Quality Grading of Atlantic Salmon (<i>Salmo salar</i>) by Computer Vision

Misimi, Ekrem; Erikson, Ulf; Skavhaug, Amund

doi:10.1111/j.1750-3841.2008.00779.x

Cited by 41 publications

(19 citation statements)

References 26 publications

(31 reference statements)

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“…The correlation of view area to weight using MV was tested and demonstrated for different foods such as produce (Eifert and others 2006) and seafood products, for example, shrimp (Balaban and others 1994), Alaska salmon (Balaban and others 2010), oysters (Damar and others 2006), Atlantic Salmon ( Salmo salar ) fillet shape and size (Misimi and others 2008a, 2008b), and the sorting of 3 species of fish (Zion and others 1999). Fish species have also been sorted according to shape, length, and orientation in a processing line (Strachan and others 1990; Strachan 1993).…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Weight of Alaskan Pollock Using Image Analysis

Balaban¹,

Chombeau²,

Cırban³

et al. 2010

Journal of Food Science

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Determining the size and quality attributes of fish by machine vision is gaining acceptance and increasing use in the seafood industry. Objectivity, speed, and record keeping are advantages in using this method. The objective of this work was to develop the mathematical correlations to predict the weight of whole Alaskan Pollock (Theragra chalcogramma) based on its view area from a camera. One hundred and sixty whole Pollock were obtained fresh, within 2 d after catch from a Kodiak, Alaska, processing plant. The fish were first weighed, then placed in a light box equipped with a Nikon D200 digital camera. A reference square of known surface area was placed by the fish. The obtained image was analyzed to calculate the view area of each fish. The following equations were used to fit the view area (X) compared with weight (Y) data: linear, power, and 2nd-order polynomial. The power fit (Y = A · X B ) gave the highest R 2 for the fit (0.99). The effect of fins and tail on the accuracy of the weight prediction using view area were evaluated. Removing fins and tails did not improve prediction accuracy. Machine vision can accurately predict the weight of whole Pollock.

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Section: Introductionmentioning

confidence: 99%

Prediction of the Weight of Alaskan Pollock Using Image Analysis

Balaban¹,

Chombeau²,

Cırban³

et al. 2010

Journal of Food Science

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“…Kohler et al (2002) developed a method for sorting quality classes of cod fillets. A method for quality grading of whole Atlantic salmon (Salmo salar) has also been developed using CV systems (Misimi et al, 2008b). Shrinkage of pre-rigor filleted rainbow trout (Oncorhyncus mykiss) was analyzed by MV.…”

Section: Colormentioning

confidence: 99%

Untitled

Gümüş¹,

Balaban²,

Ünlüsayın³

2011

Turk. J. Fish. Aquat. Sci.

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Machine vision (MV) is a rapid, economic, consistent and objective inspection and evaluation technique. This nondestructive method has applications in the aquatic food industry. MV can perform many functions at once in an aquatic food processing line: sorting by species, by size, and by visual quality attributes, as well as automated portioning. In this review, the mode of operation and the components of a MV system are introduced, its applications to foods are briefly discussed, and the advantages and disadvantages listed. The literature in the MV applications to aquatic foods is grouped under the following topics: determination of composition, measurement and evaluation of size and volume, measurement of shape parameters, quantification of the outside or meat color of aquatic foods, and detection of defects during quality evaluation. Finally, brief examples from the industrial applications of this promising technology are given. Extensive bibliography is cited in this field. ÖzetBilgisayarlı resim analizi (BRA); hızlı, ekonomik, tutarlı ve objektif olarak kontrol etme ve değerlendirme metodudur. Ürüne zarar vermeyen bu metodun, su ürünleri endüstrisine uygulamaları bulunmaktadır. BRA"nın otomatik porsiyonlama gibi, çoğu fonksiyonu veya ürünün türe, ağırlığa ve görsel kalite özelliklerine göre sınıflandırması su ürünleri işlemesinde, hızlı bir şekilde uygulanabilir. Bu derlemede, BRA sisteminin çalışma biçimi ve parçaları, kısaca gıdalara uygulanması, avantajları ve dezavantajları açıklanmaktadır. Su ürünlerine BRA uygulamalarının kaynakçaları; su ürünleri kompozisyonun belirlenmesi, ağırlık ve hacimin değerlendirmesi, şekil özelliklerinin ölçülmesi, su ürünlerinin et ya da yüzey renginin tanımlanması ve kalite değerlendirmesi sırasında istenmeyen kusurların belirlenmesi şeklindeki başlıklar altında gruplandırılmıştır. Sonuç olarak; gelecek için umut verici bu teknolojinin, endüstriyel uygulamalardaki bazı örnekleri verilmektedir. Bu konular derlemede kapsamlı kaynakça ile belirtilmektedir.Anahtar Kelimeler: Bilgisayarlı resim analizi, resim değerlendirme, su ürünleri.

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“…This method has been found suitable for industrial purposes. A method for quality grading of whole Atlantic salmon (S. salar) has also been developed using CVS [18], based on the external geometrical information from fish images.…”

Section: Visual Quality Determination Based On Size and Shapementioning

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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Quality Grading of Atlantic Salmon (Salmo salar) by Computer Vision

Cited by 41 publications

References 26 publications

Prediction of the Weight of Alaskan Pollock Using Image Analysis

Prediction of the Weight of Alaskan Pollock Using Image Analysis

Untitled

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

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