1. Chapter 6
Da-Wen Sun
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2. Figure 6. 1: Traditional methods for measuring meat quality parameters
Figure 6.1: Traditional methods for measuring meat quality parameters. (a) Measuring water holding capacity (WHC) by using EZ-Drip loss method (Rasmussen & Andersson, 1996); (b) measuring WHC by using bag method (Honikel, 1998); (c) measuring pH by using pH meter; and (d) measuring color by using a portable Minolta colorimeter.
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3. Figure 6. 2: Destructive determination methods of meat tenderness
Figure 6.2: Destructive determination methods of meat tenderness. (a–f) using slice shear force “SSF”, (g–i) using Warner–Bratzler shear force “WBSF”. [Slice shear force “SSF” method: a single slice of 5{ts}cm long from the centre of a cooked steak is removed parallel to the long dimension (a); using a double-blade knife, two parallel cuts are simultaneously made through the length of the 5{ts}cm long steak portion at a 45° angle to the long axis and parallel to the muscle fibres (b–c), this results in a slice of 5{ts}cm long and 1{ts}cm thick parallel to the muscle fibres (d), and the slice is then sheared once perpendicular to the muscle fibers using universal testing machine equipped with a flat, blunt-end blade (e–f); Warner-Bratzler shear force “WBSF” method: six core samples of 12.7{ts}mm in diameter are taken from a cooked steak parallel to the longitudinal orientation of the muscle fibres (g), each core is then sheared using universal testing machine equipped with a triangular slotted blade (h–i). In both methods the maximum shear force (meat tenderness) is the highest peak of the force–deformation curve.]
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4. Figure 6.3: Visible and NIR hyperspectral imaging system for beef tenderness prediction (Naganathan et al., 2008a and Grimes, et al., 2008 ). (1) CCD camera; (2) spectrograph; (3) lens; (4) diffuse lighting chamber; (5) tungsten halogen lamps; (6) linear slide; (7) sample plate.
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5. Figure 6.4: Distribution of samples in the canonical space (Naganathan et al., 2008a).
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6. Figure 6. 5: Hyperspectral image of optical scattering in beef steak
Figure 6.5: Hyperspectral image of optical scattering in beef steak. Y-axis represents spectral information with intervals of 4.54{ts}nm and X-axis represents spatial distance with a spatial resolution of 0.2{ts}mm. The optical scattering can be seen to vary with wavelength (Cluff , et al., 2008).
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7. Figure 6.6: Difference in the averaged optical scattering profiles of the porterhouse strip steak (WBS = 28.9 N) and tenderloin (WBS = 24.7 N) (Cluff et al., 2008).  
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8. Figure 6.7: Spectral characteristics of different quality levels of pork samples with water absorbing bands at 750 and 950{ts}nm indicated (Qaio et al., 2007a ).
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9. Figure 6.8: Layout of the main configuration of the NIR spectral imaging system (ElMasry and Wold, 2008.)
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10. Figure 6.9: Key steps for building chemical images (distribution maps) (ElMasry & Wold, 2008 ).
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11. Figure 6.10: Water and fat distribution maps in fillets, the values in the left bottom corner of the figure represent the average concentrations of water and fat in the whole fillet: (a) Atlantic halibut; (b) catfish; (c) cod; (d) herring; (e) mackerel; and (f) saithe (ElMasry and Wold, 2008).  
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12. Figure 6.11: Hyperspectral imaging system in the shortwave infrared (SWIR) region for qualitative freshness evaluation of whole fish and fillets (Chau et al., 2009).
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13. Figure 6.12: Identifying fillet flesh region of interest (red) after excluding the oversaturated specular area (green) and belly area (blue) based on their spectral data.
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14. Figure 6.13: False color images of the whole cod fish at 1164{ts}nm.
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15. (a)
(b)
Figure 6.14: Fish fillet images: (a) color image where the red, green, and blue channel is represented by spectral image at 640, 550 and 460{ts}nm, respectively; the green dashed line indicates the manually detected centreline and the blue dotted lines indicate the transition between tail to centre cut and centre cut to loin/belly-flap respectively; (b) centreline enhanced image, the axis on the left-hand side indicates the position along the fillet in percent relative to fillet length (Sivertsen et al., 2009).
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16. Figure 6. 17: Section of cod fillet
Figure 6.17: Section of cod fillet. (a) Image of the cod sample captured with RGB digital camera. (b) Spectral image at 540{ts}nm where the nematodes, K1 to K5, are indicated with white circles, a blood spot, B, marked with a dotted circle and black lining, BL, marked with a dotted circle. (c) Classification result, in green, on top of the 540{ts}nm image before thresholding. For naked eye the bloodspot may appear as a parasite. Bloodspots were not identified as parasites by the classification (Heia et al., 2007).
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17. Figure 6.19: Color composite image of a 90{ts}mg cecal mass contaminant: (a) pixels not detected (black); (b) pixels detected (gray or yellow) with a 1.10 threshold; (c) pixels detected with a 1.05 threshold; (d) pixels detected with a 1.10 threshold (Windham et al., 2005b).  
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18. Figure 6.20: Hyperspectral image processing of a poultry carcass: (a) color composite image (pseudo-RGB image); (b) calibrated color image; (c) ratio image (I565/I517); (d) background mask (Park et al ., 2006a).
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19. Figure 6.21: Band ratio of two wavelengths (517 and 565{ts}nm) selected by regression model and scanning monochromator: (a) threshold = 1.0; (b) threshold = 1.0 with filter; (c) threshold = 0.95; (d) threshold = 0.95 with filter (Park et al., 2006a ).
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20. Figure 6.24: Flowchart of poultry processing line in a real-time fecal inspection imaging system at pilot-scale plant in Russell Research Center (Park et al., 2006b).
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