Image analysis study of the perimysial connective network, and its relationship with tenderness and composition of bovine meat

International audienceImage processing method was developed to predict beef tenderness, collagen and lipids contents. The study was carried out on the semimembranosus muscle (SM). Images of sM slices were acquired under visible and ultraviolet lighting, In this work statistical technique was implemented as a method to relate the distribution of intramuscular connective tissue (IMCT), characterized by image analysis, to sensory tenderness evaluated by a trained panel and collagen and total lipids contents assessed chemically. Using Multiple Linear Regression (MLR) combining visible and ultraviolet lighting, IMCT image parameters were found to be good predictors of beef tenderness (R2 = 0.89), collagen and lipids contents (respectively R2 = 0.82 and R2 = 0,91)

Application of texture image analysis for the classification of bovine meat

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Photographic image analysis for the classification of bovine meat

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Classification of bovine muscles from texture analysis of meat slices images

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Texture image analysis : Application to the classification of bovine muscles from meat slice images

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Classement de muscles de bovins par analyse de la texture d\textquoterightimages réalisées sur des tranches de viande

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Analysis of the volatile profile and identification of odour-active compounds in Bayonne ham

International audienceThe aim of this work was to reliably identify odour-active compounds in dry-cured ham using powerful analysis methods for the volatile fraction. For this purpose, dynamic headspace gas chromatography combined with eight-way olfactometry using a panel of eight sniffers was used. One- and two-dimensional gas chromatography coupled with mass spectrometry and (or) olfactometry were also used. More than 600 compounds from the volatile fraction of dry-cured ham were identified and their biochemical origins are discussed. They covered a wide diversity of structures and chemical functions. Only 29 of them proved odour-active. Comparison of the results of GC–O analysis with those obtained by orthonasal sniffing of the dry-cured ham helped to gain a better understanding of how these substances contributed to the overall aroma of the product. Thus, “Fruity–Floral”, “Green–Vegetable” or “Plastic–Chemical” odours intensively perceived by GC–O have been poorly perceived by orthonasal sniffing. By contrast, “Animal–Meat products” or “Butter–Lactic–Cheesy” odours have been much better perceived by orthonasal sniffing. These results indicate that to understand the interactions between odour-active compounds, experimental doping with carefully selected odour-active compounds will be necessary

Effects of muscle texture on ultrasonic measurements

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Effects of bovine muscle composition and structure on ultrasonic measurements

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Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures

International audienceThe aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not highlight the most influencing variables for each separation, whereas the ICA Loadings highlighted the same variables as did CCA. This study shows the potential of CCA for the extraction of pertinent information from a data matrix, using a procedure based on an original optimisation criterion, to produce results that are complementary, and in some cases may be superior, to those of PCA and ICA