Tasting soils in Pinot noir wines of the Willamette valley, Oregon

Malolactic fermentation (MLF) is a desired process in red and acidic white wines, after alcoholic fermentation (AF), carried out by the lactic acid bacterium (LAB) Oenococcus oeni. The advantages are an increase of pH, microbiological stabilization and organoleptic improvement of the final wine. However, the presence of stress factors such as ethanol, low pH, high total SO2, lack of nutrients and presence of inhibitors, could affect the successful completion of MLF [1]. Changes in amino acid composition and deficiencies in peptides after AF, showed that MLF can be delayed, signaling its importance for bacterial growth and L-malic acid degradation during MLF [2].

Developing a tractable genetic engineering and gene editing system is an essential tool for grapevine. We initiated a plant transformation and biotechnology program at Oregon State University using the grape microvine system (V. vinifera) in 2018 to interrogate gene-to-trait relationships using traditional genetic engineering and gene editing. The microvine model is also used for nanomaterial-assisted RNP, DNA, and RNA delivery. Most reference genomes and annotations for grapevine are collapsed assemblies of homologous chromosomes and do not represent the specific microvine cultivar ‘043023V004’ under study at our institution.

A new immunoassay kit, called Botrytis Lateral Flow Device has been tested to detect Botrytis cinerea on musts and wines. The comparison of the immunoassay result with the quantitative analysis of usual markers (gluconic acid, sugars and polyols) showed the relevance of this innovative tool.

All techniques usually used to assay proteins was not reliable in vegetable extract due to interferences with the components included in extracts like polyphenols, tanins, pectines, aromatics compounds. Absorbance at 280nm, Kjeldhal assay, Biuret and Lowry methods, Acid Bicinchonique technique and Bradford assay give the results depending on the composition of extract, on the presence or not of detergent and on the raw material (Marchal, 1995). Another difficulty in these extracts for the quantification of proteins comes from the large amount of water included in vegetable and the low concentration of proteins. Thus in red wines, proteins are usually not taken into account due to their low concentration (typically below 10 mgL-1) and to the presence of anthocyanis and polyphenols.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.