920 resultados para Citrus Honey


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Seven phenolic acids related to the botanical origins of nine monofloral Eucalyptus honeys from Australia, along with two abscisic isomers, have been analyzed. The mean content of total phenolic acids ranges from 2.14 mg/100 g honey of black box (Eucalyptus largiflorens) honey to 10.3 mg/100 g honey of bloodwood (Eucalyptus intermedia) honey, confirming an early finding that species-specific differences of phytochemical compositions occur quantitatively among these Eucalyptus honeys. A common profile of phenolic acids, comprising gallic, chlorogenic, coumaric and caffeic acids, can be found in all the Eucalyptus honeys, which could be floral markers for Australian Eucalyptus honeys. Thus, the analysis of phenolic acids could also be used as an objective method for the authentication of botanical origin of Eucalyptus honeys. Moreover, all the honey samples analyzed in this study contain gallic acid as the main phenolic acid, except for stringybox (Eucalyptus globoidia) honey which has ellagic acid as the main phenolic acid. This result indicates that the species-specific differences can also be found in the honey profiles of phenolic acids. Further-more, the analysis of abscisic acid in honey shows that the content of abscisic acid varies from 0.55 mg/100 g honey of black box honey to 4.68 mg/ 100 g honey of bloodwood honey, corresponding to the contents of phenolic acids measured in these honeys. These results have further revealed that the HPLC analysis of honey phytochemical constituents could be used individually and/or jointly for the authentication of the botanical origins of Australian Eucalyptus honeys. (C) 2003 Elsevier Ltd. All rights reserved.

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Flavonoids in Australian honeys from five botanical species (Melaleuca, Guioa, Lophostemon, Banksia and Helianthus) have been analyzed in relation to their floral origins. Tea tree (Melaleuca quinquenervia) and heath (Banksia ericifolia) honeys show a common flavonoid profile comprising myricetin (3,5,7,3',4',5'-hexahydroxyflavone), tricetin (5,7,3',4,5'-pentahydroxyflavone), querectin (3,5,7,3',4'-pentahydroxyflavone) and luteolin (5,7,3',4'-tetrahydroxyflavone), which was previously suggested as a floral marker for an Australian Eucalyptus honey (bloodwood or Eucalyptus intermedia honey). These honeys of various floral species can be differentiated by their levels of total flavonoids, being 2.12 mg/100 g for heath honey and 6.35 m/100 g for tea tree honey. In brush box (Lophostemon conferta) honey, the flavonoid profile comprising mainly tricetin, luteolin and quercetin is similar to that of another Eucalyptus honey (yellow box or Eucalyptus melliodora honey). These results indicate that the flavonoid profiles in some of the Australian non-Eucalyptus honeys may contain more or less certain flavonoids from Eucalyptus floral sources because of the diversity and extensive availability of Eucalyptus nectars for honeybee foraging yearly around or a possible cross contamination of the monofloral honeys during collection, transportation and/or storage. Further analyses are required to differentiate and/or verify the botanical sources of the flavonoids that contribute to the flavonoid profiles of these honeys, by restricting honey sampling areas and procedures, employing other complementary analytical methods (e.g. pollen analysis, sugar profile) and using materials (e.g. nectar) directly sourced from the flowering plant for comparative studies. In Australian crow ash (Guioa semiglauca) honey, myricetin, tricetin, quercetin, luteolin and an unknown flavonoid have been found to be the main flavonoids, which is characteristic only to this type of honey, and could thus be used as the floral marker, while in Australian sunflower (Helianthus annuus) honey, the content of total flavonoids is the smallest amount comparing to those in the other honeys analysed in this study. However, the flavonoid quercetin and the flavonoid profile mainly consisting of quercetin, quercetin 3,3'-dimethyl ether (5,7,4'-trihydroxy3,3'-dimethoxyflavone), myricetin and luteolin are characteristic only to this sunflower honey and could thus be used for the authentication.

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Eight phenolic acids and two abscisic acid isomers in Australian honeys from five botanical species (Melaleuca, Guioa, Lophostemon, Banksia and Helianthus) have been analyzed in relation to their botanical origins. Total phenolic acids present in these honeys range from 2.13 mg/100 g sunflower (Helianthus annuus) honey to 12.11 mg/100 g tea tree (Melaleuca quinquenervia) honey, with amounts of individual acids being various. Tea tree honey shows a phenolic profile of gallic, ellagic, chlorogenic and coumaric acids, which is similar to the phenolic profile of an Australian Eucalyptus honey (bloodwood or Eucalyptus intermedia honey). The main difference between tea tree and bloodwood honeys is the contribution of chlorogenic acid to their total phenolic profiles. In Australian crow ash (Guioa semiglauca) honey, a characteristic phenolic profile mainly consisting of gallic acid and abscisic acid could be used as the floral marker. In brush box (Lophostemon conferta) honey, the phenolic profile, comprising mainly gallic acid and ellagic acid, could be used to differentiate this honey not only from the other Australian non-Eucalyptus honeys but also from a Eucalyptus honey (yellow box or Eucalyptus melliodora honey). However, this Eucalyptus honey could not be differentiated from brush box honey based only on their flavonoid profiles. Similarly, the phenolic profile of heath (Banksia ericifolia) honey, comprising mainly gallic acid, an unknown phenolic acid (Phl) and coumaric acid, could also be used to differentiate this honey from tea tree and bloodwood honeys, which have similar flavonoid profiles. Coumaric acid is a principal phenolic acid in Australian sunflower honey and it could thus be used together with gallic acid for the authentication. These results show that the HPLC analysis of phenolic acids and abscisic acids in Australian floral honeys Could assist the differentiation and authentication of the honeys. © 2005 Elsevier Ltd. All rights reserved.

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Tese de dout., Química, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2012

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Citrus canker is a disease of citrus and closely related species, caused by the bacterium Xanthomonas citri subsp. citri. This disease, previously exotic to Australia, was detected on a single farm [infested premise-1, (IP1). IP is the terminology used in official biosecurity protocols to describe a locality at which an exotic plant pest has been confirmed or is presumed to exist. IP are numbered sequentially as they are detected] in Emerald, Queensland in July 2004. During the following 10 months the disease was subsequently detected on two other farms (IP2 and IP3) within the same area and studies indicated the disease first occurred on IP1 and spread to IP2 and IP3. The oldest, naturally infected plant tissue observed on any of these farms indicated the disease was present on IP1 for several months before detection and established on IP2 and IP3 during the second quarter (i.e. autumn) 2004. Transect studies on some IP1 blocks showed disease incidences ranged between 52 and 100% (trees infected). This contrasted to very low disease incidence, less than 4% of trees within a block, on IP2 and IP3. The mechanisms proposed for disease spread within blocks include weather-assisted dispersal of the bacterium (e.g. wind-driven rain) and movement of contaminated farm equipment, in particular by pivot irrigator towers via mechanical damage in combination with abundant water. Spread between blocks on IP2 was attributed to movement of contaminated farm equipment and/or people. Epidemiology results suggest: (i) successive surveillance rounds increase the likelihood of disease detection; (ii) surveillance sensitivity is affected by tree size; and (iii) individual destruction zones (for the purpose of eradication) could be determined using disease incidence and severity data rather than a predefined set area.

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Queensland fruit fly, Bactrocera tryoni (Froggatt), is a polyphagous pest, and many citrus types are included among its hosts. While quantification of citrus host use by B. tryoni is lacking, citrus is generally considered a ‘low pressure’ crop. This paper investigates B. tryoni female oviposition preference and offspring performance in five citrus types; Murcott mandarin (Citrus reticulata), Navel orange and Valencia orange (Citrus sinensis), Eureka lemon (Citrus limon) and yellow grapefruit (Citrus paradisi). Oviposition preference was investigated in laboratory-based choice and no-choice experiments, while immature survival and offspring performance were investigated by infesting fruits in the laboratory and evaluating pupal recovery, pupal emergence and F1 fecundity. Fruit size, Brix level and peel toughness were also measured for correlation with host use. Bactrocera tryoni demonstrated an oviposition preference hierarchy among the citrus fruits tested; Murcott and grapefruit were most preferred for oviposition and lemon the least, while preference for Navel and Valencia was intermediate. Peel toughness was negatively correlated with B. tryoni oviposition preference, while no significant correlations were detected between oviposition and Brix level or fruit size. Immature survival in the tested fruit was very low. Murcott was the best host (21% pupal recovery), while all other citrus types that showed pupal recovery of 6% or lower and no pupae were recovered from Valencia orange. In pupae recovered from Navel orange and lemon, adult eclosion was greatly reduced, while in grapefruit and lemon, no eggs were recovered from F1 adults. Based on these laboratory results, many commercial citrus varieties appear to be poor hosts for B. tryoni and may pose a low post-harvest and quarantine risk. These findings need to be confirmed in the field, as they impact on both pre-harvest and post-harvest countermeasures.

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Fruit flies are the insects which cause maggots in your backyard fruit and vegetables. They are not just a nuisance to gardeners, but the single greatest insect threat to commercial and subsistence fruit growers throughout Asia, Australia and the Pacific. Queensland fruit fly, the focus of this PhD, costs Australia an estimated $100million per year. I focused specifically on how Queensland fruit fly uses different commercial citrus varieties. I identified specific plant related mechanisms which increase a fruit’s resistance to fruit fly attack. This information can be used by plant breeders to make fruit less prone to fruit fly damage.

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This paper presents a new algorithm based on honey-bee mating optimization (HBMO) to estimate harmonic state variables in distribution networks including distributed generators (DGs). The proposed algorithm performs estimation for both amplitude and phase of each harmonics by minimizing the error between the measured values from phasor measurement units (PMUs) and the values computed from the estimated parameters during the estimation process. Simulation results on two distribution test system are presented to demonstrate that the speed and accuracy of proposed distribution harmonic state estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as weight least square (WLS), genetic algorithm (GA) and tabu search (TS).

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This paper presents an efficient algorithm for multi-objective distribution feeder reconfiguration based on Modified Honey Bee Mating Optimization (MHBMO) approach. The main objective of the Distribution feeder reconfiguration (DFR) is to minimize the real power loss, deviation of the nodes’ voltage. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. So the metahuristic algorithm has been applied to this problem. This paper describes the full algorithm to Objective functions paid, The results of simulations on a 32 bus distribution system is given and shown high accuracy and optimize the proposed algorithm in power loss minimization.