Design and Analysis of Experiments for Post Harvest Management and Value Addition Studies with Applications to Essential Oils

Abstract

Appropriately designed experiments are the key for discovering the effect of factors on responses measured at the different stages of the Post Harvest Management and Value Addition (PHMVA) value chain. There are several types of statistical methods that can be used to meet specific objectives of the study that match the specific experimental design used to produce the data. In this paper, the most commonly used experimental designs and types of analyses are highlighted using examples from studies conducted on essential oil content and composition from several aromatic plants grown in Mississippi and Wyoming, USA; and Bulgaria. The examples illustrate the importance of designed experiments and appropriate type of analyses to unlock the effect of agronomic (pre-harvest), processing (post-harvest) and post-processing (re-utilization of waste) factors on essential oil content and composition. The statistical methods used in these studies include Analysis of Variance (ANOVA), Repeated Measures Analysis, Linear Regression, Nonlinear Regression, and Multivariate Analysis. The aromatic plants considered include Peppermint, Spearmint, Japanese cornmint, Lavender, Hyssop, Junipers, and Rose.