Different sieving methods for determining the physical characteristics in ground corn

We evaluated various sieving methods to estimate particle size (PS) and geometric standard deviation (GSD) of ground corn. The corn had been previously divided in six fractions and each one ground in a hammermill (1-, 2-, 3-, 4-, 5- or 12-mm sieves). The stacked sieving method, with prior drying at 105ºC without agitators was the reference. We evaluated eight sieving methods, distributed in a factorial design (2 x 2 x 2 x 6), consisting of the following treatments: i) with and without agitators (two 25-mm rubber spheres), ii) with and without previous drying, iii) with a nest of test sieves set in a stacked or reverse, and iv) employing six ground corn degrees, totaling 48 treatments (four replicates). There was a linear increase in PS estimation for methods without drying and stacking and quadratic increases for the others. Reverse, drying, and agitator methodologies gave better sieving of corn, and consequently gave the lowest PS and highest GSD. The results were more pronounced for high-intensity grinding (hammermill sieve with small apertures) in which the differences between the reference method with the drying and reverse methods were up to 210 µm. Reverse sieving combined with agitators allowed the greatest passage of corn particles through the test sieves and promoted better characterization of ground corn.(AU)

Different sieving methods for determining the physical characteristics in ground corn

We evaluated various sieving methods to estimate particle size (PS) and geometric standard deviation (GSD) of ground corn. The corn had been previously divided in six fractions and each one ground in a hammermill (1-, 2-, 3-, 4-, 5-or 12-mm sieves). The stacked sieving method, with prior drying at 105ºC without agitators was the reference. We evaluated eight sieving methods, distributed in a factorial design(2 x 2 x 2 x 6), consisting of the following treatments: i) with and without agitators (two 25-mm rubber spheres), ii) with and without previous drying, iii) with a nest of test sieves set in a stacked or reverse, and iv) employing six ground corn degrees, totaling 48 treatments (four replicates). There was a linear increase in PS estimation for methods without drying and stacking and quadratic increases for the others. Reverse, drying, and agitator methodologies gave better sieving of corn, and consequently gave the lowest PS andhighest GSD. The results were more pronounced for high-intensity grinding (hammermill sieve with small apertures) in which the differences between the reference method with the drying and reverse methods were up to 210 &956;m. Reverse sieving combined with agitators allowed the greatest passage of corn particles through the test sieves and promoted better characterization of ground corn.(AU)

Different sieving methods for determining the physical characteristics in ground corn / Different sieving methods for determining the physical characteristics in ground corn

We evaluated various sieving methods to estimate particle size (PS) and geometric standard deviation (GSD) of ground corn. The corn had been previously divided in six fractions and each one ground in a hammermill (1-, 2-, 3-, 4-, 5- or 12-mm sieves). The stacked sieving method, with prior drying at 105ºC without agitators was the reference. We evaluated eight sieving methods, distributed in a factorial design (2 x 2 x 2 x 6), consisting of the following treatments: i) with and without agitators (two 25-mm rubber spheres), ii) with and without previous drying, iii) with a nest of test sieves set in a stacked or reverse, and iv) employing six ground corn degrees, totaling 48 treatments (four replicates). There was a linear increase in PS estimation for methods without drying and stacking and quadratic increases for the others. Reverse, drying, and agitator methodologies gave better sieving of corn, and consequently gave the lowest PS and highest GSD. The results were more pronounced for high-intensity grinding (hammermill sieve with small apertures) in which the differences between the reference method with the drying and reverse methods were up to 210 µm. Reverse sieving combined with agitators allowed the greatest passage of corn particles through the test sieves and promoted better characterization of ground corn.

We evaluated various sieving methods to estimate particle size (PS) and geometric standard deviation (GSD) of ground corn. The corn had been previously divided in six fractions and each one ground in a hammermill (1-, 2-, 3-, 4-, 5- or 12-mm sieves). The stacked sieving method, with prior drying at 105ºC without agitators was the reference. We evaluated eight sieving methods, distributed in a factorial design (2 x 2 x 2 x 6), consisting of the following treatments: i) with and without agitators (two 25-mm rubber spheres), ii) with and without previous drying, iii) with a nest of test sieves set in a stacked or reverse, and iv) employing six ground corn degrees, totaling 48 treatments (four replicates). There was a linear increase in PS estimation for methods without drying and stacking and quadratic increases for the others. Reverse, drying, and agitator methodologies gave better sieving of corn, and consequently gave the lowest PS and highest GSD. The results were more pronounced for high-intensity grinding (hammermill sieve with small apertures) in which the differences between the reference method with the drying and reverse methods were up to 210 µm. Reverse sieving combined with agitators allowed the greatest passage of corn particles through the test sieves and promoted better characterization of ground corn.

Use of modified Richards model to predict isothermal and non-isothermal microbial growth

Mathematical models are often used to predict microbial growth in food products. An important class of these models involves the adaptation of classical sigmoid functions, such as the Gompertz and logistic functions. This study aimed to validate the use of the modified Richards model in various situations, which have not previously been tested. The model was obtained through solving a system of two differential equations and could be applied to both isothermal and non-isothermal environments. To test and validate this model, we used published datasets containing data for the growth of Pseudomonas spp. in fish products. The results obtained after fitting the model showed that it could be effectively used to describe and predict the Pseudomonas growth curves under various temperature regimens. However, the influence of the shape parameter on the growth curve is an issue that needs further evaluation.(AU)

Comparing non-linear mathematical models to describe growth of different animals / Avaliação comparativa de modelos matemáticos não lineares para descrever o crescimento animal

The main objective of this study was to compare the goodness of fit of five non-linear growth models, i.e. Brody, Gompertz, Logistic, Richards and von Bertalanffy in different animals. It also aimed to evaluate the influence of the shape parameter on the growth curve. To accomplish this task, published growth data of 14 different groups of animals were used and four goodness of fit statistics were adopted: coefficient of determination (R2 ), root mean square error (RMSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC). In general, the Richards growth equation provided better fits to experimental data than the other models. However, for some animals, different models exhibited better performance. It was obtained a possible interpretation for the shape parameter, in such a way that can provide useful insights to predict animal growth behavior.(AU)

O principal objetivo deste estudo foi comparar a qualidade do ajuste de cinco modelos matemáticos recorrentemente utilizados na literatura para a descrição do ganho de peso animal. Ele também teve o objetivo de estudar a influência do parâmetro de forma sobre as curvas de crescimento. Os modelos de Brody, Gompertz, Logístico, von Bertalanffy e Richards, foram ajustados a dados experimentais de 14 grupos de animais diferentes. Como critério de qualidade de ajuste quatro índices estatísticos foram adotados: coeficiente de determinação (R2 ), raiz do quadrado médio do erro (RMSE) e os critérios de informação, Akaike (AIC) e Bayesian (BIC). Em geral, o modelo de Richards forneceu os melhores ajustes aos dados experimentais comparados aos demais modelos. No entanto, para alguns animais, diferentes modelos exibiram melhor desempenho. Foi possível obter uma possível interpretação para o significado do parâmetro de modo a fornecer ferramentas úteis para prever o comportamento do crescimento animal.(AU)

Comparing non-linear mathematical models to describe growth of different animals / Avaliação comparativa de modelos matemáticos não lineares para descrever o crescimento animal

The main objective of this study was to compare the goodness of fit of five non-linear growth models, i.e. Brody, Gompertz, Logistic, Richards and von Bertalanffy in different animals. It also aimed to evaluate the influence of the shape parameter on the growth curve. To accomplish this task, published growth data of 14 different groups of animals were used and four goodness of fit statistics were adopted: coefficient of determination (R2 ), root mean square error (RMSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC). In general, the Richards growth equation provided better fits to experimental data than the other models. However, for some animals, different models exhibited better performance. It was obtained a possible interpretation for the shape parameter, in such a way that can provide useful insights to predict animal growth behavior.

O principal objetivo deste estudo foi comparar a qualidade do ajuste de cinco modelos matemáticos recorrentemente utilizados na literatura para a descrição do ganho de peso animal. Ele também teve o objetivo de estudar a influência do parâmetro de forma sobre as curvas de crescimento. Os modelos de Brody, Gompertz, Logístico, von Bertalanffy e Richards, foram ajustados a dados experimentais de 14 grupos de animais diferentes. Como critério de qualidade de ajuste quatro índices estatísticos foram adotados: coeficiente de determinação (R2 ), raiz do quadrado médio do erro (RMSE) e os critérios de informação, Akaike (AIC) e Bayesian (BIC). Em geral, o modelo de Richards forneceu os melhores ajustes aos dados experimentais comparados aos demais modelos. No entanto, para alguns animais, diferentes modelos exibiram melhor desempenho. Foi possível obter uma possível interpretação para o significado do parâmetro de modo a fornecer ferramentas úteis para prever o comportamento do crescimento animal.

Different sieving methods for determining the physical characteristics in ground corn / Different sieving methods for determining the physical characteristics in ground corn

We evaluated various sieving methods to estimate particle size (PS) and geometric standard deviation (GSD) of ground corn. The corn had been previously divided in six fractions and each one ground in a hammermill (1-, 2-, 3-, 4-, 5- or 12-mm sieves). The stacked sieving method, with prior drying at 105ºC without agitators was the reference. We evaluated eight sieving methods, distributed in a factorial design (2 x 2 x 2 x 6), consisting of the following treatments: i) with and without agitators (two 25-mm rubber spheres), ii) with and without previous drying, iii) with a nest of test sieves set in a stacked or reverse, and iv) employing six ground corn degrees, totaling 48 treatments (four replicates). There was a linear increase in PS estimation for methods without drying and stacking and quadratic increases for the others. Reverse, drying, and agitator methodologies gave better sieving of corn, and consequently gave the lowest PS and highest GSD. The results were more pronounced for high-intensity grinding (hammermill sieve with small apertures) in which the differences between the reference method with the drying and reverse methods were up to 210 µm. Reverse sieving combined with agitators allowed the greatest passage of corn particles through the test sieves and promoted better characterization of ground corn.

We evaluated various sieving methods to estimate particle size (PS) and geometric standard deviation (GSD) of ground corn. The corn had been previously divided in six fractions and each one ground in a hammermill (1-, 2-, 3-, 4-, 5- or 12-mm sieves). The stacked sieving method, with prior drying at 105ºC without agitators was the reference. We evaluated eight sieving methods, distributed in a factorial design (2 x 2 x 2 x 6), consisting of the following treatments: i) with and without agitators (two 25-mm rubber spheres), ii) with and without previous drying, iii) with a nest of test sieves set in a stacked or reverse, and iv) employing six ground corn degrees, totaling 48 treatments (four replicates). There was a linear increase in PS estimation for methods without drying and stacking and quadratic increases for the others. Reverse, drying, and agitator methodologies gave better sieving of corn, and consequently gave the lowest PS and highest GSD. The results were more pronounced for high-intensity grinding (hammermill sieve with small apertures) in which the differences between the reference method with the drying and reverse methods were up to 210 µm. Reverse sieving combined with agitators allowed the greatest passage of corn particles through the test sieves and promoted better characterization of ground corn.