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Optimization of microwave digestion and inductively coupled plasma-based methods to characterize cassava, corn and wheat flours using chemometrics

2017; Elsevier BV; Volume: 135; Linguagem: Inglês

10.1016/j.microc.2017.09.007

1095-9149

Felipo Obed Correia, Denisson S. Silva, Silvânio Silvério Lopes da Costa, Izabel Kalini V. Silva, Djalma Ribeiro da Silva, José do Patrocínio Hora Alves, Carlos Alexandre Borges Garcia, Tatiane de Andrade Maranhão, Elisângela de Andrade Passos, Rennan G.O. Araújo,

Cassava research and cyanide

In this work, microwave-assisted digestion sample treatment and two ICP-based method operational conditions were optimized to determine 17 elements in cassava, corn and wheat flours using experimental design. The proposed analytical methods showed acceptable sensitivity, with limits of quantification for Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Ni, P, S, Sb, Sr and Zn ranging from 0.02 μg g− 1(Cd, Co and Sb) up to 0.002% (S) employing inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP OES). The accuracy of the analytical methods was confirmed by analysis of certified reference materials of rice flour (NIST 1568a), wheat flour (NIST 1567a) and Tea (NCS DC 73351), with agreement between 82 ± 3% (Cr) and 108 ± 10% (Fe). Precision was expressed as relative standard deviation (RSD) and the found values were lower than 9% (Fe, n = 3) for all elements, except for Cd by ICP-MS, whose RSD value was 14%. The proposed methods were then used to determine the mineral composition of 37 samples of cassava, corn and wheat flours bought in commercial establishments in Aracaju city, Sergipe State, Brazil. The Ca, K, P, Mg and S concentrations ranged from 2.72 ± 1.09 μg g− 1 for Ca up to 4253 ± 103 μg g− 1 for K. The concentrations of the microelements and trace elements, such as Ba, Cd, Co, Cu, Fe, Mn, Sr and Zn, varied between < 0.02 μg g− 1 for Sr up to 65.5 ± 9.7 μg g− 1 for Fe. Cassava, corn and wheat flours were subsequently characterized through multivariate data analysis (principal component analysis and hierarchical cluster analysis). Three distinct groups of flour were observed according to the elemental compositions.