Surface texture characterization of an Italian pasta by means of univariate and multivariate feature extraction from their texture images
Surface texture characterization of an Italian pasta by means of univariate and multivariate feature extraction from their texture images
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DOI：
10.1016/j.foodres.2013.01.044
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发表时间：
2013-05
影响因子：
8.1
通讯作者：
L. Fongaro;K. Kvaal
中科院分区：
文献类型：
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作者：
L. Fongaro;K. Kvaal
表面质地是食品的一个重要特征，以及颜色，形状，稠度和味道。它在消费者的决策中起着重要的作用，它可以影响产品在制备过程中的性能。这项工作显示了三种不同的图像分析技术来表征三个意大利面食样品的表面纹理的能力。第一种方法是基于异质性评价（HTG）;第二种方法是基于灰度共生矩阵（GLCM）和Haralic统计量;第三种方法是基于图像多变量特征提取的角度测量技术（AMT）。所获得的结果表明，可以突出烹饪前后面食样品表面方面的差异，并且还可以将它们与它们的一些化学物理特性（例如，总淀粉和蛋白质含量、蒸煮水中的固体损失量、面食风味; r>0.6，p<0.05）。应用于GLCM和AMT结果的偏最小二乘判别分析（PLS-DA）允许仅基于其表面纹理特征对不同面食样品进行分类（灵敏度>0.963;特异性>0.648）。
Surface texture is an important characteristic of foods, as well as color, shape, consistency and taste. It plays an important role in consumers' decision and it can affect the properties of a product during its preparation. This work shows the ability of three different image analysis techniques to characterize the surface texture of three Italian pasta samples. The first method is based on the evaluation of Heterogeneity (HTG); the second on the gray level co-occurrence matrix (GLCM) and Haralic statistics; the third, the angle measure technique (AMT), is based on image multivariate feature extraction. The results obtained showed that it is possible to highlight differences in the surface aspect of pasta samples both before and after cooking, and that it is also possible to correlate them to some of their chemical–physical characteristics (e.g., total starch and protein contents, solids lost in the cooking water, pasta adhesiveness; r>0.6, p<0.05). A partial least square discriminant analysis (PLS-DA) applied on GLCM and AMT results allowed the classification of the different pasta samples only on the basis of their surface texture features (sensitivity>0.963; specificity>0.648).