SCHEDULED MAINTENANCE
Artificial neural network employment for element determination in Mugil cephalus by ICP OES in Pontal Bay, Brazil
a
Ivon Pinheiro
Lôbo,
a
Cleber Galvão
Novaes,
c
Wesley Nascimento
Guedes,
d
Raildo Mota
de Jesus,
a
Fábio Alan Carqueija
Amorim,
a
Clissiane Soares Viana
Pacheco,
ab
Luana Santos
Moreira
a
and
Erik Galvão Paranhos
da Silva
*a
Abstract
Fish are important sources of protein, making them very significant in the human diet. Although the consumption of this food is beneficial for health, it is essential that the product does not contain inorganic components above the limits recommended by the current legislation. Therefore, a method for determination of elements in fish (Mugil cephalus) samples was optimized. A simplex centroid mixture design with restriction was applied for optimization of the acid digestion of samples in an open system under reflux in order to evaluate the best ratio between the reagents HNO3, H2O2 and H2O. The results indicated that more intense analyte signals were obtained when a mixture containing 3.6 mL of HNO3 (65% v/v), 0.4 mL of H2O2 (30% v/v) and 6.0 mL of H2O was used. The accuracy of the method was assessed with a CRM of oyster tissue (NIST 1566b). The method presented relative standard deviations (RSDs) of 3.54%; 3.82%; 4.81% and 3.50% for Zn, Fe, Cu and S, respectively. The detection limits were 0.002 mg kg−1 for Cu and Zn and 0.02 mg kg−1 for Fe and S. The proposed method was applied for the determination of Zn, Fe, Cu and S in fish samples. A Kohonen Self-Organizing Map (KSOM) with K-means implementation was applied to better delimit the boundary between groups and the spatial and temporal influence on how concentrations of the chemical elements were perceived. To verify the separation, the Davies–Bouldin and Silhouette indices were used, obtaining 0.5374 and 0.8541, respectively, indicating satisfactory separation.
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