Simultaneous Determination of Chlorpyrifos and Carbaryl by Spectrophotometry and Boosting Partial Least Squares

Zare-Shahabadi, Vahid; Abbasitabar, Fatemeh; Akhond, Morteza; Shamsipour, M

doi:10.5935/0103-5053.20130197

Cited by 7 publications

(28 citation statements)

References 1 publication

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“…Li used boosting PLS and MC‐UVE combined with NIR spectroscopy to determine diesel cetane number in diesel samples. Shahabadi et al investigated the feasibility of the combination of boosting PLS with ultraviolet spectra for the simultaneous determination of chlorpyrifos and carbaryl. The results demonstrate that boosting PLS is resistant to overfitting and can gain more stable and accurate prediction than PLS without wavelength selection.…”

Section: Ensemble Calibration Methodsmentioning

confidence: 99%

Ensemble calibration for the spectral quantitative analysis of complex samples

Bian

Diwu

Liu

et al. 2017

Journal of Chemometrics

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Ensemble strategies have gained increasing attention in multivariate calibration for quantitative analysis of complex samples. The aim of ensemble calibration is to obtain a more accurate, stable, and robust prediction by combining the predictions of multiple submodels. The generation and calibration of the training subsets, as well as the integration of the submodels, are three keys to the success of ensemble calibration. Many training subset generating and submodel integrating strategies have been developed to form numerous ensemble calibration methods for improving the performance of the basic calibration method. This contribution focuses on the recent ensemble strategies in relation to calibration, especially the ensemble modeling for quantitative analysis of complex samples. The limitations and perspectives of ensemble strategies are also discussed.

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Section: Ensemble Calibration Methodsmentioning

confidence: 99%

Ensemble calibration for the spectral quantitative analysis of complex samples

Bian

Diwu

Liu

et al. 2017

Journal of Chemometrics

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“…It was shown previously that prediction by an ensemble model would be more accurately than a single model. 19,34,35 Through a simple averaging strategy, an ensemble model was constructed using all of the QSPR models. R2training, R2test, and q2 of the ensemble model were found to be 0.96, 0.94, and 0.95, respectively.…”

Section: O N L I N E F I R S Tmentioning

confidence: 99%

New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes

Faramarzi

Abbasitabar

Jahromi

et al. 2021

JSCS

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Recently, development of the QSPR models for mixtures has received much attention. The QSPR modeling of mixtures requires the use of appropriate mixture descriptors. In this study, 12 mathematical equations were considered to compute mixture descriptors from the individual components for the prediction of normal boiling points of 78 ternary azeotropic mixtures. Multiple linear regression (MLR) was employed to build all QSPR models. Memorized_ACO algorithm was employed for subset variable selection. An ensemble model was also constructed using averaging strategy to improve the predictability of the final QSAR model. The models have been validated by a test set comprised of 24 ternary azeotropes and by different statistical tests. The resulted ensemble QSPR model had R2training, R2test, and q2 of 0.97, 0.95, and 0.96, respectively. Mean absolute error (MAE) as a good indicator of model performance were found to be 3.06 and 3.52 for training and testing sets, respectively.

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“…e chemical structure of chlorpyrifos is shown in Figure 1(b). Chlorpyrifos is analyzed by various methods such as spectrophotometry and derivative spectrophotometry [11][12][13][14][15] and spectrophotometry using multivariate calibration methods [16], partial least squares [17,18], GC [19], GC equipped with electron capture detector (GC-ECD) [20], GC-MS [21], GC-MS/MS [22], and HPLC with UV detection [23][24][25] was also used for estimation of chlorpyrifos.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Determination of Ternary Mixture of Carboxin, Chlorpyrifos, and Tebuconazole Residues in Cabbage Samples Using Three Spectrophotometric Methods

Omer

Fakhre

2020

Journal of Analytical Methods in Chemistry

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Three simple precise and accurate spectrophotometric methods are developed for simultaneous determination of ternary mixtures of carboxin, chlorpyrifos, and tebuconazole residues in cabbage grown in the experimental field. The first method is a double divisor-ratio spectra derivative that relies on the derivative of ratio spectra and attained through dividing the absorption spectra of the ternary mixture by the sum of standard spectrum of a mixture of two from three components, using methanol as a solvent and measuring CAR at 242 nm, CHL at 236 nm, 276 nm, and 300 nm, and TEB at 226 nm. The second method is a successive derivative of ratio spectra which determined CAR at 256 nm and 258 nm, CHL at 290 nm and 292 nm, and TEB at 226 nm and 228 nm. The third method is a mean centering of ratio spectra where CAR, CHL, and TEB were measured at 306 nm, 280 nm, and 240 nm, respectively. These procedures do not involve any previous separation. The extraction of analytes was carried out by using acetonitrile, and the procedure of purification was fulfilled by dispersive solid-phase extraction with a primary-secondary amine (PSA). The proposed methods showed excellent linearity range for three spectrophotometric methods over the concentration ranges of 1–30 μg/mL, 1–50 μg/mL, and 1–45 μg/mL for carboxin, chlorpyrifos, and tebuconazole, respectively. The analytical characteristics such as detection limit, determination limit, relative standard deviation, and accuracy of the three methods were performed. The limits of detection were in the range of 0.153–0.260 μg/mL for carboxin, 0.137–0.272 μg/mL for chlorpyrifos, and 0.109–0.205 μg/mL for tebuconazole with limits of quantification lower than 0.790, 0.824, and 0.621 μg/mL for CAR, CHL, and TEB, respectively. The recoveries ranged from 87.02% to 94.53% for carboxin, 92.32% to 108.53% for chlorpyrifos, and 87.19% to 98.00% for tebuconazole with relative standard deviations less than 5.91%, 5.99%, and 5.53% in all instances for carboxin, chlorpyrifos, and tebuconazole, respectively. The results obtained from the proposed methods were compared statistically by using one-way ANOVA, and the results revealed that there were no significant differences between three different spectrophotometric methods. The suggested methods can be applied with great success to the simultaneous estimation of carboxin, chlorpyrifos, and tebuconazole residues in cabbage samples.

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Simultaneous Determination of Chlorpyrifos and Carbaryl by Spectrophotometry and Boosting Partial Least Squares

Cited by 7 publications

References 1 publication

Ensemble calibration for the spectral quantitative analysis of complex samples

Ensemble calibration for the spectral quantitative analysis of complex samples

New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes

Simultaneous Determination of Ternary Mixture of Carboxin, Chlorpyrifos, and Tebuconazole Residues in Cabbage Samples Using Three Spectrophotometric Methods

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