Development of an olive oil-based spread fortified with an active ingredient from olive pomace - potential cardiovascular benefits

Funder

Organizational Unit

Publications

Evaluation of fatty acids of salmon from different origins: comparison of extraction and derivatization methodologies

Publication . Grazina, Liliana; Nunes, Maria A.; Mafra, Isabel; Oliveira, Beatriz; Amaral, Joana S.

Global demand for fish and fish products has increased significantly over the last decades, which led to a simultaneous increase of aquaculture production around the world, currently corresponding to almost 50% of the global fish market [1]. Among different concerns regarding the fish that consumers are eating, nowadays, there is a demand for correct information about the species, production method (farmed vs. wild) and the catch origin/provenience of fish. Salmon, one of the most popular fish in Europe, can have different geographical origins and generally command higher prices when caught in the wild. Moreover, the commercially important species of salmon belong to different genus, namely Salmo and Oncorhynchus. Therefore, this work intended to compare the fatty acid composition of salmon from diverse origins, testing different extraction and derivatization methodologies. Farmed salmon specimens were obtaining from Chile, Canada and Norway. Two lipid extraction methods, namely conventional Soxhlet extraction using n-hexane added with butylated hydroxytoluene (BHT) and an adaptation of the Bligh and Dyer extraction using ultra-turrax homogenisation with 1% NaCl, followed by extraction with chloroform and methanol, were tested. Additionally, fatty acid methyl esters (FAME) were prepared by two methodologies, namely by alkaline transmethylation using KOH and by acidcatalysed transmethylation using boron trifluoride-methanol solution. FAME were analysed in a Shimadzu GC-2010 Plus gas chromatograph equipped with a Shimadzu AOC-20i auto-injector and a flame ionisation detector (Shimadzu, Japan). A CP-Sil 88 silica capillary column (50 x 0.25 mm i.d, 0.20 μm) from Varian (Middelburg, Netherlands) was used for FAME separation. Injector and detector temperatures were 250 and 270 °C, respectively. The compounds were identified by comparison with standards (FAME 37, Supelco, Bellefonte, PA, USA). Based on the obtained results, the ultra-turrax method was chosen for lipid extraction since it allowed obtaining higher amounts of long chain unsaturated fatty acids, particularly of docosahexaenoic acid (DHA). Similar results were obtained for both tested derivatization methodologies. Nonadecanoic acid (C19:0) was submitted to BF3/MeOH derivatization resulting in a high transmethylation yield (90.3%). In general, salmon samples showed high contents of polyunsaturated fatty acids, including ω-3 fatty acids, which supports its consumption as part of a healthy diet.

2017Conference object

Open access

Comparative analysis of fatty acid composition of wild vs. farmed salmon

Publication . Grazina, Liliana; Nunes, Maria; Mafra, Isabel; Oliveira, Beatriz; Amaral, Joana S.

To respond to the increasing global demand for fish, nowadays, almost 50% of the global fish market comes from aquaculture production [1]. Thus, there is the need to assure a correct information, not only about the species, but also about the production method (farmed vs. wild) and the catch origin of fish. Salmon, a hightrophic- level carnivorous species with high economic value due to its popularity, is among the fish species that is frequently produced in aquaculture. Although the feed given to farm-raised salmon is designed to meet its nutritional requirements, it can present differences compared to the diet of wild salmon that can be reflected on the muscle composition of farmed versus wild salmons. Therefore, this work aims at comparing the fatty acid composition of salmon from aquaculture and caught in the wild. Salmon specimens caught in the wild (n = 25) and farm-raised (n = 25) were obtained from West of Vancouver Island and Campbell River (Canada), respectively. Two lipid extraction methods (Soxhlet extraction with n-hexane and an adaptation of the Bligh and Dyer extraction method) and two derivatization procedures (alkaline transmethylation using KOH and acid-catalyzed transmethylation using BF3/MEOH solution) were tested. Fatty acid methyl esters (FAME) were analyzed in a Shimadzu GC-2010 Plus gas chromatograph equipped with a Shimadzu AOC-20i auto-injector, a flame ionization detector and a CP-Sil 88 silica capillary column (50 x 0.25 mm i.d., 0.20 μm). The injector and detector temperatures were 250 and 270 °C, respectively. The compounds were identified by comparison with standards (FAME 37, Supelco). Based on the obtained results, the modified Bligh and Dyer method was chosen for lipid extraction since it allowed obtaining higher amounts of long chain unsaturated fatty acids, particularly of docosahexaenoic acid (DHA). Similar results were obtained for both tested derivatization methodologies. In general, the two groups of salmon samples showed different profiles, with wild samples presenting significantly higher contents of omega-3 fatty acids, in particular docosahexaenoic and eicosapentaenoic acids, while farmed salmon had higher amounts of oleic and linoleic acids.

2018Conference object

Open access

Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon

Publication . Grazina, Liliana; Rodrigues, Pedro João; Igrejas, Getúlio; Nunes, Maria A.; Mafra, Isabel; Arlorio, Marco; Oliveira, Beatriz; Amaral, Joana S.

In the last decade, there has been an increasing demand for wild-captured fish, which attains higher prices compared to farmed species, thus being prone to mislabeling practices. In this work, fatty acid composition coupled to advanced chemometrics was used to discriminate wild from farmed salmon. The lipids extracted from salmon muscles of different production methods and origins (26 wild from Canada, 25 farmed from Canada, 24 farmed from Chile and 25 farmed from Norway) were analyzed by gas chromatography with flame ionization detector (GC-FID). All the tested chemometric approaches, namely principal components analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and seven machine learning classifiers, namely k-nearest neighbors (kNN), decision tree, support vector machine (SVM), random forest, artificial neural networks (ANN), naïve Bayes and AdaBoost, allowed for differentiation between farmed and wild salmons using the 17 features obtained from chemical analysis. PCA did not allow clear distinguishing between salmon geographical origin since farmed samples from Canada and Chile overlapped. Nevertheless, using the 17 features in the models, six out of the seven tested machine learning classifiers allowed a classification accuracy of ≥99%, with ANN, naïve Bayes, random forest, SVM and kNN presenting 100% accuracy on the test dataset. The classification models were also assayed using only the best features selected by a reduction algorithm and the best input features mapped by t-SNE. The classifier kNN provided the best discrimination results because it correctly classified all samples according to production method and origin, ultimately using only the three most important features (16:0, 18:2n6c and 20:3n3 + 20:4n6). In general, the classifiers presented good generalization with the herein proposed approach being simple and presenting the advantage of requiring only common equipment existing in most labs.

2020Journal article

Open access