Effect of different packaging materials on table grape quality preservation during cold storage

The fresh mushroom off-flavour (FMOff) has been appearing in wines since the 2000s. Some C8 compounds such as 1-octen-3-one, 1-octen-3-ol, 1-hydroxyoctan-3-one, 3-octanol and others are involved in this specific off-flavour [1-3]. At the same time, glycosidic precursors of some FMOff compounds have been identified in musts contaminated by Crustomyces subabruptus [4], highlighting the role of aroma precursors in this specific taint. However, the fate of these volatile molecules and glycosidic fractions during fermentation is not well known.

The study of cultural practices to delay ripening and the characterization of their effect on wine composition is important in the mitigation of accelerated ripening caused by higher temperatures

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

Fermentation derived sulfidic off-odors due to hydrogen sulfide (H2S) and low molecular weight thiols are commonly encountered in wine production and removed by Cu(II) fining. However, the mechanism underlying Cu(II) fining remains poorly understood, and generally results in increased Cu concentration that lead to deleterious reactions in finished wine. The present study describes a mechanistic investigation of the iron and copper mediated reaction of H2S, cysteine, 3-sulfanylhexan-1-ol, and 6-sulfanylhexan-1-ol with oxygen. The concentrations of H2S, thiols, oxygen, and acetaldehyde were monitored over time. It was found that Cu(II) was rapidly reduced by both H2S and thiols to Cu(I).

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.