A simple and accurate analytical method for determination of three commercial dyes in different water systems using partial least squares regression

Al‐Degs, Yahya S.; El‐Sheikh, Amjad H.; Issa, Ayman A.; Al-Ghouti, Mohammad A.; Sunjuk, Mahmoud

doi:10.2166/wst.2012.364

Cited by 20 publications

(19 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both correlation coefficients r 2 and relative error of predictions (REP%) were estimated to evaluate the performance of PLS‐Kernel for dyes prediction in the mixture. The earlier parameters were estimated as follows :

r^{2} = 1 - {true(\frac{\sum_{i = 1}^{n} {false(C_{i,pred} - C_{i,act} false)}^{2}}{\sum_{i = 1}^{n} {false(C_{i,act} - true \overset{true¯}{C} false)}^{2}} true)}^{2}

REP % = 100 \times {true(\frac{\sum_{i = 1}^{n} {false(C_{i,pred} - C_{i,act} false)}^{2}}{\sum_{i = 1}^{n} {false(C_{i,act} false)}^{2}} true)}^{1 / 2}

…”

Section: Resultsmentioning

confidence: 99%

“…Data‐solver (Excel®) was adopted to run all necessary numerical calculations. The degree of fit of a certain model to the experimental data was evaluated by estimating prediction error sum of squares (PRESS) :

PRESS = \sum_{i = 1}^{n} {false(q_{i,pred} - q_{i,exp} false)}^{2}

where q e,pred. and q e,exp are the predicted and experimental adsorption values, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Partial Least Squares‐Kernel Calibration in Competitive Adsorption Studies Using an Effective Chemically Activated Biochar

Issa

Al‐Degs

El‐Sheikh

et al. 2017

CLEAN Soil Air Water

Self Cite

View full text Add to dashboard Cite

In this work, chemically activated pine wood was used to prepare biochar, which was used to study the competitive adsorption behavior of four colorants (allura red (AR), brilliant black (BB), tartrazine (TT), and sunset yellow (SY)). Dye concentrations in mixed solutions were simultaneously estimated by using partial least squares Kernel calibration. The analytical recovery of food dyes was 96.7–103.4% and prediction error was 1.4–9.4%. Adsorption isotherms of dyes (at pH 2 and 25°C) from multi‐solute solutions were measured and the maximum adsorption capacities (according to the Jovanovich's model) were 68.4, 40.0, 29.6, and 23.4 mg/g for SY, AR, TT, and BB, respectively. The biochar had a combined retention capacity of 186.9 mg/g for dyes. The effects of dosage of adsorbent, pH and number of competing solutes on %Removal‐retention capacity were studied. The extent of competition between dyes toward the surface was assessed from the magnitudes of competition factors. The competition factors were 0.69, 0.70, 0.75, and 0.76 for AR, TT, BB, and SY, respectively. During adsorption of mixtures from solution, SY manifested stable performance while allura red negatively competed with the rest of the dyes. Partial least squares Kernel calibration was usefully applied for studying competitive adsorption behavior of four food‐dyes with minimum experimental setup.

show abstract

r^{2} = 1 - {true(\frac{\sum_{i = 1}^{n} {false(C_{i,pred} - C_{i,act} false)}^{2}}{\sum_{i = 1}^{n} {false(C_{i,act} - true \overset{true¯}{C} false)}^{2}} true)}^{2}

REP % = 100 \times {true(\frac{\sum_{i = 1}^{n} {false(C_{i,pred} - C_{i,act} false)}^{2}}{\sum_{i = 1}^{n} {false(C_{i,act} false)}^{2}} true)}^{1 / 2}

…”

Section: Resultsmentioning

confidence: 99%

PRESS = \sum_{i = 1}^{n} {false(q_{i,pred} - q_{i,exp} false)}^{2}

where q e,pred. and q e,exp are the predicted and experimental adsorption values, respectively.…”

Section: Methodsmentioning

confidence: 99%

Application of Partial Least Squares‐Kernel Calibration in Competitive Adsorption Studies Using an Effective Chemically Activated Biochar

Issa

Al‐Degs

El‐Sheikh

et al. 2017

CLEAN Soil Air Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…A mathematical nonlinear fitting procedure was adopted to determine the parameters of the equilibrium and kinetic models. The degree of fitness of the model to the experimental data was assessed by determining the prediction error sum of squares (PRESS), expressed as the following: [24] X n i¼1…”

mentioning

confidence: 99%

Manganese‐Loaded Activated Carbon for the Removal of Organosulfur Compounds from High‐Sulfur Diesel Fuels

Al-Ghouti

Al‐Degs

2014

Energy Tech

Self Cite

View full text Add to dashboard Cite

The adsorptive capacity of activated carbon (AC) is significantly enhanced toward weakly interacting organosulfur compounds (OSC) from sulfur‐rich diesel fuel. Sulfur compounds are selectively removed from diesel after surface modification by manganese dioxide (MnO2). A selective surface for OSC removal was created by loading MnO2 on the surface; π‐complexation between the partially filled d‐orbitals of Mn4+ and the S atom is the controlling mechanism for OSC removal. Principal component analysis (PCA) of adsorption data indicated the significance of the Mn–AC mass and the temperature as experimental parameters according to the following correlation: OSC adsorption (mg Sg−1)=3.94 (mass)−0.06 (part. diam.)−3.28 (temp)+0.02 (conc.). The kinetics of OSC adsorption confirmed that OSC removal by Mn–AC is a fast process in which 66 % of the surface is filled in 30 min. Kinetic data were best fitted by pseudo‐first‐ and second‐order models compared to diffusion‐based models. Surface and pore diffusion has only a minor role in the process.

show abstract

“…For comparison, the multivariate data were also analyzed by principal component regression (PCR). The statistical parameters including the number of latent ) in the presence of 2.0 mL of the concentrated sulfuric acid Table 3, some of the parameters were calculated based on the net analyte signal (NAS) concept (Booksh and Kowalski 1994;Al-Degs et al 2012). These are multivariate figure of merits.…”

Section: Interference Studymentioning

confidence: 99%

Introduction of a spectrophotometric method for simultaneous determination of nitrite and nitrate in water samples using partial least squares

Shariati-Rad

Irandoust

Haghighi

2015

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

A simple and sensitive spectrophotometric method for simultaneous determination of nitrite and nitrate was introduced. The method is based on the reaction of nitrite and nitrate with 2,4-xylenol as reagent in the concentrated sulfuric acid. Calibration curves were linear in the range of 0.2-20.0 and 0.5-7.0 mg L -1 for nitrite and nitrate, respectively. Due to the severe spectral overlapping of the reaction products of two anions with 2,4-xylenol, partial least squares (PLS) was used for simultaneous determination of nitrite and nitrate in the mixtures. For calibration and validation, 25 binary mixtures of nitrite and nitrate (17 calibration and eight external test samples) were designed by simplex lattice design with lattice degree of one. PLS models with Q 2 values of 0.988 and 0.976 were constructed for nitrite and nitrate, respectively. The validated models were used to analyze real water samples. With the recommended method, simultaneous determination of nitrite and nitrate in water samples is possible with satisfactory results.

show abstract

A simple and accurate analytical method for determination of three commercial dyes in different water systems using partial least squares regression

Cited by 20 publications

References 28 publications

Application of Partial Least Squares‐Kernel Calibration in Competitive Adsorption Studies Using an Effective Chemically Activated Biochar

Application of Partial Least Squares‐Kernel Calibration in Competitive Adsorption Studies Using an Effective Chemically Activated Biochar

Manganese‐Loaded Activated Carbon for the Removal of Organosulfur Compounds from High‐Sulfur Diesel Fuels

Introduction of a spectrophotometric method for simultaneous determination of nitrite and nitrate in water samples using partial least squares

Contact Info

Product

Resources

About