Geochemical and environmental assessment of potential effects of trace elements in soils, water, and sediments around abandoned mining sites in the northern Iberian Peninsula (NW Spain).

The mineral deposits and traces in the Iberian Peninsula are very numerous and of varied mineralogy. This study aimed to analyze the geochemical and environmental changes detected in soil, water, and sediments around the La Sierre mine, and to determine if contamination persists over time. The concentrations of ten trace elements (As, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn) were measured in 20 soil samples, 10 water samples, and 6 sediment samples, sampling at the most affected points. Soil and sediment samples were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and water samples by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). From the Principal Component Analysis (PCA), soil samples SOI-6, 7, and 20 presented high amounts ranging from 14489.86a±7 to 3031.72b ± 1 mg/kg of Co, Cu, Ni, and As, respectively. Water samples WAT-6, 8, and 10 presented high contents of As, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn, being sample WAT-8 with high values of As, Co, Cu, Fe, and Ni with 48.1 ± 0.82, 368 ± 4, 68.3 ± 0.1, 97.5 ± 1.2, and 152 ± 2 µg/L, respectively, exceeding the regulation given by R.D 314/2016 (Water for human consumption). The sediment samples were compared with the IInterim Sediment Quality Guideline (ISQG) and Probable Effect Level (PEL) values given by the Canadian Sediment Quality Guidelines for the Protection of Aquatic Life. When presenting a high ISQG value, but low PEL, they partially comply with the regulations, which happens with samples SED-1, 2, and 8 for As and SED-5, 6, and 7 for Pb. On the contrary, Cr and Cu do not comply with what is established in samples SED-8 and SED-1 and 8, respectively, whereas, in samples SED-2 and 5, Cu partially complies with the established regulations. The values found for the trace elements present in the abandoned traditional mining area with abundant epithermal deposits prevail over time in soil, water, and sediments.

A green analytical assay for the quantitation of the total saponins in quinoa (Chenopodium quinoa Willd.) based on macro lens-coupled smartphone.

The aim of this study was to develop and validate a microtiter macro lens-coupled smartphone (MCS) assay for the quantitation of the total saponins in quinoa based on foam measurement. The 96-well micro plate with a black bottom and an inclination angle = 12.102° of macro lens-coupled smartphone allows to acquire images with a high resolution. The foam stability, a critical aspect for the MCS assay, was significantly improved by the inclusion of a chelating agent (EDTA 50 mmol L-1) and bovine serum albumin (0.5 mg mL-1). The MCS assay was linear within the range of 9.039-180.773 × 10-4 mg mL-1 of saponin (R2 = 0.9929), using the integrated density/area as a foam measurement (Y) and the logarithm of saponin concentration (X) (Y = 372.1 + 104.2LogX). The MCS assay was 50-folds more sensitive than afrosimetric assay -AA- with LOD = 3.168 × 10-4 mg mL-1 and LOQ = 4.784 × 10-4 mg mL-1. MCS assay was more reproducible (relative standard deviation (RSD) = 0.632-9.646%) than AA (RSD = 3.44-44.04%). The correlation analysis, Bland-Altman analysis and Passing-Bablok regression showed good agreement between total saponin content in quinoa as measured by the MCS assay and AA. Based on the green analytical procedure index, MCS assay can be considered as a green procedure.

Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks.

Several procedures are currently used to assess fermentation index (FI) of cocoa beans (Theobroma cacao L.) for quality control. However, all of them present several drawbacks. The aim of the present work was to develop and validate a simple image based quantitative procedure, using color measurement and artificial neural network (ANNs). ANN models based on color measurements were tested to predict fermentation index (FI) of fermented cocoa beans. The RGB values were measured from surface and center region of fermented beans in images obtained by camera and desktop scanner. The FI was defined as the ratio of total free amino acids in fermented versus non-fermented samples. The ANN model that included RGB color measurement of fermented cocoa surface and R/G ratio in cocoa bean of alkaline extracts was able to predict FI with no statistical difference compared with the experimental values. Performance of the ANN model was evaluated by the coefficient of determination, Bland-Altman plot and Passing-Bablok regression analyses. Moreover, in fermented beans, total sugar content and titratable acidity showed a similar pattern to the total free amino acid predicted through the color based ANN model. The results of the present work demonstrate that the proposed ANN model can be adopted as a low-cost and in situ procedure to predict FI in fermented cocoa beans through apps developed for mobile device.