Assessment of Metal Contamination in Doñana National Park (Spain) using Crayfish (Procamburus Clarkii)

López, Francisco J.; García, Miguel de Cara; Vidal, José Luis Martı́nez; Aguilera, Pedro A.; Frenich, Antonia Garrido

doi:10.1023/b:emas.0000016789.13603.e5

Cited by 59 publications

(26 citation statements)

References 14 publications

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“…It was reported in some field studies that Procambarus clarkii can be employed as an accumulative indicator of heavy metals giving information about the pollutants contamination in different sites (ROWE et al, 2001;SANCHEZ LOPEZ et al, 2004).…”

Section: Discussionmentioning

confidence: 99%

Glutathione and Antioxidant Enzymes in the Hepatopancreas of Crayfish Procambarus Clarkii (Girard, 1852) of Lake Trasimeno (Italy)

Elia

Dörr

Mastrangelo

et al. 2006

Bull. Fr. Pêche Piscic.

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Antioxidant parameters, such as total glutathione, glutathione S-transferase, glutathione peroxidase, glutathione reductase, glyoxalases, catalase, and some heavy metals such as, lead, cadmium and chromium were examined in hepatopancreas of both sexes of Procambarus clarkii collected seasonally from Lake Trasimeno, from winter 2000-2003 to autumn 2003. Heavy metals content in hepatopancreas in males and females of P. clarkii was low and did not vary through the sampling periods and between sexes. On the contrary, crayfish exhibited sex-dependent differences in levels of some enzyme activities and of total glutathione, and no apparent relationship was found between contaminant burdens and antioxidant indexes in hepatopancreas. Because measured metal concentrations were low, other factors, presumably, were involved in antioxidant variations in P. clarkii and these latter seemed to be affected more by biological and environmental factors, other than those related to pollutants body burdens.

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Section: Discussionmentioning

confidence: 99%

Glutathione and Antioxidant Enzymes in the Hepatopancreas of Crayfish Procambarus Clarkii (Girard, 1852) of Lake Trasimeno (Italy)

Elia

Dörr

Mastrangelo

et al. 2006

Bull. Fr. Pêche Piscic.

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“…Monthly and combined variables were analyzed with the Spearman correlation analysis to search for relationships among them. Hierarchical ward cluster analysis after Z score correction was used to determine closely related variables (Lopez et al 2004;Tunca et al 2013). Further relationships among the variables were searched using the regression analysis.…”

Section: Discussionmentioning

confidence: 99%

Morphometric aspects and growth modeling of exotic bivalve blood cockle Scapharca inaequivalvis from the Black Sea, Turkey

2014

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This study was planned to determine biometric features and relationships between them in blood cockle, Scapharca inaequivalvis, a benthic exotic bivalve species of the Black Sea, Turkey during a period of 12 months. Over the study, 4,543 samples were collected and their shell length (SL), width (SWi), thickness (ST) and weight (SWe), as well as total (TW) and meat weights (MW) were measured. SL changed between 0.45 and 7.18 cm with an average value of 3.77 ± 0.02 cm. Average TW of the samples was 22.37 ± 0.26 g. Average MW of S. inaequivalvis was calculated as 10.66 ± 0.32 g with a rate of 26.85% of TW. The findings obtained showed that monthly measurements of SWe and MW had a small variation. On the other hand, monthly ST varied greatly. SL and SWe increment (%) models were also generated according to SL classes using regression analysis. Correlation and cluster analysis showed that MW had a weak association with other variables. The strongest correlation was between ST and SWe in October (R 2 = 1.0). The results present valuable information for this exotic species but more studies are required to better understand its biological characteristics and long term effects on the ecosystem.

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“…It operates on raw data and the technique constructs a discriminant function for each group (Lattin et al, 2003;Wunderlin et al, 2011). A simple linear discriminant function transforms an original set of measurements on a sample into a single discriminant score (Sanchez et al, 2004). LDA involves the determination of a linear equation that will predict which group the case belongs to.…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Simple method for classification of mango varieties on the basis of their physicochemical properties by chemometric techniques

Uddin

Ara

Motalab

et al. 2016

Bangladesh J. Sci. Ind. Res.

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Nutritional parameters vary significantly among different varieties of mango. It is therefore necessary to have simple method to classify mango pulps according to their nutritional parameters by effective and economic technique. The present study was carried out to develop a method to classify mango varieties by using two chemometric techniques namely, Artificial Neural Network (ANN) and Linear Discrimination Analysis (LDA). At first, 9 physico-chemical parameters have been chosen from 18 of them by applying Factor Analysis (FA), as quantification of each parameter involves time and cost. Nine varieties of mango available in Bangladesh were studied her. LDA can classify the mango pulps on the basis of their nutritional properties 100 percent accurately, and this rate for ANN is 96.3 percent. Therefore, a method is being proposed where mango can be classified with their 9 physico-chemical parameters into their right varieties by LDA. The proposed chemometric method could be used for regular classification of mango pulps at laboratory and mango product manufacturing industries to improve the quality of mango products.

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Assessment of Metal Contamination in Doñana National Park (Spain) using Crayfish (Procamburus Clarkii)

Cited by 59 publications

References 14 publications

Glutathione and Antioxidant Enzymes in the Hepatopancreas of Crayfish Procambarus Clarkii (Girard, 1852) of Lake Trasimeno (Italy)

Glutathione and Antioxidant Enzymes in the Hepatopancreas of Crayfish Procambarus Clarkii (Girard, 1852) of Lake Trasimeno (Italy)

Morphometric aspects and growth modeling of exotic bivalve blood cockle Scapharca inaequivalvis from the Black Sea, Turkey

Simple method for classification of mango varieties on the basis of their physicochemical properties by chemometric techniques

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