A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning.

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 of the scheme. 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 machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build 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 developed 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 predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.

Application of HMQC spectroscopy to the observation of the metal resonance in polymetallic compounds: the effect of multiple-metal spin transitions.

Multiple-metal spin transitions which distort the HMQC spectra of rhodium carbonyl clusters are discussed. These effects are seen whenever the detector nucleus, e.g. 13C or 31P, couples to more than one metal spin and are not restricted to detector ligands occupying edge- or face-bridging sites. These effects are illustrated in, but not limited to, the 13C-{103Rh} and 31P-{103Rh} HMQC spectra of [Rh6(CO)15L], (where L = P(4-F-C6H4)3), [Rh4(CO)11{P(OPh)3}], [Rh6C(CO)15]2- and [Rh2(carboxylate)2PPh3]. The effect is to modulate the intensity and position of the correlations in the metal dimension; cross peaks are displaced from the true chemical shift, additional cross peaks are seen and the intensity of the coherences varies as a function of the preparation delay, d2, and coupling constant, and may go to zero at the conventional value of 1/(2J). Analyses of the relevant spin systems are given together with experimental strategies to overcome these effects.