Fig 1.
Methodology similarities between WDF and forensic DNA-testing.
Methodological similarities between WDF and human genotyping both require DNA purification, and in forensic applications, DNA admixture analysis. The individual genotype is generally reconstructed by nuclear genomic DNA amplification targeting multiple SSR loci. The identification power of the test is designed to detect genotype rarity in billions of individuals (human) or in thousands (grapevine varieties). WDF test conditions can be less stringent than those carried out for humans due to smaller population size and the less complicated population structure, principally determined by the clonal propagation of the grapevine. Bioinformatic data elaboration from wine is an essential step for WDF’s validation.
Table 1.
Wines listed from 1 to 18 with increasing varietal complexity either by nature (experimental to commercial), or detailed knowledge of their composition (grapevine variety, number of varieties used for the blend).
Fig 2.
Graphical representation of the Sangiovese-based WDFs.
Sangiovese Wines DNA fingerprints (WDF) were used for constructing graphical outputs of the genetic distances among wines and grapevines genotypes. 2 A: wines: 1 = small scale fermented CB17; 2 = small scale fermented IN7. Grapevines: 3 = Sangiovese (SG); 4 = SG-related variant Caprili; 5 = SG-related variant CB17; 6 = SG-related variant IN7. 2 B: wines: 1 = Brunello di Montalcino; 2 = Rosso di Montalcino. Grapevines: 3 = Sangiovese (SG); 4 = Cabernet Sauvignon (CS); 5 = Merlot (M); 6 = Pinot Noir (PN); 7 = Zinfandel (Z). Red branches link the wines, black ones the grapevines. Only the wines are shown with explicit names; the numbers associated to a two-capital letter acronym code refer to the reference grapevines.
Fig 3.
Graphical representation of the Valpolicella region (Veneto, Italy) and an IGT wine from Tuscany.
Amarone, Valpolicella Classico (Valpolicella, Veneto, Italy) and Alicante wine (Tuscany, Italy) DNA fingerprints (WDFs) graphical outputs. 3 A: wines: 1 = Amarone; 2 = Valpolicella classico. Grapevines: 3 = Corvina (CO); 4 = Corvina-like (CO-like); 5 = Rondinella (RO); 6 = Riesling (R). 3 B: 1 = Alicante wine. Grapevines: 2 = Alicante genetic variant (AL-genetic variant); 3 = Alicante (AL); 4 = Cabernet Sauvignon (CS); 5 = Merlot (M); 6 = Pinot Noir (PN); 7 = Zinfandel (Z); 8 = Sangiovese (SG). Red branches link the wines, black ones the grapevines.
Fig 4.
Graphical representation of the Vernaccia di San Gimignano wines WDF.
Vernaccia di San Gimignano wines are closer to the Vernaccia di San Gimignano major varietal component. Wines: 1 and 2 = Vernaccia di San Gimignano. Grapevines: 3 = Chardonnay (CH); 4 = Viognier (VI); 5 = Manzoni Bianco (MB); 6 = Pinot Gris (PG); 7 = Trebbiano Toscano (TT); 8 = Sauvignon Blanc (SB); 9 = Riesling (R); 10 = Vernaccia di San Gimignano (VSG). Red branches link the wines, black ones the grapevines.
Fig 5.
Graphical representation of the WDFs of four varietals, blended, unknown wines from the US market.
Red wines 947, 949, 950, 951 from the American market were subjected to WDF and subsequent bioinformatics elaboration of genetic profiles. In all graphs 1 is the wine, the grapevines are: 2 = Cabernet Sauvignon (CS); 3 = Merlot (M); 4 = Pinot Noir (PN); 5 = Zinfandel (Z). 947 was revealed to be a varietal Zinfandel, 949 a varietal Cabernet Sauvignon, 950 Pinot Noir, 951 a Merlot. Secondary, undeclared varietal components might be present in the blend, such as Zinfandel in the 950 and 951, Pinot Noir in the 949. Red branches link the wines, black ones the grapevines.
Fig 6.
Consistency of WDF testing over time.
Three varietal wines were tested for WDFs after one, three, five and seven years, since wine production. The varietal Cabernet Sauvignon wine (1 = CS wine) is grouped with Cabernet Sauvignon grapevine (4 = CS, light pink bubbles), after three, and seven years. The Pinot Noir varietal wine (3 = PN wine) is correctly grouped with Pinot Noir grapevine (6 = PN, blue bubbles), after one and five years. The Merlot wine (2 = M wine) is genetically related to the Merlot grapevine variety (5 = M, pale green bubbles), in the WDF performed after one year, while it loses the correct closeness to the main varietal component after five years. The Zinfandel (7 = Z) added to the Merlot wine at 1.238% appears to be detectable only after one-year. Red branches link the wines, black ones the grapevines.
Fig 7.
Clustering of Sangiovese-based wines.
After data merging from multiple WDF experiments into a single dataset, the clustering reveals how the Sangiovese-based wines and experimental wines remain close to their varietal origin. Wines: 1 = Brunello di Montalcino Caprili 2014; 2 = Rosso Di Montalcino, Loia 2013; 3 = Small-scale fermented wine CB17; 4 = Small-scale fermented wine IN7. Grapevines: 5 = Sangiovese (SG); 6 = Cabernet Sauvignon (CS); 7 = Merlot (M); 8 = Pinot Nero (PN); 9 = Zinfandel (Z);10 = Sangiovese-related variant Caprili (SG-C); 11 = Sangiovese-related variant Case Basse CB17 (SG-CB17);12 = Sangiovese-related variant Case Basse IN7 (SG-IN7).
Fig 8.
After data merging from multiple WDF experiments into a single dataset, the clustering of the commercial US wines unambiguously resolves the varietal nature of the wines. 8 A: White varietal wines (from left): 940 = 3, 953 = 1, 948 = 2, cluster with their respective varietal main components: 6 = Chardonnay (CH), 5 = Sauvignon Blanc (SB), and 4 = Riesling (R). 8 B: Red varietal wines (from left): 947 = 2, 950 = 1, 949 = 4, 951 = 3 cluster with their respective varietal main components: 8 = Zinfandel (Z), 7 = Pinot Noir (PN), 5 = Cabernet Sauvignon (CS) and 6 = Merlot (M). The wines 949 (4) and 951 (3) and their respective original varieties Cabernet Sauvignon and Merlot, are put in the same branch due the high similarity between these two varieties at the molecular marker panel tested.
Fig 9.
Method of aggregating different layers using regression or clustering approaches.
Varying ω using the method of aggregating on unfiltered layers which represent biological, chemical, commercial, manifacturing, environmental, historical and other information.
Fig 10.
Interdependent informative inputs and data networking for wine ontology implementation.
Multidimensional networking among the main features influencing a wine. Wine traits (DNA profile, metabolomic and chemical profiling) are in turn interconnected to personal tastes and general environmental factors (e.g. climate). Bioinformatics elaborates multilayer input data contributing to wine ontology fed by semantic web languages.
Fig 11.
Towards a new wine ontology databank system.
According to a new wine ontology model based on a bioinformatics elaboration of multidimensional wine traits, a scientific unambiguous labeling of wine can be obtained. Bioinformatics is a common, essential tool for the validation of multiple, analytical approaches to wine authentication: molecular, chemical, metabolomic profiling, which merge into a comprehensive wine ontology databank fed by bioinformatic tools.