Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon

Yağız, Yavuz; Balaban, Murat Ö.; Kristinsson, Hörður G.; Welt, Bruce A.; Marshall, Maurice R.

doi:10.1002/jsfa.3467

Cited by 64 publications

(45 citation statements)

References 16 publications

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“…The colors of broiler breast meats, determined using imaging analysis, had higher L* and b* values, but a lower a* value, compared with those obtained using direct measurement. Previous studies have found that the L* and b* values of salmon (Yagiz et al, 2009), chicken, and pork (Girolami et al, 2013), measured using a machine vision system, demonstrated higher values compared with those obtained using a colorimeter. The color of the surface of the meat could be affected by the amount of overall light scattering and reflected electromagnetic wavelength, which was related to the amount of applied light that penetrates into the meat sample (Girolami et al, 2013).…”

Section: Colors Of Breast Meat Determined By Direct Measurement and Imentioning

confidence: 81%

Imaging Analysis by Digital Camera for Separating Broiler Breast Meat with Low Water-Holding Capacity

2017

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Separating breast meat with low water-holding capacity, conformation parameters (thickness, volume, bottom sarea, and perimeter), and color of chicken breast meat were measured by direct measurement and by imaging analysis with a digital camera. Samples were obtained from a production line. The L* value was used to separate the samples by three characteristics designating the quality of the meat: dark-colored samples (L*＜50), normal-colored samples (50≤L*≤56), and light-colored samples (L*＞56). Light-colored samples had higher moisture content, thawing loss, drip loss, and lower pH compared with those of normal-and dark-colored samples. Lower thickness was observed in the light-colored samples compared with those of normal-and dark-colored samples. Light-and normalcolored samples had a greater volume of meat than did the dark-colored samples. Imaging analysis showed that lightcolored samples had a greater bottom area and perimeter compared with those of normal-and dark-colored samples. However, these conformation parameters showed low correlation with water-holding capacity, which was determined as thawing and drip loss of the samples. Therefore, the conformation parameters, determined by direct measurement or imaging analysis, could not be used to predict the water-holding capacity of breast meat. Nevertheless, waterholding capacity showed high correlation with the L* value of breast meat. Imaging analysis could be used to separate light-colored breast meat with mostly low water-holding capacity. The accuracy of determining the characteristics of light-, normal-, and dark-colored samples by imaging analysis was evaluated. The characteristics of lightcolored samples were determined with higher accuracy by imaging analysis than were the characteristics of normaland dark-colored samples. This result indicated that imaging analysis using a digital camera could be used to separate light-colored breast meat with mostly low water-holding capacity from normal-and dark-colored meat.

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Section: Colors Of Breast Meat Determined By Direct Measurement and Imentioning

confidence: 81%

Imaging Analysis by Digital Camera for Separating Broiler Breast Meat with Low Water-Holding Capacity

2017

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“…The pictures acquired were resized, cleaned and converted to bmp files using Adobe Photoshop 13.0.1.3 CS 6 (Adobe Systems Inc, California, U.S.A.) before they were analyzed using LensEye Version 11.4.1 software (ECS, Gainesville, FL, USA). LensEye provided the average L*, a* and b* values of the samples (Yagiz et al, 2009). …”

Section: Computer Based Image Analysismentioning

confidence: 99%

“…Furthermore, if there is a high-fat ratio in fillets, the uneven colour distribution increases after smoking (Espe et al, 2002). Therefore, it is not possible to determine the true colour of seafood by using conventional instrumental colour analysis methods (Kong, et al, 2015b;Yagiz et al, 2009). Computer-based image analysis (CBI) could be an alternative at this point.…”

Section: Introductionmentioning

confidence: 99%

The Use of Computer-Based Image Analysis on Colour Determination of Liquid Smoked Trout (Oncorhynchus mykiss) Treated with Different Dry Salt-Sugar Mixtures

Ayvaz

Çakır

Gündüz

et al. 2017

Turkish JAF Sci.Tech.

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In this study, the changes in % yield, dry matter, ash, lipid, protein content, water activity, pH, total volatile basic nitrogen (TVB-N), total viable aerobic count (TVC), yeast and mold count, lactic acid bacteria (LAB), colour parameters and sensorial properties were analysed in rainbow trout (Oncorhynchus mykiss) exposed to either salt only or two different salt-sugar mixture treatments. For this purpose, three groups were formed. For the first, second and third group, fish samples were treated with only salt (S), salt and sugar blend (WS) and salt and brown sugar blend (BS), respectively. Then, the samples were vacuum packaged and stored at +4°C for 3 months. Overall, salt treatments, liquid smoking and cooking as well as storage generally caused remarkable changes in the parameters of interest. However, except for the sensory analysis, not a remarkable change was seen when the three groups were compared among themselves. The results of experienced panelists suggested that group BS samples had superior appearance, taste, odor and texture and therefore expected to be more preferred by the potential consumers.

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“…MVS also provides the colour spectrum and other visual attributes of the sample [24,25]. The performance of a hand-held Minolta colorimeter and a MVS in measuring the colour of Atlantic salmon (S. salar) fillets treated with different electron beam doses (0, 1, 1.5, 2, and 3 kGy) was compared [26]. The average L * , a * , and b * values measured by MVS resulted in orange colours very close to that of the original sample ( Fig.…”

Section: Visual Quality Determination Based On Colourmentioning

confidence: 99%

Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue

Korel

Balaban

2010

Handbook of Seafood Quality, Safety and Health Applications

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The increase in demand for seafood products has catalyzed the desire for higher standards regarding safety and quality issues. Since seafoods are perishable, freshness is a major quality parameter to be considered [1,2]. There is no unique freshness or spoilage indicator for seafood, therefore combinations of selected indicators need to be used to evaluate freshness [3,4]. An important and widely used method to determine freshness is sensory evaluation [5]. The Quality Index Method (QIM) uses a demerit point scoring system [6] based on the evaluation of the important sensory attributes (odour, texture, and appearance) of fish and other aquatic foods. The sensory quality is expressed by the sum of the demerit points, and a linear correlation between these points and the storage time is used to predict the freshness of the target seafood [5,7,8]. The QIM has been developed for various seafood species and products, such as Atlantic mackerel (Scomber scombrus), horse mackerel (Trachurus trachurus), European sardine (Sardina pilchardus) [9], gilthead seabream (Sparus aurata)[10], farmed Atlantic salmon (Salmo salar) [11,12], and cod (Gadus morhua) [13], etc. Even though QIM is fast and reliable in determining the freshness of seafood, it still requires experts to evaluate the quality attributes. Alternatively, appearance, odour, and taste can be measured by machine vision system (MVS), electronic nose (e-nose), and electronic tongue (e-tongue), respectively.In this chapter, the measurement of visual, odour, and taste quality of seafood using MVS, e-nose, and e-tongue is discussed. A few literature examples are given for all techniques, some of which are given from research conducted in our own laboratories. Visual qualityVisual quality of seafood includes appearance (size, shape, and colour) attributes. These have a direct influence on the seafood's value and acceptance. One of the methods of measuring them is by using a MVS, which consists of a digital camera to acquire images, an illumination system (e.g. a light box with fluorescent bulbs as lighting source), and computer software to analyze the image [14,15]. This is a rapid, objective, repeatable, and non-destructive method, Handbook of Seafood Q uality, Safety and Health ApplicationsEdited by Cesarettin Alasalvar, Fereidoon Shahidi, Kazuo Miyashita and Udaya Wanasundara

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Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon

Cited by 64 publications

References 16 publications

Imaging Analysis by Digital Camera for Separating Broiler Breast Meat with Low Water-Holding Capacity

Imaging Analysis by Digital Camera for Separating Broiler Breast Meat with Low Water-Holding Capacity

The Use of Computer-Based Image Analysis on Colour Determination of Liquid Smoked Trout (Oncorhynchus mykiss) Treated with Different Dry Salt-Sugar Mixtures

Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue

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