Quantification of surface iridescence in meat products by digital image analysis

Journal Pre-proof Quantification of surface iridescence in meat products by digital image analysis

Chiara Rüdt, Monika Gibis, Jochen Weiss PII:

S0309-1740(19)30601-1

DOI:

https://doi.org/10.1016/j.meatsci.2020.108064

Reference:

MESC 108064

To appear in:

Meat Science

Received date:

8 July 2019

Revised date:

8 January 2020

Accepted date:

20 January 2020

Please cite this article as: C. Rüdt, M. Gibis and J. Weiss, Quantification of surface iridescence in meat products by digital image analysis, Meat Science (2019), https://doi.org/10.1016/j.meatsci.2020.108064

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© 2019 Published by Elsevier.

Journal Pre-proof

Quantification of surface iridescence in meat products by digital image analysis

Chiara Rüdta, Monika Gibisa, Jochen Weissa,* [email protected] Department of Food Physics and Meat Science, Institute of Food Science and

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Biotechnology, University of Hohenheim, Garbenstraße 21/25, 70599 Stuttgart, Germany

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*

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Corresponding author.

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ABSTRACT

Iridescence extent is commonly evaluated by sensory analysis but it is a time-consuming

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and cost-intensive method. A low-cost, rapid and objective alternative is digital image

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analysis. Here we report the development of an image analysis method for quantification of iridescence in meat products. Two segmentation techniques (global thresholding and kmeans clustering algorithm) were tested for their capability to divide images into segments of iridescent and non-iridescent areas. Images segmented using k-means clustering algorithm resulted in slightly higher iridescent areas than images segmented with global thresholding (mean difference of 1.24%) but no significant difference (P > 0.05) between the iridescent areas calculated by both methods was observed. Almost perfect agreement (κ = 0.800, p = 0.001) was observed between the image analysis and

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Journal Pre-proof the visual evaluation. The results from this study showed that digital image analysis is an effective tool for evaluating surface iridescence in meat and meat products. Keywords: iridescence, meat, ham, image analysis, quality, meat color

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1. INTRODUCTION

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Surface iridescence is a physical phenomenon often found in raw meat and meat

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products consisting of intact muscle tissue like cooked ham from pork and pastrami from beef (Wang, 1991). Although many different explanations for iridescence in meat have

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been proposed, the source of the interference causing iridescence is still not completely

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understood. Martinez-Hurtado, Akram, and Yetisen (2013) attributed iridescence to a

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diffraction grating of protruding fibrils from the meat surface. However, this diffraction theory must be seen critically as argued conclusively by Swatland (2018) stating that

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iridescence caused by a diffraction grating, as for example in a peacock feather,

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disappears under water whereas iridescence caused by multilayer interference (e.g. in minerals) persists. Swatland (2012a) hypothesized that multilayer interference caused by reflections from refractive index boundaries (A-bands and I-Bands) at the sarcomere discs is the source of meat iridescence. A problem about meat iridescence is that consumers might misinterpret the rainbow-colored shine as an undesired addition of chemical additives or a microbial spoilage that may also result in green discolorations of the meat surface due the formation of green derivatives of myoglobin caused by the bacterial production of hydrogen peroxide or the growth of hydrogen sulfide-producing microorganisms, e.g. 2

Journal Pre-proof Pseudomonas spp. or Alteromonas putrefaciens (Faustman & Cassens, 1990; Swatland, 1984; Wang, 1991). Since consumers use the appearance and color as an indicator for freshness and have specific color expectations on meat and meat products, this misconception can lead to consumer rejection of iridescent meat products (Wang, 1991). Those products might either be discarded by the consumer or not be purchased at all and disposed by the retailers after exceeding the best before date. Especially in high income

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countries food waste of meat and meat products occurs mainly at the end of the food

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supply chain and large quantities of food are wasted due to quality standards over-

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emphasize appearance (FAO, 2011). According to the annual reports of the international

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DLG (German Agricultural Society) quality test for ham and sausages, iridescence is one of the most common quality deviations in both raw cured and cooked pork hams (Gibis,

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2018; Lautenschläger, Hillgärtner, & Thumel, 2016). Considering the importance of food

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waste in terms of economic, environmental and social impacts, there is a need for further research on meat iridescence to investigate factors affecting iridescence and to gain a

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better understanding of the underlying mechanisms in order to prevent consumer

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concerns and refusal. A major obstacle to researchers interested in this field is the difficulty of quantification and the lack of suitable methods for investigating structural colors in meat and meat products (Mancini, 2007). Previous studies quantified the iridescence extent by sensory evaluation (Fulladosa, Serra, Gou, & Arnau, 2009; Kukowski, Wulf, Shanks, Page, & Maddock, 2004; Obuz & Kropf, 2002; Oliver et al., 2006). However, there are several limitations to this method. Iridescence is an angle-dependent reflection. In meat products, it only appears in a certain combination of observation angle, lighting angle and sample orientation (Swatland,

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Journal Pre-proof 1984). Furthermore, the illumination type (direct vs. diffuse) is critical for the occurrence of iridescence. Our own observations showed stronger iridescence in samples illuminated with direct light than samples illuminated with diffuse light. In a standard sensory analysis, this angle and illumination dependency may not be taken into account when evaluating surface iridescence. Therefore, the extent and intensity of the iridescence might be evaluated as being lower than it actually is, or the iridescence may not be

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perceived at all by the panelists. Another limiting factor of the conventional method is the

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panelists’ training. To obtain accurate data, the sensory panel must consist of qualified

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and trained panelists but panel training as well as the actual performance of sensory tests

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are accompanied by high cost and time aspects.

Digital image analysis is a fast, low-cost and objective alternative to conventional

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methods for measurements of color, shape, size, texture and for quantification of surface

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characteristics (Brosnan & Sun, 2004). Many studies have been performed to develop computer vision systems for quality evaluation, color measurements and texture analysis

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in different types of food (Briones & Aguilera, 2005; Mendoza, Dejmek, & Aguilera,

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2006; Tan, 2004). In particular, the application of computer vision systems for the classification and quality evaluation of meat with regard to meat color (Girolami, Napolitano, Faraone, & Braghieri, 2013), texture (Li, Tan, & Shatadal, 2001) or marbling characteristics (Faucitano, Huff, Teuscher, Gariepy, & Wegner, 2005) has been studied extensively. However, no approaches using digital image analysis for the evaluation of iridescence as a quality defect of meat and meat products have been presented yet. In this study, the development of a digital image analysis method for the quantification of iridescence in meat products is presented. An image acquisition system

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Journal Pre-proof that allows the adjustment of the observation angle, illumination angle and sample orientation was constructed to observe maximum iridescence. To reduce costs and ease operation for untrained users a simple digital camera and open-source software were used. Since segmentation is a critical step in image processing, two different segmentation techniques (global thresholding and k-means clustering algorithm) were tested for their capability to segment digital images of cooked pork ham into iridescent

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and non-iridescent regions. The results of the digital image analysis were compared to the

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results obtained by conventional sensory analysis. This new method allows for an

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objective, low-cost and rapid quantification of the iridescence in meat products and

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therefore might help to overcome the particular challenges of methodology when

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investigating iridescent colors.

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Materials

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2. MATERIALS AND METHODS

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Nine cooked pork hams were produced from longissimus thoracis et lumborum (LTL) purchased from a local wholesaler (Mega eG, Stuttgart, Germany). LTL was chosen due to the predominantly parallel orientation of the muscle fibers and their susceptibility to iridescence when cut perpendicular to the slicing blades. The muscles were injected (automatic pickle injector type PI 17, Günther Maschinenbau GmbH, Dieburg, Germany) with 15% w/w brine consisting of cooled water, 20% w/w curing salt with nitrite (NaCl + NaNO2, 0.04 – 0.05 g kg-1, Zentrag eG, Frankfurt, Germany) and 3.3 g kg-1 sodium ascorbate (C6H7NaO6, Gewürzmüller GmbH, 5

Journal Pre-proof Ditzingen, Germany). The injected muscles were vacuum-packed, vacuum-tumbled for 2 h at 2 °C (Vakona GmbH, Ditzingen, Germany) and cooked (universal heating smoking chamber type Unigar 1800 BE, Ness-Smoke GmbH & Co. KG, Remshalden, Germany) after a resting period of 12 h with saturated steam and a chamber temperature of 74 °C to a core temperature of 70 °C. The weight of the injected hams was between 831.8 g and 1008.0 g and the total cooking time ranged between 60 and 90 min depending on the ham

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dimensions. After cooling at 2 °C, two slices per ham were sliced perpendicular to the

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muscle fiber orientation with a slicing machine (VS8 A, Bizerba SE & Co. KG, Balingen,

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Germany) to a thickness of about 1 cm, packaged in vacuum bags (PA/PE 90 µm, Mega

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eG, Stuttgart, Germany) and stored for further use at 2 °C. For the comparison between the digital image analysis and the sensory analysis, a digital image was acquired and

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processed from each of the two slices as described in the following section. In the sensory

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analysis, one of these two slices was visually evaluated by a sensory panel. For the comparison of the different segmentation techniques a total of 30 images were acquired

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and processed. Swatland (2012b) stated that iridescence only appears when myofibers are

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sectioned transversely and therefore has some connection with light passing along the length of myofibers. Therefore, one slice was additionally cut longitudinal to the fiber orientation to obtain a sample showing no iridescence. The slicing blade was very sharp and resulted in smooth surfaces with no disruptions of the microstructure.

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Journal Pre-proof Quantification of iridescence by image processing Digital image processing comprises specific steps to visualize a scene, collect and analyze data for further interpretation (Figure 1). A description of each step with regard to our method follows:

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Image acquisition

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Images were captured using a self-constructed image acquisition system (Figure 2).

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The system is comprised of a semicircular camera slider mounted on an aluminum plate painted black. A digital camera (Sony Alpha 58, Sony Corporation, Tokyo, Japan) with a

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lens (Sony DT 18-55 mm, F 3.5 – 5.6 SAM II, Sony Corporation, Tokyo, Japan) was

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mounted on the camera carriage. The distance between the camera lens and the sample stage was 30.5 cm. A fully rotatable sample platform was fitted on the base plate so that

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the stage midpoint aligned with the center of the camera viewing field. The sample and

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the base plate were covered with a matt blue film to facilitate image segmentation

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between the background and the object of interest. Direct illumination was realized with a lamp and LED bulb (4.5 W, 220 – 240 V, 6500 K, CRI ≥ 80, Osram Licht AG, Munich, Germany) clipped on the slider in the desired position. The image acquisition system was surrounded by cardboard walls painted black and the system was darkened with a blackout fabric to prevent undesired reflections and incidence of light from surroundings when capturing the images. A standardized gray color chart (B.I.G. GmbH, Weiden, Germany) was used for a white balance of the camera. The images were captured in the camera’s manual mode with fixed settings for ISO (=100) and aperture (=f/25) to prevent blurred background and noisy images. The shutter speed was individually set for the correct 7

Journal Pre-proof exposure, which was ensured by checking the image histogram. A self-timer delay of 10 s was utilized to prevent camera movement. The image size was 5456 x 3632 pixels (maximum resolution) and the images were saved in an RAW data format. The observation angle, illumination angle, and sample orientation were set to observe maximum iridescence. The strongest iridescence was observed with an observation angle and illumination angle of 60° from the normal, and a light incident ray and observation

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pathway in the same vertical plane. The colocalized observation and illumination at 60°

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was found to cause maximum iridescence in a preliminary study and was therefore

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chosen for this setup. Moreover, Wang (1991) similarly found that iridescence in beef M.

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semitendinosus was stronger when lighting and observation angle were smaller (measured from the sample surface) and light incident ray and observation pathway were

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in the same vertical plane. Maximum iridescence was defined visually by rotating the

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sample to the angle with the maximum size of iridescent area. The rotation angle was set

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for each sample individually.

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Image preprocessing

Image preprocessing is necessary to remove noise or any kind of irregularities in the captured image before further segmentation and analysis. The digital images from the RAW data format were converted to uncompressed 8-bit TIFF files using the RawTherapee software (Version 5.4, http://rawtherapee.com/). The RGB images were processed and analyzed in ImageJ 1.52i (Fiji implementation) (Schindelin et al., 2012). The outline of the ham slices was roughly selected and the image cropped to remove most of the background. Due to the color difference between the blue background and the 8

Journal Pre-proof red ham samples, a simple color threshold was performed to remove the remaining background from the object and calculate the total area of the slice. Image quality was improved by performing contrast enhancement using histogram equalization. Since histogram equalization redistributes pixel values so that intensities are better distributed on the histogram, the contrast is increased at the peaks (iridescent areas) and lessened at

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the tails (non-iridescent areas).

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Image segmentation

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Segmentation is a critical step in image analysis, since the purpose is to identify the

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regions of interest and segment the image, in our case, into iridescent and non-iridescent

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areas. This segmentation highly influences the subsequently extracted data and measurements. Therefore, we tested two different segmentation techniques:

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(a) Global Thresholding

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Thresholding methods are the simplest methods for image segmentation. They divide the pixels with respect to their intensity. In global thresholding, a threshold value T

𝑔(𝑥, 𝑦) = {

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is constant for the whole image f (x,y) and the thresholded image g (x,y) is defined as: 0, 𝑖𝑓 𝑓(𝑥, 𝑦) ≤ 𝑇(𝑥, 𝑦) 1, 𝑖𝑓 𝑓(𝑥, 𝑦) > 𝑇(𝑥, 𝑦)

(1)

Since the color of the iridescent surfaces differ from the actual product color, the pixels’ hue value can be used to separate iridescent from non-iridescent areas (Figure 3). Preprocessed RGB images were converted to HSV color space and separated into 8-bit grayscale images of each channel (hue, saturation, brightness). The grayscale image of the hue channel was used for segmentation. Each gray value represents the color attribute, with black and white representing the red color of the color wheel and dark grey 9

Journal Pre-proof representing yellow in HSV color space. Non-iridescent areas with the original red product color had, therefore, high pixel values (white), whereas iridescent areas with yellow, green or orange iridescence colors had low pixel values (dark grey). The images had a bimodal histogram, thus allowing a global threshold to be manually set (T = 120) to select iridescent areas and applied on the grayscale image. The segmented image was a binary image consisting of black pixels (pixel value 0) representing non-iridescent areas

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and white pixels (pixel value 1), representing iridescent areas.

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(b) K-means clustering algorithm

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K-means clustering (Macqueen, 1967) is an algorithm to divide a set of data into a

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specific number of groups. If an image with a resolution x x y and input pixels p (x, y) is clustered into k number of clusters, the algorithm is as follows (Dhanachandra, Manglem,

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& Chanu, 2015):

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1. Initialize number of cluster k and center.

2. For each pixel of an image, calculate the Euclidean distance d, between the center and

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each pixel of an image using the relation:

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𝑑 = ‖𝑝(𝑥, 𝑦) − 𝑐𝑘 ‖

(2)

3. Assign all the pixels to the nearest center based on distance d. 4. After all the pixels have been assigned, recalculate the new position of the center using the relation: 𝑐𝑘 =

1 ∑ ∑ 𝑝(𝑥, 𝑦) 𝑘

(3)

𝑦∈𝑐𝑘 𝑥∈𝑐𝑘

5. Repeat the process until it satisfies the tolerance or error value. 6. Reshape the cluster pixels into an image.

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Journal Pre-proof K-means clustering was applied to preprocessed color images using the Fiji Plugin “weka.clusterers.SimplekMeans” (Arthur & Vassilvitskii, 2007) as a clustering technique for color-based segmentation. The clustering was performed in the HSV color space on the hue and brightness channel, since iridescent surfaces can be differentiated from noniridescent surfaces by differences in the attributes of hue and brightness. A k-means classifier was trained with a number of samples of 15% (percentage of pixels to be used

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when training the clusterer). An important task when using k-means clustering for image

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segmentation is the determination of the number of clusters and an initial number of

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centroids that influence the segmentation highly. In our study, the number of clusters was

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set to 4 for the dark background, bright non-iridescent areas, dark non-iridescent areas,

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and iridescent areas. An initial number of centroids was set to 10.

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Image analysis

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The iridescence extent was calculated as the ratio of the iridescent area to the total area:

𝐴𝑟𝑒𝑎 𝑜𝑓 𝑖𝑟𝑖𝑑𝑒𝑠𝑐𝑒𝑛𝑡 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 (𝑝𝑖𝑥𝑒𝑙𝑠) ∙ 100% 𝑇𝑜𝑡𝑎𝑙 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑠𝑙𝑖𝑐𝑒 (𝑝𝑖𝑥𝑒𝑙𝑠)

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𝐼𝑟𝑖𝑑𝑒𝑠𝑐𝑒𝑛𝑐𝑒 𝑒𝑥𝑡𝑒𝑛𝑡 =

(4)

For a comparison between the image analysis method and sensory analysis, the calculated iridescence extent was converted to the sensory scale (Table 1).

Sensory analysis The cooked ham slices were visually evaluated for iridescence extent according to the Meat Color Measurement Guidelines by the American Meat Science Association

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Journal Pre-proof (Association, 2012) on a scale from 1 (no iridescence, 0%) to 6 (very strong iridescence, 81 to 100%) (Table 1). A sensory panel was formed of 20 trained individuals from the meat science department. The sensory panel evaluated the slices that were previously analyzed by digital image analysis. Each panelist examined a total of 9 slices in one sensory session. Direct illumination was realized by a light source (4.5 W, 220 – 240 V, 6500 K, CRI ≥ 80, Osram Licht AG, Munich, Germany) adjustable in position. Samples

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were covered with a plastic film between the evaluations to prevent surface drying and

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hence fading of iridescent colors. The panelists were instructed to set the illumination and

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rotate and tilt the samples to evaluate maximum iridescence. Prior to the testing, each

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panelist was trained individually on the sensory scale with reference images showing slices of iridescent cooked pork hams with a detailed description of the iridescent colors

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and the extent of iridescence as well as on the correct adjustment of the illumination and

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Statistical Analysis

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observation angle. Reference images were also provided during the sensory evaluation.

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Statistical analysis was conducted with IBM SPSS Statistics 25 (IBM Corp., Armonk, NY, USA). The Shapiro-Wilk test and F-test for variance were used to test statistical assumptions. Data from digital image analysis that were normally distributed were analyzed with an Independent Samples t-Test. Data that did not meet the assumption of normality or homoscedasticity were analyzed with the Mann-Whitney-Wilcoxon test. Sensory data was analyzed using the Kruskal-Wallis test. Bland-Altman plots, simple linear regressions and the Pearson´s correlation coefficient were used for comparison between segmentation techniques. The comparison between digital image analysis and 12

Journal Pre-proof sensory analysis was performed by calculating Spearman´s rank correlation coefficient and Cohen’s kappa coefficient. A confidence level of α = 0.05 was used for all statistical

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calculations.

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Journal Pre-proof 3. RESULTS AND DISCUSSIONS

Digital image analysis Both segmentation techniques were able to segment images (n=30) of cooked pork ham correctly into iridescent and non-iridescent areas (Figure 4). No significant difference (P > 0.05) between the iridescent area in pixels and as an area percentage

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calculated by thresholding and clustering was observed. The correlation between the two

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measurements of iridescent area was assessed by calculating Pearson’s correlation

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coefficient. A very high positive correlation was found for the iridescent area in the pixels

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(r = 0.999, P < 0.05, n = 30) and a high positive correlation for the iridescent area as an

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area percentage (r = 0.758, P < 0.05, n = 30) between the thresholding and clustering method (Figure 5). However, the use of correlation can be misleading when comparing

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two measurement techniques and an alternative analysis should be considered (Bland & Altman, 1986). Data can produce high correlation even if they are poor in agreement. For

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that reason, the extent of agreement between the two different segmentation techniques

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was investigated using the Bland-Altman method (Bland & Altman, 1986). The difference in iridescent area obtained by the two segmentation techniques (global thresholding minus k-means clustering) was plotted against the mean of the two measurements for each digital image. The clustering segmentation yielded slightly higher iridescent areas with a mean difference of 1.24% respectively 13050 pixels (Figure 4). The limits of agreement show that 95 % of the differences in iridescent area between the two techniques will lie between within a range of -16.32% to 13.84% respectively, 13050 to 81007 pixels (Figure 6). Thus clustering segmentation may result in an

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Journal Pre-proof iridescent area of 16% below or 14% above the thresholding segmentation. The BlandAltman analysis does not say whether the limits of agreement are acceptable or not and acceptable limits must be defined a priori (Giavarina, 2015). Considering that the scale for evaluation of meat iridescence as suggested by the American Meat Science Association assigns a range of 20% to one score (Table 1: Scale for evaluation of iridescence in meat products according to American Meat Science Association (2012).,

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these differences between the segmentation techniques might be acceptable. The two

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segmentation techniques tested in this study are simple and widely-used methods to

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divide images into similar regions based on a criterion like color, intensity or texture

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(Panwar & Girdhar Gopal, 2016) which are prerequisites for a rapid and easy quantification. Furthermore segmentation quality is of fundamental significance for the

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following analysis and calculations. The segmentation quality of the global thresholding

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method depends on the right choice of a threshold. In general, the threshold is located at the obvious and deep valley in the histogram (Sonka, Hlavac, & Boyle, 2014). However,

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problems arise when the histogram is unimodal and it is not clear where to set the

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threshold value (Gonzales-Barron & Butler, 2006). In our study, the histograms of the grayscale images (hue channel) were bimodal with low pixel intensities representing iridescent areas (yellow, orange, green iridescence colors) and high pixel intensities representing non-iridescent areas (original red product color) and an optimum threshold value could be set to 120 (Figure 7). Global thresholding segmentation of an image of a non-iridescent sample resulted in a false positive detected area of 1.17% (Figure 8). Since the segmentation is based on the color differences between iridescent and non-iridescent regions, the white intramuscular fat that differs from the red product color is falsely

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Journal Pre-proof detected as iridescent areas. For the k-means clustering, the final segmentation depends on the initial cluster centers and the number of clusters chosen (Dhanachandra et al., 2015). The number of clusters chosen is of the most concern, particularly when segmenting color images with the k-means clustering methods since prior knowledge of the number of clusters represented in the data is required (Ray & Turi, 1999). We chose four clusters (image background, iridescent area, bright non-iridescent area, and dark

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non-iridescent area), and the initial cluster centers were randomly selected from input

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data. The algorithm grouped the images into four regions of similar color based on the

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pixel features from the HSV color space, and the images were accurately segmented into

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iridescent and non-iridescent regions. The selection of a number of four clusters worked well because iridescence colors present on the ham surfaces were mostly yellow and

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orange. The hue values lay close together in the HSV color space and the pixels are

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therefore assigned to the same cluster (Figure 9). However, a problem could arise when segmenting images that show a wide variety of iridescence colors. A number of four

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clusters would then not be sufficient to accurately divide the image and number of

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clusters should be increased. The clustering segmentation of an image of a non-iridescent sample resulted in a false positive detected area of 4.4% (Figure 9). As with the thresholding segmentation, the image is partitioned based on color differences and for the clustering also on brightness differences. Regions differing in color and brightness from the original product color, as in our sample the fat tissue, are therefore falsely detected as iridescent. This is a limitation of image segmentation in general since pixels with similar attributes are grouped together to a set of regions and none of the segmentation approaches are applicable to all images. Pixel characteristics for partitioning can be color,

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Journal Pre-proof texture or edges between two adjacent regions. In our case, pixels between iridescent and non-iridescent vary only in color but not in texture or shape. Color is therefore the only characteristic that can be used to segment an image into iridescent and non-iridescent regions. Segmentation of an image of a non-iridescent sample misclassifies pixels with certain color properties as iridescent. But the amount of misclassified pixels was low and resulted in falsely detected iridescence extents below 10%. Here, it must be borne in

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mind that the intended application of the image analysis method is the objective

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quantification of iridescence in basic research and not the detection or quantification in

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an industrial environment. Researches can visually detect iridescence in their limited

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amount of samples and apply the image analysis method only on iridescent samples. It is also important to emphasize that, for both segmentation techniques, the preprocessing

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step is essential to obtain valid segmentation based on differences in hue such as the color

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attribute and brightness between iridescent (interference colors) and non-iridescent areas (product color). A clear distinction between the iridescent and non-iridescent surfaces is

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the requirement for an objective quantification. The complex human visual system allows

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the understanding and interpretation of a picture in a way that has not been reached by computer vision so far. For the human visual system, it is easy to identify even slight iridescence that overlays the original product color, even on unprocessed images. However, computer vision is inferior to human vision and has certain difficulties in clearly distinguishing between the iridescent and non-iridescent areas only based on unprocessed images with low contrast. Interference colors from iridescent surfaces are characterized by directed reflection in contrast to diffuse reflection from the noniridescent surfaces. Therefore, the pixel intensities in the iridescent areas are higher than

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Journal Pre-proof in the non-iridescent areas, and the contrast can be increased by histogram equalization, yielding images with a bimodal histogram that is better segmentable. In our study, the color attribute was used to segment images into foreground (iridescent area) and background. Hence, the differences in hue between the iridescent and non-iridescent areas are essential for quantification by the image analysis method presented here. Swatland (1984) reported that green, orange-red and yellow iridescence are the most

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common colors in cooked beef samples. Our own observations showed mainly yellow

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and gold iridescence on cooked pork ham that differed obviously from the original

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product color. The use of the color attribute was well suited for our quantification method

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as the results showed with limitations for images of non-iridescent samples and samples with a strong product color variation. In addition, it must be considered that an

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iridescence color similar to the product color would not be identified as iridescent area

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and quantification would result in lower iridescence extents. However, it is also questionable whether panelists in a sensory analysis or consumers would perceive and

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Sensory analysis

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evaluate iridescence colors similar to the product color.

For validation of the new method, a conventional visual evaluation (20 panelists) of cooked pork hams samples (n=9) was performed, and the results compared to the results from image analysis. Figure 11 shows the results from sensory analysis in box plots. The box represents the lower (Q1 = 25%) and upper (Q3 = 75%) quartile range and the median (dark band). The whiskers represent the minimum and maximum scores given by the panel. Six of the samples were evaluated as slightly iridescent (median 3.0). Two samples (sample 6 and 8) were evaluated as moderate iridescent (median 4.0) and one 18

Journal Pre-proof sample as very slightly iridescent (median 2.0). The box plots show that the values measured were scattered widely. For example, the evaluation of Sample nine ranged from a minimum score of 1 (no iridescence) to a maximum score of 6 (very strong iridescence) and an interquartile range (IQR) between 2.5 and 4.5. The interquartile range was between 1.5 (Sample 6) and 3 scores (Sample 9). The large spread of the visual evaluation of the iridescence extent between the panelists for one sample points out a

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great weakness of this method for the quantification of surface iridescence on meat

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products. The panelists evaluated the extent as an area percentage affected by iridescence

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(Table 1) but this evaluation can only be seen as a rough estimation of the actual

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iridescent area and is highly affected by the panelists’ subjective perception of iridescence. Even with training and reference images, panelists might have a different

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perception and assessment of iridescence colors and therefore give different scores for the

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same sample. Wang (1991) also observed that individual panelists had different sensitivities to iridescence and gave different scores for the same sample. Another

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difficulty in visual evaluation arises from the angle-dependency of the iridescence.

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Iridescence colors only appear in a certain combination of illumination angle, observation angle, and sample orientation, hence, the measuring geometry should be taken into account when visually evaluating the iridescence extent, as is described by Wang (1991) and Kukowski et al. (2004). Additionally, adjustment of the measuring geometry by the panelist to obtain a maximum iridescence is highly subjective. An objective alternative that overcomes the previously described disadvantages of the sensory analysis is digital image analysis. A comparison between the conventional visual evaluation and the newlydeveloped digital image analysis method follows in the next section.

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Journal Pre-proof Comparison between digital image analysis and sensory analysis

Image analysis with both segmentation techniques yielded good results for iridescence extent compared to the results obtained by visual evaluation (Table 2). No significant difference (P > 0.05) in iridescence extent was observed between image analysis with thresholding or clustering, and sensory analysis. Correlation of the results

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for iridescence extent obtained by the novel image analysis method and the conventional

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sensory analysis was assessed by calculating Spearman’s rank correlation coefficient. A

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very high positive correlation was observed for image segmentation with global thresholding (rs = 0.887, p = 0.001, n = 9) and a high positive correlation for cluster

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segmentation (rs = 0.720, p = 0.029, n = 9). Thresholding showed a higher Spearman’s

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coefficient and seems to be better suited for segmentation when calculating iridescence extent by digital image analysis. For the measurement of agreement between sensory and

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image analysis a concordance analysis was performed by calculating Cohen’s kappa

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coefficient (Cohen, 1960). Image analysis with thresholding segmentation had a kappa

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coefficient of 0.800 (p = 0.001 , n = 9), showing an almost perfect agreement between the two methods according to Cohen (McHugh, 2012). Image analysis with clustering segmentation had a lower kappa coefficient (κ = 0.561, p = 0.018, n = 9) which shows a substantial agreement. Correlation and concordance analysis showed better results for image analysis with thresholding segmentation than with k-means clustering segmentation. Better agreement between sensory analysis and image analysis with thresholding segmentation can be traced back to the results for sample 8. Digital image analysis with thresholding segmentation calculated an iridescent area of 41.9 ± 3.3% which equals a score of 4 on the sensory scale whereas image analysis with clustering 20

Journal Pre-proof segmentation calculated an iridescent area of 33.0 ± 0.2%, which equals a sensory score of 3. Iridescence extent was visually evaluated by the sensory panel with a score of 4. Due to the different evaluation between clustering segmentation and sensory analysis, concordance is lower than for thresholding segmentation. But it should be noted that overall results from digital image analysis were in good accordance with results from

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visual evaluation.

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4. CONCLUSION

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The purpose of this research was the development of an alternative, objective

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quantification method for surface iridescence in meat and meat products. This was

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achieved by the construction of an image acquisition system that allowed adjustments of measuring geometry to take the angle dependency of iridescence into account. A simple,

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rapid and low-cost image analysis method was then implemented to calculate iridescent area. A clustering and thresholding method was used for segmenting the ham slices, and

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the results showed that both segmentation methods were able to accurately segment the

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iridescent areas. Thresholding segmentation showed higher accordance with sensory analysis, and thus seems to be better suited for this purpose. In summary, this new method is an interesting alternative or addition to the conventional sensory analysis for quantification of surface iridescence, not only in cooked pork ham but also in other meat and meat products by selecting appropriate settings for image acquisition and processing.

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Journal Pre-proof ACKNOWLEDGEMENTS

This research project was supported financially by the German Ministry of Economics

and

Technology

(via

AiF)

and

the

FEI

(Forschungskreis

der

Ernährungsindustrie e.V., Bonn). Project AiF 20011N. We would like to thank our head

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butcher Kurt Herrmann for his help during ham production.

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REFERENCES

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Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. Paper presented at the Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Association, A. M. S. (2012). AMSA Meat Color Measurement Guidelines: AMSA: American Meat Science Association. Bland, J. M., & Altman, D. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307-310. doi:10.1016/S0140-6736(86)90837-8 Briones, V., & Aguilera, J. M. (2005). Image analysis of changes in surface color of chocolate. Food Research International, 38(1), 87-94. doi:10.1016/j.foodres.2004.09.002 Brosnan, T., & Sun, D.-W. (2004). Improving quality inspection of food products by computer vision––a review. Journal of Food Engineering, 61(1), 3-16. doi:10.1016/S0260-8774(03)00183-3 Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37-46. doi:10.1177/001316446002000104 Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image Segmentation Using K means Clustering Algorithm and Subtractive Clustering Algorithm. Procedia Computer Science, 54, 764-771. doi:10.1016/j.procs.2015.06.090 FAO. (2011). Global food losses and food waste - Extent, causes and prevention. Rome Faustman, C., & Cassens, R. G. (1990). The biochemical basis for discoloration in fresh meat: a review. Journal of Muscle Foods, 1(3), 217-243. doi:10.1111/j.17454573.1990.tb00366.x Fulladosa, E., Serra, X., Gou, P., & Arnau, J. (2009). Effects of potassium lactate and high pressure on transglutaminase restructured dry-cured hams with reduced salt content. Meat Science, 82(2), 213-218. doi:10.1016/j.meatsci.2009.01.013 Giavarina, D. (2015). Understanding Bland Altman analysis. Biochemia Medica, 25(2), 141-151. doi:10.11613/BM.2015.015 Gibis, M. (2018). Prämierungen spiegeln Produktqualität. Internationale DLGQualitätsprüfungen 2018 für Kochpökelwaren und Kochwürste(10), 44-50. Retrieved from https://www.wiso-net.de/document/FLW__20181012471283 Girolami, A., Napolitano, F., Faraone, D., & Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111-118. doi:10.1016/j.meatsci.2012.08.010 Gonzales-Barron, U., & Butler, F. (2006). A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis. Journal of Food Engineering, 74(2), 268-278. doi:10.1016/j.jfoodeng.2005.03.007 Kukowski, A. C., Wulf, D. M., Shanks, B. C., Page, J. K., & Maddock, R. J. (2004). Factors associated with surface iridescence in fresh beef. Meat Science, 66(4), 889-893. doi:10.1016/j.meatsci.2003.08.011 23

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Lautenschläger, R., Hillgärtner, K., & Thumel, H. (2016). Bedienware wieder im Aufwind. Internationale DLG-Qualitätsprüfung 2016 – Rohwürste und Rohpökelwaren(10), 47-52. Retrieved from https://www.wisonet.de/document/FLW__20161017380002 Li, J., Tan, J., & Shatadal, P. (2001). Classification of tough and tender beef by image texture analysis. Meat Science, 57(4), 341-346. doi:10.1016/S03091740(00)00105-4 Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability: Biology and problems of health (Vol. 4, pp. 281-297): University of California Press. Mancini, R. A. (2007). Iridescence: A rainbow of colors, causes, and concerns. 4. Martinez-Hurtado, J. L., Akram, M. S., & Yetisen, A. K. (2013). Iridescence in Meat Caused by Surface Gratings. Foods, 2(4), 499-506. doi:10.3390/foods2040499 McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276-282. Retrieved from https://www.biochemiamedica.com/assets/images/upload/xml_tif/McHugh_ML_Interrater_reliability.pdf Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41(3), 285-295. doi:10.1016/j.postharvbio.2006.04.004 Obuz, E., & Kropf, D. H. (2002). Will Blade Tenderization Decrease Iridescence in Cooked Beef Semitendinosus Muscle?1. Journal of Muscle Foods, 13(1), 75-79. doi:10.1111/j.1745-4573.2002.tb00322.x Oliver, M. A., Polo, J., Panella, N., Arnau, J., Contreras, M., Morera, S., . . . Gil, M. (2006). Effect of Natural Stabilised Pork Haem Pigment on the Colour, Colour Stability and Texture of Cooked Hams from Pale, Soft and Exudative Meat. Food Science and Technology International, 12(5), 429-435. doi:10.1177/1082013206070161 Panwar, P., & Girdhar Gopal, R. K. (2016). Image Segmentation using K-means clustering and Thresholding. Image, 3(05), 1787-1793. Ray, S., & Turi, R. H. (1999). Determination of number of clusters in k-means clustering and application in colour image segmentation. Paper presented at the Proceedings of the 4th international conference on advances in pattern recognition and digital techniques. Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., . . . Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676-682. doi:10.1038/nmeth.2019 Sonka, M., Hlavac, V., & Boyle, R. (2014). Image Processing, Analysis, and Machine Vision: Cengage Learning. Swatland, H. (2018). Iridescence in cooked venison–an optical phenomenon. J Nutr Health Food Eng, 8(2), 105-108. Swatland, H. J. (1984). Optical characteristics of natural iridescence in meat. Journal of Food Science, 49(3), 685–686. Swatland, H. J. (2012a). Iridescence in beef caused by multilayer interference from sarcomere discs. Meat Science, 90(2), 398-401. doi:10.1016/j.meatsci.2011.08.006 24

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Swatland, H. J. (2012b). Muscle iridescence in yellowfin tuna (Thunnus albacares). Food Research International, 48(2), 449-453. doi:10.1016/j.foodres.2012.05.018 Tan, J. (2004). Meat quality evaluation by computer vision. Journal of Food Engineering, 61(1), 27-35. doi:10.1016/S0260-8774(03)00185-7 Wang, H. (1991). Causes and solutions of iridescence in precooked meat. Kansas State University,

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TABLES

Table 1: Scale for evaluation of iridescence in meat products according to American Meat Science Association (2012).

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Iridescence Intensity (-)

1

no iridescence

2

very slight iridescence

1 – 20

3

slight iridescence

21 – 40

moderate iridescence

41 – 60

strong iridescence

61 – 80

very strong iridescence

81 – 100

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6

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5

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4

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Score

Extent (%) 0

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Journal Pre-proof Table 2: Quantitative assessment of iridescence on cooked ham samples (n = 9) using digital image analysis with two different segmentation techniques (global thresholding; kmeans clustering algorithm) and results from sensory analysis (n = 20). Digital Image Analysis

Thresholding

SD

SE

(Score)

(%)

SD

SE

Extent

Extent*

(Score)

(Score)

3

3a

0.32

1.47 1.04

3

3b

0.34

37.11

3.41 2.41

3

3c

0.32

29.17

1.85

1.31

3

28.03

2

32.91

5.61

3.97

3

3

32.91

7.81

5.52

4

25.32

1.27

0.90

3

31.74

3.63 2.57

3

3d

0.25

5

13.94

7.78

2

18.51

6.56 4.64

2

2a-f

0.26

6

43.63

0.19

0.13

4

41.54

2.90 2.05

4

4a-f

0.32

7

15.98

3.47

2.45

2

20.74

0.38 0.27

2

3f

0.30

8

41.87

3.29

2.33

4

33.02

0.21 0.15

3

4a-f

0.24

9

28.96

1.38

0.98

3

29.57

0.17 0.12

3

3e

0.37

30.62

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5.51

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1

3

0.56 0.39

SE

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Extent

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(%)

Extent

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Extent

K-means Clustering

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Sample

Sensory

*Median. Values within the column with different letters (a-f) significantly differ (P < 0.05). All other values: mean; SD: standard deviation; SE: standard error (n=2). 27

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FIGURE CAPTIONS

Figure 1: Schematic representation of the image analysis procedure. Figure 2: Image acquisition system. A digital camera and light source can be individually positioned in a semicircle over the sample and sample orientation can be

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adjusted by a fully rotatable platform. Digital camera and light source were positioned at

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60° from the normal since strongest iridescence in the cooked ham samples was observed

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with these angles. Sample was then rotated on the platform to the angle with the

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maximum iridescent area.

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Figure 3: Color distribution in the HSL color space of a digital image of an iridescent cooked ham after contrast enhancement by histogram equalization (a) and

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(Barthel, 2006).

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before processing (b). Images were produced with ImageJ plugin “Color Inspector 3D”

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Figure 4: Preprocessed images of iridescent cooked ham samples and binary images after segmentation with global thresholding and k-means clustering algorithm. White pixels represent the foreground (= iridescent areas), black pixels represent the background. Figure 5: Correlation of iridescent area in (a) percentage (y = 0.6338x + 12.6438; R2 = 0.5738) and (b) pixels (y = 1.3250x – 7324.3374; R2 = 0.9973) between global thresholding segmentation and k-means clustering segmentation.

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Journal Pre-proof Figure 6: Bland-Altman plots comparing iridescent area in (a) percentage and (b) pixels obtained by global thresholding segmentation and k-means clustering segmentation. Blue lines indicate mean differences. Red lines indicate limits of agreement (± 1.96 standard deviation). Data represent a total of 30 images. Figure 7: Grayscale image and histogram of the hue channel of an iridescent

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cooked ham sample. Gray value represents the hue in HSV color space (white pixels represent red color and gray values correspond to different hues). High pixel values

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represent non-iridescent areas, low pixel values represent iridescent areas. The threshold

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( = 120) was manually selected on the valley) to divide the image into iridescent and non-

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iridescent regions.

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Figure 8: Preprocessed image (a), grayscale image (b) and histogram (d) of the hue

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channel and segmented binary image of a non-iridescent cooked ham sample. Gray value represents the hue in HSV color space (white pixels represent red color and gray values

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correspond to different hues). High pixel values represent non-iridescent areas, low pixel

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values represent iridescent areas. The threshold ( = 120) was manually selected on the valley) to divide the image into iridescent and non-iridescent regions. Figure 9: K-means cluster (k = 4) segmentation (a) of a digital image of an iridescent ham sample with the attributes of hue and brightness. Preprocessed image (b) and grayscale image (c) after k-means clustering with each grayscale representing one cluster. Cluster 1 represents the image background, cluster 2 bright non-iridescent regions, cluster 3 iridescent regions and cluster 4 dark non-iridescent areas. Diagram

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Journal Pre-proof shows pixels and their classification to a cluster based on the hue and brightness value. Black crosses show the cluster´s centroids. Figure 10: K-means cluster (k = 4) segmentation (a) of a digital image of a noniridescent cooked ham sample with the attributes of hue and brightness. Preprocessed image (b) and grayscale image (c) after k-means clustering with each grayscale

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representing one cluster. Cluster 1 represents the dark non-iridescent regions, cluster 2 iridescent regions, cluster 3 the image background and cluster 4 non-iridescent bright

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regions. Diagram shows pixels and their classification to a cluster based on the hue and

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brightness value. Black crosses show the cluster´s centroids.

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Figure 11: Box plot diagram of iridescence extent for nine cooked ham samples

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evaluated visually by a trained panel (n = 20). The line within the box represents the

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median and the square represents the mean.

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FIGURES

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Journal Pre-proof CRediT author statement. Jochen Weiss: Conceptualization, Project administration, Supervision, Funding acquisition, Writing-Reviewing and Editing Monika Gibis: Conceptualization, Project administration, Validation, Funding acquisition, Writing-Reviewing and Editing

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Chiara Rüdt: Methodology, Formal analysis, Investigation, Writing-Original Draft, Visualization

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Declaration of Interest: None

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