Prediction of beef eating quality from colour, marbling and wavelet texture features

Jackman, Patrick; Sun, Da‐Wen; Du, Cheng-Jin; Allen, Paul; Downey, Gérard

doi:10.1016/j.meatsci.2008.06.001

Cited by 125 publications

(52 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before reviewing the use of ultrasound technology to examine these features, the differences between IMF and marbling should be outlined. IMF refers to the chemically extractable fat in a muscle (Shi-Zheng and Su-Mei, 2009) and is an objective measurement, whereas marbling, assessed visually, refers to the appearance of evenly distributed white flecks or streaks of fatty tissue between bundles of muscle fibres (Tume, 2004) and can be subjectively assessed with grading scores or objectively assessed when image analysis is used (e.g., Faucitano et al ., 2005;Jackman et al ., 2008). Both are relevant for meat quality evaluation and are closely related to each other (correlation coefficients of up to 0.8 in Savell et al ., 1986;Devitt and Wilton, 2001;Kemp et al ., 2002).…”

Section: Using Real-time Ultrasonography To Predict Intramuscular Fatmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Using Real-time Ultrasonography To Predict Intramuscular Fatmentioning

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

View full text Add to dashboard Cite

show abstract

“…Computer vision systems are very popular choices for delivering fast, reliable and robust food classification; since the grading of foodstuffs by human graders has essential weaknesses of subjectivity, inconsistency and unreliability Sun 2004, 2006;Jackman and Sun 2011a, b, c;Jackman et al 2008;. The application of computer vision technology has been highly successful in food classification and quality prediction in the past and it has continued this success in recent times (Jackman and Sun 2013).…”

Section: Introductionmentioning

confidence: 99%

Effect of frying temperature and time on image characterizations of pellet snacks

2014

View full text Add to dashboard Cite

The development of non-destructive methods for the evaluation of food properties has important advantages for the food processing industries. So, the aim of this study was to evaluate the effects of frying temperature (150, 170, and 190°C) and time (0.5, 1.5, 2.5, 3.5 and 4.5 min) on image properties (L*, a* and b*, fractal dimension, correlation, entropy, contrast and homogeneity) of pellet snacks. Textures were computed separately for eight channels (RGB, R, G, B, U, V, H and S). Enhancing the frying time from 0.5 min to 2.5 min increased the fractal dimension; but its increase from 2.5 min to 4.5 min could not expand the samples. Then, the highest volume of pellet snacks was observed at 2.5 min. Features derived from the image texture contained better information than color features. The best result was for U channel which showed that increasing the frying time increased the contrast, entropy and correlation. Developing the frying temperature up to 170°C decreased contrast, entropy and correlation of images; however these factors were increased when frying temperature was 190°C. These results were invert for homogeneity.

show abstract

“…NMR technologies for these purposes are still in the laboratory experiment stage of development, and there is much work to be done before their commercialization is possible. Computer vision technology (Sun and Brosnan 2003;Valous et al 2009;Jackman et al 2008;Sun 2004;Jackman et al 2009;Wang and Sun 2002) may be useful for evaluating external attributes of fruit, but it is not helpful in detecting bruises under the peel, nor can it be used to evaluate the internal quality of fruit (e.g., moisture content (MC), total soluble solid (TSS), and firmness). Therefore, the market still awaits a simple, fast, accurate, and objective method for measuring the quality of fruit.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in the Application of Hyperspectral Imaging for Evaluating Fruit Quality

et al. 2015

Self Cite

View full text Add to dashboard Cite

The quality of fruit is a very important measure in determining the market value of the fruit. Good fruit quality provides great potential benefits for antioxidant properties of abundant bioactive compounds such as flavonoids, anthocyanins, polyphenols, and ascorbic acid. Therefore, giving enough attention to fruit quality and its changes is important for assuring high quality of fruits. Hyperspectral imaging (HSI) technique has showed great potential for evaluating quality attributes of fruit due to its nature of simple, continuous noninvasiveness and economy. However, for inherent peculiarity of fruit, some problems arose in the practice of estimating fruit quality by HSI, including focusing, determination of scanning parameters, image processing, data mining, and data analysis. The purpose of this paper is to review the use of HSI in the estimation of physical-chemical attributes, detection of common defects and contaminants, and evaluation of maturity stage of fruit. Remaining problems are also identified, along with suggested solutions and possible future trends in the field.

show abstract

Prediction of beef eating quality from colour, marbling and wavelet texture features

Cited by 125 publications

References 14 publications

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

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

Effect of frying temperature and time on image characterizations of pellet snacks

Recent Advances in the Application of Hyperspectral Imaging for Evaluating Fruit Quality

Contact Info

Product

Resources

About