Application of chemometrics methods for the simultaneous kinetic spectrophotometric determination of aminocarb and carbaryl in vegetable and water samples
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作者:
Ni, Yongnian
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Nanchang Univ, Dept Chem, Nanchang 330047, Jiangxi, Peoples R China
Nanchang Univ, State Key Lab Food Sci & Technol, Nanchang 330047, Jiangxi, Peoples R ChinaNanchang Univ, Dept Chem, Nanchang 330047, Jiangxi, Peoples R China
Ni, Yongnian
[1
,2
]
Xiao, Weiqiang
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Nanchang Univ, Dept Chem, Nanchang 330047, Jiangxi, Peoples R ChinaNanchang Univ, Dept Chem, Nanchang 330047, Jiangxi, Peoples R China
Xiao, Weiqiang
[1
]
Kokot, Serge
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Queensland Univ Technol, Inorgan Mat Res Program, Sch Phys & Chem Sci, Brisbane, Qld 4001, AustraliaNanchang Univ, Dept Chem, Nanchang 330047, Jiangxi, Peoples R China
Kokot, Serge
[3
]
机构:
[1] Nanchang Univ, Dept Chem, Nanchang 330047, Jiangxi, Peoples R China
[2] Nanchang Univ, State Key Lab Food Sci & Technol, Nanchang 330047, Jiangxi, Peoples R China
[3] Queensland Univ Technol, Inorgan Mat Res Program, Sch Phys & Chem Sci, Brisbane, Qld 4001, Australia
A procedure for the simultaneous kinetic spectrophotometric determination of aminocarb and carbaryl in vegetable and water samples was described. The method was based on the differential oxidation rate of aminocarb and carbaryl when they were reacted with the oxidant, potassium ferricyanide (K3Fe(CN)6), in an appropriate alkaline medium. Both species were instantly oxidized, and resulted in a decrease of ferricyanide concentration. This anion has a maximum spectral absorbance at about 420 nm. Under the optimum experimental conditions, the linear ranges were 0.05-0.6 mg L-1 and 0.1-1.2 mg L-1 for aminocarb and carbaryl, respectively. The kinetic data collected were processed by chemometrics methods, such as classical least squares (CLS), partial least squares (PLS), principal components regression (PCR), back propagation-artificial neural network (BP-ANN), radial basis function-artificial neural network (RBF-ANN). and principal component-radial basis function-artificial neural network (PC-RBF-ANN). These methods were applied for the prediction of the two carbamate pesticides. The results showed that the PLS and PC-RBF-ANN calibration models gave the lowest prediction errors. The proposed method was successfully applied to the simultaneous determination of aminocarb and carbaryl in vegetable and water samples, and satisfactory results were obtained. (C) 2009 Elsevier B.V. All rights reserved.
机构:
School of Physical and Chemical Sciences, Queensland University of TechnologySchool of Physical and Chemical Sciences, Queensland University of Technology
机构:
October Univ Modern Sci & Arts, Fac Pharm, Analyt Chem Dept, 6th October City 11787, EgyptCairo Univ, Fac Pharm, Analyt Chem Dept, 13 Kasr El Aini St, Cairo 11562, Egypt
Elbalkiny, Heba T.
Riad, Safa'a M.
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Cairo Univ, Fac Pharm, Analyt Chem Dept, 13 Kasr El Aini St, Cairo 11562, Egypt
October Univ Modern Sci & Arts, Fac Pharm, Analyt Chem Dept, 6th October City 11787, EgyptCairo Univ, Fac Pharm, Analyt Chem Dept, 13 Kasr El Aini St, Cairo 11562, Egypt
Riad, Safa'a M.
Elsaharty, Yasser S.
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Cairo Univ, Fac Pharm, Analyt Chem Dept, 13 Kasr El Aini St, Cairo 11562, EgyptCairo Univ, Fac Pharm, Analyt Chem Dept, 13 Kasr El Aini St, Cairo 11562, Egypt