Climatic factors influencing the isotope composition of Italian olive oils and geographic characterisation

Abstract

The purpose of this study was to investigate the possibility of identifying oil source areas by means of simple measurements on the natural samples avoiding time-consuming sample treatments. The oxygen and carbon isotopic values of 150 samples of extra-virgin olive oil from eight different Italian regions and from three different years of production were measured according to well-established techniques. Statistical treatments of the results obtained show a very good correlation of the delta(18)O of oil with latitude, mean annual temperature, and mean relative humidity at the collection site. No correlation is found with elevation and mean annual precipitation. The shift of the oil delta(18)O per degree centigrade of the mean annual temperature is quantitatively close to that calculated for atmospheric precipitation in continental areas. Accordingly, in our measurements, the year of oil production can be identified on the basis of the delta(18)O value (mean 2004 temperatures were higher than 2005 temperatures). On the contrary, the oil delta(13)C values show no correlation with the above variables but only with latitude and, consequently, are less suitable for discriminating the geographic origin of oil. However, the delta(13)C values are suitable to indicate biological differentiation while the delta(18)O values are not.

Stable isotope composition of cocoa beans of different geographical origin

“…Hydrogen and oxygen stable isotopes of plant materials are strongly linked to the climatic conditions (relative humidity, temperature and amount of precipitation) and geographical characteristics (distance from the sea or other evaporation source, altitude and latitude) of the area where the plants grew …”

“…Hydrogen and oxygen stable isotopes of plant materials are strongly linked to the climatic conditions (relative humidity, temperature and amount of precipitation) and geographical characteristics (distance from the sea or other evaporation source, altitude and latitude) of the area where the plants grew. [18,29,[41][42][43][44] East Africa (Uganda) shows significantly higher δ 18 O (Fig. 1) than the other macro-areas, while Peru and Asia have the lowest.…”

Discrimination of unprocessed cotton on the basis of geographic origin using multi-element stable isotope signatures

“…This term is generally understood to mean the process of relating a compound or a material to the geographic region from which it originated. The most frequently employed analytical techniques for determining links or correlations between a given material and geographic origin are stable isotope abundance analysis, trace element analysis, trace element isotope ratio analysis either in isolation, or in combination or in conjunction with other analytical techniques …”

“…[2] This term is generally understood to mean the process of relating a compound or a material to the geographic region from which it originated. The most frequently employed analytical techniques for determining links or correlations between a given material and geographic origin are stable isotope abundance analysis, trace element analysis, [2] trace element isotope ratio analysis either in isolation, [3][4][5][6] or in combination [7][8][9] or in conjunction with other analytical techniques. [10][11][12][13][14] Early work by Yakir and co-workers demonstrated links between water use and δ 2 H, δ 13 C and δ 18 O values in cotton plants, [15] while results from a proof-of-concept study carried out on spun cotton yarn showed it was possible in principle to distinguish samples from different geographic regions based on differences in their stable isotope signature.…”

Fatty acid composition and δ13C of bulk and individual fatty acids as marker for authenticating Italian PDO/PGI extra virgin olive oils by means of isotopic ratio mass spectrometry

“…[18] Isotopic ratio mass spectrometry (IRMS) methods were used for the geographical characterization of olive oil in previous works. [19][20][21] This technique presents the advantage of giving output data that are practically independent of the employed cultivar and the production technique and, in some cases, has already been introduced as an official method. [22][23][24] This kind of methods relies on commodity databases updated each year.…”