Chemometric techniques in oil classification from oil spill fingerprinting

Ismail, Azimah; Toriman, Mohd Ekhwan; Juahir, Hafizan; Kassim, Azlina Md.; Zain, Sharifuddin M.; Ahmad, Wan Kamaruzaman Wan; Wong, Kok Fah; Retnam, Ananthy; Zali, Munirah Abdul; Mokhtar, Mazlin; Yusri, Mohd Ayub

doi:10.1016/j.marpolbul.2016.06.089

Cited by 28 publications

(11 citation statements)

References 22 publications

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“…The factor (VF2) exhibits significant positive correlation and communality among other EPA priority PAH pollutants which have a relatively high contribution from waste oil discharge which is either illegal or unintentional as well as factory petroleum discharge. The complex oil mixture dominated by hydrocarbon PAH compounds with high-molecular-weight (four to six rings) [26]. In contrary, VF3 and VF4 exhibit factors with strong collinearity with n-alkanes compound from diesel and lubricating oil.…”

Section: Implementing Changes To Demonstrate Improvement Of Processmentioning

confidence: 98%

“…Discriminant functions (DFs) perform the validation of raw data sets or set of variables known as predictors. DA enables the determination of the oil spill variables and discriminate them into two or more groups (clusters) [19][20][21][22][23][24][25][26]. Discriminant Factor (DF) of DA is constructed based on the original dataset that have similar discriminant ability of each group towards the original data set with or without standardization [20,33].…”

Section: Dmaic Six Sigma Approaches In Oil Spill Classificationmentioning

confidence: 99%

“…Principal components (PCs) which represent the underlying factors while sustaining the information of the original dataset are employed [20,34]. PCA/PCR is one of the effective tools for a meaningful data reduction and interpretation and can be effective in detecting correlation between oil spill variables from different pollution sources [22,[25][26]. PCs with eigenvalue > 1.0 are considered significant.…”

Section: Dmaic Six Sigma Approaches In Oil Spill Classificationmentioning

confidence: 99%

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DMAIC Six Sigma Methodology in Petroleum Hydrocarbon Oil Classification

Ismail

Mohamed

Juahir

et al. 2018

IJET

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This research focuses on the use of the DMAIC method (Define, Measure, Analyze, Improve and Control) as a Six Sigma approach in studying oil spill fingerprint of samples recovered from Peninsular Malaysia and Sabah (East Malaysia). The DMAIC approach in this study was used as a way to classify oil types based on data obtained from GC-FID and GC-MS measurements. The cause-effect diagram was used to define the factors leading to the failure of the oil spill fingerprinting based on inaccurate oil type clustering. Discriminant Analysis (DA) was also applied to quantify the root-cause of the failure. An Ishikawa diagram obtained in the analysis phase identifies the potential failure causal. Principal component analysis (PCA) was applied and was successful in discriminating four clusters of oil types, namely diesel, heavy fuel oil (HFO), mixture oil lube and fuel oil (MOLFO) and waste oil (WO) with a total variance of 85.3%. In the control phase, the use of a Pareto chart indicated 100% cumulative percentage of oil type clustering with a 95% confidence level. The DMAIC approach to be effective in solving oil spill fingerprinting problems and results in quality improvement in the clustering of oil spills into the different hydrocarbon types.

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Section: Implementing Changes To Demonstrate Improvement Of Processmentioning

confidence: 98%

Section: Dmaic Six Sigma Approaches In Oil Spill Classificationmentioning

confidence: 99%

Section: Dmaic Six Sigma Approaches In Oil Spill Classificationmentioning

confidence: 99%

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DMAIC Six Sigma Methodology in Petroleum Hydrocarbon Oil Classification

Ismail

Mohamed

Juahir

et al. 2018

IJET

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“…where i denotes the number of groups (G), k i is the constant coherent to each group, n is the number of parameter used to classify a set of data into given group and w ij is the weight coefficient assigned by discriminant factor (DF) to a given parameter (p ij ) [4,5,7,30].…”

Section: Principle Component Analysismentioning

confidence: 99%

“…In the chemistry and environmental science field, Chemometric technique is a statistical method has been practiced [3]. Principal component analysis (PCA), cluster analysis (CA) and discriminant analysis (DA), as well as factor analysis (FA) are extensively used for data analysis via chemometric techniques [4][5]. Multiuse of chemometric technique let the decrease of difficulty sum of dataset for the presentable data elaboration [6][7].…”

Section: Introductionmentioning

confidence: 99%

Spatial Assessment of Selangor, Malaysia Water Treatment Plant Performance Using Chemometric Technique

Zolkipli

Juahir

Ghazali

et al. 2018

IJET

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This study aims to identify the most significant parameters in drinking water quality, spatial disparities of treated water (TW) and performance of water treatment plant (WTP) in Selangor. Physico- chemical (PCPs), Inorganic (IPs), Heavy metal and organic (HMOPs) and pesticide (PPs) were selected as parameters to discriminate the source of WTP pollutant. Chemometric technique such as principle component analysis (PCA), one-way analysis of variance (ANOVA) and discriminant analysis (DA) was applied to validate the performance of water treatment plant. PCA identified the most significant parameters which are highlighted six out of eight parameters for PCPs, six out of twelve parameters for IPs, nine out of sixteen parameters for HMOPs and all seventh parameters for PP. ANOVA for distinguish two categories region in WTP and showed both of PCPs and IPs had significant differences due to their concentration (p < 0.5) and HMOPs suggested fifth of significant differences within regions (p < 0.05). PP doesn’t give any significant differences (p > 0.05). DA was suggested PCPs, IPs and HMOPs in good performance (76.96%, 91.90% and 93.27%) except PP (50.43%). We can conclude that this chemometric technique can expose which area of WTP need to be properly maintains their performance to produce high quality of drinking water.

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