Development of novel heat-treated sausage with Pleurotus ostreatus: Low energy, enhanced bioactivity, reduced amount of meat and fat

The use of Pleurotus ostreatus to reduce calories and increase bioactivity in processed meat products is an increasingly common practice. In this study, a novel sausage containing P. ostreatus was developed based parameters like protein, antioxidants, total phenolic content, cohesiveness, energy, fat, ΔE, hardness, and gumminess values using a D-optimal mixture design. The results showed that over 20% of P. ostreatus could be used in sausage production. Moreover, it was concluded that the optimized sausages contain less fat (33.05%) and meat (18.6%) and therefore have a lower calorie value (19.03%). In addition, P. ostreatus added to the sausage formulation decreased the use of animal raw materials, increased the bioactive properties of the sausages, and did not cause a significant change in the amino acid profile.

Modeling of crude oil biodegradation using two phase partitioning bioreactor

In this work, crude oil biodegradation has been optimized in a solid-liquid two phase partitioning bioreactor (TPPB) by applying a response surface methodology based d-optimal design. Three key factors including phase ratio, substrate concentration in solid organic phase, and sodium chloride concentration in aqueous phase were taken as independent variables, while the efficiency of the biodegradation of absorbed crude oil on polymer beads was considered to be the dependent variable. Commercial thermoplastic polyurethane (Desmopan®) was used as the solid phase in the TPPB. The designed experiments were carried out batch wise using a mixed acclimatized bacterial consortium. Optimum combinations of key factors with a statistically significant cubic model were used to maximize biodegradation in the TPPB. The validity of the model was successfully verified by the good agreement between the model-predicted and experimental results. When applying the optimum parameters, gas chromatography-mass spectrometry showed a significant reduction in n-alkanes and low molecular weight polycyclic aromatic hydrocarbons. This consequently highlights the practical applicability of TPPB in crude oil biodegradation.

Comparison of Optimal Design Methods in Inverse Problems

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher Information Matrix (FIM). A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criteria with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model [13], the standard harmonic oscillator model [13] and a popular glucose regulation model [16, 19, 29].