Modelo de reconhecimento de padrão aplicado no processo de esterilização a vapor com enfâse em ciclos falhos

Abstract

Data mining presents itself as a powerful tool to achieve the standardization of microbial behavioral parameters, so it can help positively in the analysis of the step monitoring in the steam sterilization process. Therefore, mathematical models can help the understanding of the microorganisms’ survival probability present in the culture media of biological indicators, used in the validation and monitoring of sterilization processes. In addition, statistical techniques such as discriminant analysis and logistic regression analysis, allows the search of the bacterial activity behavior correlating the failed cycles in the steam sterilization process. Therefore, a quick and effective predictive evaluation of the monitoring step of the failed cycles in the sterilization process, carried out in up to 3 hours when compared to the conventional method 48 hours, implies a reduction in the result time, as well as in decision-making based on this result. This possibility allows the manufacturing industry of biological indicators to have a differential focus on its performance and competitive capacity in an existing market segment, but currently unexplored. Hereupon, the present study aimed to develop a model for pattern recognition, allowing the definition and evaluation of the efficiency of a steam sterilization process, when subjected to failed cycles, through observation of the monitoring and validation test result in up to 3 hours. The application of the factorial analysis method, logistic regression method, and quadratic score method, were the prediction model effectiveness. The behavior of the predicted pattern recognition model linked to the logistic regression method presented at least 97% of acceptability for data related to the result of the monitoring and validation test in up to 3 hours for failed cycles. Thus, it can be concluded that the results observed in this work are consistent and meets the given requirements.