We propose and develop a genetic algorithm (GA) for generating D‐optimal designs where the experimental region is an irregularly shaped polyhedral region. Our approach does not require selection of points from a user‐defined candidate set of mixtures and allows movement through a continuous region that includes highly constrained mixture regions. This approach is useful in situations where extreme vertices (EV) designs or conventional exchange algorithms fail to find a near‐optimal design. For illustration, examples with three and four components are presented with comparisons of our GA designs with those obtained using EV designs and exchange‐point algorithms over an irregularly shaped polyhedral region. The results show that the designs produced by the GA perform better than, if not as well as, the designs produced by the exchange‐point algorithms; however, the designs produced by the GA perform better than the designs produced by the EV. This suggests that GA is an alternative approach for constructing the D‐optimal designs in problems of mixture experiments when EV designs or exchange‐point algorithms are insufficient. Copyright © 2012 John Wiley & Sons, Ltd.

This study was conducted to evaluate the effects of supplementing methionine (Met) in a low-protein (Low-CP) diet during d 11 to 24 and subsequently feeding with a low-metabolizable energy diet (Low-ME; -75 kcal/kg) or a normal ME diet during d 25 to 42 on the productive performance, blood chemical profile, and lipid metabolism-related gene expression of broiler chickens. The 1,600 broiler chicks were divided into 5 groups as follows: 1) Normal CP, then Normal ME; 2) Low-CP, then Normal ME; 3) Low-CP, then Low-ME; 4) Low-CP+Met, then Normal ME; and 5) Low-CP+Met, then Low-ME. During d 11 to 24, the growth performance of the control group was better than those of the other groups (P < 0.01). In Low-CP diets, the addition of Met resulted in an improvement in the growth performance, breast meat yield, protein conversion ratio, plasma total protein, and albumin (P < 0.01). Moreover, the supplementation significantly increased the plasma triglyceride content (P < 0.01). Feeding Low-CP or Low-CP+Met diets increased the abdominal fat content compared to the control group (P < 0.01). Feeding the Low-CP+Met, then Normal ME (d 25 to 42) resulted in compensation in the feed conversion ratio (FCR), protein conversion ratio, and energy conversion ratio equal to or better than the control group (P < 0.01). The body weights of birds fed Low-CP diets were still inferior to the control group (P < 0.01), except in the Low-CP+Met group followed by the normal ME diet. Feeding with the Low-ME diet tended to decrease the expression of the carnitine palmitoyl transferase I gene in the liver (P = 0.08). In conclusion, supplementing Met in the Low-CP diet during the grower period and subsequently feeding with a control diet improved the feed and protein conversion ratios, reduced fat accumulation, and reduced the production cost of broiler chickens with regard to fat deposition compared to the control diet.

The objective of this study is to evaluate resonant frequency, firmness and soluble solids for pineapple classification using artificial neural networks (ANNs) as the analytical tool. A sample of 149 pineapples was classified based on their internal qualities into five classes: unripe, partially ripe, ripe, partially overripe and completely overripe. The developed ANN model successfully classified pineapples into merely three classes as unripe, ripe and completely overripe. The most effective model was obtained when both resonant frequency and soluble solids were included in the model. The classification accuracy was more than 83% for all three classes.

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Among the numerous alphabetical optimality criteria is the IV-criterion that is focused on prediction variance. We propose a new criterion, called the weighted IV-optimality. It is similar to IV-optimality, because the researcher must first specify a model. However, unlike IV-optimality, a suite of "reduced" models is also proposed if the original model is misspecified via over-parameterization. In this research, weighted IV-optimality is applied to mixture experiments with a set of prior weights assigned to the potential mixture models of interest. To address the issue of implementation, a genetic algorithm was developed to generate weighted IV-optimal mixture designs that are robust across multiple models. In our examples, we assign models with p parameters to have equal weights, but weights will vary based on varying p. Fraction-of-design-space (FDS) plots are used to compare the performance of an experimental design in terms of the prediction variance properties. An illustrating example is presented. The result shows that the GA-generated designs studied are robust across a set of potential mixture models.