Juahir Hafizan, 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
East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Malaysia.
East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Malaysia; Faculty of Design, Innovation and Technology (FRIT), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Malaysia.
Mar Pollut Bull. 2017 Jul 15;120(1-2):322-332. doi: 10.1016/j.marpolbul.2017.04.032. Epub 2017 May 20.
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 F>F 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.
本研究涉及在基于气相色谱 - 火焰离子化检测器（GC - FID）和气相色谱 - 质谱联用仪（GC - MS）的溢油指纹识别进行溢油分类中运用质量工程，并采用六西格玛方法。溢油样本取自马来西亚半岛和沙巴（东马来西亚）的不同水域。该研究方法使用六西格玛方法，有效地解决了从溢油数据集复杂混合物中提取的油品分类问题。六西格玛与质量工程的关联分析提高了组织绩效，以实现其环境法医鉴定的目标。研究表明，根据内在化学性质的相似性，溢油可分为四组，即柴油、烃类燃料油（HFO）、混合油润滑剂和燃料油（MOLFO）以及废油（WO）。通过验证，确认了柴油、烃类燃料油（HFO）、混合油润滑剂和燃料油（MOLFO）以及废油（WO）这四个判别成分主导了油类类型，总方差为99.51%，方差分析在95%置信水平下F>F，卡方拟合优度检验为74.87。本研究获得的结果表明，在溢油分类等数据驱动问题中采用六西格玛方法，可以加快做出良好决策。
