Chicken meat is becoming the most consumed in the world for both economic and nutritional reasons; regarding the latter, the lipid profile may play positive or negative roles in the prevention and treatment of diseases. In this study, we define the state of the art of lipid-based nutritional indexes and used the lipid content and fatty acid profile (both qualitative and quantitative) of breast meat of two poultry genotypes with different growth rates and meat traits. Further, we summarize and review the definitions, implications, and applications of nutritional indexes used in recent years and others of our own design to provide a useful tool to researchers working in the field of meat quality (not only in poultry) to select the most appropriate index for their own scientific purposes. All indexes show advantages and disadvantages; hence, a rational choice should be applied to consider the nutritional effect of meat on human health and for a possible assessment of the most suitable rearing systems (genotype, feeding, farming system or postmortem handling).

Chicken meat is rich in unsaturated fatty acids. Therefore, it is more susceptible to lipid oxidation and production of volatile organic compounds (VOC). In this study, we evaluated the fatty acids, antioxidants, and VOC profiles of raw and cooked meat samples derived from 4 strains of chicken differing in their growth rates, which were as follows: slow-growing (SG, Leghorn), medium-growing (MG, Hubbard and Naked Neck), and fast-growing (FG, Ross). The VOC profile of meat was measured using proton-transfer reaction-mass spectrometry (PTR-MS). The VOC were identified using PTR-time of flight-MS (PTR-ToF-MS). The data were analyzed using both univariate and multivariate models. Twenty main VOC were identified, which were classified into the following chemical categories: aldehydes, alkadienes, alkenes, furans, amides, alcohols, and other compounds. Our results revealed that the chicken genotype and the method of cooking strongly influenced the VOC profile of the meat. Identifying the relationships between these traits allowed us to highlight the trade-off of the main substrates such as n-3 and n-6 polyunsaturated fatty acids (PUFA), protective substances (antioxidants), and degradation products (VOC) of the poultry meat produced during cooking. The extent of VOC production and n-3 loss was found to be higher for the SG genotype. Reduction of n-6 was higher in MG, whereas small losses in antioxidants and PUFA were observed in the FG genotype, consequently, resulting in the lowest production of VOC. The SG and MG are genotypes more active from a kinetic point of view respect to the FG ones. For this reason, in the FG genotypes, the antioxidants are less involved in the oxidative stress induced by the movement; thus, they were available to protect the lipid of the meat during the cooking process. These results suggested that the use of SG and MG genotypes requires a specific dietary protocol (i.e., increasing the antioxidants content) to counteract the lipid oxidations in all the phases: in vivo, postmortem, and during/after cooking.

The beneficial effects of n-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) on human health are widely known. Humans are rather inefficient in synthesizing n-3 LC-PUFA; thus, these compounds should be supplemented in the diet. However, most Western human diets have unbalanced n-6/n-3 ratios resulting from eating habits and the fact that fish sources (rich in n-3 LC-PUFA) are not sufficient (worldwide deficit ~347,956 t/y) to meet the world requirements. In this context, it is necessary to find new and sustainable sources of n-3 LC-PUFA. Poultry products can provide humans n-3 LC-PUFA due to physiological characteristics and the wide consumption of meat and eggs. The present work aims to provide a general overview of the main strategies that should be adopted during rearing and postproduction to enrich and preserve n-3 LC-PUFA in poultry products. The strategies include dietary supplementation of α-Linolenic acid (ALA) or n-3 LC-PUFA, or enhancing n-3 LC-PUFA by improving the LA (Linoleic acid)/ALA ratio and antioxidant concentrations. Moreover, factors such as genotype, rearing system, transport, and cooking processes can impact the n-3 LC-PUFA in poultry products. The use of a multifactorial view in the entire production chain allows the relevant enrichment and preservation of n-3 LC-PUFA in poultry products.

The demand for poultry meat, being cheaper than red meat, will drive worldwide production of this product. Accordingly, an increase in production up to 16% is expected in 2025, most of which will occur in developing countries. Most poultry meat production is realized with intensive production systems, and extensive rearing systems (ERS) of poultry (organic, free-range, and low-input) represent only a small portion of poultry production in the EU (about 5%). However, there is an increasing interest in such rearing systems to maintain the good image of product and environmental sustainability, improved animal welfare, and meat quality with an annual trend of growth of about 10%. The aims of this work were to summarize the activities and the viewpoint of the researchers of the Department of Agricultural, Food, and Environmental Science of the University of Perugia (Italy). One of the most important goals of the research unit was the challenge of identifying the best poultry genotypes for ERS, which are important not only for the food industry but also for the improvement of human nutrition. Only the definition of the best genotypes adapted to ERS through the measurement of a wide panel of traits—genetic, physiologic, and behavior—and not only relying on daily weight gain will allow us to achieve this goal.

The aim of this research was to validate the effectiveness of the Healthy Fatty Index (HFI) regarding some foods of animal origin (meat, processed, fish, milk products, and eggs) typical of the Western diet and to compare these results with two consolidated indices (atherogenic—AI, and thrombogenic—TI) in the characterization of the nutritional features of their lipids. The fatty acids profile (% of total fatty acids and mg/100 g) of 60 foods, grouped in six subclasses, was used. The AI, TI, and HFI indexes were calculated, and the intraclass correlation coefficients and the degree of agreement were evaluated using different statistical approaches. The results demonstrated that HFI, with respect to AI and TI, seems better able to consider the complexity of the fatty acid profile and the different fat contents. HFI and AI are the two most diverse indices, and they can provide different food classifications. AI and IT exhibit only a fair agreement in regards to food classification, confirming that such indexes are always to be considered indissolubly and never separately, in contrast to the HFI, which can stand alone.