Consumer acceptance and sensory profiling of reengineered kitoza products

Kitoza refers to a traditional way of preparing beef and pork in Madagascar. However, in order to improve some drawbacks previous identified, the product was submitted to a reengineering process. The acceptance and sensory profiling of improved Kitoza products among Portuguese consumers was investigated. A local smoked loin sausage was selected as basis for comparison. Firstly, a Focus Group study was performed to identify sensory descriptors for Kitoza products and explore product perception. Subsequently, a Flash Profile and a consumer sensory acceptance study were conducted. Flash Profile’s results showed that beef- and pork-based Kitoza products investigated differed considerably in all sensory dimensions. The Portuguese sausage was characterized as having a more intense and lasting after taste, as well as displaying a higher degree of (meat) doneness. The acceptance study yielded higher overall liking ratings for pork- than for beef-based Kitoza, although the Portuguese sausage remained the most appreciated product

Use of sensors and in silico models for the prediction of meat colour: Tools to reduce meat wastage

International audienc

The Impact of Cooking of Beef on the Supply of Heme and Non-Heme Iron for Humans

International audienceRed meat contains a high proportion of heme iron (HI) which is absorbed at a far higher extent into the blood than the non-heme iron (NHI) found in plants. However, HI and NHI are expelled in the juice during cooking while a fraction of HI is converted into NHI, thus decreasing iron bioavailability. This paper relies on experiments and the use of modeling. The kinetics of the conversion of HI into NHI was measured and modeled in juice extracted from uncooked beef meat, and beef cubes were cooked to measure the variations of HI/NHI contents. In meat, HI/NHI ratio decreased from 2.0 when it was raw to less than 1.0 for the longest heat treatments and highest temperatures. The model was used to predict the effect of cooking conditions on the variations of the iron supplied by beef meat. The lowest contribution of meat to iron supply was found for under-pressure cooking at temperatures above 100˚C

Effect of dietary n-6 and n-3 polyunsaturated fatty acids on peroxidability of lipoproteins in steers

International audienc

Impacts des suppléments lipidiques riches en AGPI sur la sensibilité à la péroxydation des lipides sanguins et tissulaires du ruminant

National audienc

Effets des suppléments lipidiques des rations pour bovins en finition sur la qualité nutritionnelle des acides gras de la viande

National audienc

Predicting the Oxidative Degradation of Raw Beef Meat during Cold Storage Using Numerical Simulations and Sensors—Prospects for Meat and Fish Foods

Preventing animal-source food waste is an important pathway to reducing malnutrition and improving food system sustainability. Uncontrolled color variation due to oxidation is a source of waste as it prompts food rejection by consumers. Evaluation of oxidation–reduction potential (ORP) can help to predict and prevent oxidation and undesirable color changes. A new sensor and two modeling approaches—a phenomenological model and a reaction–diffusion model—were successfully used to predict the oxidative browning of beef ribeye steaks stored under different temperature and oxygen concentration conditions. Both models predicted similar storage durations for acceptable color, although deviating for higher and lower redness levels, which are of no interest for meat acceptance. Simulations under higher oxygen concentrations lead to a few days of delay in the redness change, as observed in practice, under modified atmosphere packaging. In meat juice, variation in ORP measured by the sensor correlated with the redness variation. However, in meat, sensors promote oxidation in the adjacent area, which is unacceptable for industrial use. This paper discusses the potential, limits, and prospects of the mathematical models and sensors, developed for beef. A strategy is proposed to couple these approaches and include the effect of microorganisms

Predicting the Oxidative Degradation of Raw Beef Meat during Cold Storage Using Numerical Simulations and Sensors—Prospects for Meat and Fish Foods

Preventing animal-source food waste is an important pathway to reducing malnutrition and improving food system sustainability. Uncontrolled color variation due to oxidation is a source of waste as it prompts food rejection by consumers. Evaluation of oxidation–reduction potential (ORP) can help to predict and prevent oxidation and undesirable color changes. A new sensor and two modeling approaches—a phenomenological model and a reaction–diffusion model—were successfully used to predict the oxidative browning of beef ribeye steaks stored under different temperature and oxygen concentration conditions. Both models predicted similar storage durations for acceptable color, although deviating for higher and lower redness levels, which are of no interest for meat acceptance. Simulations under higher oxygen concentrations lead to a few days of delay in the redness change, as observed in practice, under modified atmosphere packaging. In meat juice, variation in ORP measured by the sensor correlated with the redness variation. However, in meat, sensors promote oxidation in the adjacent area, which is unacceptable for industrial use. This paper discusses the potential, limits, and prospects of the mathematical models and sensors, developed for beef. A strategy is proposed to couple these approaches and include the effect of microorganisms