Comparison of microalgal biomass profiles as novel functional ingredient for food products
Autor: | Luísa Gouveia, Anabela Raymundo, José M. Franco, Ana Paula Batista, Narcisa M. Bandarra |
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Rok vydání: | 2013 |
Předmět: |
Haematococcus pluvialis
Low protein biology Food industry Haematococcus Pluvialis business.industry Chlorella Vulgaris Chlorella vulgaris Food technology biology.organism_classification Isochrysis galbana Ingredient Spirulina Maxima Functional food Diacronema Vlkianum Botany Food science Isochrysis Galbana business Agronomy and Crop Science |
Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
ISSN: | 2211-9264 |
DOI: | 10.1016/j.algal.2013.01.004 |
Popis: | Microalgae are one of the most promising sources for new food and functional food products, and can be used to enhance the nutritional value of foods, due to their well-balanced chemical composition. Knowing their physicochemical characteristics is fundamental for the selection of the most suitable microalgae to specific food technology applications and consequently successful novel foods development. The aim of this study is to screen the chemical composition (e.g., proteins, pigments, fatty acids) and thermogravimetry properties of five microalgae species with potential application in the food industry: Chlorella vulgaris (green and carotenogenic), Haematococcus pluvialis (carotenogenic), Spirulina maxima, Diacronema vlkianum and Isochrysis galbana. C. green and S. maxima presented high protein (38% and 44%, respectively), low fat content (5% and 4%, respectively). The carotenogenic C. vulgaris and H. pluvialis showed a higher carotenoid content, higher fat, low protein and better resistance to thermal treatment. D. vlkianum and I. galbana presented high protein (38–40%) and fat (18–24%) contents with PUFA's ω3, mainly EPA and DHA. Finally, the results from microalgae chemical and thermal analysis were grouped and correlated through Principal Components Analysis (PCA) in order to determine which variables better define and differentiate them. |
Databáze: | OpenAIRE |
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