Malcata, F. Xavier

Reis, Patrícia J. M.

English

Repositório Institucional da Universidade Católica Portuguesa

Use of image analysis on cheese sections for microstructural characterizationPatrícia J M Reis and F Xavier Malcata . .   .  Escola Superior Biotecnologia, Universidade Católica PortuguesaRua Dr. António Bernardino de Almeida, P-4200-072 Porto, PortugalE.mail: pmreis@mail.esb.ucp.ptIntroduction Materials and methods  The perceived texture of Serra da Estrela cheese is an importantquality parameter for typical consumers. As the microstructure ofcheese is closed related to its texture, light microscopy has been usedfor a long time for structural studies. Microscopy provides visualqualitative information about the structures and changes in thesamples, but this type of information is difficult to handle if one wants tomake comparisons between a number of samples. Therefore, methodsLight Microscopy. Stained sections were observed at x400magnification with an Olympus CX-31 microscope, and the imageswere digitised using an Olympus camera.Image analysis. Images were processed on a PC using theUTHSCSA Image Tool IT V2.0 (University of Texas Health ScienceCenter, San António). Images were binarized by simple thresholdingin order to extract numeric information. The threshold grey-level wasSample preparation. Serra da Estrela cheesewas examined for its microstructure using lightmicroscopy. The cheese was produced using rawewe’s milk as described by Macedo et al. (1993).Blocks of cheese (10 x 10 x 20 mm) were takenvertically through the top of the cheeses,completely submerged in 4%(v/v) formalin andfi d f t l t 1 b f tti Th bl kR lt d di ito quantify the visual information have been applied to microscopicdata. Optical microscopy techniques are often employed to reveal theinternal geometrical structure of a product in detail; stereology andimage analysis can be employed as auxiliary tools for these purposes(Langton, 1993; Schoonman, 2001). The present study was carried outso as to investigate the structural differences in the gel network of thecheese throughout maturation via 2D image analysis, and to correlatemicrostructural with textural parameters.selected after examination of the histogram of the grey-level valuesof the image. Geometrical parameters such as porosity, area,perimeter, elongation and roundness were measured using theobject analysis option of the software. For each cheese, 2 blockswere analysed, 6 sections were obtained and 2 images from eachsection were produced. Images were digitised on a matrix of 2272 x1704 pixels, with 32 bit resolution.xe or a eas mo e ore cu ng. ese oc swere cut into approximately 2 x 2 x 1 mm sections(perpendicular to the base) and dehydrated in anethanol series (10 - 100%). The dry samples wereembedded in Epon 812, sectioned (~0.25 m) andstained using a mixture of 1% toluidine blue and1% sodium borax (1:1).esu s an  scuss onA typical LM micrograph of the inner structure of Serra da Estrelacheese is shown in Fig. 1-a. From section images, three characteristicsizes have been determined: (a) “porosity”; (b) pore area distribution;and (c) roundness (or shape) factor, which describes the deviation ofan object from a true circle. In the following image-processing step,utilizing binarized images, “porosity” measurements were performed.Table 1 Porosity evaluated using image analysisRipening time (day) Porosity (%) 1 45,12 ± 2,35 7 44,80 ± 3,47 14 46,02 ± 3,31 21 44,65 ± 3,10 70 42,25 ± 2,38  0,00,51,01,52,02,50 10 20 30 40 50 60 70 801 dRoundnessLength (m)0,00,51,01,52,02,50 10 20 30 40 50 60 70 8014 dRoundnessLength (m)ss2,02,570 dss2,02,521 dIn Fig. 1-b, the black pixels represent solid matrix and the white pixelsrepresent voids (pores).a bThe roundness of the pores is plotted versus pore size inFig. 4. Independently of the ripening time, the roundness ofthe pores decreases with increasing size This fact couldFigure 4. Roundness vs. length (major axis) of the pores atdifferent ripenning times.Frequency0 30,40,50,60,70,80,91,01 d7 d14 d21d70 d1d7d14d21d70d.on micrographs of cheese throughout ripeningtime (Mean  Standard Deviation). RoundnesLength (m)0,00,51,01,50 10 20 30 40 50 60 70 80RoundnesLength (m)0,00,51,01,50 10 20 30 40 50 60 70 80Figure 1. Light micrograph of the inner structure of 14-d-old Serra daEstrela cheese at x400 original magnification (a). Image binarized bythresholding to extract the pores (b).The software used counts the black and white pixels and evaluatesporosity as the ratio between white pixels and total pixels in the image.The Porosity so calculated (under stereological assumptions) is equalto the pore volume proportion. Results obtained for different ripeningtimes show that the porosity is highest in cheeses by 14 d of ripening.suggest that the small irregular particles can be producedby fragmentation of the large smooth ones.Furthermore, the void space can be regarded as a networkof pores connected by smaller void channels, so themeasurement of size are not a simpler matter. By imageanalysis, voids do appear as patches that vary in size andshape Regarding the morphological complexity in theFigure 2. Histogram of “number-weighted” distributionsof pore area, and corresponding cumulative curves atvarious ripening times.Pore area (m2)0,00,10,2,0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190(Table 1); however, statistically significant differences were not foundbetween ripening days (p > 0,05). In contrast, the ANOVA indicatedthat porosity was significantly affected by cheese block (p = 0,002).This observation reveals the high heterogeneity present amountdistinct regions in cheese.The frequency of pores sorted by area is depicted in Fig. 2. While themost probable particle size is rather small, it is the few larger pores thatcontribute most of the volume. The importance of the larger particles is.plane of the image (Figure 1), such patches can representsingle pores or a network consisting of connected pores.Regarding textural properties, the cheeses were creamyand exhibited a decrease of firmness over the ripening time(data not shown). A relationship between microstructuraland textural parameters in cheese seems to exist, althoughfurther studies have still to be conducted in this area.Figure 3 Cumulative curves of “area weighted”FrequencyPore area (m2)0,00,10,20,30,40,50,60,70,80,91,00 100 200 300 400 500 600 700 800 900 1000 1100 12001 d7 d14 d21 d70 dLiterature AcknowledgmentsFinancial support for author P. J. M. Reis was provided by a PhD fellowship(BD/19752/99), granted by FCT within program Praxis XXI (Portugal).Langton, M., Hermansson, A. M. (1993). Food Hydrocolloids 7: 11-22Macedo, A. C., Malcata, F. X., Oliveira, J. C. (1993). J. Dairy Sci. 76: 1725-1739.Schoonman, A. et al. (2001). Food Res. Int. 34: 913-929.apparent in Fig. 3, which shows the area-weighted distribution of porearea.. -distributions of pore area at various ripening times.

Use of image analysis on cheese sections for microstructural characterization

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Use of image analysis on cheese sections for microstructural characterization

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