Excitation-emission fluorescence spectroscopy coupled with PARAFAC and MCR-ALS with area correlation for investigation of jet fuel contamination

Câmara, Anne B.F.; da Silva, Wellington J.O.; Neves, Ana C.de O.; Moura, Heloise O.M.A.; de Lima, Kassio M.G.; de Carvalho, Luciene S.

doi:10.1016/j.talanta.2023.125126

Talanta

2024

DOI: 10.1016/j.talanta.2023.125126

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Excitation-emission fluorescence spectroscopy coupled with PARAFAC and MCR-ALS with area correlation for investigation of jet fuel contamination

Anne B.F. Câmara,

Wellington J.O. da Silva,

Ana C.de O. Neves

et al.

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Cited by 6 publications

References 27 publications

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The MCR‐ALS Trilinearity Constraint for Data With Missing Values

Gómez‐Sánchez,

Vitale,

Loza‐Alvarez

et al. 2024

Journal of Chemometrics

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Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating Least Squares (MCR‐ALS), can provide trilinear models by implementing the trilinearity condition as a constraint. However, some trilinear analytical measurements, such as excitation–emission matrix (EEM) measurements, usually exhibit systematic patterns of missing data due to the nature of the technique, which imply a challenge to the classical implementation of the trilinearity constraint. In this instance, extrapolation or imputation methodologies may not provide optimal results. Recently, a novel algorithmic strategy to constrain trilinearity in MCR‐ALS in the presence of missing data was developed. This strategy relies on the sequential imposition of a classical trilinearity restriction on different submatrices of the original investigated dataset, but, although effective, was found to be particularly slow and requires a proper submatrix selection criterion. In this paper, a much simpler implementation of the trilinearity constraint in MCR‐ALS capable of handling systematic patterns of missing data and based on the principles of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is proposed. This novel approach preserves the trilinearity of the retrieved component profiles without requiring data imputation or subset selection steps and, as with all other constraints designed for MCR‐ALS, offers the flexibility to be applied component‐wise or data block‐wise, providing hybrid bilinear/trilinear models. Furthermore, it can be easily extended to cope with any trilinear or higher‐order dataset with whatever pattern of missing values.

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The MCR‐ALS Trilinearity Constraint for Data With Missing Values

Gómez‐Sánchez,

Vitale,

Loza‐Alvarez

et al. 2024

Journal of Chemometrics

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Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics

Lujun,

Nuo,

Xiaodong

et al. 2024

J Fluoresc

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This study explores the application of excitation-emission matrix uorescence (EEMF) in conjunction with chemometric techniques for the rapid identi cation and quanti cation of adulteration in olive oil, particularly in cases where sample quantities are limited. Soybean oil, peanut oil, and linseed oil are introduced into olive oils to simulate various adulterated samples. Our approach involves the application of parallel factor analysis (PARAFAC) for data decomposition, with a following focus on establishing correlations between the decomposed components and the actual adulterated components. This is accomplished through a thorough comparison of the spectral characteristics and score results of the decomposed components, allowing us to attribute them to the actual adulterated components and thereby ultimately enabling us to quantify the levels of actual adulteration. The results proves that EEMF spectroscopy combined with the proposed analysis methods serves as a powerful tool for the rapid detection and quanti cation of adulteration in olive oil. We also utilize principal component analysis (PCA) to cluster adulterated samples and identify e cient excitation wavelengths, and conduct a comparative analysis between PCA and PARAFAC methods. This study offers a novel perspective and method for quantitatively analyzing adulterants in olive oil through spectral detection, holding the promise of practical application in real-world detection scenarios.

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Determination of DEET, Icaridin, and IR3535 in insect repellents using excitation-emission matrix (EEM) fluorescence spectroscopy and multiway calibration

Francisco Rafael Lemes,

Luis Felipe Soares,

Nagata

2024

Microchemical Journal

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Excitation-emission fluorescence spectroscopy coupled with PARAFAC and MCR-ALS with area correlation for investigation of jet fuel contamination

Cited by 6 publications

References 27 publications

The MCR‐ALS Trilinearity Constraint for Data With Missing Values

The MCR‐ALS Trilinearity Constraint for Data With Missing Values

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics

Determination of DEET, Icaridin, and IR3535 in insect repellents using excitation-emission matrix (EEM) fluorescence spectroscopy and multiway calibration

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