White wines aroma recovery and enrichment: Sensory-led aroma selection and consumer perception

Accepted Manuscript White wines aroma recovery and enrichment: Sensory-led aroma selection and consumer perception

Alvaro Lezaeta, Edmundo Bordeu, Eduardo Agosin, J. Ricardo Pérez-Correa, Paula Varela PII: DOI: Reference:

S0963-9969(18)30220-5 doi:10.1016/j.foodres.2018.03.044 FRIN 7481

To appear in:

Food Research International

Received date: Revised date: Accepted date:

2 November 2017 14 March 2018 14 March 2018

Please cite this article as: Alvaro Lezaeta, Edmundo Bordeu, Eduardo Agosin, J. Ricardo Pérez-Correa, Paula Varela , White wines aroma recovery and enrichment: Sensory-led aroma selection and consumer perception. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Frin(2017), doi:10.1016/ j.foodres.2018.03.044

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ACCEPTED MANUSCRIPT White wines aroma recovery and enrichment: sensory-led aroma selection and consumer perception Alvaro Lezaeta*a, Edmundo Bordeua, Eduardo Agosinb, J. Ricardo Pérez-Correab, Paula Varelac

Universidad Católica de Chile, Facultad de Agronomía e Ingeniería Forestal,

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a Pontificia

Departamento de Fruticultura y Enología, Casilla 306 Correo 22, Santiago, Chile Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Departamento de

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b

Nofima AS, Osloveien 1, P.O. Box 210, N-1431 Ås, Norway

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c

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Ingeniería Química y Bioprocesos, Casilla 306 Correo 22, Santiago, Chile

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*Corresponding author:

E-mail address: [email protected]; [email protected] (A. Lezaeta). Full postal address: Pontificia Universidad Católica de Chile, Facultad de

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Agronomía e Ingeniería Forestal, P.O. Box 306-22, Santiago, Chile.

Abbreviations: CO2, carbon dioxide; CATA, check all that apply; GDA, generic

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descriptive analysis; HCl, hydrochloric acid; SO2L, free sulfur dioxide; CaCO3, calcium carbonate; H2O2, hydrogen peroxide; Rg, commercial regular bottling of

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wine; Rv, commercial reserve bottling of wine; AG, aromatic group; PCA, principal component analysis; GM, geometric means; HCA, hierarchical cluster analysis; AFC, alternative forced choice; D, doses of aromatic condensate; AHC, Agglomerative Hierarchical Clustering.

Abstract

ACCEPTED MANUSCRIPT We developed a sensory-based methodology to aromatically enrich wines using different aromatic fractions recovered during fermentations of Sauvignon Blanc must. By means of threshold determination and generic descriptive analysis using a trained sensory panel, the aromatic fractions were characterized, selected, and clustered. The selected fractions were grouped, re-assessed, and

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validated by the trained panel. A consumer panel assessed overall liking and

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answered a CATA question on some enriched wines and their ideal sample.

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Differences in elicitation rates between non-enriched and enriched wines with respect to the ideal product highlighted product optimization and the role of

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aromatic enrichment. Enrichment with aromatic fractions increased the aromatic

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quality of wines and enhanced consumer appreciation.

Keywords: Wine; Sauvignon Blanc; Aroma; Preference; Sensory; Consumers;

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CATA.

Highlights

Sauvignon Blanc wines were enriched with natural aromas condensed

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

from fermentation. By means

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

of

a

sensory-based

methodology,

positive

aromatic

condensates were selected. 

The selected aromatic groups improved the perception of wine quality.



CATA showed the positive effect of aromatic enrichment on consumer perception.

ACCEPTED MANUSCRIPT 1. Introduction Aroma is one of the main sensory drivers of consumer preferences (Abbott, 1999) that determines the perceived quality and value of wine (Swiegers, Bartowsky, Henschke, & Pretorius, 2005). In wine, aroma is a complex sensory quality parameter (Cacho, 2003), which is due to a unique blend of several volatile and compounds

originated

from

grapes,

secondary

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semi-volatile

products

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(fermentative aromas), and aging (Lambrechts & Pretorius, 2000; Swiegers &

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Pretorius, 2005). These compounds belong to different chemical families and are present in a wide range of concentrations (Ruiz & Martínez, 1997). Moreover, the

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perception threshold of these compounds can vary from ng/L to mg/L, and, in several cases, they do not show an evident relationship between concentration

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and sensory impact. Sensory impact is usually related to complex interactions among volatile compounds (Riu-Aumatell, 2005). This complexity explains why

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small differences in the concentration of aromatic compounds can result in an

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excellent or ordinary wine (Swiegers et al., 2005). The CO2 released during wine fermentation strips a series of aromatic

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compounds (Colibaba, Cotea, Niculaua, & Schmarr, 2012; Gomez, Martinez, & Laencina, 1993; Morakul et al., 2013; Mouret, Morakul, Nicolle, Athes, &

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Sablayrolles, 2012). The losses in aromatic compounds may be significant (Mouret et al., 2014), affecting the final concentration of volatile aromatic compounds (Morakul et al., 2013) and the sensory profile of red wines (Guerrini et al., 2016). Aroma losses during fermentation have been a significant concern in the wine industry because they can lead to a reduction in wine quality (Morakul, 2011). Several of the lost volatile compounds may contribute to positive sensory attributes in wines (Hodson, 2004). In a consumer study using sensory profiling

ACCEPTED MANUSCRIPT methods (CATA and Projective mapping based on choice), Lezaeta, Bordeu, Næs, & Varela (2017) observed that aromatic enrichment improved quality perception of white wines by increasing the positive attributes (good, intense and fruity

aroma,

apple/pear,

etc.)

and

decreasing

negative

attributes

(vegetable/herbaceous, a term always considered negative in the Chilean

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oenological environment).

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During wine fermentation, aromatic complexity increases dramatically

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(Swiegers et al., 2005). The highest levels of volatile compounds are produced during the yeast growth phase (Bely, Sablayrolles, & Barre, 1990; Miller, Amon,

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& Simpson, 1987). However, some volatile fermentation compounds (e.g., sulfur compounds) negatively affect wine quality (Hodson, 2004). Muller, Wahlstrom, &

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Fugelsang (1993) reported that the aromatic fractions at the beginning of fermentation were desirable, while those produced close to the end of

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fermentation were detrimental to wine quality.

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A wide range of technologies has been implemented to reduce aromatic losses (Sablayrolles, 2009). Fermentation temperature plays an important role in

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the final aromatic profile of wines and is an important parameter to fine tune the aromatic quality during wine fermentation (Molina, Swiegers, Varela, Pretorius, &

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Agosin, 2007). Higher fermentation temperatures contribute to higher losses of volatile compounds, while lower temperatures have the opposite effects (Muller et al., 1993). Therefore, many winemakers ferment at very low temperatures (8°C to 12°C) to reduce aroma losses and improve the aromatic profile of wines (Killian & Ough, 1979; Kunkee, 1984). However, fermentation at low temperatures increases production costs due to energy expenditure, reduces the cellar installed capacity and increases the risk of arrested fermentations.

ACCEPTED MANUSCRIPT Different capture and return methodologies have been developed to control aromatic losses and improve wine quality (Muller et al., 1993; Todd, Castronovo, Fugelsang, Gump, & Muller, 1990; Zoecklein, Herns, Whiton, & Mansfield, 2000). Condensation is a clean and simple technique; however, to the best of our knowledge, there are no widespread commercial applications of this technology.

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Most of the technologies have not taken fractioned or characterized condensates

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during fermentation. In contrast, during aromatic enrichment, these technologies

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incorporated whole aroma condensates, thereby adding undesirable compounds to wines. In addition, the use of sensory evaluation as a technical tool has not

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been considered. Therefore, it is important to fractionate and characterize the condensates obtained throughout fermentation to unveil their specific aroma

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contribution. Obtaining the sensory profile of each condensate fraction using sensorial techniques would allow a better understanding of their effect on wine.

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In this study, different aromatic fractions were condensed throughout the

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fermentation of Sauvignon Blanc wines. By applying generic descriptive analysis (GDA) as described by Lawless & Heymann (2010), which is based on QDA® and

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using a trained sensory panel, we characterized and grouped each condensed fraction based on aromatic similarities. Grouped fractions that positively

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contributed to the aromatic wine profile were selected. The aim of this study was to develop a sensory-based methodology of the aromatic enrichment of wines through the identification of aromatic fractions recovered during fermentation.

2. Materials and methods 2.1 Fermentation

ACCEPTED MANUSCRIPT The must was obtained from Sauvignon Blanc grapes from Leyda valley that were crushed, pressed and settled to obtain clear juice. Isothermal fermentations were carried out in triplicate at 12°C and 18°C (three and two weeks respectively) in 600-L tanks filled with 500 L of clear must following the standard white wine fermentative protocol used by the winery. To ensure constant fermentation

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temperatures, the tanks had a refrigeration control system equipped with a

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temperature sensor PT100 (Veto, Chile) coupled to a temperature controller

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(Danfoss, USA). To guarantee its availability for all fermentations, 5,000 L of must was stored in a stainless-steel tank at 4°C during vintage. To minimize

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spontaneous fermentation, pH was reduced from 3.2 to 2.8 using HCl and free sulfur dioxide (SO2L) was increased to 60 ppm. Prior to each replicate

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fermentation, pH was adjusted to 3.2 with CaCO3 and SO2L was reduced to 20 ppm using H2O2. Nutritional supplements such as Biovin (Engel, Santiago, Chile),

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FermoPlus Blanc (AEB, Brescia, Italy), and diammonium phosphate (Sigma-

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Aldrich, USA) were used (20 g/hL) throughout fermentation. The must was inoculated at 18°C, and the fermentation temperature was subsequently

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adjusted. Commercial Saccharomyces cerevisiae strain VIN7 (20 g/hl, Anchor Wine yeast, South Africa) was used. We monitored the fermentations on a daily

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basis by measuring must density. The tanks were fitted with closures allowing the CO2 gas stream to pass through two condensers in series, which continuously condensed at two different temperatures. Wines obtained at both fermentation temperatures (12°C and 18ºC) were bottled and stored at 4ºC.

2.2 Samples 2.2.1 Condensate sampling

ACCEPTED MANUSCRIPT Condensate from each condenser was independently collected every eight density points (from 1092 g/L), obtaining 10 condensed fractions until the CO2 flow rate was too low to recover more condensate (end of fermentation). Each fraction (1077, 1069, 1061, 1053, 1045, 1037, 1029, 1021, 1013, and 995 g/L) corresponded to a density interval during the fermentation; e.g., fraction 1077

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corresponded to the interval between 1092 and 1077 g/L. These fractions were

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collected and stored at −80ºC in individual canisters topped with argon for a

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period of two months, between the completion of fermentations and the start of sensory work. The volumetric condensate recovery was of 0.8 and 1.0 mL/L for

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the fermentations at 12°C and 18°C respectively. Subsequent chemical analysis allowed to identify and quantify volatile components which were analysed by gas

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chromatography with mass spectrometry (GC-MS) and quantified using the standard addition method. These analyses revealed that mainly water was

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condensed in one of the condensers; therefore, the fractions from that condenser

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were discarded. The fractions from the second condenser, which contained high concentrations of aromatic compounds, were used in the remainder of the study.

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2.2.2 Wine samples

Different fermentation temperatures and categories of Sauvignon Blanc wines



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were used to evaluate the effect of aromatic enrichment (Table 1a): Regular quality wines: wine fermented at 18ºC (B) and commercial regular bottling of wine (Rg; Weil, 2005). These wines were enriched with different aromatic groups (AG1, AG2 and AG3, details below). 

High quality wines: wine fermented at 12ºC (A) and commercial reserve bottling of wine (Rv; Weil, 2005);

ACCEPTED MANUSCRIPT All commercial wines (Rg and Rv) corresponded to 2014 harvest and were selected from different categories of competitor wineries in the Chilean market. All wines were acquired at local supermarkets. 2.2.3 Sample enrichment Each fraction was diluted (0.1% v/v) in a hydro-alcoholic solution (12% v/v, pH

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3.2) in a canister topped with nitrogen, protected from light with aluminum foil,

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and stored at 4ºC. The hydro-alcoholic solutions and wine samples (75 mL) were

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enriched the day before the aromatic evaluation and were presented in ISOtasting glasses covered with Petri dishes 10 min before the test and served at

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8ºC. The enrichment levels (concentrations) were defined based on sensory

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testing, as detailed below.

2.3 Trained panel

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GDA was carried out using 12 trained assessors (seven women and five men,

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median age: 35 y of age), with at least one-year experience in white wine. The assessors were recruited from “Centro de Aromas y Sabores - DICTUC S.A.” and

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an internal sensory panel from the winery (40 and 60% respectively) and were

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trained specifically for this experiment (details below). Panel performance was ensured by checking discrimination, agreement, and repeatability (Pineau, 2006).

2.4 Sensory testing procedures 2.4.1 Sensory profiling of condensate fractions The sensory profile of the fractions was performed across 13 sessions, using GDA sensory methodology (Stone & Sidel, 2004) and included the following steps:

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The panel was exposed to five samples representative of the sensory space, with the purpose of developing a common language for the description of the fractions. The preliminary aromatic list generated 41 attributes, and was subsequently reduced to a manageable number through consensus. Assessors evaluated the fractions by rating the

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generated attributes from 0 (not present) to 5 (very intense). From intensity

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(I) and frequency of citation (F) of each attribute, geometric means (GM =

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(F x I)/2) were compiled (Dravnieks, 1985). The attributes were analysed by applying PCA on mean scores to assess which attributes were

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correlated. Based on the results, there was a new discussion with the panel. The attributes were subsequently reduced to 12 (pineapple, linalool,

banana,

passion

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apple/pear,

fruit,

grapefruit,

roses,

vegetable/herbaceous, moisture/earthy, mineral, cat urine, and reduced),

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following the elimination of non-pertinent terms and grouping of synonyms

(1994).

The assessors were trained on the selected attributes using specific

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

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in subsequent sessions (four sessions) according ISO NORM 11035

definitions and aroma standards (four sessions, Table 2). Formal evaluation: five fractions were evaluated in duplicate during each

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

of the five sessions (the fifth session was done to control repeatability and reproducibility of the data, selecting randomly between the samples previously evaluated). Samples were coded with three-digit numbers and were presented in a sequential monadic randomized order, according to William’s Latin-square arrangement. Assessors were asked to evaluate each fraction orthonasally (previous evaluations with this panel allowed to

ACCEPTED MANUSCRIPT determine that there was no taste contribution) and to score the intensity of each attribute on a 10-point scale (unipolar and discrete response scale) in which 0 represented ‘none’ and 9 represented ‘extremely strong’. Scores were collected on paper and exported to an Excel spreadsheet. A 20-min break was enforced in the middle of each session to limit assessor

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fatigue.

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2.4.2 Formation and characterization of aromatic groups.

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The sensory characteristics of the fractions were analysed to identify similarities and generate groups with defined and distinctive aromatic compounds. The main

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objective of the generation of aromatic groups was to define the tools with which the oenologist could aromatically enrich wines. For this purpose, Principal

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Component Analysis (PCA) was performed on the mean sensory scores of the attributes, and a Hierarchical Cluster Analysis (HCA) was performed using

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Euclidean distances, Ward's aggregation criterion, and automatic truncation

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(Moussaoui & Varela, 2010).

After obtaining the "theoretical" aromatic groups (those obtained through PCA

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and HCA), the corresponding condensed fractions were proportionally mixed

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respecting the volumes obtained during fermentation, in a conditioned environment (16ºC) by means of a micropipette in individual canisters topped with argon. Using GDA (one session) with the same panel, the methods and attributes previously used, we characterized the resulting mixtures to confirm the results previously obtained. 2.4.3 Wine aromatic enrichment Prior to aromatic enrichment, the absolute threshold of aromatic groups was determined in wine to define the level at which the aromatic enrichment could be

ACCEPTED MANUSCRIPT detected. The absolute threshold of aromatic groups (AG, details below) in hydroalcoholic solutions and wine samples was determined using 3-AFC (Alternative Forced Choice) tests by the same trained panel. Wine samples were enriched with the aromatic groups using the methodology described above (Fig. 1). The effect of aromatic enrichment on wines of different

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qualities was evaluated, first in wines obtained at different temperatures (one

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session, two replicates), and subsequently in commercial wines from different

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categories (one session, two replicates).

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2.5 Consumer test 2.5.1 Samples

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Eight samples of Sauvignon Blanc commercial wines were evaluated (75mL). Two reserve wines (Rv samples) and two regular wines (Rg samples) from one

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winery (company A) were enriched with two different doses of aromatic

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condensate (d1 and d2) using one previously selected aromatic group (AG2). Dose d1 (0.1% v/v) was the lowest dose (Rv.d1 and Rg.d1 samples), while d2

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(0.2% v/v) was the highest dose (Rv.d2 and Rg.d2 samples). We included two samples without enrichment (Rv and Rg) and two wines from two competitor

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wineries, one reserve (Rv2 sample) and one regular (Rg2 sample; Table 1b). 2.5.2 Consumers Consumers (n = 144; 21–65 y of age), who were from different household compositions and had different income levels and education levels, were recruited based on their interest and availability to participate in the study. All of them consumed white wine more than twice per month. 2.5.3 Testing procedure

ACCEPTED MANUSCRIPT The consumer perception of aromatically enriched wines was evaluated by consumers in 15-d period, using overall liking measurements and CATA (checkall-that-apply) questions including an ideal product evaluation. Samples were assessed in a sequential monadic approach according to the balanced random design (Williams' design). The CATA questions consisted of 30 terms, including

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17 sensory terms and 13 extrinsic wine attributes. These terms were selected

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based on previous testing with a trained sensory panel and with internal

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marketing information obtained from the winery. The sensory terms evaluated were bitter, balanced, unbalanced, vegetable/herbaceous, intense aroma, weak

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aroma, bad aroma, good aroma, bad flavor, good flavor, fruity, floral, tropical, citric, sweet fruit, apple/pear, and moisture/earthy. The extrinsic wine attributes

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evaluated were “it is an elegant high-quality wine”, “I would consume it frequently with meals”, “it is a fresh wine”, “it is too complex”, “I would pay less for it than I

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normally do”, “I would pay more for it than I normally do”, “it is new and different”,

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“I would buy it”, “I would not buy it”, “I would drink it for a special occasion”, “I would recommend it”, “it is a young/modern wine”, and “I would give it as a gift”.

consumers.

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The attributes were randomized within each group and among products and

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After scoring overall and aroma liking using a structured nine-point hedonic scale, consumers answered CATA questions and described their ideal sample using the same CATA list. Details of the set-up of the test have been described by Lezaeta et al. (2017). The authors of that study used consumer data for methodological comparison objectives and other consumer data not presented here. All protocols were approved by the scientific ethics committee of the University (id protocol 151019003).

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2.6 Data analysis Statistical analyses were performed using PanelCheck (Nofima Mat and DTU– Informatics and Mathematical Modelling, Norway) and XLStat 2014 (Addinsoft, Paris, France).

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Two-way analysis of variance (ANOVA) was performed to evaluate product,

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assessor, and replication effects and their interactions, to check for discrimination, agreement, and repeatability.

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GDA data were analysed using PCA (based on Pearson’s correlation matrix)

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and AHC (Euclidian distances, Ward’s criterion) using mean values averaged over assessors and replicates. Two-way ANOVA with the use of a mixed model

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(assessors as random effect) and Tukey’s test were conducted for all pairwise comparisons (p ≤ 0.05). There was no missing data in the descriptive analysis.

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Data processing of consumer test was carried out in Norway. Overall and

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aroma liking scores were analysed by one-way ANOVA. The samples represented the fixed source of variation, and the consumers represented the

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random effects. Mean differences between samples were compared using Tukey's test at a 5% significance level (p ≤ 0.05). Missing values were replaced

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by the mean liking scores of each individual sample. Frequency of mention of each attribute of CATA questions was determined by counting the number of consumers that used each term to describe each sample. Differences in elicitation rates between non-enriched (Rv and Rg) and enriched (Rv.d2 and Rg.d2) wines with respect to the ideal product highlighted product optimization and the role of aromatic enrichment. The analysis was conducted as reported by Meyners, Castura, & Carr (2013).

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3. Results and discussion 3.1 Sensory profiling of condensate fractions The collected fractions had significantly different aromatic profiles. In general, the aromatic perception of fruity attributes (e.g., pineapple, apple/pear, and banana)

vegetable/herbaceous,

moisture/earthy,

mineral,

and

reduced

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contrast,

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increased during fermentation and decreased towards the end of the process. In

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decreased during the early fermentation process and remained relatively constant throughout. Data analysis revealed differences in perception (p < 0.05)

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for eight of the 12 evaluated attributes: pineapple, apple/pear, banana, roses, vegetable/herbaceous, moisture/earthy, mineral, and reduced (Table 3a). Among

highest

intensity

values.

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these attributes, pineapple, apple/pear, banana, and reduced presented the Conversely,

roses,

vegetable/herbaceous,

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total intensity scale).

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moisture/earthy, and mineral presented intensity values lower than 3.0 (1/3 of the

When plotting the fractions in a multidimensional space, the first two principal

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components of the PCA explained 85.7% of the total variance. All attributes were correlated with the perceptual space determined by the first two components (Fig.

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2). The first component accounted for 70.8% of the total variance and placed the fruity attributes (pineapple, apple/pear, and banana) and roses in the negative area of the dimension and undesirable and mineral attributes in the positive area of the dimension. HCA was used to group the fractions with similar aromatic characteristics. Based on the results, there were three "theoretical" aromatic groups. Aromatic group 1 (AG1: beginning of fermentation) included only fraction 1077, which was significantly associated with negative attributes such as

ACCEPTED MANUSCRIPT moisture/earthy, vegetable/herbaceous, and reduced. Aromatic group 2 (AG2: middle of the fermentation) included fractions 1061, 1053, 1045, 1037, and 1029; which were strongly associated with fruity attributes such as pineapple, apple/pear, and banana. Finally, aromatic group 3 (AG3: 1069, 1021, 1013, and

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3.2 Formation and characterization of aromatic groups

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995) included fractions with intermediate intensity levels.

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To evaluate the relevance of the aromatic groups, a new GDA was performed with the sensory panel. Sensory analysis highlighted differences in the perception

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of seven attributes (Table 3b). The most discriminant attributes were pineapple, apple/pear, banana, roses, moisture/earthy, mineral, and reduced. The aromatic

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groups had different sensory profiles, confirming the expected "theoretical" results based on the evaluation of the individual fractions. AG1 was associated

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with mineral and some undesirable attributes (moisture/earthy and reduced)

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commonly linked to consumer rejection (Mueller, Osidacz, Francis, & Lockshin, 2010) and low quality wines (Varela & Gámbaro, 2006). AG2 was associated with

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positive and fruity attributes such as pineapple, apple/pear, and banana, and AG3

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was associated with roses and to a lesser extent with fruity attributes. Both AG2 and AG3 had low intensity levels in undesirable attributes (moisture/earthy and reduced).

3.3 Wine aromatic enrichment 80% of the panelists detected 0.05 and 0.075, 100% panel detected the 0.1% concentration, so this one was selected. 3.3.1 Wines fermented at different temperatures

ACCEPTED MANUSCRIPT The differences between wines obtained at different temperatures (12°C and 18ºC) were investigated (Table 3c). As expected, wine A (12ºC) had more intense fruity attributes (e.g., pineapple and banana) than wine B (18ºC). Several studies have shown that during fermentation, low temperatures increase the production of volatile compounds (Sablayrolles, 2009). When evaluating the effect of

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aromatic enrichment on wine B, we observed that AG2 increased the aromatic

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intensity of fruity attributes (pineapple, apple/pear, and banana). These fruity

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attributes were not significantly different between wine B.AG2 and wine A, which evidences the contribution of enrichment in the increase of the aromatic intensity.

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AG3 increased to a lesser extent the aromatic quality of wine B.AG3, which had similar pineapple intensity as wine A. AG1 slightly increased the negatives

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attributes of wine B.AG1, with no differences in the positive attributes between wine B.AG1 and wine B.

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3.3.2 Commercial wines of different categories

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The commercial wines evaluated (Rv and Rg) had different aromatic profiles (Table 3d). The panel perceived wine Rv as strongly mineral while wine Rg was

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characterized as fruity and high in the pineapple attribute. Wine Rg.AG2 was considered highly fruity with high pineapple and banana attributes. In addition,

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AG2 decreased the reduced attribute perception. This is important since reduced aromas (among others) reduce the consumers’ willingness to buy (Mueller et al., 2010). In line with the results obtained for wines A and B, wine Rg.AG3 was perceived as slightly fruitier with increased perception of banana and pineapple attributes, which were higher than in wine Rv. AG3 increased the perception of the mineral attribute in Rg.AG3, which was similar to that of Rv. In contrast,

ACCEPTED MANUSCRIPT Rg.AG1 was described as a more reduced wine with no increased perception of fruitiness.

3.4 Consumer test 3.4.1 Overall liking

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The results showed differences in consumer overall liking scores. Wines enriched

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with d2 (Rv.d2 and Rg.d2) had the highest liking scores (p < 0.05) allowing

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differentiate from the Rv wine, while Rv2 had the lowest liking score (Table 4). The best-rated wines on aroma liking were those enriched with d2. Aroma liking

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increased in both regular and reserve enriched wines. In fact, this dose allowed to the Rv.d2 and Rg.d2 wines, to differentiate itself from the competitor (Rv2) and

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to be more accepted than Rg wine respectively. In most cases, the lower dose (d1) of aromatic enrichment had no effect on the perceived quality of the

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evaluated wines.

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3.4.2 CATA question for real and ideal samples Lezaeta et al. (2017), who compared CATA and Projective Mapping based on

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Choice, reported the mechanism by which aromatic enrichment affects consumer perception by means of CATA questions. The authors revealed that with

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increasing aromatic enrichment, the frequency of selection of sensory and nonsensory attributes considered to be positive (fruity, apple/pear, good aroma, etc.) increases and the frequency of selection of attributes considered to be negative (vegetable/herbaceous, bitter, weak aroma, etc.) decreases. In this study, CATA results are not presented in detail (the interested reader can refer to the previous paper). We collected CATA data with overall liking on the real and ideal samples

ACCEPTED MANUSCRIPT to better understand the effect of the sensory-based selection of the fractions for enrichment on consumer perception. When a CATA attribute is selected either for the real or ideal product (incongruences), it provides information important for product reformulation. It can be that consumers would like to have an attribute in their ideal, but they do

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not find it in the product, negatively impacting the liking (must have attribute); or

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else, that an attribute is present in the real product but consumers would not like

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to have it, with a negative impact on liking (must not have attribute). When an attribute has been selected for both or none of the products (congruence), it does

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not provide information for product optimization (Meyners et al., 2013). The differences between the evaluated wines and the ideal wine in terms of elicitation

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rates and the 95% confidence intervals are shown in Figure 3, where the attributes were ordered by decreasing effective sample size. The difference in

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elicitation rates was based on the total base size of 144. When comparing

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individual attributes of Rg and Rv with their hypothetical ideals, the terms balanced, bitter and fruity were less frequently elicited. The opposite results were

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obtained for the attributes tropical and unbalanced. The aromatic enrichment affected the elicitation frequency for the attributes fruity (Rg.d2 and Rv.d2),

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tropical and balanced (Rg.d2), with no differences from the ideal sample. Therefore, the aromatic enrichment allowed to the wines to approach their ideal. Volatile compounds loss during fermentation have been identified, quantified, and modeled (Colibaba et al. 2012, Gomez et al. 1993, Morakul et al. 2013, Mouret et al. 2012), and these results confirm that these losses affect the wine sensory profile. Consumers were able to perceive the aromas provided by the enrichment. Besides, the new sensory profile created by the addition of the

ACCEPTED MANUSCRIPT sensory-based selected aroma fractions had a positive impact on consumer appreciation, with a significant increase in liking.

4. Conclusions The purpose of this study was to apply a sensory-based methodology to identify

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and select condensates with positive impact on the aromatic profile of white

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wines. Through successive stages of fractionation, characterization, and

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grouping of condensates, we identified different aromatic groups that could be employed by oenologists for aromatic enrichment, thereby providing a novel

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oenological tool to improve wine aroma and potentially define different wine styles.

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The selected aromatic groups improved the perception of wine quality, as demonstrated by a trained panel and consumers. AG2 contributed to an increase

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in the overall wine fruitiness, while AG3 had minor effects. However, AG3

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significantly improved the perception of enriched wines. AG1 reduced wine quality. The identification of this aromatic group and the lack of a sensory-based

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methodology to determine the most interesting fractions could explain why there is no widespread commercial application of aroma recovery in wine

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fermentations.

Aromatic enrichment generated a positive response from consumers, increasing overall and aroma liking of enriched wines. To change consumer perception, the enrichment dose has to be adjusted. Consumer product description via CATA as compared to the ideal product emphasized the weakness of unenriched wines, highlighting the fruity attribute as driver of product improvement.

ACCEPTED MANUSCRIPT A sensory-based methodology was proposed to guide oenologists in using these fermentation condensates to enhance the aromatic quality of wines and increase consumer acceptance. This technique could potentially assist the wine industry to ferment at higher temperature conditions, thereby saving energy and increasing winery capacity. Moreover, this technique could be used to improve

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the aromatic quality of other fermented beverages.

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Acknowledgements

Financial support for the experiments conducted in Chile was received from the

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FONDEF IDeA-CA12i10119 and CONICYT-PAI “Concurso nacional tesis de doctorado en la empresa”, convocatoria 2014 folio 781412004. For the

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experiments performed in Norway, support was received from the Norwegian Foundation for Research Levy on Agricultural Products FFL, through the

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research program “FoodSMaCK, Spectroscopy, Modelling and Consumer

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Knowledge” (2017–2020), and the Research Council of Norway through the

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ACCEPTED MANUSCRIPT Table 1 Sample coding and treatments: (a) Wines from different temperatures and categories used in aromatic enrichment. (b) Wines used in consumer test. Rv: Reserve, Rg: Regular wines. a r o m a t i c

Sample

Wines from different temperatures

Sample

B

Wine fermented at 18ºC

Rg

B.AG2 B.AG3

Rg.AG1 Rg.AG2 Rg.AG3

Wine fermented at 12ºC (b)

Rv

C o n s u m e r Aromatic enrichment

Rv

A

--

Rv.d1

A

Rv.d2 Rv2

Company

Aromatic enrichment

A

AG2/dose 1

Rg.d1

A

AG2/dose 1

A

AG2/dose 2

Rg.d2

A

AG2/dose 2

B

Rg2

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Rg

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Commercial regular wine

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Company

--

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Commercial reserve wine

Commercial regular bottling of wine Commercial regular bottling of wine enriched with AG1 Commercial regular bottling of wine enriched with AG2 Commercial regular bottling of wine enriched with AG3 Commercial reserve bottling of wine

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A

Wine fermented at 18ºC enriched with AG1 Wine fermented at 18ºC enriched with AG2 Wine fermented at 18ºC enriched with AG3

Wines from different categories

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B.AG1

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(a)

--

--

ACCEPTED MANUSCRIPT Table 2 Attributes and reference standards used in training for GDA. Attributes

Standard reference

Pineapple

Frozen concentrated juice of pineapple

Apple/pear

Ethyl octanoate

2,005 ppb

10% Ethanol/water

Linalool

Linalool

1,000 ppm

10% Ethanol/water

Banana

Isoamyl acetate

30 ppb

10% Ethanol/water

Passion fruit

3-mercaptohexyl acetate

63.15 ng/L

Grapefruit

Frozen natural fruit

Roses

Phenylethyl alcohol

2,000 ppb

Hexanol

8,000 ppb

2,4,6-tribromoanisole (TBA)

30 ppt

4-mercapto-4-methylpentan2-one

Reduced

Reduced wine

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10% Ethanol/water

10% Ethanol/water 10% Ethanol/water 10% Ethanol/water

19 ppt

10% Ethanol/water

385.9 ng/L

10% Ethanol/water

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Benzenemethanethiol

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Mineral

Matrix

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Vegetable/ herbaceous Moisture/ earthy

Concentration

-

-

Vendor Watts Cramer SigmaAldrich SigmaAldrich SigmaAldrich SigmaAldrich SigmaAldrich SigmaAldrich SigmaAldrich SigmaAldrich -

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Table 3a. Analysis of variance for the different fractions obtained. Significant P-values (5% level according to Tukey’s test) are highlighted in bold letters. Pineapple

Apple/ pear

Banana

Roses

Vegetable/ herbaceous

Moisture/ earthy

Mineral

Reduced

P-value

< 0.0001

< 0.0001

0.004

0.017

0.004

< 0.0001

0.005

< 0.0001

1077

4.1 c

4.3 c

3.2 b

0.7 b

2.2 a

2.8 a

2.9 a

3.6 a

1069

5.0 bc

4.8 abc

3.7 ab

1.0 ab

0.9 b

1.3 b

1.8 b

1.4 bc

1061

5.5 ab

5.2 abc

3.7 ab

1.6 ab

1.8 ab

1.8 b

2.3 ab

1.1 bc

1053

6.6 a

5.8 a

4.3 ab

2.0 a

1.2 ab

1.3 b

2.1 ab

0.9 c

1045

5.6 ab

5.2 abc

4.4 a

1.3 ab

1.4 ab

1.5 b

2.1 ab

1.3 bc

1037

6.5 a

5.7 ab

4.5 a

1.2 ab

0.8 b

1.6 b

1.2 bc

1029

6.0 ab

5.3 abc

4.2 ab

1.4 ab

1.0 b

1.3 b

1.6 b

1.3 bc

1021

5.2 bc

4.6 bc

4.2 ab

1.1 ab

1.3 ab

1.6 b

2.2 ab

2.1 b

1013

4.2 c

4.4 c

3.7 ab

1.2 ab

1.5 ab

1.4 b

2.0 ab

1.4 bc

995

4.9 bc

4.6 bc

4.1 ab

1.2 ab

1.6 ab

2.0 ab

2.1 ab

2.0 bc

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1.4 b

Table 3b. Analysis of variance for the aromatic groups obtained. Significant P-values (5% level according to Tukey’s test) are highlighted in bold letters. Pineapple

Apple/ pear

Banana

Roses

Moisture/ earthy

Mineral

Reduced

0,008

Vegetable/ herbaceous 0,091

P-value

< 0.0001

0,014

0,001

< 0.0001

< 0.0001

< 0.0001

AG1

4.1 a

4.3 a

3.2 a

0.7 a

n.s.

2.8 b

2.9 b

3.6 b

AG2

5.9 b

5.5 b

4.8 b

1.2 ab

n.s.

1.1 a

0.9 a

1.2 a

AG3

4.4 a

4.5 ab

4.4 b

1.7 b

n.s.

1.6 a

1.7 a

1.8 a

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Samples

Pineapple

P-value

0.042

B

4.4 b

B.AG1

5.3 ab

B.AG2

Apple/ pear

Banana

Roses

0.042

0.033

3.2 a

3.7 ab

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Samples

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Table 3c. Analysis of variance for aromatic enrichment of wines fermented at different temperatures, A: 12ºC, B-samples: 18ºC. Significant P-values (5% level according to Tukey’s test) are highlighted in bold letters. 0.717

Vegetable/ herbaceous 0.713

Moisture/ earthy 0.717

3.0 b

n.s.

n.s.

3.6 ab

n.s.

Mineral

Reduced

0.185

0.526

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

4.9 b

3.6 ab

n.s.

n.s.

n.s.

n.s.

n.s.

5.5 ab

3.7 ab

3.3 ab

n.s.

n.s.

n.s.

n.s.

n.s.

A

5.9 a

4.2 ab

4.4 a

n.s.

n.s.

n.s.

n.s.

n.s.

AC

5.8 a

B.AG3

Table 3d. Analysis of variance for aromatic enrichment of commercial wines from different categories. Significant P-values (5% level according to Tukey’s test) are highlighted in bold letters. Samples

Pineapple

Apple/ pear

Banana

Roses

Vegetable/ herbaceous

Moisture/ earthy

Mineral

Reduced

P-value

0.001

0.379

0.009

0.656

0.269

0.559

0.034

< 0.0001

Rg

5.6 ab

n.s.

2.9 b

n.s.

n.s.

n.s.

0.9 b

0.6 bc

Rg.AG1

5.7 ab

n.s.

3.6 ab

n.s.

n.s.

n.s.

1.6 ab

2.1 a

Rg.AG2

6.7 a

n.s.

4.1 ab

n.s.

n.s.

n.s.

1.1 ab

0.3 c

Rg.AG3

5.9 a

n.s.

4.6 a

n.s.

n.s.

n.s.

1.3 ab

0.5 bc

Rv

4.0 b

n.s.

3.0 b

n.s.

n.s.

n.s.

2.4 a

1.4 ab

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Table 4 Overall liking for all consumers and per cluster, and aroma liking for consumers. Overall liking*

Aroma liking*

Rg Rg.d1 Rg.d2 Rg2 Rv Rv.d1 Rv.d2 Rv2

5.4 ab 5.5 ab 6.0 a 5.5 ab 5.8 ab 5.8 ab 5.9 a 5.2 b

5.2 c 5.8 abc 6.2 ab 5.9 abc 6.1 ab 6.0 ab 6.5 a 5.6 bc

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Mean liking scores were significantly different according to Tukey’s test (confidence level of 95%). * Evaluated using a structured nine-point hedonic scale.

ACCEPTED MANUSCRIPT Figure captions Fig. 1. Schematic representation of the condensed fractions from fermentation, formed aromatics groups, and aromatically-enriched wines. Fig. 2. PCA plot (total variability: 85.7%) of the relationship between the discriminant attributes and the samples. The circles represent the three clusters highlighted by HCA.

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Fig. 3. Differences in elicitation rates between non-enriched (Rv and Rg) and enriched wines (Rv.d2 and Rg.d2), with respect to the ideal product including a 95% confidence interval for this difference.

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ACCEPTED MANUSCRIPT Highlights 

Sauvignon Blanc wines were enriched with natural aromas condensed from fermentation.



By means

of

a

sensory-based

methodology,

positive

aromatic

condensates were selected. The selected aromatic groups improved the perception of wine quality.



CATA showed the positive effect of aromatic enrichment on consumer

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perception.