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1.
Environ Geochem Health ; 41(3): 1559-1576, 2019 Jun.
Article in English | MEDLINE | ID: mdl-30607524

ABSTRACT

Beer is a popular alcoholic beverage worldwide, traditionally made from water, barley and hop (Humulus lupulus L.) strobili. The strobili contain lupulin glands whose components (mostly bitter acids and polyphenols) confer unique and locally different flavours to beer types. It is therefore relevant for brewers and consumers to precisely know the geographical origin of hop plants used for high-quality beer. Hop plants belonging to the variety Hallertau Perle, grown in two locations, Cavalese and Imèr, of the Trentino Region (Italy) were analysed to establish a direct relationship between the chemical elements detected in soil and in plant parts. Chemical elements were determined by X-ray fluorescence and inductively coupled plasma mass spectrometry in soil, leaf and strobili samples from Cavalese and Imèr. The data from the two areas were compared by a nonparametric test (Mann-Whitney) and multivariate statistics (principal component analysis and partial least squares discriminant analysis). The geochemical characterization and the statistical analyses showed different concentrations of major and trace elements in soil and plant parts from the two areas. A reliable correlation could be established between some elements in soil and strobili samples, that is Nb, Fe, Rb and Zr for Cavalese and Mg, Ni, Zn and Zr for Imèr. These elements could therefore be used as geochemical fingerprints to identify the geographical origin of strobili from the two study areas, an approach useful to verify the origin of hop plants for the production of high-quality beer.


Subject(s)
Food Analysis/methods , Humulus/chemistry , Soil/chemistry , Spectrometry, X-Ray Emission/methods , Trace Elements/analysis , Discriminant Analysis , Food Analysis/statistics & numerical data , Italy , Mass Spectrometry , Plant Leaves/chemistry , Principal Component Analysis , Spectrometry, X-Ray Emission/statistics & numerical data
2.
Mar Environ Res ; 69(1): 10-7, 2010 Feb.
Article in English | MEDLINE | ID: mdl-19679346

ABSTRACT

Karavasta is the widest and most important lagoon in Albania. This study aimed to assess the ecological quality status of the lagoon, acquire knowledge of a natural environment which might be exploited for aquaculture, and give management hints on the basis of anthropogenic impact and ecological conditions. A sampling campaign was carried out in 2008: at six stations, benthic fauna, water, and sediment parameters were considered. Statistical analyses were carried out through multivariate procedures (PCA, classification-clustering, SIMPER, RDA, DISTLM, PERMANOVA). Ecological quality was assessed through the AZTI Marine Biotic Index (AMBI), the multivariate AMBI (M-AMBI) and the Benthic Index based on Taxonomic Sufficiency (BITS). Sediment characteristics (percent organic matter, %OM; redox potential discontinuity layer depth, RPDL; particle size composition) and salinity represented contributory influences on lagoon communities. It was possible to distinguish and characterise a confined area, and benthic communities, from a marine-influenced area and its biota. The number of species was quite low when compared with other open Adriatic lagoons. The M-AMBI and BITS classifications gave quite similar results, which seemed consistent with the ecological conditions of the lagoon, that is a distinction in the ecological quality between the seaward and landward stations, with higher ecological quality (EcoQ) at the seaward stations. Given the pressures and the ecological condition of Karavasta, an intensification of aquaculture activities must be considered with caution, since the lagoon seems at significant risk of serious hypereutrophication. This situation is made worse by the limited water exchange with the marine environment due to the irregular dredging of the communication channels.


Subject(s)
Ecosystem , Seawater/analysis , Albania , Animals , Conservation of Natural Resources , Geologic Sediments/analysis , Oceans and Seas , Principal Component Analysis , Salinity
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