Application of image texture analysis for online determination of curd moisture and whey solids in a laboratory-scale stirred cheese vat.

A noninvasive technology, which could be employed online to monitor syneresis, would facilitate the production of higher quality and more consistent cheese products. Computer vision techniques such as image texture analysis have been successfully established as rapid, consistent, and nondestructive tools for determining the quality of food products. In this study, the potential of image texture analysis to monitor syneresis of cheese curd in a stirred vat was studied. A fully randomized 2-factor (milk pH and stirring speed), 2-level factorial design was carried out in triplicate. During syneresis, images of the surface of the stirred curd-whey mixture were captured using a computer vision system. The images were subjected to 5 image texture analysis methods by which 109 image texture features were extracted. Significant correlations were observed between a number of image texture features and curd moisture and whey solids. Multiscale analysis techniques of fractal dimension and wavelet transform were demonstrated to be the most useful for predicting syneresis indices. Fractal dimension features predicted curd moisture and whey solids during syneresis with standard errors of prediction of 1.03% (w/w) and 0.58 g/kg, respectively. It was concluded that syneresis indices were most closely related to the image texture features of multiscale representation. The results of this study indicate that image texture analysis has potential for monitoring syneresis.

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