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Utilizing visible and near infrared spectroscopy based on multi-class support vector machines classification to characterize olive oil adulteration CIGR Journal
Ghasemi-Varnamkhasti, Mahdi; Amini-Pozveh, Samaneh; Mireei, Seyed Ahmad; Mishra, Puneet; Ghosh, Satyabrata; Ghanbarian, Davoud; Izadi, Zahra.
Rapid and non-destructive adulteration detection is of particular importance to oil industries. This paper presents an application of visible and near-infrared spectroscopy (VNIR) for detection of adulteration levels in olive oil. Sunflower oil was used as an adulterant to olive oil and adulteration samples with different levels ranging from 0 to 40% were prepared and used for the experiments. The spectra were first considered in the range of 500-900 nm and then smoothened and normalized to reduce the light scattering effects. Principal component analysis (PCA) was performed on the spectra to have a primary data visualization and feature extraction. The extracted PCA scores were used to calculate the Mahalanobis distances of the adulterated samples from...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Food and olive oil Olive oil industry; Support vector machine; Computer aided classification; Spectroscopy.
Ano: 2018 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/4681
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VIS/NIR spectral signature for the Identification of Peanut Contamination of Powder Foods CIGR Journal
Ghosh, Satyabrata; Domínguez, Teresa R. Cuadrado; Diezma, Belén; Lleó, Lourdes; Barreiro, Pilar; Lacarra, Teresa García; Roger, Jean-Michel.
Visible-Near Infrared reflectance spectra are proposed for the characterization IRMM 481 peanuts in comparison to powder food materials: wheat flour, milk and cocoa. Multidimensional analysis of spectra of powder samples shows a specific NIR band centred at 1200 nm that identifies peanut compared to the rest of food ingredients, regardless compaction level and temperature. Spectral range 400-1000 nm is not robust for identification of blanched peanut. The visible range has shown to be reliable for the identification of pre-treatment and processing of unknown commercial peanut samples. A spectral index is proposed based on the combination of three wavelengths around 1200 nm that is 100% robust against pre-treatment (raw or blanched) and roasting (various...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Adulterant; Food analysis; Food composition; Food quality; Allergen; PCA; Spectroscopy.
Ano: 2015 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/3216
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