Thesis Abstract

Abstract
This research project focuses on the statistical modeling and optimization of the drying characteristics of Musa paradisiaca, specifically unripe plantain. Drying is a critical process in food preservation and value addition, making it essential to understand and optimize the drying characteristics of plantain to enhance its shelf life and quality. The study aims to investigate the effects of various drying parameters such as temperature, air velocity, and thickness of the plantain slices on the drying kinetics and quality attributes of the dried product. Statistical modeling techniques will be employed to develop predictive models for the drying characteristics of unripe plantain. Response surface methodology (RSM) will be used to design experiments and analyze the data to establish the relationship between the drying parameters and the quality of the dried plantain. The study will also involve the use of mathematical models such as Page, Henderson and Pabis, and Midilli et al. to describe the drying kinetics of unripe plantain under different drying conditions. Optimization of the drying process will be carried out using numerical optimization techniques to determine the optimal drying conditions that result in the highest quality dried plantain. The goal is to minimize the drying time and energy consumption while maximizing the retention of nutrients and sensory attributes of the dried product. The study will consider the trade-offs between drying efficiency and product quality to identify the most favorable drying conditions for unripe plantain. In addition to the experimental work, the research will also investigate the effect of drying on the nutritional composition, color, texture, and sensory properties of the dried plantain. Analytical techniques such as proximate analysis, color measurement, texture analysis, and sensory evaluation will be used to assess the changes in the quality attributes of plantain during the drying process. Overall, this research project aims to contribute to the development of efficient drying processes for unripe plantain through statistical modeling and optimization techniques. The findings of this study are expected to provide valuable insights for food processors and researchers in improving the drying efficiency and quality of plantain products, ultimately leading to increased value addition and market competitiveness in the food industry.

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.