Assessment of technological maturity parameters and anthocyanins in berries of cv. Sangiovese (Vitis vinifera L.) by a portable vis/NIR device

Abstract In grape berries the balance between technological parameters such as soluble solids and titratable acidity and phenolic maturity such as anthocyanins concentration, is a key factor for obtaining quality wines. Grapevine berries are commonly harvested on the base of technological maturity parameters determined by traditional analysis methods, often without considering properly phenolic maturity. We investigated the potential use of a portable and non-invasive device based on visible and near infrared (vis/NIR) spectroscopy (Cherry-Meter), which provides an Index of Absorbance Difference (IAD) based on two wavelengths peaks (560 and 640 nm), to measure soluble solids concentration (SSC), titratable acidity (TA), firmness (DI) and anthocyanins (total and monomeric) in cv. Sangiovese grapes. Berries were separated in ten IAD classes according to the Cherry meter data ranging from 0.4 to 1.8, and then analyzed for technological parameters and anthocyanins by using conventional methods. Linear and non-linear regression analysis showed that IAD values were significantly correlated to SSC (R2 = 0.92), TA (R2 = 0.87), DI (R2 = 0.89), and monomeric and total anthocyanin concentration (R2 ranging from 0.68 to 0.97). A Principal Component Analysis (PCA) was applied to analyze relationship among IAD classes, obtaining four different clusters based on increasing level of maturity defined by means of technological parameters and anthocyanins concentration. This is the first approach demonstrating that the use of IAD values obtained from Cherry-Meter could be useful for monitoring both technological maturity parameters and anthocyanin concentration and composition of grape berries.

[1]  Giovanni Agati,et al.  Rapid and non‐destructive method to assess in the vineyard grape berry anthocyanins under different seasonal and water conditions* , 2011 .

[2]  Giovanni Agati,et al.  New portable optical sensors for the assessment of winegrape phenolic maturity based on berry fluorescence , 2008 .

[3]  R. Infante.  Harvest maturity indicators in the stone fruit industry , 2012 .

[4]  R. Boulton.  The Copigmentation of Anthocyanins and Its Role in the Color of Red Wine: A Critical Review , 2001, American Journal of Enology and Viticulture.

[5]  Richard G. Brereton,et al.  Chemometrics: Data Analysis for the Laboratory and Chemical Plant , 2003 .

[6]  L. Bodria,et al.  Apples Nutraceutic Properties Evaluation Through a Visible and Near-Infrared Portable System , 2013, Food and Bioprocess Technology.

[7]  Ø. Hammer,et al.  PAST: PALEONTOLOGICAL STATISTICAL SOFTWARE PACKAGE FOR EDUCATION AND DATA ANALYSIS , 2001 .

[8]  E. Agosin,et al.  A Multipurpose Portable Instrument for Determining Ripeness in Wine Grapes Using NIR Spectroscopy , 2008, IEEE Transactions on Instrumentation and Measurement.

[9]  David C. Baker,et al.  Effects of reaction mixture and other components on the determination of the equilibrium and rate constants of the hydration reactions of anthocyanins , 2008 .

[10]  Carl A. Anderson,et al.  Efficient Spectroscopic Calibration Using Net Analyte Signal and Pure Component Projection Methods , 2005 .

[11]  Ning Wang,et al.  Studies on banana fruit quality and maturity stages using hyperspectral imaging , 2012 .

[12]  L. Bodria,et al.  Prediction of blueberry (Vaccinium corymbosum) ripeness by a portable Vis-NIR device , 2009 .

[13]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[14]  V. Sadras,et al.  Elevated temperature decouples anthocyanins and sugars in berries of Shiraz and Cabernet Franc , 2012 .

[15]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[16]  Roberto Beghi,et al.  Wavelength Selection with a View to a Simplified Handheld Optical System to Estimate Grape Ripeness , 2014, American Journal of Enology and Viticulture.

[17]  Carlos Conde,et al.  Biochemical Changes throughout Grape Berry Development and Fruit and Wine Quality , 2007 .

[18]  Fei He,et al.  Biosynthesis of Anthocyanins and Their Regulation in Colored Grapes , 2010, Molecules.

[19]  M. Noferini,et al.  NON-DESTRUCTIVE TECHNIQUE TO ASSESS INTERNAL FRUIT QUALITY , 2003 .

[20]  Ralph Brown,et al.  Visible-Near Infrared Reflectance Spectroscopy for Nondestructive Analysis of Red Wine Grapes , 2016, American Journal of Enology and Viticulture.

[21]  Riccardo Velasco,et al.  Metabolite profiling of grape: Flavonols and anthocyanins. , 2006, Journal of agricultural and food chemistry.

[22]  S. Poni,et al.  Effects of Cluster Thinning and Preflowering Leaf Removal on Growth and Grape Composition in cv. Sangiovese , 2012, American Journal of Enology and Viticulture.

[23]  R. Infante,et al.  Non-destructive monitoring of flesh softening in the black-skinned Japanese plums ‘Angeleno’ and ‘Autumn beaut’ on-tree and postharvest , 2011 .

[24]  L. Bodria,et al.  Evaluation of Grape Quality Parameters by a Simple Vis/NIR System , 2010 .

[25]  P. Ribereau-gayon,et al.  Handbook of Enology , 2001 .

[26]  M. Noferini,et al.  An innovative use of DA-meter for peach fruit postharvest management , 2016 .

[27]  J. Kanner,et al.  PH-dependent forms of red wine anthocyanins as antioxidants. , 1999, Journal of agricultural and food chemistry.

[28]  G. Casadoro,et al.  A new index based on vis spectroscopy to characterize the progression of ripening in peach fruit , 2008 .

[29]  Luca Rolle,et al.  Mechanical behaviour and quality traits of highbush blueberry during postharvest storage , 2009 .

[30]  V. Freitas,et al.  Influence of the heterogeneity of grape phenolic maturity on wine composition and quality , 2011 .

[31]  Z. Schmilovitch,et al.  Utilizing the IAD index to determine internal quality attributes of apples at harvest and after storage , 2013 .

[32]  R. Bramley,et al.  Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintages , 2005 .

[33]  Da-Wen Sun,et al.  Hyperspectral imaging for food quality analysis and control , 2010 .

[34]  C. Camps,et al.  Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy , 2009 .

[35]  J. Hernández-Hierro,et al.  Relationship between skin cell wall composition and anthocyanin extractability of Vitis vinifera L. cv. Tempranillo at different grape ripeness degree. , 2014, Food chemistry.

[36]  Robert G. Dambergs,et al.  Analysis of Grapes and Wine by near Infrared Spectroscopy , 2006 .

[37]  L. Bonora,et al.  USING NON-DESTRUCTIVE METHODS TO CORRELATE CHILLING INJURY IN NECTARINES WITH FRUIT MATURITY , 2013 .

[38]  H. Wada,et al.  Fruit ripening in Vitis vinifera: spatiotemporal relationships among turgor, sugar accumulation, and anthocyanin biosynthesis , 2011, Journal of experimental botany.

[39]  D. Stefanelli,et al.  Nectarine Fruit Ripening and Quality Assessed Using the Index of Absorbance Difference () , 2013 .

[40]  M. Diago,et al.  Estimation of total soluble solids in grape berries using a hand-held NIR spectrometer under field conditions. , 2016, Journal of the science of food and agriculture.

[41]  Gwendal Latouche,et al.  Non-Destructive Optical Monitoring of Grape Maturation by Proximal Sensing , 2010, Sensors.

[42]  Beńed́icte Lorrain,et al.  Evolution of Analysis of Polyhenols from Grapes, Wines, and Extracts , 2013, Molecules.

[43]  C. D'onofrio,et al.  Potential of a multiparametric optical sensor for determining in situ the maturity components of red and white Vitis vinifera wine grapes. , 2013, Journal of agricultural and food chemistry.