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

Separations ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 28 ◽  
Author(s):  
Edward J. Soares ◽  
Alexandra J. Clifford ◽  
Carolyn D. Brown ◽  
Ryan R. Dean ◽  
Amber M. Hupp
Keyword(s):  
Gas Chromatography ◽  
Mahalanobis Distance ◽  
Probability Distributions ◽  
Methyl Esters ◽  
P Value ◽  
Data Sets ◽  
Biodiesel Feedstock ◽  
F Distribution ◽  
Biodiesel Fuels ◽  
Gas Chromatography Columns

In this work, a statistical metric called the Mahalanobis distance (MD) is used to compare gas chromatography separation conditions. In the two-sample case, the MD computes the distance between the means of the multivariate probability distributions of two groups. Two gas chromatography columns of the same polarity but differing length and film thickness were utilized for the analysis of fatty acid methyl esters in biodiesel fuels. Biodiesel feedstock samples representing classes of canola, coconut, flaxseed, palm kernal, safflower, soy, soyabean, sunflower, tallow, and waste grease were used in our experiments. Data sets measured from each column were aligned with the correlated optimized warping (COW) algorithm prior to principal components analysis (PCA). The PC scores were then used to compute the MD. Differences between the data produced by each column were determined by converting the MD to its corresponding p-value using the F-distribution. The combination of COW parameters that maximized the p-value were determined for each feedstock separately. The results demonstrate that chromatograms from each column could be optimally aligned to minimize the MD derived from the PC-transformed data. The corresponding p-values for each feedstock type indicated that the two column conditions could produce data that were not statistically different. As a result, the slight loss of resolution using a faster column may be acceptable based on the application for which the data are used.

scholarly journals Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

2016 ◽  
Vol 3 (1) ◽  
pp. 19-25
Author(s):  
T. Petkov ◽  
Z. Mustafa ◽  
S. Sotirov ◽  
R. Milina ◽  
M. Moskovkina
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Artificial Neural Network ◽  
Gas Chromatography ◽  
Fatty Acid Methyl Esters ◽  
Methyl Esters ◽  
Gas Chromatography Mass Spectrometry ◽  
Acid Methyl ◽  
Chemometric Approach ◽  
Biodiesel Fuels

Abstract A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography - mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, “unknown” were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety “unknown” samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

A comparison of structural–functional equation models to identify fatty acids on three common gas chromatography columns

2018 ◽  
Vol 10 (15) ◽  
pp. 1747-1759 ◽  
Author(s):  
A. T. L. Wotherspoon ◽  
K. L. Reeves ◽  
J. Crawford
Keyword(s):  
Fatty Acids ◽  
Functional Equation ◽  
Gas Chromatography ◽  
Fatty Acid Methyl Esters ◽  
Functional Relationship ◽  
Structural Information ◽  
Methyl Esters ◽  
Acid Methyl ◽  
Chromatography Columns ◽  
Gas Chromatography Columns

Reported herein is a series of new structural–functional relationship equations which provide relevant structural information of unknown fatty acid methyl esters (double-bonds, chain-length, and omega-bond position) based upon temperature induced shifts in equivalent chain length's (ECLs).

Bayesian Inference of Recent Migration Rates Using Multilocus Genotypes

Genetics ◽  
2003 ◽  
Vol 163 (3) ◽  
pp. 1177-1191 ◽  
Author(s):  
Gregory A Wilson ◽  
Bruce Rannala
Keyword(s):  
Probability Distributions ◽  
Gray Wolf ◽  
Data Sets ◽  
Genotype Data ◽  
Migration Rates ◽  
Multilocus Genotypes ◽  
Monte Carlo Techniques ◽  
Inbreeding Coefficients ◽  
Genotype Frequencies ◽  
Marker Loci

Abstract A new Bayesian method that uses individual multilocus genotypes to estimate rates of recent immigration (over the last several generations) among populations is presented. The method also estimates the posterior probability distributions of individual immigrant ancestries, population allele frequencies, population inbreeding coefficients, and other parameters of potential interest. The method is implemented in a computer program that relies on Markov chain Monte Carlo techniques to carry out the estimation of posterior probabilities. The program can be used with allozyme, microsatellite, RFLP, SNP, and other kinds of genotype data. We relax several assumptions of early methods for detecting recent immigrants, using genotype data; most significantly, we allow genotype frequencies to deviate from Hardy-Weinberg equilibrium proportions within populations. The program is demonstrated by applying it to two recently published microsatellite data sets for populations of the plant species Centaurea corymbosa and the gray wolf species Canis lupus. A computer simulation study suggests that the program can provide highly accurate estimates of migration rates and individual migrant ancestries, given sufficient genetic differentiation among populations and sufficient numbers of marker loci.

scholarly journals An FDA-Based Approach for Clustering Elicited Expert Knowledge

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 184-204
Author(s):  
Carlos Barrera-Causil ◽  
Juan Carlos Correa ◽  
Andrew Zamecnik ◽  
Francisco Torres-Avilés ◽  
Fernando Marmolejo-Ramos
Keyword(s):  
Functional Data ◽  
Expert Knowledge ◽  
Probability Distributions ◽  
Knowledge Elicitation ◽  
Parallel Analysis ◽  
Data Sets ◽  
Dimensional Problem ◽  
Prior Distributions ◽  
Infinite Dimensional ◽  
Finite Dimensional

Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

scholarly journals Review on stationary phases and coating methods of MEMs gas chromatography columns

2020 ◽  
Vol 39 (1) ◽  
pp. 247-259
Author(s):  
Liu Yang ◽  
Molin Qin ◽  
Junchao Yang ◽  
Genwei Zhang ◽  
Jiana Wei
Keyword(s):  
Gas Chromatography ◽  
Microelectromechanical Systems ◽  
Stationary Phases ◽  
Preparation Methods ◽  
Site Analysis ◽  
Advantages And Disadvantages ◽  
Coating Methods ◽  
On Line ◽  
Inorganic Adsorbents ◽  
Gas Chromatography Columns

Abstract Gas chromatography (GC) is an important and widely used technique for separation and analysis in the field of analytical chemistry. Micro gas chromatography has been developed in response to the requirement for on-line analysis and on-site analysis. At the core of micro gas chromatography, microelectromechanical systems (MEMs) have the advantages of small size and low power consumption. This article introduces the stationary phases of micro columns in recent years, including polymer, carbon materials, silica, gold nanoparticles, inorganic adsorbents and ionic liquids. Preparation techniques ranging from classical coating to unusual sputtering of stationary phases are reviewed. The advantages and disadvantages of different preparation methods are analyzed. The paper introduces the separation characteristics and application progress of MEMs columns and discusses possible developments.

Evaluation of "Dexsil 300 GC" as Stationary Phase in Gas Chromatography of Triiodothyronine and Thyroxine in Human Serum

1973 ◽  
Vol 19 (1) ◽  
pp. 109-112 ◽  
Author(s):  
Mohammed Tajuddin ◽  
Stanley G Elfbaum
Keyword(s):  
Gas Chromatography ◽  
Stationary Phase ◽  
Human Serum ◽  
Methyl Esters ◽  
Phase Separations ◽  
Heat Stable

Abstract The N,O-dipivalyl methyl esters of triiodothyronine and thyroxine were prepared and gas chromatographed. "Dexsil 300 GC," a heat-stable polycarboranesiloxane, was used as the stationary phase. Separations were good and prompt. As little as 250 pg of triiodothyronine was detectable. Dexsil 300 GC was used satisfactorily in measuring serum triiodothyronine

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