Phenolic Composition Influences the Effectiveness of Fining Agents in Vegan-Friendly Red Wine Production
Abstract
:1. Introduction
2. Results and Discussion
2.1. Total Phenolic Compounds and Flavanols
2.2. Total Anthocyanins and Color Parameters
2.3. Comparison between Flavanol Reduction and Color Modification
2.4. Astringency and Visual Color Assessment
3. Materials and Methods
3.1. Reagents and Standards
3.2. Wines
3.3. Wine Fining Trials
3.4. Chemical Analysis after Wine Fining
3.5. Color Characteristics after Wine Fining
3.6. Sensory Analysis after Wine Fining
3.7. Statistical Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Marangon:, M.; Vincenzi, S.; Curioni, A. Wine fining with plant proteins. Molecules 2019, 24, 2186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cosme, F.; Ricardo-Da-Silva, J.M.; Laureano, O. Effect of various proteins on different molecular weight proanthocyanidin fractions of red wine during wine fining. Am. J. Enol. Vitic. 2009, 60, 74–81. [Google Scholar]
- Zoecklein, B.W.; Fugelsang, K.C.; Gump, B.H.; Nury, F.S. Fining and fining agents. In Wine analysis and production; Springer: Boston, MA, USA, 1999. [Google Scholar]
- Stockley, C.S.; Johnson, D.L. Adverse food reactions from consuming wine. Aust. J. Grape Wine Res. 2015, 21, 568–581. [Google Scholar] [CrossRef]
- European Commission. Commission Implementing Regulation (EU) No 579/2012 of 29 June 2012 amending Regulation (EC) No. 607/2009 laying down certain detailed rules for the implementation of Council Regulation (EC) No. 479/2008 as regards protected designations of origin and geographical indications, traditional terms, labelling and presentation of certain wine sector products. Off. J. Eur. Union 2012, 171, 4–7. [Google Scholar]
- Gambuti, A.; Rinaldi, A.; Moio, L. Use of patatin, a protein extracted from potato, as alternative to animal proteins in fining of red wine. Eur. Food Res. Technol. 2012, 235, 753–765. [Google Scholar] [CrossRef]
- Kang, W.; Niimi, J.; Putnam Bastian, S.E. Reduction of red wine astringency perception using vegetable protein fining agents. Am. J. Enol. Vitic. 2018, 69, 22–31. [Google Scholar] [CrossRef]
- Gazzola, D.; Vincenzi, S.; Marangon, M.; Pasini, G.; Curioni, A. Grape seed extract: The first protein-based fining agent endogenous to grapes. Aust. J. Grape Wine Res. 2017, 23, 215–225. [Google Scholar] [CrossRef]
- Vincenzi, S.; Dinnella, C.; Recchia, A.; Monteleone, E.; Gazzola, D.; Pasini, G.; Curioni, A. Grape seed proteins: A new fining agent for astringency reduction in red wine. Aust. J. Grape Wine Res. 2013, 19, 153–160. [Google Scholar] [CrossRef]
- Iturmendi, N.; Durán, D.; Marín-Arroyo, M.R. Fining of red wines with gluten or yeast extract protein. Int. J. Food Sci. Technol. 2010, 45, 200–207. [Google Scholar] [CrossRef]
- Lochbühler, B.; Manteau, S.; Morge, C.; Caillet, M.-M.; Charpentier, C.; Schnell, S.; Grossmann, M.; Rauhut, D. Yeast protein extracts: An alternative fining agent for red wines. Eur. Food Res. Technol. 2015, 240, 689–699. [Google Scholar] [CrossRef]
- Bautista-Ortín, A.B.; Ruiz-García, Y.; Marín, F.; Molero, N.; Apolinar-Valiente, R.; Gomez-Plaza, E. Remarkable proanthocyanidin adsorption properties of Monastrell pomace cell wall material highlight its potential use as an alternative fining agent in red wine production. J. Agric. Food Chem. 2015, 63, 620–633. [Google Scholar] [CrossRef] [PubMed]
- Bindon, K.A.; Smith, P.A. Comparison of the affinity and selectivity of insoluble fibres and commercial proteins for wine proanthocyanidins. Food Chem. 2013, 136, 917–928. [Google Scholar] [CrossRef] [PubMed]
- Guerrero, R.F.; Smith, P.; Bindon, K.A. Application of insoluble fibers in the fining of wine phenolics. J. Agric. Food Chem. 2013, 61, 4424–4432. [Google Scholar] [CrossRef] [PubMed]
- Jiménez-Martínez, M.D.; Gómez-Plaza, E.; Molero, N.; Bautista-Ortín, A.B. Fining of red wines with pomace cell wall material: Effect on wine phenolic composition. Food Bioprocess. Technol. 2017, 10, 1531–1539. [Google Scholar] [CrossRef]
- Sarni-Manchado, P.; Deleris, A.; Avallone, S.; Cheynier, V.; Moutounet, M. Analysis and characterization of wine condensed tannins precipitated by proteins used as fining agent in enology. Am. J. Enol. Vitic. 1999, 50, 81–86. [Google Scholar]
- Granato, T.M.; Piano, F.; Nasi, A.; Ferranti, P.; Iametti, S.; Bonomi, F. Molecular basis of the interaction between proteins of plant origin and proanthocyanidins in a model wine system. J. Agric. Food Chem. 2010, 58, 11969–11976. [Google Scholar] [CrossRef]
- Granato, T.M.; Ferranti, P.; Iametti, S.; Bonomi, F. Affinity and selectivity of plant proteins for red wine components relevant to color and aroma traits. Food Chem. 2018, 256, 235–243. [Google Scholar] [CrossRef]
- Luck, G.; Liao, H.; Murray, N.J.; Grimmer, H.R.; Warminski, E.E.; Williamson, M.P.; Lilley, T.H.; Haslam, E. Polyphenols, astringency and proline rich proteins. Phytochemistry 1994, 37, 357–371. [Google Scholar] [CrossRef]
- Vidal, S.; Francis, L.; Guyot, S.; Marnet, N.; Kwiatkowski, M.; Gawel, R.; Cheynier, V.; Waters, E.J. The mouth-feel properties of grape and apple proanthocyanidins in a wine-like medium. J. Sci. Food Agric. 2003, 83, 564–573. [Google Scholar] [CrossRef]
- Simonato, B.; Mainente, F.; Selvatico, E.; Violoni, M.; Pasini, G. Assessment of the fining efficiency of zeins extracted from commercial corn gluten and sensory analysis of the treated wine. LWT - Food Sci. Technol. 2013, 54, 549–556. [Google Scholar] [CrossRef]
- Simonato, B.; Mainente, F.; Suglia, I.; Curioni, A.; Pasini, G. Evaluation of fining efficiency of corn zeins in red wine: A preliminary study. Ital. J. Food Sci. 2009, 21, 97–105. [Google Scholar]
- Tschiersch, C.; Nikfardjam, M.P.; Schmidt, O.; Schwack, W. Degree of hydrolysis of some vegetable proteins used as fining agents and its influence on polyphenol removal from red wine. Eur. Food Res. Technol. 2010, 231, 65–74. [Google Scholar] [CrossRef]
- Granato, T.M.; Nasi, A.; Ferranti, P.; Iametti, S.; Bonomi, F. Fining white wine with plant proteins: Effects of fining on proanthocyanidins and aroma components. Eur. Food Res. Technol. 2014, 238, 265–274. [Google Scholar] [CrossRef]
- Cosme, F.; Capão, I.; Filipe-Ribeiro, L.; Bennett, R.N.; Mendes-Faia, A. Evaluating potential alternatives to potassium caseinate for white wine fining: Effects on physicochemical and sensory characteristics. LWT - Food Sci. Technol. 2012, 46, 382–387. [Google Scholar] [CrossRef]
- Ma, W.; Guo, A.; Zhang, Y.; Wang, H.; Liu, Y.; Li, H. A review on astringency and bitterness perception of tannins in wine. Trends Food Sci. Technol. 2014, 40, 6–19. [Google Scholar] [CrossRef]
- Creusot, N.; Wierenga, P.A.; Laus, M.C.; Giuseppin, M.L.F.; Gruppen, H. Rheological properties of patatin gels compared with β-lactoglobulin, ovalbumin, and glycinin. J. Sci. Food Agric. 2011, 91, 253–261. [Google Scholar] [CrossRef] [PubMed]
- Boulton, R. The copigmentation of anthocyanins and its role in the color of red wine: A critical review. Am. J. Enol. Vitic. 2001, 52, 67–87. [Google Scholar]
- de Freitas, V.A.P.; Fernandes, A.; Oliveira, J.; Teixeira, N.; Mateus, N. A review of the current knowledge of red wine colour. OENO One 2017, 51. [Google Scholar] [CrossRef]
- Heredia, F.J.; Escudero-Gilete, M.L.; Hernanz, D.; Gordillo, B.; Meléndez-Martínez, A.J.; Vicario, I.M.; González-Miret, M.L. Influence of the refrigeration technique on the colour and phenolic composition of Syrah red wines obtained by pre-fermentative cold maceration. Food Chem. 2010, 118, 377–383. [Google Scholar] [CrossRef]
- Torchio, F.; Urcan, D.E.; Lin, L.; Gerbi, V.; Giacosa, S.; Río Segade, S.; Pop, N.; Lambri, M.; Rolle, L. Influence of different withering conditions on phenolic composition of Avanà, Chatus and Nebbiolo grapes for the production of ‘Reinforced’ wines. Food Chem. 2016, 194, 247–256. [Google Scholar] [CrossRef]
- Boulet, J.-C.; Trarieux, C.; Souquet, J.-M.; Ducasse, M.-A.; Caillé, S.; Samson, A.; Williams, P.; Doco, T.; Cheynier, V. Models based on ultraviolet spectroscopy, polyphenols, oligosaccharides and polysaccharides for prediction of wine astringency. Food Chem. 2016, 190, 357–363. [Google Scholar] [CrossRef] [PubMed]
- McRae, J.M.; Kennedy, J.A. Wine and grape tannin interactions with salivary proteins and their impact on astringency: A review of current research. Molecules 2011, 16, 2348–2364. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Quijada-Morín, N.; Williams, P.; Rivas-Gonzalo, J.C.; Doco, T.; Escribano-Bailón, M.T. Polyphenolic, polysaccharide and oligosaccharide composition of Tempranillo red wines and their relationship with the perceived astringency. Food Chem. 2014, 154, 44–51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pérez-Magariño, S.; González-Sanjosé, M.L. Application of absorbance values used in wineries for estimating CIELAB parameters in red wines. Food Chem. 2003, 81, 301–306. [Google Scholar] [CrossRef]
- Martínez, J.A.; Melgosa, M.; Pérez, M.M.; Hita, E.; Negueruela, A.I. Note. Visual and instrumental color evaluation in red wines. Food Sci. Technol. Int. 2001, 7, 439–444. [Google Scholar] [CrossRef]
- Negueruela, A.I.; Echávarri, J.F.; Pérez, M.M. A study of correlation between enological colorimetric indexes and CIE colorimetric parameters in red wines. Am. J. Enol. Vitic. 1995, 46, 353–356. [Google Scholar]
- González-Neves, G.; Favre, G.; Gil, G. Effect of fining on the colour and pigment composition of young red wines. Food Chem. 2014, 157, 385–392. [Google Scholar] [CrossRef]
- Petrozziello, M.; Torchio, F.; Piano, F.; Giacosa, S.; Ugliano, M.; Bosso, A.; Rolle, L. Impact of increasing levels of oxygen consumption on the evolution of color, phenolic and volatile compounds of Nebbiolo wines. Front. Chem. 2018, 6, 137. [Google Scholar] [CrossRef]
- Harbertson, J.F.; Picciotto, E.A.; Adams, D.O. Measurement of polymeric pigments in grape berry extracts and wines using a protein precipitation assay combined with bisulfite bleaching. Am. J. Enol. Vitic. 2003, 54, 301–306. [Google Scholar]
- OIV. Compendium of International Methods of Analysis of Wines and Musts; International Organisation of Vine and Wine: Paris, France, 2016. [Google Scholar]
- ISO 3591 Norm. Sensory Analysis—Apparatus—Wine-Tasting Glass; The International Organization for Standardization, Vernier: Geneva, Switzerland, 1977. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019; Available online: https://www.R-project.org/ (accessed on 9 December 2019).
- Kassambara, A.; Mundt, F. Factoextra: Extract and Visualize the Results of Multivariate Data Analyses, R package version 1.0.6; Available online: https://CRAN.R-project.org/package=factoextra (accessed on 9 December 2019).
Sample Availability: Samples of the compounds are not available from the authors. |
Primitivo | Montepulciano | Syrah | Nebbiolo | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Treatment | A280 | PRO | FRV | A280 | PRO | FRV | A280 | PRO | FRV | A280 | PRO | FRV |
(AU, OP 10 mm) | (mg/L) | (mg/L) | (AU, OP 10 mm) | (mg/L) | (mg/L) | (AU, OP 10 mm) | (mg/L) | (mg/L) | (AU, OP 10 mm) | (mg/L) | (mg/L) | |
CO | 68.8 ± 2.1ab | 3254 ± 83a | 1323 ± 42abc | 84.1 ± 1.1 | 3440 ± 40a | 1330 ± 39a | 72.5 ± 1.4a | 3094 ± 19a | 1659 ± 9abc | 56.7 ± 0.5a | 3589 ± 16a | 2129 ± 33ab |
GEL | 65.1 ± 2.1bc | 2919 ± 76cd | 1318 ± 59abc | 84.7 ± 0.6 | 3025 ± 33c | 1236 ± 55bc | 70.9 ± 1.4ab | 2991 ± 43ab | 1587 ± 35abcd | 52.4 ± 0.4cd | 3330 ± 81cd | 2085 ± 26b |
GEH | 62.9 ± 1.0c | 2801 ± 66d | 1229 ± 4cd | 82.1 ± 0.9 | 2788 ± 44d | 1092 ± 10d | 66.7 ± 1.3b | 2704 ± 30de | 1495 ± 39d | 51.8 ± 1.1d | 3038 ± 0f | 1836 ± 45e |
PE1L | 68.2 ± 1.4abc | 3022 ± 87bc | 1176 ± 25d | 83.2 ± 0.2 | 3209 ± 79abc | 1237 ± 50bc | 70.5 ± 1.3ab | 2863 ± 75bcd | 1649 ± 6a | 53.9 ± 0.8bc | 3243 ± 77de | 1971 ± 49cd |
PE1H | 68.5 ± 2.1ab | 2969 ± 30bcd | 1235 ± 13bcd | 83.4 ± 2.1 | 3134 ± 71abc | 1186 ± 31c | 69.0 ± 2.4ab | 2947 ± 33ab | 1591 ± 30abcd | 53.8 ± 0.4bc | 3240 ± 100de | 1907 ± 33de |
PE2L | 67.3 ± 1.9abc | 3172 ± 64ab | 1347 ± 20ab | 85.1 ± 1.4 | 3262 ± 80ab | 1304 ± 3ab | 70.0 ± 1.5ab | 2910 ± 30abc | 1621 ± 40abc | 54.8 ± 0.5b | 3380 ± 14bcd | 2196 ± 36a |
PE2H | 69.0 ± 0.4ab | 3234 ± 43a | 1255 ± 9bcd | 84.9 ± 1.0 | 3178 ± 49abc | 1306 ± 17ab | 69.7 ± 2.0ab | 2732 ± 103cde | 1544 ± 40bcd | 53.7 ± 0.3bc | 3306 ± 75cde | 2190 ± 22a |
PT1L | 71.4 ± 2.0a | 3000 ± 56bcd | 1337 ± 82abc | 85.2 ± 1.2 | 3041 ± 30c | 1181 ± 14c | 70.0 ± 1.9ab | 2935 ± 95ab | 1492 ± 39d | 54.6 ± 0.5b | 3284 ± 103cde | 1970 ± 47cd |
PT1H | 65.5 ± 1.0bc | 3159 ± 81ab | 1380 ± 39a | 82.4 ± 2.2 | 3112 ± 57bc | 1160 ± 5cd | 68.0 ± 1.9ab | 2620 ± 82e | 1527 ± 14cd | 52.5 ± 0.5cd | 3125 ± 68ef | 2058 ± 14bc |
PT2L | 68.6 ± 2.5ab | 3182 ± 20ab | 1372 ± 32a | 84.4 ± 0.3 | 3334 ± 14a | 1367 ± 19a | 69.9 ± 1.0ab | 2988 ± 56ab | 1632 ± 9ab | 54.1 ± 0.3bc | 3464 ± 68abc | 2151 ± 8ab |
PT2H | 69.8 ± 2.3ab | 3125 ± 30abc | 1261 ± 5bcd | 85.7 ± 0.5 | 3212 ± 44abc | 1312 ± 24ab | 70.8 ± 1.4ab | 2885 ± 62bcd | 1507 ± 50d | 53.3 ± 0.9bcd | 3536 ± 47ab | 2060 ± 24bc |
ANOVA | *** | *** | *** | ns | *** | *** | * | *** | *** | *** | *** | *** |
Contrasts’ significance p-values respect to control | ||||||||||||
CO vs. GEL | 0.076 | 0.000 | 1.000 | 0.999 | 0.000 | 0.006 | 0.789 | 0.309 | 0.627 | 0.000 | 0.001 | 0.530 |
CO vs. GEH | 0.001 | 0.000 | 0.021 | 0.290 | 0.000 | 0.000 | 0.002 | 0.000 | 0.001 | 0.000 | 0.000 | 0.000 |
CO vs. PE1L | 1.000 | 0.004 | 0.000 | 0.939 | 0.201 | 0.006 | 0.610 | 0.002 | 0.991 | 0.000 | 0.000 | 0.000 |
CO vs. PE1H | 1.000 | 0.000 | 0.193 | 0.983 | 0.015 | 0.000 | 0.095 | 0.064 | 0.727 | 0.000 | 0.000 | 0.000 |
CO vs. PE2L | 0.937 | 0.928 | 0.799 | 0.945 | 0.711 | 0.873 | 0.369 | 0.013 | 1.000 | 0.009 | 0.008 | 0.134 |
CO vs. PE2H | 1.000 | 1.000 | 0.189 | 0.983 | 0.074 | 0.914 | 0.263 | 0.000 | 0.049 | 0.000 | 0.000 | 0.205 |
CO vs. PT1L | 0.268 | 0.001 | 1.000 | 0.874 | 0.000 | 0.000 | 0.376 | 0.038 | 0.001 | 0.003 | 0.000 | 0.000 |
CO vs. PT1H | 0.154 | 0.807 | 0.101 | 0.458 | 0.006 | 0.000 | 0.020 | 0.000 | 0.014 | 0.000 | 0.000 | 0.100 |
CO vs. PT2L | 1.000 | 0.682 | 0.197 | 1.000 | 1.000 | 0.590 | 0.335 | 0.280 | 1.000 | 0.000 | 0.199 | 0.974 |
CO vs. PT2H | 0.973 | 0.359 | 0.296 | 0.592 | 0.220 | 0.985 | 0.765 | 0.004 | 0.003 | 0.000 | 0.933 | 0.116 |
Primitivo | Montepulciano | Syrah | Nebbiolo | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Treatment | TA | Color Intensity | Hue | TA | Color Intensity | Hue | TA | Color Intensity | Hue | TA | Color Intensity | Hue |
(mg/L) | (AU, OP 10 mm) | (mg/L) | (AU, OP 10 mm) | (mg/L) | (AU, OP 10 mm) | (mg/L) | (AU, OP 10 mm) | |||||
CO | 255 ± 1abc | 13.97 ± 0.20a | 0.708 ± 0.002ab | 275 ± 4a | 15.12 ± 0.06a | 0.664 ± 0.002b | 367 ± 11a | 13.73 ± 0.14a | 0.677 ± 0.001a | 112 ± 4a | 4.73 ± 0.06a | 1.028 ± 0.005bc |
GEL | 238 ± 3d | 13.02 ± 0.01bc | 0.708 ± 0.001ab | 265 ± 2c | 14.22 ± 0.12de | 0.669 ± 0.002ab | 367 ± 15a | 13.08 ± 0.01bc | 0.673 ± 0.001bc | 92 ± 3de | 4.25 ± 0.03de | 1.028 ± 0.003bc |
GEH | 238 ± 2cd | 12.19 ± 0.10d | 0.709 ± 0.001a | 257 ± 2d | 13.35 ± 0.08f | 0.668 ± 0.001ab | 351 ± 7a | 12.50 ± 0.12d | 0.672 ± 0.001bc | 92 ± 2de | 3.99 ± 0.04f | 1.022 ± 0.000c |
PE1L | 261 ± 3a | 13.68 ± 0.05a | 0.707 ± 0.001ab | 271 ± 2abc | 14.69 ± 0.07bc | 0.666 ± 0.001b | 361 ± 11a | 13.31 ± 0.23bc | 0.672 ± 0.001bc | 102 ± 3b | 4.45 ± 0.09c | 1.030 ± 0.005abc |
PE1H | 236 ± 2cd | 13.55 ± 0.05a | 0.706 ± 0.001b | 267 ± 0bc | 14.45 ± 0.07cd | 0.665 ± 0.002b | 355 ± 9a | 13.20 ± 0.13bc | 0.672 ± 0.002bc | 102 ± 4bc | 4.42 ± 0.03cd | 1.022 ± 0.003c |
PE2L | 253 ± 2abcd | 13.59 ± 0.12a | 0.707 ± 0.001ab | 270 ± 2abc | 14.82 ± 0.08ab | 0.665 ± 0.001b | 360 ± 10a | 13.20 ± 0.06bc | 0.674 ± 0.001ab | 96 ± 1bcd | 4.64 ± 0.05ab | 1.036 ± 0.003ab |
PE2H | 243 ± 5bcd | 13.39 ± 0.02ab | 0.706 ± 0.001b | 265 ± 3c | 14.45 ± 0.16cd | 0.664 ± 0.001b | 371 ± 6a | 13.17 ± 0.08bc | 0.672 ± 0.001bc | 88 ± 2e | 4.46 ± 0.06bc | 1.034 ± 0.008abc |
PT1L | 248 ± 8abcd | 13.18 ± 0.06ab | 0.708 ± 0.001ab | 267 ± 0bc | 14.48 ± 0.11cd | 0.669 ± 0.002ab | 364 ± 15a | 12.94 ± 0.10c | 0.674 ± 0.002abc | 103 ± 3b | 4.46 ± 0.08bc | 1.035 ± 0.004ab |
PT1H | 245 ± 2bcd | 12.65 ± 0.04cd | 0.707 ± 0.001ab | 258 ± 2d | 13.90 ± 0.24e | 0.672 ± 0.005a | 352 ± 6a | 12.50 ± 0.11d | 0.670 ± 0.002c | 92 ± 2de | 4.22 ± 0.11e | 1.041 ± 0.005a |
PT2L | 255 ± 2ab | 13.67 ± 0.04a | 0.709 ± 0.000ab | 275 ± 1a | 14.71 ± 0.07bc | 0.666 ± 0.002b | 363 ± 6a | 13.43 ± 0.11ab | 0.673 ± 0.001bc | 94 ± 1cde | 4.54 ± 0.03bc | 1.035 ± 0.005ab |
PT2H | 251 ± 5abcd | 13.48 ± 0.10ab | 0.708 ± 0.000ab | 272 ± 1ab | 14.57 ± 0.08bc | 0.666 ± 0.001b | 358 ± 8a | 13.20 ± 0.23bc | 0.674 ± 0.002ab | 100 ± 4bc | 4.41 ± 0.01cd | 1.031 ± 0.000abc |
ANOVA | *** | *** | * | *** | *** | ** | ns | *** | *** | *** | *** | *** |
Contrasts’ significance p-values respect to control | ||||||||||||
CO vs. GEL | 0.004 | 0.001 | 1.000 | 0.000 | 0.000 | 0.081 | 1.000 | 0.000 | 0.006 | 0.000 | 0.000 | 1.000 |
CO vs. GEH | 0.037 | 0.000 | 0.635 | 0.000 | 0.000 | 0.240 | 0.295 | 0.000 | 0.005 | 0.000 | 0.000 | 0.413 |
CO vs. PE1L | 0.081 | 1.000 | 0.606 | 0.122 | 0.001 | 0.763 | 0.980 | 0.008 | 0.002 | 0.004 | 0.000 | 0.999 |
CO vs. PE1H | 0.014 | 0.997 | 0.189 | 0.002 | 0.000 | 0.999 | 0.671 | 0.001 | 0.001 | 0.002 | 0.000 | 0.342 |
CO vs. PE2L | 1.000 | 1.000 | 0.395 | 0.033 | 0.028 | 0.999 | 0.949 | 0.001 | 0.128 | 0.000 | 0.435 | 0.271 |
CO vs. PE2H | 0.339 | 0.405 | 0.251 | 0.000 | 0.000 | 1.000 | 0.998 | 0.000 | 0.002 | 0.000 | 0.000 | 0.612 |
CO vs. PT1L | 0.843 | 0.013 | 1.000 | 0.002 | 0.000 | 0.048 | 1.000 | 0.000 | 0.093 | 0.006 | 0.000 | 0.322 |
CO vs. PT1H | 0.373 | 0.000 | 0.635 | 0.000 | 0.000 | 0.000 | 0.364 | 0.000 | 0.000 | 0.000 | 0.000 | 0.011 |
CO vs. PT2L | 0.939 | 1.000 | 0.999 | 1.000 | 0.002 | 0.912 | 1.000 | 0.073 | 0.010 | 0.000 | 0.012 | 0.413 |
CO vs. PT2H | 1.000 | 0.852 | 0.996 | 0.670 | 0.000 | 0.827 | 0.864 | 0.001 | 0.128 | 0.000 | 0.000 | 0.996 |
Treatment | Primitivo | Montepulciano | Syrah | Nebbiolo | ||||
---|---|---|---|---|---|---|---|---|
Sensory | BSA Index (mg/L catechin) | Sensory | BSA Index (mg/L catechin) | Sensory | BSA Index (mg/L catechin) | Sensory | BSA Index (mg/L catechin) | |
CO | 6.54 ± 0.47 | 1008 ± 113abc | 5.43 ± 0.33 | 601 ± 74abc | 4.75 ± 0.67 | 889 ± 68a | 7.54 ± 0.43 | 585 ± 67ab |
GEL | 5.25 ± 0.65 | 957 ± 59bc | 4.70 ± 0.84 | 431 ± 47c | 3.69 ± 1.02 | 652 ± 85b | 6.16 ± 0.39 | 723 ± 49a |
GEH | 4.89 ± 0.47 | 912 ± 28c | 4.69 ± 0.43 | 539 ± 62abc | 3.51 ± 0.79 | 646 ± 85b | 7.06 ± 0.44 | 527 ± 27b |
PE1L | 4.79 ± 0.61 | 1189 ± 83ab | 4.54 ± 0.67 | 744 ± 87a | 4.50 ± 0.80 | 681 ± 63ab | 6.47 ± 0.53 | 631 ± 68ab |
PE1H | 4.73 ± 0.86 | 1057 ± 151abc | 4.37 ± 0.60 | 467 ± 111bc | 4.38 ± 1.02 | 665 ± 105b | 6.63 ± 0.79 | 594 ± 15ab |
PE2L | 3.70 ± 0.63 | 1035 ± 144abc | 3.94 ± 0.65 | 477 ± 61bc | 3.84 ± 0.95 | 712 ± 33ab | 6.59 ± 0.45 | 585 ± 34ab |
PE2H | 4.35 ± 0.70 | 1214 ± 27a | 4.52 ± 0.80 | 494 ± 44abc | 4.24 ± 0.80 | 660 ± 79b | 5.60 ± 0.82 | 586 ± 86ab |
PT1L | 6.04 ± 0.46 | 889 ± 96cd | 4.20 ± 0.62 | 630 ± 74abc | 4.33 ± 0.69 | 675 ± 75ab | 6.57 ± 0.42 | 615 ± 20ab |
PT1H | 5.39 ± 0.68 | 660 ± 92d | 3.41 ± 0.64 | 417 ± 102c | 4.19 ± 0.96 | 578 ± 76b | 6.02 ± 0.38 | 570 ± 24b |
PT2L | 5.04 ± 0.73 | 968 ± 46bc | 4.26 ± 0.76 | 718 ± 77ab | 3.38 ± 1.13 | 693 ± 50ab | 6.68 ± 0.65 | 597 ± 25ab |
PT2H | 4.95 ± 0.49 | 835 ± 98cd | 3.60 ± 0.64 | 646 ± 182abc | 3.66 ± 0.90 | 760 ± 95ab | 6.30 ± 0.38 | 573 ± 69b |
ANOVA | ns | *** | ns | ** | ns | ** | ns | * |
Contrasts’ significance p-values respect to control | ||||||||
CO vs. GEL | 0.637 | 0.981 | 0.981 | 0.193 | 0.972 | 0.006 | 0.375 | 0.019 |
CO vs. GEH | 0.352 | 0.640 | 0.978 | 0.972 | 0.932 | 0.005 | 0.996 | 0.663 |
CO vs. PE1L | 0.289 | 0.075 | 0.937 | 0.344 | 1.000 | 0.019 | 0.666 | 0.843 |
CO vs. PE1H | 0.254 | 0.984 | 0.843 | 0.418 | 1.000 | 0.010 | 0.820 | 1.000 |
CO vs. PE2L | 0.015 | 1.000 | 0.515 | 0.500 | 0.990 | 0.056 | 0.781 | 1.000 |
CO vs. PE2H | 0.105 | 0.035 | 0.928 | 0.663 | 1.000 | 0.009 | 0.086 | 1.000 |
CO vs. PT1L | 0.998 | 0.413 | 0.721 | 1.000 | 1.000 | 0.015 | 0.761 | 0.984 |
CO vs. PT1H | 0.751 | 0.000 | 0.189 | 0.135 | 1.000 | 0.000 | 0.277 | 1.000 |
CO vs. PT2L | 0.461 | 1.000 | 0.764 | 0.577 | 0.884 | 0.029 | 0.855 | 1.000 |
CO vs. PT2H | 0.396 | 0.097 | 0.280 | 0.996 | 0.968 | 0.256 | 0.503 | 1.000 |
Code | Origin | Recommended Dose (g/hL) | Low Dose (g/hL) | High Dose (g/hL) |
---|---|---|---|---|
CO | - | - | - | - |
GE | Animal | 5–30 | 10 | 25 |
PE1 | Pea | 10–20 | 12 | 18 |
PE2 | Pea | 5–20 | 8 | 17 |
PT1 | Potato | 5–30 | 10 | 25 |
PT2 | Potato | 2–5 | 2.6 | 4.4 |
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Río Segade, S.; Paissoni, M.A.; Vilanova, M.; Gerbi, V.; Rolle, L.; Giacosa, S. Phenolic Composition Influences the Effectiveness of Fining Agents in Vegan-Friendly Red Wine Production. Molecules 2020, 25, 120. https://doi.org/10.3390/molecules25010120
Río Segade S, Paissoni MA, Vilanova M, Gerbi V, Rolle L, Giacosa S. Phenolic Composition Influences the Effectiveness of Fining Agents in Vegan-Friendly Red Wine Production. Molecules. 2020; 25(1):120. https://doi.org/10.3390/molecules25010120
Chicago/Turabian StyleRío Segade, Susana, Maria Alessandra Paissoni, Mar Vilanova, Vincenzo Gerbi, Luca Rolle, and Simone Giacosa. 2020. "Phenolic Composition Influences the Effectiveness of Fining Agents in Vegan-Friendly Red Wine Production" Molecules 25, no. 1: 120. https://doi.org/10.3390/molecules25010120
APA StyleRío Segade, S., Paissoni, M. A., Vilanova, M., Gerbi, V., Rolle, L., & Giacosa, S. (2020). Phenolic Composition Influences the Effectiveness of Fining Agents in Vegan-Friendly Red Wine Production. Molecules, 25(1), 120. https://doi.org/10.3390/molecules25010120