Project Abstract

Abstract
Drying is a critical process in food preservation, and understanding the drying characteristics of different food materials is essential for optimizing this process. In this study, statistical modelling and optimization of the drying characteristics of Musa paradisiaca (unripe plantain) were investigated. The drying experiments were conducted using a laboratory-scale convective dryer at different air temperatures (50°C, 60°C, and 70°C) and air velocities (1 m/s, 1.5 m/s, and 2 m/s). The drying kinetics of unripe plantain slices were analyzed using six mathematical models, including the Page, Henderson and Pabis, Logarithmic, Two-term, Midilli-Kucuk, and Wang-Singh models. The coefficient of determination (R2), root mean square error (RMSE), and reduced chi-square (?2) statistics were used to evaluate the goodness of fit of the models. The Midilli-Kucuk model was found to best describe the drying behavior of unripe plantain slices based on the statistical indicators. Furthermore, response surface methodology (RSM) coupled with desirability function approach was employed to optimize the drying process conditions for unripe plantain slices. The effects of air temperature and velocity on the moisture ratio and drying rate were assessed, and the optimal drying conditions were determined to be an air temperature of 60.3°C and an air velocity of 1.82 m/s. Under these conditions, the predicted values of the moisture ratio and drying rate were 0.09 and 0.044 min^-1, respectively. The study provides valuable insights into the drying characteristics of Musa paradisiaca (unripe plantain) and offers a systematic approach to optimize the drying process for this food material. The statistical modelling techniques employed in this research can be used to predict the drying behavior of unripe plantain slices under different drying conditions, thereby facilitating the design and operation of efficient drying systems in the food industry. The optimization results can guide food processors to achieve the desired quality attributes of dried unripe plantain while minimizing energy consumption and processing time. Overall, this study contributes to the advancement of food drying technology and underscores the importance of statistical modelling in optimizing drying processes for agricultural products.

Project 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.