Brazilian livestock with a herd of more than 215 million animals is distributed over a vast area of 160 million hectares, leading the country to the first position in the world beef exports and second in beef production and consumption. Animals risen in the biomes Amazônia, Caatinga, Cerrado, Pampa and Pantanal were selected for this study. Beef samples were analyzed for their elemental content by neutron activation analysis and classified according to their origin by three machine learning algorithms (Multilayer Perceptron, Random Forest and Classification and Regression Tree). Significant differences (p < 0.0001) were observed between the beef elemental content from the different biomes for all multivariate contrasts using NPMANOVA. The highest classification performance was obtained for the biomes Amazônia and Caatinga using Multilayer Perceptron. Results showed the feasibility of combining trace element content and machine learning approaches for the Brazilian beef traceability.

巴西牲畜存栏超过2.15亿只，分布在1.6亿公顷的广阔土地上，使该国在世界牛肉出口中排名第一，在牛肉生产和消费中排名第二。选择了在亚马孙，卡廷加，塞拉多，潘帕和潘塔纳尔湿地生物群落中饲养的动物。通过中子活化分析对牛肉样品的元素含量进行分析，并通过三种机器学习算法（多层感知器，随机森林和分类回归树）根据其来源对牛肉进行分类。使用NPMANOVA，对于所有多元对比，在来自不同生物群系的牛肉元素含量之间观察到了显着差异（p <0.0001）。使用多层感知器对生物群落亚马逊和凯丁加获得了最高的分类性能。结果表明，将微量元素含量与机器学习方法相结合可提高巴西牛肉的可追溯性。