10 resultados para olive transpiration

em Universidade do Minho


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Although most of the accidents occurred in Olive Oil Mill (OOM) resulted from “basic” risks, there is a need to apply adequate tools to support risk decisions that can meet the specificities of this sector. This study aims to analyse the views of Occupational, Safety & Health (OSH) practitioners about the risk assessment process in OOM, identifying the key difficulties inherent to the risk assessment process in these sector, as well as identifying some improvements to the current practice. This analysis was based on a questionnaire that was developed and applied to 13 OSH practitioners working at OOM. The results showed that the time available to perform the risk assessment is the more frequent limitation. They believe that the methodologies available are not an important limitation to this process. However, a specific risk assessment methodology, that includes acceptance criteria adjusted to the OOM reality, using risk metrics supported on the frequency of accidents and workdays lost, were indicated as being also an important contributions improve the process. A semi-quantitative approach, complemented with the use of the sector accident statistics, can be a good solution for this sector. However, further strategies should also be adopted, mainly those that can lead to an easy application of the risk assessment process.

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Olive oils may be commercialized as intense, medium or light, according to the intensity perception of fruitiness, bitterness and pungency attributes, assessed by a sensory panel. In this work, the capability of an electronic tongue to correctly classify olive oils according to the sensory intensity perception levels was evaluated. Cross-sensitivity and non-specific lipid polymeric membranes were used as sensors. The sensor device was firstly tested using quinine monohydrochloride standard solutions. Mean sensitivities of 14±2 to 25±6 mV/decade, depending on the type of plasticizer used in the lipid membranes, were obtained showing the device capability for evaluating bitterness. Then, linear discriminant models based on sub-sets of sensors, selected by a meta-heuristic simulated annealing algorithm, were established enabling to correctly classify 91% of olive oils according to their intensity sensory grade (leave-one-out cross-validation procedure). This capability was further evaluated using a repeated K-fold cross-validation procedure, showing that the electronic tongue allowed an average correct classification of 80% of the olive oils used for internal-validation. So, the electronic tongue can be seen as a taste sensor, allowing differentiating olive oils with different sensory intensities, and could be used as a preliminary, complementary and practical tool for panelists during olive oil sensory analysis.

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Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

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Olive mill wastewaters (OMW) and vinasses (VS) are effluents produced respectively by olive mills and wineries, both sectors are of great economic importance in Mediterranean countries. These effluents cause a large environmental impact, when not properly processed, due to their high concentration of phenolic compounds, COD and colour. OMW may be treated by biological processes but, in this case, a dilution is necessary, increasing water consumption. The approach here in proposed consists on the bioremediation of OMW and VS by filamentous fungi. In a screening stage, three fungi (Aspergillus ibericus, Aspergillus uvarum, Aspergillus niger) were selected to bioremediate undiluted OMW, two-fold diluted OMW supplemented with nutrients, and a mixture of OMW and VS in the proportion 1:1 (v/v). Higher reductions of phenolic compounds, colour and COD were achieved mixing both residues; with A. uvarum providing the best results. In addition, the production of enzymes was also evaluated during this bioremediation process, detecting in all cases lipolytic, proteolytic and tannase activities. A. ibericus, A. uvarum and A. niger achieved the highest value of lipase (1253.7 ± 161.2 U/L), protease (3700 ± 124.3 U/L) and tannase (284.4 ± 12.1 U/L) activities, respectively. Consequently, this process is an interesting alternative to traditional processes to manage these residues, providing simultaneously high economic products, which can be employed in the same industries.

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Dissertação de mestrado em Bioengenharia

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Yarrowia lipolytica, a yeast strain with a huge biotechnological potential, capable to produce metabolites such as γ-decalactone, citric acid, intracellular lipids and enzymes, possesses the ability to change its morphology in response to environmental conditions. In the present study, a quantitative image analysis (QIA) procedure was developed for the identification and quantification of Y. lipolytica W29 and MTLY40-2P strains dimorphic growth, cultivated in batch cultures on hydrophilic (glucose and N-acetylglucosamine (GlcNAc) and hydrophobic (olive oil and castor oil) media. The morphological characterization of yeast cells by QIA techniques revealed that hydrophobic carbon sources, namely castor oil, should be preferred for both strains growth in the yeast single cell morphotype. On the other hand, hydrophilic sugars, namely glucose and GlcNAc caused a dimorphic transition growth towards the hyphae morphotype. Experiments for γ-decalactone production with MTLY40-2P strain in two distinct morphotypes (yeast single cells and hyphae cells) were also performed. The obtained results showed the adequacy of the proposed morphology monitoring tool in relation to each morphotype on the aroma production ability. The present work allowed establishing that QIA techniques can be a valuable tool for the identification of the best culture conditions for industrial processes implementation.

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Dissertação de mestrado integrado em Engenharia Civil

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Dissertação de mestrado em Técnicas de Caracterização e Análise Química

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In this work, oil mill wastewater (OMW), a residue generated during olive oil extraction, was evaluated as an inducer of rhamnolipid production. Using a medium containing as sole ingredients corn steep liquor (10%, v/v), sugarcane molasses (10%, w/v) and OMW (25%, v/v), Pseudomonas aeruginosa #112 produced 4.5 and 5.1 g of rhamnolipid per liter in flasks and reactor, respectively, with critical micelle concentrations as low as 13 mg/l. Furthermore, in the medium supplemented with OMW, a higher proportion of more hydrophobic rhamnolipid congeners was observed comparing with the same medium without OMW. OMW is a hazardous waste which disposal represents a serious environmental problem; therefore, its valorization as a substrate for the production of added-value compounds such as rhamnolipids is of great interest. This is the first report of rhamnolipid production using a mixture of these three agro-industrial by-products, which can be useful for the sustainable production of rhamnolipids.

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Programa Doutoral em Engenharia Mecânica.