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. • Meta-learning usage in pork quality evaluations. • L∗, WHC and dataset entropy were the most important meta-features. • We provide a comprehensive identification of porks standards. • Understanding of causes of higher incidence in poor meat quality categories.
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