One drop only. Easy and rapid Raman evaluation of ß-carotene in olive oil and its relevance as an index of olive fly attack.

This paper presents, for the first time, a method for the rapid quantification of ß-carotene in olive oil by Raman spectroscopy. Using a 532 nm Raman laser source, our procedure requires only one drop (100 µL) of oil, for ß-carotene content to be determined. Results show that ß-carotene content is associated with the lutein/ß-carotene ratio, a parameter whose value describes how healthy the olives were before processing, specifically whether an olive fly attack occurred. Since olive fly attacks are not always visible to the oil producers, this method gives them the means to control the validity of the prevention strategies they adopted.

Relationships between air particulate matter capture efficiency and leaf traits in twelve tree species from an Italian urban-industrial environment.

Air pollution in the urban environment is widely recognized as one of the most harmful threats for human health. International organizations such as the United Nations and the European Commission are highlighting the potential role of nature in mitigating air pollution and are now funding the implementation of Nature-Based Solutions, especially at the city level. Over the past few decades, the attention of the scientific community has grown around the role of urban forest in air pollution mitigation. Nevertheless, the understanding on Particulate Matter (PM) retention mechanisms by tree leaves is still limited. In this study, twelve tree species were sampled within an urban park of an industrial city. Two techniques were used for leaf analysis: Vacuum/Filtration and Scanning Electron Microscopy coupled with Energy Dispersive X-ray spectroscopy, in order to obtain a quali-quantitative analysis of the different PM size fractions. Results showed that deposited PM loads vary significantly among species. Different leaf traits, including micro and macromorphological characteristics, were observed, measured and ranked, with the final aim to relate them with PM load. Even if no significant correlation between each single leaf characteristic and PM deposition was observed (p > 0.05), multivariate analysis revealed relationships between clusters of leaf traits and deposited PM. Thus, by assigning a score to each trait, an Accumulation index (Ai) was calculated, which was significantly related to the leaf deposited PM load (p ≤ 0.05).

Geographical discrimination of extra-virgin olive oils from the Italian coasts by combining stable isotope data and carotenoid content within a multivariate analysis.

We have determined the isotopic composition and the carotenoid contents of 38 extra-virgin olive oils (EVOOs) from seven regions along the Italian coasts, by means of isotope ratio mass spectrometry (IRMS) and resonant Raman spectroscopy (RRS), respectively. The application of linear discriminant analysis to our overall results demonstrated the combination of isotope and carotenoid data is a promising method to discriminate EVOOs from production sites that are impacted by similar geographical and climatic parameters. In particular, this dual approach allowed correct classification of 82% EVOO samples, while separate IRMS and RRS investigations were able to discriminate only samples from Sicily and Latium, respectively.

Characterization of leaf-level particulate matter for an industrial city using electron microscopy and X-ray microanalysis.

This study reports application of monitoring and characterization protocol for particulate matter (PM) deposited on tree leaves, using Quercus ilex as a case study species. The study area is located in the industrial city of Terni in central Italy, with high PM concentrations. Four trees were selected as representative of distinct pollution environments based on their proximity to a steel factory and a street. Wash off from leaves onto cellulose filters were characterized using scanning electron microscopy and energy dispersive X-ray spectroscopy, inferring the associations between particle sizes, chemical composition, and sampling location. Modeling of particle size distributions showed a tri-modal fingerprint, with the three modes centered at 0.6 (factory related), 1.2 (urban background), and 2.6µm (traffic related). Chemical detection identified 23 elements abundant in the PM samples. Principal component analysis recognized iron and copper as source-specific PM markers, attributed mainly to industrial and heavy traffic pollution respectively. Upscaling these results on leaf area basis provided a useful indicator for strategic evaluation of harmful PM pollutants using tree leaves.