Dejan Škorjanc

Predicting intramuscular fat content in pork and beef by near infrared spectroscopy

Prediction ability of near infrared (NIR) spectroscopy for intramuscular fat content (IMF) determination was studied. The material comprised 126 muscle samples; 46 pig longissimus dorsi and semitendinosus and 34 beef longissimus dorsi muscle samples. The IMF content was chemically determined in duplicate using two different chemical methods; fat extraction according to Folch et al. and Soxhlet extraction with hydrolysis according to SIST ISO 1443. Folch extraction underestimated IMF content compared to Soxhlet extraction with hydrolysis (-0.32%, P < 0.0001). Similar repeatability was obtained for Folch and Soxhlet extraction with hydrolysis (0.17% and 0.18%, respectively, P < 0.0001). Sample spectra were scanned from 400–2500 nm by the NIR Systems model 6500 spectrophotometer (Silver Spring, MD, USA) and analysed by WinISI II on minced and intact (pork only) samples. Modified partial least squares regression was used to develop models and to obtain calibration statistics: coefficient of determination in calibration(R2 C ) and cross-validation (R2 CV ) and standard error in calibration (SEC) and cross-validation (SECV). We prepared different models (for a single muscle/common, by applying NIR spectrum or the whole spectrum, on intact and minced samples). Obtained models proved the remarkable prediction ability of NIR spectroscopy to determine IMF content (R2 CV between 0.84 and 0.99; SECV between 0.14% and 0.53%) and confirms the potential of NIR spectroscopy to replace laborious chemical procedures. Regarding the factors studied, calibrations were less accurate for intact than for minced samples; the use of an NIR spectrum compared to the whole spectrum had no important effect on the prediction ability. According to calibration statistics, the prediction using a common equation for several muscles seems more reliable than the equations within the muscle, but the latter showed lower bias.

Partial fast-to-slow conversion of regenerating rat fast-twitch muscle by chronic low-frequency stimulation.

Chronic low-frequency stimulation (CLFS) of rat fast-twitch muscles induces sequential transitions in myosin heavy chain (MHC) expression from MHCIIb → MHCIId/x → MHCIIa. However, the ‘final’ step of the fast-to-slow transition, i.e., the upregulation of MHCI, has been observed only after extremely long stimulation periods. Assuming that fibre degeneration/regeneration might be involved in the upregulation of slow myosin, we investigated the effects of CLFS on extensor digitorum longus (EDL) muscles regenerating after bupivacaine-induced fibre necrosis. Normal, non-regenerating muscles responded to both 30- and 60-day CLFS with fast MHC isoform transitions (MHCIIb → MHCIId → MHCIIa) and only slight increases in MHCI. CLFS of regenerating EDL muscles caused similar transitions among the fast isoforms but, in addition, caused significant increases in MHCI (to ∼30% relative concentration). Stimulation periods of 30 and 60 days induced similar changes in the regenerating bupivacaine-treated muscles, indicating that the upregulation of slow myosin was restricted to regenerating fibres, but only during an early stage of regeneration. These results suggest that satellite cells and/or regenerating fast rat muscle fibres are capable of switching directly to a slow program under the influence of CLFS and, therefore, appear to be more malleable than adult fibres.

An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks.

The ability to predict meat drip loss by using either near infrared spectra (SPECTRA) or different meat quality (MQ) measurements, such as pH(24), Minolta L(∗), a(∗), b(∗), along with different chemometric approach, was investigated. Back propagation (BP) and counter propagation (CP) artificial neural networks (ANN) were used and compared to PLS (partial least squares) regression. Prediction models were created either by using MQ measurements or by using NIR spectral data as independent predictive variables. The analysis consisted of 312 samples of longissimus dorsi muscle. Data were split into training and test set using 2D Kohonen map. The error of drip loss prediction was similar for ANN (2.2-2.6%) and PLS models (2.2-2.5%) and it was higher for SPECTRA (2.5-2.6%) than for MQ (2.2-2.3%) based models. Nevertheless, the SPECTRA based models gave reasonable prediction errors and due to their simplicity of data acquisition represent an acceptable alternative to classical meat quality based models.

Specific impulse patterns regulate acetylcholinesterase activity in skeletal muscles of rats and rabbits.

In rats, acetylcholinesterase (AChE) activity in the fast muscles is several times higher than in the slow soleus muscle. The hypothesis that specific neural impulse patterns in fast or slow muscles are responsible for different AChE activities was tested by altering the neural activation pattern in the fast extensor digitorum longus (EDL) muscle by chronic low‐frequency stimulation of its nerve. In addition, the soleus muscle was examined after hind limb immobilization, which changed its neural activation pattern from tonic to phasic. Myosin heavy‐chain (MHC) isoforms were analyzed by gel electrophoresis. Activity of the molecular forms of AChE was determined by velocity sedimentation. Low‐frequency stimulation of the rat EDL for 35 days shifted the profile of MHC II isoforms toward a slower MHCIIa isoform. Activity of the globular G1 and G4 molecular forms of AChE decreased by a factor of 4 and 10, respectively, and became comparable with those in the soleus muscle. After hind limb immobilization, the fast MHCIId isoform, which is not normally present, appeared in the soleus muscle. Activity of the globular G1 form of AChE increased approximately three times and approached the levels in the fast EDL muscle. In the rabbit, on the contrary to the rat, activity of the globular forms of AChE in a fast muscle increased after low‐frequency stimulation. The results demonstrate that specific neural activation patterns regulate AChE activity in muscles. Great differences, however, exist among different mammalian species in regard to muscle AChE regulation. J. Neurosci. Res. 47:49–57, 1997.

Growth of piglets in relation to milk intake and anatomical location of mammary glands

Abstract The quantity of milk consumed by piglets was estimated with the aid of the ‘weigh–suckle–weigh’ method. During the four weeks of lactation we closely observed the changes in the quantity of milk taken in by piglets with respect to the defined area of the mammary complex (anterior (A), middle (M) and posterior (P)). Daily milk production was estimated, and comparison was made between the growth rate of the piglets and their birth weight (light, heavy). Ten sows (Landrace×Large White) with their suckled litters (9.8±1.9 piglets) were evaluated in the study. There were no significant differences between the A and M areas in the quantity of milk consumed by the piglets, yet both areas differed significantly from the P area (p<0.05). During the entire period of lactation, piglets that suckled at the cranial (A and M area) teats took in more milk and consequently achieved higher daily weight gain than the piglets positioned in the P area. The body mass growth of piglets was positively and significantly correlated with gland milk yield during lactation stages (p<0.05).

Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks

An attempt to classify dry-cured hams according to the maturation time on the basis of near infrared (NIR) spectra was studied. The study comprised 128 samples of biceps femoris (BF) muscle from dry-cured hams matured for 10 (n=32), 12 (n=32), 14 (n=32) or 16 months (n=32). Samples were minced and scanned in the wavelength range from 400 to 2500 nm using spectrometer NIR System model 6500 (Silver Spring, MD, USA). Spectral data were used for i) splitting of samples into the training and test set using 2D Kohonen artificial neural networks (ANN) and for ii) construction of classification models using counter-propagation ANN (CP-ANN). Different models were tested, and the one selected was based on the lowest percentage of misclassified test samples (external validation). Overall correctness of the classification was 79.7%, which demonstrates practical relevance of using NIR spectroscopy and ANN for dry-cured ham processing control.

Effect of hydrolysable tannins on intestinal morphology, proliferation and apoptosis in entire male pigs

ABSTRACT This study aimed to evaluate the effect of hydrolysable tannin supplementation on morphology, cell proliferation and apoptosis in the intestine and liver of fattening boars. A total of 24 boars (Landrace × Large white) were assigned to four treatment groups: Control (fed commercial feed mixture) and three experimental groups fed the same diet supplemented with 1%, 2% and 3% of hydrolysable tannin-rich extract. Animals were housed individually with ad libitum access to feed and then slaughtered at 193 d of age and 122 ± 10 kg body weight. Diets supplemented with hydrolysable tannin affected the morphometric traits of the duodenum mucosa as reflected in increased villus height, villus perimeter and mucosal thickness. No effect was observed on other parts of the small intestine. In the large intestine, tannin supplementation reduced mitosis (in the caecum and descending colon) and apoptosis (in the caecum, ascending and descending colon). No detrimental effect of tannin supplementation on liver tissue was observed. The present findings suggest that supplementing boars with hydrolysable tannins at concentrations tested in this experiment has no unfavourable effects on intestinal morphology. On the contrary, it may alter cell debris production in the large intestine and thus reduce intestinal skatole production.

Kinetic Versus Endpoint Measurement for Quantitative Histochemical Determination of Enzyme Activity in Muscle Fibers

I mage analysis is increasingly used for quantitative evaluation of histochemically assessed enzyme activity. Two methods are presently used: (a) estimation of enzyme activity by image analysis of tissue sections stained for a specific enzyme reaction (single-point or endpoint measurement), (b) determination of maximal initial reaction rates by monitoring changes in optical density during the early phase of the reaction (kinetic microphotometric measurement). Determination of enzyme activity by single time point measurement is feasible if the reaction follows zero-order kinetics. However, this condition may be fulfilled only during the initial phase of the reaction and may vary for the same enzyme according to its cellular or tissue distribution. This phenomenon is illustrated in Figure 1, which compares microphotometrically monitored reaction rates of succinate dehydrogenase (SDH) in muscle fibers with high, intermediate, and low activity. Obviously, reaction rates are linear with time only during the first 1–2 min and then level off. After 12 min they have declined to approximately 48% (high), 56% (intermediate), and 61% (low) of the initial rates. Because nonspecific formazan production was monitored individually for each fiber in a parallel section and subtracted from the reaction rate in the presence of substrate, decreasing reaction rates could not have resulted from “nothing dehydrogenase” artifacts. Similar results were obtained when corrections for nothing dehydrogenase were performed for each fiber by measurements on the same section using assay mixtures without and with succinate ( korjanc and Pette 1997; and data not shown). It is more likely, therefore, that declining reaction rates reflect a progressive inhibition of SDH, probably due to product inhibition or the precipitation of formazan on the mitochondrial membranes. Because reS action rates decrease nonuniformly, the ratios between high and low activity fibers tend to become smaller with prolonged incubation (Figure 1), obscuring the true range of variations in cellular SDH activity ( korjanc and Pette 1997; korjanc et al. 1997). Measurements on larger sample sizes further illustrate this phenomenon (Table 1), supporting the notion that evaluation of enzyme activity in tissue sections by quantitative histochemistry should be restricted to maximal initial reaction rates (Pette and Wimmer 1979; see also van Noorden and Butcher 1991). S S

Tissue-specific regulation of CYP3A by hydrolysable tannins in male pigs

Abstract 1. Little is known about the activities and regulation of cytochrome P4503A (CYP3A) enzymes in porcine colon in response to specific feeding components. 2. We added hydrolyzable tannins to the diet of fattening boars and studied its effect on the expression of hepatic and intestinal CYP3A. 3. In total, 51 Landrace × Large White boars were assigned to the following treatment groups: control (without the addition of hydrolysable tannins), T1 (diet-containing 1% hydrolysable tannin extract), T2 (diet-containing 2% hydrolysable tannin extract) and T3 (diet-containing 3% hydrolysable tannin extract). CYP3A expression and activity were measured in microsomes prepared from liver and colon tissue. 4. CYP3A protein expression and activity were increased in the colon of pigs fed 2% and 3% tannins, while no changes were observed with lower tannin concentrations, or in the liver of any treatment groups. Also, it was demonstrated that colon mucosa possess CYP3A activity similar to that measured in the liver. 5. The present results provide the first evidence that tannin supplementation can modulate CYP3A in porcine colon mucosa in vivo. The physiological significance of this finding for the health status of the individual animal needs further investigation.

Application of Artificial Neural Networks in Meat Production and Technology

The market of meat and meat products is growing continuously. In the sector of meat, there are many problems and challenges associated with the evaluation of meat quality at industrial level. The methods with the potential of industrial application should be accurate but also rapid, non-destructive, with no health or environment hazards, with benefits of automation and lower risk of human error. The lack of such methods represents a drawback for meat industry and the research focusing on the possible application of rapid methods is emerging. Many new promising techniques are being tested in meat science such as NIR (near infrared) and FT-IR (Fourier transformed infrared) spectroscopy, mass spectrometry, hyperand multispectral imaging techniques, machine/computer vision, biosensors, electronic noses (array of sensors), ultrasound techniques, etc. However, the enormous amount of information provided by these instruments demands an advanced data treatment approach. The artificial intelligent methods can be used for such purposes since their primary target is to distinguish objects or groups or populations. Artificial neural networks (ANN) are a well-known mathematical tool widely used and tested lately for the problems in meat production and technology. Its advantages are in the ability to handle with nonlinear data, highly correlated variables and the potential for identification of problems or classification. In particular promising applications of ANN in relation to meat sector is in carcass classification, quality control of raw material, meat processing, meat spoilage or freshness and shelf-life evaluation, detecting off-flavours, authenticity assessment, etc. In this chapter an overview of published studies dealing with the application of ANN in meat science is given. In the first part of the chapter basic concepts of artificial neural networks (ANN) are presented and described. The next part of the chapter summarizes the relevant publications on the use of ANN in case of meat production and technology issues and is divided in several paragraphs presenting the relevant research work with the most interesting applications of ANN.