Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture JAVIER MERÁS FERNÁNDEZ MSc GIS Cheng-Jin Du, Da-Wen Sun Meat Science 72 (2006)

Introduction Material and methods –Preparation of pork ham –Image acquisition –Image processing Ham extraction Image enhancement Pore segmentation –Characterisation of pores –Determination of water content –Measurement of texture Results Conclusions Outline

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 distribution influences the mechanical and textural characteristics of the meat. Pores also affect sensory properties of foods and have a direct effect on the other physical properties. Therefore, the information on pores is important for evaluating the quality of a food product and to predict other properties. Porosity is still mostly measured using manual methods which are destructive and laborious. A reliable, relatively quick and easy method for pore characterisation would be a very desirable tool. Therefore, it is necessary to develop an automatic method for pore structure characterisation of pork ham. Such a method has to provide an acceptable level of pore information using computer vision techniques.

Material and Methods (1) Preparation of pork ham –Sixty circular disks (25 mm in diameter and 4 mm thick) of ham were used. Image acquisition –Images of these circular disks of pork ham were captured on a black background under two fluorescent lamps with plastic light diffusers of pore information. –The image acquisition system used in this study consists of a Dell Workstation 400 equipped with an IC-RGB frame grabber. –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. –The same exposure and focal length were used for all the images. –413,280 pixels/image with 24 bits per pixel, and saved in TIFF format. Cheng-Jin Du, Da-Wen Sun, (2006)

Material and Methods (2) Image processing –Ham extraction An image processing algorithm was developed to extract the region of ham. The RGB image of ham was firstly partitioned from the black background using thresholding-based image segmentation method. Some background pixels in the area near the border were assigned as ham. After that, several morphological operations were implemented on the binary image to remove noises and gaps within the object. A mask of ham with homogeneous region was constructed. The mask was applied to each colour component of the original ham image. Cheng-Jin Du, Da-Wen Sun, (2006)

Material and Methods (3) –Image Enhancement Some structures of ham have similar colours to that of certain pores. Difficult to extract only pores in the ham image by colour characterisation, ham images converted to grey scale by elimination the hue and saturation information but retaining the luminance. Images are subject of various types of noise: signal noise, readout noise, dark noise. A median filter was employed to filter out the unwanted noise within the image. Image characterised by low contrast. Contrast enhancing technique (histogram equalisation) was applied. Cheng-Jin Du, Da-Wen Sun, (2006)

Material and Methods (4) –Pore segmentation Watershed algorithm was employed to extract pores from the grey level images of ham as precisely as possible. It 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. The regional minima are detected and looked upon as holes. Due to some noises, the 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.

Material and Methods (5) Characterisation of pores –From the segmented pores, the porosity, number of pores, pore size, and size distribution were measured. Porosity (area of pores/total area of ham). Determination of water content –The water content was obtained by drying the meat in an oven at 100 C to constant weight. Measurement of texture –Measure the texture attributes, including hardness, springiness, cohesion, gumminess and chewiness. Cheng-Jin Du, Da-Wen Sun, (2006)

Results The noises around border area were successfully removed. Colour-based segmentation methods could not perform well when applied to partition pores in a pork ham image. Almost all the pores in the image were segmented properly % of pores have area sizes between and mm2. 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. It can be observed that the total number of pore (TNP) significantly negatively related with the water content of pork ham (P < 0.05).

Conclusions The results have demonstrated the ability of the method based on computer vision to characterise pore structure of pork ham. Using image processing techniques, the pores can be partitioned automatically, and the porosity, number of pores, pore size, and size distribution can be calculated efficiently. Processing time is negatively correlated with total number of pores and porosity. Negative relationship between water content and pore characteristics. The relations between the pore characteristics and the texture attributes of pork ham are very complex in nature.

References Cheng-Jin Du,& Da-Wen Sun (2006). “Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture”. Meat Science 72, 294–302.