Kulmyrzaev A.A.,Unite de Recherches Typicite des Produits Alimentaires |
Dufour E.,Unite de Recherches Typicite des Produits Alimentaires
Food and Bioprocess Technology | Year: 2010
Using standard technologies, 36 experimental hard, semi-hard, and soft cheeses were manufactured from milk produced at three different locations. Infrared, fluorescence, and physicochemical properties of cheeses and corresponding milks and wheys were measured, and a data collection consisting of 16 tables characterizing measured properties was built. This data collection was then subjected to statistical treatment using common components and specific weights analysis (CCSWA). As a result, specific weights of the data tables relating them to the common components, latent variables, and loadings of the data tables were calculated. Analysis of the CCSWA results along with the physicochemical properties of the experimental cheeses demonstrated that cheeses can be discriminated according to the properties of corresponding milk and whey. © 2008 Springer Science + Business Media, LLC.
Kulmyrzaev A.,Unite de Recherches Typicite des Produits Alimentaires |
Bertrand D.,French National Institute for Agricultural Research |
Lepetit J.,French National Institute for Agricultural Research |
Listrat A.,Unite de Recherches sur les Herbivores |
And 2 more authors.
Food Chemistry | Year: 2012
The potential of fluorescence imaging to discriminate different bovine muscles in relation with animal age, muscle type, chemical and mechanical properties was examined. Twenty-four muscles of three types (Gluteus medius, Longissimus dorsi, and Semitendinosus) and two animal age groups (10-13-years old and 12-24-months old) were obtained from the carcasses of Limousin breed cows. One hundred and forty-four images were collected at three illuminating conditions (exc 320 nm, exc 380 nm, and white light) using a custom-designed imager. The image cubes were processed using "regionprops" algorithm developed earlier in order to extract image shape features (number of shapes, area, major-axis-length, eccentricity, and solidity). Extracted image shape features were processed using custom-designed programs. The results of the PLSDA performed on image shape features showed 100% good discrimination for the three types of muscles. Muscle samples were also subjected to chemical analysis (dry matter, fat, pyridinoline, total, insoluble and soluble collagen) and mechanical tests (shear stress and breaking energy). PLSR models indicated relations between extracted image shape features and mechanical properties, i.e., R 2 = 0.69 and RMSEV = 0.514 were observed for breaking energy for adult-animal muscles. Regarding chemical composition, image shape features allowed to predict total collagen of L. dorsi with R 2 = 0.61 and RMSEV = 0.756. This study has demonstrated a promising potential of the custom-designed fluorescence imager combined with multivariate statistical tools in the study of beef meat. © 2011 Elsevier Ltd. All rights reserved.