Journal ArticleDOI
Quality based classification of gasoline samples by ATR-FTIR spectrometry using spectral feature selection with quadratic discriminant analysis
TLDR
In this paper, a chemometric approach has been developed for characterization of gasoline samples regarding their quality, which was aimed to classify the fuel samples according to their quality passed/failed data.About:
This article is published in Fuel.The article was published on 2013-09-01. It has received 26 citations till now. The article focuses on the topics: Feature selection & Quadratic classifier.read more
Citations
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Journal ArticleDOI
A contemporary review on Data Preprocessing (DP) practice strategy in ATR-FTIR spectrum
TL;DR: The status quo of DP practice strategy is outlined and critically discussed on whether the contemporary practice has been malpractice or best practice, and rationales that could have possibly contributed to some of the malpractices are discussed.
Journal ArticleDOI
Automatic individual identification of Holstein dairy cows using tailhead images
TL;DR: Results show that the low-order Zernike moment feature, along with the QDA and SVM algorithms is an effective approach for individual dairy cow identification and has significant applications in precision animal management.
Journal ArticleDOI
Discrimination of the type of biodiesel/diesel blend (B5) using mid-infrared spectroscopy and PLS-DA
Sarmento J. Mazivila,Felipe Bachion de Santana,Hery Mitsutake,Lucas Caixeta Gontijo,Douglas Queiroz Santos,Waldomiro Borges Neto +5 more
TL;DR: In this article, the use of the mid-infrared (MIR) spectroscopy combined with supervised chemometric tool, partial least squares discriminant analysis (PLS-DA) was used.
Journal ArticleDOI
Gasoline analysis by headspace mass spectrometry and near infrared spectroscopy
TL;DR: In this article, a set of 60 samples belonging to two different research octane numbers (95# and 98#) was analyzed by both headspace mass spectrometry and near infrared spectroscopy.
Journal ArticleDOI
Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods.
Mahsa Mohammadi,Mohammadreza Khanmohammadi Khorrami,Ali Vatani,Hossein Ghasemzadeh,Hamid Vatanparast,Alireza Bahramian,Afshin Fallah +6 more
TL;DR: A new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for adressing regression and classification tasks for crude oils analysis based on °API gravity values, showing 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.
References
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Journal ArticleDOI
Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra
TL;DR: In this article, the standard normal variate (SNV) and de-trending (DT) approaches are applied to individual NIR diffuse reflectance spectra to remove the multiplicative interferences of scatter and particle size.
Book
Machine Learning: Neural and Statistical Classification
TL;DR: A survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning can be found in this article, where the authors also discuss their own work.
Journal ArticleDOI
Petroleomics: The Next Grand Challenge for Chemical Analysis
Alan G. Marshall,Ryan P. Rodgers +1 more
TL;DR: The key features that have opened up this new field have been ultrahigh-resolution FT-ICR mass analysis, specifically, the capability to resolve species differing in elemental composition by C(3) vs SH(4) (i.e., 0.0034 Da), thereby extending to >900 Da the upper limit for unique assignment of elemental composition based on accurate mass measurement.
Journal ArticleDOI
Book review: Machine Learning, Neural and Statistical Classification Edited by D. Michie, D.J. Spiegelhalter and C.C. Taylor (Ellis Horwood Limited, 1994)
TL;DR: This well-edited book provides an up-to-date review of different approaches to classification, compares their performance on a wide range of challenging datasets, and draws conclusions on their applicability to realistic industrial problems.
Journal ArticleDOI
A flexible classification approach with optimal generalisation performance: support vector machines
TL;DR: Support vector machines (SVM) as a recent approach to classification implement classifiers of an adjustable flexibility, which are automatically and in a principled way optimised on the training data for a good generalisation performance.
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