Shelf life enhancement technique of Musa acuminata in a controlled environment and optimization of process parameters affecting shelf life using genetic algorithm.

An economical and effective storage solution has been designed in this work for the storage of postharvest fruits and vegetables. Musa acuminata or banana has a shelf life of 5-6 days in open uncontrolled environment. This article reports a storage solution of M. acuminata in a controlled enclosure containing titanium oxide (TiO2 )-coated inner walls and irradiated with ultraviolet ray of band "C," an air filtration unit, 5% by volume potassium permanganate (KMnO4 ) solution in a clay pot, grow lights, and activated charcoal granules. The same fruit was kept in an uncontrolled environment too. The percentages of dark spots on banana (M. acuminata) upon storage in controlled and uncontrolled environments have been estimated using an image-processing algorithm. The prediction of dark spots was conducted using multi-linear and multivariate polynomial regression. Experimentation with optimum process parameters obtained with genetic algorithm resulted in a shelf life extension of 6 days as compared to its storage in an uncontrolled environment. The setup can be used in vegetable and fruit markets for the extension of shelf life of postharvest perishable items in a compact and cost-effective manner. The setup does not use any refrigeration process thereby decreasing energy requirement.

Experimentation and Mathematical Modelling of Process Parameters for Prevention of Infectious Disease Caused by Staphylococcus aureus Bacteria in Indoor Environment.

Performance optimization using process parameters of an indoor air filtration system is a requirement that has to be established through experimental and analytical means for increasing machine efficacy. A closed casing containing a motor-driven blower is placed in a glass-encapsulated control volume. Air flows axially through an inlet filter and is thrown radially by the blower. In the radial path, air is treated with free radicals from the UVC-irradiated nano-TiO2 coated in the inner wall of casing. A known quantity of Staphylococcus aureus bacteria is populated (Courtesy: EFRAC Laboratories) in the glass-encapsulated control volume. The bacterial colony count is measured at different time intervals after the machine is switched on. Machine learning approaches are applied to develop a hypothesis space and the hypothesis based on best R2 score is used as a fitness function in genetic algorithm to find the optimal values of input parameters. The present research aims to determine the optimum time for which the setup is operated, the optimum air flow velocity in the chamber, the optimum setup-chamber-turning-radius affecting the air flow chaos, and the optimum UVC tube wattage, which when maintained yields the maximum reduction in bacterial colony count. The optimal values of the process parameters were obtained from genetic algorithm using the hypothesis obtained from multivariate polynomial regression. A reduction of 91.41% in bacterial colony count was observed in the confirmation run upon running the air filter in the optimal condition.