Visible and near-infrared light transmission: A hybrid imaging method for non-destructive meat quality evaluation

Ziadi, Adel; Maldague, Xavier; Saucier, Linda; Duchesne, Carl; Gosselin, Ryan

doi:10.1016/j.infrared.2012.05.004

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…With respect to muscle type, the best classification considering textural features were both obtained with the 940 nm excitation LED suggesting that muscle discrimination could be assigned to difference in muscle marbling. [32] The results of the present study are in agreement with previous investigation but enabled to identify a new discrimination LED in the NIR region (i.e., 940 nm) but with lower classification accuracy. The best LED for discrimination of muscle type was identified as being the white-light by Kulmyrzaev et al [11] The white-light enabled to obtain a 91% of good classification for three muscles.…”

Section: Sample Discrimination By Msi Datasupporting

confidence: 92%

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Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis

Aït‐Kaddour

Jacquot

Micol

et al. 2016

International Journal of Food Properties

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The potential of multi-spectral visible-near infrared imaging to discriminate beef meat muscles in relation with their type and animal origin was examined in the present study. Two hundred forty muscles of three types (longissimus thoracis, biceps femoris, and semimembranosus) were obtained from the carcasses of three types of animals, two late-maturing cattle types of animals (Limousin and Blond d'Aquitaine) that grow slowly and deposit more muscles and less fat, compared to one early-maturing cattle types of animals (Angus) which tends to have muscles richer in collagen and in intramuscular fat. Two hundred forty cube images were collected with nineteen Ligth Emitting Diodes (405 to 1050 nm) using the Videometer Lab2 device. The image cubes were processed in order to extract image mean spectra and image shape features from co-occurrence and difference of histogram matrices. The results of the partial least square discriminant analysis performed on image texture features and spectral data show a maximum ranging from 63.5 to 83% of good classification depending on the muscle and breed considered. This study demonstrated the promising potential of the visible-near infrared multi-spectral imager to characterize beef meat muscles based on muscle type and its animal origin.

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Section: Sample Discrimination By Msi Datasupporting

confidence: 92%

“…[4,[29][30][31] The band at 940 nm can be related to meat fat. [32] The 1050 nm band is close to the bands assigned to fat and proteins. [33][34][35] These spectral differences were consistent with biochemical analysis of beef meat.…”

Section: Sample Discrimination By Msi Datamentioning

confidence: 52%

Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis

Aït‐Kaddour

Jacquot

Micol

et al. 2016

International Journal of Food Properties

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“…Infrared spectroscopy (IS) techniques are based on the absorption of infrared light due to the changes in the vibrational states of organic molecules. Due to the opacity of certain samples, their use is limited (Rinnan et al, 2009), although the viability of IS to quantify and to characterize different components in meat and fish products has been evaluated (Ziadi et al, 2012). Moreover, the ability of IS based techniques for the identification of different meat species, allowing the detection of food fraud, has been reported (Damez and Clerjon, 2013;Kamruzzaman et al, 2012).…”

Section: Infrared Spectroscopymentioning

confidence: 99%

Application of non-invasive technologies in dry-cured ham: An overview

Pérez-Santaescolástica

Fraeye

Barba

et al. 2019

Trends in Food Science & Technology

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Background: Dry-cured ham is one of the most valued food products by Mediterranean consumers. In this sense, the appropriate development of its different production stages is essential to ensure the quality requirements. For this reason, non-invasive technologies have gained popularity and have been reported as useful not only to ensure the food safety of different products, but also to monitor fundamental stages in the production process, such as the salting stage, to analyze the content of different compounds without sample losses, and to correct possible defects in the final product. Scope and approach: This work has been focused on summarizing the studies that describe and have successfully applied these techniques, as well as on mentioning other technologies with potential use in dry-cured ham manufacture which have not been studied enough. Finally, the potential next steps to improve and optimize the process, as well as the suitability of creating new products with added value based on the new quality standards, have also been evaluated. Key findings and conclusions: Innovative non-invasive technologies such as high pressure (HP), ultrasound (US), pulsed electric fields (PEF), microwaves, irradiation, etc. can be used as promising tools to effectively control salting and curing stages as well as for checking defects of the final product and/or ensuring food safety. HP and US are useful tools for the determination of salt and fat content, and for monitoring the salting process. Moreover, HP enhances salty taste perception, which makes it a useful tool to reduce salt addition. Both, HP and US, can correct texture defects. In addition, NIRS allows predicting the state of the meat to remove those pieces that could result in defective products. Moreover, RAMAN or MRI are able to detect anomalous textures at the end of the process. Microwaves could be useful for the online estimation of salt, water and fat contents easily with portable equipment. Finally, data mining, that allows to make predictions based on an immense data file, is the most promising discovery in recent years for detecting defects or classifying products according to sensory attributes.

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“…A microscopy image can be stored as a multi-dimensional matrix, in which elements are pixel intensities (Chan & Shen, 2005; Gosselin et al, 2011; Ziadi et al, 2012; Juneau et al, 2017). With the matrix of images, the key idea is to detect the boundary of cells and segment cells from the background of images.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images

Budman

Duever

2017

Microsc Microanal

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Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

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Visible and near-infrared light transmission: A hybrid imaging method for non-destructive meat quality evaluation

Cited by 13 publications

References 22 publications

Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis

Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis

Application of non-invasive technologies in dry-cured ham: An overview

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images

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