Grape quality control app with AI Quality Management:

Grape quality control app with AI Quality Management for rapid grape quality testing, and complete grape packing business management. Consistent quality grapes, less waste. Reduce quality control costs. Eliminate price negotiations, and QC mistakes.

Easy grape packing; better traceability.

Grape quality control app with AI Quality Management:

Grape quality control app with AI Quality Management for rapid grape quality testing, and complete grape packing business management. Consistent quality grapes, less waste. Reduce quality control costs. Eliminate price negotiations, and QC mistakes. 

Grape Quality controls during production

Quality tests can be performed on fresh produce and other ingredients used during packing or manufacturing, these quality tests relate directly to the materials (and their suppliers & PO's) tested, and also to the specific packing / manufacturing batch. 

Daily Grape packhouse hygiene checklist

Perform common tests like Daily Packhouse Hygiene checklist, Daily Factory Hygiene checklist, Monthly External Site control and more.  You can created unlimited quality programs and relate them to your Quality Management System. 

Grape Quality control

Perform QC tests for incoming inventory, packed, pre-shipping. Configure QC tests for ANYTHING you want to test, supplier quality control tracking.  Attach unlimited photos & documents to QC tests from your cell or tablet - integrate with your QMS.

Grape Supplier quality control

Rapidly perform quality control tests on fresh produce from suppliers.  Compare the quality  performance of multiple suppliers, and compare quality criteria performance.  Provide quality feedback to suppliers, integrate into  your QMS.

Grape Quality control dashboard

Instantly turn your quality control data into useful and interpretable quality information. Internal quality monitoring, supplier performance.  Discover quality trends and provide suppliers with useful quality feedback.  

Grape Quality control labels

Optionally show a QR code on customer or consumer units that will instantly show the quality control results for that batch of fresh produce.

Farmsoft QC Quality control app makes fresh produce quality control rapid and accurate for all fresh produce packers:  cherry, berry, onion, pepper & capsicum, avocado, potato quality, broccoli, salad quality control, spinach, lettuce, cucumber, tomato quality, citrus, asparagus, garlic quality control app, carrot quality, bean, mango, leafy greens, fresh cut quality control, food service quality app, coleslaw quality, strawberry quality control app, grape quality, meat quality control app, flower quality.

Practical methods of measuring grape quality
Grape quality depends to a large extent on various metabolites, timing and completeness of ripening and the ripening synchrony of skins, seeds, stems, and pulp. This chapter outlines grape quality issues of importance in stylistic winemaking.

How Do You Define Quality In grapes?
We hear and read the adage, “you can’t make quality wine without quality grapes” all the time, and there is certainly truth in that statement. But again, what are we calling quality? How do we measure it? And how does a grower manage for “quality?” We obviously can’t measure quality directly, and you can’t manage what you can’t measure, so there have to be alternative measurements that we can use to determine if we are producing quality fruit. This is where communication between growers and wineries becomes paramount. If there isn’t an understanding ahead of time as to what constitutes quality grapes, it can lead to problems down the road.

Determining Quality

So what are some things that can be used to try to get at “quality?”

• Brix: This has always been, and continues to be, the most commonly used parameter by growers and wineries to make picking decisions, partly because it is so easy to measure. In some cases, sugar development tracks very well with other factors like color or acidity. However, we have seen a lot of situations out West where fruit achieves very high Brix levels but does not develop acceptable color or flavor. And on the flip side, fruit with lower sugar levels, and thus lower alcohol levels, can make some very good wines depending on the desired style.

• Acidity: In some situations, there may be more concern about acidity levels in the fruit than sugars. Techniques and materials are now used in many wineries, however, to adjust acidity levels in the cellar to where the winemaker wants them, so this is perhaps a bit less of an issue than other quality measurements.

• Fruit Condition: Here in the East, it is very difficult not to have at least a little bit of bunch rot develop on some clusters, and any winemaker who has experienced more than one harvest out here knows this and learns to manage it. At some point, though, enough rot will start to cause off-flavors and aromas that can negatively affect the quality of the finished product. Some rots are worse than others, too. A load of Riesling with 2% botrytis may actually enhance flavors, while 2% sour rot infection may be highly problematic.

• Color: Red grapes with poor visible coloration certainly can be an indication of fruit that is underripe or has other problems. But the factors that really influence just how much color potential there is in a final wine, like the anthocyanin content of the berries, are hard to measure without some serious lab equipment and personnel. And a lot of what determines the final color of a red wine happens during and after fermentation, so even pressed or macerated fruit may not provide a very good indication of what the final color will be.

• Flavor: Ah, yes, the ultimate arbiter of quality. In a finished wine, this is certainly true, but can you tell what the flavors of the wine will be like by tasting the grapes? Some recent studies have suggested there isn’t necessarily much correlation between the two. What is more realistic is to identify the absence of certain flavors, like methoxypyrazines that produce bell pepper flavors or harsh, bitter tannins.

Ultimately, of course, the final answer to the question, “What is quality?” comes down whoever is buying the fruit, and those answers may very well change from business to business. Growers have to be willing to adapt their practices and communications to each buyer’s needs in order to produce quality fruit, however that may be defined.

Assessing wine grape quality parameters using plant traits derived from physical model inversion of hyperspectral imagery
Together with ensuring a stable yield, improving grape composition and aroma is the main goal of wine grape production management as it determines consumer acceptance and ultimately revenue. Understanding the triggers of the synthesis of aromatic components and finding methods to map their variability in the field can aid management practices during the season and planning selective harvest in views of maximizing benefit. Vegetation indices have been shown to track grape colour, sugar and acidity content but it has been demonstrated that aromatic components are the main drivers of the final palate of wine and are not correlated to sugar concentration. Leaf pigments such as chlorophyll, carotenoids and anthocyanins are involved in the metabolic pathways of aroma compounds in grapes. The physiological connections between grape aromatic components and primary and secondary photosynthetic pigments suggest that they could be used to detect processes related to aroma composition.

This study investigates the links between grape quality parameters such as aromatic components and image-quantified spectral indices and photosynthetic plant traits derived by physical model inversion methods. Two sets of high-spatial resolution hyperspectral and thermal imagery were collected with an unmanned platform at veraison and harvest. The variability found in the field was partly but not fully explained by the thermal-based crop water stress index as an indicator of water stress (r2= 0.51–0.58, p-value<0.01). Fluspect-CX leaf model was coupled to 4SAIL canopy model and inverted to map the main photosynthetic pigment groups and the fraction of pigments acting in photoprotection. Results obtained through radiative transfer model inversion outperformed traditional vegetation indices related to pigment content and degradation. We found statistically significant relationships between image-retrieved pigments and terpenoids responsible for wine aroma (p-value<0.005).

Grape quality parameters that influence wine flavour and aroma: identification, confirmation and application to industry
Cabernet Sauvignon and Chardonnay grape sensory attributes and chemical compositions were mined to predict relationships with wine sensory characteristics. In parallel, winemaker panels defined sensory characteristics associated with quality Cabernet Sauvignon or Chardonnay wines. Relating the sensory characteristics of Cabernet Sauvignon or Chardonnay grapes to wines was challenging, as was modelling the entire wine sensory space using grape measures. However, modelling individual wine characteristics successfully linked blocks of grape measures to wine attributes. Knowledge generated from this project will form the basis for future development of measures of grape flavour potential and strategies to produce fit for purpose fruit.

Considerable research into the chemical basis for sensory attributes in wine has been undertaken but there has been less focus on understanding the links of grape composition to wine chemistry and wine sensory properties. In limited cases, a wine sensory attribute can be assigned directly to a specific grape metabolite; for example, pepper due to rotundone, green capsicum resulting from 3-isobutyl-2-methoxypyrazine, and floral characters from monoterpenes. Beyond that, sugars, amino acids, lipids, micronutrients, and other grape constituents will contribute to the suite of compounds produced during winemaking that are important to wine sensory properties. An increasing body of evidence emphasises the importance of grape composition on the potential of a wine to have certain sensory characteristics. Nonetheless, the previously identified gap in the literature still remains, and there is a lack of knowledge that explains how parcels of grapes from the same variety, and possibly same vineyard, can result in very different wine sensory outcomes. In addition, there is little information tracing vineyard management practices and effects of the environment on production of grape metabolites that subsequently influence wine chemistry and sensory. With regards to “quality” measures, some success has been achieved with red grape colour and this project aimed to develop other measures that predict wine sensory outcomes.

The project methodology consisted of three main parts. 1) In each of the first three vintages of the project (2013-15), 25 Cabernet Sauvignon grape parcels were obtained from our industry partners from both warm and cool growing regions and vinified using an identical small-scale fermentation protocol. A subsample of each grape parcel was analysed using multiple methods to quantify various classes of compounds including: amino acids, volatile compounds, bound volatile compounds, anthocyanins, flavonols, tannins, fatty acids and total phenolics. Other measures such as CIELab colour, normal harvest parameters and activities from lipoxygenase pathway enzymes were also conducted, as well as berry sensory analysis (BSA). The corresponding wines were analysed by a trained descriptive analysis (DA) sensory panel and rated for quality by a winemaker panel. The multiple datasets were then analysed for predictive relationships. 2) A similar experimental setup was applied to Chardonnay for two vintages (2015-16). Again, 25 grape parcels were obtained each year from our industry partners in vineyards spread across South Australia. These were vinified using a controlled protocol, and the wines profiled by DA and assessed for quality by a winemaker panel. BSA was conducted on the grape parcels and measures deemed to be relevant to Chardonnay fruit composition were applied to the fruit. Predictive relationships among the data were explored in a similar way to Cabernet Sauvignon. 3) Biochemical methods were used to study the pathways of two classes of secondary metabolites during Cabernet Sauvignon berry development, to explore whether genetic and biochemical markers could indicate changes in berry metabolism.

The study of Cabernet Sauvignon over three vintages showed that the major driver of differences in the sensory attributes of the wines was the region of origin. In general, the wines from the Riverland had lower sensory scores for dark fruit flavour and aroma, body, overall flavour and aroma and astringency, compared to the wines from other regions. These same wine sensory characteristics were identified by the winemaker sensory panel as indicators of higher quality, whereas lower quality wines were described as green, simple and poor in colour. Interestingly, some Riverland Cabernet Sauvignon wines that were graded higher than others, possessed “balance”, which is a holistic sensory percept that the detailed sensory profiling did not capture. To achieve a more complete understanding of wine quality, the drivers for concepts such as balance (and complexity), and not merely a list of specific wine sensory attributes, clearly require further investigations.

Because the study involved such a large number of data sets collected during an extensive metabolomics analysis of the grapes (12 data blocks for Cabernet Sauvignon, 9 for Chardonnay), a novel data analysis method called sequential and orthogonalised partial least squares (SO-PLS) was used to select data blocks that were predictive of wine sensory attributes. It also highlighted the data blocks that were least frequently used to model sensory perception and can likely be removed from future studies. The current study confirmed previous findings that some grape volatile measures are important for modelling sensory attributes in both Cabernet Sauvignon and Chardonnay wines. However, many other data blocks, arising from the quantification of groups of grape metabolites beyond grape volatiles, were better at modelling a range of wine sensory characteristics, and novel correlations between particular sensory attributes and blocks of grape metabolites were demonstrated. The grape target compounds may not necessarily be precursors to wine aroma volatiles, but may act as markers that indicate altered berry metabolism and composition. These grape biochemical markers of wine sensory outcomes would be useful in streaming or grading fruit once suitable protocols for their measurement can be developed and verified.

Winemaker quality assessment proved successful with the Cabernet Sauvignon wines but did not significantly discriminate the Chardonnay wines. When vinified with a simple, identical protocol, sensory differences across Chardonnay wines were detectable albeit subtle, and these results suggest that quality drivers of commercial Chardonnay wines are most likely not derived solely from grapes. Sensory differences were not consistent across the vintages and often did not relate to regionality. All nine blocks of grape measures were used in the models developed to predict wine sensory attributes so there appear to be many potential indicators of Chardonnay wine flavour. Our results suggest that vinification factors likely contribute more to the variation in sensory characteristics of commercial Chardonnay wines than the grapes, and perhaps grape measures of quality would be more suited to red varieties or other white varieties with distinct varietal characteristics.

Principal component analysis (PCA) of the data blocks enabled observation of the year to year variation in grape measures, yielding information about their stability across regions and vintages. This enables the identification of aspects of grape composition that can potentially be manipulated in the vineyard and those that may be more prone to variability due to unknown or uncontrollable environmental factors. Amino acid composition of the Cabernet Sauvignon grape samples was similar in samples taken from the same vineyard across the vintages, and was not primarily driven by region. This suggests that something intrinsic to the vineyard influences grape amino acid composition but exploration is required to determine whether this can be managed. Other variables, such as anthocyanin or tannin composition, appeared to be driven mainly by region, suggesting that broad climatic or regional management differences may be important determinants. Bound volatile compound and fatty acid compositions were somewhat related to region but they also varied from year to year. Their concentrations may be altered by environmental variables and could be managed if conditions in the bunch zone could be altered to mimic environmental changes.

Studies of the expression of genes from the lipoxygenase pathway and those responsible for the breakdown of carotenoids showed that the enzyme activity in the fruit was often the result of multiple genes and that maximal gene expression was often separated temporally from the peak in enzyme activity. This would make it difficult to use gene expression assays to predict pathway flux at harvest. Nevertheless, understanding where and when these pathways are active in the fruit is important for the development of strategies to manage the production of important aroma precursor compounds in the vineyard. This has important implications given the outcome that carotenoid content in Cabernet Sauvignon may predict the concentration of β-damascenone, a compound implicated in red wine quality.