Screening analysis of biodiesel feedstock using UV–vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm

Insausti, Matías; Gomes, A. A.; Cruz, Fernanda V.; Pistonesi, Marcelo Fabián; Araújo, Mário César Ugulino de; Galvão, Roberto Kawakami Harrop; Pereira, Claudete Fernandes; Band, Beatriz Susana Fernández

doi:10.1016/j.talanta.2012.04.056

Cited by 32 publications

(19 citation statements)

References 30 publications

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“…SPA performs simple projection operations in a vector space to obtain subsets of useful variables with small collinearity. The selected variables can be used as the inputs for calibration models; in this context, the SPA models have advantages because they are uncomplicated and the results are easy to interpretation [28][29][30].…”

Section: Chemometrics Methodsmentioning

confidence: 99%

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Guo

Kokot

2016

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Section: Chemometrics Methodsmentioning

confidence: 99%

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Guo

Kokot

2016

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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“…The best choice for measuring the calibration merits is RMSECV that explains the analytical solutions of the problem [13]. Owing to a higher selectivity and simplicity, synchronous fluorescence spectroscopy (SFS) has been successfully explode in our recent works to discriminate biodiesel samples with respect to the base oil employed in their production [14] and quantified several parameters in diesel blends [15]. Another solution for multicomponent determination is the excitation emission matrix fluorescence spectroscopy (EEM) [16] and the non-linear angle synchronous spectrofluorimetry [17].…”

Section: Modelingmentioning

confidence: 99%

Determination of 2-ethylhexyl Nitrate in Diesel Oil Using a Single Excitation Emission Fluorescence Spectra (EEF) and Chemometrics Analysis

Insausti¹,

B²

2017

J Fundam Renewable Energy Appl

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A highly sensitive spectrofluorimetric method has been developed for the determination of 2-ethylhexyl nitrate in diesel fuel. Usually, this compound is used as an additive in order to improve cetane number.The analytical method consists in building the chemometric model as a first step. Then, it is possible to quantify the analyte with only recording a single excitation-emission fluorescence spectrum (EEF), whose data are introduced in the chemometric model above mentioned. Another important characteristic of this method is that the fuel sample was used without any pre-treatment for EEF.This work provides an interest improvement to fluorescence techniques using the rapid and easily applicable EEF approach to analyze such complex matrices. Exploding EEF was the key to a successful determination, obtaining a detection limit of 0.00434% (v/v) and a limit of quantification of 0.01446% (v/v overlapping of the spectral information might be overcome by using multicomponent analysis such as partial least squares (PLS) [21]. This method allowed an approach using the full spectral region rather than unique and isolated analytical bands. PLS performs data decomposition into spectral score and loading matrices prior to model building with the aid of these new variables, using spectral and concentration data. The algorithm was based on the ability to mathematically correlate spectral data to a property of interest while simultaneously accounting for all other significant spectral factors that interfered in the spectrum.The full cross-validation leave-one-out procedure was applied, which consists of systematically removing one of the training samples in turn, and using only the remaining ones for the construction of the latent factors and regression models [22].The predictive capability of calibration model was expressed by the determination coefficient (R-square), root mean square error of cross validation (RMSECV), and standard error of prediction (SEP).The optimal number of principal components (PCs) was chosen in function of the first local minimum based on the RMSEC curve after cross validation in the calibration set. Predictive capability of model was evaluated by predicting the external prediction set with commercial formulation. Results and DiscussionConventional fluorescence may be preferred to determine one fluorophor concentration or when the mixture being analyzed contains only a limited number of compounds with fluorescence properties and a quenching effect are not to be expected. Ignoring the excitation spectra by using only the emission spectra could not be enough to obtain the solutions of the problem. So, besides the emission spectra, it seems essential to include also the analysis of excitation spectra possessing abundant information. The determination in diesel oil may contain many fluourophors. This task is further complicated by the fact that the fluorophors of interest have different characteristic excitation and emission ranges in the different samples. An inherent problem of many fluorimetric ...

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“…Derivative spectra were calculated by the Savitsky-Golay method with a second order polynomial and 3 point smoothing. Principal component analysis (PCA) was performed on first derivative and second derivative spectra with cross validation to establish classifications for subsequent modeling [9].…”

Section: Spectroscopymentioning

confidence: 99%

Near-Infrared Analysis of Peanut Seed Skins for Catechins

Holser¹

2014

AJAC

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Peanuts (Arachis hypogaea) produce several phenolic compounds such as catechins that may be isolated as co-products from peanut seed testae or skins during peanut processing. Peanut skins are currently underutilized although they are a potential source of bioactive compounds for use in functional foods and animal feeds. The phenolic compounds in the skins can degrade during processing and lose bioactivity. Extracted peanut skins were analyzed by near-infrared (NIR) spectroscopy to detect phenolic compounds. Spectra were collected from 10000 -4000 cm −1 and evaluated by principal component analysis (PCA) to compare differences in peanut skins before and after alcohol extraction. PCA of derivative spectra showed clear separation into two groups. These results support the development of predictive models to measure catechins in blanched peanut skins.

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Screening analysis of biodiesel feedstock using UV–vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm

Cited by 32 publications

References 30 publications

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Determination of 2-ethylhexyl Nitrate in Diesel Oil Using a Single Excitation Emission Fluorescence Spectra (EEF) and Chemometrics Analysis

Near-Infrared Analysis of Peanut Seed Skins for Catechins

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