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, hydroponic gutter 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.
The control and sort treatments were fermented in triplicate and the reject treatments were fermented in duplicate. 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. Phenolic compounds were also analyzed by a method using reverse-phase high performance liquid chromatography previously described in the literature. Briefly, compounds were measured at four wavelengths; 280 nm -catechin, dimer B1, -epicatechin, epicatechin gallate, and polymeric phenols; 320 nm ; 360 nm ; and 520 nm . Wine samples were stored in HPLC vials in a −20 ◦C freezer until analysis. An Agilent 1260 Infinity HPLC with a diode array detector was used. An Agilent PLRP-S column with an Agilent PLRP-S guard cartridge was maintained at 35 ◦C. Agilent CDS ChemStation software was used for instrument control and data analysis. The injection volume was 20 µL and a gradient mobile phase of water with 0.3% phosphoric acid and acetonitrile with 0.2%phosphoric acid was used at a flow rate of 1.0 mL/min. The solvent gradient was 6–31% B at 0–73 min, 31–62% B at 73–78 min, isocratic 62% B at 78–86 min, 62–6% B at 86–90 min. Compounds were identified using retention time and spectral comparison to standards. 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, and malvidin-3- glucoside. Caftaric acid was quantified as caffeic acid equivalents, hydroponic nft channel 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 there were 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. 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 .Analysis of Brix, pH, and TA of the musts showed minimal differences among treatments for each variety . There were no significant differences for all three parameters of the BA must and only the reject treatment for GN had a significantly higher TA; however, this difference was not large. It is possible that this difference could be the result of the inclusion of under ripe berries in the must, which have a higher TA. Raisins were also rejected from the sorter, which are high in sugar and could have compensated for the difference in sugar from the less ripe berries. The CS must exhibited the most differences among treatments, which was unexpected considering this variety had the lowest percentage of rejected fruit . The Brix was significantly higher in the sorted treatment compared to the control and reject treatments. This may indicate that the sorter was effective at removing less ripe berries for CS. The pH also differed significantly among treatments for CS; pH was highest in the reject must at 3.8, followed by sort and control at 3.71 and 3.67 respectively. Although the difference in pH between the sort and control was statistically significant, they are very similar with only a 0.04 pH unit difference. Overall, the differences seen in the must chemistry were minimal and likely made little to no difference in the progression of the wines.