Extraction of volatile oil from aromatic plants with supercritical carbon dioxide: experiments and modeling

An overview of the studies carried out in our laboratories on supercritical fluid extraction (SFE) of volatile oils from seven aromatic plants: pennyroyal (Mentha pulegium L.), fennel seeds (Foeniculum vulgare Mill.), coriander (Coriandrum sativum L.), savory (Satureja fruticosa Beguinot), winter savory (Satureja montana L.), cotton lavender (Santolina chamaecyparisus) and thyme (Thymus vulgaris), is presented. A flow apparatus with a 1 L extractor and two 0.27 L separators was built to perform studies at temperatures ranging from 298 to 353 K and pressures up to 30.0 MPa. The best compromise between yield and composition compared with hydrodistillation (HD) was achieved selecting the optimum experimental conditions of extraction and fractionation. The major differences between HD and SFE oils is the presence of a small percentage of cuticular waxes and the relative amount of thymoquinone, an oxygenated monoterpene with important biological properties, which is present in the oils from thyme and winter savory. On the other hand, the modeling of our data on supercritical extraction of volatile oil from pennyroyal is discussed using Sovova's models. These models have been applied successfully to the other volatile oil extractions. Furthermore, other experimental studies involving supercritical CO2 carried out in our laboratories are also mentioned.

Comparison between monopolar and bipolar microsecond range pulsed electric fields in enhancement of apple juice extraction

The effect of monopolar and bipolar shaped pulses in additional yield of apple juice extraction is evaluated. The applied electric field strength, pulsewidth, and number of pulses are assessed for both pulse types, and divergences are analyzed. Variation of electric field strength is ranged from 100 to 1300 V/cm, pulsewidth from 20 to 300 mu s, and the number of pulses from 10 to 200, at a frequency of 200 Hz. Two pulse trains separated by 1 s are applied to apple cubes. Results are plotted against reference untreated samples for all assays. Specific energy consumption is calculated for each experiment as well as qualitative indicators for apple juice of total soluble dry matter and absorbance at 390-nm wavelength. Bipolar pulses demonstrated higher efficiency, and specific energetic consumption has a threshold where higher inputs of energy do not result in higher juice extraction when electric field variation is applied. Total soluble dry matter and absorbance results do not illustrate significant differences between application of monopolar and bipolar pulses, but all values are inside the limits proposed for apple juice intended for human consumption.

Pulsed electric field pre-treatment for apple juice extraction: Evaluation of monopolar and bipolar pulses effects

Additional apple juice extraction with pulsed electric field pretreated apple cubes towards control samples is evaluated. Monopolar and bipolar shaped pulses are compared and their effect is studied with variation of electric field, pulse width and number of pulses. Variation of electric field strength is ranged from 100 V/cm to 1300 V/cm, pulse width from 20 mu s to 300 mu s and number of pulses from 10 to 200, at frequency of 200Hz. Two pulse trains separated by 1 second are applied to all samples. Bipolar pulses showed higher apple juice yields with all studied parameters. Calculation of specific energies consumed was assessed and a threshold where higher energy inputs do not increase juice yield is found for a number of used parameters. Qualitative parameters of total soluble matter (Brix) and absorbance at 390 nm wavelength were determined for each sample and results show that no substantial differences are found for PEF pre-treated and control samples.

Hepatic parasympathetic nerve dysfunction involved in the deregulation of postprandial whole-body insulin sensitivity:a road to diabetes and associated pathologies

FCM: UC Fisiologia - Teses de Doutoramento

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Optimization of antioxidants extraction from coffee silverskin, aroasting by-product, having in view a sustainable process

Coffee silverskin is a major roasting by-product that could be valued as a source of antioxidant compounds. The effect of the major variables (solvent polarity, temperature and extraction time) affecting the extraction yields of bioactive compounds and antioxidant activity of silverskin extracts was evaluated. The extracts composition varied significantly with the extraction conditions used. A factorial experimental design showed that the use of a hydroalcoholic solvent (50%:50%) at 40 °C for 60 min is a sustainable option to maximize the extraction yield of bioactive compounds and the antioxidant capacity of extracts. Using this set of conditions it was possible to obtain extracts containing total phenolics (302.5 ± 7.1 mg GAE/L), tannins (0.43 ± 0.06 mg TAE/L), and flavonoids (83.0 ± 1.4 mg ECE/L), exhibiting DPPHradical dot scavenging activity (326.0 ± 5.7 mg TE/L) and ferric reducing antioxidant power (1791.9 ± 126.3 mg SFE/L). These conditions allowed, in comparison with other “more effective” for some individual parameters, a cost reduction, saving time and energy.

Road drivers performance appraisal using a MCDA method: a case study in the road transport company

Performance evaluation increasingly assumes a more important role in any organizational environment. In the transport area, the drivers are the company’s image and for this reason it is important to develop and increase their performance and commitment to the company goals. This evaluation can be used to motivate driver to improve their performance and to discover training needs. This work aims to create a performance appraisal evaluation model of the drivers based on the multi-criteria decision aid methodology. The MMASSI (Multicriteria Methodology to Support Selection of Information Systems) methodology was adapted by using a template supporting the evaluation according to the freight transportation company in study. The evaluation process involved all drivers (collaborators being evaluated), their supervisors and the company management. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used.

Fast unsupervised technique for extraction of endmembers spectra from hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the linear unmixing. The key to linear unmixing is to find the set of reference substances, also called endmembers, that are representative of a given scene. This paper presents the vertex component analysis (VCA) a new method to unmix linear mixtures of hyperspectral sources. The algorithm is unsupervised and exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O (n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.

Fast unsupervised extraction of endmembers spectra from hyperspectral data

Linear unmixing decomposes an hyperspectral image into a collection of re ectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art methods with much lower computational complexity.

Comparison of Disposable Pipette Extraction and Dispersive Solid-Phase Extraction in the QuEChERS Method for Analysis of Pesticides in Strawberries

In this study, we sought to assess the applicability of GC–MS/MS for the identification and quantification of 36 pesticides in strawberry from integrated pest management (IPM) and organic farming (OF). Citrate versions of QuEChERS (quick, easy, cheap, effective, rugged and safe) using dispersive solid-phase extraction (d-SPE) and disposable pipette extraction (DPX) for cleanup were compared for pesticide extraction. For cleanup, a combination of MgSO4, primary secondary amine and C18 was used for both the versions. Significant differences were observed in recovery results between the two sample preparation versions (DPX and d-SPE). Overall, 86% of the pesticides achieved recoveries (three spiking levels 10, 50 and 200 µg/kg) in the range of 70–120%, with <13% RSD. The matrix effects were also evaluated in both the versions and in strawberries from different crop types. Although not evidencing significant differences between the two methodologies were observed, however, the DPX cleanup proved to be a faster technique and easy to execute. The results indicate that QuEChERS with d-SPE and DPX and GC–MS/MS analysis achieved reliable quantification and identification of 36 pesticide residues in strawberries from OF and IPM.

Georeferencing Road Accidents with Google Earth: Transforming Information into Knowledge for Decision Support

Over the last fifty years mobility practices have changed dramatically, improving the way travel takes place, the time it takes but also on matters like road safety and prevention. High mortality caused by high accident levels has reached untenable levels. But the research into road mortality stayed limited to comparative statistical exercises which go no further than defining accident types. In terms of sharing information and mapping accidents, little progress has been mad, aside from the normal publication of figures, either through simplistic tables or web pages. With considerable technological advances on geographical information technologies, research and development stayed rather static with only a few good examples on dynamic mapping. The use of Global Positioning System (GPS) devices as normal equipments on automobile industry resulted in a more dynamic mobility patterns but also with higher degrees of uncertainty on road traffic. This paper describes a road accident georeferencing project for the Lisbon District involving fatalities and serious injuries during 2007. In the initial phase, individual information summaries were compiled giving information on accidents and its majour characteristics, collected by the security forces: the Public Safety Police Force (Polícia de Segurança Pública - PSP) and the National Guard (Guarda Nacional Republicana - GNR). The Google Earth platform was used to georeference the information in order to inform the public and the authorities of the accident locations, the nature of the location, and the causes and consequences of the accidents. This paper also gives future insights about augmented reality technologies, considered crucial to advances to road safety and prevention studies. At the end, this exercise could be considered a success because of numerous consequences, as for stakeholders who decide what to do but also for the public awareness to the problem of road mortality.

Data Extraction Tool to Analyse, Transform and Store Real Data from Electricity Markets

The study of electricity markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring process produced. Currently, lots of information concerning electricity markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge to define realistic scenarios, which are essential for understanding and forecast electricity markets behavior. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of electricity markets and of the behaviour of the involved entities. In this paper an adaptable tool capable of downloading, parsing and storing data from market operators’ websites is presented, assuring constant updating and reliability of the stored data.

Automatic Electricity Markets Data Extraction for Realistic Multi-agent Simulations

Electricity markets worldwide suffered profound transformations. The privatization of previously nationally owned systems; the deregulation of privately owned systems that were regulated; and the strong interconnection of national systems, are some examples of such transformations [1, 2]. In general, competitive environments, as is the case of electricity markets, require good decision-support tools to assist players in their decisions. Relevant research is being undertaken in this field, namely concerning player modeling and simulation, strategic bidding and decision-support.

Declarative approach to data extraction of web pages

Thesis submitted to Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, in partial fulfilment of the requirements for the degree of Master in Computer Science