Browsing by Author "Arlorio, Marco"
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- Applicability of HRM analysis for carnaroli rice authentication based on polymorphisms of the waxy genePublication . Grazina, Liliana; Costa, Joana; Amaral, Joana S.; Garino, Cristiano; Arlorio, Marco; Oliveira, Beatriz; Mafra, IsabelRice (Oryza sativa L.) is a staple food and one of the most important cereals in the worldwide. Italy, the leading rice producer in Europe, holds nearly 200 different varieties in the available germplosm [1]. The Carnaroli rice is a high quality and priced variety belonging to the group of ja ponica ecotype, produced mainly in Piedmont. it is considered one of the finest Italian rice varieties due to its excellent cooking resistance, given by a low tendency to lose starch and a good ability to absorb liquid while creaming, being, thus, ideal for the preparation of traditional risotto. Italian rice varieties hove different characteristics, from which the starch composition is a highly relevant parameter. Together with amylopectin, amylose is the main component of starch, whose ratio is determinant for the rice cooking properties. After cooking, varieties with high amylose content have dry, firm and separate groins, while low amylose ones usually hove tender, cohesive and glossy texture [2]. Amylose synthesis is catalysed by the granule bound starch synthase (GBSS) that is encoded by the Waxy gene (Wx), being located on the chromosome 6. Various nucleotide polymorphisms have been associated with the Wx gene, namely (CT)n repeats and several single nucleotide polymorphisms (SNP) [2]. The aim of this work was to propose a new method based on high resolution melting (HRM) analysis, exploiting those polymorphisms to differentiate Carnaroli rice from other closely related varieties.
- Authentication of carnaroli rice by HRM analysis targeting nucleotide polymorphisms in the Alk and Waxy genesPublication . Grazina, Liliana; Costa, Joana; Amaral, Joana S.; Garino, Cristiano; Arlorio, Marco; Mafra, IsabelCarnaroli is a high quality and priced variety, being considered as one of the finest Italian rice varieties due to its sensorial and rheological properties and, thus being a potential adulteration target. The present work aimed at exploiting polymorphisms in the Alk (A/G and GC/TT in exon 8) and Waxy ((CT)n and G/T in intron 1) genes by HRM analysis to differentiate Carnaroli rice from closely related varieties. The HRM method targeting the Alk gene did not allow gathering the Carnaroli subgroup genotypes in the same cluster. The HRM approach targeting Waxy gene successfully discriminated the varieties sold as Carnaroli from all the others with high level of confidence (>98%), which corroborated sequencing data. Its applicability to commercial rice samples was successful. Therefore, the proposed new HRM method can be considered a simple, specific, high-throughput and cost-effective tool for the authentication of Carnaroli rice, contributing to valorise such premium variety.
- Exploiting polymorphisms in the Waxy and Alk genes of Italian rice varieties to identify DNA markers for Carnaroli authenticationPublication . Grazina, Liliana; Costa, Joana; Amaral, Joana S.; Garino, Cristiano; Arlorio, Marco; Oliveira, Beatriz; Mafra, IsabelRice (Oryzasativa L) is one of the most important cereals in the world, being Italy its main producer in Europe with nearly 200 different varieties present in the germplasm [1] Italian rice varieties have different characteristics, from which the starch composition is a highly relevant parameter.
- 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.