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Fast and reliable methods for the direct determination of Al, Cu and Fe in unprocessed coconut water by electrothermal atomic absorption spectrometry are proposed. For Fe determination, 100 µL of sample were diluted with 1400 µL of 0.1% v/v HNO3. Aliquots of 10 µL of this solution were injected into the graphite tube without any chemical modifier. For Al and Cu determinations, samples were slightly diluted (1250 µL of sample + 250 µL of 0.6% v/v HNO3 + 0.6% m/v Triton X-100) directly into the auto sampler cups. In this case, aliquots of 10 µL of an oxidant mixture (15% v/v H2O2 + 1.0% v/v HNO3) were co-injected with 15 µL of samples or analytical solutions into the graphite tube. The oxidant mixture minimized the carbonaceous residues formation and increased graphite tube lifetime in 75%, allowing up to 200 heating cycles. The detection limits and characteristic masses were 1.0 mg L-1 and 30 pg for Al, 0.7 µg L-1 and 20 pg for Cu, and 2.0 µg L-1 and 10 pg for Fe, respectively. The reliability of the proposed methods was evaluated by addition and recovery tests (104 ? 1% for Al, 98 ? 3% for Cu, and 97 ? 1% for Fe).

A systematic study of Na+, K+, Ca2+, Mg2+, Cl-, NO3-, SO4(2-) and PO4(3-) interference on the simultaneous atomization of As, Cd, Pb and Se was carried out using an electrothermal atomic absorption spectrometer. The mixture 250 mg W + 250 mg Rh was thermally deposited on the graphite tube platform and used as permanent chemical modifier. The compromised heating program adjustments (Tp = 650&ordmC and Ta = 2200&ordmC) diminished the thermal decomposition and volatilization efficiency of the interfering species during the pyrolysis step. The Cl- and SO4(2-) anions caused the most severe interference, while NO3- only affected the analytes' atomization when high amounts (> 27 mg) were used. The anion PO4(3-) produced a strong negative interference on the Se absorbance signals. The presence of Na+, K+, Ca2+ and Mg2+ prejudiced mainly Cd, and the interference effects of Cl- salts were more pronounced than NO3- salts.

A flow injection system coupled to focused microwave-assisted oven was used for on-line orange juice sample digestion for determination of Ca, Cu, Fe, K, Mg, Mn, Na, P, and Zn by inductively coupled plasma optical emission spectrometry. The reactor coil was a PTFE tube (4.0 m long and 1.6 mm i.d.) positioned into the commercial glass tube of the focused microwave oven. Aliquots of 500 muL of sample and 1000 muL of reagent (80% v/v HNO3) were mixed in a confluence and carried out to the reactor coil by air carrier. The relative standard deviation for five replicates of sample was lower than 5.0%. Good recoveries varying from 91 to 111% were obtained for added concentrations of the interest elements. The results obtained using the proposed digestion system are in agreement with those obtained for total digestion at the 95% confidence level. With this on-line digestion system was possible to carry out 12 samples h-1, minimizing contamination, saving consumption of samples and reagent and low residue generation.

The atomization of lead in an electrothermal tungsten coil atomizer in the presence and absence of Na+, K+, Ca2+ and Mg2+ was investigated with the objective of understanding the interference processes. The lead atomization was less affected by Ca2+ and Mg2+ than by Na+ and K+. In the absence of concomitants, lead atomization efficiency was improved by the presence of H2 (10% v/v) in the purge gas composition, during pyrolysis and atomization steps. The interference caused by Na+ and Ca2+ was negligible when the pyrolysis step was accomplished without H2 in the purge gas composition. The results showed that Na+, K+, Ca2+ and Mg2+ are directly involved in competition reactions for H2 in condensed phase.

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height. [ABSTRACT FROM AUTHOR]