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Image analysis study of the relationship between total collagen content and distribution
of the perimysial connective network in a bovine muscle
S. Abouelkaram1, P. Berge1, J.-F. Hocquette2, J. Culioli1, A. Listrat2
INTRODUCTION
The connective tissue of skeletal muscle is composed mainly of collagen and lipids which respective functions, and consequently, distributions within this tis- sue, are different. Collagen is found more or less homogenously distributed throu- ghout the muscle whereas fat is found in deposits corresponding to localised groups of adipocytes. The way these two components are distributed affects both the sensorial and the mechanical characteristics of the muscle [1]. Most previous studies have addressed the chemical composition of muscle and there has been little work carried out on the spatial distribution of the connective network, and even less on the relative distribution of fat and collagen within this network.
A preliminary study was carried out, focussing on collagen as the basic cri- teria for assessing the role of connective tissue in meat tenderness. The purpose was to establish relationships between parameters of connective tis- sue distribution, obtained by image analysis, and total collagen content.
MATERIAL AND METHODS
Animals
The study was carried out on the semimembranosus muscle (SM) from two groups of culled cows (CC) of the Holstein (n=12) and Salers (n=12) breeds,
1. Station de Recherches sur la Viande.
2. Unité de Recherches sur les Herbivores.
INRA, Theix, 63122 Saint Genès Champanelle, France
© Lavoisier – La photocopie non autorisée est un délit
aged 6-7 years. The cows were slaughtered at the same in vivo fatness condi- tion score assessed visually. All animals were fed the same diet based on pressed beet pulp silage supplemented with maize cob, soy cake and urea.
Biochemical collagen assay
A sample (mean weight 550 g) was taken from each muscle and homoge- nised. Total collagen content was assessed for each muscle on 5 sub-samples of 3 g of fresh muscle, by hydroxyproline assay according to the Bergman and Loxley (1963) [2] method as modified by Bonnet and Kopp (1984) [3].
Image analysis on muscle slice
Images of SM muscle slices were made using a photographic measurement bench comprising a B&W CCD camera (Sony MACC77), a polarised white light system and a PC computer for image digitisation. The images comprised 256 levels of grey with 768 ✕ 576 pixel resolution for a slice of muscle of surface area 5 ✕ 4.5 cm.
The images were processed using threshold methods and object extraction (labelling). A segmentation algorithm allowed extraction of the surface of objects selected as being part of the connective network. The surface of these objects was expressed as a percentage of the total surface of the image. These were then classified by size to produce the characteristic image parameters for the samples. The parameters thus obtained give information on the organisation of connective tissue in terms of distribution of the size of the elements in the connective network. The results were expressed as the percentage of objects in each class relative to the total number of objects per image. We worked with 20 variables, each corresponding to the proportion of objects in each size class.
Statistical analysis
All the data was analysed using the SAS software package (version 8.01).
The total collagen content prediction models were established by multiple stepwise linear regression using the SAS REG procedure (STEPWISE method).
This method meant that out of the 20 available variables only the most pertinent were retained with an error probability threshold of 5%, in order to build the prediction model.
RESULTS
Images of slices of meat were taken in order to visualize the connective net- work. An example of these images is given in Figure 1 where the connective network shows up white against a black background. The images presented in this figure show the muscles of two animals with extreme total collagen con- tents (2.30 and 3.95 µg hydroxyproline/mg dry matter respectively for Figures 1a and 1b).
Figure 1a Salers cow
Figure 1b Holstein cow
The 4 variables related most explicitly to the total collagen content out of the 20 studied are presented in Table 1, coded as C0575, C0086, C1000 and C0064.
These variables are the percentages of objects belonging to the classes (C) corres- ponding respectively to the following size intervals (as % of total surface of the image): ]0.433; 0.575], ]0.075; 0.086], ]0.859; 1.00] and ]0.054; 0.064].
Table 1
Result of the selection procedure for the parameters of total collagen content calculation model
Step Variable Partial R2 Model R2 F Prob. > F
1 C0575 0.21 0.21 5.8 0.024
2 C0086 0.19 0.40 6.9 0.015
3 C1000 0.17 0.58 8.2 0.009
4 C0064 0.09 0.67 5.7 0.026
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Figure 2 gives a graphic representation of the model established by regres- sion. The correlation between observed and calculated data was R = 0.82 resul- ting in a simple determination coefficient of R2 = 0.68 (significant; P < 5%). This means that 68% of the variation in collagen content could be explained by only 4 parameters. Inclusion of an additional parameter (error probability threshold 10%) improved this result slightly (R2 = 0.74). The model can be improved fur- ther however because (1) the areas of muscle used for biochemical assay and imaging were not exactly the same, (2) the image parameters extracted were not selective for collagen alone, but also for intramuscular fat, (3) the images were made in the visible light spectrum that does not favour any muscle com- ponent above the others, meaning that a more selective lighting of the compo- nents is required, (4) the sample size could be optimised to make it more representative of the muscle.
Figure 2
Total collagen content calculation model (collagen expressed as µg OH-Pro/mg dry mater)
CONCLUSION
The model developed allowed a good estimation of the total collagen con- tent of muscle to from the distribution of the connective network, although the imaging parameters extracted were not specific to collagen. This study there- fore needs to be taken further, taking also into account the importance of intra- muscular fat tissue.
2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0
2.4 2.9 3.4 3.9
Observed values
Calculated values
y = 0,6776 x + 1,0035 R2 = 0,67
ACKNOWLEDGEMENTS
This work was funded by the "Commissariat au Développement Economique et à l’Aménagement du Massif Central". The authors thank the "Herbivores Research Unit", Meat Research Station and the INRA Theix slaughterhouse staffs for the management and the slaughter of the animals under controlled conditions, the meat sampling and the laboratory analysis.
REFERENCES
1. ABOUELKARAM S., SUCHORSKI K., BUQUET B., BERGE P., CULIOLI J., DELA- CHARTRE P. and BASSET O., 2000. Effects of muscle texture on ultrasonic measure- ments. Food Chemistry, 69, 447-455.
2. BERGMAN I. and LOXLEY R., 1963. Two improved and simplified methods for the
spectrophotometric determination of hydroxyproline. Analytical Chemistry, 35, 1961:1965.
3. BONNET M. et KOPP J., 1984. Dosage du collagène dans les tissus conjonctifs, la viande et les produits carnés. Cahiers Techniques de l’INRA, 5, 19:30.
