Texture parameters were analyzed by instrumental, image analysis techniques and by Warner–Bratzler shear force. Findings: Significant differences (p<0.05) were obtained for image and instrumental texture features. Higher amount of porous were observed in freeze dried samples of beef cuts from Gluteus Medius and semintendinosus muscles. A linear trend with a linear correlation was applied for instrumental and image texture. High correlations were found between image and instrumental texture features. Instrumental parameters showed a positive correlation with image texture feature. Originality/value: This research suggests that the addition of image texture features improves the accuracy to predict texture parameter. The prediction of quality parameters can be performed easily with a computer by recognizing attributes within an image.