Improving oil classification quality from oil spill fingerprint beyond six sigma approach
This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Juahir, Hafizan [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
|---|
| Umfang: |
11 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs - Lai, Xiaohong ELSEVIER, 2022, the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:120 ; year:2017 ; number:1 ; day:15 ; month:07 ; pages:322-332 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.marpolbul.2017.04.032 |
|---|
| Katalog-ID: |
ELV020636156 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV020636156 | ||
| 003 | DE-627 | ||
| 005 | 20230625132232.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2017 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.marpolbul.2017.04.032 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000268A.pica |
| 035 | |a (DE-627)ELV020636156 | ||
| 035 | |a (ELSEVIER)S0025-326X(17)30339-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 550 |a 333.7 | |
| 082 | 0 | 4 | |a 550 |q DE-600 |
| 082 | 0 | 4 | |a 333.7 |q DE-600 |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 15,3 |2 ssgn | ||
| 084 | |a PHARM |q DE-84 |2 fid | ||
| 084 | |a 44.40 |2 bkl | ||
| 100 | 1 | |a Juahir, Hafizan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| 264 | 1 | |c 2017transfer abstract | |
| 300 | |a 11 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. | ||
| 520 | |a This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. | ||
| 700 | 1 | |a Ismail, Azimah |4 oth | |
| 700 | 1 | |a Mohamed, Saiful Bahri |4 oth | |
| 700 | 1 | |a Toriman, Mohd Ekhwan |4 oth | |
| 700 | 1 | |a Kassim, Azlina Md. |4 oth | |
| 700 | 1 | |a Zain, Sharifuddin Md. |4 oth | |
| 700 | 1 | |a Ahmad, Wan Kamaruzaman Wan |4 oth | |
| 700 | 1 | |a Wah, Wong Kok |4 oth | |
| 700 | 1 | |a Zali, Munirah Abdul |4 oth | |
| 700 | 1 | |a Retnam, Ananthy |4 oth | |
| 700 | 1 | |a Taib, Mohd. Zaki Mohd. |4 oth | |
| 700 | 1 | |a Mokhtar, Mazlin |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Lai, Xiaohong ELSEVIER |t Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs |d 2022 |d the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers |g Amsterdam [u.a.] |w (DE-627)ELV007549504 |
| 773 | 1 | 8 | |g volume:120 |g year:2017 |g number:1 |g day:15 |g month:07 |g pages:322-332 |g extent:11 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.marpolbul.2017.04.032 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-PHARM | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a SSG-OPC-PHA | ||
| 936 | b | k | |a 44.40 |j Pharmazie |j Pharmazeutika |q VZ |
| 951 | |a AR | ||
| 952 | |d 120 |j 2017 |e 1 |b 15 |c 0715 |h 322-332 |g 11 | ||
| 953 | |2 045F |a 550 | ||
| author_variant |
h j hj |
|---|---|
| matchkey_str |
juahirhafizanismailazimahmohamedsaifulba:2017----:mrvnollsiiainultfoolplfnepit |
| hierarchy_sort_str |
2017transfer abstract |
| bklnumber |
44.40 |
| publishDate |
2017 |
| allfields |
10.1016/j.marpolbul.2017.04.032 doi GBV00000000000268A.pica (DE-627)ELV020636156 (ELSEVIER)S0025-326X(17)30339-9 DE-627 ger DE-627 rakwb eng 550 333.7 550 DE-600 333.7 DE-600 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Juahir, Hafizan verfasserin aut Improving oil classification quality from oil spill fingerprint beyond six sigma approach 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Ismail, Azimah oth Mohamed, Saiful Bahri oth Toriman, Mohd Ekhwan oth Kassim, Azlina Md. oth Zain, Sharifuddin Md. oth Ahmad, Wan Kamaruzaman Wan oth Wah, Wong Kok oth Zali, Munirah Abdul oth Retnam, Ananthy oth Taib, Mohd. Zaki Mohd. oth Mokhtar, Mazlin oth Enthalten in Elsevier Science Lai, Xiaohong ELSEVIER Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs 2022 the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers Amsterdam [u.a.] (DE-627)ELV007549504 volume:120 year:2017 number:1 day:15 month:07 pages:322-332 extent:11 https://doi.org/10.1016/j.marpolbul.2017.04.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 120 2017 1 15 0715 322-332 11 045F 550 |
| spelling |
10.1016/j.marpolbul.2017.04.032 doi GBV00000000000268A.pica (DE-627)ELV020636156 (ELSEVIER)S0025-326X(17)30339-9 DE-627 ger DE-627 rakwb eng 550 333.7 550 DE-600 333.7 DE-600 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Juahir, Hafizan verfasserin aut Improving oil classification quality from oil spill fingerprint beyond six sigma approach 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Ismail, Azimah oth Mohamed, Saiful Bahri oth Toriman, Mohd Ekhwan oth Kassim, Azlina Md. oth Zain, Sharifuddin Md. oth Ahmad, Wan Kamaruzaman Wan oth Wah, Wong Kok oth Zali, Munirah Abdul oth Retnam, Ananthy oth Taib, Mohd. Zaki Mohd. oth Mokhtar, Mazlin oth Enthalten in Elsevier Science Lai, Xiaohong ELSEVIER Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs 2022 the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers Amsterdam [u.a.] (DE-627)ELV007549504 volume:120 year:2017 number:1 day:15 month:07 pages:322-332 extent:11 https://doi.org/10.1016/j.marpolbul.2017.04.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 120 2017 1 15 0715 322-332 11 045F 550 |
| allfields_unstemmed |
10.1016/j.marpolbul.2017.04.032 doi GBV00000000000268A.pica (DE-627)ELV020636156 (ELSEVIER)S0025-326X(17)30339-9 DE-627 ger DE-627 rakwb eng 550 333.7 550 DE-600 333.7 DE-600 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Juahir, Hafizan verfasserin aut Improving oil classification quality from oil spill fingerprint beyond six sigma approach 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Ismail, Azimah oth Mohamed, Saiful Bahri oth Toriman, Mohd Ekhwan oth Kassim, Azlina Md. oth Zain, Sharifuddin Md. oth Ahmad, Wan Kamaruzaman Wan oth Wah, Wong Kok oth Zali, Munirah Abdul oth Retnam, Ananthy oth Taib, Mohd. Zaki Mohd. oth Mokhtar, Mazlin oth Enthalten in Elsevier Science Lai, Xiaohong ELSEVIER Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs 2022 the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers Amsterdam [u.a.] (DE-627)ELV007549504 volume:120 year:2017 number:1 day:15 month:07 pages:322-332 extent:11 https://doi.org/10.1016/j.marpolbul.2017.04.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 120 2017 1 15 0715 322-332 11 045F 550 |
| allfieldsGer |
10.1016/j.marpolbul.2017.04.032 doi GBV00000000000268A.pica (DE-627)ELV020636156 (ELSEVIER)S0025-326X(17)30339-9 DE-627 ger DE-627 rakwb eng 550 333.7 550 DE-600 333.7 DE-600 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Juahir, Hafizan verfasserin aut Improving oil classification quality from oil spill fingerprint beyond six sigma approach 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Ismail, Azimah oth Mohamed, Saiful Bahri oth Toriman, Mohd Ekhwan oth Kassim, Azlina Md. oth Zain, Sharifuddin Md. oth Ahmad, Wan Kamaruzaman Wan oth Wah, Wong Kok oth Zali, Munirah Abdul oth Retnam, Ananthy oth Taib, Mohd. Zaki Mohd. oth Mokhtar, Mazlin oth Enthalten in Elsevier Science Lai, Xiaohong ELSEVIER Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs 2022 the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers Amsterdam [u.a.] (DE-627)ELV007549504 volume:120 year:2017 number:1 day:15 month:07 pages:322-332 extent:11 https://doi.org/10.1016/j.marpolbul.2017.04.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 120 2017 1 15 0715 322-332 11 045F 550 |
| allfieldsSound |
10.1016/j.marpolbul.2017.04.032 doi GBV00000000000268A.pica (DE-627)ELV020636156 (ELSEVIER)S0025-326X(17)30339-9 DE-627 ger DE-627 rakwb eng 550 333.7 550 DE-600 333.7 DE-600 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Juahir, Hafizan verfasserin aut Improving oil classification quality from oil spill fingerprint beyond six sigma approach 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Ismail, Azimah oth Mohamed, Saiful Bahri oth Toriman, Mohd Ekhwan oth Kassim, Azlina Md. oth Zain, Sharifuddin Md. oth Ahmad, Wan Kamaruzaman Wan oth Wah, Wong Kok oth Zali, Munirah Abdul oth Retnam, Ananthy oth Taib, Mohd. Zaki Mohd. oth Mokhtar, Mazlin oth Enthalten in Elsevier Science Lai, Xiaohong ELSEVIER Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs 2022 the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers Amsterdam [u.a.] (DE-627)ELV007549504 volume:120 year:2017 number:1 day:15 month:07 pages:322-332 extent:11 https://doi.org/10.1016/j.marpolbul.2017.04.032 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 120 2017 1 15 0715 322-332 11 045F 550 |
| language |
English |
| source |
Enthalten in Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs Amsterdam [u.a.] volume:120 year:2017 number:1 day:15 month:07 pages:322-332 extent:11 |
| sourceStr |
Enthalten in Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs Amsterdam [u.a.] volume:120 year:2017 number:1 day:15 month:07 pages:322-332 extent:11 |
| format_phy_str_mv |
Article |
| bklname |
Pharmazie Pharmazeutika |
| institution |
findex.gbv.de |
| dewey-raw |
550 |
| isfreeaccess_bool |
false |
| container_title |
Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs |
| authorswithroles_txt_mv |
Juahir, Hafizan @@aut@@ Ismail, Azimah @@oth@@ Mohamed, Saiful Bahri @@oth@@ Toriman, Mohd Ekhwan @@oth@@ Kassim, Azlina Md. @@oth@@ Zain, Sharifuddin Md. @@oth@@ Ahmad, Wan Kamaruzaman Wan @@oth@@ Wah, Wong Kok @@oth@@ Zali, Munirah Abdul @@oth@@ Retnam, Ananthy @@oth@@ Taib, Mohd. Zaki Mohd. @@oth@@ Mokhtar, Mazlin @@oth@@ |
| publishDateDaySort_date |
2017-01-15T00:00:00Z |
| hierarchy_top_id |
ELV007549504 |
| dewey-sort |
3550 |
| id |
ELV020636156 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV020636156</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625132232.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.marpolbul.2017.04.032</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000268A.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV020636156</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0025-326X(17)30339-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">550</subfield><subfield code="a">333.7</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">333.7</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">15,3</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">PHARM</subfield><subfield code="q">DE-84</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.40</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Juahir, Hafizan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving oil classification quality from oil spill fingerprint beyond six sigma approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ismail, Azimah</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mohamed, Saiful Bahri</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Toriman, Mohd Ekhwan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kassim, Azlina Md.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zain, Sharifuddin Md.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ahmad, Wan Kamaruzaman Wan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wah, Wong Kok</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zali, Munirah Abdul</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Retnam, Ananthy</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Taib, Mohd. Zaki Mohd.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mokhtar, Mazlin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Lai, Xiaohong ELSEVIER</subfield><subfield code="t">Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs</subfield><subfield code="d">2022</subfield><subfield code="d">the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV007549504</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:120</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1</subfield><subfield code="g">day:15</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:322-332</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.marpolbul.2017.04.032</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-PHARM</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.40</subfield><subfield code="j">Pharmazie</subfield><subfield code="j">Pharmazeutika</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">120</subfield><subfield code="j">2017</subfield><subfield code="e">1</subfield><subfield code="b">15</subfield><subfield code="c">0715</subfield><subfield code="h">322-332</subfield><subfield code="g">11</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">550</subfield></datafield></record></collection>
|
| author |
Juahir, Hafizan |
| spellingShingle |
Juahir, Hafizan ddc 550 ddc 333.7 ddc 610 ssgn 15,3 fid PHARM bkl 44.40 Improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| authorStr |
Juahir, Hafizan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV007549504 |
| format |
electronic Article |
| dewey-ones |
550 - Earth sciences 333 - Economics of land & energy 610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
550 333.7 550 DE-600 333.7 DE-600 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| topic |
ddc 550 ddc 333.7 ddc 610 ssgn 15,3 fid PHARM bkl 44.40 |
| topic_unstemmed |
ddc 550 ddc 333.7 ddc 610 ssgn 15,3 fid PHARM bkl 44.40 |
| topic_browse |
ddc 550 ddc 333.7 ddc 610 ssgn 15,3 fid PHARM bkl 44.40 |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
a i ai s b m sb sbm m e t me met a m k am amk s m z sm smz w k w a wkw wkwa w k w wk wkw m a z ma maz a r ar m z m t mzm mzmt m m mm |
| hierarchy_parent_title |
Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs |
| hierarchy_parent_id |
ELV007549504 |
| dewey-tens |
550 - Earth sciences & geology 330 - Economics 610 - Medicine & health |
| hierarchy_top_title |
Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV007549504 |
| title |
Improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| ctrlnum |
(DE-627)ELV020636156 (ELSEVIER)S0025-326X(17)30339-9 |
| title_full |
Improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| author_sort |
Juahir, Hafizan |
| journal |
Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs |
| journalStr |
Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science 300 - Social sciences 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2017 |
| contenttype_str_mv |
zzz |
| container_start_page |
322 |
| author_browse |
Juahir, Hafizan |
| container_volume |
120 |
| physical |
11 |
| class |
550 333.7 550 DE-600 333.7 DE-600 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Juahir, Hafizan |
| doi_str_mv |
10.1016/j.marpolbul.2017.04.032 |
| dewey-full |
550 333.7 610 |
| title_sort |
improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| title_auth |
Improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| abstract |
This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. |
| abstractGer |
This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. |
| abstract_unstemmed |
This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA |
| container_issue |
1 |
| title_short |
Improving oil classification quality from oil spill fingerprint beyond six sigma approach |
| url |
https://doi.org/10.1016/j.marpolbul.2017.04.032 |
| remote_bool |
true |
| author2 |
Ismail, Azimah Mohamed, Saiful Bahri Toriman, Mohd Ekhwan Kassim, Azlina Md Zain, Sharifuddin Md Ahmad, Wan Kamaruzaman Wan Wah, Wong Kok Zali, Munirah Abdul Retnam, Ananthy Taib, Mohd. Zaki Mohd Mokhtar, Mazlin |
| author2Str |
Ismail, Azimah Mohamed, Saiful Bahri Toriman, Mohd Ekhwan Kassim, Azlina Md Zain, Sharifuddin Md Ahmad, Wan Kamaruzaman Wan Wah, Wong Kok Zali, Munirah Abdul Retnam, Ananthy Taib, Mohd. Zaki Mohd Mokhtar, Mazlin |
| ppnlink |
ELV007549504 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth oth oth oth oth oth oth |
| doi_str |
10.1016/j.marpolbul.2017.04.032 |
| up_date |
2024-07-06T18:07:55.328Z |
| _version_ |
1803854047171575808 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV020636156</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625132232.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.marpolbul.2017.04.032</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000268A.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV020636156</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0025-326X(17)30339-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">550</subfield><subfield code="a">333.7</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">333.7</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">15,3</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">PHARM</subfield><subfield code="q">DE-84</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.40</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Juahir, Hafizan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving oil classification quality from oil spill fingerprint beyond six sigma approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC–MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat >Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ismail, Azimah</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mohamed, Saiful Bahri</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Toriman, Mohd Ekhwan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kassim, Azlina Md.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zain, Sharifuddin Md.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ahmad, Wan Kamaruzaman Wan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wah, Wong Kok</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zali, Munirah Abdul</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Retnam, Ananthy</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Taib, Mohd. Zaki Mohd.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mokhtar, Mazlin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Lai, Xiaohong ELSEVIER</subfield><subfield code="t">Purification and mass spectrometry study of Maillard reaction impurities in five acyclic nucleoside antiviral drugs</subfield><subfield code="d">2022</subfield><subfield code="d">the international journal for marine environmental scientists, engineers, administrators, politicians and lawyers</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV007549504</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:120</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1</subfield><subfield code="g">day:15</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:322-332</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.marpolbul.2017.04.032</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-PHARM</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.40</subfield><subfield code="j">Pharmazie</subfield><subfield code="j">Pharmazeutika</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">120</subfield><subfield code="j">2017</subfield><subfield code="e">1</subfield><subfield code="b">15</subfield><subfield code="c">0715</subfield><subfield code="h">322-332</subfield><subfield code="g">11</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">550</subfield></datafield></record></collection>
|
| score |
7.399701 |