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Ana Sayago   Dr.  Other 
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Ana Sayago published an article in November 2018.
Research Keywords & Expertise
0 A
0 Olive Oil
0 Spectrophotometry
0 phenolic compounds
Top co-authors See all
Carlos Vílchez

59 shared publications

Algal Biotechnology Group, CIDERTA, RENSMA and Faculty of Sciences, University of Huelva, 21007 Huelva, Spain;(Z.M.-L.);(M.V.);(J.L.F.);(E.B.);(I.G.);(M.C.)

Rosa María Martínez-Espinosa

47 shared publications

Departamento de Agroquímica y Bioquímica, División de Bioquímica y Biología Molecular, Facultad de CienciasUniversidad de Alicante, Carretera San Vicente del Raspeig s/n ‐ 03690 San Vicente del Raspeig Alicante Spain

Juan Urbano Baena

39 shared publications

Laboratorio de Catálisis Homogénea; Unidad Asociada al CSIC; CIQSO-Centro de Investigación en Química Sostenible and Departamento de Química; Universidad de Huelva; Campus de El Carmen 21007 Huelva Spain

José M. Vega

35 shared publications

Department of Plant Biochemistry and Molecular Biology, Faculty of Chemistry, University of Seville, 41012 Seville, Spain

Rafael Beltrán

13 shared publications

University of Huelva

40
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96
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12
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203
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Publication Record
Distribution of Articles published per year 
(2000 - 2018)
Total number of journals
published in
 
25
 
Publications See all
CONFERENCE-ARTICLE 29 Reads 0 Citations Application of targeted and non-targeted approaches to investigate the effect of genotype and growing conditions on the ... Raúl González-Domínguez, Ana Sayago, Ángeles Fernández-Recam... Published: 15 November 2018
Proceedings of 3rd International Electronic Conference on Metabolomics, doi: 10.3390/iecm-3-05838
DOI See at publisher website ABS Show/hide abstract

Strawberry is composed of numerous primary metabolites (sugars, amino acids, organic acids) and secondary metabolites (anthocyanins, flavan-3-ols, phenolic acids), which play an essential role in fruit quality, organoleptic characteristics and healthy benefits. In this context, metabolomics presents a great potential to get a deep overview of this complex chemical meshwork, which can provide valuable information on the effect of multiple growing factors in the strawberry composition. In this work, we show the utility of different metabolomic approaches to investigate the influence of variety and agronomic conditions in the strawberry metabolome on the basis of data acquired in two published studies conducted in our research group. First, we conducted a GC/MS-based non-targeted metabolomic analysis in strawberries of three varieties with different sensitivity to environmental conditions (Camarosa, Festival and Palomar), which in turn were grown in soilless systems by using various agronomic conditions (electrical conductivity, coverage and substrates) (1). Complementarily, a targeted metabolomic approach based on UHPLC-MS/MS was also applied to identify and quantitate the main polyphenol compounds in these strawberry fruits (2). The most discriminant metabolites were several amino acids, sugars, organic acids, anthocyanins, ellagic acid derivatives, flavan-3-ols, chlorogenic acid and quercetin 3-O-glucuronide, which could be associated with differences in organoleptic characteristics and the biosynthesis of strawberry antioxidants.

(1) I. Akhatou, R. González-Domínguez, A. Fernández-Recamales. Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress. Plant Physiol. Biochem. 101 (2016) 14-22

(2) I. Akhatou, A. Sayago, R. González-Domínguez, Á. Fernández-Recamales. Application of targeted metabolomics to investigate optimum growing conditions to enhance bioactive content of strawberry. J. Agric. Food Chem. 65 (2017) 9559-9567

CONFERENCE-ARTICLE 53 Reads 0 Citations Comparison of complementary statistical analysis approaches in metabolomic food traceability Raúl González-Domínguez, Ana Sayago, Ángeles Fernández-Recam... Published: 15 November 2018
Proceedings of 3rd International Electronic Conference on Metabolomics, doi: 10.3390/iecm-3-05839
DOI See at publisher website ABS Show/hide abstract

Metabolomics generates large datasets that require the use of advanced and complementary statistical tools in order to extract the maximum amount of useful information. Traditionally, various non-supervised and supervised pattern recognition methods have been employed in food traceability and authentication, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or soft independent model class analogy (SIMCA), among others. Complementarily, the use of new machine learning algorithms is emerging in food metabolomics during the last years due to their excellent performance for the analysis of complex datasets, such as random forest (RF) and support vector machines (SVM). In this work, we show the advantages, limitations and complementarities of these statistical tools in food analysis, on the basis of data acquired in various traceability studies performed in our research group with strawberry and extra virgin olive oil (1-4).

(1) I. Akhatou, R. González-Domínguez, A. Fernández-Recamales. Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress. Plant Physiol. Biochem. 101 (2016) 14-22

(2) I. Akhatou, A. Sayago, R. González-Domínguez, Á. Fernández-Recamales. Application of targeted metabolomics to investigate optimum growing conditions to enhance bioactive content of strawberry. J. Agric. Food Chem. 65 (2017) 9559-9567

(3) A. Sayago, R. González-Domínguez, R. Beltrán, Á. Fernández-Recamales. Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition. Food Chem. 261 (2018) 42–50

(4) A. Sayago, R. González-Domínguez, J. Urbano, Á. Fernández-Recamales. Combination of vintage and new-fashioned analytical approaches for varietal and geographical authentication of olive oils. Under preparation

Article 0 Reads 0 Citations Optimization of Growth and Carotenoid Production by Haloferax mediterranei Using Response Surface Methodology Zaida Montero-Lobato, Adrián Ramos-Merchante, Juan Luis Fuen... Published: 09 October 2018
Marine Drugs, doi: 10.3390/md16100372
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Haloferax mediterranei produces C50 carotenoids that have strong antioxidant properties. The response surface methodology (RSM) tool helps to accurately analyze the most suitable conditions to maximize C50 carotenoids production by haloarchaea. The effects of temperature (15–50 °C), pH (4−10), and salinity (5–28% NaCl (w/v)) on the growth and carotenoid content of H. mediterranei were analyzed using the RSM approach. Growth was determined by measuring the turbidity at 600 nm. To determine the carotenoid content, harvested cells were lysed by freeze/thawing, then re-suspended in acetone and the total carotenoid content determined by measuring the absorbance at 494 nm. The analysis of carotenoids was performed by an HPLC system coupled with mass spectrometry. The results indicated the theoretical optimal conditions of 36.51 or 36.81 °C, pH of 8.20 or 8.96, and 15.01% or 12.03% (w/v) salinity for the growth of haloarchaea (OD600 = 12.5 ± 0.64) and production of total carotenoids (3.34 ± 0.29 mg/L), respectively. These conditions were validated experimentally for growth (OD600 = 13.72 ± 0.98) and carotenoid production (3.74 ± 0.20 mg/L). The carotenoid profile showed four isomers of bacterioruberin (89.13%). Our findings suggest that the RSM approach is highly useful for determining optimal conditions for large-scale production of bacterioruberin by haloarchaea.
Article 0 Reads 0 Citations High-Throughput Direct Mass Spectrometry-Based Metabolomics to Characterize Metabolite Fingerprints Associated with Alzh... Raúl González-Domínguez, Ana Sayago, Ángeles Fernández-Recam... Published: 18 September 2018
Metabolites, doi: 10.3390/metabo8030052
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Direct mass spectrometry-based metabolomics has been widely employed in recent years to characterize the metabolic alterations underlying Alzheimer’s disease development and progression. This high-throughput approach presents great potential for fast and simultaneous fingerprinting of a vast number of metabolites, which can be applied to multiple biological matrices including serum/plasma, urine, cerebrospinal fluid and tissues. In this review article, we present the main advantages and drawbacks of metabolomics based on direct mass spectrometry compared with conventional analytical techniques, and provide a comprehensive revision of the literature on the use of these tools in the investigation of Alzheimer’s disease.
Article 1 Read 0 Citations Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on m... Ana Sayago, Raúl González-Domínguez, Rafael Beltrán, Ángeles... Published: 01 September 2018
Food Chemistry, doi: 10.1016/j.foodchem.2018.04.019
DOI See at publisher website
PREPRINT 0 Reads 0 Citations High-Throughput Direct Mass Spectrometry-Based Metabolomics to Characterize Metabolite Fingerprints Associated with Alzh... Raúl González-Domínguez, Ana Sayago, Ángeles Fernández-Recam... Published: 23 August 2018
doi: 10.20944/preprints201808.0410.v1
DOI See at publisher website ABS Show/hide abstract
Direct mass spectrometry-based metabolomics has been widely employed in the last years to characterize metabolic alterations underlying to Alzheimer’s disease development and progression. This high-throughput approach presents a great potential for fast and simultaneous fingerprinting of a vast number of metabolites, which can be applied to multiple biological samples such as serum/plasma, urine, cerebrospinal fluid and tissues. In this review article we present the main advantages and drawbacks of metabolomics based on direct mass spectrometry compared with conventional analytical techniques, and provide a comprehensive revision of the literature on the application of these tools in Alzheimer’s disease research.
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