Comparison of accuracy of intramuscular fat prediction in live pigs using five different ultrasound intensity levels

The objective of this study was to evaluate the possibility of prediction of intramuscular fat (IMF) in live pigs using ultrasound method. Moreover, the accuracy of prediction at five different ultrasound intensity levels was investigated. Cross-sectional images of longissimus dorsi muscle (LD) at right last rib area, from hybrid pigs, were taken. Each pig was scanned at the same frequency (3.5 MHz) and at the five different ultrasound intensity levels 70%, 75%, 80%, 85% and 90% of total amplifying of sonograph, using the device ALOKA SSD-500. The video image analysis was used to predict IMF content (ultrasound intramuscular fat (UIMF) 70 to UIMF90). The second day after slaughter, the dissection of right half carcass was done. A sample of LD at the last rib was taken for laboratory analysis of IMF content (LAIMF). Scatter plots with UIMF on the x-axis and LAIMF on the y-axis were constructed to account for individual variability within and between intensity levels. Correlations between LAIMF and UIMF were significantly different from zero (r = 0.40–0.52), except for correlation between LAIMF and UIMF90 (r = 0.14). Statistical model with LAIMF (the dependent variable), UIMF (the same model for each intensity level), live weight (the covariates) and sex (the fixed effect) was developed. Coefficients of determination (R²) were 0.33, 0.38, 0.34, 0.25 and 0.17 with UIMF at the intensity level 70%, 75%, 80%, 85% and 90%. Root mean square errors ranged from 0.516% to 0.639%. Standard errors of individual prediction ranged from 0.523% to 0.649%. Goodness-of-fit of the model was also justified by testing the residuals for normality. Although the results are not quite unequivocal in favour of the one intensity level, it seems that intensity levels 75% and 80% are the most suitable to predict IMF in live pigs. Further research is needed, mainly to increase accuracy of collecting, processing and evaluating the sonograms using video image analysis.