a Department of Health, Animal Science and Food Safety , Università degli Studi di Milano , Milan , Italy.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2013;30(10):1745-51. doi: 10.1080/19440049.2013.821715. Epub 2013 Aug 2.
The aims of this study were (1) to evaluate the potential of image analysis measurements, in combination with the official analytical methods for the detection of constituents of animal origin in feedstuffs, to distinguish between poultry versus mammals; and (2) to identify possible markers that can be used in routine analysis. For this purpose, 14 mammal and seven poultry samples and a total of 1081 bone fragment lacunae were analysed by combining the microscopic methods with computer image analysis. The distribution of 30 different measured size and shape bone lacunae variables were studied both within and between the two zoological classes. In all cases a considerable overlap between classes meant that classification of individual lacunae was problematic, though a clear separation in the means did allow successful classification of samples on the basis of averages. The variables most useful for classification were those related to size, lacuna area for example. The approach shows considerable promise but will need further study using a larger number of samples with a wider range.
(1)评估图像分析测量与饲料中动物源性成分检测的官方分析方法相结合的潜力,以区分家禽与哺乳动物;(2)确定可用于常规分析的可能标志物。为此,通过将微观方法与计算机图像分析相结合,对 14 个哺乳动物和 7 个家禽样本以及总共 1081 个骨碎片腔进行了分析。研究了 30 种不同测量尺寸和形状的骨腔变量在两个动物类群内和之间的分布情况。在所有情况下,类群之间的重叠都相当大,这意味着单个腔的分类存在问题,尽管平均值的明确分离确实允许基于平均值成功地对样本进行分类。最有用的分类变量与大小有关,例如腔面积。该方法显示出很大的前景,但需要使用更多样本和更广泛的范围进行进一步研究。