Classification of Cabernet Sauvignon from Two Different Countries in South America by Chemical Compounds and Support Vector Machines
ABSTRACT A simple approach is proposed for the classification of Cabernet Sauvignon wines from two different countries in South America (Brazil and Chile). The strategy combines the wines’ functionality, designed as antioxidant activity (DPPH and ORAC), total polyphenols (TP), total anthocyanins (TA), and color, with a data mining technique known as support vector machines (SVM). The original dataset has 16 wine samples from Brazil and 113 from Chile. Algorithms were used to balance the dataset. Using resampling algorithms, we extended the Brazilian wines to 32 samples and reduced the Chilean wines to 32. With the proposed methodology, it was possible to classify the origin of the wine with an accuracy of 89% when using the 20 original elements. An accuracy of 83% was found using only 5 elements (L, DPPH, delph-3-acetylglu, peon-3-(coum)glu, and pet-3-acetylglu). Our methodology can be used for origin certification of other wines.
A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry.
A practical and easy control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry is proposed. Reference ranges for 32 chemical elements in 22 samples of sugarcane (13 organic and 9 non organic) were established and then two algorithms, Naive Bayes (NB) and Random Forest (RF), were evaluated to classify the samples. Accurate results (>90%) were obtained when using all variables (i.e., 32 elements). However, accuracy was improved (95.4% for NB) when only eight minerals (Rb, U, Al, Sr, Dy, Nb, Ta, Mo), chosen by a feature selection algorithm, were employed. Thus, the use of a fingerprint based on trace element levels associated with classification machine learning algorithms may be used as a simple alternative for authenticity evaluation of organic sugarcane samples.
The Impact of EU Unfair Contract Terms Law on U.S. Business-to-Consumer Internet Merchants
When the Court of Appeals for Versailles issued its decision in AOL France v. UFC Que Choisir2 in September 2005, it sent the message to U.S. Internet businesses that even contractual boilerplate localized for European markets may be invalid in European consumer transactions. In that case, a French consumer advocacy group challenged the terms AOL offered its French customers after AOL had made extensive revisions to its standard form contracts to respond to the concerns regarding those terms expressed in an advisory opinion by a French consumer protection agency. In 2004, a trial court invalidated nearly every term in the revised agreement; the appeals court affirmed on all counts. Subsequently, in July 2006 following concerns raised by the Office of Fair Trading (“OFT”), a United Kingdom government agency charged with “making sure markets work well for consumers,”3 Dell Corporation Limited changed its online terms and conditions to make them fairer to consumers.4 European consumer contract law had an impact in both cases. At issue for AOL France and Dell Corporation Limited was the application of France’s and the UK’s consumer contract law which is based on the Unfair Contract Terms Directive of 19945 and calls into question the validity of many of the terms that U.S. courts routinely enforce in transactions involving American consumers and merchants.