Abstract

Bananas are one of the most nutritious and widely available food products in the world. An important step of the mass production of bananas (Musa acuminate) is determining it’s ripening stages, indicating that it is ready for consumption, and with the most concentration of important nutrients, what will not be achieved in under-ripening or over-ripening. The use of faster and easier assays to determinate the optimum ripening stage will allow the production of fruits with quality and consistence, increasing also the producer income. The industry standard for this process relies on the use of the Von Loesecke scale through visual analysis of the fruits which can result in significant discrepancies and commercial losses. Biochemical analyses can also be used but are time-consuming. The objective here is to investigate and develop a ripening classification test for bananas by using electrical impedance spectroscopy together with a support vector machine algorithm. 100 samples of bananas at 3 ripening stages were used for measuring both impedance modulus and phase of the impedance in the frequency range from 50 Hz to 1MHz. Results showed that this process is able to identify ripeness stages with 100% accuracy with low requirements for processing time. The techniques presented here might be applied in large scale production in order to make the automation process of the harvest time more reliable and profitable.

Keywords

electrical impedance spectroscopy, biological tissue, ripening classification, support vector machine