Prediction Of Intramuscular Fat Research Articles

Beef-on-dairy: Meat quality of veal and prediction of intramuscular fat using the Q-FOM™ Beef camera at the 5th–6th thoracic vertebra

This study aims to describe the meat quality of young Holstein (HOL) beef-on-dairy heifers and bulls sired by Angus (ANG, n = 109), Charolais (CHA, n = 101) and Danish Blue (DBL, n = 127), and to investigate the performance of the handheld vision-based Q-FOM™ Beef camera in predicting the intramuscular fat concentration (IMF%) in M. longissimus thoracis from carcasses quartered at the 5th–6th thoracic vertebra. The results showed significant differences between crossbreeds and sexes on carcass characteristics and meat quality. DBL × HOL had the highest EUROP conformation scores, whereas ANG × HOL had darker meat with higher IMF% (3.52%) compared to CHA × HOL (2.99%) and DBL × HOL (2.51%). Bulls had higher EUROP conformation scores than heifers, and heifers had higher IMF% (3.70%) than bulls (2.31%). These findings indicate the potential for producing high-quality meat from beef-on-dairy heifers and ANG bulls. The IMF% prediction model for Q-FOM performed well with R2 = 0.91 and root mean squared error of cross validation, RMSECV = 1.33%. The performance of the prediction model on the beef-on-dairy veal subsample ranging from 0.9 to 7.4% IMF had lower accuracy (R2 = 0.48) and the prediction error (RMSEveal) was 1.00%. When grouping beef-on-dairy veal carcasses into three IMF% classes (2.5% IMF bins), 62.6% of the carcasses were accurately predicted. Furthermore, Q-FOM IMF% predictions and chemically determined IMF% were similar for each combination of sex and crossbreed, revealing a potential of Q-FOM IMF% predictions to be used in breeding, when aiming for higher meat quality.

Hyperspectral imaging and chemometrics assessment of intramuscular fat in pork Longissimus thoracic et lumborum primal cut

To assess the intramuscular fat (IMF) of Longissimus thoracis et lumborum (LTL) primal cut excised from an animal carcass, rib sections are dissected for chemical or subjective evaluation. Dissection destroys the integrity of the LTL. This study examined the feasibility of developing a method based on hyperspectral imaging to predict the IMF of pork LTL using hyperspectral imaging. Spectral information from different tissues was collected at the cranial (Serratus ventralis, Spinalis dorsi, longissimus , backfat), and the caudal ( Gluteus medius, Psoas major ) ends of the LTL. The spectral information and the IMF content chemically extracted from the mid-loin (3rd and 4th last rib of the LTL) were used to develop regression models for the prediction of the IMF content in the 3rd and 4th. The individual results of the analysis from the various tissues at both the cranial and caudal ends show that models exhibited performances for IMF prediction with root-mean-square-error (RMSEP) ranging from 0.36 to 0.92% and coefficient of determination in prediction ranging from 0.43 to 0.81. Gluteus medius showed the highest accuracy. This study has demonstrated that hyperspectral imaging could be used to accurately predict the IMF content in the mid-loin by using spectral data from Gluteus medius . The result could be useful in segregating pork LTL based on IMF content. • Intramuscular fat in whole pork Longissimus thoracic et lumborum was evaluated. • Muscles from the cranial and caudal of the Longissimus thoracic et lumborum were scanned. • PLSR was used to develop models between tissues and intramuscular fat at 3rd/4th last ribs. • The Gluteus medius muscle predicted intramuscular fat accurately.

Prediction and visualization of fat content in polythene-packed meat using near-infrared hyperspectral imaging and chemometrics

There is a growing demand for non-destructive techniques in the meat industry. This study aimed to use hyperspectral imaging to predict intramuscular fat (IMF) in pork vacuum-packed with polyethylene (WPE) film. Data pre-processing was applied to the WPE data by using second-order derivative (SD) followed by correlated optimized warping (COW) in order to align and remove the contributions of the PE film from the spectral data, respectively. The IMF values were correlated with the spectral data using support vector machines regression (SVMR) or partial least squares regression (PLSR) to develop the calibration models. Results based on SVMR applied to the WPE showed high accuracy (R2 = 0.89, RMSEP = 0.37%, RPD = 3.38) in the prediction. The results indicated that the SD and COW were effective in pre-processing hyperspectral images of PE-packed pork for IMF prediction. Overall, the study reveals that hyperspectral imaging could be used to accurately evaluate IMF content in vacuum-packed pork.

Intramuscular fat prediction of the semimembranosus muscle in hot lamb carcases using NIR

The aim of the current study was to identify whether changes in pH or temperature during the development of rigor mortis influence spectral quality and thus the prediction of intramuscular fat (IMF) in hot lamb carcases. Semimembranosus muscles (SM) of 75 lamb carcases were measured pre-rigor 3 times (Runs 1–3) using a near infrared (NIR) spectrometer with muscle pH and temperature also recorded. Calibration models for the prediction of IMF showed that spectra measurements taken at Run 2 (R2 = 0.42, RMSE = 0.75) and Run 3 (R2 = 0.38, RMSE = 0.78) provided more accurate and precise models compared to Run 1 (R2 = 0.27, RMSE = 0.84). An association between spectral variance, internal and surface temperature of the SM was observed across runs. These results suggest that, as muscle temperature declined during the early post-mortem period there was an improved ability of NIR spectra to predict IMF.

Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles.

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R2 test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R2P = 0.99 and RMSEP = 0.103 g × 100 g−1 DM.

Prediction of intramuscular fat in lamb by visible and near-infrared spectroscopy in an abattoir environment

The study used visible and near-infrared spectroscopy (Vis-NIR) in a large commercial processing plant, to test a system for meat quality (intramuscular fat; IMF) data collection within a supply chain for UK lamb meat. Crossbred Texel x Scotch Mule lambs (n = 220), finished on grass on 4 farms and slaughtered across 2 months, were processed through the abattoir and cutting plant and recorded using electronic identification. Vis-NIR scanning of the cut surface of the M. longissimus lumborum produced spectral data that predicted laboratory-measured IMF% with moderate accuracy (R2 0.38–0.48). Validation of the Vis-NIR prediction equations on an independent sample of 30 lambs slaughtered later in the season, provided similar accuracy of IMF prediction (R2 0.54). Values of IMF from four different laboratory tests were highly correlated with each other (r 0.82–0.95) and with Vis-NIR predicted IMF (r 0.66–0.75). Results suggest scope to collect lamb loin IMF data from a commercial UK abattoir, to sort cuts for different customers or to feed back to breeding programmes to improve meat quality.

Utilization of Bioelectrical Impedance to Predict Intramuscular Fat and Physicochemical Traits of the Beef Longissimus Thoracis et Lumborum Muscle.

The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% ≤ CV ≤ 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (−0.50 ≤ r ≤ 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Application of texture analysis of b-mode ultrasound images for the quantification and prediction of intramuscular fat in living beef cattle: A methodological study

Intramuscular fat (IMF) contributes significantly to the aroma and tenderness of the meat, therefore playing a key role in quality determination. Yet, IMF determination methods rely on visual inspection or on fat extraction from meat samples after animals' slaughter. The aim of this methodological study was the elaboration of a process capable of predicting IMF% using real-time ultrasound (RTU) images in live beef cattle.The longissimus dorsi (LD) muscle of 26 Charolaise heifers was investigated. In vivo ultrasound images were taken and texture analysis was performed. One week after the animals' slaughter, the whole twelfth rib cut was collected, and IMF% was determined by extraction with petrol ether (Randall) method.Animals were divided in 3 groups depending on their mean lipid content percentage in 100 g meat (Group 1: IMF ≤ 4.24%; Group 2: 4.25% ≤ IMF ≤ 5.75%; Group 3: IMF ≥ 5.76%).Texture parameters were selected by a stepwise linear discriminant analysis using IMF% measured by chemical extraction (IMFqa) as the dependent variable, and the results of the texture analysis as explanatory variables. 6 variables were found predictive and molded into a multiple regression equation, this equation was then validated using IMFqa as ground truth. A high linear correlation between IMFqa and IMFpred was evident (r2 = 0.8504), ROC analysis perfomed on IMFpred comparing it to IMFqa showed a sensitivity of 80% and a specificity of 93.7%, while results from the Bland-Altman plot were ± 1.96 (±1.11SD).

Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat.

The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute "combined attribute features" (CAF) which yielded acceptable results with (Rc2 = 0.877, 0.891; RMSEC = 2.410, 1.109; Rp2 = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (Rc2 = 0.783, 0.877; RMSEC = 4.591, 0.653; Rp2 = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (Rc2 = 0.936, 0.955; RMSEC = 1.209, 0.997; Rp2 = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat. Graphical abstract.

Intramuscular fat content in different muscles, locations, weights and genotype-sexes and its prediction in live pigs with computed tomography

Intramuscular fat (IMF) content depends on sex, genotype and diet and varies with pig growth. The aim of the present work was to determine the evolution of IMF by genotype-sex, muscle and muscle location, to determine relationships between IMF content of different muscles and to predict IMF in live pigs with computed tomography (CT). For this purpose, 155 pigs of seven combinations of genotype-sex were CT scanned and slaughtered at 70, 100 and 120 kg. From the carcasses, fat thickness was measured at several locations along the midline. Loin samples from three anatomical positions (between the eighth and ninth last ribs, between the third and fourth last ribs and between the third and fourth lumbar vertebrae) and three ham muscles (biceps femoris, semimembranosus and gluteus medius) were extracted, weighed and IMF was determined with near-IR equipment. From CT images, the distribution of volume by Hounsfield value (unit related with the density) was obtained for each muscle and anatomical location. Marbling was evaluated in the three loin locations. The effects of genotype-sex and live weight and their interaction were included in the statistical model. For prediction of IMF with CT images, partial least square regression was used. The results show differences in IMF content by genotype-sex and muscle. In general, the most cranial part of the loin presented higher IMF content, as well as the biceps femoris muscle of the ham. Depending on the genotype-sex, IMF content increased during all growth or increased until 100 kg and then became constant. Correlation coefficients between IMF content by muscle/location were between 0.74 and 0.83 within loin locations and between 0.53 and 0.70 for ham muscles. Correlation coefficients between marbling and IMF content evaluated at the same location varied between 0.51 and 0.66. Prediction of IMF content from CT images is not accurate enough (residual predictive deviation statistical values lower than 1.3). Muscle weight increase with animal growth and allometric coefficients varied between 0.89 and 0.97 for the muscles evaluated. The conclusions of the present work are that IMF content differs between and within muscle, during growth and by genotype-sex and that prediction of IMF in CT images of live pigs is not accurate.

Computed tomography has improved precision for prediction of intramuscular fat percent in the M. longissimus thoracis et lumborum in cattle compared to manual grading

This study assessed the ability of computed tomography (CT) to predict intramuscular fat (IMF) % in the M. longissimus thoracis et lumborum of 64 cattle undergoing Meat Standards Australia (MSA) marbling score. The striploin was dissected from carcasses at the 12th rib and CT scanned, with IMF% determined at the 12th rib of the M. longissimus thoracis (LT) and the caudal M. longissimus lumborum (LL). Prediction of IMF% using CT in the LT was moderate (R2 = 0.89, RMSE = 1.91) utilising CT pixel density and standard deviation, and better than prediction using MSA marbling score (R2 = 0.81, RMSE = 2.47). Prediction of IMF% in the caudal aspect of the LL was comparatively poor (R2 = 0.63, RMSE = 4.69), with IMF% 1.97% higher in this region (P < .05). CT is a promising non-destructive method for determination of IMF% that was more accurate than MSA marbling score.

Prediction of intramuscular fat content and shear force in Texel lamb loins using combinations of different X-ray computed tomography (CT) scanning techniques

Computed tomography (CT) parameters, including spiral computed tomography scanning (SCTS) parameters, intramuscular fat (IMF) and mechanically measured shear force were derived from two previously published studies. Purebred Texel (n = 377) of both sexes, females (n = 206) and intact males (n = 171) were used to investigate the prediction of IMF and shear force in the loin. Two and three dimensional CT density information was available. Accuracies in the prediction of shear force and IMF ranged from R2 0.02 to R2 0.13 and R2 0.51 to R2 0.71 respectively, using combinations of SCTS and CT scan information. The prediction of mechanical shear force could not be achieved at an acceptable level of accuracy employing SCTS information. However, the prediction of IMF in the loin employing information from SCTS and additional information from standard CT scans was successful, providing evidence that the prediction of IMF and related meat eating quality (MEQ) traits for Texel lambs in vivo can be achieved.

Composition and intramuscular fat estimation of Holstein bull and steer rib sections by using one or more computed tomography cross-sectional images

Predictions of the composition and intramuscular fat (IMF) of beef rib sections were estimated from 1 cross-sectional image taken at the 10th rib level, from one at the 11th rib level (IMF) or from a series of cross-sectional images taken between the 9th and 11th ribs with computed tomography (CT) scanner. Two hundred and twelve beef cuts from Holstein bulls and steers were scanned, with two thirds of the samples used for calibration and the rest for validation. The 9–11th rib section was CT scanned and manually dissected. In addition, the IMF was determined in minced loin. For each scan, the volume associated with each Hounsfield value was obtained for the whole rib section and for the Longissimus thoracis muscle as regions of interest. Volumes associated with a range of Hounsfield values were selected in a stepwise manner and used as predictors in the regression equation. With the exception of IMF prediction, the accuracy was higher when the entire rib section was scanned in comparison to a single cross-sectional image taken at the 10th rib level. However, as one single image is cheaper than scanning the entire rib section, a compromise between precision and cost must be considered to select an adequate prediction method using one cross-sectional image or a series of images comprising the whole rib section. The CT results from IMF content determinations of L. thoracis muscle improve when one cross-sectional image of all the cut (including other muscles and types of fat) is considered, probably because the high correlation between IMF content and fat content of the whole section. Moreover further studies are needed to improve CT scanning predictive accuracy.

Prediction of intramuscular fat levels in Texel lamb loins using X-ray computed tomography scanning

For the consumer, tenderness, juiciness and flavour are often described as the most important factors for meat eating quality, all of which have a close association with intramuscular fat (IMF).X-ray computed tomography (CT) can measure fat, muscle and bone volumes and weights, in vivo in sheep and CT predictions of carcass composition have been used in UK sheep breeding programmes over the last few decades. This study aimed to determine the most accurate combination of CT variables to predict IMF percentage of M. longissimus lumborum in Texel lambs.As expected, predicted carcass fat alone accounted for a moderate amount of the variation (R2=0.51) in IMF. Prediction accuracies were significantly improved (Adj R2>0.65) using information on fat and muscle densities measured from three CT reference scans, showing that CT can provide an accurate prediction of IMF in the loin of purebred Texel sheep.

Rapid and non-invasive quantification of intramuscular fat content of intact pork cuts

Having acquired near infrared (NIR) hyperspectral images of intact pork loin samples through an NIR hyperspectral imaging system, the efficiency of a variety of image processing techniques including texture pattern analysis techniques were applied to process hyperspectral images so as to determine the intramuscular fat (IMF) content non-destructively. After the segmentation of region of interest (ROI), the raw spectral, texture-based spectral and textural characteristics of pork images were extracted by spectral averaging and pattern recognition techniques namely Gabor filter and improved gray level co-occurrence matrix (GLCM), respectively. First derivatives of the non-filtered and the Gabor filtered spectra were also investigated. Full waveband partial least squares regression (PLSR) was employed to determine the optimal parameters of Gabor filter and GLCM, and to select optimal wavelengths for IMF prediction. A stepwise procedure was applied to the optimal wavelengths to further optimize them to key wavelengths. Multiple linear regression (MLR) models were built based on the key wavelengths. Mean spectra and the Gabor filtered spectra outperformed GLCM. The best result, represented by correlation coefficients of calibration (Rc), cross validation (Rcv) and prediction (Rp) of 0.89, 0.89, and 0.86, respectively, was achieved using the first derivative of Gabor filtered spectra at 1193 and 1217 nm. To visualize the IMF content in pork, the distribution maps of IMF content in pork were drawn using a mean spectra-based MLR model. These promising results highlight the great potential of NIR hyperspectral imaging for non-destructive prediction of IMF content of intact pork.

Use of linear regression and partial least square regression to predict intramuscular fat of pig loin computed tomography images

The intramuscular fat (IMF) content is related to the sensory acceptability of pork, and it can be non-destructively estimated using computed tomography (CT). The aim of this paper is to evaluate the potential use of ordinary linear regression (OLR) of the relative volumes associated with ranges of Hounsfield (HU) values and partial least square (PLS) regression applied to the relative volumes associated with each individual HU value to predict the IMF content using data from one or two different tomograms. The tomograms were obtained from pork loins, and the relative volume associated with each HU value was calculated. The IMF was measured in the loins using a near infrared transmittance device. The best prediction of IMF was obtained by OLR when data from 2 tomograms were used (R2=0.83 and RMSEPCV=0.46%). The results suggest that CT has good potential for measuring the IMF in loins and that the accuracy improves when the data from 2 tomograms were combined. The use of partial volumes as predictors with OLR allows for improved accuracy compared to the use of all of the individual volumes with PLS.

Prediction of the intramuscular fat content in loin muscle of pig carcasses by quantitative time-resolved ultrasound

A novel method for non-destructive intramuscular fat (IMF) estimation via spectral ultrasound backscatter analysis of signals obtained from pig carcasses early post mortem is described. A commercial hand-held ultrasound device (center frequency: 2.7MHz) was modified to focus the sound beam to the longissimus muscle at the 2nd/3rd last rib. Time-resolved ultrasound backscatter signals of loin muscle were recorded 45minp.m. on 82 pig carcass sides. Backfat width (dBF=18.9±3.8mm) and muscle attenuation (αmuscle=.77±.15dBMHz−1cm−1) were assessed from the measured pulse echo data. Other propagation properties of skin, backfat and muscle tissue obtained in a previous investigation were incorporated into the signal pre-processing to minimize parameter estimation artifacts. Spectral and cepstral parameters were derived from time-gated backscattered signals measured in the central muscle region. The range of intramuscular fat (IMF) determined by ether extraction was representative for German pig populations (.7%≤IMFchem≤3.6%, coefficient of variation CV(IMFchem)=44.8%). Variations of IMF were associated with variations of backfat width (CV(dBF)=20.2%), muscle attenuation (CV(αmuscle)=19.3%), and slope of the backscattered amplitude spectrum (CV(m)=28.8%). A full cross validated multiple linear regression model using these parameters resulted in good predictability of IMFchem (R2=.76, RMSEP=.34%). Among all tested carcasses, 73% could be correctly classified into one of three IMF classes (LOW: <1%, MID: 1–2%, HIGH: >2%). Using a single threshold (2% IMF), about 92% of all carcasses were correctly classified. With respect to the inherent variability of IMF within a single muscle and the different tissue volumes used for the chemical and ultrasound based IMF estimations the remaining prediction errors are acceptable. Compared to previous ultrasound based studies, the number of acoustic parameters used for the IMF prediction could be reduced. Moreover, the used parameters are based on time-of-flight and spectral slope estimations, which are i) more robust with respect to measurement artifacts and ii) have a causal link to structural variations associated with IMF variations in pork loin.

Results from six generations of selection for intramuscular fat in Duroc swine using real-time ultrasound. I. Direct and correlated phenotypic responses to selection1

A study was conducted to evaluate the efficacy of selection for intramuscular fat (IMF) in a population of purebred Duroc swine using real-time ultrasound. Forty gilts were purchased from US breeders and randomly mated for 2 generations to boars available in regional boar studs, resulting in a base population of 56 litters. Littermate pairs of gilts from this population were randomly assigned to a select line (SL) or control line (CL) and mated to the same sire to establish genetic ties between lines. At an average BW of 114 kg, a minimum of 4 longitudinal ultrasound images were collected 7 cm off-midline across the 10th to 13th ribs of all pigs for the prediction of IMF (UIMF). At least 1 barrow or gilt was slaughtered from each litter, and carcass data were collected. A sample of the LM from the 10th to 11th rib interface was analyzed for carcass IMF (CIMF). Breeding values for IMF were estimated by fitting a 2-trait (UIMF and CIMF) animal model in MATVEC. In the SL, selection in each subsequent generation was based on EBV for IMF with the top 10 boars and top 75 gilts used to produce the next generation. One boar from each sire family and 50 to 60 gilts representing all sire families were randomly selected to maintain the CL. Through 6 generations of selection, an 88% improvement in IMF has been realized (4.53% in SL vs. 2.41% in CL). Results of this study revealed no significant correlated responses in measures of growth performance. However, 6 generations of selection for IMF have yielded correlated effects of decreased loin muscle area and increased backfat. Additionally, the SL obtained more desirable objective measures of tenderness and sensory evaluations of flavor and off-flavor. Meat quality characteristics of pH, water holding capacity, and percent cooking loss were not significantly affected by selection for IMF. Selection for IMF using real-time ultrasound is effective but may be associated with genetic ramifications for carcass composition traits. Intramuscular fat may be used in purebred Duroc swine breeding programs as an indicator trait for sensory traits that influence consumer acceptance; however, rapid improvement should not be expected when simultaneous improvement in other trait categories is also pursued.

Prediction of intramuscular fat in live bulls using real-time ultrasound and image analysis

1. Use of image analysis of cross-sectional cuts to estimate the composition of the 10th–11th–12th rib-cut of European lean beef bulls R. Santos, F. Peña, M. Juárez, C. Avilés, A. Horcada, A. Molina Meat Science CrossRef

The effects of probe type and intensity of ultrasound on accuracy of intramuscular fat prediction in longissimus dorsi muscle of pigs

This study evaluates the possibility of intramuscular fat prediction in live pigs using two different ultrasound probes (3.5 and/or 5.0 MHz) at three various levels of total ultrasound intensity. One hundred and forty-five hybrid pigs were weighed and scanned one to three days before slaughter by device ALOKA SSD 500. Each pig was scanned by both probes but at different levels of intensity. Cross-sectional images of longissimus dorsi muscle were processed by video image analysis for prediction of intramuscular fat content. Day after slaughter a sample of longissimus dorsi muscle was taken for laboratory analysis of true content of intramuscular fat. Correlation coefficients between predicted and true IMF content were calculated. Better results were achieved by probe 3.5 MHz than 5.0 MHz. Statistically significant correlation was found between analyzed IMF and estimated one at intensity of 80 % (r = 0.31), less significant at intensity of 75 % (r = 0.20). After the improvement of accuracy of data proccessing and evaluation of sonographic images the ultrasound prediction of intramuscular fat in live pigs is feasible.