Prediction of egg freshness during storage using electronic nose

The aim of the present study was to investigate the potential of a fast gas chromatography (GC) e-nose for freshness discrimination and for prediction of storage time as well as sensory and internal quality changes during storage of hen eggs. All samples were obtained from the same egg production farm and stored at 20 °C for 20 d. Egg sampling was conducted every 0, 3, 6, 9, 12, 16, and 20 d. During each sampling time, 4 egg cartons (each containing 10 eggs) were randomly selected: one carton for Haugh units, one carton for sensory evaluation and 2 cartons for the e-nose experiment. The e-nose study included 2 independent test sets; calibration (35 samples) and validation (28 samples). Every sampling time, 5 replicates were prepared from one egg carton for calibration samples and 4 replicates were prepared from the remaining egg carton for validation samples. Sensors (peaks) were selected prior to multivariate chemometric analysis; qualitative sensors for principal component analysis (PCA) and discriminant factor analysis (DFA) and quantitative sensors for partial least square (PLS) modeling. PCA and DFA confirmed the difference in volatile profiles of egg samples from 7 different storage times accounting for a total variance of 95.7% and 93.71%, respectively. Models for predicting storage time, Haugh units, odor score, and overall acceptability score from e-nose data were developed using calibration samples by PLS regression. The results showed that these quality indices were well predicted from the e- nose signals, with correlation coefficients of R² = 0.9441, R² = 0.9511, R² = 0.9725, and R² = 0.9530 and with training errors of 0.887, 1.24, 0.626, and 0.629, respectively. As a result of ANOVA, most of the PLS model results were not significantly (P > 0.05) different from the corresponding reference values. These results proved that the fast GC electronic nose has the potential to assess egg freshness and feasibility to predict multiple egg freshness indices during its circulation in the supply chain.

Evolution of helping and harming in heterogeneous groups

Social groups are often composed of individuals who differ in many respects. Theoretical studies on the evolution of helping and harming behaviors have largely focused upon genetic differences between individuals. However, nongenetic variation between group members is widespread in natural populations, and may mediate differences in individuals' social behavior. Here, we develop a framework to study how variation in individual quality mediates the evolution of unconditional and conditional social traits. We investigate the scope for the evolution of social traits that are conditional on the quality of the actor and/or recipients. We find that asymmetries in individual quality can lead to the evolution of plastic traits with different individuals expressing helping and harming traits within the same group. In this context, population viscosity can mediate the evolution of social traits, and local competition can promote both helping and harming behaviors. Furthermore, asymmetries in individual quality can lead to the evolution of competition-like traits between clonal individuals. Overall, we highlight the importance of asymmetries in individual quality, including differences in reproductive value and the ability to engage in successful social interactions, in mediating the evolution of helping and harming behaviors.