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A highly reliable, quantitative and sensitive analytical method for determining the residues of the fungicide, hexaconazole in black tea is described. The proposed method is based on liquid-liquid extraction followed by gas chromatographic determination, using nitrogen phosphorus detector (GC-NPD) for the identification and quantitation of hexaconazole. The most was cleaned up by adsorption column chromatography using activated florisil. Performance of the method was assessed by evaluating quality parameters such as recovery value, repeatability, reproducibility, linearity and limits of detection and quantitation. When the method was assessed for repeatability, the percentage of recovery ranged between 86% and 96% while the relative standard deviation was between 0.30% and 2.35%. In studies on reproducibility the recovery ranged from 81% to 85% and relative standard deviation from 1.68% to 5.13%, implying that the method was reliable. A field trial was conducted to verify the application of this method with real samples. Results prove that the validated method was suitable for extracting hexaconazole residues. 相似文献
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Chinnachamy Karthika Narayanan Nair Muraleedharan 《Journal of Zhejiang University. Science. B》2009,10(6):422-426
The sprayed chemicals on tea leaves disappear over a period of time by the influence of rainfall elution, evaporation,growth dilution, and photodegradation. Influence of plant growth on the four fungicides (hexaconazole, propiconazole, tridemorph,and c) was studied to know the constructive loss of fungicides. The study shows that residues of fungicides sprayed on tea shoots got diluted by the growing process. The expansion of a leaf took 8 to 11 d and more than 50% of the fungicide residues were cleaned out during this leaf expansion period. Under south Indian agroclimatic condition, the fungicides are sprayed at an interval of 10 d, so it is safe that the tea is harvested on the 10th day of the application of fungicides. 相似文献
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Rubin’s classic missingness mechanisms are central to handling missing data and minimizing biases that can arise due to missingness. However, the formulaic expressions that posit certain independencies among missing and observed data are difficult to grasp. As a result, applied researchers often rely on informal translations of these assumptions. We present a graphical representation of missing data mechanism, formalized in Mohan, Pearl, and Tian (2013). We show that graphical models provide a tool for comprehending, encoding, and communicating assumptions about the missingness process. Furthermore, we demonstrate on several examples how graph-theoretical criteria can determine if biases due to missing data might emerge in some estimates of interests and which auxiliary variables are needed to control for such biases, given assumptions about the missingness process. 相似文献
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