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A study in order to differentiate Tempranillo and Tempranillo blanco grapes and wines from A.O.C. Rioja (Spain) has been carried out. The three most important groups of chemical compounds in grapes and wines were determined: nitrogen and phenolic compounds by HPLC–DAD, and volatile compounds by GC–MS. A machine learning approach was carried out to achieve the most important chemical parameters to differentiate Tempranillo and Tempranillo blanco grapes and wines based on the Kruskal–Wallis test, F-score feature selector, and support vector machine classification. Four importance levels were established for both grape and wine discrimination. The first level is composed of variables that can differentiate Tempranillo and Tempranillo blanco grapes and wines by analyzing the chemical concentrations. The second, third, and fourth level of importance is composed of a variable set that can correctly classify the samples, each one determined according to the F-score importance ranking and the SVM discriminations. Some chemical compounds demonstrated to be good predictors to wine and grape discriminations, whereas some chemicals are only a good predictor to one discrimination approach.

The purpose of this paper is to classify wines from 4 different countries in South America. Each class of wines is formed by samples considered by experts as representatives of the following commercial categories: “Argentinean Malbec (AM)”, “Brazilian Merlot (BM)”, “Uruguayan Tannat (UT)” and “Chilean Carmenere (CC)”. The 83 samples collected were analyzed according to their composition of volatiles, semi-volatiles and phenolic compounds. We built a decision system for classification based on support vector machines (SVM), along with Correlation-based Feature selection (CFS), and Random Forest Importance (RFI), which measures the relative importance of the input variables. First, we use CFS to select a subset of variables among 190 chemical compounds. Thirteen chemicals were selected as correlated to the category and uncorrelated with each other. Afterwards, these chemical compounds were organized according to the importance ranking given by the RFI and classified with SVM. The study clearly indicated that SVM in combination with feature selection methods was able to identify the most important chemicals to classify the wine samples. Among the compounds identified in the wine samples, the variable subset defined by the feature selection methods, which were catechin, gallic, octanoic acid, myricetin, caffeic, isobutanol, resveratrol, kaempferol, and ORAC, were able to achieve an accuracy of 93.97% in classifying the commercial categories.

Beer is considered one of the top three most popular drinks, being consumed all over the world. During the last few decades, discrimination of beverages and food products has gained attention with many application research studies based on chemical parameters and chemometric or machine learning algorithms. However, no reviews about the evaluation of beers have been reported. Therefore, this review presents applications of beer classification among brands, styles and types, aging, origin, and the prediction of quality attributes of interest based on chemometric, machine learning methods, and chemical parameters. After analyzing the literature, it was found that chemometric and machine learning methods are successful tools for qualitative and quantitative examination of beers. However, more work needs to be done to evaluate machine learning methods and data mining algorithms, such as sampling, feature selection, and advanced classification algorithms.

Osteoporosis and its consequence of fragility fracture represent a major public health problem. Human exposure to heavy metals has received considerable attention over the last decades. However, little is known about the influence of co-exposure to multiple heavy metals on bone density. The present study aimed to examine the association between exposure to metals and bone mineral density (BMD) loss. Blood and urine concentrations of 20 chemical elements were selected from 3 cycles (2005–2010) NHANES (National Health and Nutrition Examination Survey), in which we included white women over 50 years of age and previously selected for BMD testing (N = 1892). The bone loss group was defined as participants having T-score

Concern for the consumption of organic vegetables is growing throughout the world. We verified the efficiency of machine learning techniques in the classification of vegetables produced under both organic and conventional systems in the state of Pernambuco, Brazil. The contents of 25 elements (Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, P, Pb, S, Se, Si, Ti, V, Zn) were determined in 364 vegetable samples. Principal component analysis (PCA) was displayed to get a primary distribution overview of samples. Data mining techniques such as linear discriminant analysis (LDA) were carried out to develop discrimination models based on organic vegetable samples, and feature selection (F-score and chi-squared) combined with classification algorithms (support vector machine—SVM, multilayer perceptron—MLP, and random forest—RF) was applied to these samples. LDA reached 100% in the discrimination models in tomato samples and bell pepper samples, while SVM, combined with chi-squared, outperformed the other algorithms obtaining accuracy of 100% in bell pepper samples (Capsicum annuum) and onion (Allium cepa Hysam) and 97% in tomato (Solanum Lycopersicum) samples, of which 95% was the hit rate in organic samples. For lettuce (Lactuca sativa) samples, the accuracy obtained was 92%, with a 90% hit rate of samples grown in organic systems. This high success rate highlights the potential of using elemental quantification and algorithms as support techniques in the process of authenticity and inspection of organic products.

Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called F–score, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables.

The echocardiogram (ECHO) is an ultrasound of the heart used to diagnose heart diseases (DHC). The analysis and interpretation of ECHO are dependent on the doctor's experience. However, software that uses artificial intelligence to analyze ECHO images or videos is contributing to support the physician's decision. This paper aims to perform a Systematic Literature Review (SLR) on artificial intelligence (AI) techniques applied in the automation of Transthoracic Echocardiogram (TTE) processes, to support medical decisions. The study identified more than 800 articles on the topic in the leading scientific research platforms. To select the most relevant studies, inclusion and exclusion criteria were applied, where 45 articles were selected to compose the detailed study of the SRL. The results obtained with the extraction of information from the papers, identified 3 groups of primary studies, namely: identification of the cardiac vision plan, analysis of cardiac functions and detection of cardiac diseases. SRL identifies that the set of Machine learning (ML) techniques are being widely applied in the tasks of segmentation, detection and classification of images obtained from ECHO. The techniques based on Convolutional Neural Network (CNN), presented the best Accuracy rates. Research shows a strong interest in automating ECHO processes. However, it is still an open research field, with the potential to generate many publications for researchers.

There is a general consensus that the consumption of organic food can contribute to a healthy diet; nevertheless, large-scale production of organic food is not an easy task since it requires intense care due to the number of pests, fungi, and diseases that can wipe out an entire crop. Researchers evaluating food quality are often concerned with the use of pesticides, antibiotics, and hormones in agriculture, along with genetic modification (GMOs) and additives in food processing. Thus, a major challenge that arises in this context is how to obtain products that are free of these toxic elements. In this review, we give an overview of the research conducted in relation to the chemometric tools for extraction of variables in several types of food and the use of data mining techniques and statistical analysis to classify samples grown in organic and conventional systems. The expansion of the organic sector, driven by growing demand and high prices, could lead to fraud. Then, creating mechanisms that can be used by regulators, supervisory bodies, or even installed in supermarkets so the client can do this verification may be a deterrent for this type of deception. Results presented by recent research have shown that chemometric methods associated with data mining algorithms or statistical methods can be used to successfully classify products grown in organic and conventional systems.

Recently, least squares twin multi-class support vector machine (LSTKSVC) was proposed as a least squares version of twin multi-class classification support vector machine (Twin-KSVC), both based on twin support vector machine (TWSVM). In this paper, we propose a novel multi-class classifier termed as Improvements on least squares twin multi-class classification support vector machine that is motivated by LSTKSVC and Twin-KSVC. Similarly to LSTKSVC that evaluates all the training data into a ` ` 1 − v e r s u s − 1 − v e r s u s − r e s t ″ structure, the algorithm here proposed generates ternary output { − 1 , 0 , + 1 } . Whereas Twin-KSVC needs to solve two quadratic programming problems (QPPs), the solution of the two modified primal problems for our algorithm is reduced to two systems of linear equations. Besides that, in our algorithm the structural risk minimization (SRM) principle is implemented by introducing a regularization term, along with minimizing the empirical risk. To test the efficacy and validity of the proposed method, numerical experiments on ten UCI benchmark data sets are performed. The results obtained further corroborate the effectiveness of the proposed algorithm.

The geographical recognition of wines has been extensively attempted based on chemical parameters. However, few studies have used wine sensory properties to characterize wines according to their geographical origin. This paper presents a machine learning study to classify and to find the most important sensory descriptors of Cabernet Sauvignon, Syrah, Tannat, and Merlot wines from Argentina, Brazil, Chile and Uruguay. Four feature selection methods (F score, relief, $${\chi ^2}$$ , and random forest importance) were used to generate the order of importance of the sensory descriptors. The feature subsets were generated based on the feature selection ranking order to use as input features for the support vector machines classifier. Very good results with 85–100% accuracy were achieved, and the results showed that few sensory descriptors discriminate the origin of wines better than when using all the descriptors and that there is a specific subset of most important features to each wine variety. As far as we know, this is the first study to analyze South American wines based solely on sensory descriptors and support vector machines along with feature selection methods.

Because honey is a natural product synthetized by bees from secretions of various flowers and plants, its composition and properties are determined by those unique floral origins. Aside from honey’s sweet and distinctive flavor, it can provide various human health benefits, which makes its market value favorable to those of other sweeteners. Consequently, honey has been a very common and easy target of adulteration for economic profit, making authenticity of honey a longtime concern for researchers and producers around the world. The oldest and most popular of these methods is melissopalynology, the analysis of pollen contained in honey, which can determine its floral origin. However, because this method is time consuming, requires a specialist, and is unable to detect fraudulent pollen contamination, several other methods for the determination of the floral and geographical origin of honey have been proposed in the scientific literature. Multivariate data analysis and machine learning are emerging areas that offer performance and cost advantages for extracting valuable information from raw data sets. They are capable of performing both exploratory and predictive analyses, which can help to uncover trends and hidden patterns within data. Most of the studies reviewed base their methods on atomic spectra and physicochemical properties as descriptive variables for honey. Sensorial data obtained from electronic tongue and nose, and color histograms of honey images also show high discriminative power for ascertaining honey origin. Principal component analysis (PCA), discriminant analysis (DA), and cluster analysis are the preferred techniques for performing exploratory and predictive analyses for the purpose of identifying origin. PCA and DA continue to be preferred due to their ease of application and interpretation, while machine learning algorithms are more complex to model. Nevertheless, both machine learning algorithms and PCA-DA models achieved excellent results in discriminating honey origin. Finally, a commonly observed tendency is the use of hybrid methods that combine multivariate data analysis and machine learning techniques, as each technique has its own strengths and weaknesses regarding uncovering and classifying patterns in honey data.

In many college courses in several countries are used exams in a national scale, such as Gaokao, in China, Scholastic Aptitude Test - SAT and the American College Testing - ACT in the United States of American, Yuksekogretime Gecis Sinavi – YGS in Turkey, among others. This paper examines microdata from the High School National Examination (ENEM) database from Brazil. The database has 8,627,367 records, 166 attributes, and all experiments were performed based on the Spark architecture. The objective of this work is to examine microdata of the ENEM database applying data mining algorithms and creating an approach to handle big data and to predict the profile of those enrolled in ENEM. Through the standards found by the data mining algorithms with classification algorithms, it was possible to observe that family income, access to information, profession, and academic history of the parents were directly related to the performance of the candidates. And with a rules induction algorithm, it was possible to identify the patterns presented in each of the regions of Brazil, such as common characteristics when a candidate was approved and when not, essential factors as disciplines and particular characteristics of each region. This approach also enables the execution of large volumes of data in a simplified computational structure.

Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint&rsquo, s chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew&rsquo, s correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America.

A set $S$ of vertices of graph $G$ is a \textit{defensive alliance} of $G$ if for every $v \in S$, it holds $|N[v] \cap S| \geq |N[v]-S|$. An alliance $S$ is called $global$ if it is also a dominating set. In this paper, we determine the exact values of the global defensive alliance number of lexicographic products of path and cycles., 25 pages, 12 tables

Wines with a clear geographical origin are an issue of interest for consumers and food industries. This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geographical origin. A group of samples from Argentina (n = 17), Brazil (n = 12), Chile (n = 48), and Uruguay (n = 6) was analyzed. Twenty chemical compounds were determined by high-performance liquid chromatography (HPLC). These compounds include antioxidant activity, total polyphenols, total anthocyanins, individual anthocyanins and color. Four binary classification problems were performed (Brazil versus non-Brazil, Argentina versus non-Argentina, Chile versus non-Chile, and Uruguay versus non-Uruguay) to investigate the geographic characteristics of each country. Through the evaluation of binary classifications in our dataset it was possible to identify the main variables (chemical compounds) that discriminate between the countries. We used the following algorithms: Synthetic Minority over-sample Technique and under-sampling to balance the dataset of each classification approach, the Relief algorithm to obtain a variable importance ranking and the classifiers Support Vector Machines, Multilayer Perceptron and Radial Basis Function Network with dynamic decay adjustment. SVM model obtained the highest performance measures among the classifiers for each dataset (93.73% of accuracy for the Brazil versus non-Brazil, 91.18% for the Argentina versus non-Argentina, 79.16% for the Chile versus non-Chile, and 91.67% for the Uruguay versus non-Uruguay classification). These accuracies were achieved by the search of the possible variable subsets according to Relief for each classification approach. We found that some variables, such as DPPH, wine color and individual anthocyanins, are among the most important variables in the characterization of Merlot wines. Keywords: Support Vector Machine, Multilayer Perceptron, Anthocyanins, Feature selection, Merlot wines, South America wines

In this study, 83 wines representating four commercial categories: "Argentinean Malbec", "Brazilian Merlot", "Uruguayan Tannat" and "Chilean Carménère" were analyzed according to their phenolic and volatile compounds. The objective was to identify the chemical compounds that would typify each category. From approximately about 600 peaks obtained by chromatographic techniques, 169 were identified and 53 of them were selected for multivariate statistical analysis. Chilean Carménère was the best discriminated group by the methods applied in our study, followed by Argentinean Malbec. Brazilian Merlot mixed mainly with some Carménère, whileTannat mixed with all wines categories, especially Malbec. In general, Chilean Carménère wines can be characterized by a bluish color, higher amounts of sulphur dioxide, higher content of octanoic acid, isobutanol, ethyl isoamyl succinate and catechin and a smaller amount of quercetin. These data can contribute for further process of authenticity or typification of South American red wines.

Let G be a finite, simple, and undirected graph and let S be a set of vertices of G. If no vertex of G that does not belong to S has two neighbours in S, then S is P 3 -convex. The P 3 -convex hull H G ( S ) of S is the smallest P 3 -convex set containing S. The P 3 -Caratheodory number of G is the smallest integer c such that for every set S and every vertex u in H G ( S ) , there is a set F ⊆ S with | F | ⩽ c and u ∈ H G ( F ) . We study structural and algorithmic aspects of the P 3 -Caratheodory number. We characterize the P 3 -Caratheodory number of trees and block graphs, establish upper bounds on the P 3 -Caratheodory number of general graphs and of claw-free graphs and prove that it is NP-complete to decide for a given bipartite graph G and a given integer k, whether the P 3 -Caratheodory number of G is at least k.

Helly's theorem for convex sets motivated the definition of the p-Helly property for hypergraphs. On the other hand, the colorful Helly theorem for collections of convex sets, by Lovasz, generalizes Helly's theorem. Motivated by Lovasz's theorem, we define the colorful p-Helly property for a family of p hypergraphs. We describe complexity results related to the latter. We show that it is Co-NP-complete to decide if a family of p hypergraphs is colorful p-Helly, even if p = 2 . However, for any fixed p, we describe a polynomial time algorithm to decide if such family is colorful p-Helly, provided p − 1 of the hypergraphs are p-Helly.