Analysis of Biodiesel Feedstock Using GCMS and Unsupervised Chemometric Methods

Abstract

Various biodiesel feedstocks were evaluated using gas chromatography-mass spectrometry data combined with unsupervised chemometric methods of analysis. Peak areas of the fatty acid methyl esters (FAMEs) present in the biodiesel feedstocks (soybean oil, canola oil, waste grease, animal tallow, etc.) were utilized. The importance of chromatographic parameters, such as temperature program and column polarity, was examined with respect to the clustering that was observed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Biodiesels in this study clustered based on feedstock type regardless of temperature program or column type, as long as FAME isomers were resolved from one another. As such, the number and type of FAME components required to observe this clustering was investigated further. In general, the minor components in the sample did not provide improved clustering and thus did not need to be included. In addition, data from various temperature programs or column types were combined to yield similar clustering, showing potential versatility in analyzing similar samples across laboratories using different columns and column properties. Overall, we determined that (1) minor FAME components are non-essential for feedstock identification and (2) PCA and HCA clustering is based on feedstock, regardless of column selection, so long as resolution of FAME isomers is achieved. Keywords Fatty acid methyl esters (FAMEs) Á Biodiesel fuels Á Gas chromatography-mass spectrometry (GCMS) Á Principal component analysis (PCA) Á Hierarchical cluster analysis (HCA) Á Correlation M. E. Flood and J. C. Goding contributed equally to this work.

Evaluation of single and multi-feedstock biodiesel – diesel blends using GCMS and chemometric methods

“…This analysis method can be used with little knowledge of chemometric and preprocessing methods and simple, user friendly chemometric software. Previous research in this area has shown that using peak areas rather than the entire chromatogram does not bias simple data sets [28]. Multifeedstock samples were treated in a similar way, with the six most abundant FAMEs in the mixture used.…”

“…Previous work in our research laboratory has determined optimal separation conditions of several biodiesel feedstocks on three different GC column chemistries and utilized unsupervised chemometric methods to characterize feedstock type [27,28,29]. The current research expands previous research by employing separation and chemometric methods for biodiesel-diesel blends made from these same feedstocks and several diesel sources.…”

Greener procedures for biodiesel quality control

“…Although these methods are not directly related to parameters for evaluation of the quality of the biofuel, they are useful to classify the biodiesel based on the feedstock [142][143][144][145][146][147] or to check the biodiesel content and adulteration in diesel/biodiesel blends. Although these methods are not directly related to parameters for evaluation of the quality of the biofuel, they are useful to classify the biodiesel based on the feedstock [142][143][144][145][146][147] or to check the biodiesel content and adulteration in diesel/biodiesel blends.…”

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

“…For separations involving biodiesel-diesel blends, many analyzers want to determine feedstock type or concentration of biodiesel [4,18]. The identification of feedstock depends on the identity of the FAMEs present, often with large differences in components, but for some it can be minor differences in isomers [19]. Our lab has investigated the use of chemometric methods for the determination of feedstock and concentration using the full chromatogram as well as using peak areas of major biodiesel and diesel components [20,21].…”