Pork quality classification is supported by different reference standards that are widely reported in the literature. However, selecting the most suitable standard for each type of meat samples remains a challenge, due to their intrinsic variation according to the quality parameters’ interval. The usage of meta-learning was proposed to automatically recommend the most adequate standard for a determined sample collection, leading to a more accurate classification. The meta-learning procedure has emerged from the machine learning research field to solve the algorithm selection dilemma, outlining a new method for pork quality classification. The applicability and advantages of using a suitable classification standard for pork quality were addressed using the J48 Decision Tree (DT) algorithm, which serves as the meta-recommender. Experiments conducted with six pork standards revealed promising results based on a few meta-attributes (L∗, water hold capacity, and dataset entropy) as the approach successfully recommended all scenarios.

猪肉质量分类得到了文献中广泛报道的不同参考标准的支持。然而，为每种类型的肉类样品选择最合适的标准仍然是一个挑战，因为它们根据质量参数区间的内在变化。建议使用元学习为确定的样本集合自动推荐最合适的标准，从而实现更准确的分类。元学习程序从机器学习研究领域出现，以解决算法选择难题，概述了猪肉质量分类的新方法。使用作为元推荐的 J48 决策树 (DT) 算法解决了使用合适的猪肉质量分类标准的适用性和优势。L *、持水能力和数据集熵），因为该方法成功推荐了所有场景。