Classification and Class-Modelling

Bevilacqua, Marta; Bucci, Remo; Magrì, Andrea; Magrı̀, Antonio; Nescatelli, Riccardo; Marini, Federico

doi:10.1016/b978-0-444-59528-7.00005-3

Cited by 52 publications

(21 citation statements)

References 47 publications

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“…PCA is known to be a good tool for information extraction from multivariate matrices and concentrate it in only few components (Bevilacqua, Bucci, Magrì, Magrì, & Nescatelli, 2013). The scores of the obtained components are then used to plot the data in an interpretable way.…”

Section: Resultsmentioning

confidence: 99%

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Physicochemical characterization and antioxidant activity of Palestinian honey samples

Imtara

Elamine

Lyoussi

2018

Food Science & Nutrition

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Physicochemical characteristics, main minerals, and antioxidant activity were determined for Palestinian honey samples belonging to different floral and geographical origins. The features of the analyzed samples were within the established international standards for honey quality control. One clear exception was the hydroxymethylfurfural (HMF) of the Ziziphus sample purchased from the Jericho region, which is the lowest city in the word characterized by a hot desert climate. The observed HMF value was 81.86 ± 2.64 mg/kg being two folds the maximum allowed in honey samples (40 mg/kg). As a second objective of the present work, the parameters were divided into two groups with different discriminatory power. The assessed physicochemical parameters, and the antioxidant activities, specific to the botanical origin discrimination, were used to run the first PCA. A strong correlation could be seen between the bioactive compounds and the antioxidant activities despite the geographical origin of the samples. Thyme and Ziziphus samples were the best samples, while citrus sample presented the lowest activity. Regarding the geographical discrimination, Ash and mineral contents in addition to the electrical conductivity were used. The output PCA conserved high represent ability of the data in the two‐first components being 82.72% and 9.60%. A little discrimination between the samples produced in the north and those produced in the south of the country, but it was not perfect. The intervention of the botanical variability could be the reason.

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Section: Resultsmentioning

confidence: 99%

“…To further understand the distribution of the analyzed samples, based on the assessed parameters, principal component analysis was used (PCA). PCA is known to be a good tool for information extraction from multivariate matrices and concentrate it in only few components (Bevilacqua, Bucci, Magrì, Magrì, & Nescatelli, 2013).…”

Section: Multivariate Analysismentioning

confidence: 99%

Physicochemical characterization and antioxidant activity of Palestinian honey samples

Imtara

Elamine

Lyoussi

2018

Food Science & Nutrition

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“…Menos habitual ha sido la utilización de esta técnica para el análisis cualitativo de datos, de manera que sean las características espectrales de las muestras, las que permitan establecer la posible existencia de subgrupos con características propias y diferenciadas del resto. Para ello se requiere la aplicación de técnicas quimiométricas específicas que parten de sistemas de clasificación no supervisados que, en su caso, evolucionan a sistemas clasificados (Bevilacqua et al 2013).…”

Section: Muestras De Grasa Y Espectro Nirsunclassified

Comparación de herramientas quimiométricas de clasificación para la identificación de grasa perirrenal en corderos

et al. 2020

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En el presente trabajo se prueba la capacidad de la técnica NIRS para clasificar correctamente dos tipos de grasa perirrenal de corderos provenientes de distintos tipos de alimentación. Se combinan distintos tratamientos espectrales (Asorbancia-A, Reflectancia-R, A-EMSC, R-EMS) con distintas técnicas de clasificación no supervisada (PCA-Projection) y supervisada SIMCA, PLS-DA, LDA, C-SVM. Los resultados indican que los espectros sin tratar, contienen información valiosa en términos de clasificación, a pesar de la dispersión luminosa existente. Igualmente, los métodos de clasificación más sencillos PCA-Projection y LDA resultan los más efectivos. De los métodos más sofisticados, el C-SVM resulta el más eficaz, aunque es sensible al tipo de semilla de iniciación utilizada. Se concluye que la espectroscopía NIRS combinada con técnicas quimiométricas es un método válido para clasificar corderos en matadero en función de su alimentación.

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“…SIMCA, however, proved more accurate when testing unknown and control oil admixtures by producing less classification errors using the prediction set. More specifically, SIMCA is not 'forced' to classify all samples to a particular class in contrast to PLS-DA (Wold, 1976;Wold & Sjostrom, 1977;Bevilacqua, Bucci, Magrì, Magrì, Nescatelli & Marini, 2013). In fact, it will return samples unclassified, i.e.…”

Section: Choice Of Screening Spectral Technique Classification Algormentioning

confidence: 99%

Identification of vegetable oil botanical speciation in refined vegetable oil blends using an innovative combination of chromatographic and spectroscopic techniques

et al. 2015

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Classification and Class-Modelling

Cited by 52 publications

References 47 publications

Physicochemical characterization and antioxidant activity of Palestinian honey samples

Physicochemical characterization and antioxidant activity of Palestinian honey samples

Comparación de herramientas quimiométricas de clasificación para la identificación de grasa perirrenal en corderos

Identification of vegetable oil botanical speciation in refined vegetable oil blends using an innovative combination of chromatographic and spectroscopic techniques

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