Biomedical subjects

Alejandro Castillo

Publications and source records attributed to Alejandro Castillo.

4 recordsLinked to original sources

PubMed2006

Surrogates for validation of electron beam irradiation of foods.

The aim of this study was to identify a potential surrogate to describe the radiation sensitivity of the most common pathogens encountered in fruits. Three pathogens: Escherichia coli O157:H7 933, Listeria monocytogenes ATCC 51414, and Salmonella Poona, and five non-pathogens: E. coli K-12 MG1655, Listeria innocua Seeliger 1983 (NRRL B-33003 and NRRl B-33014), Enterobacter aerogenes, and Salmonella LT2 were inoculated (populations of 10(7)-10(9) CFU/ml) into model food systems (10% w/w gelatin) and exposed to doses up to 1.0 kGy using a 2 MeV Van der Graaf linear accelerator. The non-pathogen E. coli K-12 MG1655 was highly resistant to radiation (D(10)=0.88 kGy) in comparison to the other strains while L. monocytogenes was the more radiation-resistant pathogen (D(10)=1.09 kGy). Thus, E. coli K-12 MG1655 could be a suitable surrogate for e-beam studies with L. monocytogenes as the indicator pathogen. L. innocua strains were more radiation-sensitive (D(10)=0.66, 0.72 kGy) than their pathogenic counterpart. S. Poona and E. coli O157:H7 were even more radiation-sensitive (D(10)=0.38, 0.36 kGy, respectively). S. LT2 was the least radiation-resistant pathogen with D(10)=0.12 kGy. In a later study, the radiation resistance of the pathogens and the surrogate was evaluated when inoculated in a real food (i.e., fresh cantaloupe). The D(10) values obtained in this experiment were higher than those obtained with the model foods. However, the surrogate was still more radiation-resistant and could therefore be used to indicate decontamination of the target pathogens under electron beam irradiation.

Colony Count, Microbial↗

PubMed2006

Improving the microbiological quality and safety of fresh-cut tomatoes by low-dose electron beam irradiation.

The effect of electron beam irradiation on microbiological quality and safety of fresh-cut tomatoes was studied. Fresh tomatoes were obtained from a local supplier and then cut into cubes that were separated from the stem scars. Both cubes and stem scars were inoculated with a rifampin-resistant strain of either Salmonella Montevideo or Salmonella Agona, separated into treatment groups, and treated by electron beam irradiation at 0.0 (control), 0.7, or 0.95 kGy. The effect of electron beam irradiation on Salmonella, lactic acid bacteria, yeast, and mold counts and pH of tomato cubes and stem scars was determined over a 15-day storage period at 4 degrees C. Results indicated that although irradiation treatment significantly reduced most microbial populations on tomato samples, there were no differences in the reduction of microbial populations between treatments of 0.7 and 0.95 kGy. Irradiation at either dose resulted in a significant reduction in Salmonella when compared with the control (P < 0.05). Lactic acid bacteria, yeasts, and molds were more resistant to irradiation than were Salmonella. No differences were detected between the two Salmonella serotypes in response to irradiation treatment. These results indicate that irradiation at doses of at least 0.7 kGy can be used for pathogen reduction in fresh-cut tomatoes. If the use of doses greater than 1 kGy were approved, this technology might be very effective for use in fresh-cut tomatoes to eliminate significant populations of pathogens and to ensure the microbial quality of the product.

Consumer Product Safety↗

PubMed2004

Carbon monoxide concentration forecasting in Santiago, Chile.

In the city of Santiago, Chile, air quality is defined in terms of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) concentrations. An air quality forecasting model based on past concentrations of PM10 and meteorological conditions currently is used by the metropolitan agency for the environment, which allows restrictions to emissions to be imposed in advance. This model, however, fails to forecast between 40 and 50% of the days considered to be harmful for the inhabitants every year. Given that a high correlation between particulate matter and carbon monoxide (CO) concentrations is observed at monitoring stations in the city, a model for CO concentration forecasting would be a useful tool to complement information about expected air quality in the city. Here, the results of a neural network-based model aimed to forecast maximum values of the 8-hr moving average of CO concentrations for the next day are presented. Forecasts from the neural network model are compared with those produced with linear regressions. The neural network model seems to leave more room to adjust free parameters with 1-yr data to predict the following year's values. We have worked with 3 yr of data measured at the monitoring station located in the zone with the worst air quality in the city of Santiago, Chile.

Air Pollutants↗

PubMed2002

Multivariable control of grinding plants: a comparative simulation study.

In this paper five multivariable adaptive and classical control strategies have been studied and implemented in a simulator of the copper grinding plant of CODELCO-Andina. The strategies presented were compared and, according to theory, exhibit good behavior. The extended horizon, pole-placement and model reference multivariable adaptive control strategies were formulated in discrete-time and use a model of the plant whose parameters are updated on line using the recursive least squares method along with UD factorization of the covariance matrix and variable forgetting factor. The direct Nyquist array and sequential loop closing techniques were also studied and simulated. The two-by-two multivariable system chosen to represent the grinding plant has the percentage of solids (density) of the pulp fed to the hydrocyclones (which is highly correlated with the percentage of +65 mesh in the overflow of hydrocyclones) and the sump level as output (controlled) variables. The water flow added to the sump and the speed of the pump are its input (manipulated) variables. All the algorithms tested by simulation exhibited good performance and were able to control the grinding plant in a stable fashion. Adaptive algorithms showed better performance than classical techniques, with the extended horizon and pole-placement algorithms proving to be the best. The fact that adaptive algorithms continuously adjust their parameters renders such controllers superior to those based on fixed parameters.

Algorithms↗