Effect of γ radiation on physiological parameters of the ethanolic fermentation

This work examines the efficacy of radiation in reducing the viability of certain contaminating bacteria of sugar-cane must and the consequential beneficial effect of lethal doses of radiation on some physiological parameters of the yeast-based ethanolic fermentation. The must from sugar-cane juice was inoculated with different bacteria that usually contaminate the must in the production facilities: Bacillus and Lactobacillus. The contaminated must was irradiated at 2.0, 4.0, 6.0, 8.0 and 10.0 kGy of gamma radiation. The population density of the bacteria in the irradiated must was recorded. Ethanolic fermentation by yeast (Saccharomyces cerevisiae) was carried out and the total acidity, the volatile acidity and the organic acids (lactic and acetic) produced during the fermentation were determined. The ethanol yield was also recorded. The treatment with radiation reduced the population of the contaminating microorganisms of the sugar-cane must. The acidity and the organic acids (lactic and acetic) produced during the fermentation decreased as the dose of radiation applied to the must increased. It is concluded that irradiation was efficient in decontaminating the sugar-cane must and improved the biochemical parameters of the ethanolic fermentation, including the ethanol yield by 2%.     

Exposing the confounding in experimental designs to understand and evaluate them,and formulating linear mixed models for analyzing the data from a designed experiment

Comparative experiments involve the allocation of treatments to units, ideally by randomization. This necessarily confounds treatment information with unit information, which we distinguish from the other forms of information blending, in particular aliasing and marginality. We outline a factor-allocation paradigm for describing experimental designs with the aim of (i) exhibiting the confounding in a design, using analysis-of-variance-like tables, so as to understand and evaluate the design and (ii) formulating a linear mixed model based on the factor allocation that the design involves. The approach exhibits the dispersal of treatments information between units sources, allows designers a choice in the strategy that they adopt for including block-treatment interactions, clarifies differences between experiments, accommodates systematic allocation of factors, and provides a consolidated analysis of nonorthogonal designs. It provides insights into the process of designing experiments and issues that commonly arise with designs. The paradigm has pedagogical advantages and is implemented using the R package dae .