In an effort to improve wine quality, many smaller high-end wineries employ laborers to hand sort individual berries after destemming to remove unwanted material such as raisins, diseased berries, unripe berries, and materials other than grapes such as leaves and stems. This can be costly, labor intensive, and it can slow down the process line. To reduce costs and increase throughput, many wineries have adopted optical sorting technology. Using this technology, MOG can be removed more efficiently, and parameters such as color, shape, and size can be used to sort individual berries. Depending on the type of sorter, processing speeds can range between 2 and 15 tons per hour. Furthermore, fewer workers are needed to operate an optical sorter than to hand sort the respective amount of fruit. In addition to saving time and money, optical sorters have the potential to decrease the impact of inconsistent ripening in grapes. One study successfully sorted Carlos Muscadine grapes into four different ripeness levels using light at two different wavelengths in the visible spectrum. The researchers found that with successive sorting levels, there was an increase in Brix and pH, along with a decrease in titratable acidity in grape samples. In the wines, an increase in tannin and pH and a decrease in titratable acidity was found with increasing sorting. In sensory analysis, the first and fourth sorting levels were found to be inferior compared to the middle two treatments. Even though this study used outdated equipment compared to today’s standards, growing raspberries in pots it shows that white grapes can be sorted into different ripeness levels and this can affect the quality of the wine produced. A recent study used visible near-infrared spectroscopy to classify table grapes into different groups based on soluble solid and phenolic content.

The researchers were able to differentiate berries of different classes with accuracy ranging from 77% to 94%. Another study found that wine made from optically sorted Chardonnay grapes had higher residual sugar, pH, and total phenols than the unsorted control. The wines were analyzed sensorially with descriptive analysis and the judges scored the sorted wines significantly higher in tropical fruit and sweetness. However, with only two significant attributes out of twenty, the wines were determined to be similar in character. Another study investigating the effect of mechanical harvesting and optical berry sorting on Pinot noir grapes found that, in general, wines made from optically sorted fruit were significantly lower in total phenol and tannin, potentially due to the removal of MOG during sorting. In sensory analysis only two significant attributes out of eighteen were found and it was concluded that the wines were similar in character. A study published in 2014 used an optical sorter on Riesling, Müller-Thurgau, and Pinot gris grapes infected with Botrytis cinerea to investigate the effect of optical sorting on sulfur binding compounds in the finished wine. The researchers found that wine made from optically sorted fruit contained significantly less 2-oxoglutaric acid and pyruvic acid . They concluded that optical sorting is an effective method for reducing the amount of sulfur dioxide needed in the winemaking process using these varieties. There is a lack of published research investigating the impact of optical berry sorting on wine composition and only a few cultivars of Vitis vinifera have been tested. The objective of the current study was to provide more information on the effect of optical berry sorting on different varieties and investigate the capabilities of today’s optical sorters to sort for different ripeness levels using red grapes and using color as a sorting parameter. The current study found that although optical sorting can efficiently replace hand sorting, the overall impact on wine sensory attributes was minimal. Therefore, in general, the study supported the findings of previous researchers.Three varieties were tested in 2016: Barbera , Cabernet Sauvignon , and Grenache .

BA was harvested on 19 August 2016, CS was harvested 30 August 30 2016, and GN was harvested 8 September 2016. All three varieties were hand harvested early in the morning from UC Davis campus vineyards and processed the same day. Fruit condition was good with seemingly little variation, although GN fruit showed more variation in color than the other cultivars. Half-ton bins were dumped by a forklift into a receiving hoper. Clusters were carried by a Delta TR elevator into a Delta E2 destemmer . Destemmed berries fell onto a moving belt and were carried onto a ChromaxHD Berrytek Optical Sorter . Rejection parameters were established by capturing color profiles of optimal berries, suboptimal berries , and MOG. These parameters were optimized with the assistance of a WECO technician for removing suboptimal berries and MOG while rejecting as few optimal berries as possible. This process was repeated, and parameters were adjusted for each variety. The must was pumped directly into 200 L stainless steel research fermentors, which were filled incrementally to reduce vineyard variation. The rejected material was collected in buckets and transferred into research fermentors. The grapes were processed in three treatments, control , sort , and reject . The rejection rates were 14.9%, 3.9%, and 1.5% for GN, BA, and CS, respectively. Juice collected in trays from the rolling belts during processing operations was added back to each treatment in proportional amounts. This was done to maintain a consistent solid-to-juice ratio in the must among treatments. Wines were made in the UC Davis Teaching and Research Winery using 200 L stainless steel research fermentors. Duplicate fermentations were used for the reject treatment wines because only a small amount of reject material was obtained during grape processing.

Fermentation replications were kept separate through the entire experiment. Juice samples were taken from each fermentation vessel after mixing. Fifty milligrams per liter of SO2 was added to the must after processing using a 15% potassium metabisulfite solution. Yeast assimilable nitrogen was adjusted to 250 mg/L using diammonium phosphate , titratable acidity was adjusted to 6 g/L using tartaric acid. The must was heated to 25 ◦C before inoculation with Lalvin EC1118 yeast using the manufacturers rehydration procedure. One tank volume was pumped over twice per day using automated pump overs for all wines except for the reject treatment for CS. The volume in these tanks was too low for the automated pumps to create suction, therefore, the wines were punched down manually once per day during the fermentation. Once wines were dry, they were pressed using a basket press and allowed to settle for 5 days before being racked and transferred to a temperature-controlled room held at 20 ◦C. The wines were then inoculated with Viniflora CH16 Oenococcus oeni bacteria by Chr. Hansen . Upon finishing the malolactic fermentation, 50 mg/L SO2 was added to the wines and they were held in a 9 ◦C cold room until bottling. Free SO2 was adjusted to 30 mg/L before bottling. All samples for basic wine chemical analyses were taken at the time of bottling. Ethanol was measured using an Alcolyzer and pH was measured using an Orion 5-star pH meter . Titratable acidity and free SO2 were measured using a Mettler Toledo DL50 auto titrator . Residual sugar, malic acid, and volatile acidity were measured using a Thermo Fisher Scientific Gallery automated analyzer . Wines were sterile filtered using 0.45 µm membrane filters prior to bottling using green Bordeaux style bottles and screw cap closures with Saranex liners by Amcor . Samples for phenolic analysis were taken from bottles at the time of sensory analysis. The modified Adams-Harbertson assay was used to determine levels of anthocyanin , tannin , and total iron-reactive phenolics. A Genesis 10S UV-Vis spectrophotometer was used for this assay.An external calibration was used for the quantification of phenolic compounds and curves were made for gallic acid, -catechin , -epicatechin , caffeic acid , quercetin-3-rhamnoside, plant pot with drainage and malvidin-3- glucoside. Caftaric acid was quantified as caffeic acid equivalents, quercetin-glycosides as quercetin-3-O-rhamnoside units and all pigments as malvidin-3-glucoside units. Bottle duplicates for each fermentation replication were analyzed and the sequence was randomized. Wine aroma compounds were analyzed using head-space solid-phase microextraction gas chromatography mass spectrometry . The method used was adapted from a previous study. Samples used for wine volatile analysis were taken at the time of sensory analysis and stored at 4 ◦C for no more than one month. Identified volatile peaks are normalized against an internal standard and the obtained data is thus semiquantitative only. Twenty mL amber glass headspace vials were used, containing 10 mL milliliters of wine sample, 3 g of NaCl salt and 50 µL of a 10 mg/L solution of 2-undecanone . Twenty millimeter green magnetic caps with 3 mm PTFE silicone septa were crimped onto the vials and the samples were mixed by vortexing. The analysis was done using an Agilent Technologies 7890A GC system with a Gerstel MPS2 multipurpose sampler . The mass analyzer was an Agilent Technologies 5975C inert XL EI/CI MSD. The column used was an Agilent Technologies DB-Waxetr with a temperature range of 30 ◦C to 260 ◦C. The dimensions of the column were 30 m, 0.250 mm, and 0.25 µm. Maestro software was used to control the instrument and data were collected using ChemStation software . During the analysis, the oven was held at 40 ◦C for 5 min and then increased 3 ◦C/min to 180 ◦C, followed by 30 ◦C/min to 250 ◦C and held for 7.67 min. The MSD interface was kept at 260 ◦C. HS-SPME-GC-MS conditions were as previously described. Shortly, samples were heated to 30 ◦C for five minutes while agitating with a speed of 500 rpm prior to exposing the fiber to the sample for 45 min at 30 ◦C with agitation at 250 rpm. The SPME fiber was desorbed in split mode with a 10:1 split ratio and the inlet temperature was kept at 260 ◦C. Bottle duplicates were analyzed in triplicate for each treatment. Compound details are provided in Table S1. Wines were analyzed sensorially using descriptive analysis in the J. Lohr Wine Sensory Room, University of California, Davis, CA. GN, BA, and CS wines were analyzed approximately two, three, and four months, respectively, after bottling. Three separate descriptive analysis panels were utilized, one for each variety. Eleven panelists were recruited for GN, and ten each for BA and CS.

The panelists were offered $30 gift certificates for completion of the study. The study was approved by the International Review Board and all participants reviewed and agreed to the terms of the experiment. None of the panelists knew details of the experiment. Two fermentation replicates were selected from each treatment totaling six wines for each descriptive analysis study. There were six training sessions and three evaluation sessions. The panelists were given 30 mL of each wine sample for both the training sessions and the evaluation sessions. The wines were presented blind using black wine glasses and the order was randomized for each session. In the first training session, panelists generated descriptors used for differentiating the wines. In subsequent sessions, the reference standards for each descriptor were optimized through panel discussions until there was general agreement. The list of descriptors was narrowed down until therewere twenty descriptors for GN , twenty-six for BA , and twenty-two for CS . Panelists were asked to rate the intensity of each attribute using an unmarked line scale. Reference standards were given as an anchor for the high end of the intensity scale of each attribute. In addition to these attributes, panelists also analyzed color by matching each wine with a color chart . Panelists were given 30 mL of wine in a clear glass and instructed to hold the glass at arm’s length with a white background and match with the closest color on the poster. Scores were reported by assigning number values to each color on the poster. Perceived color differences from sensory analysis were compared to wine colors determined using a CR-400 Chroma Meter using the CIELAB color space.Wines were analyzed by the panelists in triplicate using a randomized block design over a one-week period. All analyses were completed in isolated booths with positive air flow and red lighting. Randomized three-digit codes were assigned to the wines to eliminate biases. Panelists were given breaks in between each wine and were encouraged to drink water and eat an unsalted cracker as a pallet cleanser. All samples were expectorated. Data were collected using FIZZ software .All statistical analyses were carried out using XLSTAT .