Chemical Characteristics of Sangiovese Wines from California and

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Food and Beverage Chemistry/Biochemistry

Chemical Characteristics of Sangiovese Wines from California and Italy of 2016 Vintage Valentina Canuti, Scott Clifford Frost, Larry Lerno, Courtney K. Tanabe, Jerry A Zweigenbaum, Bruno Zanoni, and Susan E. Ebeler J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 13 Feb 2019 Downloaded from http://pubs.acs.org on February 13, 2019

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Journal of Agricultural and Food Chemistry

Chemical Characteristics of Sangiovese Wines from California and Italy of 2016 Vintage Valentina Canuti*†, Scott Frost‡,§, Larry A. Lerno§, Courtney K. Tanabe‡, Jerry Zweigenbaum#, Bruno Zanoni†, Susan E. Ebeler‡,§ †GESAAF-Department of Agriculture, Food and Forestry Systems, University of Florence, via Donizetti 6, 50144, Florence, Italy. ‡Department of Viticulture and Enology, University of California, Davis, One Shields Avenue, Davis, CA, 95616, United States. §Food Safety and Measurement Facility, University of California, Davis, One Shields Avenue, Davis, CA, 95616, United States. #Agilent

Technologies, Inc., 2850 Centerville Rd, Wilmington, DE, 19808, United States.

Corresponding Author *Telephone: +39 055 2755517; Fax: +39 055 2755500; E-mail: [email protected]

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ABSTRACT: Sangiovese is the most widespread Italian red cultivar and constitutes the basis of

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internationally known wines such as Chianti and Brunello di Montalcino. Outside of Europe,

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Argentina is the largest producer, followed by the United States. This study sought to define and

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compare 2016 vintage Sangiovese wine composition from various production regions in California

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and Italy. Forty-six commercial Sangiovese wines from California and Italy were analyzed for

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volatile profile, color, phenolic and elemental content. This study demonstrates that it is possible to

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determine regional differences among wines based on these chemical profiles. However, some

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Californian and Italian wine had similar chemical compositions. In order to compare Californian

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and Italian wines, Californian wine reference models were developed using the chemical parameters

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from Sangiovese wines, performing a Soft Independent Modelling of Class Analogy (SIMCA). To

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our knowledge, this is the first time that an extensive regionality study has been attempted for

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Sangiovese wines.

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KEYWORDS: elemental analysis; phenolic compounds; Sangiovese; SIMCA; volatile profile;

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wine regionality.

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INTRODUCTION Sangiovese is the most cultivated red grape variety in Italy (53,865 ha representing 7.9 % of

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the national vineyard in 2017) and, constitutes the basis of internationally known wines such as

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Chianti, Brunello di Montalcino, and Nobile di Montepulciano.1,2 Furthermore, Sangiovese is

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certified for the production of several DOCG (Denominazione di Origine Controllata e Garantita),

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DOC (Denominazione di Origine Controllata) and IGT (Indicazione Geografica Tipica) wines all

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over Italy. Worldwide, Sangiovese has been introduced in different countries and regions by Italian

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immigrants. According to 2017 data, Argentina is the largest cultivator of Sangiovese outside

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Europe (1,805 ha), followed by the United States with 802 ha, mostly in California (727 ha), and

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Australia with 438 ha.2 According to the California Wine Institute3 and to the Cal-Italia.com,4 there

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were 4,653 wineries in California by 2017 and 133 of them produce Sangiovese wines (around 3%

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of California’s wineries). Despite its global distribution and importance, there is a lack of

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international studies on Sangiovese grapes and wines. The most substantial scientific works were

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presented at the International Symposium of Sangiovese5,6 between 2000 and 2004 in Italy These

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works showed the influence of regionality and terroir on the flavor profile of Sangiovese wine.7

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According to the International Organisation of Vine and Wine (OIV) vitivinicultural terroir

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is a holistic concept which refers to an area in which collective knowledge of the interactions

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between the identifiable physical and biological environment and applied vitivinicultural practices

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provides distinctive characteristics for the products originating from this area. Regionality, terroir,

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or typicality, is the unique characteristic that the geography, geology, and climate of a particular

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place bestows upon a wine. It can provide recognition of a style specific to an area in a

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representative wine sample.8 Terroir includes growing conditions, microbial differences in

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vineyards9 and wineries as well as common regional winemaking practices. However, in the

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popular media and wine marketing efforts, discussions of the term terroir are further simplified and

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commonly limited to defining a delimited geographical area with distinct soils as it is believed soils ACS Paragon Plus Environment

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are responsible for the unique sensory characteristics of wine. Additionally, soil differences are

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thought to provide key points of differentiation when comparing wines from differing terroirs.10

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A recently proposed a multivariate approach related the typicality of a red Protected

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Denomination of Origin wine to its chemical composition and the chemical composition of the

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original grapes.11 Chemical characteristics of grapes and wines demonstrate that their quality is

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geographically controlled since they frequently vary with the geographical origin of the grapes or

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with the place of the manufacture of wine.12 For these reasons, when comparing wines produced

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from the same cultivar, it is essential to understand how varietal characteristics are recognized.

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Grape derived volatile compounds can define the typicality of a wine and represent an expression of

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terroir. For example, different soils can influence the aromatic composition of grapes,13 and

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therefore distinguish countries by chemical means. This could be useful in the elucidation of

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regionality, in particular when studying wines on a broader scale.14

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There have been numerous studies characterizing regional chemical differences in wines.

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Typical characterization of wines includes volatile compounds,15 polyphenols16-17 and metals.18 In

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particular, a first-time extensive regional study on Malbec was conducted in Argentina and

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California to show the compositional differences of the wines between the two countries;8 a similar

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study19 was conducted on Cabernet Sauvignon wines from Australia to investigate the role of

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geographic origin on sensory and chemical characteristics

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Due to a large number of compounds present in wine, discrimination and classification

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could be better accomplished by grouping compounds by chemical family.20 For this purpose, some

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authors combine various physicochemical parameters of wine, such as trace and major

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elements,21,22 polyphenolic fingerprint,23 amino acids,24 and volatile compounds.25 The elemental

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profile of finished wines, in particular, has been used to identify the geographical origin.21,26-29

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Numerous studies were performed to explain the differences between wines from different regions

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within a country, but only a few of them have compared elemental composition to distinguish

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various countries of origin.29,30 ACS Paragon Plus Environment

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This study sought to define and compare the regional chemical characteristics of Californian

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and Italian Sangiovese wines from the 2016 vintage. Two aspects were considered: a) chemical

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characterization and differentiation of wines for the definition and comparison of Sangiovese wines

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from California and Italy; b) the expression of Sangiovese varietal character in two different regions

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by creating predictive models based on compositional profiles. To our knowledge, this is the first

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extensive regionality study attempted for Sangiovese wines.

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MATERIALS AND METHODS Wines. Forty-six commercial wines from the 2016 harvest (20 from Italy and 26 from

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California) were collected to be representative samples of both regions. All the wines used in this

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study were sourced from commercial producers and required to be 100% Sangiovese, no oak barrel

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aged. As the wines were made solely from Sangiovese grapes under commercial winemaking

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conditions, the differences in composition should reflect the regional styles. A minimum of two

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bottles was received for each wine sample. The wines were coded as reported in Table 1. The pH,

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titratable acidity and alcohol content were evaluated following the OIV official analytical

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methods.31 In California, the wines were chosen from four different American Viticultural Areas

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(AVAs); Central Coast, Sierra Foothills, North Coast, and the San Joaquin Valley region of

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California. The AVAs incorporate the following counties/regions: Amador County, Paso Robles,

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Napa Valley, San Joaquin Valley, Sonoma County, Mendocino County, and Alameda County. In

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Italy, the wines were chosen from two different regions, Tuscany and Emilia Romagna,

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representing the wine areas of Chianti, Chianti Classico, Montalcino and Maremma. In this study,

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we had no control over the viticultural practices and winemaking except for asking for a “best

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representation of the Sangiovese”.

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Volatiles analysis by Headspace SPME GC-MS. Free volatile compounds were quantitated according to the method developed previously.32 Thirty-three compounds were ACS Paragon Plus Environment

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identified in the wines (Table 2) and verified by analyzing reference compounds, except for

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(2R,5R)-2,6,6-trimethyl-10-methylidene-1-oxaspiro[4.5]dec-8-ene (vitispirane), ethyl 2-

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methylbutanoate, 1,1,4a-trimethyl-3,4,4a,5,6,7-hexahydro-2(1H)-naphthalenone, ethyl dec-9-

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enoate, (6E)-7, 11-dimethyl-3-methylidenedodeca-1,6,10-triene (-farnesene), 1,1,6-trimethyl-1,2-

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dihydronaphthalene (TDN), and decanoic acid. Volatile standards ethyl acetate ≥ 99%, ethyl

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butanoate 99%, propan-1-ol ≥ 99.5%, 2-methylpropan-1-ol 99%, ethyl 3-methylbutanoate ≥ 99.7%,

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3-methylbutyl acetate ≥ 95%, 3-methylbutan-1-ol ≥ 99%, ethyl hexanoate 98%, hexyl acetate 99%,

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methyl octanoate 99%, octan-2-ol 99%, ethyl octanoate ≥ 98%, benzaldehyde 99%, ethyl nonanoate

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97%, ethyl decanoate ≥ 98%, 2-phenylethyl acetate 99%, ethyl dodecanoate ≥ 98%, octanoic acid ≥

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99%, were purchased from Aldrich (Milwaukee, WI, USA). 2-Phenylethanol, 99%, and octan-1-ol,

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99% were purchased from Sigma-Aldrich (St. Louis, MO, USA). Hexan-1-ol, 99.9%, and 3,7-

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dimethyloct-6-en-1-ol (-citronellol) 95% were purchased from Fluka (Sigma-Aldrich, St. Louis,

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MO, USA). (E)-1-(2,6,6-Trimethylcyclohexa-1,3-dien-1-yl) but-2-en-1-one (-damascenone), 1.1–

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1.3% (w/w) in absolute ethanol 99.5%, ethyl 2-hydroxypropanoate 99% and diethyl butanedioate

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(diethyl succinate) 99% were purchased from SAFC Supply Solution (Sigma-Aldrich, St. Louis,

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MO, USA). Hexanoic acid 99% was purchased from Acros Organics (Thermo Fisher Scientific,

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Geel, Belgium). The retention times of the authentic standards were matched to the compounds

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measured. The compounds were also verified using quantifier/qualifier ion ratios and published

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retention indices reported for a DB-Wax column. Chemical volatiles standard mixtures were

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prepared in a model wine solution consisting of 5 g/L of tartaric acid 99% (Sigma-Aldrich, St.

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Louis, MO, USA) dissolved in Milli-Q water, pH adjusted to 3.6 with NaOH (Sigma-Aldrich, St.

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Louis, MO, USA) and 12% v/v absolute ethanol. Water was purified through a Milli-Q Water

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System (Millipore, Billerica, MA, USA) prior to use. Absolute ethanol, 200 proof, was purchased

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from Rossville Gold Shield (Hayward, CA, USA).

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Standard concentrations were selected to bracket the concentrations of each individual compound in the wine samples. All standards were analyzed in triplicate. The peak area of each ACS Paragon Plus Environment

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standard (calculated as total ion), relative to the peak area of the octan-2-ol internal standard were

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plotted against the standard concentration to create a standard curve. All the compounds where the

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reference standards were not available were quantitated based on relative response to the octan-2-ol

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internal standard. The linear regression equations obtained were used to calculate the concentration

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(g/L) of each compound in the wine samples.

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Samples were prepared by transferring an 8 mL aliquot of wine to a 20 mL glass headspace

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sample vial containing 3 g of NaCl (Fisher Scientific, Fair Lawn, NJ, USA) then adding 5 L of the

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octan-2-ol internal standard solution (82 mg/L in ethanol solution), for a final concentration of 5.1 ×

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10−2 mg/L. The mixture was carefully shaken to dissolve the NaCl and then left for 1 h in the dark

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at room temperature (22 ± 1 °C) to equilibrate before the analysis. The SPME fiber used for the

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extraction was polydimethylsiloxane (PDMS), 100 m thickness, 23 gauge. The fiber was

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purchased from Supelco (Sigma-Aldrich, St. Louis, MO, USA) and thermally conditioned before

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the first use in accordance with the manufacturer’s recommendations.

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A Gerstel MPS2 autosampler (Gerstel, Baltimore, MD, USA) mounted to an Agilent 6890N

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gas chromatograph (Little Falls, DE, USA) paired with an Agilent 5975 mass selective detector

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constituted the analytical system for the GC-MS analysis. The software used was MSD

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ChemStation (G1701-90057, Agilent Technology, Little Falls, DE, USA). The prepared wine

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samples were warmed to 40 °C for 10 min before exposing the SPME fiber to the sample

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headspace. Headspace extraction times of 30 min, at the temperature of 40 °C, were performed with

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continuous stirring (500 rpm). Thermal desorption of analytes from the SPME fiber occurred during

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splitless injection of the fiber (straight glass liner, 0.8 mm I.D.) at 240 °C for 1 min. Following the

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SPME desorption, the inlet was switched to purge-on for the remainder of the GC-MS run, and the

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SPME fiber was conditioned for 9 min more before it was removed from the injector. There was no

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carry-over between samples observed with a 10 min desorption time.

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A DB-wax column (30 m x 0.25 mm O.D., 0.25 m film thickness, Agilent Technology, Little

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Falls, DE, USA) was used for all the analyses. Helium carrier gas was used with a total flow of 2.33 ACS Paragon Plus Environment

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mL/min (constant pressure). The oven parameters were as follows: initial temperature of 40 °C held

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for 4.0 min, followed by an increase to 80 °C at a rate of 2.5 °C/min, a second increase to 110 °C at

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a rate of 5 °C/min, and a final increase to 220 °C at a rate of 10°C/min. The oven was then held at

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220 °C for 5 min before returning to the initial temperature (40 °C). The total cycle time, including

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oven cool-down, was 50 min. The MS detector was operated in scan mode (mass range 50-200 m/z)

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and the transfer line to the MS system was maintained at 240 °C. All the wine samples were

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analyzed in triplicates.

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Color indices. Color intensity (CI) and hue (Hue) were measured according to the method

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of Glories33 and the total phenols index (TPI) as described by Ribereau-Gayon.34 CI was measured

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using a 1 mm path length quartz cell, and expressed as the sum of absorbances at 420 nm (A420),

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520 nm (A520), and 620 nm (A620). Wine Hue was measured using a 1 mm path length quartz cell,

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and expressed as the ratio between absorbance at 420 nm (A420) and 520 nm (A520). TPI was

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measured as absorbance at 280 nm using a 1 mm path length quartz cell and samples were diluted

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1:10 with Milli-Q water. The ultraviolet-visible (UV/Vis) absorbance of the samples were measured

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on an Agilent spectrophotometer Cary 8454 UV-Visible diode array detector and the software used

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was MSD ChemStation (G1701-90057, Agilent Technology, Little Falls, DE, USA). Milli-Q water

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was use as a reference. All the analyses were performed in triplicate.

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Phenolic analysis by UHPLC q-TOF MS. All wines were analyzed for monomeric

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phenolic and wine pigments using an Agilent 6545 quadrupole time-of-flight mass spectrometer (Q-

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TOF MS; Santa Clara, CA) equipped with a dual JetStream ESI source operated in positive ion

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mode and based on a previously published method.35 A modified solvent system and gradient was

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used for the analyses in order to obtain better chromatographic performance of the anthocyanins. A

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binary solvent system consisting of water + 1% formic acid (v/v) (mobile phase A) and acetonitrile

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+ 1% formic acid (v/v) (mobile phase B) was used and the gradient employed was: 0 min., 3% B;

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10 min., 80% B; 13 min., 80% B; 14 min., 3% B; 18 min., 3% B. Q-TOF MS tuning, source ACS Paragon Plus Environment

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conditions, and reference masses were the same as those presented in the previously published

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method.35 Aliquots of wine were taken from unopened bottles and spiked with decylglycoside (0.1

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g/mL) as an internal standard. Samples were centrifuged for 5 minutes at 15,000 rpm and the

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supernatant was collected for analysis. Data was analyzed in Agilent Profinder (ver. 8, Santa Clara,

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CA) using the find by formula algorithm with database matching, using an in-house database

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consisting of 88 grape and wine phenolics (Table 4). The retention time of the phenolics in the

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database were previously verified using authentic standard or by tandem mass spectrometry to

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confirm the identity of potential phenolics in the samples. All potential matches were confirmed by

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inspection of mass spectral data and retention time. Relative quantitation amongst samples was

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performed.

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Elemental analysis by ICP-MS/MS. Fifty-four elements were quantitated in each wine using a

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similar dilute-and-shoot method previously established/used at UC Davis36 (Table 5). The samples

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were diluted 5-fold with a solution of 3% nitric acid (70%, J.T. Baker, Ultrex II Ultrapure, Center

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Valley, PA, USA) and 1% hydrochloric acid (35%, Fisher Science, Optima Grade, Pittsburgh, PA,

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USA) in metal-free centrifuge tubes (VWR, Radnor, PA, USA). Ultrapure water (18 Mcm, EMD

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Millipore Bellerica, MA, USA) was used to prepare all solutions. In order to analyze elements with

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high concentrations (>2 g/mL; Ca, Na, Mg, K, S, P), samples were also analyzed after a 250-fold

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dilution. Each wine was prepared and analyzed in triplicate at the two different dilution levels. The

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diluted samples were centrifuged (15,000 rpm) and stored at 4 C up to a week before analysis.

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Calibration functions were made using multi-element calibration standards 1, 2A, 3, and 4

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from SPEX CertiPrep (Metuchen, NJ, USA) and Calibration Mix Majors from Agilent

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Technologies (Santa Clara, CA, USA). Single-element standards were also used for elements: B

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(High Purity Standards, Charleston, SC, USA), K (Agilent Technologies, Santa Clara, CA, USA), S

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(High Purity Standards, Charleston, SC, USA), and P (SPEX CertiPrep, Metuchen, NJ, USA). An

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environmental spike mix was obtained from Agilent Technologies (Santa Clara, CA, USA) for a ACS Paragon Plus Environment

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recovery study. Due to the lack of certified reference material (CRM) prepared in a wine matrix,

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NIST 1643e Trace Metals in Water (Gaithersburg, MD, USA) was analyzed to monitor the method

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performance and accuracy and also aid in the selection of analysis mode for each element.

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All calibration standards, blanks, and CRMs were made with a matrix-matched solution of

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3% nitric acid, 1% hydrochloric acid, and 3% ethanol (200 proof, Koptec, King of Prussia, PA,

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USA) when analyzing the 5-fold diluted wine samples. Because the percentage of ethanol in the

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higher diluted samples was negligible, ethanol was not included in the calibration standards, blanks,

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and CRMs solutions while analyzing the higher diluted samples. In all cases, 6-point calibration

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curves were created for all quantitated elements.

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All elemental profiling was completed with an Agilent 8800 triple quadrupole inductively

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coupled plasma-mass spectrometer (ICP-QQQ, Santa Clara, CA, USA) equipped with a Scott-type

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double-pass quartz spray chamber, MicroMist concentric nebulizer (Glass Expansion, Pty, Ltd,

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Australia), and nickel sampler and skimmer cones. Internal Standard Mix 1 (SPEX CertiPrep,

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Metuchen, NJ, USA) diluted 1:10 in 1% nitric acid was constantly mixed with the sample stream

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during analysis. Elements were monitored in both single quadrupole MS and MS/MS modes in

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triplicate with 100 sweeps per replicate. To ensure the instrument performance and validity of the

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method, the ICP-QQQ was tuned and calibrated daily and a set of quality controls, consisting of

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continuous calibration blank (CCB), continuous calibration verification (CCV), and CRMs, were

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analyzed every 20 samples.

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The monitored elemental isotopes were quantitated using the MassHunter ICP-MS software

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(version 4.3, Agilent Technologies, Santa Clara, CA, USA). Limits of detection (LOD) were

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calculated according to the IUPAC from the analysis of 10 replicates of the lowest level calibration

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standards. A specific gas mode and isotope were selected for each element based on the LOD and

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reported background equivalent concentrations (BEC). For statistical testing, in all cases where the

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element concentration was measured below the LOD, a value of that element’s LOD/10 was

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assigned instead. ACS Paragon Plus Environment

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Statistical analysis. Analysis of variance (ANOVA), considering country/growing region

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and replications as factors, and frequency distribution, analyzed by the Chi-square test, were

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performed using Statgraphics Centurion (Ver.XV, StatPoint Technologies, Warrenton, VA).

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Principal Component Analysis (PCA) and the Soft Independent Modelling of Class Analogy

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(SIMCA) were performed using Unscrambler (V9.1, CAMO Process AS, Oslo, Norway). PCA

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allows visualizing the information of the data set in a few principal components retaining the

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maximum possible variability within that set, the SIMCA method instead is a very useful modelling

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technique that builds a box for each category. With SIMCA, a PCA is applied to each group

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separately and provides additional information on the different groups such as the relevance of the

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different variables and measures of separation.

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The classification method (SIMCA) consisted in describing each class of samples (wines),

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identified by their chemical composition, in independent Principal Component Analysis (PCA)

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models. Wine samples were classified on the basis of their membership limit within the different

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PCA models.37

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RESULTS AND DISCUSSION

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Chemical characterization and differentiation of Sangiovese wines from California and Italy.

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Volatile profile. Thirty-three volatile aroma compounds were identified and quantitated in

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both Californian and Italian Sangiovese wines using HS-SPME-GC-MS. Table 2 shows the

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compounds list and the ANOVA results comparing the differences between California and Italy,

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and regions within each country. No significant differences among the analytical replicates were

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observed for any volatile compounds (data not shown). Volatile aroma compounds were all

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significantly different (p-value < 0.05) between the wine from California and Italy, except for 2-

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methyl-1-propanol (#7), (2R,5R)-2,6,6-trimethyl-10-methylidene-1-oxaspiro[4.5]dec-8-ene

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(vitispirane I) (#16), benzaldehyde (#17), ethyl nonanoate (#19), ethyl decanoate (#21), (6E)-7,11-

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dimethyl-3-methylidenedodeca-1,6,10-triene (-farnesene) (#24), 1,1,6-trimethyl-1,2ACS Paragon Plus Environment

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dihydronaphthalene (TDN) (#25), (E)-1-(2,6,6-Trimethylcyclohexa-1,3-dien-1-yl)but-2-en-1-one

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(-damascenone) (#28) and ethyl dodecanoate (#29). The compounds vitispirane I, benzaldehyde,

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-farnesene, TDN and -damascenone are considered grape derived flavors.38 -Damascenone is a

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commonly identified norisoprenoid and has a floral/fruity odor with a reported aroma detection

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threshold in water of 9 ng/L.39,40 Vitispirane has an aroma note described as eucalpytus-like or

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camphoraceous and an aroma detection threshold in wine of 800 g/L.40,41 TDN contributes a

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characteristic diesel or kerosene-like aroma to aged Riesling and has an aroma detection threshold

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of 20 g/L.40,42 The norisoprenoids occur in grapes and wines predominately as glycosidically

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bound precursors. Other authors7,10 have identified -damascenone in Sangiovese grapes and wines

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and TDN in Sangiovese wines. Benzaldehyde also originates in grape43 and in high concentrations

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it is associated with fruit aromas.44 -farnesene, an acyclic sesquiterpene related with floral

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characteristics of wines, was previously found at trace levels in Syrah, Nero d’Avola and Frappato

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wines.45 Sesquiterpenes are originally from grapes.46 In this study, these data demonstrated that all

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the Sangiovese wines have some common grape-derived volatile compounds and, thus, can be

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related to the expression of the variety in California and Italy regions.

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Based on an ANOVA, only the compounds that were significantly different between

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California and Italy were used to create PCA scores and loadings plots of the wine samples (Figure

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1 a and b). The first two dimensions of the PCA explained 49% of the variance in the data set. The

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wines were divided in two groups corresponding to the Italian wines on the left side of the graph

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and Californian wines on the right side (Figure 1a). Along the first PC (35% of the total explained

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variance) the Italian wines were characterized by the compounds ethyl 2-methyl butanoate (#5),

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ethyl 3-methylbutanoate (#6), 3-methylbutan-1-ol (#9), 1,1,4a-trimethyl-3,4,4a,5,6,7-hexahydro-

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2(1H)-naphthalenone (#18) and 2-phenylethanol (#31) that explained between 50 and 100% of the

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variance according to the correlation loadings plot in Figure 1b. Italian wines also had higher

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amounts of 3,7-dimethyloct-6-en-1-ol (-citronellol) (#26) compared to the California wines. The


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California wines, on the right side of Figure 1a, were characterized by 2-methylpropyl acetate (#2),

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hexyl acetate (#11), ethyl 2-hydroxypropanoate (ethyl lactate) (#12), hexan-1-ol (#13), octan-1-ol

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(#20), octanoic acid (#32) and decanoic acid (#33), that explained between 50 and 100% of the

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variance as shown in the correlation loadings plot (Figure 1b). The results demonstrate that the

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Californian and the Italian wines are separated into two groups mostly according to the fermentative

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volatiles, which were generally higher in the California wines.

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A similar analysis was carried out for the Californian and the Italian wines separately to

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determine if the different wine regions within each country could be distinguished from each other.

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Only the volatiles that were significantly different among regions for the California wines were

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used for the analysis. Figure 2 a and b shows the PCA scores and correlation loadings plots for the

296

volatile compounds for Sangiovese wines from California. Along the first PC (27% of variance

297

explained) the wines were separated according to the different amounts of ethyl acetate (#1), 2-

298

methylpropyl acetate (#2), ethyl 2-methylbutanoate (#5), 2-methylpropan-1-ol (#7), ethyl 2-

299

hydroxypropanoate (ethyl lactate) (#12); the wines on the right side of the graph are richer in these

300

compounds. Along the second PC (22%) the wines were associated with higher amounts of ethyl

301

butanoate (#3), ethyl hexanoate (#10), hexyl acetate (#11), methyl octanoate (#14), ethyl octanoate

302

(#15), ethyl decanoate (#21), 3,7-dimethyloct-6-en-1-ol (-citronellol) (#26), and ethyl dodecanoate

303

(#29), which explains 50-100% of the variance. In general wines from Amador County (AM) and

304

the North Coast (NV, Napa Valley; ME, Mendocino; RV, Redwood Valley) were distinguished

305

from each other. However, other regions/AVAs could not be completely distinguished from these

306

two areas.

307

The Italian wines were also evaluated, and no clear separations based on growing regions

308

were obtained (figure not shown). Overall < 50% of the total variance was explained indicating that

309

volatiles alone may not differentiate the wines. The Italian wines all came from Tuscany (with the

310

exception of two wines from Emilia Romagna). The Tuscany region in Italy comprises a defined

311

region and thus might have a more uniform composition within the region compared to other ACS Paragon Plus Environment

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regions in Italy. The California wines represent a number of different regions that have greater

313

diversity in climate, soil, and elevation than that represented by the Tuscan region.

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Color indices. CI, Hue, and TPI were used to discriminate the wine samples from California

316

and Italy. All three indices were highly significant according to ANOVA. A PCA was used to

317

visualize the difference between the wines from California and Italy (Figure 3 a and b) and the first

318

two PCs explained 98% of the total variance. The Sangiovese Italian wines were mostly on the right

319

side of the plot and were characterized by a high CI and a high TPI; on the left side of the plot, the

320

California Sangiovese wines were characterized by a high Hue measurement. These differences

321

between the two groups of wine could be explained by the data in Figure 4 (a, b and c) and Table 3

322

that showed the minimum and maximum, 25% and 75% percentiles, median, average and standard

323

deviation for the three different color indices. Italian wines had a higher CI and TPI with an average

324

of 17.52 and 56.79, respectively, compared to the Californian wines with average values of 10.20

325

and 41.33, respectively. However, there were seven Italian wines that were not distinguished from

326

the Californian wines showing/inferring that they have similar color indices (Figure 3). The values

327

shown in Table 3 were consistent with other findings for Sangiovese wines.47

328

Phenolic composition. Seventy-four phenolic compounds were identified in the Sangiovese

329

wines (Table 4) but only 47 were significantly different between Californian and Italian wines

330

according to the ANOVA (p < 0.05). Within each region, a significant difference in phenolic

331

composition was not found among the different Californian wines or Italian wines. A PCA was

332

performed using only the phenolics that were significantly different between the two regions

333

(Figure 5 a and b). The first two PCs explained 53% of the total variance. The wine samples were

334

positioned according different phenols compounds.

335

The Californian and Italian wines were separated in two different groups, with Italian wines

336

on the left side of the graph (Figure 5 a); these wines were mostly Sangiovese wines from the

337

Chianti Classico area (CC). These wines were characterized by the phenolic compounds malvidinACS Paragon Plus Environment

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glucoside-ethyl-epicatechin (#4), malvidin-glucoside-ethyl-catechin (#5), malvidin-glucoside-

339

pyruvic acid (vitisin A) (#12), catechin-malvidin-glucoside (# 33), malvidin-glucoside- epicatechin

340

(#35), (+)-catechin (#41), (+)-(epi)gallocatechin (#43), quercetin (#46), myricetin (#47), dimer

341

(#68), and dimer (#69).

342

On the right side (Figure 5 b), the Californian wines were characterized mostly by

343

compounds kaempferol 3-O-galactoside (#48), kaempferol 3-O-glucoside (#49), kaempferol 3-O-

344

glucuronide (#50), quercetin 3-O-galactoside (#51), and quercetin 3-O-glucoside (#52). Some of the

345

Italian Sangiovese wines, e.g., those from the Chianti area (CH) and Montalcino (MO), were also

346

positioned close to the California Sangiovese wines from the Amador county region (AM). This

347

could be a result of similarities in winemaking techniques among these regions and wineries in AM,

348

CH and MO, but further studies are needed to test this hypothesis.

349

One of the main characteristics distinguishing Sangiovese from other red wines is its

350

delicate pigment profile.48 In fact, regarding the relative abundance of anthocyanins, the pigments

351

were mainly represented by delphinidin-3-O-glucoside (DEL), cyanidin-3-O-glucoside (CYA),

352

peonidin-3-O-glucoside (PEO), petunidin-3-O-glucoside (PET) and malvidin-3-O-glucoside

353

(MAL). These are all very unstable and oxidizable phenols. In agreement with results reported

354

elsewhere,48,49 the more chemically stable acylated anthocyanins were found in very low amounts

355

(sum of total relative area below 2% of the total content in anthocyanins, data not shown).

356

Elemental profiling. A total of 54 elements in the mass range from 6-238 m/z were detected

357

in the 46 wines using the described ICP-QQQ method. Of these 54 elements, 38 were significantly

358

different between the Californian and Italian wines (p < 0.05) in the ANOVA (Table 5). K and Ca,

359

are natural components of grape, with K being the predominant element in Vitis vinifera juice;50 K

360

and Ca levels were not significantly different between California and Italy. Fe levels were also not

361

significantly different between the two regions. These findings are in contrast with other research

362

that reported that Fe, Ca and K could be used to characterize wines according to geographical

363

origin,51 because their concentrations in wines can be influenced by regional variations in fertilizing ACS Paragon Plus Environment

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practices and winemaking. Additionally, some authors52 considered that K and Fe were the key

365

elements to discriminate between two Andalusian Denomination of Origin fine wines.

366

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The OIV (Organisation Internationale de la Vigne et du Vin) has set maximum allowable

367

limits for some elements such as B (80 mg/L), Cu (1 mg/L), Zn (5 mg/L), As (0.2 mg/L), Cd (0.01

368

mg/L), and Pb (0.15 mg/L). All wines analyzed here were below the maximum acceptable limits

369

according to the OIV.

370

Only the 38 elements that were significantly different between the wines from California

371

and Italy were used in the PCA to obtain the elemental space of the studied wines, shown in Figure

372

6 a and b. The first two PCs explained 57% of the total variance. The wines were divided in two

373

groups corresponding to the Italian wines on the lower left side of the graph and Californian wines

374

on the right side (Figure 6 a). California wines from Amador County (CAM) tended to group

375

together in the upper half of the plot (Figure 6 a). Along the first PC (43% of the total explained

376

variance), the wines were separated based on their concentration of the elements Yb, Tm, Er, Mo,

377

Nb, La, Re, Ce, Ho, Ce, Ga and U on the right side of the plot; according to the correlation loadings

378

plot (Figure 6 b) these elements explained between 50-100% of variance. The Italian wines on the

379

left side were principally characterized by higher levels of Lu, Cu and Pb. Along the second

380

dimension PC2, (14% of the total explained variance), the wines were separated based on their

381

levels in Na and Sr (top side of plot). The Na soil content can depend on the distance from the sea

382

or from soil composition.51 From these results, Californian wines resulted to be richer in most of the

383

measured elements.

384 385

Californian Sangiovese wine models and evaluation of the variety expression in two different

386

regions.

387

In order to better understand similarities and differences that emerged from the PCA

388

between wines from California and Italy, California Sangiovese wine models were built using

389

SIMCA. This analysis was applied to compare the Sangiovese wines from the different regions of ACS Paragon Plus Environment

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Italy to the different wines in California in order to understand whether the California wine regions

391

produced wines with similar chemical profiles to the Italian wines. Italian wines were used as the

392

basis of comparison since Italy is the country of origin for the Sangiovese cultivar.

393

Five models were built by applying PCA to the 26 Californian wines. The variables

394

considered for the models were the volatiles, phenolic, color indices, elements and combined

395

variable profiles as reported in Table 6. The models were then used to classify a total of 20 Italian

396

Sangiovese wines (11 wines from Chianti Classico area, 4 from Chianti area, 2 from Montalcino

397

area, 1 from Maremma and 2 from Emilia Romagna) according to their chemical composition. The

398

SIMCA analysis reported in Table 6 allowed the classification of a different number of Italian

399

wines for each of the California Sangiovese wine models. When using the volatile compounds alone

400

as variables to create the model, it was observed that the highest numbers of Italian wines matched:

401

9 wines from Chianti Classico area, 3 from Chianti area, 2 from Montalcino, 1 from Emilia

402

Romagna and 1 from Maremma area. The phenols model was the least representative for the Italian

403

wines since only four wines fit this model. The vintage and climate could have a greater impact

404

than terroir on wine color and polyphenols as evidenced in a recent study on Sangiovese grapes and

405

wines through three different years.53 For these reasons an extended study on more vintages could

406

be carried out to better understand the differences related to the different terroir or the climatic

407

conditions. Further studies also needed to evaluated the influence of the winemaking methods on

408

the wines composition.

409

This study sought to define and compare the regional chemical characteristics of Sangiovese

410

wines from two regions, California and Italy, by combining multiple chemical analyses. The

411

findings demonstrated that the California Sangiovese wines have common characteristics with the

412

Italian wines, particularly related to the volatile composition. Thus the chemical composition of the

413

Sangiovese variety can be largely maintained when it is cultivated abroad, as the wines in each

414

region retain a common set of characteristics. These compounds were grape derived and can be

415

associated with the varietal character of Sangiovese in both regions. The results of this study ACS Paragon Plus Environment

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416

expand our current knowledge of Sangiovese wines and the contribution of regional characteristics

417

to the composition of wine.

418

By combining multiple chemical analyses, volatile profile, color indices, phenol

419

composition and elemental profiles, it was possible to describe the differences and similarities

420

between the two regions. Volatile and elemental profiles were most effective at characterizing the

421

wine from the two regions.

422

This is the first time that an extensive regionality study has been attempted for Sangiovese

423

wines made in California and Italy. The results of this study expand our current knowledge of

424

Sangiovese wines and the contribution of regional characteristics to the composition of wine. Wines

425

from the 2016 vintage were considered for the study. In the future, comparison among two or more

426

vintages would help, to better understand the year to year variability between the wines produced in

427

the two regions. Future studies to characterize the sensory profiles and relate the chemical

428

properties to sensory characteristics are also needed.

429 430

AKNOWLEDGMENTS

431

We thank all the Californian and Italian wineries and the “Vignaioli di Radda Association” for

432

supplying and donate the Sangiovese wine samples.

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REFERENCES

438

(1) Bergamini, C.; Caputo, A. R.; Gasparro, M.; Perniola, R.; Cardone, M. F.; Antonacci, D.

439

Evidences for an alternative genealogy of ‘Sangiovese’. Molecular Biotech. 2013, 53(3), 278-

440

288.

441

(2) Distribution of the world’s grapevine varieties. OIV Focus 27 February 2017. URL

442

(http://www.oiv.int/en/oiv-life/the-distribution-of-the-worlds-grapevine-varieties-new-oiv-

443

study-available)

444

(3) California Wine Institute. URL (https://www.wineinstitute.org)

445

(4) Cal-Italia.com URL (http://cal-italia.com)

446

(5) Various authors. In Il Sangiovese vitigno tipico e internazionale: identità e peculiarità.

447

Proceedings of the 2nd International Sangiovese Symposium, Florence 17-19 November 2004,

448

Ed. Arsia Firenze.

449 450 451 452 453

(6) Various authors. In Il Sangiovese Proceedings of the 1st International Sangiovese Symposium, Florence 15-17 February 2000, Ed. Arsia Firenze. (7) Bertuccioli, M.; Rosi, I.; Canuti, V.; Giovani, G.; Picchi, M. La tipicità dei vini: il Sangiovese. Industria delle bevande. 2011, 233, 7-14. (8) King, E. S.; Stoumen, M.; Buscema, F.; Hjelmeland, A. K.; Ebeler, S. E.; Heymann, H.;

454

Boulton, R. B. Regional sensory and chemical characteristics of Malbec wines from Mendoza

455

and California. Food Chem. 2014, 143, 256-267.

456

(9) Knight, S.; Klaere, S.; Fedrizzi, B.; Goddard, M. R. Regional microbial signatures positively

457

correlate with differential wine phenotypes: evidence for a microbial aspect to terroir.

458

Scientific reports. 2015, 5, 14233.


19


459 460 461

Page 20 of 43

(10) Herderich, M.; Barter, S.; Black, C. A.; Bramley, R.; Capone, D.; Dry, P.; Siebert, T.; Zhang, P. Terroir effects on grape and wine aroma compounds. ACS Symp. Ser. 2015, 1203, 131−146. (11) Canuti, V.; Picchi, M.; Zanoni, B.; Fia, G.; Bertuccioli, M. A multivariate methodological

462

approach to relate wine to characteristics of grape composition: The case of typicality. Am. J.

463

Enol. Vitic. 2017, 68 (1), 49-59.

464 465

(12) Vaudour, E. The quality of grapes and wine in relation to geography: Notions of terroir at various scales. J. Wine Res. 2002, 13 (2), 117-141.

466

(13) Sabon, I.; De Revel, G.; Kotseridis, Y.;Bertrand, A. Determination of volatile compounds in

467

Grenache wines in relation with different terroirs in the Rhone Valley. J. Agric. Food Chem.

468

2002, 50 (22), 6341-6345.

469

(14) Heymann, H.; Robinson, A. L.; Buscema, F.; Stoumen, M. E.; King, E. S.; Hopfer, H.; R.B.

470

Boulton; Ebeler, S. E. Effect of region on the volatile composition and sensory profiles of

471

Malbec and Cabernet Sauvignon wines. ACS Symp. Ser. 2015, 109-122.

472

(15) Sabon, I.; de Revel, G.; Kotseridis, Y.; Bertrand, A. Determination of volatile compounds in

473

Grenache wines in relation with different terroirs in the Rhone Valley. J. Agric. Food Chem.

474

2002, 50 (22), 6341–6345

475

(16) Csomós, E.; Héberger, K.; Simon-Sarkadi, L. Principal component analysis of biogenic amines

476

and polyphenols in Hungarian wines. J. Agric. Food Chem. 2002, 50 (13), 3768–3774.

477

(17) Zanoni, B.; Siliani, S.; Canuti, V.; Rosi, I.; Bertuccioli, M. A kinetic study on extraction and

478

transformation phenomena of phenolic compounds during red wine fermentation. Int. J. Food

479

Sci. Tech. 2010, 45 (10), 2080-2088.

480

(18) Brainina, K. Z.; Stozhko, N. Y.; Belysheva, G. M.; Inzhevatova, O. V.; Kolyadina, L. I.;

481

Cremisini, C. Determination of heavy metals in wines by anodic stripping voltammetry with

482

thick-film modified electrode. Anal. Chim. Acta. 2004, 514 (2), 227–234. ACS Paragon Plus Environment

20

Page 21 of 43


483

(19) Robinson, A. L.; Adams, D. O.; Boss, P. K.; Heymann, H.; Solomon, P. S.; Trengove, R. D.

484

Influence of geographic origin on the sensory characteristics and wine composition of Vitis

485

vinifera cv. Cabernet Sauvignon wines from Australia. Am. J. Enol. Vitic. 2012, 63, 467−476.

486

(20) Serrano-Lourido, D.; Saurina, J.; Hernández-Cassou, S.; Checa, A. Classification and

487

characterisation of Spanish red wines according to their appellation of origin based on

488

chromatographic profiles and chemometric data analysis. Food Chem. 2012, 135 (3), 1425-

489

1431.

490

(21) Almeida, C.M.R.; Vasconcelos M.T.SD. Multielement composition of wines and their

491

precursors including provenance soil and their potentialities as fingerprints of wine origin. J.

492

Agric. Food Chem. 2003, 51 (16),4788–4798

493

(22) Gonzálvez, A; Llorens, A; Cervera, M.L.; Armenta, S.; De la Guardia, M. Elemental

494

fingerprint of wines from the protected designation of origin Valencia. Food Chem. 2009, 112

495

(1), 26–34.

496

(23) Makris, D.P.; Kallithraka, S.; Mamalos, A. Differentiation of young red wines based on

497

cultivar and geographical origin with application of chemometrics of principal polyphenolic

498

constituents. Talanta. 2006, 70 (5),1143–1152.

499

(24) Hernandez-Orte, P.; Cacho, J.F.; Ferreira, V. Relationship between varietal amino acid profile

500

of grapes and wine aromatic composition. Experiments with model solutions and chemometric

501

study. J. Agric. Food Chem. 2002, 50 (10), 2891–2899.

502

(25) Ivanova, V.; Stefova, M.; Vojnoski, B.; Stafilov, T.; Bìrò, I.; Bufa, A.; Felinger, A.; Kilàr, F.

503

Volatile composition of Macedonian and Hungarian wines assessed by GC/MS. Food

504

Bioprocess Technol. 2013, 6 (6) 1609–1617.

505 506

(26) Ashurst, P. R.; Dennis, M. J. In Analytical Methods of Food Authentication, Springer Science & Business Media, Eds. Blackie: London, U.K., 1998; 368 pp.


21


507 508 509 510

Page 22 of 43

(27) Coetzee, P. P.; Van Jaarsveld, F. P.; Vanhaecke, F. Intraregional classification of wine via ICPMS elemental fingerprinting. Food Chem. 2014, 164, 485-492. (28) Saurina, J. Characterization of wines using compositional profiles and chemometrics. Trends Anal. Chem. 2010, 29 (3), 234-245.

511

(29) Serapinas, P.; Venskutonis, P. R.; Aninkevičius, V.; Ežerinskis, Ž.; Galdikas, A.; Juzikienė, V.

512

Step by step approach to multi-element data analysis in testing the provenance of wines. Food

513

Chem. 2008, 107 (4), 1652-1660.

514 515 516 517 518

(30) Baxter, M. J.; Crews, H. M.; Dennis, M. J.; Goodall, I.; Anderson, D. The determination of the authenticity of wine from its trace element composition. Food Chem. 1997, 60 (3), 443-450. (31) OIV, Compendium of international methods of wine and must analysis. International Organisation of Vine and Wine: Paris, France, 2009, 154-196. (32) Canuti, V.; Conversano, M.; Calzi, M. L.; Heymann, H.; Matthews, M. A.; Ebeler, S. E.

519

Headspace solid-phase microextraction–gas chromatography–mass spectrometry for profiling

520

free volatile compounds in Cabernet Sauvignon grapes and wines. J. Chrom. A. 2009, 1216

521

(15), 3012-3022.

522 523 524 525 526

(33) Glories, Y. La couler des vins rouges. II Partie – mesure, origine et interpretation. Connaiss. la Vigne du Vin. 1984, 18, 253–271. (34) Ribereau-Gayon, P. Le dosage des composés phénoliques totaux dans les vins rouges. Chim. Anal. 1970, 52, 627–631. (35) Bokulich, N.; Collins, T.; Masarweh, C.; Allen, G.; Heymann, H.; Ebeler, S. E.; Mills, D. A.

527

Associations among wine grape microbiome, metabolome, and fermentation behavior suggest

528

microbial contribution to regional wine characteristics. MBio. 2016, 7 (3), e00631-16.


22

Page 23 of 43

529


(36) Hopfer, H.; Nelson, J.; Collins, T. S.; Heymann, H.; Ebeler, S. E. (2015). The combined impact

530

of vineyard origin and processing winery on the elemental profile of red wines. Food Chem.

531

2015, 172, 486-496.

532 533 534 535 536 537 538 539 540 541 542 543

(37) Wold, S.; Sjostrom, M. SIMCA: a method for analyzing chemical data in terms of similarity and analogy. Chemometr Theory Appl. 1977, 52, 243-282. (38) Ebeler, S. E. (2001). Analytical chemistry: Unlocking the secrets of wine flavor. Food Rev. Int. 2001, 17 (1), 45-64. (39) Strauss, C. R.; Wilson, B.; Anderson, R.; Williams, P. J. Development of precursors of C13 nor-isoprenoid flavorants in Riesling grapes. Am. J. Enol. Vitic. 1987, 38 (1), 23-27. (40) Ohloff, G. Importance of minor components in flavors and fragrances. Perfum. Flav. 1978, 3, 11. (41) Simpson, R. F. Aroma and compositional changes in wine with oxidation, storage and ageing. Vitis. 1978, 17 (3), 274-287. (42) Simpson, R. F. 1, 1, 6-Trimethyl-1, 2-dihydronaphthalene-important contributor to bottle aged bouquet of wine. Chem. Ind. 1978, 1, 37.

544

(43) Rosillo, L.; Salinas, M. R.; Garijo, J.; Alonso, G. L. Study of volatiles in grapes by dynamic

545

headspace analysis: Application to the differentiation of some Vitis vinifera varieties. J.

546

Chrom. A. 1999, 847 (1), 155-159.

547

(44) Spillman, P. J.; Sefton, M. A.; Gawel, R. The effect of oak wood source, location of seasoning

548

and coopering on the composition of volatile compounds in oak‐matured wines. Aust. J. Grape

549

Wine Res. 2004, 10 (3), 216-226.

550 551

(45) Cincotta, F.; Verzera, A.; Tripodi, G.; Condurso, C. Determination of sesquiterpenes in wines by HS-SPME coupled with GC-MS. Chromatography. 2015, 2 (3), 410-421.


23


552 553 554

(46) May, B.; Wüst, M. Temporal development of sesquiterpene hydrocarbon profiles of different grape varieties during ripening. Flav. Fragr. J. 2012, 27 (4), 280-285. (47) Canuti, V.; Puccioni, S.; Giovani, G.; Salmi, M.; Rosi, I.; Bertuccioli, M. Effect of oenotannin

555

addition on the composition of Sangiovese wines from grapes with different characteristics.

556

Am. J. Enol. Vitic. 2012, 63, 220–231

557

Page 24 of 43

(48) Mattivi, F.; Scienza, A.; Failla, O.; Villa, P.; Anzani, R.; Tedesco, G.; Gianazza, E.; Righetti,

558

P. Vitis vinifera − a chemotaxonomic approach: anthocyanins in the skin. Vitis (Special Issue)

559

1990, 29, 119−133.

560

(49) Canuti, V.; Puccioni, S.; Storchi, P.; Zanoni, B.; Picchi, M.; Bertuccioli, M. Enological

561

eligibility of grape clones based on the SIMCA method: the case of the Sangiovese cultivar

562

from Tuscany. Ital. J. Food Sci. 2018, 30 (1), 184-199.

563 564

(50) Marengo, E.; Aceto, M. Statistical investigation of the differences in the distribution of metals in Nebbiolo-based wines. Food Chem. 2003, 81 (4), 621-630.

565

(51) Latorre, M. J.; Garcia-Jares, C.; Medina, B.; Herrero, C. Pattern recognition analysis applied to

566

classification of wines from Galicia (Northwestern Spain) with certified brand of origin. J.

567

Agric. Food Chem. 1994, 42 (7), 1451-1455.

568

(52) Álvarez, M.; Moreno, I. M.; Jos, Á. M.; Cameán, A. M.; González, A. G. Study of mineral

569

profile of Montilla-Moriles “fino” wines using inductively coupled plasma atomic emission

570

spectrometry methods. J. Food Comp. Anal. 2007, 20 (5), 391-395.

571

(53) Priori, S.; Pellegrini, S.; Perria, R.; Puccioni, S.; Storchi, P.; Valboa, G.; Costantini, E. A.

572

Scale effect of terroir under three contrasting vintages in the Chianti Classico area (Tuscany,

573

Italy). Geoderma. 2019, 334, 99-112.

574 575

(54) Humpf, H. U.; Schreier, P. Bound aroma compounds from the fruit and the leaves of blackberry (Rubus laciniata L.). J. Agric. Food Chem. 1991, 39, 1830−1832. ACS Paragon Plus Environment

24

Page 25 of 43

576


(55) Selli, S.; Cabaroglu, T.; Canbas, A.; Erten, H.; Nurgel, C.; Lepoutre, J. P.; Gunata, Z. Volatile

577

composition of red wine from cv. Kalecik Karasι grown in central Anatolia. Food Chem. 2004,

578

85 (2), 207-213.

579

(56) Kelebek, H.; Selli, S. Determination of volatile, phenolic, organic acid and sugar components

580

in a Turkish cv. Dortyol (Citrus sinensis L. Osbeck) orange juice. J. Sci. Food Agr. 2011, 91

581

(10), 1855-1862.

582

(57) Ugliano, M.; Moio, L. Free and hydrolytically released volatile compounds of Vitis vinifera L.

583

cv. Fiano grapes as odour-active constituents of Fiano wine. Anal. Chim. acta. 2008, 621 (1),

584

79-85.

585 586 587 588 589 590

(58) Fariña, L.; Villar, V.; Ares, G.; Carrau, F.; Dellacassa, E.; Boido, E. Volatile composition and aroma profile of Uruguayan Tannat wines. Food Res. Int. 2015, 69, 244-255. (59) Qian, M.; Reineccius, G. Identification of aroma compounds in Parmigiano-Reggiano cheese by gas chromatography/olfactometry. J. Dairy Sci. 2002, 85 (6), 1362-1369. (60) Wei, Z.; Liu, X.; Huang, Y.; Lu, J.; Zhang, Y. Volatile aroma compounds in wines from Chinese wild/hybrid species. J. Food Biochem. 2018, e12684, 1-20.

591 592 593 594 595 596 597 598 ACS Paragon Plus Environment

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Figure captions

600 601

Figure 1. Principal Component Analysis (PCA) scores (a) and loadings (b) plots of the volatile

602

compounds for Sangiovese wines from California and Italy. See Table 1 for wine codes and Table 2

603

for compounds numbers. a) score plot showing the volatile space of the wines. Italian wines in bold;

604

b) correlation loadings plot (ellipse represent the interval 50-100% of explained variance).

605 606

Figure 2. Principal Component Analysis (PCA) scores (a) and loadings (b) plots of the volatile

607

compounds for Sangiovese wines from California. See Table 1 for wine codes and Table 2 for

608

compounds numbers. a) score plot showing the volatile space of the wines. Same color represents

609

the same wine area; b) correlation loadings plot (ellipse represent the interval 50-100% of explained

610

variance).

611 612

Figure 3. Principal Component Analysis (PCA) scores (a) and loadings (b) plots of the color indices

613

(color intensity, hue and total phenols index) for Sangiovese wines from California and Italy. See

614

Table 1 for wine codes. a) score plot showing the color indices space of the wines. Italian wines in

615

bold; b) correlation loadings plot (ellipse represent the interval 50-100% of explained variance).

616 617

Figure 4. Color intensity (a), hue (b), and total phenols index (c) for Sangiovese wines from

618

California and Italy: minimum and maximum, 25% and 75% percentiles, and median.

619 620

Figure 5. Principal Component Analysis (PCA) scores (a) and loadings (b) plots of the phenolic

621

composition for Sangiovese wines from California and Italy. See Table 1 for wine codes and Table

622

4 for phenolic compounds codes. a) score plot showing the phenols space of the wines. Italian

623

wines in bold; b) correlation loadings plot (ellipses represent the interval 50-100% of explained

624

variance).


26

Page 27 of 43


625 626

Figure 6. Principal Component Analysis (PCA) scores (a) and loadings (b) plots of the elemental

627

profile for Sangiovese wines from California and Italy. See Table 1 for wine codes. a) score plot

628

showing the elemental space of the wines. Italian wines in bold; b) correlation loadings plot (ellipse

629

represent the interval 50-100% of explained variance).

630


27


Page 28 of 43

Tables Table 1. Details of the wines in the study from California (USA) and Italy, including the regions of origin and standard chemical parameters.


28

Page 29 of 43


Wine code

State/Country

Region

pH

Titratable acidity (g/L tartaric acid)

Alcohol content (v/v%)

01CAM

California

Amador County (Sierra Foothills)

3.50

6.50

13.30

02CAM

California


3.59

7.62

14.70

03CAM

California


3.48

8.80

12.52

04CAM

California


3.53

6.10

14.10

05CAM

California


3.39

6.51

14.30

06CAM

California


3.38

6.50

14.54

07CAM

California


3.49

5.24

14.50

08CAM

California


3.32

6.40

13.30

09CAM

California


3.29

6.50

13.31

10CAM

California


3.63

7.90

13.50

11CNV

California

Napa Valley (North Coast)

4.00

4.68

14.50

12CVN

California


3.92

4.85

14.60

13CNV

California


3.79

5.08

14.10

14CNV

California


3.86

5.12

13.90

15CNV

California


3.46

5.24

14.57

16CNV

California


3.64

6.27

14.46

17CNV

California


3.54

6.03

14.78

18CNV

California


3.65

5.11

15.10

19CME

California

Mendocino (North Coast)

3.75

4.78

15.24

20CRV

California

Redwood Valley (North Coast)

3.48

4.94

14.70

21CSY

California

Santa Ynez Valley (Central Coast)

3.33

6.10

15.73

22CSY

California

Santa Ynez Valley (Central Coast)

3.50

5.27

14.77

23CMC

California

Monterey County (Central Coast)

3.61

4.32

13.44

24CSL

California

San Louis Obispo County, Paso Robles (Central Coast)

3.62

6.40

14.20

25CAC

California

Alameda County (Central Coast)

3.62

5.21

14.98

26CSJ

California

San Joaquin Valley (Inland Valleys)

4.04

3.94

14.48

27IER

Italy

Emilia Romagna

3.96

6.20

14.29

28IER

Italy

Emilia Romagna

4.08

4.7

14.30

29IMA

Italy

Maremma (Tuscany)

3.15

8.40

14.68

30IMO

Italy

Montalcino (Tuscany)

3.44

7.29

13.83

31IMO

Italy

Montalcino (Tuscany)

3.55

6.15

13.94

32ICH

Italy

Chianti (Tuscany)

3.58

7.00

12.57

33ICH

Italy

Chianti (Tuscany)

3.32

6.37

14.05

34ICH

Italy

Chianti (Tuscany)

3.39

5.73

14.04

35ICH

Italy

Chianti (Tuscany)

3.52

6.05

13.70

36ICC

Italy

Chianti Classico (Tuscany)

3.36

5.73

13.30

37ICC

Italy


3.27

6.08

14.33

38ICC

Italy


3.18

6.45

14.24

39ICC

Italy


2.98

7.66

12.90

40ICC

Italy


3.29

5.77

14.30

41ICC

Italy


3.17

6.74

13.71

42ICC

Italy


3.32

6.63

15.13


29


Page 30 of 43

43ICC

Italy


3.05

8.00

13.32

44ICC

Italy


3.22

7.03

14.11

45ICC

Italy


3.37

6.21

13.81

46ICC

Italy


3.06

7.97

14.00


30

Page 31 of 43


Table 2. A list of the compound measured in the HS-SPME-GC-MS, retention time, linear retention index (literature and calculated) and significance levels of main effects (95% significance level). Main effects

Compound #

1 2 3 4 5 6 7 8 9 10 11 12 13 14

15

16 17

18 19 20

Linear Retention Index (calculate d)

State/C ountry Califor nia vs Italy (pvalue)

Region within Califor nia (pvalue)

Region within Italy (pvalue)

Califor nia (averag e) g/L

Italy (aver age) g/L

8.00c 266.6 6c 280.7 8

Volatile compounds

Retention time (min)

Linear Retention Index (literature )

Ethyl acetate

3.189

890d

890

< 0.05

< 0.05

< 0.05

11.72c

5.904

1007e

1015

< 0.05

< 0.05

< 0.05

317.86c

6.656

1028f

1036

< 0.05

1040g

1042

< 0.05

< 0.05 0.3290 ns

464.03

6.860

< 0.05 0.8613 ns

7.190

1050f

1051

< 0.05

< 0.05

< 0.05

1852.11

7.740

1069d

1066

< 0.05

< 0.05

4.43

9.008

1085g

1101

< 0.05 0.2468 ns

< 0.05

< 0.05

353.45c

10.027

1132f

1123

< 0.05

< 0.05

< 0.05

2702.63

14.521

1230g

1217

< 0.05

< 0.05

318.74c

15.611

1220f

1238

< 0.05

< 0.05

17.611 Ethyl 2-hydroxypropanoate (ethyl lactate) 21.248

1270f

1276

< 0.05

< 0.05

< 0.05 0.1764 ns 0.5458 ns

1341d

1350

< 0.05

< 0.05

< 0.05

229.81c

21.977

1360f

1365

< 0.05

< 0.05

< 0.05

2717.51

134.5 3c 1544. 84

Methyl octanoate

23.381

1392

1395

< 0.05

< 0.05

< 0.05

23.02

22.93

Octan-2-olIS

24.786

< 0.05

< 0.05

< 0.05

885.26

548.6 5

0.9309 ns 0.4023 ns

0.8900 ns

1.60

2.26

< 0.05

0.5372 ns 0.8052 ns

7.88

7.47

< 0.05 0.2334 ns

< 0.05

< 0.05

12.50

18.84

< 0.05

1.79

2.11

< 0.05

< 0.05 0.8365 ns

19.64

8.95

< 0.05

< 0.05

54.82

54.63

2-Methylpropyl acetate Ethyl butanoate Propan-1-ol Ethyl 2-methylbutanoatea,b Ethyl 3-methylbutanoate 2-Methylpropan-1-ol 3-Methylbutyl acetate 3-Methylbutan-1-ol Ethyl hexanoate Hexyl acetate

Hexan-1-ol

Ethyl octanoate (2R,5R)-2,6,6-trimethyl10-methylidene-1oxaspiro[4.5]dec-8-ene (Vitispirane I)a,b Benzaldehyde 1,1,4a-trimethyl3,4,4a,5,6,7-hexahydro2(1H)-naphthalenonea,b Ethyl nonanoate Octan-1-ol

d

68.92c

442.11 457.39

25.01c 3422. 67 6.94 435.9 8c 1664. 83 357.6 0c 259.6 4 139.1 5

1433

25.318

1436f

1448

27.570

1515g

1517

27.670

1516d

1521

28.050

1537g

1537

28.250

1528e

1546

28.839

1565d

1571

30.438

1636f

1650

< 0.05 0.7831 ns

22

Diethyl butanedioate (Diethyl succinate)

31.057

1690e

1685

< 0.05

< 0.05

< 0.05

7310.92

5743. 65

23

Ethyl dec-9-enoatea,b

31.288

1675g

1697

< 0.05

< 0.05

< 0.05

29.37

34.44

1674e

1736

0.0579 ns

0.6796 ns

< 0.05

0.83

2.29

1748g

1758

0.6335 ns

< 0.05

< 0.05

1.34

1.31

1778f

1775

< 0.05

< 0.05

< 0.05

13.00

14.91

21

24 25 26

Ethyl decanoate

(6E)-7,11-dimethyl-3methylidenedodeca-1,6,10triene -Farnesene)a,b 31.870 1,1,6-Trimethyl-1,2dihydronaphthalene (TDN)a,b 32.209 3,7-dimethyloct-6-en-1-ol -Citronellol) 32.459

31 ACS Paragon Plus Environment


27

28 29 30 31 32 33 ISinternal

2-Phenylethyl acetate Phenethyl acetate) (E)-1-(2,6,6Trimethylcyclohexa-1,3dien-1-yl)but-2-en-1one -Damascenone) Ethyl dodecanoate Hexanoic acid 2-Phenylethanol Phenylethanol) Octanoic acid Decanoic acida,b

Page 32 of 43

< 0.05

< 0.05

0.0906 ns

178.19

135.9 4

0.9844 ns

0.0586 ns

0.61

0.54

1852

0.2371 ns 0.5880 ns

< 0.05

41.40

42.92

1855d

1874

< 0.05

< 0.05

28.61c

28.55c

34.487

1925f

1928

< 0.05

52.27c

36.250

2075d

2076

< 0.05

< 0.05 0.0650 ns

< 0.05 0.4791 ns 0.0743 ns 0.1178 ns

69.10c 3903. 17

38.835

2300d

2292

< 0.05

< 0.05

33.213

1814f

1829

33.332

1841d

1838

33.520

1851g

33.808

standard; ns indicates no statistical significant (p-values >

4100.84

< 0.05 25.06 a 0.05); expressed

16.26

as octan-2-ol

equivalents; bcompounds tentatively identified by matching to the NIST MS library spectra and comparison of Kovats’ retention indices (I) to literature values; cexpressed as mg/L; dhttp://www.odour.org.uk/; ehttp://www.pherobase.com; fwww.flavornet.org; gLinear

Retention

Index on a DB-WAX column were obtained from refs 54−60.


Page 33 of 43


Table 3. Color Intensity, Hue, and Total Phenols Index for Sangiovese wines from California and Italy: average and standard deviation. Color Intensity

Hue

Total Phenols Index

California

Italy

California

Italy

California

Italy

average

10.20

17.52

0.89

0.64

41.33

56.79

standard deviation

2.29

5.09

0.16

0.15

7.07

9.23



Page 34 of 43

Table 4. A list of the phenol compounds measured in the Q-TOF-MS method with the corresponding main effect significance levels (95% significance level) and the average concentrations.

Main effect

Phenolic compounds

State/Country California - California vs (average) Italy (p-value) area

Italy (average) area

1

DecylglucosideIS

0.064 ns

479358

423823

2

Petunidin-glucoside-ethyl-epicatechin

< 0.05

155987

1102997

3

Petunidin-glucoside-ethyl-catechin

< 0.05

171833

444874

4

Malvidin-glucoside-ethyl-epicatechin

< 0.05

686417

3494086

5

Malvidin-glucoside-ethyl-catechin

< 0.05

1379386

9109046

6

Delphinidin-glucoside-acetaldehyde

< 0.05

336393

1297841

7

Peonidin-glucoside-acetaldehyde

0.912 ns

81591

92924

8

Petunidin-glucoside-acetaldehyde

0.5665 ns

3560671

46832

9

Malvidin-glucoside-acetaldehyde (Vitisin B)

< 0.05

5361196

16803270

10

Peonidin-glucoside-pyruvic acid

0.7681 ns

120291

138414

11

Petunidin-glucoside-pyruvic acid

< 0.05

677137

419515

12

Malvidin-glucoside-pyruvic acid (Vitisin A)

< 0.05

4169507

11376389

13

Malvidin-3-glucoside-acetone

0.9100 ns

225784

240425

14

Peonidin-3-glucoside-acetone

0.1144 ns

1120166

250024

15

Petunidin-3-glucoside-acetone

< 0.05

6969614

17752130

16

Cyanidin 3-O-glucoside

< 0.05

20020943

26861105

17

Peonidin-3-glucoside

0.0898 ns

44829061

58802091

18

Delphinidin-3-glucoside

0.9436 ns

18173279

17426104

19

Petunidin-3-glucoside

0.1271 ns

49600677

43952082

20

Malvidin-3-glucoside

0.0802 ns

91287104

88477312

21

Petunidin-glucoside-vinylcatechol

< 0.05

1252138

918384

22

Malvidin-glucoside-vinylcatechol (pinotin A)

0.1062 ns

1732527

1715150

23

Malvidin-glucoside-vinyl-catechin

< 0.05

407249

1294880

24

Malvidin-glucoside-vinyl-epicatechin

< 0.05

469974

518322

25

Peonidin-glucoside-vinylguaicol

< 0.05

6669776

4729136

26

Delphinidin-glucoside-vinylguaicol

0.6976 ns

720766

221724

27

Petunidin-glucoside-vinylguaicol

< 0.05

3294401

2112256

28

Malvidin-glucoside-vinylguaicol

< 0.05

2524034

1707442

29

Peo-glucoside-vineylphenol

< 0.05

711194

1529313

30

Petunidin-glucoside-vinylphenol

0.4896 ns

1588670

1268311

31

Malvidin-glucoside-vinylphenol (pigment A)

0.2275 ns

6579046

8976040

32

Malvidin-glucoside-vinylsyringol

0.1993 ns

268143

26719

33

Catechin-malvidin-glucoside (F-A type)

< 0.05

576701

1148212

34

Epicatechin-malvidin-glucoside (F-A type)

< 0.05

194981

498078

35

Malvidin-glucoside-catechin (A-F type)

< 0.05

119767

176353

36

Malvidin-glucoside-epicatechin (A-F type) Malvidin-glucoside-(epi)gallocatechin (F-A type)

< 0.05

128790

140035

0.1533 ns

292413

457423

Compound #

37


Page 35 of 43


38

Malvidin-glucoside-(epi)gallocatechin (A-F type)

0.2673 ns

55998

65464

39

Malvidin-glucoside-di(epi)catechin (F-A type)

0.0587 ns

57802

85576

40

Malvidin-glucoside-di(epi)catechin (A-F type)

< 0.05

146945

20061

41

(+)-Catechin

< 0.05

1436111

3374955

42

(-)-Epicatechin

< 0.05

2009848

3041230

43

(+)-(Epi)Gallocatechin

< 0.05

790536

1249277

44

(-)-Epicatechin 3-O-gallate

< 0.05

369169

15415

45

Kaempferol

< 0.05

3233349

2307194

46

Quercetin

< 0.05

7375499

11294235

47

Myricetin

< 0.05

983349

2448175

48

Kaempferol 3-O-galactoside

< 0.05

1029646

175791

49

Kaempferol 3-O-glucoside

< 0.05

2119228

143320

50

Kaempferol 3-O-glucuronide

< 0.05

559271

180572

51

Quercetin 3-O-galactoside

< 0.05

2262031

841163

52

Quercetin 3-O-glucoside

< 0.05

7765399

673766

53

Quercetin 3-O-glucuronide

< 0.05

6825996

4869161

54

Myricetin 3-O-glucoside

< 0.05

1520276

502442

55

Quercetin 3-O-rutinoside

< 0.05

200463

-

56

Ellagic acid

< 0.05

119084

88247

57

Gentisic acid

< 0.05

213441

358835

58

Gallic acid

0.9567 ns

432900

461043

59

p-Coumaric acid

0.2408 ns

193942

229460

60

Caffeic acid

0.1491 ns

142423

196426

61

Caffeoyl tartaric acid

0.1082 ns

3015650

105545

62

2-S-Glutathionyl caftaric acid

< 0.05

20254126

14396929

63

Vanillic acid

< 0.05

324432

237082

64

Syringic acid

< 0.05

474188

868926

65

Ferulic acid

0.6387 ns

38373

49536

66

pyranone-malvidin-glucoside

0.7717 ns

975363

70375

67

Dimer

< 0.05

7153257

5630402

68

Dimer

< 0.05

2711013

4544455

69

Dimer

0.2649 ns

1089776

1327254

70

Dimer

0.2758 ns

641146

646240

71

Dimer

< 0.05

2085092

3603043

72

Dimer

< 0.05

244361

345926

73

Resveratrol -glucoside

< 0.05

105308

693667

74

Piceatannol 3-O-glucoside

0.1984 ns

13828

24579

ISinternal

standard; ns indicates not statistically significant (p-values > 0.05)



Page 36 of 43

Table 5. Elements analyzed by ICP-MS, corresponding significance levels of main effects (95% significance level) and average concentrations for California and Italian wines. Main effects State/Country California vs Italy (p-value)

Italy (average) g/La

California (average) g/La

Li

0.9217 ns

0.0153a

0.0186a

Be

Element 7 9

< 0.05

0.0520

0.0934

B

0.0510 ns

5.6631

a

6.8537a

Al

0.7818 ns

0.1632a

0.1571a

Ti

0.3787 ns

0.0170

0.0184a

V

< 0.05

0.2215

0.5997

Cr

< 0.05

9.9840

7.6642

Fe

0.6046 ns

1.3932a

1.2847a

Co

< 0.05

2.6118

3.0725

Ni

< 0.05

0.0153a

0.0283a

Cu

< 0.05

0.0746

a

0.0432a

Zn

< 0.05

0.7099a

0.9303a

Ga

11 27 47 51 52 56 59 60 63 66 71

a

< 0.05

0.0291

0.0679

75->75

< 0.05

0.0078

3.2821

Rb

< 0.05

1.5166

a

1.9347a

Sr

< 0.05

0.6185a

0.9657a

Zr

0.1693 ns

0.9982

1.2777

Nb

< 0.05

0.0053

0.0103

Mo

< 0.05

0.8412

1.3358

< 0.05

0.9868

2.1057

Ru

0.9856 ns

0.0005

0.0005

Rh

0.9727 ns

0.0003

0.0002

Cd

< 0.05

0.1200

0.4012

Sb

0.0580 ns

0.0433

0.1807

Te

0.3824 ns

0.0019

0.0026

Cs

0.9142 ns

2.6638

13.5277

Ba

< 0.05

111.88

277.81

La

< 0.05

0.0087

0.0364

Ce

< 0.05

0.0225

0.0556

Pr

< 0.05

0.0019

0.0070

Nd

< 0.05

0.0110

0.0320

Sm

< 0.05

0.0013

0.0059

Eu

< 0.05

0.0041

0.0111

Gd

< 0.05

0.0030

0.0122

Dy

< 0.05

0.0040

0.0126

Ho

< 0.05

0.0009

0.0035

Er

< 0.05

0.0053

0.0163

Tm

< 0.05

0.0008

0.0032

Yb

< 0.05

0.0059

0.0227

As

85 88 90 93 95

Se

80->96 101 103 111 121 125 133 137 139 140 141 146 147 153 157 163 165 166 169 172


Page 37 of 43


Lu

< 0.05

0.1568

0.0526

Hf

< 0.05

0.0957

0.2046

Ta

< 0.05

0.0020

0.0209

W

0.1061 ns

0.0598

0.1346

Re

< 0.05

0.0091

0.0436

Tl

< 0.05

0.4680

0.5708

Pb

< 0.05

6.2458

1.7148

Th

0.6632 ns

0.0011

0.0016

U

< 0.05

0.0048

0.0135

Na

< 0.05

6.100

1.4057a

Mg

175 178 181 182 185 205 207 232 238 23 26

a

< 0.05

113.22a

135.99a

P

< 0.05

246.74

297.96a

32->48

S

0.2128 ns

124.88a

K

0.3687 ns

1007.82

1024.69a

0.2779 ns

78.288a

76.681a

31->47

39

Ca

44

aexpressed

a

134.02a a

as mg/L; ns indicates not statistically significant (p-values > 0.05)



Page 38 of 43

Table 6. Classification of the Sangiovese Italian wines using SIMCA as a function of the Sangiovese Californian wines (5% significance limit) and the different group of variables for model development (all variables, volatiles, phenols, color indices, and elements). The wines indicated with the symbol (●) fit the model, while (-) indicates Italian wines not fitting the model. Wine code

All variables

Phenols

Volatiles

Color indices

Elements

27IER

-

-

●

●

●

28IER

-

-

●

-

29IMA

●

-

30IMO

●

● ●

31IMO

●

-

●

●

●

32ICH

●

-

●

●

33ICH

-

-

●

●

34ICH

-

●

35ICH

-

●

●

●

36ICC

-

-

●

-

●

37ICC

-

-

●

-

-

38ICC

-

-

39ICC

●

-

●

40ICC

-

-

●

●

●

41ICC

-

●

-

●

42ICC

-

●

●

-

-

43ICC

-

-

●

-

44ICC

-

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Figure 1 a and b

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Figure 3 a and b


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Figure 4 a, b and c



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Figure 5 a and b

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Chemical Characteristics of Sangiovese Wines from California and

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