Universidade da Madeira
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Browsing Universidade da Madeira by Field of Science and Technology (FOS) "Ciências Agrárias::Outras Ciências Agrárias"
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- Development of a methodology based on QuEChERS-dSPE/UHPLC-PDA to quantify pesticide residues in potatoesPublication . Reis, Débora Carina Freitas; Câmara, José de Sousa; Perestrelo, Rosa Maria de SáPesticides have been associated to human health hazards, ranging from headaches to cancer or, reproductive and endocrine system disruption. Inappropriate use of pesticides may cause damage to the environment, increase resistance in the target pest organisms and deleterious effects of non-target organisms. The European Union (EU) set directives for pesticides usage, establishing maximum residue limits (MRLs) in fruits and vegetables. It is very important to monitor if the pesticide residues are below the MRLs in food matrices and to evaluate if they pose a risk to the health of consumer and environment. The purpose of this work was to develop a fast and sensitive analytical method to identify and quantify common pesticide residues in potatoes based on a quick, easy, cheap, effective, rugged, and safe (QuEChERS) procedure combined with ultra-highpressure liquid chromatography tandem with photodiode array system (UHPLC-PDA). The parameters that affect the QuEChERS/UHPLC-PDA efficiency, such as extraction solvent, buffered salts, stationary phases, gradient conditions, and eluents were optimized. The optimal parameters were 50% of acetonitrile (ACN) acidified with 0.1% phosphoric acid (PhA) and, magnesium sulfate: sodium chloride: disodium hydrogen citrate sesquihydrate: trisodium citrate dihydrate (1:1:1/2:1) ratio was used. Moreover, 30 min ultrasound time was added and for clean-up magnesium sulfate and primary secondary were used. The selected column was CORTECS with a gradient program combining an aqueous solution acidified with 0.1% PhA and ACN, a flow rate of 150 µL/min at 30 ºC. After optimization, the method was validated according to IUPAC guidelines. The validated method showed to be selective for the studied pesticides, and showed satisfactory performance in terms of linearity, with correlation coefficient (r2) higher than 0.997. The limits of detection (LOD) ranging from 0.005 (chlorpyrifos) to 2.581 (thiabendazole) mg/L, whereas the limit of quantification (LOQ) limit from 0.015 (chlorpyrifos) to 7.821 (thiabendazole) mg/L. In relation to the accuracy, the obtained values varied between 87.7 and 214.2 %, while in precision, the coefficients of variation remained in general below to 20 %. In terms of matrix effect, the values ranged between 87.6 and 185.8 %, which agreed with results reported by other studies.
- Estabelecimento da composição volátil do nutriente FNI 210 usado pela Ceratitis capitata (Wiedemann, 1824) e dos extratos de diferentes espécies de plantas avaliação do potencial atrativo e repelente das plantas: avaliação do potencial atrativo e repelente das plantasPublication . Sousa, Maria de Fátima Rodrigues; Pombo, Dora Aguim; Dantas, Luís Miguel FernandesA mosca da fruta é uma das principais pragas agrícolas. Neste trabalho determinou-se a composição volátil do nutriente FNI 210 (proteína alimentar) e dos extratos de cinco plantas: Cedronella canariensis, Eucalyptus globulus, Laurus novocanariensis, Myrtus communis e Ruta chalepensis e avaliou-se o seu potencial atrativo e repelente em moscas adultas num olfatómetro em Y. A composição volátil do nutriente e dos extratos foi semelhante à encontrada por outros autores e apresentou compostos atrativos para a mosca da fruta. Nos bioensaios com o olfatómetro as moscas foram atraídas à proteína mas a percentagem média de respostas variou de acordo com o sexo, estado sexual, idade e número de indivíduos por grupo sendo mais alta aos 8 dias em grupos de 5. No geral, as fêmeas virgens responderam mais do que as não virgens e mais do que os machos virgens. O número de insetos que se dirigiram à proteína foi superior na primeira repetição nos primeiros 10 e 20 minutos. Contudo, em todos os bioensaios houve um número elevado de indivíduos não responderam. Nos bioensaios das plantas a resposta do mesmo grupo de 5 indivíduos com 8 dias foi testada três vezes no olfatómetro pela ordem seguinte: sem amostra, com proteína e com extrato de planta. Nos três casos as respostas dos adultos variaram de acordo com o sexo e estado sexual. As percentagens médias de respostas aos extratos foram superiores às obtidas nos ensaios sem amostra e menores que à proteína, á exceção do extrato de L. novocanariensis que apresentou um potencial atrativo superior ao da proteína nos machos virgens. Nos testes com o extrato, as respostas ao braço com amostra foram superiores ao braço sem amostra, à exceção das respostas das fêmeas não virgens ao extrato de R. chalepensis, o que sugere ser esta a única planta com potencial repelente.
- TerraSenseTK: a toolkit for remote soil nutrient estimationPublication . Pereira, Manuel Afonso Soares; Quintal, Filipe Magno Gouveia; Pereira, Amâncio Lucas de SousaIntensive farming endangers soil quality in various ways. Researchers show that if these practices continue, humanity will be faced with food production issues. For this matter, Earth Observation, more concretely Soil Sensing, along with Machine Learning, can be employed to monitor several indicators of soil degradation, such as soil salinity, soil heavy metal contamination and soil nutrients estimation. More concretely, Soil Nutrients are of great importance. For instance, to understand which crop better suits the land, the soil nutrients must be identified. However, sampling soil is a laborous and expensive task, which can be leveraged by Remote Sensing and Machine Learning. Several studies have already been developed in this matter, although many gaps still exist. Among them, the lack of cross-dataset evaluations of existing algorithms, and also the steep learning curve to the Earth Observation domain that prevents many researchers from embracing this field. In this sense, we propose TerraSense ToolKit (TSTK), a python toolkit that addresses these challenges. In this work, the possibility to use Remote sensing along with Machine Learning algorithms to per form Soil Nutrient Estimation is explored, additionally, a nutrient estimation toolkit is proposed, and the effectivity of it is tested in a soil nutrient estimation case study. This toolkit is capable of simplifying Remote Sensing experiments and aims at reducing the barrier to entry to the field of Earth Observation. It comes with a preconfigured case study which implements a soil sensing pipeline. To evaluate the usability of the toolkit, experiments with five different crops were executed, namely with Wheat, Barley, Maize, Sunflower and Vineyards. This case study gave visibility to an underlying unbalanced data problem, which is not well addressed in the current State of the Art.