Finding the Most Significant Elements for the Classification of Organic Orange Leaves: A Data Mining Approach

Abstract

ABSTRACTBrazil is the world’s largest producer of oranges. The Brazilian conventional citrus crop requires repeated application of agrochemicals to achieve satisfactory levels of productivity. The organic citriculture is an alternative production system, which is environmentally friendly and offers a safe food to consumers. However, it is difficult to determine if a food or plant was cultivated in organic or conventional system by just common observation, which makes the customers of organic food market vulnerable against fraudulent entrepreneurs. In this study, we present a data mining approach for the study of Brazilian organic citrus leaves which can aid in the certification of authenticity of the citrus leaves. The elemental composition is determined by inductively coupled plasma-mass spectrometry (ICP-MS). We developed classification models based on support vector machines and artificial neural networks capable of predicting whether a citrus leaf is organic or conventional through analysis of the concentration levels of the 14 chemical elements (Al, Ba, Co, Cr, Cs, Cu, Fe, Mg, Mn, Ni, Rb, Si, Sr, and V) found in both types of leaves. Feature selection filter methods are used to determine the most relevant elements for the classification process. Our best model obtained was a support vector machine with approximately 88% prediction accuracy. The elements Mn, Mg, and Rb were evaluated as the most significant for the classification decision. This is the first paper which addresses the problem of classification of organic orange leaves based on chemical composition. The presented methodology is useful for attesting authenticity of organic citrus leaves and can be adapted for other organic food or substances.