首页|Kwame Nkrumah University of Science and Technology Reports Findings in Machine L earning (A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning)
Kwame Nkrumah University of Science and Technology Reports Findings in Machine L earning (A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Kumasi, Ghana, by News Rx editors, research stated, “Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation o f the scheme.” Our news journalists obtained a quote from the research from the Kwame Nkrumah U niversity of Science and Technology, “There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and m achine learning algorithms to detect adulteration in diesel fuel. The training s ets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classifi cation model is built to classify diesel samples as neat or adulterated. Adulter ated samples are passed on to the second stage where a second classification mod el identifies the type of adulterant (kerosene, naphtha, or premix) present. Sam ples were analyzed by H NMR spectroscopy and the data obtained were used to buil d and validate support vector machine (SVM) classification models at both levels . At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model develop ed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic p redictions with adulterant levels below 20% w/w as the training se ts for both models contained adulterants above 20% w/w.”
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