@article {Ni:2009:0306-7319:939,
title = "Simultaneous enzymatic kinetic determination of carbamate pesticides with the aid of chemometrics",
journal = "International Journal of Environmental and Analytical Chemistry",
parent_itemid = "infobike://tandf/geac",
publishercode ="tandf",
year = "2009",
volume = "89",
number = "13",
publication date ="2009-01-01T00:00:00",
pages = "939-955",
itemtype = "ARTICLE",
issn = "0306-7319",
eissn = "1369-1619",
url = "https://www.ingentaconnect.com/content/tandf/geac/2009/00000089/00000013/art00001",
doi = "doi:10.1080/03067310902756151",
keyword = "pesticides, carbamates, acetylcholinesterase, chemometrics, enzymatic kinetic method",
author = "Ni, Yongnian and Deng, Na and Kokot, Serge",
abstract = "A method for the simultaneous enzymatic kinetic determination of the pesticides, oxamyl, aldicarb and aminocarb in fruit, vegetables and water samples, has been researched and developed. It was based on enzymatic reaction kinetics and spectrophotometric measurements, and results were
interpreted with the aid of chemometrics. The analytical method relies on the inhibitory effect of the pesticides on acetylcholinesterase (AChE), and the use of 5,5'-dithiobis (2-nitrobenzoic) acid (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine
iodide (ATChI) substrate. The complex rate equation for the formation of the chromogenic product, P, was solved under certain experimental conditions, and this enabled the absorbance (Ap, at max = 412 nm) from the mixtures of the three pesticide inhibitors to be directly related to
their concentrations. The detection limits of the enzymatic kinetic spectrophotometric procedures for the determination of the oxamyl, aldicarb and aminocarb were 0.81, 2.13 and 1.25 ng mL-1, respectively. Calibration models were constructed for principal component regression (PCR), partial
least squares (PLS), and radial basis function-artificial neural network (RBF-ANN), and verified with synthetic samples of the three pesticides. The prediction performance of these models showed generally satisfactory results, and the RBF-ANN one performed slightly better than the other two
(RPET = 7.59% and average %recovery = 99%). This model was then successfully applied to estimate the amounts of the three compounds in fruit, vegetables and water with satisfactory results.",
}