Abstract The objective of this study was to evaluate the effect of the addition of Moringa oleifera Lam. seed flour for partial fat replacement in chicken mortadella on physicochemical characteristics, chemical composition, and lipid oxidation. Four mortadellas were prepared: C (control), T1, T3, and T5 (addition of 1%, 3%, and 5% of moringa seed flour, respectively). T5 mortadella had the lowest lipid content (p < 0.05), whereas C mortadella had the highest (p < 0.05). The addition of 5% of moringa seed flour affected color parameters, leading to an increase in L*, a*, and b* values in comparison with the control. Color changes (ΔE) in T3 and T5 mortadellas were the lowest among the samples during a 90-day storage period at 4 °C. The addition of 3 or 5% of moringa seed flour promoted a reduction in lipid oxidation during storage. Moringa seeds have antioxidant activity and, therefore, have the potential to be used as a natural functional ingredient in meat products. The addition of 3% of moringa seed flour seemed to be ideal for chicken mortadella, as it reduced lipid content and promoted lipid stability without causing noticeable color changes during the 90 days of storage.

Este trabalho teve como objetivo implementar um modelo computacional para simular a dinâmica operacional de uma linha industrial de abate de suínos. O sistema real modelado pertence à empresa Frigorífico Frimesa, sediada no município de Medianeira (PR). O modelo implementado é tipo dinâmico, discreto e estocástico. Este simula 34 operações unitárias e foi estruturado com o uso da linguagem de simulação EXTEND TM. Para validação do modelo foram coletados dados relativos a cinco dias de operação, em que foram abatidos 1.346, 1.630, 1.360, 1.585 e 1.550 suínos, respectivamente. Como parâmetros de comparação entre os dados obtidos a partir do sistema e gerados pelo modelo foram selecionadas as seguintes variáveis: (i) tempo de duração da operação; (ii) tempo de deslocamento da insensibilização até a depiladeira; (iii) tempo deslocamento da insensibilização até a câmara fria; (iv) número de carcaças re-inspecionadas; e (v) número final de carcaças. Na validação do modelo, foi constatado que, para a variável tempo de duração da operação por meio do teste Tukey a 1% de significância, não foram detectadas diferenças estatísticas entre os valores obtidos do sistema real e os gerados pelo modelo. Considerando-se esta e outras análises, foi concluído que o modelo aplica-se à finalidade para a qual foi implementado. Sendo assim, foram realizadas duas análises de sensibilidade. Na primeira, ao alterar o número de suínos a abater de 1 mil para 2 mil, foi determinado que os tempos de duração da operação variariam de 6,20 a 10,10 h, respectivamente. Para a segunda, ao abater 1.340 suínos e alterar a velocidade das nórias de 300 para 600 animais por hora, o tempo de duração da operação passou de 8,10 para 7,40 h, respectivamente.

This work was carried out with the objective to implement a computational model for simulating the dynamics of an industrial slaughtering for swine. Modeled real system belongs the Frimesa enterprise, located in Medianeira city, Paraná State, Brazil. Implemented model was classified as: dynamic, discrete and stochastic, and simulates thirty-four unity operations. For model implementation it was employed the simulation language EXTEND TM. Model validation was carried out comparing data related to five operational shifts, when it was slaughtered 1,346, 1,630, 1,360, 1,585 and 1,550 pigs, respectively. As contrasting parameters between data gotten from real system and generated by model, it was selected the following variables: (i) spent operational time; (ii) spent time from stunning area to scrap machine, (iii) spent time from stunning area to cold storage chamber; (iv) number of re-inspected carcasses; and (v) final number of edible slaughtered swine. At model validation was evidenced that for spent operational time averages, according to Tukey's test, at the significance level of 1%, it was not detected statistical differences between real system and generated simulation model data. According to this and other analyses, lead to the conclusion that model and system outcomes were in good agreement. Furthering, two sensitivity were carried out. In the first it was modified the number of swine to be slaughtered from 1,000 to 2,000. It was observed that spent operational time increase from 6.20 to 10.20 h. At the second, it was simulated the slaughter of 1,340 pigs, and was varied processing rates from 300 to 600 pigs per hour, at this situation operational times decreased from 8.10 to 7.40 h, respectively