Validation of a macroscopic classification at the processing plant of breast muscles affected by growth-related abnormalities through meat quality evaluation in broilers

The present study was developed as preliminary research within the PRIN PACHOL4 project, which aims at investigating the molecular basis of growth-related abnormalities (e.g., wooden breast; WB) affecting the P. major muscle (PM) of fast-growing (FG) broilers and impairing the quality traits of affected meat. An accurate classification of PM is required to investigate factors ascribable to the onset of these abnormalities, thus avoiding misleading results. This work was thus performed to validate the macroscopic classification of PMs (as affected and unaffected by WB) to be further investigated. PMs belonging to three homogenous flocks (Ross 308 males45-d old 3.2 kg of live weight; ADWG>85g) were collected at a commercial processing plant 3h post-mortem and classified relying on their macroscopic traits as normal (NORM, 30 PM/flock)and abnormal (ABN, severe WB; 30 PM/flock). Then, the main quality traits and technological properties were assessed: ultimate pH (pHu), color (L*, a*, and b*), drip loss, cooking loss, and shear force. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied using meat quality data to validate the sample classification implemented at the slaughterhouse. The effect of WB on the quality traits of chicken PMs (N=180; 30PMs/group/flock) was tested by Student t-test using STATISTICA 10 (StatSoft). PCA and HCA analyses were performed in R environment (v.4.3.2). As expected, meat quality evaluation evidenced significant differences (P<.05) between NORM and ABN concerning the pHu, redness (a*), cooking loss and shear force. PCA allowed to explore the meat quality traits ofNORM and ABN samples and identified pHu, lightness (L*), cooking loss, and shear force as potentially useful for sample classification. HCA showed sample clustering partially resembling the macroscopic classification implemented at the processing plant. Overall, the present results allowed us to perform a more accurate classification of PM samples to be selected for further analysis. The research was supported by MUR, PRIN 2022 PACHOL4 (Prot. n. 2022EPWEPW).