Browsing by Author "Nunes, Maria A."
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- Evaluation of fatty acids of salmon from different origins: comparison of extraction and derivatization methodologiesPublication . 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.
- Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmonPublication . 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.