Enhanced bio-decolourisation of acid orange 7 by Shewanella oneidensis through co-metabolism in a microbial fuel cell

The decolourisation of acid orange 7 (AO7) (C.I.15510) through co-metabolism in a microbial fuel cell by Shewanella oneidensis strain 14063 was investigated with respect to the kinetics of decolourisation, extent of degradation and toxicity of biotransformation products. Rapid decolourisation of AO7 (>98% within 30 h) was achieved at all tested dye concentrations with concomitant power production. The aromatic amine degradation products were recalcitrant under tested conditions. The first-order kinetic constant of decolourisation (k) decreased from 0.709 ± 0.05 h−1 to 0.05 ± 0.01 h−1 (co-substrate – pyruvate) when the dye concentration was raised from 35 mg l−1 to 350 mg l−1. The use of unrefined co-substrates such as rapeseed cake, corn-steep liquor and molasses also indicated comparable or better AO7 decolourisation kinetic constant values. The fully decolourised solutions indicated increased toxicity as the initial AO7 concentration was increased. This work highlights the possibility of using microbial fuel cells to achieve high kinetic rates of AO7 decolourisation through co-metabolism with concomitant electricity production and could potentially be utilised as the initial step of a two stage anaerobic/aerobic process for azo dye biotreatment.

Identification of meat spoilage by FTIR spectroscopy and neural networks

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology