Deposition and residues of azoxystrobin and imidacloprid on greenhouse lettuce with implications for human consumption

Lettuce greenhouse experiments were carried out from March to June 2011 in order to analyze how pesticides behave from the time of application until their intake via human consumption taking into account the primary distribution of pesticides, field dissipation, and post-harvest processing. In addition, experimental conditions were used to evaluate a new dynamic plant uptake model comparing its results with the experimentally derived residues. One application of imidacloprid and two of azoxystrobin were conducted. For evaluating primary pesticide distribution, two approaches based on leaf area index and vegetation cover were used and results were compared with those obtained from a tracer test. High influence of lettuce density, growth stage and type of sprayer was observed in primary distribution showing that low densities or early growth stages implied high losses of pesticides on soil. Washed and unwashed samples of lettuce were taken and analyzed from application to harvest to evaluate removal of pesticides by food processing. Results show that residues found on the Spanish preharvest interval days were in all cases below officially set maximum residue limits, although it was observed that time between application and harvest is as important for residues as application amounts. An overall reduction of 40–60% of pesticides residues was obtained from washing lettuce. Experimentally derived residues were compared with modeled residues and deviate from 1.2 to 1.4 for imidacloprid and azoxystrobin, respectively, presenting good model predictions. Resulting human intake fractions range from... for imidacloprid to ... for azoxystrobin.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Metabolic origin of δ15N values in nitrogenous compounds from Brassica napus L. leaves

Nitrogen isotope composition (δ15N) in plant organic matter is currently used as a natural tracer of nitrogen acquisition efficiency. However, the δ15N value of whole leaf material does not properly reflect the way in which N is assimilated because isotope fractionations along metabolic reactions may cause substantial differences among leaf compounds. In other words, any change in metabolic composition or allocation pattern may cause undesirable variability in leaf δ15N. Here, we investigated the δ15N in different leaf fractions and individual metabolites from rapeseed (Brassica napus) leaves. We show that there were substantial differences in δ15N between nitrogenous compounds (up to 30 ) and the content in (15N enriched) nitrate had a clear influence on leaf δ15N. Using a simple steady-state model of day metabolism, we suggest that the δ15N value in major amino acids was mostly explained by isotope fractionation associated with isotope effects on enzyme-catalysed reactions in primary nitrogen metabolism. δ15N values were further influenced by light versus dark conditions and the probable occurrence of alternative biosynthetic pathways. We conclude that both biochemical pathways (that fractionate between isotopes) and nitrogen sources (used for amino acid production) should be considered when interpreting the δ15N value of leaf nitrogenous compounds