Exploiting Data Mining for Authenticity Assessment and Protection of High-Quality Italian Wines from Piedmont

This paper discusses the data mining approach followed in a project called TRAQUASwine, aimed at the definition of methods for data analytical assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont region in Italy. This is a big issue in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The objective is twofold: to show that the problem can be addressed without expensive and hyper-specialized wine analyses, and to demonstrate the actual usefulness of classification algorithms for data mining on the resulting chemical profiles. Following Wagstaff\u2019s proposal for practical exploitation of machine learning (and data mining) approaches, we describe how data have been collected and prepared for the production of different datasets, how suitable classification models have been identified and how the interpretation of the results suggests the emergence of an active role of classification techniques, based on standard chemical profiling, for the assesment of the authenticity of the wines target of the stud

Authentication of carnaroli rice by HRM analysis targeting nucleotide polymorphisms in the Alk and Waxy genes

Carnaroli 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.This work was funded by the European project FOODINTEGRITY (FP7-KBBE-2013-single-stage, under grant agreement No 613688), the project UIDB/50006/2020 subsidised by FCT/MCTES (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior) through national funds and the project NORTE-01-0145- FEDER-000052. L. Grazina thanks FCT and ESF (European Social Fund) through POCH (Programa Operacional Capital Humano) for her PhD grant (SFRH/BD/132462/2017). J. Costa thanks FCT for funding throughinfo:eu-repo/semantics/publishedVersio

Cooking of Artemide Black Rice: Impact on Proximate Composition and Phenolic Compounds

The consumption of black rice has grown in recent years due to its particular organoleptic properties and high content of antioxidant polyphenols, which make it a sort of natural functional food. However, heat treatment applied during cooking can influence the content and the composition of antioxidant components, particularly anthocyanins, the main compounds of black rice, responsible for its color. The aim of this work was to evaluate the impact of different cooking techniques (boiling, microwaves oven, under pressure pot and risotto preparation) on the chemical and nutritional composition of the Italian Artemide black rice. Different cooking methods had significant and different impact on rice composition. Proximate composition was not affected by cooking, except for moisture, which increased, and fiber content, which decreased. Total polyphenols, total anthocyanin content, and antioxidant capacity were reduced; moreover, anthocyanins and phenolic acids determined by HPLC-DAD generally decreased, with the only exception of protocatechuic acid. The risotto preparation was the most useful cooking technique to preserve anthocyanins and antioxidant activity. Our results demonstrated the importance to study cooking methods and to evaluate their impact on rice characteristics, in order to preserve its nutritional and beneficial properties

Music Teacher Education at a Liberal Arts College

In 2012, a committee at a small Midwestern liberal arts college, Lake Forest College, embarked on a journey to create a music education teacher licensure major. Drawing from narrative inquiry, this article reports how the dean of faculty, education department chair, music department chair, and assistant professor of music/music education coordinator collaborated on a curricular creation. Findings from this process included (a) the created music education major, (b) each participant’s rationale for wanting the new music education major, (c) valued components of the music education major, and (d) unique elements of a music education major at a liberal arts college. Implications from this experience could be valuable for music education programs at small liberal arts colleges, those involved in university/school partnerships such as professional development schools, and those looking to advocate for their music education programs across campus

Applicability of HRM analysis for carnaroli rice authentication based on polymorphisms of the waxy gene

Rice (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.This work has been supported by the European project FOODINTEGRITY (FP7-KBBE- 2013-single-stage, No 613688), by FCT (Fundação para a Ciencia e Tecnologia) through project UID/QUI/50006/2013 - POCI/ 01/0145/FEDER/ 007265 with financial support from FCT /MEC through national funds and eo-financed by FED ER. under the Partnership Agreement PT2020 and by the project NORTE-01-0145-FEDER-000011. L. Grazina and J. Costa ore grateful to grants (SFRH/ BD/132462/2017 and SFRH/BPD/102404/2014, respectively) from FCT financed by POPH-QREN (subsidised by FSE and MCTES).info:eu-repo/semantics/publishedVersio

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

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.This work was supported by the European project FOODINTEGRITY (FP7-KBBE-2013-single-stage, under grant agreement No 613688) and FCT (Fundação para a Ciência e Tecnologia, Portugal) under the Partnership Agreements UIDB 50006/2020, UIDB 00690/2020 (CIMO) and UIDB/5757/2020 (CeDRI). L. Grazina and M.A. Nunes acknowledge the FCT grant SFRH/BD/132462/2017 and SFRH/BD/130131/2017 financed by POPH-QREN (subsidised by FSE and MCTES).info:eu-repo/semantics/publishedVersio