Research Progress in Imaging Technology for Assessing Quality in Wine Grapes and Seeds
Abstract
:1. Introduction
1.1. Wine Phenolics
1.2. Spectroscopic Techniques for Inspection in the Wine Industry
1.3. Imaging Techniques
1.3.1. Standard RGB Cameras
1.3.2. Hyperspectral Cameras
2. Materials and Methods
2.1. Sampling
2.2. Images Acquisition
2.2.1. DigiEye Imaging System
2.2.2. Hyperspectral Measurements
2.3. Chemical Analyses
2.3.1. Sugar Content in Must
2.3.2. Extraction and Analysis of Phenolics in Grape Seeds and Skins
2.4. Image Processing and Statistics
3. Results
3.1. Chemical Analyses
3.2. Image Processing
3.3. Prediction Models
3.4. Chemical Imaging
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- 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]
- Foster, R. Organic Charge-Transfer Complexes; Academic Press: London, UK, 1969; ISBN 978-0-12-262650-0. [Google Scholar]
- Gordillo, B.; Rodríguez-Pulido, F.J.; Escudero-Gilete, M.L.; González-Miret, M.L.; Heredia, F.J. Comprehensive Colorimetric Study of Anthocyanic Copigmentation in Model Solutions. Effects of PH and Molar Ratio. J. Agric. Food Chem. 2012, 60, 2896–2905. [Google Scholar] [CrossRef]
- Kennedy, J.A.; Matthews, M.A.; Waterhouse, A.L. Changes in Grape Seed Polyphenols during Fruit Ripening. Phytochemistry 2000, 55, 77–85. [Google Scholar] [CrossRef]
- Ristic, R.; Iland, P.G. Relationships between Seed and Berry Development of Vitis Vinifera, L. Cv Shiraz: Developmental Changes in Seed Morphology and Phenolic Composition. Aust. J. Grape Wine Res. 2005, 11, 43–58. [Google Scholar] [CrossRef]
- Zamora-Marín, F. El Cambio Climático, Una Amenaza Para Nuestra Viticultura. Enólogos 2006, 29, 28–31. [Google Scholar]
- Rousseau, J.; Delteil, D. Présentation d’une Méthode d’analyse Sensorielle Des Raisins. Principe, Méthode et Grille d’interprétation. Rev. Française Oenol. 2000, 183, 10–13. [Google Scholar]
- Sen, I.; Ozturk, B.; Tokatli, F.; Ozen, B. Combination of Visible and Mid-Infrared Spectra for the Prediction of Chemical Parameters of Wines. Talanta 2016, 161, 130–137. [Google Scholar] [CrossRef] [Green Version]
- Fragoso, S.; Aceña, L.; Guasch, J.; Mestres, M.; Busto, O. Quantification of Phenolic Compounds during Red Winemaking Using FT-MIR Spectroscopy and PLS-Regression. J. Agric. Food Chem. 2011, 59, 10795–10802. [Google Scholar] [CrossRef]
- Cozzolino, D.; Kwiatkowski, M.J.; Parker, M.; Cynkar, W.U.; Dambergs, R.G.; Gishen, M.; Herderich, M.J. Prediction of Phenolic Compounds in Red Wine Fermentations by Visible and near Infrared Spectroscopy. Anal. Chim. Acta 2004, 513, 73–80. [Google Scholar] [CrossRef]
- Cozzolino, D.; Cynkar, W.U.; Dambergs, R.G.; Mercurio, M.D.; Smith, P.A. Measurement of Condensed Tannins and Dry Matter in Red Grape Homogenates Using Near Infrared Spectroscopy and Partial Least Squares. J. Agric. Food Chem. 2008, 56, 7631–7636. [Google Scholar] [CrossRef]
- Ferrer-Gallego, R.; Hernández-Hierro, J.M.; Rivas-Gonzalo, J.C.; Escribano-Bailón, M.T. A Comparative Study to Distinguish the Vineyard of Origin by NIRS Using Entire Grapes, Skins and Seeds. J. Sci. Food Agric. 2013, 93, 967–972. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ferrer-Gallego, R.; Hernández-Hierro, J.M.; Rivas-Gonzalo, J.C.; Escribano-Bailón, M.T. Determination of Phenolic Compounds of Grape Skins during Ripening by NIR Spectroscopy. LWT Food Sci. Technol. 2011, 44, 847–853. [Google Scholar] [CrossRef] [Green Version]
- Nogales-Bueno, J.; Baca-Bocanegra, B.; Rooney, A.; Miguel Hernández-Hierro, J.; José Heredia, F.; Byrne, H.J. Linking ATR-FTIR and Raman Features to Phenolic Extractability and Other Attributes in Grape Skin. Talanta 2017, 167, 44–50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ferrer-Gallego, R.; Hernández-Hierro, J.M.; Rivas-Gonzalo, J.C.; Escribano-Bailón, M.T. Feasibility Study on the Use of near Infrared Spectroscopy to Determine Flavanols in Grape Seeds. Talanta 2010, 82, 1778–1783. [Google Scholar] [CrossRef] [Green Version]
- Ali, K.; Maltese, F.; Fortes, A.M.; Pais, M.S.; Choi, Y.H.; Verpoorte, R. Monitoring Biochemical Changes during Grape Berry Development in Portuguese Cultivars by NMR Spectroscopy. Food Chem. 2011, 124, 1760–1769. [Google Scholar] [CrossRef]
- Lee, J.-E.; Hwang, G.-S.; Van Den Berg, F.; Lee, C.-H.; Hong, Y.-S. Evidence of Vintage Effects on Grape Wines Using 1H NMR-Based Metabolomic Study. Anal. Chim. Acta 2009, 648, 71–76. [Google Scholar] [CrossRef] [PubMed]
- Son, H.-S.; Hwang, G.-S.; Kim, K.M.; Ahn, H.-J.; Park, W.-M.; Van Den Berg, F.; Hong, Y.-S.; Lee, C.-H. Metabolomic Studies on Geographical Grapes and Their Wines Using 1H NMR Analysis Coupled with Multivariate Statistics. J. Agric. Food Chem. 2009, 57, 1481–1490. [Google Scholar] [CrossRef]
- Fotakis, C.; Kokkotou, K.; Zoumpoulakis, P.; Zervou, M. NMR Metabolite Fingerprinting in Grape Derived Products: An Overview. Food Res. Int. 2013, 54, 1184–1194. [Google Scholar] [CrossRef]
- Rodríguez-Pulido, F.J.; Gordillo, B.; Heredia, F.J.; González-Miret, M.L. CIELAB—Spectral Image MATCHING: An App for Merging Colorimetric and Spectral Images for Grapes and Derivatives. Food Control 2021, 125, 108038. [Google Scholar] [CrossRef]
- Rodríguez-Pulido, F.J.; Gómez-Robledo, L.; Melgosa, M.; Gordillo, B.; González-Miret, M.L.; Heredia, F.J. Ripeness Estimation of Grape Berries and Seeds by Image Analysis. Comput. Electron. Agric. 2012, 82, 128–133. [Google Scholar] [CrossRef]
- Talaverano, M.I.; Moreno, D.; Rodríguez-Pulido, F.J.; Valdés, M.E.; Gamero, E.; Jara-Palacios, M.J.; Heredia, F.J. Effect of Early Leaf Removal on Vitis Vinifera, L. Cv. Tempranillo Seeds during Ripening Based on Chemical and Image Analysis. Sci. Hortic. 2016, 209, 148–155. [Google Scholar] [CrossRef]
- Rodríguez-Pulido, F.J.; Ferrer-Gallego, R.; Lourdes González-Miret, M.; Rivas-Gonzalo, J.C.; Escribano-Bailón, M.T.; Heredia, F.J. Preliminary Study to Determine the Phenolic Maturity Stage of Grape Seeds by Computer Vision. Anal. Chim. Acta 2012, 732, 78–82. [Google Scholar] [CrossRef]
- Aquino, A.; Diago, M.P.; Millán, B.; Tardáguila, J. A New Methodology for Estimating the Grapevine-Berry Number per Cluster Using Image Analysis. Biosyst. Eng. 2017, 156, 80–95. [Google Scholar] [CrossRef]
- Aquino, A.; Barrio, I.; Diago, M.-P.; Millan, B.; Tardaguila, J. VitisBerry: An Android-Smartphone Application to Early Evaluate the Number of Grapevine Berries by Means of Image Analysis. Comput. Electron. Agric. 2018, 148, 19–28. [Google Scholar] [CrossRef]
- Aquino, A.; Millan, B.; Diago, M.-P.; Tardaguila, J. Automated Early Yield Prediction in Vineyards from On-the-Go Image Acquisition. Comput. Electron. Agric. 2018, 144, 26–36. [Google Scholar] [CrossRef]
- Schöler, F.; Steinhage, V. Automated 3D Reconstruction of Grape Cluster Architecture from Sensor Data for Efficient Phenotyping. Comput. Electron. Agric. 2015, 114, 163–177. [Google Scholar] [CrossRef]
- ElMasry, G.; Sun, D.-W. Principles of Hyperspectral Imaging Technology. In Hyperspectral Imaging for Food Quality Analysis and Control; Sun, D.-W., Ed.; Academic Press: San Diego, CA, USA, 2010; pp. 3–43. ISBN 978-0-12-374753-2. [Google Scholar]
- Koehler IV, F.W.; Lee, E.; Kidder, L.H.; Lewis, N.E. Near Infrared Spectroscopy: The Practical Chemical Imaging Solution. Spectrosc. Eur. 2002, 14, 12–19. [Google Scholar]
- Gowen, A.A.; O’Donnell, C.P.; Cullen, P.J.; Downey, G.; Frias, J.M. Hyperspectral Imaging—An Emerging Process Analytical Tool for Food Quality and Safety Control. Trends Food Sci. Technol. 2007, 18, 590–598. [Google Scholar] [CrossRef]
- Rodríguez-Pulido, F.J.; Barbin, D.F.; Sun, D.-W.; Gordillo, B.; González-Miret, M.L.; Heredia, F.J. Grape Seed Characterization by NIR Hyperspectral Imaging. Postharvest Biol. Technol. 2013, 76, 74–82. [Google Scholar] [CrossRef]
- Rodríguez-Pulido, F.J.; Hernández-Hierro, J.M.; Nogales-Bueno, J.; Gordillo, B.; González-Miret, M.L.; Heredia, F.J. A Novel Method for Evaluating Flavanols in Grape Seeds by near Infrared Hyperspectral Imaging. Talanta 2014, 122, 145–150. [Google Scholar] [CrossRef]
- Quijada-Morín, N.; García-Estévez, I.; Nogales-Bueno, J.; Rodríguez-Pulido, F.J.; Heredia, F.J.; Rivas-Gonzalo, J.C.; Escribano-Bailón, M.T.; Hernández-Hierro, J.M. Trying to Set up the Flavanolic Phases during Grape Seed Ripening: A Spectral and Chemical Approach. Talanta 2016, 160, 556–561. [Google Scholar] [CrossRef] [Green Version]
- Martínez-Sandoval, J.R.; Nogales-Bueno, J.; Rodríguez-Pulido, F.J.; Hernández-Hierro, J.M.; Segovia-Quintero, M.A.; Martínez-Rosas, M.E.; Heredia, F.J. Screening of Anthocyanins in Single Red Grapes Using a Non-Destructive Method Based on the near Infrared Hyperspectral Technology and Chemometrics. J. Sci. Food Agric. 2016, 96, 1643–1647. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nogales-Bueno, J.; Ayala, F.; Hernández-Hierro, J.M.; Rodríguez-Pulido, F.J.; Echávarri, J.F.; Heredia, F.J. Simplified Method for the Screening of Technological Maturity of Red Grape and Total Phenolic Compounds of Red Grape Skin: Application of the Characteristic Vector Method to Near-Infrared Spectra. J. Agric. Food Chem. 2015, 63, 4284–4290. [Google Scholar] [CrossRef] [PubMed]
- Fernandes, A.M.; Franco, C.; Mendes-Ferreira, A.; Mendes-Faia, A.; da Costa, P.L.; Melo-Pinto, P. Brix, PH and Anthocyanin Content Determination in Whole Port Wine Grape Berries by Hyperspectral Imaging and Neural Networks. Comput. Electron. Agric. 2015, 115, 88–96. [Google Scholar] [CrossRef]
- Agati, G.; Traversi, M.L.; Cerovic, Z.G. Chlorophyll Fluorescence Imaging for the Noninvasive Assessment of Anthocyanins in Whole Grape (Vitis vinifera L.) Bunches†. Photochem. Photobiol. 2008, 84, 1431–1434. [Google Scholar] [CrossRef]
- Baiano, A.; Terracone, C.; Peri, G.; Romaniello, R. Application of Hyperspectral Imaging for Prediction of Physico-Chemical and Sensory Characteristics of Table Grapes. Comput. Electron. Agric. 2012, 87, 142–151. [Google Scholar] [CrossRef]
- Rodríguez-Pulido, F.J.; Gordillo, B.; Lourdes González-Miret, M.; Heredia, F.J. Analysis of Food Appearance Properties by Computer Vision Applying Ellipsoids to Colour Data. Comput. Electron. Agric. 2013, 99, 108–115. [Google Scholar] [CrossRef]
- Nogales-Bueno, J.; Hernández-Hierro, J.M.; Rodríguez-Pulido, F.J.; Heredia, F.J. Determination of Technological Maturity of Grapes and Total Phenolic Compounds of Grape Skins in Red and White Cultivars during Ripening by near Infrared Hyperspectral Image: A Preliminary Approach. Food Chem. 2014, 152, 586–591. [Google Scholar] [CrossRef]
- Behmann, J.; Acebron, K.; Emin, D.; Bennertz, S.; Matsubara, S.; Thomas, S.; Bohnenkamp, D.; Kuska, M.T.; Jussila, J.; Salo, H.; et al. Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors 2018, 18, 441. [Google Scholar] [CrossRef] [Green Version]
- OIV. Compendium of International Methods of Wine and Must Analysis; The International Organisation of Vine and Wine: Paris, France, 2013; ISBN 979-10-91799-06-5. [Google Scholar]
- Singleton, V.L.; Rossi, J.A. Colorimetry of Total Phenolics with Phosphomolybdic-Phosphotungstic Acid Reagents. Am. J. Enol. Vitic. 1965, 16, 144–158. [Google Scholar]
- Vivas, N.; Glories, Y.; Lagune, L.; Saucier, C.; Augustin, M. Estimation Du Degré de Polymérization Des Procyanidines Du Raisin et Du Vin Par La Méthode Au P-Dimethylaminocinnamaldéhyde. J. Int. Des Sci. De La Vigne Et Du Vin 1994, 28, 319–336. [Google Scholar]
- Castillo-Muñoz, N.; Gómez-Alonso, S.; García-Romero, E.; Hermosín-Gutiérrez, I. Flavonol Profiles of Vitis Vinifera Red Grapes and Their Single-Cultivar Wines. J. Agric. Food Chem. 2007, 55, 992–1002. [Google Scholar] [CrossRef] [PubMed]
- 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]
- The Mathworks. MATLAB; The Mathworks Inc.: Natick, MA, USA, 2020. [Google Scholar]
- Baca-Bocanegra, B.; Nogales-Bueno, J.; Heredia, F.J.; Hernández-Hierro, J.M. Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools. Sensors 2018, 18, 2426. [Google Scholar] [CrossRef] [Green Version]
- Gómez-Alonso, S.; García-Romero, E.; Hermosín-Gutiérrez, I. HPLC Analysis of Diverse Grape and Wine Phenolics Using Direct Injection and Multidetection by DAD and Fluorescence. J. Food Compos. Anal. 2007, 20, 618–626. [Google Scholar] [CrossRef]
- Nogales-Bueno, J.; Baca-Bocanegra, B.; Rodríguez-Pulido, F.J.; Heredia, F.J.; Hernández-Hierro, J.M. Use of near Infrared Hyperspectral Tools for the Screening of Extractable Polyphenols in Red Grape Skins. Food Chem. 2015, 172, 559–564. [Google Scholar] [CrossRef] [Green Version]
- Castillo-Muñoz, N.; Gómez-Alonso, S.; García-Romero, E.; Gómez, M.V.; Velders, A.H.; Hermosín-Gutiérrez, I. Flavonol 3-O-Glycosides Series of Vitis Vinifera Cv. Petit Verdot Red Wine Grapes. J. Agric. Food Chem. 2009, 57, 209–219. [Google Scholar] [CrossRef]
- Cejudo-Bastante, M.J.; Pérez-Coello, M.S.; Hermosín-Gutiérrez, I. Effect of Wine Micro-Oxygenation Treatment and Storage Period on Colour-Related Phenolics, Volatile Composition and Sensory Characteristics. LWT Food Sci. Technol. 2011, 44, 866–874. [Google Scholar] [CrossRef]
- Gordillo, B.; López-Infante, M.I.; Ramírez-Pérez, P.; González-Miret, M.L.; Heredia, F.J. Influence of Prefermentative Cold Maceration on the Color and Anthocyanic Copigmentation of Organic Tempranillo Wines Elaborated in a Warm Climate. J. Agric. Food Chem. 2010, 58, 6797–6803. [Google Scholar] [CrossRef]
- Gordillo, B.; Cejudo-Bastante, M.J.; Rodríguez-Pulido, F.J.; González-Miret, M.L.; Heredia, F.J. Application of the Differential Colorimetry and Polyphenolic Profile to the Evaluation of the Chromatic Quality of Tempranillo Red Wines Elaborated in Warm Climate. Influence of the Presence of Oak Wood Chips during Fermentation. Food Chem. 2013, 141, 2184–2190. [Google Scholar] [CrossRef]
Compound | N | Mean | Minimum | Maximum | SD |
---|---|---|---|---|---|
Syrah | |||||
Total phenolic content | 57 | 3.76 | 0.79 | 23.18 | 5.4 |
Total flavanol content | 57 | 1.23 | 0.18 | 6.61 | 1.73 |
Benzoic acids: | |||||
Gallic acid | 57 | 0.08 | 0.04 | 0.25 | 0.05 |
Flavan-3-ols: | |||||
(+)-Catechin | 57 | 0.27 | 0.03 | 1.42 | 0.36 |
(−)-Epicatechin | 57 | 0.37 | 0.06 | 1.57 | 0.40 |
Tempranillo | |||||
Total phenolic content | 51 | 2.43 | 0.45 | 6.31 | 1.68 |
Total flavanol content | 51 | 0.67 | 0.01 | 3.36 | 0.74 |
Benzoic acids: | |||||
Gallic acid | 51 | 0.07 | 0.04 | 0.11 | 0.02 |
Flavan-3-ols: | |||||
(+)-Catechin | 51 | 0.14 | 0.02 | 0.91 | 0.18 |
(−)-Epicatechin | 51 | 0.1 | 0.01 | 0.41 | 0.10 |
Compound | N | Mean | Minimum | Maximum | SD |
---|---|---|---|---|---|
Total phenolic content | 57 | 11.67 | 4.62 | 20.73 | 4.58 |
Total flavanol content | 57 | 0.92 | 0.53 | 1.46 | 0.22 |
Benzoic acids: | |||||
Gallic acid | 57 | 0.29 | 0.14 | 0.57 | 0.10 |
Flavan-3-ols: | |||||
(+)-Catechin | 57 | 0.03 | 0 | 0.09 | 0.02 |
Hydroxycinnamic acid derivatives: | |||||
t-Caftaric acid | 57 | 0.01 | 0 | 0.1 | 0.01 |
p-Coumaric acid | 57 | 0.01 | 0 | 0.02 | 0.01 |
Flavonols: | |||||
Myricetin-3-glucuronide | 57 | 0.04 | 0 | 0.18 | 0.04 |
Myricetin-3-glucoside | 57 | 0.04 | 0 | 0.09 | 0.03 |
Quercetin-3-glucuronide | 57 | 0.09 | 0.01 | 0.27 | 0.08 |
Quercetin-3-glucoside | 57 | 0.16 | 0.01 | 0.49 | 0.13 |
Laricitrin-3-glucoside | 57 | 0.01 | 0 | 0.02 | 0.01 |
Isorhamnetin-3-glucoside | 57 | 0.07 | 0 | 0.23 | 0.06 |
Syringetin-3-glucoside | 57 | 0.03 | 0 | 0.13 | 0.03 |
Monomeric anthocyanins: | |||||
Delphinidin-3-glucoside | 57 | 0.21 | 0 | 0.43 | 0.07 |
Cyanidin-3-glucoside | 57 | 0.05 | 0 | 0.06 | 0.01 |
Petunidin-3-glucoside | 57 | 0.34 | 0 | 0.77 | 0.17 |
Peonidin-3-glucoside | 57 | 0.4 | 0 | 0.77 | 0.17 |
Malvidin-3-glucoside | 57 | 1.86 | 0 | 4.96 | 1.30 |
Petunidin-3-acetyl-glucoside | 57 | 0.22 | 0 | 0.35 | 0.07 |
Peonidin-3-acetyl-glucoside | 57 | 0.31 | 0 | 0.46 | 0.10 |
Malvidin-3-acetyl-glucoside | 57 | 1.18 | 0 | 2.7 | 0.75 |
Petunidin-3-p-coumaroyl-glucoside | 57 | 0.3 | 0 | 0.59 | 0.17 |
Peonidin-3-p-coumaroyl-glucoside | 57 | 0.26 | 0 | 0.35 | 0.08 |
Malvidin-3-p-coumaroyl-glucoside | 57 | 0.73 | 0 | 1.47 | 0.40 |
Compound | N | Mean | Minimum | Maximum | SD |
---|---|---|---|---|---|
Total phenolic content | 51 | 12.84 | 7.25 | 20.62 | 3.39 |
Total flavanol content | 51 | 1.65 | 0.64 | 2.87 | 0.45 |
Benzoic acids: | |||||
Gallic acid | 51 | 0.48 | 0.2 | 1.08 | 0.20 |
Flavan-3-ols: | |||||
(+)-Catechin | 51 | 0.05 | 0 | 0.16 | 0.04 |
Hydroxycinnamic acid derivatives: | |||||
t-Caftaric acid | 51 | 0.04 | 0 | 0.13 | 0.03 |
t-Coutaric acid | 51 | 0.01 | 0 | 0.06 | 0.01 |
c-Coutaric acid | 51 | 0.02 | 0 | 0.06 | 0.01 |
p-Coumaric acid | 51 | 0.01 | 0 | 0.03 | 0.01 |
Flavonols: | |||||
Myricetin-3-glucuronide | 51 | 0.04 | 0 | 0.11 | 0.03 |
Myricetin-3-glucoside | 51 | 0.04 | 0.01 | 0.07 | 0.01 |
Quercetin-3-glucuronide | 51 | 0.09 | 0.02 | 0.41 | 0.07 |
Quercetin-3-glucoside | 51 | 0.06 | 0.01 | 0.24 | 0.05 |
Laricitrin-3-glucoside | 51 | 0.01 | 0 | 0.03 | 0.01 |
Isorhamnetin-3-glucoside | 51 | 0.01 | 0 | 0.02 | 0.01 |
Syringetin-3-glucoside | 51 | 0.01 | 0 | 0.02 | 0.01 |
Monomeric anthocyanins: | |||||
Delphinidin-3-glucoside | 51 | 0.33 | 0.17 | 0.66 | 0.10 |
Cyanidin-3-glucoside | 51 | 0.06 | 0.04 | 0.08 | 0.01 |
Petunidin-3-glucoside | 51 | 0.47 | 0.19 | 0.85 | 0.15 |
Peonidin-3-glucoside | 51 | 0.29 | 0.19 | 0.43 | 0.06 |
Malvidin-3-glucoside | 51 | 1.75 | 0.54 | 3.14 | 0.57 |
Petunidin-3-acetyl-glucoside | 51 | 0.19 | 0.16 | 0.23 | 0.01 |
Peonidin-3-acetyl-glucoside | 51 | 0.17 | 0.16 | 0.18 | 0.00 |
Malvidin-3-acetyl-glucoside | 51 | 0.35 | 0.18 | 0.46 | 0.07 |
Petunidin-3-p-coumaroyl-glucoside | 51 | 0.24 | 0.16 | 0.35 | 0.05 |
Peonidin-3-p-coumaroyl-glucoside | 51 | 0.18 | 0.16 | 0.21 | 0.01 |
Malvidin-3-p-coumaroyl-glucoside | 51 | 0.49 | 0.18 | 0.77 | 0.16 |
Compounds | #LV | R2cal | RMSEcal | R2val | RMSEval |
---|---|---|---|---|---|
Must | |||||
Sugars | 11 | 0.92 | 1.2 | 0.91 | 1.4 |
Skins | |||||
Total phenols | 4 | 0.76 | 1.7 | 0.73 | 1.8 |
Total flavonols | 6 | 0.52 | 0.2 | 0.51 | 0.2 |
Delphinidin-3-glucoside | 7 | 0.72 | 0.06 | 0.70 | 0.06 |
Cyanidin-3-glucoside | 7 | 0.56 | 0.01 | 0.54 | 0.006 |
Petunidin-3-glucoside | 9 | 0.75 | 0.08 | 0.73 | 0.09 |
Peonidin-3-glucoside | 10 | 0.51 | 0.05 | 0.50 | 0.05 |
Malvidin-3-glucoside | 6 | 0.75 | 0.3 | 0.72 | 0.3 |
Petunidin-3-p-coumaroyl-glucoside | 5 | 0.78 | 0.02 | 0.76 | 0.02 |
p-Coumaric acid | 7 | 0.67 | 0.004 | 0.65 | 0.004 |
Gallic acid | 5 | 0.34 | 0.04 | 0.36 | 0.04 |
(+)-Catechin | 7 | 0.63 | 0.01 | 0.62 | 0.01 |
Seeds | |||||
Total phenols | 12 | 0.82 | 1.8 | 0.83 | 1.7 |
Total flavanols | 2 | 0.72 | 0.7 | 0.59 | 0.8 |
Gallic acid | 4 | 0.63 | 0.02 | 0.70 | 0.02 |
(+)-Catechin | 22 | 0.87 | 0.1 | 0.83 | 0.1 |
(−)-Epicatechin | 2 | 0.69 | 0.2 | 0.69 | 0.2 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rodríguez-Pulido, F.J.; Mora-Garrido, A.B.; González-Miret, M.L.; Heredia, F.J. Research Progress in Imaging Technology for Assessing Quality in Wine Grapes and Seeds. Foods 2022, 11, 254. https://doi.org/10.3390/foods11030254
Rodríguez-Pulido FJ, Mora-Garrido AB, González-Miret ML, Heredia FJ. Research Progress in Imaging Technology for Assessing Quality in Wine Grapes and Seeds. Foods. 2022; 11(3):254. https://doi.org/10.3390/foods11030254
Chicago/Turabian StyleRodríguez-Pulido, Francisco J., Ana Belén Mora-Garrido, María Lourdes González-Miret, and Francisco J. Heredia. 2022. "Research Progress in Imaging Technology for Assessing Quality in Wine Grapes and Seeds" Foods 11, no. 3: 254. https://doi.org/10.3390/foods11030254