37 resultados para Chlorophyll fluorescence, Dr. Haardt Instruments


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coffea arabica L. is considered to be sensitive to low temperatures throughout its life cycle. In some Brazilian regions, seedling production occurs under shade conditions and during the winter, with average temperatures of around 10 °C. The formation and functioning of the photosynthetic apparatus are strongly controlled by temperature. This study aimed to assess the changes that occurred in pigment contents, lipid peroxidation and variables of chlorophyll a fluorescence during the greening process of coffee seedlings submitted to chilling. Results indicate that saturation of the photosynthetic activity of coffee seedlings occurred before saturation of the accumulation of chloroplastid pigments. Pigment accumulation during the greening process is far beyond the metabolic needs for the maintenance of photosynthetic activity, more specifically of photosystem II. Coffee seedlings attained a quantum yield equivalent to that of the control with approximately half the chlorophyll a and b contents and around 40% of the carotenoid. Low temperature decreases the metabolism of seedlings, consequently reducing free radical production and lipid peroxidation. The chilling temperature (10 °C) used inhibited the accumulation of chloroplast pigments, in turn altering the capacity of the photosynthetic tissue of etiolated coffee seedlings to capture and transfer photon energy to the photosystem II reaction centre. These alterations were better demonstrated by O-J-I-P chlorophyll a fluorescence transients, rather than F v/F m and F v/F 0 ratios. © 2009 Elsevier B.V. All rights reserved.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.