Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy

Silva, Neirivaldo C. da; Pimentel, Maria Fernanda; Honorato, Ricardo S.; Talhavini, Márcio; Maldaner, Adriano O.; Honorato, Fernanda Araújo

doi:10.1016/j.forsciint.2015.05.011

Cited by 24 publications

(8 citation statements)

References 29 publications

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“…A sample was considered to be correctly classified if the predicted value lay on the same side of the midpoint of the assigned value of 0.5, a cut-off criterion that is often used. [22][23][24][25] A coffee sample was classified as peaberry coffee if its value was above 0.5 and classified as normal coffee if the value was below 0.5.…”

Section: Sample Preparationmentioning

confidence: 99%

Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA

Suhandy

Yulia²

2017

International Journal of Food Properties

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Ultraviolet-visible (UV-Vis) spectral information (190-400 nm) was used to classify coffee samples into either pure peaberry or pure normal coffee classes using two chemometric methods: soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The spectral data of peaberry and normal coffee were acquired using a UV-Vis spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, USA). For both supervised discrimination methods, SIMCA and PLS-DA, all samples were correctly classified into their corresponding classes. The SIMCA model classified all samples accurately (100%) into either the peaberry or normal coffee class, even at a 5% confidence level; however, the PLS-DA model also correctly classified all samples (100%). Investigation of the major wavelengths contributing to the classifications using x-loading weights and loading of latent variables (LVs) indicated wavelengths at 230, 250, 270, 310, and 350 nm were important for determination of the coffee types. These wavelengths were closely related to the absorbance wavelengths of several important chemical components in roasted coffee: caffeine, caffeic acid, and chlorogenic acids (CGA). These results provided the basis for developing a simple and reliable method for peaberry coffee authentication (including more complex samples where peaberry coffee is blended with normal coffees) based on UV-Vis spectra.

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Section: Sample Preparationmentioning

confidence: 99%

Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA

Suhandy

Yulia²

2017

International Journal of Food Properties

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“…35 For the PLS-DA model, a threshold value of 0.5 was adopted, as described in others studies. [36][37][38] When a value above 0.5 is predicted, the sample is considered to belong to the class under study, while a value below 0.5 indicates that the sample does not.…”

Section: Chemometric Procedures and Sowarementioning

confidence: 99%

Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis

Milanez

Pontes

2015

Anal. Methods

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“…Silva et al 68 developed a method to classify gasoline according to its origin (Brazil, Venezuela and Peru), using IR spectroscopy and multivariate classification. A set of 126 gasoline samples: 56 Brazilian, 66 Venezuelan, and 4 Peruvian, was analyzed.…”

Section: Fuelsmentioning

confidence: 99%

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018

et al. 2019

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A compilation of papers published between 2014 and 2018 was evaluated. Many papers related to multivariate calibration and classification have been reported, as well as, design of experiments applications and artificial intelligence methods. Some applications were highlighted, as medical and pharmaceutical, food analysis, fuels, biological and forensic for the chemometric techniques on this review. Most studies are related to developing methods for practical solutions in industry or routine analysis. A promising scenario is shown considering the number of published papers: a total of 832 for this period using the keywords, multivariate classification, multivariate calibration, analysis, chemometrics, prediction, analytical chemistry, artificial neural networks (ANN), design of experiments (DoE) and factorial design. An useful overview for Analytical Chemistry researchers´ combined with Chemometrics is presented in this review.

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Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy

Cited by 24 publications

References 29 publications

Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA

Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA

Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018

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