The wines made from control and sort treatments are more closely associated with each of the significant attributes; this trend generally matches the results from the chemical analyses. Both the wines made from control and sort treatments were higher in ethanol content, which can explain their greater association with “alcohol hotness” when compared to reject treatment wines. It is possible that the higher ethyl ester concentration in the control and sort treatment wines could explain why they are rated significantly higher in the “apple” aroma. Most ethyl esters have fruity aromas which the judges could have rated as “apple”. Curiously, the control and sort treatment wines are rated significantly higher in “sweet” as well despite the residual sugar content of all wines being less than 1 g/L , which is below the sensory threshold . All three significant attributes for the CS panel were rated similarly between control and sort treatment wines. This suggests that these wines made from these treatments had similar sensory properties.Analysis of wine color revealed that there were perceivable differences among treatments for all three varieties . For BA the reject treatments were rated lighter in color compared to the control and sort treatments, whereas a similar trend was observed in the CS treatments. This was expected because berries with less color were removed by the optical sorter and included in the reject fermentations. This agrees with results from Table 6; the rejected treatments were significantly lower in anthocyanin content for BA and CS, plastic gutter which can explain the difference in color perception. For GN wines, the control treatment was perceived to be slightly darker than the sort and reject treatments.
Although fermentations were prepared to have similar solid-to-juice ratios in the must among treatments, it is possible that variations between replicates may have resulted in the control treatments being slightly more concentrated, which could provide an explanation for this result. Color perception from the panelists matches well with the wine color determined in the CIELAB color space . It can be concluded that optical sorting was generally successful in removing berries with less color; however, this did not lead to a large difference in the final color of the wines between the sort and control treatments.Multiple Factor Analysis was performed for each variety using all sensory attributes and only volatile compounds that differed significantly among treatments . This was done to observe the association, if any, of the significant volatile compounds and sensory attributes. For GN wines, the only significant attribute was “SO2”. From Figure 7, isobutanol, which can impart a solventlike aroma in wine, is grouped closely with “SO2”. It is possible that wines with a higher isobutanol concentration were perceived to be higher in “SO2” aroma. For BA wines, there does not appear to be a trend among sensory attributes and volatile compounds . For CS wines, “apple” is grouped closely with ethyl esters , which provides evidence that this may have caused the increased perception of this attribute in the control and sort treatments .Overall, optical sorting had minimal impact on the sensory properties of the three varieties tested. It is possible that the chemical differences noted earlier were too small to result in consistent differences by descriptive analysis.
Even though the wines made from reject material contained significantly higher concentrations of higher alcohols, it did not result in a difference in sensory perception. Higher alcohols have a relatively high sensory threshold . It is possible that the concentration of these compounds in the reject wines was below the sensory threshold.The purpose of this study was to determine what effects, if any, optical berry sorting had on wine made from different red grape varieties, and to investigate the potential to use optical sorters to sort for different ripeness levels using color as a main criterion. Given the observed differences in Brix and final ethanol content, optical sorting seemed to be successful in removing underripe berries for CS and possibly for BA; however, this did not result in a significant difference in the final ethanol content between the sort and control treatments. The removal of underripe berries was also evident by the difference in color among treatments. For BA, the rejected treatments were significantly lighter in color; however, the color of the sort and control treatments was very similar, whereas a similar trend was observed in the CS treatments. Wines made from GN generally did not follow these trends; possibly because sorting parameters were too aggressive for this cultivar, resulting in a high percent rejection of optimal berries. This may have minimized potential differences between reject wine with the other treatments. Another possibility is that color differences in the GN fruit did not correspond to differences in sugar content. From these results, it may be concluded that, when using color as a criterion, optical sorting based on ripeness level was successful but may be dependent on variety and fruit variability. Additionally, the impact on the resulting wine is likely dependent on the initial variability in grape ripeness.
The optical sorter was successful in removing MOG. Thisresult was reflected in the phenolic analyses; reject treatments were generally higher in total phenolics and tannin, most likely due to the greater proportion of MOG included in the must. The decrease in anthocyanins is likely due to the higher percentage of green, underripe berries in the reject treatment musts. A study that made wine with the addition of MOG found that this addition significantly increased the phenolic and tannin content in the resulting wines. Despite the differences observed in the phenolic composition of the reject wines, the control and sort treatments were very similar for all three varieties. This is in contrast with some previous studies that have found wine made from optical sorted fruit had significantly different levels of phenolics. One study found that optical sorting led to wines with higher levels of total phenolics. It should be mentioned that the researchers here did whole cluster pressing for their control wines , whereas the sorted wines were destemmed. It is possible that higher levels of phenolics were extracted due to the damage caused by the destemming process on the seeds and skins. Another study found that wine made from optically sorted grapes that were machine harvested generally had lower levels of phenolics; levels that were similar to the same wines made from a handpick treatment. Given that the rejects were, in general, significantly higher in total phenolics and tannin than the control and sort treatments, it can be suggested that optical sorting has the potential to decrease the phenolic content in wine; however, there was not enough MOG to show a large impact in the current study. Optical sorting likely has a greater impact on mechanically harvest fruit due to generally higher levels of MOG observed from this harvest method. Some differences were found among treatments in the aroma profiles of the wines. Few compounds differed significantly between sort and control treatment and, in general, the reject treatments had greater concentrations of higher alcohols and control and sort treatments had greater concentrations of ethyl esters. The higher ethanol content of the sort and control treatments as well as their lower pH can lead to a higher production of esters. In general, blueberry container reject treatments contained significantly more suspended solids then the control and sort treatments for all varieties studied. Research has shown that high levels of suspended solids during fermentation can lead to greater production of higher alcohols. Descriptive analysis indicated only one significantly different attribute among GN treatments and only two significantly different attributes among BA treatments. BA control and sort wines were associated with the “alcohol” descriptor which correlated with the higher ethanol levels in these treatments compared to the reject treatment. Similarly, there were only three significant attributes among the CS treatments. “Alcohol hotness” related to ethanol content as previously described. The control and sort treatments were also rated significantly higher in “apple” and “sweet” aromas compared to the reject treatment. Some studies have shown that higher levels of ethanol can increase the perception of sweetness in a wine. However, as King et al. noted, there is disagreement in this regard, as other studies have shown that ethanol content can either decrease or have no effect on the perception of sweetness. Thus, this may not be a sufficient explanation as to why the control and sort wines were rated significantly higher in sweetness. Perhaps the higher concentration of total phenolics and tannin in reject wines could explain the difference given that phenolics in wine contribute to bitterness and astringency. From the PCA in Figure 6, it can be noted that “bitter” and “drying” are more associated with reject wines. Although these attributes are not significantly different among the treatments there appears to be a trend which could impact the perception of sweetness. One study found that increasing bitterness in coffee decreased the perception of sweetness. It is possible that reject wines were rated lower in “sweet” due to the higher concentration of phenolic compounds thus decreasing the perception of sweetness. The higher perception of sweetness in the control and sort wines may also be attributed to the higher intensity of the “apple” aroma, which the judges could have associated with a sweet taste.
One study found that retronasal aromaperception of fruity compounds increased with an increasing level of sweetness in a model wine solution. The authors also noted several other studies which found that aroma compounds can enhance the perception of sweetness in different foods and beverages. Another study found that samples described as “fruity” were also often associated with a “sweet” aroma. This provides further evidence that the judges in the current study may have associated these attributes together. The overall sensory differences were minimal, and the wines were determined to be similar. The results from this study largely agree with results from previous studies investigating the effects of optical sorters. It is possible that there was not enough variation in the starting material of the current study for optical sorting to have a large impact. Optical sorters may be used to greater effect during vintages with inconsistent ripening, issues with raisining, or large amounts of berry damage, possibly caused by either birds and/or fungal infections. Future research should investigate the impact of optical sorters in these scenarios.Grapevine has indeterminate growth habits compared to other perennial fruit crops. Latent growth of the dormant grapevine bud may be induced by favorable conditions with little to no dormancy period required . Therefore, semi-tropical regions may raise two crops a year, and in fact, it is not uncommon for the latent bud to produce some fruit when correlative inhibition is removed in temperate regions. Furthermore, the grape berry does not have the same fruit abscission mechanism as apple or peach revealed under carbon starvation. It is therefore possible for grapevine canopy size and crop level manipulations leading to a wider range of source or sink limiting conditions within a growing season. The number and size of the flower primordia is associated with number of clusters and berries per cluster through the formation flowers and fruit set . However, fruit set is largely variable among years, weather, location, and cultivars . Poor fruit set may be a limitation to crop yield, although weather is often considered to be the leading cause. However, the mechanism of poor fruit set is not fully understood. Carbon supply or mineral nutrition are related to the amount of fruit set , which is an acclimation mechanism to unfavorable conditions. Ultimately, yield of grapevine is affected by berry size, and within the berry, pulp enlargement is the largest contributor to yield gain rather than skin or seed biomass . Conversely, vegetative growth is far less influenced by latent bud formation, as competition amongst growing buds tends to buffer the impact of growing shoot tips on its length and total leaf area . This is likely due to the great limiting effect of nitrogen among other nutrients or hydraulic pressure . The ratio between leaf area and fruit mass is closely related to the amount of carbohydrates accumulated in the must . Thus, an excessive crop level or less than ideal canopy size may result in over cropping and may lead to delayed ripening .