Oak seedling microbiome assembly under climate warming and drought.

Despite that climate change is currently one of the most pervasive challenges, its effects on the plant-associated microbiome is still poorly studied. The aim of this study was to evaluate the impact of the independent and combinatory effect of climate warming and drought on the microbiome assembly of oak from seed to seedling. In a multifactorial experimental set up, acorns were subjected to different temperatures (15 °C, 20 °C, and 25 °C) and soil moisture levels (drought (15%) and control (60%)) from germination until the seedling stage, after which the bacterial and fungal communities associated to the rhizosphere and phyllosphere were characterized by amplicon sequencing and qPCR.

The potential of e-nose aroma profiling for identifying the geographical origin of licorice (Glycyrrhiza glabra L.) roots.

Licorice roots cultivated commercially in distinct geographical areas such as China, Iran, Italy (Abruzzo, Basilicata, Calabria and Sicily) and Turkey were classified using an artificial olfactive system (e-nose) based on metal oxide semiconductor sensors (MOS). The resultant instrumental data were processed using a multivariate statistical analysis method in order to classify the raw samples according to its origin. The e-nose odourprintings were obtained by a canonical discriminant analysis carried out with the aim of relating the specific data-sets corresponding to whole licorice roots aroma with the e-nose reference dataset.

Non-destructive flavour evaluation of red onion (Allium cepa L.) ecotypes: an electronic-nose-based approach.

This work reports preliminary results on the potential of a metal oxide sensor (MOS)-based electronic nose, as a non-destructive method to discriminate three "Tropea Red Onion" PGI ecotypes (TrT, TrMC and TrA) from each other and the common red onion (RO), which is usually used to counterfeit. The signals from the sensor array were processed using a canonical discriminant function analysis (DFA) pattern recognition technique. The DFA on onion samples showed a clear separation among the four onion groups with an overall correct classification rate (CR) of 97.