DETERMINATION OF ENERGY VALUE OF HAMBURGERS AND MEATBALLS BY DIRECT CALORIMETRY AND FOOD ANALYSIS COMPOSITION. COMPARISON WITH NUTRITION INFORMATION DISPLAYED ON PACKAGES

The nutritional composition found in the laboratory and those present on labels of manufactured foods can differ significantly. The purpose of this study was to determine the nutritional composition of hamburgers and meatballs and compare them with your labels. The food analysis was performed following the Analytical Standards Institute`s Adolfo Lutz and energy content was determined by bomb calorimetry. Regarding the energy value, all the samples had values less than informed on the label. The content of lipids of hamburgers and meatballs ( except the beef) were lower than those reported on the label. The values of protein for the meatballs and chicken hamburger had lower values than those labels. Thus, the labels may overestimate as underestimate some nutritional values, leading to population erroneous information.

Comparative analysis of two immunohistochemical methods for antigen retrieval in the optical lobe of the honeybee Apis mellifera: Myosin-v assay

The present study compared two heating methods currently used for antigen retrieval (AR) immunostaining: the microwave oven and the steam cooker. Myosin-V, a molecular motor involved in vesicle transport, was used as a neuronal marker in honeybee Apis mellifera brains fixed in formalin. Overall, the steam cooker showed the most satisfactory AR results. At 100 degrees C, tissue morphology was maintained and revealed epitope recovery, while evaporation of the AR solution was markedly reduced; this is important for stabilizing the sodium citrate molarity of the AR buffer and reducing background effects. Standardization of heat-mediated AR of formalin-fixed and paraffin-embedded tissue sections results in more reliable immunostaining of the honeybee brain.

The role of integrated information acquisition and management in the analysis of coastal ecosystem change

This book chapter represents a synthesis of the work which started in my PhD and which has been the conceptual basis for all of my research since 1993. The chapter presents a method for scientists and managers to use for selecting the type of remotely sensed data to use to meet their information needs associated with a mapping, monitoring or modelling application. The work draws on results from several of my ARC projects, CRC Rainforest and Coastal projects and theses of P.Scarth , K.Joyce and C.Roelfsema.