Project Abstract

Abstract
Drying is a critical process in the preservation of agricultural products such as Musa paradisiaca (unripe plantain). This study focused on the statistical modelling and optimization of the drying characteristics of unripe plantain to improve efficiency and quality. The drying process was conducted using a convective hot air dryer at different temperatures (50, 60, and 70°C) and air velocities (1, 1.5, and 2 m/s). The drying characteristics were analyzed using both experimental data and mathematical models. Statistical modelling was employed to develop predictive models for the drying kinetics of unripe plantain. The experimental data was fitted to various thin-layer drying models including Page, Henderson & Pabis, and Logarithmic models. The coefficients of determination (R2) and root mean square error (RMSE) were used to evaluate the goodness of fit of the models. The Page model was found to be the most suitable for describing the drying behavior of unripe plantain. Furthermore, response surface methodology (RSM) was utilized to optimize the drying process parameters such as temperature and air velocity. Central composite design (CCD) was employed to design the experiments and analyze the effects of these parameters on the drying time and quality attributes of the dried unripe plantain slices. The optimized conditions were determined based on maximizing the drying rate while minimizing the color change and total carotenoid content. The study revealed that higher drying temperatures and air velocities led to a significant reduction in the drying time of unripe plantain slices. However, excessive drying temperatures resulted in undesirable color changes and nutrient losses. The optimization process identified the optimal drying conditions as 65°C temperature and 1.5 m/s air velocity, which achieved a balance between drying efficiency and product quality. In conclusion, the statistical modelling and optimization of the drying characteristics of Musa paradisiaca (unripe plantain) can help enhance the drying process efficiency and product quality. The developed models and optimized conditions provide valuable insights for the design and operation of industrial-scale drying systems for unripe plantain. This research contributes to the sustainable preservation and utilization of agricultural products, particularly in regions where Musa paradisiaca is a significant crop.

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.