# Can Bioelectrical Impedance Analysis (BIA) Be Used to Predict Pig's Meat Quality In Vivo?
## 3 Results and Discussion
Cox et al. [39] indicated that making the relation between the variables of BIA and the body composition of animals is possible. The principle is the laboratory evaluation of the moisture level, protein, mineral, and lipids content The position of the electrodes is crucial, and it is recognized that the more accurate BIA results are obtained with the electrodes placed on the back of the animals compared to the ventral readings. According to Bertotti [40], right after the slaughter, there are no significant changes in the BIA, even considering temperature variation. However, when the carcass starts to cool, biochemical changes in the cell membranes occur due to the rigor mortis effect, as the carcass loses its ionic gradients with the rise of the temperature and the time-lapse of aging. In other studies, Daza et al. [34] demonstrated a positive correlation between the resistance, swine live weight, and amount of content of fat. Moreover, Alfonso et al. [41] found that the parameters assessed by BIA can be used for meat characteristics in the combination of Rs and Xc to predict intramuscular fat, demonstrating 79.3% of adjustment, while, for the physicochemical characteristics, the best adjustments were in the length of the sarcomere with 64.4% and sheer force of 60.5. The obtained correlations can lead to the conclusion that bioelectrical impedance can be useful for meat quality prediction, but it needs additional research because the relationship is not simple for interpretations. The other correlations between BIA and the analyzed features were not statistically significant. Results of research conducted by Daza et al. [34] on live Iberian pigs showed that bioimpedance measurements were correlated with live \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{4}{c}{**Pearson Correlations**} & \multicolumn{4}{c}{**Spearman Correlations**} \\ \cline{2-9}  & **Rz/Xc/HCW** & **Rz/Xc** & **Rz (f)** & **Xc (f)** & **Rz/Xc/HCW** & **Rz/Xc** & **Rz (f)** & **Xc (f)** \\ \hline pH\({}_{1}\) & \(-\)0.06 & \(-\)0.03 & 0.48 * & 0.29 & \(-\)0.03 & 0.48 * & 0.29 & \(-\)0.03 \\ Color b* & \(-\)0.40 & \(-\)0.39 & \(-\)0.32 & 0.35 & \(-\)0.62 * & 0.36 & 0.44 & \(-\)0.51 * \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The correlation between BIA variables and measurement meat quality traits (n = 18). weight and length values and body composition, such as fat quantity and fat proportion, bone amount and bone percentage, and skin quantity and skin proportion. As indicated by Bohuslavek et al. [18], after the improvement of the proportionality of adjusted distance electrodes to carcass length, the achieved results can be more favorable. Thorough research showed that BIA technology can be an accurate predictor of beef carcass composition. This work showed that it was possible to derive a regression formula (r = 0.8) for an estimate of carcass conformation on the basis of BIA analysis [18]. Zollinger et al. [42] developed equations to predict % salable carcass yield (SY%) and percentage trimmable fat (FT%). The obtained equations accounted for 81% and 84% of the variation in SY% and FT%, respectively. In our study, we applied the statistical method of analysis (PCA) to better understand the relationship between the measured traits in a multidimensional space. The results showed that two main components explained about 50% of the total variability in the set of variables (Figure 1). The first component that explained 33,18% of a total variability is strongly negatively associated with color b* value, PG, and Xc and, on the other hand, is positive associated with IMF, Rz/X/MTC, and Rz/Xc values (Table 3 and Figure 1). The second component explained about 17% of total variability and was strongly associated with pH1, pH24, and color L* and b* values (Figure 1 and Table 3). Similar effects were obtained by Suliga et al. [35] in the study of application of bioimpedance measurements as a marker for ham defects.