A deep learning-based simulator for comprehensive two-dimensional GC applications.

Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R2  = 0.9202) and 0.014 (relative average deviation = 1.67%, R2  = 0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.

Characterization of the chemical composition (mineral, lead and centesimal) in pine nut (Araucaria angustifolia (Bertol.) Kuntze) using exploratory data analysis.

In this study, mineral composition, centesimal composition and lead were evaluated in pine nut seeds (raw and cooked) from five Brazilian states. Mineral composition was determined by ICP OES and lead by GF AAS. The results for minerals were evaluated by Boxplot, PCA and HCA, using the R software. Average minerals in raw and cooked samples (mg 100 g-1) were: 15.2 and 10.8 (Ca), 0.168 and 0.113 (Cu), 0.506 and 0.330 (Fe), 536 and 420 (K), 51.3 and 40.6 (Mg), 0.373 and 0.208 (Mn), 132 and 102 (P) and 0.746 and 0.520 (Zn). The average centesimal composition (raw and cooked) was: 53.5 and 47.2% (moisture), 1.76 and 1.26% (ash), 3.90 and 3.53% (protein), 40.8 and 48.0% (carbohydrate) and 179 and 206 kcal/100 g (total caloric value) and Pb was not detected. The chemometric analysis showed a distinction of raw and cooked samples due to significant nutrient losses after thermal processing.