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	Provedor de dados CIGR Journal (2) Autor Ghosh, Satyabrata (2) Amini-Pozveh, Samaneh (1) Barreiro, Pilar (1) Diezma, Belén (1) Domínguez, Teresa R. Cuadrado (1) Ghanbarian, Davoud (1) Ghasemi-Varnamkhasti, Mahdi (1) Izadi, Zahra (1) Lacarra, Teresa García (1) Lleó, Lourdes (1) Mireei, Seyed Ahmad (1) Mishra, Puneet (1) Roger, Jean-Michel (1) Mais... Palavra-chave Spectroscopy (2) Adulterant (1) Allergen (1) Computer aided classification (1) Food analysis (1) Food and olive oil Olive oil industry (1) Food composition (1) Food quality (1) PCA (1) Support vector machine (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Ano 2018 (1) 2015 (1) País China (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última 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 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 Registros recuperados: 2 Primeira ... 1 ... Última

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