Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture

MEAT SCIENCE Meat Science 72 (2006) 294–302 www.elsevier.com/locate/meatsci

Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture Cheng-Jin Du, Da-Wen Sun

*

FRCFT Group, Department of Biosystems Engineering, University College Dublin, National University of Ireland, Earlsfort Terrace, Dublin 2, Ireland Received 15 March 2005; received in revised form 12 July 2005; accepted 28 July 2005

Abstract Pores formed in pork ham have a significant effect on its quality. However, they are mostly characterised using manual methods with special devices. In this paper, an automatic method for pore characterisation of pork ham was developed using computer vision. To segment pores from images of pork ham, three stages of image processing algorithm were developed, i.e., ham extraction, image enhancement, and pore segmentation. From the segmented pores, the porosity, number of pores, pore size, and size distribution were measured. The statistical analysis showed that 79.81% of pores have area sizes between 6.73 · 10
3 and 2.02 · 10
1 mm2. Furthermore, it was found that the total number of pore (TNP) and porosity highly negatively related to the water content of pork ham (P < 0.05), and had negative correlations with the cooking and cooling time. However, for texture analysis, positive correlations were found between the pore characterisations and WBS, hardness, cohesion, and chewiness, respectively, while springiness and gumminess were negatively related to TNP and porosity.
2005 Elsevier Ltd. All rights reserved. Keywords: Air blast cooling; Computer vision; Cooked meat; Image processing; Pork ham; Pore characterisation; Porosity; Watershed segmentation

1. Introduction Pores occur in a variety of food products and have a significant effect on their qualities. The variation in porosity, average pore size and pore size distribution influences the mechanical and textural characteristics of dried foods significantly (Huang & Clayton, 1990). Pores also affect sensory properties of foods and have a direct effect on the other physical properties, such as mass diffusion coefficient, thermal conductivity, and thermal diffusivity (Rahman, 2001). Therefore, the information on pores is important for evaluating the quality of a food product, predicting other properties, and modelling heat and mass transfer during food processing. A reliable, relatively quick and easy method for pore characterisation would be a very desirable tool. *

Corresponding author. Tel.: +353 1 7165528; fax: +353 1 4752119. E-mail address: [email protected] (D.-W. Sun). URL: www.ucd.ie/refig (D.-W. Sun).

0309-1740/$ - see front matter
2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.meatsci.2005.07.016

In the literature, much research effort has been directed at studying the porosity of different kinds of food, such as grains (Chesson, Gardner, & Wood, 1997), fruits (Palipane, Driscoll, & Srzednicki, 1992; Vincent, 1989), fish (Rahman, Al-Amri, & Al-Bulushi, 2000; Rahman, Perera, Chen, Driscoll, & Potluri, 1996), and vegetables (Madamba, Driscoll, & Buckle, 1994; Scanlon, Day, & Povey, 1998). Besides the above food products, porosity has also been investigated for meat products. In the work of McDonald and Sun (2001a), the effects of meat porosity on the cooling times of a cooked beef product and the development of porosity during vacuum cooling were examined, where the internal percentage porosity was calculated using the relationship between apparent and true densities of the beef samples. In addition, mercury porosimetry and helium pycnometry have been employed for characterisation of pores. Kassama and Ngadi (2005) used mercury porosimetry to characterise the the evolution of pore structure during deep-fat-frying of chicken meat. In another research, using mercury porosimetry and helium

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pycnometry, the effects of the pore structure of a cooked beef product on the efficiency of vacuum cooling in terms of its porosity, pore size, pore distribution and effective diffusivity of moisture were investigated (McDonald & Sun, 2001b). However, as an important physical attribute of food products, porosity is still mostly measured using manual methods with special devices such as mercury porosimetry and helium pycnometry, which are destructive, laborious, and inherently subjective. In the experimental work of McDonald and Sun (2001a), the difficulties in acquiring exact porosity of beef samples were realised from the outset. The methods used for characterising pore structure cannot provide sufficient information. Mercury porosimetry provides information in somewhat unrealistic ways and is subject to misleading artefacts. Helium pycnometry provides global-type information applicable only to the test specimen as a whole, which has no distributive value (Huang & Clayton, 1990). To our knowledge, there is still no attempt to characterise the pore structure of pork ham using automatic techniques. Therefore, it is necessary to develop an automatic method for pore structure characterisation of pork ham. Such a method might provide an acceptable level of pore information. Being a non-destructive, rapid, and objective quality evaluation tool, computer vision techniques could be employed to characterise pores in pork ham automatically. Recently, computer vision has been attracting much attention from the food industry (Sun, 2004), and has been applied increasingly for quality inspection of a wide range of food products (Du & Sun, 2004; Kavdir & Guyer, 2002; Park & Chen, 2000; Sun, 2000; Sun & Brosnan, 2003; Sun & Du, 2004; Wang & Sun, 2001). Using image analysis, Hullberg and Jallerini (2003) have investigated the effects of RN genotype and tumbling condition on the number of pores in cured-smoked pork. In their work, high correlations were found when the image analysis results were compared with the results of a trained sensory panel and a visual scoring of images, which indicates that image analysis is a good tool in this type of investigation. The objective of the current research is to develop an automatic method for pore structure characterisation of pork ham using computer vision techniques. The relationships between the pore characteristics and the processing time, water content, and texture of the product were investigated. 2. Materials and methods 2.1. Preparation of pork hams De-boned pork legs with pH 5.7–6.0 were selected and injected with a brine solution to a total weight of 20% of green legs. After injection, the samples were put into a tumbler for tumbling under vacuum. Then the tumbled sample was stuffed into elastic netting using a stuffer horn, and packed into a cooking bag to form an ellipsoidal shaped

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ham. Six replicates were cooked in a water bath at 82 C to a core temperature of 72 C. After cooking, the samples were cooled from 72 to 4 C core temperature using the traditional air blast cooling method (1 ± 1 C). During the cooking and cooling processes, the processing time of each sample was recorded. 2.2. Image acquisition After cooling, each sample was cut into 6 parts at 5 positions along the long axis. Ten slices 4 mm thick were obtained from the 6 parts using a slicer (Model TM250, Fabbrica Affecttatrici Cavaria, Cavaria, Italy). From the 10 slices, sixty circular disks (25 mm in diameter) of ham were cored using a cork borer. Images of these circular disks of pork ham were captured on a black background under two fluorescent lamps with plastic light diffusers. The average colour intensity of the black background is 38, 33, and 40 for red, green, and blue colour components, respectively, whose differences with respect to the average colour intensity of the samples are 171, 127, and 99, respectively. The colour temperature of the fluorescents is 5000 K. The image acquisition system used in this study consists of a Dell Workstation 400 equipped with an ICRGB frame grabber (Imaging Technology, Billerica, MA, US), and a high quality 3-CCD Sony XC-003P camera. A copy stand was used to support the camera and lighting unit. The CCD camera can be moved vertically to adjust magnification, and its distance to the pork ham sample is 16.5 cm. The lights are tilted and adjusted in height to obtain images with appropriate brightness and contrast, and the angles between lamps and sample are approximately 45. The same exposure and focal length were used for all the images, which were in the same size of 720 · 574 (413,280 pixels) with 24 bits per pixel, and were saved in the image format of tag image file format (TIFF). 2.3. Image processing To segment pores from the images captured by the CCD camera, three stages of image processing algorithm were developed, i.e., ham extraction, image enhancement, and pore segmentation. This image processing sequence is shown in Fig. 1. 2.3.1. Ham extraction An image processing algorithm including three steps was developed to extract the region of ham, namely image segmentation, morphological and mask operations. The RGB (red, green, and blue) image of ham was firstly partitioned from the black background using thresholding-based image segmentation method, and a binary image was obtained. After that, several morphological operations were implemented on the binary image to remove noises and gaps within the object, and a mask of ham with a homogeneous region was constructed. The region of ham was finally extracted by a mask operation on the original image.

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Image segmentation

Morphological operations

Ham extraction

was applied to the image after dilation. Thus, a mask was created. To extract the ham image, the mask was applied to each colour component of the original ham image, i.e., red, green and blue. Based on the extracted ham image, the whole area of ham can be easily obtained by counting the number of pixels.

Mask operation

Noise reduction

Contrast enhancement

Image enhancement

Image gradient computation

Marker detection

Pore segmentation

Watershed segmentation Fig. 1. The image processing sequence for ham extraction, image enhancement, and pore segmentation of ham.

Being a computationally simple method, thresholdingbased segmentation is a particularly effective technique for scenes containing solid objects resting upon a contrasting background. Since the ham images were acquired on a black background, thresholding-based segmentation technique was applied to distinguish the region of ham from the background with an optimal threshold. The threshold value was automatically obtained using OtsuÕs method (Otsu, 1979), in which the optimal threshold was chosen to maximise the interclass variance between dark and bright regions. With the optimal threshold, all pixels at or above the threshold were assigned as the region of ham and all pixels below the threshold were set as background. Unfortunately, in the area near the boundary of the binary image, there were some pixels belonging to the background that were wrongly assigned as ham because they straddled the boundary between ham and background and thus had an average brightness that happened to lie in the range selected by thresholding. Meanwhile, some parts belonging to ham with similar colour to the background were assigned as background, and formed some gaps within the region of ham. To remove these noises and gaps, several morphological operators were applied to the binary image obtained by the thresholding-based segmentation method. An erosion operation was firstly carried on the binary image to remove the noises. Then a dilation operation was implemented to fill the gaps within the region of ham. The dilation operation will also add a layer of pixels around the periphery of the ham. To remove the extra layer of pixel and the noises around the border area, another erosion operation

2.3.2. Image enhancement The preliminary results show that some structures of ham have similar colours to that of certain pores, which makes it difficult to extract only pores in the ham image by colour characterisation. Therefore, the ham images were converted to grey scale by eliminating the hue and saturation information while retaining the luminance. Ham images captured by the CCD camera are subject to various types of noise, which could be the signal noise including photon noise and shot noise (light has an inherent noise that derives from the stochastic nature of the photon flux), the readout noise (this is the noise caused by the camera electronics, mostly by the on-chip preamplifier, upon quantification of the signal but also dependent on the A/D conversion rate), and the dark noise (this is caused by current fluctuations on the photodiodes in the absence of light arising from thermally generated electrons). These noises may degrade the quality of the ham image and consequently it cannot provide correct information for subsequent image processing. In order to improve the quality of the ham image, operations need to be performed to remove or decrease degradations suffered in its acquisition. A median filter was employed to filter out the unwanted noise within the ham image. Since median filtering replaces the output pixel with the median of its neighbouring pixel values instead of a weighted sum of those values, it is able to better remove these noises without reducing the sharpness of the image. From the histogram of ham image after median filtering (Fig. 2), it can be seen that most values gather together, which indicates that the image is characterised by low con-

Fig. 2. The grey level histogram of ham image after median filtering.

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trast. One of the popular contrast enhancing techniques is histogram equalisation (Gauch, 1992), which works on the entire image. In the current work, the contrast-limited adaptive histogram equalisation (CLAHE) method was applied (Mathworks, 1998), which operates on small regions in the image, called tiles. Since each tile is enhanced, the contrast can be limited, especially in homogeneous areas. After the above image pre-processing, the minimum area size of the pores obtained is 6.73 · 10
3 mm2. 2.3.3. Pore segmentation As one of the most reliable methods for image segmentation, watershed transformation has been applied successfully to segment X-ray images of randomly oriented and touching pistachio nuts in the food industry (Casasent, Talukder, Keagy, & Schatzki, 2001). In the current study, an improved watershed algorithm was employed to extract pores from the grey level images of ham as precisely as possible. The concept of watershed was originally proposed by Beucher and Lantuejoul (1979) to solve the problem of image segmentation, which simulates a flooding process over the image surface. The ham image to be segmented is herein considered as a topographic surface, in which the altitude of a position is equal to the intensity of the corresponding pixel in the image. At the beginning, the regional minima are detected and looked upon as holes. Suppose the flooding liquid springs through the holes and progressively immerses the adjacent surface slowly into a lake, in order to prevent the merging of water streams coming from different holes, dams are built at the meeting locations. These dams correspond to the watershed lines, and the regions surrounded by the dams are the catchment basins. Since the watershed lines are the highest crest lines separating the regional minima, it seems natural to compute the watersheds of the image gradient (Vincent, 1993). The catchment basins should theoretically correspond to the homogeneous grey level regions of the image. In the current research, the Prewitt gradient operators (Fig. 3) were used to compute the gradient of the image at every point in the image (Prewitt, 1970). The two kernels Px and Py are convolved separately with the image to obtain the gradient in each direction Gx and Gy. Then the gradient magnitude can be calculated by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1Þ G ¼ G2x þ G2y . -1

0

1

-1

-1

0

1

0

0

0

-1

0

1

1

1

1

Px

-1 -1

Py

Fig. 3. The Prewitt gradient operators.

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Each minimum of the obtained gradient of the image will produce a catchment basin in the final segment result. Unfortunately, as the ham image consists of noises or local irregularities, there are a great number of minima generated in the image gradient. Therefore, a major problem with the watershed algorithm is that it may over-segment the ham image, and yield incorrect results of pores. To overcome the problem of over-segmentation, Meyer and Beucher (1990) proposed a method called marker-controlled watershed. The foreground markers are connected pixels within each of the objects, while the background markers are pixels out of any object. The design of robust marker detection techniques involves using the knowledge specific to the series of images under study (Vincent, 1993). To find the markers of the image to be segmented, an image processing algorithm with three steps was developed. To correctly mark the pores, one has to account for the fact that the pores are smaller compact spots than the non-pore areas. A dilation followed by reconstructing the original image from its dilation was firstly implemented to filter out all the pore parts in the image while preserving the others entirely. The resulting transformation is often called closing by reconstruction and belongs to the category of the algebraic closings (Vincent, 1993). After that, the original image was subtracted from its morphological closing by reconstruction. Then the markers were detected by a relatively simple thresholding method, where the white pixels belong to the pores while the background pixels are in black. Again, OtsuÕs method (Otsu, 1979) was applied to automatically find the optimal threshold value, which was chosen to maximise the interact as variance between dark and bright regions. In practice, some foreground markers will go right up to the edge of the pores, which is undesirable and will lead to wrong results. In order to shrink the marker blobs of pores a bit, a morphological closing was implemented followed by an erosion operation. Similarly, the background markers should not be too close to the edges of the pores. The background was thinned by computing the watershed transform of the distance transform of the shrunk image (Mathworks, 1998), and then finding the watershed crest lines of the result. After marker extraction, the gradient image of ham was modified, where it only had regional minima at the locations of pore and background markers. Based on the modified gradient image, the watershed transform could now be used to obtain the desired segmentation results. The image processing algorithm described above was implemented with Matlab version 5 (Mathworks, 1998) under Windows 2000 on a Dell Workstation 400. 2.4. Characterisation of pores From the segmented pores, the porosity, number of pores, pore size, and size distribution were measured. Porosity is the most common terminology used in characterising pores (Rahman, 2001). In this study, porosity was calculated as the ratio between the total area of pores

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and the area of ham disk. The pore size was computed as the area of pore, which was obtained by simply counting the number of pixels. 2.5. Determination of water content The water content was obtained by drying the meat in an oven at 100 C to constant weight. Each treatment had five replications. 2.6. Measurement of texture To measure the Warner–Bratzler shear (WBS) force, samples were cut parallel to the longitudinal orientation of the muscle fibres into 45 mm · 30 mm · 2.0 mm sizes using a slicer (Model TM250, Fabbrica Affecttatrici Cavaria, Cavaria, Italy) and examined by Instron universal testing machine (Model No. 5544, Instron Corporation, Bucks, UK). Ten strips were sheared at their central point at room temperature on the WBS device attached to the Instron universal testing machine, using a 0.5 kN load cell and a crosshead speed of 50.0 mm/min. Shear value was recorded at the peak force of the shearing. For the texture profile analysis (TPA), cylindrical samples (D = 25 mm, H = 20 mm) were cored with a cork borer. Using a 60 mm circular flat disk attached to the Instron, the samples were compressed to 50% of their original height at a crosshead speed of 50 mm/min to measure the texture attributes, including hardness, springiness, cohesion, gumminess, and chewiness. 2.7. Correlation analysis To investigate the effect of pores on the processing time, water content and texture, the correlation coefficients between the pore characterizations and the quality attributes of pork ham were computed using SAS 8.12 (Anon, 2000). However, being too analytical, the statistical processing of heterogeneous data according to classical methods does not provide a global knowledge on the relationships among the different variables (Destefanis, Barge, Brugiapaglia, & Tassone, 2000). To meet this need, principal component analysis (PCA) was applied in this work. PCA gives an interpretable overview of the key information on the relationships among the different variables in a graphical plot, i.e., the loading plot. In the loading plot, variables close together are positively correlated, while variables lying opposite to each other tend to have a negative correlation. 3. Results and discussion 3.1. Image processing An example is shown in Fig. 4(a) to demonstrate the performance of the algorithm. It can be observed that there existed some noises, especially around the border area of ham. One possible reason is that when the ham image is

Fig. 4. Results of the image processing algorithm: (a) original image; (b) extracted image; (c) enhanced image; (d) segmented image.

captured on a black background, there are some shade formed around the border area. The image processing results of ham extraction, image enhancement, and pore segmentation are shown in (b), (c), and (d) of Fig. 4, respectively. From the result of ham extraction shown in Fig. 4(b), it can be seen that the noises around the border area were successfully removed, while the inner part of the ham was well preserved. As mentioned before, it is difficult to discriminate some pores from others by colour information. Therefore, colour-based segmentation methods could not perform well when applied to partition pores in a pork ham image. Instead, the extracted ham was converted to grey level image, and the morphological watershed segmentation method was employed to separate the pores. The reason is that morphological segmentation techniques rely on morphological tools, and are very efficient to deal with object-oriented criteria such as size and contrast. However, the watershed segmentation method is very sensitive to noise and contrast in an image (Huang & Chen, 2004). To reduce the possible noises in the inner part of ham, a 3 · 3 median filter was applied to the converted grey level image of ham. After that, contrast-limited adaptive histogram equalization method (Mathworks, 1998) was applied to enhance the contrast of the de-noised ham image. It is obvious that the contrast of the ham image was enhanced as shown in Fig. 4(c). Based on the enhanced image of ham, the pores were partitioned using the method described in Section 2.3.3. It can be observed that almost all the pores in the image were segmented properly as shown in Fig. 4(d). Unfortunately, there are two pores wrongly separated into two

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parts in the segmented result. Two marks assigned for those pores might contribute to the wrong segmentation. However, statistically, it will not markedly influence the characterisation of pores. There is almost no effect on the porosity computation because the total pore area of two parts is nearly equal to the whole one. Further more, since there are sixty circular disks for each ham sample captured and only few pores were partitioned wrongly, the number of pores segmented properly is far more than the one wrongly divided. Therefore, the effect on the number of pores and the pore size distribution can be neglected. Using the developed image processing method, the pore areas were measured and the porosity was calculated. The area size distribution of pores is given in Table 1. The results indicate that there is a wide range of pore size within the samples. The smallest area of pore is only 6.73 · 10
3 mm2, while the area of the biggest pore is 1.69 mm2. The statistical analysis shows that 79.81% of pores have area sizes between 6.7 · 10
3 and 2.02 · 10
1 mm2. However, there are only 8.95% of pores with area sizes more than 4.04 · 10
1 mm. This type of size distribution is consistent with the reports from other researchers. Micropores accounted for 80% of pore volume in freeze-dried raw chicken meat (Farkas & Singh, 1991), 70% and 84% of pore volume in ground beef samples and in ground beef extended with soy protein, respectively (Kassama, Ngadi, & Raghavan, 2003). Pork ham can be considered as a multiphase system, i.e., gas–liquid–solid system (Rahman et al., 1996), which is hygroscopic and capillary porous with definite void structures that modulate mass transport during heat processing (Kassama & Ngadi, 2005). The pore formation in pork ham is very complex, which is not only a consequence of the pork itself, but also a result of the subsequent processing, i.e., cooking and cooling. For pork itself, there exist some void structures, which are primarily the result of the hierarchical arrangement of muscle fibres and connective tissues (Kassama & Ngadi, 2005). The genotype of pork could influence the number of pores formed. Hullberg, Jofanson, and Lundstro¨m (2005) found that there were more pores for RN
allele carriers, which was discovered in pigs of the Hampshire breed or crosses with Hampshire in the mid 1980s (Le Roy, Naveau, Elsen, & Sellier, 1990). In the cooked meat industry, pork hams are normally formed by small pieces of boned-out legs, injected with brine solution, and tumbled under vacuum. Therefore, besides the internal pores of pork, there are also some pores among the meat pieces before cooking for pork ham. However, tumbling has an effect on the pore characteristics caused by alterations of the cell structure. Compared with non-tumbled loins, tumbled loins contained

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much smaller numbers of pores and larger average areas (Hullberg & Jallerini, 2003). As tumbling time increased, defects such as holes and cracks were reduced for both cured pork loins (Barbieri, Ferrari, & Ghillani, 1994) and restructured cured buffalo meat (Sharma, Kumar, Nanda, & Kumar, 2002). McDonald and Sun (2001b) identified that total porosity of cooked meat comprises actual pores within the meat matrix and void space volume. The majority of small pores (see Table 1) are most likely the result of cooking. During cooking, heating caused denaturation of protein, which may lead to structural collapse, and allowed for the dehydration and shrinkage of the meat and the formation numerous actual pores. The porosity and pore sizes of samples tended to decrease with frying time (Kassama & Ngadi, 2005), which could be attributed to physicochemical changes that triggers certain visco-elastic behavioral characteristics of proteins. Intense heating may have prompted meat protein gelation, a condition that has caused agglomeration of protein and shrinkage of the muscle causing the alteration pore structure. The larger pores might mainly contribute to the void space, while some of them were developed during cooling. McDonald and Sun (2001a) reported that an effect of cooling on porosity. In their work, they also pointed out that development of porosity during cooling of the cooked meat is dependent on the initial moisture of the samples as well as their composition, muscle fibre orientation, available surface area, and physical properties such as thermal conductivity or thermal diffusivity. 3.2. Correlation analysis Being a defect often observed in processed meat products, internal pore formation is normally unappealing for the consumers and therefore negative for the meat industry (Hullberg et al., 2005). The CORR procedure (Anon, 2000) was employed to study the correlation between the pore characterisations and the processing time, water content, and texture of pork ham. The obtained correlation coefficients are shown in Table 2. It can be observed that the total number of pore (TNP) significantly negatively related with the water content of pork ham (P < 0.05). For the raw meat, the variation in the total extracellular space was found to explain 39% of the variation in early postmortem drip loss in pork (Scha¨fer, Rosenvold, Purslow, Andersen, & Henckel, 2002). During cooking, heat denaturation of myofibrillar protein and collagen would create more pores, and at the same time increase water loss (Ofstad, Kidman, Myklebust, & Hermansson, 1993). As a result, the more pores that existed, the more water was lost during processing. The

Table 1 The area size distribution of pores Range of pore size (·10
3 mm2) Numbers Percentage (%)

6.73–67.3 4008 52.82

74.0–134.6 1382 18.21

141.3–201.9 666 8.78

208.6–269.2 414 5.46

275.9–336.5 282 3.72

343.2–402.8 184 2.42

>403.8 652 8.59

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Table 2 Correlation coefficients between the pore characterisations and processing time, water content, and texture of pork ham CkT TPN Po


0.56
0.67

CIT
0.46
0.41

TPT
0.74
0.74

WC *


0.96
0.95*

WBS

Ha

Sp

Co

Gu

Ch

0.52 0.62

0.74 0.62


0.39
0.50

0.67 0.54


0.19
0.06

0.56 0.68

Note. TPN, total pore number; Po, porosity; CkT, cooking time; CIT, cooling time; TPT, total processing time; WC, water content; WBS, Warner– Bratzler shear force; Ha, hardness; Sp, springiness; Co, cohesion; Gu, gumminess; Ch, chewiness. * Levels of significance = P < 0.05.

relationship between pore development and moisture loss was investigated in the work of Kassama and Ngadi (2005). They also reported that the increase in pore development was associated with increased moisture loss. As indicated in Table 2, water content was highly negatively correlated with porosity (P < 0.05). The action of cooking caused loss of water, and consequently decreased water content and increased the porosity of pork ham. During cooling, the loss of water is a type of drying process, which has been shown to lower moisture content and increase porosity of cooked meat products (McDonald & Sun, 2001a; McDonald, Sun, & Kenny, 2000). Water evaporation plays an important role in energy exchanges during cooling (Girard, 1992). To facilitate the cooling process, it is necessary to remove a certain proportion of sample mass in the form of water vapour (McDonald & Sun, 2001a). In the mean time, as moisture transport is closely related to the formation of pores (Rahman, 2001), the more water loss during cooling, the higher the porosity. Both TNP and porosity were negatively correlated with the cooking time. More TNP (r =
0.56) and higher porosity (r =
0.67) will result in quicker cooking time. The cooking efficiency is affected by the thermal properties of foods, which can be calculated from the compositions of foods and the thermal properties of each component. The main component of pork are water, protein, and fat, while the amount of other components such as salt and ash are very small. Thermal conductivity of protein and fat is considerably less than that of water (Mittal & Blaisdell, 1984). The thermal conductivity of meats increases with increasing water content. Since the pork ham is immersed in water for cooking, the pores are filled up with water during the whole cooking procedure. More pores and higher porosity mean that more water is contained in the pork ham, leading to a shorter cooking time. The relationships between the cooling time and TNP and porosity are shown in Table 2. During the air blast cooling process, heat is transferred from the core of the pork ham to the surface by conduction and released to the cooling environment mainly by convection. The cooling rate of air blast cooling is governed by the thermal conductivity of the cooked pork ham (Wang & Sun, 2002). For the same reason higher thermal conductivity of the cooked pork ham with more TNP and higher porosity would result in a shorter cooling time. However, as the cooling procedure progresses, the thermal conductivity of pork ham decreases with the decrease in liquid water content due to moisture loss. Therefore, compared with the cooking time,

the cooling time has a poorer relation with TNP and porosity. McDonald and Sun (2001a) found a similar result for vacuum cooling of large cooked beef products. In the pore area, capillary flow and surface tension will be lower and liquid diffusion will be quicker (Luikov, 1975). The sample with greater porosity will have faster water/vapour diffusion, and thus less cooling time. As the total processing time (TPT) is the sum of cooking and cooling time, TPT has thus negative relationships with TNP and porosity. For texture analysis, positive correlations were found between the pore characterisations and WBS, hardness, cohesion, and chewiness, respectively, while springiness and gumminess were negatively related to TNP and porosity. The mechanical properties of foods are a direct consequence of microstructure that in turn resulted from tissue organization, interactions due to change in chemical composition and physical forces (Rahman & Sablani, 2003). Measured using mechanical methods, the textural characteristics are profoundly affected by their porous structure (Huang & Clayton, 1990). It has been demonstrated that both cooking and cooling could lead to an increase of porosity of pork ham due to water loss. Greater porosity indicated higher water loss of pork ham. Water is not only a medium for reaction but also an active agent in the modification of physical properties (Huang & Clayton, 1990). Loss of water might lead to the compression of muscle fibres and increase of the concentration of the interstitial fluid, and thus enhance adhesive power and strength (McDonald et al., 2000). Therefore, pork ham with greater TNP and porosity will have higher shear force values, and a reduction in tenderness but an increase in hardness, cohesion, and chewiness. The effect of porous structure on texture properties in our study supports the finding by Bertram, Aaslyng, and Andersen (2005). They showed that a reduction in juiciness and tenderness could be ascribed to changes in the size of the pore confining the myofibrillar water within the meat in combination with an expulsion of water. The apparent decrease of springiness and gumminess with increasing TNP and porosity could be explained by stress–strain analysis. Structurally, the porosity and number of cavities might influence the ability to deform. The meat sample with larger porosity and more pores becomes weaker, and less mechanical stress is needed to cause yielding and fracturing. However, the exact nature of the relations between the texture and pore characteristics is not clear, and has not been extensively studied. More experiments are needed to explore the exact nature of the effect of porous structure on the texture of pork ham.

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with large pores (DeFreitas, Sebranek, Olson, & Carr, 1997; Hermansson, 1985) and thus have a positive effect on the quality of pork ham. 4. Conclusions

Fig. 5. Plot of the first two loading vectors.

PCA analysis showed that 48.63% of the total variation was explained by the first principal component, while the second principal component explained 30.20% of the total variation. It can be observed from the loading plot (Fig. 5) that the first principal component is mainly defined by porosity, TNP, water content, and TPT, and the second one by cohesion, hardness, chewiness, gumminess, WBS, and cooking time. All the pore characterisations are loaded on the first principal component, which suggests that the effect of pore characterisations on the quality attributes of pork ham should be evaluated under this component. Since water content and TPT lie far away from porosity and TNP, there exist negative relationships between them. Cooking time, springiness, cooling time, and gumminess are located on the opposite side to porosity and TNP, which indicates that the porous structures have a negative effect on them. However, the effects are weaker than that on water content and TPT. Conversely, porosity and TNP positively correlated with cohesion, hardness, WBS, and chewiness because all of them are located on the right side of the loading plot. Compared with the classical correlation analysis, it can be seen that PCA is a very useful method to determine quickly the relationships among the variables themselves, and allows at first sight the identification of how variables correlate with each other (Destefanis et al., 2000). The relations between the pore characteristics and the quality attributes of pork ham are very complex in nature (Rahman & Sablani, 2003). Pore formation is dependent on the quality of raw meat, pre-treatment, and processing, which will influence the pore size, geometry or shape, porosity, and size distribution of the meat matrix. The variation in pore characteristics has various effects on the processing time, water content, textural, and other quality attributes of the pork ham. A well-structured matrix and a fine, uniform structure with numerous small pore or open spaces would probably result in more absorptive capacity and better retention of water compared to coarse structures

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