Bibliographic record
Abstract
INTRODUCTION The making of wine is a fungal biotransformation; in addition, fungi have the potential to affect this process at every stage from the grape vine to consumption. Several fungi are pathogenic to grape vines, infecting the roots, trunk, canes, leaves and berries (Pearson and Goheen, 1988). Fungal diseases may affect crop yield and quality, which, in turn, may affect wine at its most fundamental level the decision whether or not it is made! Fungi which infect berries (Hewitt, 1988; Emmett et al., 1992) include the mildew pathogens Erysiphe necator (Uncinula necator) and Plasmopara viticola, Alternaria spp., Aspergillus spp., Botrytis cinerea, Cladosporium spp., Penicillium spp. and Rhizopus spp. These infections are wellrecognised and either cause damage sporadically in seasons in which conditions favour disease, or are controlled through vineyard management techniques, such as fungicidal sprays. Changes in the berry due to fungal infection may in turn alter the properties of the wine. Once vinification commences, filamentous fungi are less of a consideration, although their prior activity may affect the wine. Yeast strains are selected to confer desirable flavours and aromas to the wine during fermentation; conversely, undesirable strains and species may kill the desirable yeasts, hinder fermentation or produce off-flavours (Kunkee and Bisson, 1993; Fleet, 2003; Loureiro and MalfeitoFerreira, 2003). Post-fermentation, yeasts may continue to produce off-flavours, referment residual sugars, grow on the surface as a film, or cause cloudiness and sediment formation. These problems are generally well managed in wineries; however, Dekkera spp. (anamorphs in Brettanomyces) are spoilage yeasts which appear to be of growing concern to the industry. After bottling, filamentous fungi may again affect wine through their involvement in cork taint (Lee and Simpson, 1993). A recent mycological food safety challenge to confront the wine industry is the production of ochratoxin A (OA), an isocoumarin derivative linked to phenylalanine, by black Aspergillus spp. in grapes, and its subsequent passage into wine. Aspergillus bunch rots caused by black Aspergillus spp. sporadically occur in vineyards situated in warm to temperate regions (Snowdon, 1990). These fungi typically invade berries via insect punctures or splits in the berry skin. Abarca et al. (1994) reported production of OA by two strains of A. niger from feed, and in the following year, Horie (1995) reported OA production by a second species, A. carbonarius, which has long been known to cause grape rots (Gupta, 1956). OA, a nephrotoxin, was detected in wine only recently (Majerus and Otteneder, 1996; Zimmerli and Dick, 1996), and Heenan et al. (1998) isolated ochratoxigenic black Aspergillus spp. during dried grape processing, suggesting that such species may indeed be the source of OA in grapes. This chapter summarises current research on the occurrence of OA in wine, its production in grapes, fate during vinification, and the potential contribution of DNA-based studies to this problem. The effects of some other fungal infections on wine quality are also discussed. OCHRATOXIN A IN WINE The incidence and degree of OA contamination in wine is now fairly well documented (Figure 1; tabulated in Otteneder and Majerus (2000), Bellí et al. (2002), Stefanaki et al. (2003) and Blesa et al. (2006)). OA was detected more frequently in wines from Mediterranean countries and northern Africa, following a trend for increased prevalence in wines from southern (warmer) regions compared with northern regions, and in red compared to white wine (Majerus and Otteneder, 1996; Zimmerli and Dick, 1996; Ospital et al., 1998; Otteneder and Majerus, 2000; Markarki et al., 2001; Pietri et al., 2001). Occurrence of OA in wines from the socalled new world (USA, Canada, South America, South Africa, Australia, New Zea- land) was low. In Australia and South Africa, no obvious differences were observed between OA in red and white wines, and wines from warmer areas did not show increased contamination rates (Stander and Steyn, 2002; Hocking et al., 2003; Leong et al., 2006a). Less expensive wines, such as those sold in plasticlined cardboard boxes, were more frequently contaminated than bottled wines in Australia, Italy and South Africa (Tateo et al., 2000; Tateo and Bononi, 2001; Stander and Steyn, 2002; Hocking et al., 2003; Tateo and Bononi, 2003). OA contamination was also more frequent in certain dessert or fortified wines such as Moscatel, Marsala and Malaga (Zimmerli and Dick, 1996; Burdaspal and Legarda, 1999; Stander and Steyn, 2002; Bellí et al., 2004a; Blesa et al., 2004), whereas its occurrence in ports and sherries was not greater than in still table wines (Festas et al., 2000; Ratola et al., 2004).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".