Data-Mining Methodology to Improve the Scientific Production Quality in Turkey Meat and Carcass Characterization Studies.

Simple Summary: Simple Summary: Currently, research on livestock production suffers from isolation from other disciplines and a generalist nature, which makes publishing in top-tier journals a very difficult task. This situation is even more drastic when it comes to turkey meat research, which is an underdeveloped area that has historically suffered from a lack of resources compared to other species. For this reason, the aim of the present study is to develop a tool that allows researchers to determine which carcass and meat quality traits are related to increased interest by the scientific community and the quality standards of the journals in which studies are published. Variables improving journal standards include carcass dressing traits, muscle fibers properties, pH, colorimetry, some texture and water captivity traits, and chemical composition. Contrarily, carcass or piece yield is not a recommended variable to be performed in studies, as this parameter did not show a clear impact on publication quality. Finally, measures after 72 h are contraindicated since they showed a correlation with poor journal quality standards. Thus, this work can be used as a guideline for designing turkey carcass and meat quality studies, describing parameters to prioritize in order to maximize the impact quality of publication in the scientific community. The present research aims to describe how turkey meat and carcass quality traits define the interest of the scientific community through the quality standards of journals in which studies are published. To this end, an analysis of 92 research documents addressing the study of turkey carcass and meat quality over the last 57 years was performed. Meat and carcass quality attributes were dependent variables and included traits related to carcass dressing, muscle fiber, pH, colorimetry, water-holding capacity, texture, and chemical composition. The independent variables comprised publication quality traits, including journal indexation, database, journal impact factor (JIF), quartile, publication area, and JIF percentage. For each dependent variable, a data-mining chi-squared automatic interaction detection (CHAID) decision tree was developed. Carcass or piece yield was the only variable that did not show an impact on the publication quality. Moreover, color and pH measurements taken at 72 h postmortem showed a negative impact on publication interest. On the other hand, variables including water-retaining attributes, colorimetry, pH, chemical composition, and shear force traits stood out among the quality-enhancing variables due to their low inclusion in papers, while high standards improved power. [ABSTRACT FROM AUTHOR]