Development of a multi-classification neural network model to determine the microbial growth/no growth interface

Boundary models have been recognized as useful tools to predict the ability of microorganisms to grow at limiting conditions. However, at these conditions, microbial behaviour can vary, being difficult to distinguish between growth or no growth. In this paper, the data from the study of Valero et al. [Valero, A., Pérez-Rodríguez, F., Carrasco, E., Fuentes-Alventosa, J.M., García-Gimeno, R.M., Zurera, G., 2009. Modelling the growth boundaries of Staphylococcus aureus: Effect of temperature, pH and water activity. International Journal of Food Microbiology 133 (1-2), 186-194] belonging to growth/no growth conditions of Staphylococcus aureus against temperature, pH and a_w were divided into three categorical classes: growth (G), growth transition (GT) and no growth (NG). Subsequently, they were modelled by using a Radial Basis Function Neural Network (RBFNN) in order to create a multi-classification model that was able to predict the probability of belonging at one of the three mentioned classes. The model was developed through an over sampling procedure using a memetic algorithm (MA) in order to balance in part the size of the classes and to improve the accuracy of the classifier. The multi-classification model, named Smote Memetic Radial Basis Function (SMRBF) provided a quite good adjustment to data observed, being able to correctly classify the 86.30% of training data and the 82.26% of generalization data for the three observed classes in the best model. Besides, the high number of replicates per condition tested (n = 30) produced a smooth transition between growth and no growth. At the most stringent conditions, the probability of belonging to class GT was higher, thus justifying the inclusion of the class in the new model. The SMRBF model presented in this study can be used to better define microbial growth/no growth interface and the variability associated to these conditions so as to apply this knowledge to a food safety in a decision-making process.     

Modeling the influence of electron beam irradiation on the heat resistance of Bacillus cereus spores

The effect of electron beam irradiation (EBI) on Bacillus cereus spore heat resistance was investigated. Irradiation with accelerated electrons had an important heat-sensitizing effect on distilled-water spore suspensions. After irradiation doses of 1.3, 3.1, or 5.7 kGy followed by heating at 90 degrees C, calculated D(90)-values for strains Escuela Politécnica Superior de Orihuela (EPSO)-41WR and EPSO-50UR were reduced more than 1.3, 2.4, and 4.6 times, respectively. Plots of calculated log D(T)-values versus irradiation doses (1.3, 3.1, and 5.7 kGy) yielded straight parallel lines for the 85-100 degrees C heating temperature range, which made it possible to develop an equation to predict the changes in heat sensitivity of B. cereus spores that occurred with changing irradiation dose. Radiation-induced heat-sensitivity was characterized by a z(EBI)-value which was determined as the irradiation dose that should be required to reduce the decimal reduction time (D(T)) by one log(10) cycle when log(10)D(T) was plotted against irradiation treatment. A model is proposed to describe the influence of a pre-irradiation treatment with electron beams followed by heating on the heat resistance of B. cereus spores. This study also suggests the potential use of EBI followed by heating for food preservation.     

Influence of enrofloxacin administration on the proteolytic and antioxidant enzyme activities of raw and cooked turkey products

The objective of this study was to assess the effect of enrofloxacin administration with and without withdrawal period on the quality of fresh meat and cooked products from turkey breasts. Cathepsin B and L activities were inactivated by the thermal process but were not affected by the presence of enrofloxacin. The level of enrofloxacin in samples without withdrawal time was higher than the regulatory maximum residue limit (MRL). The antioxidant enzymes glutathione peroxidase (GSHPx) and catalase (CAT) were affected by both the thermal process and the antibiotic residues in cooked meat. In no cases were differences found in superoxide dismutase (SOD) activity, supporting the theory that this enzyme could play a major role in preventing lipid oxidation of cooked meat. Enrofloxacin residues could contribute to an increase in the oxidative stress produced by thermal processing, as can be deduced by the reduction in GSHPx and CAT activities. Copyright © 2005 Society of Chemical Industry     

Early 21st Century processors

The computer architecture arena faces exciting challenges as it attempts to meet the design goals and constraints that new markets, changing applications and fast-moving semiconductor technology impose     

Vapour treatments with methyl salicylate or methyl jasmonate alleviated chilling injury and enhanced antioxidant potential during postharvest storage of pomegranates

Pomegranates were treated after harvest with methyl jasmonate (MeJa) or methyl salicylate (MeSa) at two concentrations (0.01 and 0.1 mM), and then stored under chilling temperature for 84 days. Control fruits exhibited chilling injury (CI) symptoms manifested by pitting and browning, the severity being enhanced as storage time advanced, and accompanied by softening and electrolyte leakage (EL). The CI symptoms were significantly reduced by MeJa or MeSa treatments, without significant differences among treatments or applied dose. In addition, both treatments significantly increased total phenolics and anthocyanins with respect to controls. Hydrophilic (H-TAA) and lipophilic (L-TAA) total antioxidant activity decreased in control arils, but in both MeJa and MeSa treated fruits H-TAA increased while no significant changes occurred for L-TAA. Results would suggest that both MeJa and MeSa have potential postharvest applications in reducing CI, maintaining quality and improving the health benefits of pomegranate fruit consumption by increasing the antioxidant capacity.     

A hybrid neural model (HNM) for the on‐line monitoring of lipase production by Candida rugosa

A mechanistic model was proposed by Gordillo for the representation of lipase production by Candida rugosa, with the bioreactor in batch and fed‐batch operation. However, the model was not able to represent the lipolytic activity. The objective of the present study is to propose an efficient hybrid neural‐phenomenological model (HNM) for this process. The experimental data used corresponded to fed‐batch operation with constant substrate feed rate at 2.8 × 10^?7; 5.6 × 10^?7 and 9.7 × 10^?7 kg s^?1. Artificial neural networks (ANNs) were trained to represent the aqueous and intracellular lipase activity and were further associated with a reduced version of the mechanistic model of the proposed HNM. When compared to the experimental data, the HNM exhibited higher accuracy. The HNM can be employed in process monitoring using only on‐line measurements of CO₂ and substrate feed rate to infer enzyme activities and also substrate and biomass concentrations. Copyright © 2007 Society of Chemical Industry     

Evolutionary Path Planning Algorithm for Industrial Robots

This paper proposed a new methodology to solve collision free path planning problem for industrial robot using genetic algorithms. The method poses an optimization problem that aims to minimize the significant points traveling distance of the robot. The behavior of more two operational parameters – the end effector traveling distance and computational time – are analyzed. This algorithm is able to obtain the solution for any industrial robot working in the complex environments, just it needs to choose a suitable significant points for that robot. An application example has been illustrated using robot Puma 560.