Thesis Abstract

Abstract
This research project focuses on the statistical modeling and optimization of the drying characteristics of Musa paradisiaca (unripe plantain). Drying is a crucial process in food preservation and the quality of the dried product is highly influenced by the drying conditions. In this study, the drying characteristics of unripe plantain were investigated using a convective hot air dryer. The drying process was carried out 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). Statistical models were developed to describe the drying behavior of unripe plantain slices based on the experimental data. The drying kinetics were analyzed using various mathematical models including the Page, Henderson and Pabis, and Logarithmic models. The goodness of fit of the models was evaluated using statistical parameters such as coefficient of determination (R2), root mean square error (RMSE), and reduced chi-square (?2) test. Optimization of the drying process was carried out using response surface methodology (RSM) coupled with desirability function analysis. The effects of air temperature and velocity on the drying time and quality characteristics of the dried plantain slices were investigated. The optimal drying conditions for achieving the shortest drying time and highest product quality were determined. The results indicated that the Logarithmic model provided the best fit to the experimental drying data of unripe plantain slices. The drying rate was found to increase with an increase in air temperature and velocity. The RSM optimization revealed that the optimal drying conditions for unripe plantain slices were an air temperature of 70°C and an air velocity of 2 m/s. Under these conditions, the drying time was minimized while maintaining the desired product quality attributes such as moisture content and color. In conclusion, this research provides valuable insights into the drying characteristics of Musa paradisiaca (unripe plantain) and offers a systematic approach for optimizing the drying process. The statistical models developed in this study can be used to predict the drying behavior of unripe plantain slices under different drying conditions, thereby enabling efficient and high-quality drying processes in the food industry.

Thesis Overview

INTRODUCTION1.1. BACKGROUND OF STUDYDrying 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 STATEMENTDrying 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 STUDYThe 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 JUSTIFICATIONProduction 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 facilities1.5 SCOPE OF STUDYThe 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.