STATISTICAL MODELLING AND OPTIMIZATION OF THE DRYING CHARACTERISTICS OF MUSA PARADISIACA (UNRIPE PLANTAIN)

STATISTICAL MODELLING AND OPTIMIZATION OF THE DRYING CHARACTERISTICS OF MUSA PARADISIACA (UNRIPE PLANTAIN)

Chapter one

The earliest and most significant method of food preservation used by humans is drying. Because it significantly lowers the material’s water and microbial activity and minimizes physical and chemical changes during storage, this technique increases the stability of food.

Unripe plantains, or musa paradisiacal, are a staple food in Central and West Africa that, along with bananas, supplies 25% of the calories for 60 million people. Over 2.11 million metric tons of plantains are produced in Nigeria each year, according to FAO (2004). Plantain for local consumption contributes to food and income security, and it has the ability to lower rural poverty and improve national food security.

Iron is a valuable nutrient found in unripe plantains (Aremu, et al., 1990). However, due to their high metabolic activity and moisture content, which continue after harvest, they are highly perishable and vulnerable to rapid deterioration (Demirel, et al., 2003).

Furthermore, it had been claimed that between 35 and 60% of post-harvest losses were attributable to a lack of storage facilities and ineffective food processing methods. Unripe plantain is typically dried using either air alone or air and sun drying. Plantains gain value through drying in addition to aiding in preservation.

Statement of the problem

Reducing the water activity of the materials being dried is a crucial step in the drying process. A common preservation technique for agricultural products is hot air drying due to its ease of usage and low cost. To increase the shelf life of fruits and vegetables, a common technique is thin layer drying.

However, drying any food component requires a lot of energy and has serious industrial repercussions. As a result, it must be done with the best possible energy efficiency.

The goal of this project is to identify the ideal temperature, slice thickness, and thin layer model for time optimization.

The objectives of this work are to;

Ascertain the type of thin-layer model that best fits the moisture ratio/time data during the drying of unripe plantain.

To determine the temperature and slice thickness that optimizes time (i.e. gives the shortest drying time).

1.4 JUSTIFICATION

Production of plantain is seasonal while consumption is all year round and therefore there is the need to cut down on post-harvest losses by processing them into forms with reduced moisture content.

This agricultural product has high moisture content at harvest and therefore cannot be preserved for more than some few days under ambient conditions of 20oC – 25oC (Chua, et al., 2001). This post-harvest loss results in seasonal unavailability and limitations on the use by urban populations. Plantain has however been having an increasing surplus production since 2001 (Dankye, et al., 2007). It is estimated that in 2015, there will be a surplus of about 852,000 Mt. This means that these surpluses have to be exported, processed or go to waste.

A reduction in moisture content potentially increases shelf life and hence prevents excessive post-harvest loss and that drying is an alternative to developing nations, where there is deterioration due to poor storage, weather conditions and processing facilities

The scope of this project work includes the following:

Using the ten selected thin layer models to investigate the one that best fits the data generated from drying of unripe plantain at specified temperatures, slice thicknesses, and drying time.

Using regression analysis to obtain the slice thickness and temperature for the optimum (minimum) drying time.