Fagan C C, Du C J, O'Donnell C P, Castillo M, Everard C D, O'Callaghan D J, Payne F A
Biosystems Engineering, UCD School of Agriculture, Food Science and Veterinary Medicine, Univ. College Dublin, Earlsfort Terrace, Dublin 2, Ireland.
J Food Sci. 2008 Aug;73(6):E250-8. doi: 10.1111/j.1750-3841.2008.00814.x.
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.
一种可用于在线监测乳清析出的非侵入性技术，将有助于生产出质量更高、更稳定的奶酪产品。诸如图像纹理分析等计算机视觉技术已成功确立为用于测定食品质量的快速、稳定且无损的工具。在本研究中，对图像纹理分析监测搅拌罐中凝乳乳清析出的潜力进行了研究。采用了完全随机的二因素（牛奶pH值和搅拌速度）、二水平析因设计，并重复进行了三次。在乳清析出过程中，使用计算机视觉系统捕捉搅拌的凝乳 - 乳清混合物表面的图像。对这些图像采用了5种图像纹理分析方法，从中提取了109个图像纹理特征。观察到许多图像纹理特征与凝乳水分和乳清固体之间存在显著相关性。分形维数和小波变换的多尺度分析技术被证明对预测乳清析出指数最为有用。分形维数特征预测乳清析出过程中的凝乳水分和乳清固体，预测标准误差分别为1.03%（w/w）和0.58 g/kg。得出的结论是，乳清析出指数与多尺度表示的图像纹理特征关系最为密切。本研究结果表明，图像纹理分析在监测乳清析出方面具有潜力。
