Evaluation of the quality of cold meats by computer-assisted image analysis

Zapotoczny, Piotr; Szczypiński, Piotr M.; Daszkiewicz, Tomasz

doi:10.1016/j.lwt.2015.11.042

Cited by 37 publications

(22 citation statements)

References 25 publications

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“…Mainly, this technique has been applied to characterize the microstructure of different types of food [27 -30], microstructure of beef [31] and poultry meat [32]. In relation to the use of fractals to predict quality traits of food, Tsuta et al [33] applied them to predict the sugar content of melons and Polder et al [34] measured the chlorophyll of tomato by applying of fractals.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different image analysis algorithms on MRI to predict physico-chemical and sensory attributes of loin

Caballero

Caro

Dahl

et al. 2018

Chemometrics and Intelligent Laboratory Systems

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T. (2018). Comparison of different image analysis algorithms on MRI to predict physico-chemical and sensory attributes of loin. Chemometrics and intelligent laboratory systems, 180, 54-63. ABSTRACTComputer vision algorithms on MRI have been presented as an alternative to destructive methods to determine the quality traits of meat products. Since, MRI is non-destructive, non-ionizing and innocuous methods. The use of fractals to analyze MRI could be another possibility for this purpose. In this paper, a new fractal algorithm is developed, to obtain features from MRI based on fractal characteristics. This algorithm is called OPFTA (One Point Fractal Texture Algorithm). Three fractal algorithms (Classical Fractal Algorithm -CFA-, Fractal Texture Algorithm -FTA-and OPFTA) and three classical texture algorithms (Grey level co-occurrence matrix -GLCM-, Grey level run length matrix -GLRLM-and Neighbouring grey level dependence matrix -NGLDM-) were tested in this study. The results obtained by means of these computer vision algorithms were correlated to the results obtained by means of physico-chemical and sensory analysis. CFA reached low relationship for the quality parameters of loins, the remaining algorithms achieved correlation coefficients higher than 0.5 noting OPFTA that reached the highest correlation coefficients in all cases except for the L* coordinate color that GLCM obtained the highest correlation coefficient.These high correlation coefficients confirm the new algorithm as an alternative to the other computer vision approaches in order to compute the physicochemical and sensory parameters of meat products in a non-destructive and efficient way.

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Section: Introductionmentioning

confidence: 99%

Comparison of different image analysis algorithms on MRI to predict physico-chemical and sensory attributes of loin

Caballero

Caro

Dahl

et al. 2018

Chemometrics and Intelligent Laboratory Systems

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“…Image analysis has numerous applications to the food industry, such as classifying pork hams, evaluating the quality of cold meat, assessing the tenderness of beef carcasses, controlling the freshness of gilthead sea bream based on gill and eye color changes, and automatic fishbone detection [7][8][9][10][11][12]. Visible image analysis is one of the most accessible techniques, from which many studies have offered successful results in diverse research areas on versatility and reduced cost possibilities.…”

Section: Introductionmentioning

confidence: 99%

Digital Image Filtering Optimization Supporting Iberian Ham Quality Prediction

Perán-Sánchez

Serrano

Ravé

et al. 2019

Foods

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Digital images of food for later analysis tend to be heterogeneous in terms of color and luminosity. Improving these images by using filters is necessary and crucial before further processing. This paper compares the non-use of filters and the use of high-pass filters in the images of hand-cut Iberian ham that will be used in a multifractal analysis for the study of fat and its infiltration. The yielded results show that with the use of a high-pass filter, more accurate fractal dimensions were obtained, which can be featured in predictive techniques of Iberian ham quality.

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“…Implementation of NIRS as a process analytical technology (PAT) to the food industry involves a multidisciplinary approach in which computational intelligence (CI), particularly machine learning (ML) [3][4][5][6][7][8][9][10], has been investigated. e main advantage of CI is its capacity of handling multiple parameters, facilitating fast and accurate evaluation of samples in an industrial environment [11].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

Barbon

Mantovani

et al. 2018

Journal of Spectroscopy

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Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

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Evaluation of the quality of cold meats by computer-assisted image analysis

Cited by 37 publications

References 25 publications

Comparison of different image analysis algorithms on MRI to predict physico-chemical and sensory attributes of loin

Comparison of different image analysis algorithms on MRI to predict physico-chemical and sensory attributes of loin

Digital Image Filtering Optimization Supporting Iberian Ham Quality Prediction

Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

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