Modelling conditions of storing quality commercial eggs

Egg storage has been a problem due to ineffective methods subjecting many farmers and egg retailers to losses. These techniques include various models involving statistical analysis of the storage conditions on the egg quality. Apparent deficiencies of the information from the randomized complete block design model prompted this study. The study evaluated the effect of storage temperature at three levels (5 ^°C, 19.5 ^°C, 30 ^°C) and duration at four levels (2^nd, 12^th, 22^nd, 32^nd) on egg quality using a fixed and mixed-effect model. We used a total of 618 fresh and unfertilized eggs from ISA (Institut de Sélection Animale) brown layers. We determined egg quality by the changes of physical characterization under storage conditions. The study used Restricted maximum likelihood and analysis of variance methods to assess the efficiency of fixed and mixed effect models. Results showed that the physical components of the egg were significantly affected at 5 ^°C, 19.5 ^°C, and 30 ^°C(P<0.05). The effect was more adverse on eggs stored at 30 ^°C for 32 days. However, storage temperatures of 5 ^°C and 19.5 ^°C led to an extensive reduction in the Haugh unit, yolk index, and egg white height. On the other hand, it increased the weight loss and albumen diameter under storage for 2^nd, 12^th, 22^nd and 32^nd-time intervals. Based on these findings, the study recommends 5 ^°C for egg quality preservation. The eggs should be refrigerated for 32 days, stored at 19.5 ^°C for 14 days, and lastly kept at 30 ^°C for a maximum of 7 days. The fixed-effect models exhibited more minor variances in diameter and height of albumen, yolk index, weight loss, and Haugh unit. This overlapped instances where the fixed-effect models were significantly the same as the mixed-effect models. This study proposes that the fixed effect model is the most appropriate for randomized completely block design experiments.

Towards better meta-analyses in assisted reproductive technology: Fixed, random or multivariate models?

AIM: To study the validity of the fixed, random, and multivariate meta-analytical models applied in meta-analyses in artificial reproduction technique.

METHODS: Based on common characteristics of in vitro fertilization (IVF) meta-analyses, we simulated a large number of data to compare results issued from the fixed model (FM) with the random model (RM). For multiple endpoints meta-analysis (MA), we compared the univariate RM with the multivariate model (MM). Finally, we illustrate our findings in re-analyzing a recent MA.

RESULTS: In our review, although a homogeneous effect was excluded in 89% of the MAs (11%), FM was utilized in 41 studies (82%). From simulations, a concordance of 59% ± 6% was found between the two tests, with up to 65% of falsely significant results with FM. The Q-test on studies characterized by substantial heterogeneity falsely accepted homogeneity in 46% of studies. Comparing separate univariate RM and MM on multiple endpoints studies, MM reduces the between endpoint discrepancy (BED) of 68%, and increases the power of 57% ± 8%. In the example dealing with the controversial effect of luteneizing hormone supplementation to follicle stimulating hormone during ovarian stimulation in IVF cycles, MM reduced BED by 66%, and consistent effects were found for all the endpoints, irrespective of partial reporting.

CONCLUSION: The FM generally may produce falsely significant differences. The RM should always be used. For multiple endpoints, the MM constitutes the best option.