Recognition of organic rice samples based on trace elements and support vector machines

Highlights

• 19 trace elements were determined in rice samples by ICP-MS.

• Authenticity of organic rice samples can be demonstrated via chemometrics.

• Rice is classified with 98% of accuracy by using SVM and the mineral levels.

Abstract

A simple approach is proposed for the authentication of organic rice samples. The strategy combines levels of concentration of trace elements and a data mining technique known as support vector machine (SVM). Nineteen elements (As, B, Ba, Ca, Cd, Ce, Cr, Co, Cu, Fe, La, Mg, Mn, Mo, P, Pb, Rb, Se and Zn) were determined in organic (n = 17) and conventional (n = 33) rice samples by quadrupole inductively coupled plasma mass spectrometry (q-ICP-MS) and the variations found in their elemental composition resulted in profiles with useful information for classification purposes. With the proposed methodology, it was possible to predict the authenticity of organic rice samples with an accuracy of 98% when using the 19 original elements. An accuracy of 96% was found using only the elements Ca and Cd.

Introduction

Health concerns and environmental considerations related to the use of pesticides, hormones and veterinary drugs in conventional farming practices have raised public attention to the production and consumption of organic products. Organic farming is an environmentally sustainable approach with viable solutions to a range of problems associated with conventional food production, rural development and animal welfare. In the specific case of organic cereal production, current legislation restricts the use of synthetic fertilizers, pesticides and insecticides. Farming practices should be based on the use of compost or organic waste, readily soluble mineral fertilizers and biological pest control (Capuano et al., 2012).

Due to lower production yields and higher certification costs, organic products tend to retail at higher prices than their conventional counterparts. This fact, associated with their increasing popularity, makes organic products susceptible to fraud. One possible way to differentiate among organic and conventional products is to monitor their chemical composition. Under this perspective, the authentication of organic foods becomes an analytical problem. The studies presented here deal with the authentication of organic rice.

Several analytical strategies exist for the authentication of organic foods of plant origin. These include the measurement of stable isotope ratios such as ¹⁵N/¹⁴N (Kelly et al., 2005, Rossmann, 2001), metabolomics (Zorb et al., 2006), analysis of phenolic compounds (Wang et al., 2008), copper chloride crystallization (Huber et al., 2010) and infrared spectrometry (Cozzolino et al., 2009). The approach presented here is based on quadrupole-inductively coupled plasma-mass spectrometry (q-ICP-MS) and the differences found in the elemental composition of organic and conventional rice. Although ICP-MS has been applied to the determination of metals in rice samples (Capuano et al., 2012, Cheajesadagul et al., 2013, Shen et al., 2013), our literature search reveals no previous reports for the purpose at hand. Our work considers the spectral profiles of rice samples resulting from the determination of three major (g kg⁻¹) and seventeen minor (mg kg⁻¹ – mg kg⁻¹) elements. Data analysis was carried out with the aid of a data mining technique known as support vector machine (SVM). Supervised and unsupervised chemometric tools have been used for food authenticity and traceability (Barbosa et al., 2014a, Barbosa et al., 2014b, Batista et al., 2012, Niu et al., 2011). SVM is a supervised learning model with associated learning algorithms that analyze data and recognize patterns often used for classification and regression analysis (Tan et al., 2006). A literature search has not revealed any reports of its application to data processing of chemical profiles from rice samples. Herein, we demonstrate that the combination of q-ICP-MS with SVM provides a robust analytical tool for the authentication of organic rice samples.