Prediction of IMF Percentage of Live Cattle by Using Ultrasound Technologies with High Accuracies

Li, Chengcheng; Zheng, Yefeng; Agyepong, Kwabena

doi:10.1109/icie.2009.294

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…The algorithms used to predict the IMF percentage of live animals were based on regression analysis (Whittaker et al ., 1992;Amin et al ., 1993;Newcom et al ., 2002;Li et al ., 2009); neural network (Brethour, 1994;Amin et al ., 1992;Harron and Dony, 2009;Li et al ., 2009) or support vector machine (Harron and Dony, 2009), among others. These algorithms were developed from textural RTU image features such as a histogram of pixel grey levels, Fourier-based (Amin et al ., 1997;Hassen et al ., 1999b;Newcom et al ., 2002;Harron and Dony, 2009).…”

Section: Mathematical Modelling Approaches From Rtu Image Analysismentioning

confidence: 99%

Real-time ultrasound (RTU) imaging methods for quality control of meats

Silva¹,

Cadavez²

2012

Computer Vision Technology in the Food and Beverage Industries

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Abstract:In this chapter the use of real-time ultrasonography to predict in vivo carcass composition and meat traits will be reviewed. The chapter begins by discussing background and principles of ultrasound. Then aspects affecting the suitability of realtime ultrasonography and image analysis for predicting carcass composition and meat traits of meat producing species and fish will be presented. This chapter also provides an overview of the present and future trends in the application of real-time ultrasonography in the meat industry.

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Section: Mathematical Modelling Approaches From Rtu Image Analysismentioning

confidence: 99%

Real-time ultrasound (RTU) imaging methods for quality control of meats

Silva¹,

Cadavez²

2012

Computer Vision Technology in the Food and Beverage Industries

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“…Therefore, IMF% in animals analysed in previous works such as [12,6,7,13] might be different from the animals analysed in the present work and predictors should be adjusted in this case.…”

Section: Introductionmentioning

confidence: 82%

“…The production method used in Latin America usually includes a high component of extensive farming (although feedlot is used too) impacting in the amount of IMF%, while in other regions the feedlot production is preferred [ 10 , 6 , 11 ]. Therefore, IMF% in animals analysed in previous works such as [ 12 , 6 , 7 , 13 ] might be different from the animals analysed in the present work and predictors should be adjusted in this case.…”

Section: Introductionmentioning

confidence: 82%

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Beef quality parameters estimation using ultrasound and color images

et al. 2015

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BackgroundBeef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat.ProposalAn algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis.ConclusionsThe proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat.

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“…An ultrasonic scanning system can be used for food diagnostic purposes (physicochemical properties like flow rate, structure, composition, and physical state), especially for soybean, honey, cereals, meat, and aerated foods (Table 1) [22]. Volume estimation, firmness, maturity of fruits [23][24][25], rheological properties of cereal products [26], determination of fat percentage in meat [27], and defect detection in cheese [28] have been measured or conducted with ultrasound. Ultrasound velocity, attenuation coefficient, signal and wave amplitude, acoustic impedance, and relative delay are the parameters considered for analysis of food materials [29,30].…”

Section: Ultrasonic Wave-based Analysismentioning

confidence: 99%

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

et al. 2022

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The chemical composition of any food material can be analyzed well by employing various analytical techniques. The physical properties of food are no less important than chemical composition as results obtained from authentic measurement data are able to provide detailed information about the food. Several techniques have been used for years for this purpose but most of them are destructive in nature. The aim of this present study is to identify the emerging techniques that have been used by different researchers for the analysis of the physical characteristics of food. It is highly recommended to practice novel methods as these are non-destructive, extremely sophisticated, and provide results closer to true quantitative values. The physical properties are classified into different groups based on their characteristics. The concise view of conventional techniques mostly used to analyze food material are documented in this work.

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Prediction of IMF Percentage of Live Cattle by Using Ultrasound Technologies with High Accuracies

Cited by 3 publications

References 9 publications

Real-time ultrasound (RTU) imaging methods for quality control of meats

Real-time ultrasound (RTU) imaging methods for quality control of meats

Beef quality parameters estimation using ultrasound and color images

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

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