Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine.

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach.

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.

Background: No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.

Methods: In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets.

Acid hydrolysis conditions do affect the non-extractable phenolic compounds composition from grape peel and seed.

This study aimed to evaluate the effect of hydrolysis conditions on non-extractable phenolic compounds (NEPC) composition of grape peel and seed powder. The effect of temperature (50-90 °C), hydrochloric acid concentration (0.1-15.

Uncovering the phenolic diversity of Guabiju fruit: LC-MS/MS-based targeted metabolomics approach.

The comprehensive composition of phenolic compounds (PC) from seven genotypes of guabiju were analyzed by high-performance liquid chromatography coupled to a diode array detector and mass spectrometry (HPLC-ESI-qTOF-MS/MS), and a targeted metabolomic approach was utilized to explore the PC-related similarities among the genotypes. Sixty-seven phenolic compounds were annotated and twenty-four were quantified in all genotypes of guabiju. The phenolic acids and anthocyanins were the major PC, representing more than 63% (w/w) of the total PC.

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.