Mycotoxins are secondary metabolites produced by some filamentous fungi, which can cause toxicity in animal species, including humans. Because of their high toxicological impacts, mycotoxins have received significant consideration, leading to the definition of strict legislative thresholds and limits in many areas of the world. Mycotoxins can reduce farm profits not only through reduced crop quality and product refusal, but also through a reduction in animal productivity and health. This paper briefly addresses the impacts of mycotoxin contamination of feed and food on animal and human health, and describes the main pre- and post-harvest systems to control their levels, including genetic, agronomic, biological, chemical, and physical methods. It so highlights (i) the lack of effective and straightforward solutions to control mycotoxin contamination in the field, at pre-harvest, as well as later post-harvest; and (ii) the increasing demand for novel methods to control mycotoxin infections, intoxications, and diseases, without leaving toxic chemical residues in the food and feed chain. Thus, the broad objective of the present study was to review the literature on the use of ozone for mycotoxin decontamination, proposing this gaseous air pollutant as a powerful tool to detoxify mycotoxins from feed and food.

Hyperspectral phenotyping has the potential to act as a surrogate for standard reference measurements of physiological and anatomical leaf traits related with w

Sustainable forest management is essential to confront the detrimental impacts of diseases on forest ecosystems. This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effective manner. The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed. Using several optical sensors from leaf to landscape-level, a number of forest diseases characterized by variable pathogenic processes have been detected, identified and quantified in many country sites worldwide. Overall, these reviewed studies have pointed out the green and red regions of the visible spectrum, the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom development. Late disease conditions particularly affect the shortwave-infrared region, mostly related to water content. This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas, to further develop hyperspectral sensors for early detection and discrimination of forest disturbances, to improve devices for remote sensing, to implement long-term monitoring, and to advance algorithms for exploitation of spectral data. Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.

Drought frequency is predicted to increase in future environments. Leaf water potential (Ψ_LW) is commonly used to evaluate plant water status, but traditional measurements can be logistically difficult and require destructive sampling. We used reflectance spectroscopy to characterize variation in Ψ_LW of Quercus oleoides Schltdl. & Cham. under differential water availability and tested the ability to predict pre-dawn Ψ_LW (PDΨ_LW) using spectral data collected hours after pressure chamber measurements on dark-acclimated leaves. Ψ_LW was measured with a Scholander pressure chamber. Leaf reflectance was collected at one or both of two time points: immediately (Ψ_LW) and ~5 h after pressure chamber measurements (PDΨ_LW). Predictive models were constructed using partial least-squares regression. Model performance was evaluated using coefficient of determination (R²), root-mean-square error (RMSE), bias, and the percent RMSE of the data range (%RMSE). Ψ_LW and PDΨ_LW were well predicted using spectroscopic models and successfully estimated a wide variation in Ψ_LW (light- or dark-acclimated leaves) as well as PDΨ_LW (dark-acclimated leaves only). Mean Ψ_LWR², RMSE and bias values were 0.65, 0.51 MPa and 0.09, respectively, with a %RMSE between 8% and 20%, while mean PDΨ_LWR², RMSE and bias values were 0.60, 0.44 MPa and 0.01, respectively, with a %RMSE between 9% and 20%. Estimates of PDΨ_LW produced similar statistical outcomes when analyzing treatment effects on PDΨ_LW as those found using reference pressure chamber measurements. These findings highlight a promising approach to evaluate plant responses to environmental change by providing rapid measurements that can be used to estimate plant water status as well as demonstrating that spectroscopic measurements can be used as a surrogate for standard, reference measurements in a statistical framework.