Potential use of 3-dimensional scanners for food process modeling

Uyar, Rahmi; Erdoğdu, Ferruh

doi:10.1016/j.jfoodeng.2009.01.034

Cited by 52 publications

(35 citation statements)

References 17 publications

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“…In the publications on the use of 3D scanning in food examinations, the authors focused their attention primarily on issues of precision of product volume estimation. As an example, Uyar and Erdoğdu (2009) determined that there exists a possibility to use a 3D scanning technique for the determination of volume of food products with irregular shapes, such as bananas, pears, and eggs. Goñi et al (2007) using 3D scanning technique determined the volume of pieces of meat.…”

Section: Introductionmentioning

confidence: 99%

Using Density Measurement on Semispinalis capitis as a Tool to Determinate the Composition of Pork Meat

et al. 2018

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The aim of the present study was an attempt to utilize density measurements using different methods as 3D scanning, hydrostatic, and pycnometric for the estimation of chemical composition of 20 pork semispinalis capitis. The density determined with direct (hydrostatic, pycnometric) and indirect (3D scanning) methods was correlated with water, protein, fat, and ash content. The highest correlation coefficients (absolute value) between density and water (r = 0.86), protein (r = 0.79), and fat (r = − 0.87) content of pork necks were found for hydrostatic method. Despite the standard error of prediction (SEP) obtained for density determination using 3D scanning method was close to acceptable tolerance range recommended by the EU law, which amounts to 20% mean value on the declaration of calculated fat and protein content in a product, further studies are needed on a larger population of samples.

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Section: Introductionmentioning

confidence: 99%

Using Density Measurement on Semispinalis capitis as a Tool to Determinate the Composition of Pork Meat

et al. 2018

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“…However, 3-dimensional images may provide more information or details to improve quality analysis in many applications. Uyar and Erdoğdu et al (2009) predicted the size and shape of pears, strawberries, bananas, apples, and eggs by using a 3-dimensional scanner. More complex and irregular-shaped materials can be described with fewer errors and less time.…”

Section: -Dimensional Techniquementioning

confidence: 99%

Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances

Sun

et al. 2014

Critical Reviews in Food Science and Nutrition

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With consumer concerns increasing over food quality and safety, the food industry has begun to pay much more attention to the development of rapid and reliable food-evaluation systems over the years. As a result, there is a great need for manufacturers and retailers to operate effective real-time assessments for food quality and safety during food production and processing. Computer vision, comprising a nondestructive assessment approach, has the aptitude to estimate the characteristics of food products with its advantages of fast speed, ease of use, and minimal sample preparation. Specifically, computer vision systems are feasible for classifying food products into specific grades, detecting defects, and estimating properties such as color, shape, size, surface defects, and contamination. Therefore, in order to track the latest research developments of this technology in the agri-food industry, this review aims to present the fundamentals and instrumentation of computer vision systems with details of applications in quality assessment of agri-food products from 2007 to 2013 and also discuss its future trends in combination with spectroscopy.

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“…The traditional approach in modeling studies is assuming they are homogenous, isotropic products with regular shapes (Borsa et al, 2002;Uyar and Erdoğdu, 2009). Using the exact shape of an object is a tedious and difficult task, especially for irregular shaped food products and there are not many studies published based on exact geometry of these products (Celik et al, 2011;Fabbri et al, 2011;Goñi et al, 2007;Kelkar et al, 2011;Uyar and Erdoğdu, 2009). Using the 3D…”

Section: Introductionmentioning

confidence: 99%

Temperature Dependency of Gas Diffusivity and Skin Resistance of Japanese Pear (‘Oushuu’) Based on the Fruit^|^apos;s True 3D Geometry

Rezagah

Ishida

Tanaka

et al. 2014

FSTR

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Potential use of 3-dimensional scanners for food process modeling

Cited by 52 publications

References 17 publications

Using Density Measurement on Semispinalis capitis as a Tool to Determinate the Composition of Pork Meat

Using Density Measurement on Semispinalis capitis as a Tool to Determinate the Composition of Pork Meat

Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances

Temperature Dependency of Gas Diffusivity and Skin Resistance of Japanese Pear (‘Oushuu’) Based on the Fruit^|^apos;s True 3D Geometry

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