Growth rate and TRI5 gene expression profiles of Fusarium equiseti strains isolated from Spanish cereals cultivated on wheat and barley media at different environmental conditions

Fusarium equiseti is a toxigenic species that often contaminates ce real crops from diverse climatic regions such as Northern and Southern Europe. Previous results suggested the existence of two distinct populations within this species with differences in toxin pro file which largely corresponded to North and South Europe (Spain). In this work, growth rate profiles of 4 F. equiseti strains isolated from different cereals and distinct Spanish regions were determined on wheat and barley based media at a range of temperatures (15, 20, 25, 30, 35 and 40 °C) and water potentialregimens(−0.7,−2.8,−7.0,and −9.8MPa,correspondingto 0.99,0.98,0.95 and 0.93aw values).Growth was observed at all temperatures except at 40 °C, and at all the solute potential values except at−9.8 MPa when combined with 15 °C. Optimal growth was observed at 20– 30 °C and −0.7/−2.8 MPa. The effect of these factors on trichothecene biosynthesis was examined on a F. equiseti strain using a newly developed real time RT-PCR protocol to quantify TRI5 gene expression at 15, 25 and 35 °C and −0.7, −2.8, − 7.0 and −9.8 MPa on wheat and barley based media. Induction of TRI5 expression was detected between 25 and 35 °C and −0.7 and − 2.8 MPa, with maximum values at 35 °C and −2.8 MPa being higher in barley than in wheat medium. These results appeared to be consistent with a population well adapted to the present climatic conditions and predicted scenarios for Southern Europe and suggested some differences depending on the cereal considered. These are also discussed in relation to other Fusarium species co-occurring in cereals grown in this region and to their significance for prediction and control strategies of toxigenic risk in future scenarios of climate change for this region.

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 realtime signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1 ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426 ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%.

Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.