Thesis Abstract

Abstract
This research project focuses on the statistical modelling and optimization of the drying characteristics of Musa paradisiaca, specifically unripe plantain. Drying is a critical process in food preservation and the quality of the dried product is influenced by various parameters such as temperature, air velocity, and thickness of the material. The study aims to develop mathematical models using statistical techniques to describe the drying behavior of unripe plantain and optimize the drying process for better efficiency and product quality. Experimental data on the drying characteristics of unripe plantain will be collected using a laboratory-scale convective dryer. The drying experiments will be conducted at different temperature levels and air velocities to capture the effects of these parameters on the drying kinetics. The moisture content of the plantain samples will be monitored over time to generate drying curves. Statistical analysis techniques such as regression analysis and response surface methodology will be employed to develop mathematical models that describe the drying behavior of unripe plantain. These models will be used to predict the drying kinetics under different drying conditions and optimize the process parameters for maximum efficiency. The aim is to minimize the drying time and energy consumption while maintaining the quality of the dried product. Furthermore, the study will investigate the effects of different thicknesses of plantain slices on the drying characteristics. Thin and thick slices of unripe plantain will be dried under similar conditions to compare the drying rates and develop models that account for the thickness variation. This analysis will provide insights into the impact of slice thickness on the overall drying process and help optimize the slicing operation in industrial drying setups. The optimization of the drying process for unripe plantain has practical implications for food processing industries involved in the production of dried plantain products. By developing efficient drying models and optimizing the process parameters, manufacturers can improve the quality of their products, reduce energy costs, and enhance overall productivity. The research outcomes will contribute to the advancement of drying technology in the food industry and offer valuable insights for the sustainable processing of Musa paradisiaca.

Thesis Overview

INTRODUCTION

Drying is probably the oldest and the most important method of
food preservation practiced by humans. This process improves the food
stability, since it reduces considerably the water and microbiological activity
of the material and minimizes physical and chemical changes during its storage.

Musa paradisiacal (unripe
plantain) is an important staple food in Central and West Africa, which along
with bananas provides 60 million people with 25% of their calories. According
to FAO, (2004), over 2.11 million metric tons of plantain is produced in
Nigeria annually. Plantain for local consumption, plays a role in food and
income security and has the potential to contribute to national food security
and reduce rural poverty.

Unripe
plantain has rich iron nutrient content (Aremu, et al., 1990). However, they
are highly perishable and subject to fast deteriorations, as their moisture
contents and high metabolic activity persist after harvest (Demirel, et al.,
2003).

Moreso, about 35-60%
post-harvest losses had been reported and attributed to lack of storage facilities
and inappropriate technologies for food processing. Air drying alone or
together with sun drying is largely used for preserving unripe plantain.
Besides helping preservation, drying adds value to plantain.

1.2
PROBLEM STATEMENT

Drying consists of a critical step
by reducing the water activity of the products being dried. Hot air drying of
agricultural products is one of the most popular preservation methods because
of its simplicity and low cost. Thin layer drying is a common method and widely
used for fruits and vegetables to prolong their shelf life.

However, drying of any food
substance is an energy intensive operation with grave industrial consequences,
and must be performed with optimal energy utilization.

This project work seeks to
ascertain the best thin layer model and the temperature and slice thickness
that optimizes time.

1.3.
OBJECTIVE OF STUDY

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

1.5
SCOPE OF STUDY

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.