Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches.

Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia.

Comprehensive two-dimensional gas chromatography-mass spectrometry combined with multivariate data analysis for pattern recognition in Ecuadorian spirits.

The current methodology used in quality control of Ecuadorian beverages such as Pájaro azúl, Puro and Pata de vaca is carried out by using conventional gas chromatography; however, it does not allow the fingerprinting of these Ecuadorian spirit beverages and their possible cases of adulteration. In order to overcome this drawback, comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS) was combined with multivariate data analysis, revealing that compounds like citronellal, citronellol, geraniol, methyl anthranilate, (-)-trans-α-bergamotene, (-)-cis-α-bergamotene and D-limonene can be considered key elements for pattern recognition of these traditional beverages and product adulteration cases. Thus, the two-dimensional chromatographic fingerprints obtained by GC × GC-MS coupled with chemometric analysis, using Principal Component Analysis and Fisher-ratio can be considered as a potential strategy for adulteration recognition, and it may used as a quality assurance system for Ecuadorian traditional spirits.