Meta-recommendation of pork technological quality standards

Abstract

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.