Institution
University of Almería
Education•Almería, Spain•
About: University of Almería is a education organization based out in Almería, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 4674 authors who have published 10905 publications receiving 233036 citations. The organization is also known as: University of Almeria & Universidad de Almería.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The amounts of n-3 VLCPUFA in Hermetia larvae could be altered by dietary manipulation in a short period of time and therefore a lower n-6:n-3 ratio than those of the control insect meal.
140 citations
••
TL;DR: The water soluble ruthenium complexes (C5R5)RuCl(PTA)2] were synthesized and characterised in this paper, and their evaluation as regioselective catalysts for hydrogenation of unsaturated ketones in aqueous biphasic conditions was briefly presented.
140 citations
••
TL;DR: The most productive institutions in terms of number of documents dealing with nitrate leaching research, h-index and total citations, were located in the United States, China and the Netherlands, followed by China, the United Kingdom and Germany.
139 citations
••
TL;DR: In this paper, the principal extraction and clean-up methodologies (e.g., liquid-liquid extraction, solid-phase extraction, pressurized liquid extraction, QuEChERS (quick, easy, cheap, effective, rugged and safe), gel-permeation chromatography and supercritical-fluid extraction) are compared.
Abstract: Rice consumption has increased worldwide over recent decades, as it has become one of the most common foods. Although the analysis of environmental samples coming from rice areas has been well documented, there is less information regarding the analysis of pesticide residues in rice-grain samples. Rice (paddy, brown and white) can be considered a complex matrix, leading to difficulties in the application of the different multiresidue methods described in the literature. This review addresses and compares the principal extraction and clean-up methodologies [e.g., liquid-liquid extraction, solid-phase extraction, pressurized-liquid extraction, QuEChERS (quick, easy, cheap, effective, rugged and safe), gel-permeation chromatography and supercritical-fluid extraction – with QuEChERS-based methods being the most frequently employed]. Traditionally, the determination of pesticide residues in rice has been based on gas chromatography with mass spectrometry (MS). But the application of new classes of pesticides has driven laboratories to increase the use of liquid chromatography with tandem MS. The limits of detection and quantification are in the ranges 0.09–90 μg/kg and 1–297 μg/kg, respectively, for the methodologies reported. These values agree with the current internationally-accepted maximum residue limits (MRLs). Based on the European Union (EU) database, more than 3000 analyses of pesticide residues in rice have been performed by official EU laboratories over the past decade. Of these, 6% reported pesticide residues above the MRLs. Physico-chemical properties can explain the occurrence of pesticides in rice commodities: lipophilic pesticides are frequently found in brown rice, whereas fungicides are mainly found in milled rice. Carbendazim, malathion, iprodione, tebuconazole, quinclorac and tricyclazole are the pesticides most frequently found in white rice, while buprofezin, hexaconazole, chlorpyrifos and edifenphos are most commonly found in paddy rice. Pesticide-residue concentrations can be affected during rice processing – with concentrations generally lower in the final products. However, few studies focusing on primary processing have addressed the setting of precise values applicable for the processing factors.
138 citations
••
TL;DR: In this paper, an alternative expression to calculate the variance inflation factor (VIF) in ridge regression is presented. But the expression does not necessarily lead to values of VIFs equal to or greater than 1.
Abstract: Ridge regression has been widely applied to estimate under collinearity by defining a class of estimators that are dependent on the parameter k. The variance inflation factor (VIF) is applied to detect the presence of collinearity and also as an objective method to obtain the value of k in ridge regression. Contrarily to the definition of the VIF, the expressions traditionally applied in ridge regression do not necessarily lead to values of VIFs equal to or greater than 1. This work presents an alternative expression to calculate the VIF in ridge regression that satisfies the aforementioned condition and also presents other interesting properties.
138 citations
Authors
Showing all 4758 results
Name | H-index | Papers | Citations |
---|---|---|---|
Amadeo R. Fernández-Alba | 83 | 318 | 21458 |
Sixto Malato | 80 | 315 | 24216 |
Francisco Rodríguez | 79 | 748 | 24992 |
Yusuf Chisti | 76 | 347 | 33979 |
José Luis García | 73 | 453 | 17504 |
Anne-Marie Caminade | 69 | 580 | 15814 |
Elias Fereres | 68 | 236 | 18751 |
David Mecerreyes | 66 | 324 | 16822 |
Berta Martín-López | 64 | 177 | 16136 |
Ana Agüera | 63 | 168 | 12280 |
Alberto Fernández-Gutiérrez | 62 | 312 | 13557 |
Mary F. Mahon | 59 | 539 | 14258 |
José María Carazo | 59 | 309 | 12499 |
Claudio Bianchini | 57 | 368 | 13412 |
Manuel Marquez | 55 | 126 | 12237 |