The process of image analysis

To study the kinetics of colour changing in food samples, an image analysis system has to measure representatively and accurately, the colour of the sample. The image processing steps involve acquisition, pre-processing, segmentation and statistic calculation (Batchelor & Searcy, 1989; Jain, 1989 and Gunasekaran, 2001).

2.5.2.1 Image acquisition

Images are captured using a colour digital camera. The colour digital camera is located vertically above the sample. Sample illuminators and the computer digital camera should be placed in a dark room in order to avoid light and reflection from the environment. Prior to operating the image system, the white balance needs to be set. The photographs of a colour standard are necessary to ensure the accuracy of the lighting system and digital camera (Batchelor & Searcy, 1989; Gunasekaran & Ding, 1994; Gunasekaran, 1996; Gunasekaran, 2001 and Brosnan & Sun, 2002).

2.5.2.2 Image pre-processing

Prior to analysis of the digital images, they can be pre-processed to improve their quality by manipulating the data for correction of geometric distortion, removal of noise, grey level correction and correction for blurring (Hamey et al., 1993 and Brosnan & Sun, 2004). The noise of the image can be removed and the contrast can be enhanced by using digital noise filtering. For example a linear Gaussian low pass filter or

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averaging filter can be applied (Batchelor & Searcy, 1989; Jain, 1989; Brosnan & Sun, 2004; Abdullah et al., 2004 and Gonzalea & Woods, 2008).

2.5.2.3 Segmentation

The purpose of this step is to split the part of interest from the background by classifying disjoint regions of the image intensity. This segmented image is a binary image consisting only of black and white pixels, where ‘0’ (black) and ‘1’ (white) mean background and object, respectively (Jain, 1989; Gunasekaran & Ding, 1994 and Gunasekaran, 1996). Three techniques; thresholding, edge-based segmentation and region-based segmentation are mostly used in the segmentation step (Sun, 2000; Brosnan & Sun, 2004 and Sonka et al., 2008). Thresholding is commonly used because it is a simple and fast technique for characterising image regions based on constant reflectivity or light absorption of their surface. Edge-based segmentation detects discontinuities in grey level, colour or texture. Region segmentation groups the similar pixels together to be represented as a boundary or a region of interest (Brosnan & Sun, 2004).

2.5.2.4 Analysing and measuring

After the segmentation step, the objects of interest have been extracted and are ready to be analysed. For the study of browning kinetics in food, the colour space commonly used to describe the browning reaction is the L*a*b* colour system because they are the standard colour parameters for agriculture and in the food area (Yam & Papadakis, 2004). In addition the L*a*b* colour space is close to uniform, in that numerical changes in L*a*b* are proportional to the perceived colour differences (León et al., 2006 and Hunt & Pointer, 2011). However, a digital image normally presents in the RGB colour model, which is the most often used model in capturing the intensity of the light in the red (R), green (G) or blue (B) spectrum, respectively.

The conversion of RGB into L*a*b* units is therefore an important step when applying an image analysis method to agricultural or food products. The RGB-L*a*b*

conversion cannot be done directly using a standard formula like a conversion from centimetres to inches (Ilie & Welch, 2005 and León et al., 2006). The conversion can be

Chapter 2: Literature review 21 computationally approached using the mathematical transform model with known parameters (Segnini et al., 1999;Paschos, 2001; Mendoza & Aguilera, 2004).

The summary of the image analysis method step is shown in Figure 2.7.

Figure 2.7 Schematic representation of the image analysis step (Pedreschi et al., 2006).

2.5.2.5 Colour system used in the browning kinetic study

The colour system generally found in the studies of browning kinetics is the Lab colour system. Lab is the shortened name of two different colour spaces, which are the Hunter

Lab (Hunter L, a, b) and the other is CIE (CIE 1976 L*a*b*). Lab is an informal short form of a colour-opponent space. Both spaces are absolute colour spaces and are derived based on nonlinear compression of the master space CIE 1931 XYZ colour space coordinates with the dimension L for lightness and a and b for the colour- opponent dimensions (Fairchild, 2005; Schanda, 2007 and Hunt & Pointer, 2011).

Hunter Lab Colour Space

The XYZ system has a drawback with respect to being a perceptually non-uniform colour scale and does not give a good indication of sample colour. So, the Hunter Lab

colour scale was developed in 1966. The Hunter Lab colour scale is more visually uniform than the XYZ colour scale. In a uniform scale, the difference between points plotted in the colour space corresponds to visual differences between the colours plotted. The Hunter Lab colour space is organized in a cube form. The L axis runs from

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top with the maximum value of 100 to 0 at the bottom. At the maximum of 100 is a

perfect white and the minimum of 0 is an absolute black. The ‘a’ and ‘b’ axes have no

specific numerical limits. Positive ‘a’ is red; negative ‘a’ is green. Positive ‘b’ is

yellow; negative ‘b’ is blue. A diagram of the Hunter Lab colour space is shown in Figure 2.8 (Gilchrist et al., 1999).

Figure 2.8 The Hunter Lab colour space

CIE L*, a*, b* Colour Space

CIE L*a*b* was invented in 1976 in order to solve the non-uniform scale problem in CIE XYZ colour space. CIE L*a*b* (1976) is usually used to describe the colours visible to the human eye because it is a more uniform colour space and the most complete colour model (McLaren, 1976 and Schanda, 2007). It was developed by the International Commission on Illumination (McLaren, 1976). The * after L, a and b

mean these value are derived from L, a and b in CIE colour space, therefore they present in the form of L*, a* and b*. The meaning of three parameters (L*a*b*) in the model represent the lightness, redness and yellowness of the colour, respectively. The value of the lightness is between 0 and 100 (L* = 0 yields black and L* = 100 indicates white), the redness (a*) position between red and green (a* is [-] negative values indicating green while [+] positive values indicate red) and the yellowness (b*) position between yellow and blue (b* is [-] negative values indicating blue and [+] positive values indicate yellow) (Gilchrist et al., 1999).

Different colour systems have been used in the studies of browning kinetic of foods. Some studies employed the Hunter Lab colour scale to model the browning kinetic such

Chapter 2: Literature review 23 as the study of bread baking (Zanoni et al., 1995), maize grits extrusion (Ilo & Berghofer, 1999), peach puree heating (Ávila & Silva, 1999), French fries in deep frying (Krokida et al., 2001) and sesame seeds roasting (Kahyaoglu & Kaya, 2006).

However, the CIE L*a*b* scale has been more popular to apply in the browning kinetic study area than the Hunter Lab colour system. For example in the measurement of the kinetics of colour change using L* for pea puree heating (Shin & Bhowmik, 1995), hazelnut drying (Lopez et al., 1997), cracker baking (Broyart et al., 1998), soy milk heating (Kwok et al., 1999), apple puree (Ibarz et al., 2000), tofu during frying (Baik & Mittal, 2003), potato cooking and frying (Nourian & Ramswamy, 2003), gulabjamun

balls in deep frying (Jayendra Kumar et al., 2006), almond roasting (Lukac et al., 2007), and in bread baking (Purlis & Salvadori, 2007, 2009 and Purlis, 2010).

It was found from the review of the Hunter Lab colour space system, applications in the study of browning kinetics of food processes that most of the studies applied the conventional colorimeter as the measuring instrument in evaluating the browning reaction of food processes. Therefore, it can be mostly found that Hunter Lab colour space is used in colorimeter instruments. Conversely, the CIE colour system is applicable to use in the image processing method. This may be because the CIE colour system has advantages over the Hunter Lab colour system and it is a more recently developed colour system which has a more uniform evaluation method than the Hunter

Lab colour system. Therefore the CIE colour system will be used in the image analysis method for this study.