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, Discussion Générale performances de prédiction pour les autres AGPI ont également augmenté, elles restent toujours insatisfaisantes, Chapitre, vol.5

. Zhou, le fait d'exprimer les AG en valeur relative diminue « mathématiquement » la variabilité de ceux-ci, ce qui altère les performances de la SPIR, comme nous l'avons établi précédemment. De plus, Zhou et al. (2012) rapportent que la SPIR étant basée sur la loi de Beer-Lambert, où l'absorbance varie de façon linéaire avec la concentration d'un paramètre, et ils militent en faveur d'une expression des AG en valeur absolue. À contrario, il est à noter que le mode d'expression en valeur relative peut augmenter l'expression de la variabilité des AG présents en faible quantité. Nous avons pu, très récemment, tester ces deux approches (AG exprimés en valeur relative ou absolue) à partir de notre nouvelle « échantillothèque ». Concernant les AGS et AGMI, nous confirmons que l'expression en valeur absolue améliore les performances de calibration. Cependant, Un autre point mérite d'être discuté. Il concerne le mode d'expression des AG (en valeur absolue ou relative) à partir duquel les équations de calibration sont établies. Jusqu'à présent, les étalonnages des AG de la viande ont préférentiellement été réalisés sur des AG exprimés en valeur absolue (mg.100g -1 de tissu) plutôt qu'en valeur relative (% des AG totaux). Selon Siemens & Daun, 2005.

, Prédiction des AGPI par des équations

, Nous avons donc poursuivi notre travail en cherchant à évaluer les performances de la prédiction des AGPI à partir de données très bien prédites par SPIR (AGS, AGMI) et de données facilement accessibles en abattoir (race, sexe, type d'animal, ?), Comme rapporté précédemment, la prédiction des AG d'intérêt reste insatisfaisante par SPIR

, Nous avons construit notre stratégie de détermination des équations de prédiction des AG d'intérêt autour de deux bases de données différentes, « ?3Meat » qui regroupait les données de la bibliographie et « INRA » qui regroupait les données acquises depuis plus de 10 ans dans mon laboratoire d'accueil. Cette stratégie présentait bien évidemment des risques (doublement du temps d'analyse, obtention d'équations potentiellement divergentes, ?) mais présentait pour nous plusieurs avantages que nous allons détailler par la suite

, Sur le plan statistique, le fait d'avoir utilisé deux bases de données distinctes nous a

, La prise en compte de la qualité des produits pour l'alimentation humaine, du bien-être animal, d'un meilleur respect de l'environnement sont autant de points nouveaux présentant un intérêt particulier pour les filières de production mais aussi, Les productions animales ont beaucoup évolué au cours des cinquante dernières années

, Ce travail de thèse a permis de confirmer que la SPIR était bien un outil performant, répondant aux exigences énoncées ci-dessus, tout au moins pour déterminer la composition en AG majeurs de la viande bovine. Toutefois, la SPIR présente des limites d'utilisation pour les AGPI présents en faible quantité, malgré leur fort intérêt nutritionnel. Nous avons montré qu'il était possible d'augmenter les performances de prédiction par SPIR de ces AG en introduisant dans notre « échantillothèque » des échantillons dont la variabilité et/ou la teneur en AGPI étaient plus importantes. Cependant, cette stratégie a montré des limites de prédiction encore trop importantes pour satisfaire nos commanditaires. Nous avons donc associé à notre démarche, le développement d'équations de prédiction des AGPI (mal prédits par SPIR) à partir des acides gras majeurs (AGS et AGMI, bien prédits par SPIR), Concernant la qualité nutritionnelle de la viande qui est une des préoccupations majeure du consommateur et du monde médical, il est indispensable de disposer d'informations fiables, détaillées et facilement accessibles à tous les niveaux de la filière

, Une des premières pistes à privilégier serait d'élargir encore la variabilité de notre base spectrale. Cela pourrait être réalisé en introduisant des échantillons de viande d'origine encore plus variée que ceux déjà prospectés, comme par exemple des échantillons provenant de génisses ou de taureaux ou provenant d'animaux ayant reçus des rations plus extrêmes que celles déjà investiguées (durée de pâturage ou supplémentation en lin plus longues,?.). En dehors du type d'animal et de son alimentation, nous pourrions également envisager d'intégrer d'autres types de tissus connus pour leurs teneurs plus extrêmes en lipides et/ou en AGPI comme le coeur ou la langue. Enfin, nous pourrions également envisager de créer une base spectrale « viande » plus large en intégrant des échantillons de viande provenant d'autres espèces telles que le mouton, le porc, Au vue des demandes des filières, il nous semble indispensable de continuer à améliorer les performances de prédiction de la SPIR

. Il, Pour ce faire, deux pistes d'amélioration seraient à envisager, la technique de mesure physique elle-même, ou plus simplement la méthode d'exploitation des spectres. En effet, il est rapporté que l'étalonnage des constituants de la SPIR peut s'envisager par 2 approches différentes : l'approche « globale » ou l'approche « locale ». Nous avons utilisé dans notre travail l'approche « globale », la totalité des spectres ayant été utilisés pour réaliser les prédictions. La méthode dite « locale » consiste à créer un étalonnage spécifique pour un spectre donné à partir d'un petit groupe d'échantillons spectralement similaires afin d'établir une prédiction réellement adaptée à chacun des spectres

. Toutefois, pour réaliser cette procédure, il est nécessaire de disposer d'un grand nombre de spectres (>1000) ce qui n'était pas notre cas dans le contexte de ce travail

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