Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Abstract

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.

Modeling second-order data for classification issues: Data characteristics, algorithms, processing procedures and applications

“…In fact, modern analytical platforms of separation followed by different detection techniques, e.g. tandem gas chromatography-mass spectrometry (GC-MS) [5][6][7], liquid chromatography-mass spectrometry or diode-arrays spectrophotometer (LC-MS [8,9], LC diode-array spectroscopy (LC-DAD) [10,11]), and capillary electrophoresis (CE) with DAD [12], have been scarcely applied. Conversely, fluorescence spectroscopy is the direct detection technique that has been mostly exploited in order to solve classification issues.…”

“…processes, storage, harvest or variety). Other relevant works have been orientated to more specific research, such as the forensic analysis of textile fibers [40,41], the characterization of organic constituents of soils [42], discrimination of geographical origin and detection of adulteration of burdook and kudzu roots [43,44], and for the identification of biodiesel and petroleum oils with environmental purposes [7,45,46]. Several recent reviews have been published about this ever increasing research field where relevant applications can be found [47][48][49][50][51][52][53].…”

“…In these cases, the use of simpler and faster conditions may lead to unresolved analyte peaks in a reasonably short time Consequently, hyphenation from separation techniques such as GC, LC or CE beside spectroscopic techniques such as DAD or, fast scanning fluorescence detection (FSFD), can provide many possible analytical strategies for classification studies [12,30,37]. Furthermore, due to advanced technologies in mass detectors, in recent years, the coupling of LC, GC or CE with MS has been employed in various classification studies [5][6][7][8][9]65].…”

Balancing Resolution with Analysis Time for Biodiesel–Diesel Fuel Separations Using GC, PCA, and the Mahalanobis Distance

“…For biodiesel-diesel blended fuels, a long, very polar column is traditionally used to separate the many diesel components as well as to isolate the long chain fatty acid methyl esters (FAMEs) present in biodiesels [2][3][4]. The diesel components elute at low to mid-range temperatures and are often difficult to completely isolate, leading to unresolved baseline humps [5,6]. The alkanes have large, easily recognizable peaks with various aromatic components present in diesel and are interspersed throughout the chromatogram.…”

Comparison of Pre-Processing and Variable Selection Strategies in Group-Based GC×GC-TOFMS Analysis

“…Comprehensive two-dimensional gas chromatography (GC×GC) coupled to time-of-flight mass spectrometry (TOFMS) has become an established technique for the analysis of complex samples [1][2][3]. The increased peak capacity and sensitivity obtained through non-targeted GC×GC-TOFMS analysis result in complex data sets, which are time-consuming to analyze [4,5]. As such, multiple data reduction strategies have been implemented for the analysis of GC×GC-TOFMS data [6][7][8][9][10][11][12].…”