Determination of the most informative chemical elements for discrimination of rice samples according to the producing region.

Several approaches to assess the authenticity of food products have been developed, given that fraudulent products may impact consumers' confidence, affect commercial trades and lead to health risks. This paper proposes an approach to identify the chemical elements that optimally discriminate rice samples according to their producing region in the South of Brazil, the largest rice producer outside Asia. A combinatorial procedure on the concentration of 26 elements determined using inductively coupled mass spectrometry (ICP-MS) and liquid chromatography hyphenated with ICP-MS from 640 rice samples was coupled with Support Vector Machine. The assessed elements included nonmetal and metal elements of 3 types of rice collected from 5 rice-producing regions. The framework selected Mn, Fe, Cu, Zn, Ni, Mo, Cd, Cs, As, Rb, Se, and iAs as the most informative elements for tracking samples' origin. The concentration of such elements is strongly affected by fertilization procedures and soil composition.

Communication regarding the article "An efficient primary screening COVID-19 by serum Raman spectroscopy".

When performing computational modeling and machine learning experiments, it is imperative to follow a protocol that minimizes bias. In this communication, we share our concerns regarding the article "An efficient primary screening COVID-19 by serum Raman spectroscopy" published in this journal. We consider that the authors may have inadvertently biased their results by not guaranteeing complete independence of test samples from the training data. We corroborate our point by reproducing the experiment with the available data, showing that if full independence of the test set was ensured, the reported results should be lower. We ask the authors to provide more information regarding their article, as well as making available all code used to generate their results. Our experiments are available at https://doi.org/10.6084/m9.figshare.14124356.

On the crucial role of multilingual biomedical databases in epidemic events (SARS-CoV-2 analysis).

The need for multilingual biomedical databases was already pointed out by different authors. They argue about the need for making translations available in other languages and centralized access to regional databases and that one should not disregard citations in other languages. This fact could not be any more real in the current situation regarding the novel coronavirus. When considering treatment, diagnosis and prevention, around 44% of the articles in PubMed were written in Chinese. This prompts the urgent need for quality automatic translation to make such extremely valuable information available to medical personnel in as many languages as possible. We also point out that the community should also make efforts to guarantee editorial quality and to follow the best practices in editing and publishing. This is of critical importance as well, such that the content is properly scrutinized before being published.

A multivariate-based variable selection framework for clustering traffic conflicts in a brazilian freeway.

More than one million people die or suffer non-fatal injuries annually due to road accidents around the world. Understanding the causes that give rise to different types of conflict events, as well as their characteristics, can help researchers and traffic authorities to draw up strategies aimed at mitigating collision risks. This paper proposes a framework for grouping traffic conflicts relying on similar profiles and factors that contribute to conflict occurrence using self-organizing maps (SOM). In order to improve the quality of the formed groups, we developed a novel variable importance index relying on the outputs of the nonlinear principal component analysis (NLPCA) that intends to identify the most informative variables for grouping collision events. Such index guides a backward variable selection procedure in which less relevant variables are removed one-by-one; after each removal, the clustering quality is assessed via the Davies-Bouldin (DB) index. The proposed framework was applied to a real-time dataset collected from a Brazilian highway aimed at allocating traffic conflicts into groups presenting similar profiles. The selected variables suggest that lower average speeds, which are typically verified during congestion events, contribute to conflict occurrence. Higher variability on speed (denoted by high standard deviation, and speed's coefficient of variation levels on that variable), which are also perceived in the assessed freeway near to congestion periods, also contribute to conflicts.

A genetic algorithm-based framework for wavelength selection on sample categorization.

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd.