2 resultados para PM3 semi-empirical method
em Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal
Resumo:
In this study the feasibility of different extraction procedures was evaluated in order to test their potential for the extraction of the volatile (VOCs) and semi-volatile constituents (SVOCs) from wines. In this sense, and before they could be analysed by gas chromatography–quadrupole first stage masss spectrometry (GC–qMS), three different high-throughput miniaturized (ad)sorptive extraction techniques, based on solid phase extraction (SPE), microextraction by packed sorbents (MEPS) and solid phase microextraction (SPME), were studied for the first time together, for the extraction step. To achieve the most complete volatile and semi-volatile signature, distinct SPE (LiChrolut EN, Poropak Q, Styrene-Divinylbenzene and Amberlite XAD-2) and MEPS (C2, C8, C18, Silica and M1 (mixed C8-SCX)) sorbent materials, and different SPME fibre coatings (PA, PDMS, PEG, DVB/CAR/PDMS, PDMS/DVB, and CAR/PDMS), were tested and compared. All the extraction techniques were followed by GC–qMS analysis, which allowed the identification of up to 103 VOCs and SVOCs, distributed by distinct chemical families: higher alcohols, esters, fatty acids, carbonyl compounds and furan compounds. Mass spectra, standard compounds and retention index were used for identification purposes. SPE technique, using LiChrolut EN as sorbent (SPELiChrolut EN), was the most efficient method allowing for the identification of 78 VOCs and SVOCs, 63 and 19 more than MEPS and SPME techniques, respectively. In MEPS technique the best results in terms of number of extractable/identified compounds and total peak areas of volatile and semi-volatile fraction, were obtained by using C8 resin whereas DVB/CAR/PDMS was revealed the most efficient SPME coating to extract VOCs and SVOCs from Bual wine. Diethyl malate (18.8 ± 3.2%) was the main component found in wine SPELiChrolut EN extracts followed by ethyl succinate (13.5 ± 5.3%), 3-methyl-1-butanol (13.2 ± 1.7%), and 2-phenylethanol (11.2 ± 9.9%), while in SPMEDVB/CAR/PDMS technique 3-methyl-1-butanol (43.3 ± 0.6%) followed by diethyl succinate (18.9 ± 1.6%), and 2-furfural (10.4 ± 0.4%), are the major compounds. The major VOCs and SVOCs isolated by MEPSC8 were 3-methyl-1-butanol (26.8 ± 0.6%, from wine total volatile fraction), diethyl succinate (24.9 ± 0.8%), and diethyl malate (16.3 ± 0.9%). Regardless of the extraction technique, the highest extraction efficiency corresponds to esters and higher alcohols and the lowest to fatty acids. Despite some drawbacks associated with the SPE procedure such as the use of organic solvents, the time-consuming and tedious sampling procedure, it was observed that SPELiChrolut EN, revealed to be the most effective technique allowing the extraction of a higher number of compounds (78) rather than the other extraction techniques studied.
Resumo:
The Bologna Process introduced some changes in the curriculum of higher education institutions (HEIs) and defined that academic learning should consider the needs of the labour market. HEIs and employers agree that personal skills are the most important set of competence of graduates (Pavlin, Akkuyunlu, Kovacic, & Svetlik, 2009). The goals of this work were to explore how the work experienced by teams of students in HEIs might help them improve their personal skills, namely empirically explore the perception of teamwork and personality into two groups of students. The study was based on the theoretical model of Team Evolution and Maturation (TEAM, Fransen, 2012). The sample consisted of 99 students of the 3rd year of the degree (1st cycle) in Computer Science (49 students) and the 2nd year of the Bachelor's Degree (1st cycle) in Psychology (50 students), from the University of Madeira, Portugal. Areas of personality and team collaboration were evaluated with a Pre- and Post-test. Findings show that the perception of the teamwork collaboration of students in Computer Science and Psychology majors seems to be influenced by their scientific area, by gender, by the selection method of the time-organiser, the self-perceived personality of the time-organiser, the self perceived personality of the non-time-organiser, and the size of the team. It is expected that this data will contribute to further theoretical and practical reflection on the teamwork among college students and their performance in the labour market.