Evaluating the Atypical Aging Potential Development in Sparkling Wines Can Be Achieved by Assessing the Base Wines at the End of the Alcoholic Fermentation

Traditional sparkling wine production is a lengthy and costly process, involving a double fermentation step and a period of aging sur lie; thus, monitoring quality during the key manufacturing stages is crucial. The effects of the second fermentation on the development of 2-aminoacetophenone (AAP), the main marker of the atypical aging (ATA) defect, were investigated on 55 base wines (BWs) and corresponding sparkling wines (SWs) produced in an experimental winery. While the AAP content of the SWs was observed to be higher than the BWs, it was found that an artificial aging test carried out on the BWs could be a good predictor of ATA development in SWs. Further, the antioxidant capacity of the SWs was noticed to correlate well with the potential AAP formed during accelerated aging. Finally, an analysis of covariance (ANCOVA) model of linearization capable of predicting AAP formation in SWs using the data obtained from the corresponding BWs was created

Left: bi-plot after Principal components and classification analysis based on correlation of the content of the 54 chemical elements (empty marker, not all labelled) in the 29 soil samples (full marker).

<p>The variance explained by the each principal component is shown in parentheses. Right: plot of canonical scores after Discriminant analysis with canonical analysis performed with the four chemical elements most, and significantly, correlated (absolute value) with factor 1 (Cs, Ga, Ca, S) and factor 2 (Cd, Zn, Hg, Sr). Classification of soil samples has an high probability to be correct (93%) as the predicted classification deviates only for sample “Le”, that is not distinct from “X” (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020222#pone-0020222-g001" target="_blank">fig. 1d</a>). Since these two samples are in the same group (the green one), group classification is 100% correct. For technical details of multivariate techniques see Statistica 7.0 Electronic Manual (Statsoft Inc., 2005).</p

Air view maps of the crime scene and surroundings depicting the sites from which samples were taken, starting from the murder spot, and at increasing distances.

<p>The inset map of Italy shows the area location as well as the sites of two soils taken at far distance as outgroup references. X: spot where the corpse was found (margin of a corn field), CF (inside corn field), Le (ridge of the levee bordering the corn field). 1.7 Km, 3 Km, 18 Km, 19 Km: sites located at 1.7, 3, 18, 19 kilometers from the murder site and sharing the same crop (corn); 1.8 KmF: (fallow), site located at 1.8 km but not cropped for over 50 years and featuring natural vegetation and secondary growth. Sar: soil from an uncultivated area in Sardinia (Castelsardo); Alp: soil from an uncultivated area in the Alps (Soranzen). All samples, except the two outgroup references, were chosen in equivalent soil conditions as regards parent material, depositional basin river and soil type (Hypercalcaric-Fluvic Cambisols, WRB 1998, or Oxyaquic Eutrudept fine-silty, carbonatic, mesic, USDA 1998) to minimize the variability that would occur across different soil types. For details and exact coordinates, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020222#pone-0020222-t001" target="_blank">Tab.1</a>. Scale bars equal: 500 km (a); 5 km (b); 500 m (c); 50 m (d).</p

Results of the analyses on the soil samples compared with two specimens of soil (CarR, CarL), found respectively on the right and left carpets of the suspect's car floor.

<p>a, b) ICP analysis of the content of 54 mineral elements. a): Box and whiskers synthetic representation of the variability of the 12 zones; b) Cluster analysis (single linkage, Euclidean distance) of the data; c) Amplified Ribosomal DNA Restriction Analysis (ARDRA) of the soil bacterial communities. The Neighbour Joining dendrogram resulting from Pearson correlation analysis of the combined three enzymes electrophoretic profiles is shown. The horizontal scale indicates the percent distance. In b) and c) sample replicates are included to show the degree of inter-replicate variability.</p

Location of sampling sites: latitude and longitude coordinates in decimal values.

<p>The “Car” samples were found within the car impounded from the suspect.</p

Additional file 10: Figure S6. of Shared and divergent pathways for flower abscission are triggered by gibberellic acid and carbon starvation in seedless Vitis vinifera L

Distribution of differentially changed transcripts and metabolites according to functional categories. Each pie chart corresponds to changes occurred as response to GAc and shade treatments at 5 and 7 days after 100 % cap fall (d). (PDF 234 kb

Additional file 4: Table S2. of Shared and divergent pathways for flower abscission are triggered by gibberellic acid and carbon starvation in seedless Vitis vinifera L

Parameters for hormone identification in Mass Spectrometry. Cone voltage potential, collision energy (CE) and other performance characteristics. (PDF 67 kb

Additional file 11: Table S5. of Shared and divergent pathways for flower abscission are triggered by gibberellic acid and carbon starvation in seedless Vitis vinifera L

Enzymatic classification of differentially expressed genes and respective fold-change for each treatment at 5 and 7d. (XLSX 68 kb

Additional file 6: Table S3. of Shared and divergent pathways for flower abscission are triggered by gibberellic acid and carbon starvation in seedless Vitis vinifera L

List of genes significantly affected by GAc and shade treatments. Gene annotation, functional categories and respective fold-change. The abbreviations GAc5d, GAc7d, SH5d and SH7d mean the log2 fold-change between gene relative expression obtained in treated and control inflorescences, at 5 and 7 days after 100 % cap fall. (XLSX 777 kb