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Research Article
Received: 7 November 2022 Revised: 26 January 2023 Accepted article published: 31 January 2023 Published online in Wiley Online Library: 14 February 2023
(wileyonlinelibrary.com) DOI 10.1002/jsfa.12479
Natural versus conventional production
of Spanish white wines: an exploratory study
María-Pilar Sáenz-Navajas,a
*
Carlota Sánchez,a
Marivel Gonzalez-Hernandez,aMónica Bueno,bCristina Peña,b
Purificación Fernández-Zurbano,aJordi Ballester,cEva Parga-Dansdand
Pablo Alonso Gonzálezd
Abstract
BACKGROUND: Natural wine (NW) lacks an official or agreed definition, but it can be generally described as a wine produced
with organic or biodynamic grapes with minimal intervention in the cellar, and with minimal or no use of oenological additives.
The present study aimed to test the hypotheses that self-defined NWs differ from conventional wines (CW) in their chemical
composition and main sensory characteristics. The levels of conventional oenological parameters, turbidity, biogenic amines,
ochratoxin A, ethyl carbamate, sulphites, chlorides, some metals, major, trace and Strecker aldehyde volatile compounds were
determined in 28 wines, including natural and conventional Spanish commercial white wines. Wines were also sensory
described following a labelled free sorting task.
RESULTS: NWs presented higher pH, volatile acidity (VA) and turbidity values, and a more intense yellow colour, whereas they
have a lower malic acid content compared to theor conventional counterparts. NWs presented lower levels of total sulphur
dioxide but significantly higher levels of biogenic amine putrescine, although both compounds are within the legal limits in
all cases. None of the dimensions of the similarity space discriminated NWs from CWs. However, 70% of the NWs were grouped
on the basis of various aromatic defects related to their higher content in 4-ethylphenols and VA. The remaining 30% were not
differentiated from their conventional counterparts.
CONCLUSION: It could be confirmed that NW can be globally differentiated from CW with respect to to their chemical and their
sensory profiles, whereas the content in toxicants was not significantly different, with the exception of total sulphur dioxide
and putrescine levels.
© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of
Chemical Industry.
Supporting information may be found in the online version of this article.
Keywords: natural wine; sensory description; Spain; wine; sorting task; toxics
INTRODUCTION
In recent years, consumers have been paying more attention to
the effects of conventional agriculture on the environment,
human health, and food and beverage sustainability.
1
One of
the main reasons for this is a change in consumer behaviour.
Customers are increasingly more knowledgeable about what they
buy and consume regarding not only the intrinsic characteristics
of the product, but also its environmental and social influence.
2
Greater consumer awareness of the negative effects of conven-
tional/industrial agriculture on human health and the environ-
ment has led to a growing demand for ‘natural’or healthier
foods and beverages, which are perceived as safer and with a
lower environmental impact.
3
This trend has affected all sectors, particularly that of wine. As a
result, a new category called ‘natural wine’(NW) has emerged in
*Correspondence to: M P Sáenz-Navajas, Department of Enology, Instituto de
Ciencias de la Vid y del Vino (UR-CSIC-GR), Logroño, La Rioja, Spain.
E-mail: mpsaenz@icvv.es
aDepartment of Enology, Instituto de Ciencias de la Vid y del Vino (UR-CSIC-GR),
Logroño, Spain
bLaboratorio de Análisis del Aroma y Enología (LAAE). Departamento de
Química Analítica, Facultad de Ciencias, Universidad de Zaragoza. Instituto
Agroalimentario de Aragón-IA2 (Universidad de Zaragoza-CITA), Zaragoza,
Spain
cCentre des Sciences du Goût et de l'Alimentation, CNRS, INRAE, Institut Agro,
Université Bourgogne Franche-Comté, Dijon, France
dInstituto de Productos Naturales y Agrobiología, IPNA-CSIC, La Laguna, Santa
Cruz de Tenerife, Spain
© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
3540
recent years, which extends beyond organic and biodynamic
wines.
4
The use of different production methods greatly influ-
ences the style and composition of wine.
5
In addition to the wine
itself, such methods can influence the perception and conceptua-
lisation of food and beverage products, modifying consumer
expectations about hedonic benefits associated with organolep-
tic properties, health and environmental benefits.
6
In the case of
NW, the controversies are growing
7
and the present study aimed
to address them. NWs do not comply with any internationally
endorsed regulations, being a declaration of the individual pro-
ducer or an association of winemakers, who follow their own spe-
cific interpretation of naturalness in both the vineyard and
winery.
8
Moreover, winemakers declare that they use no commer-
cial yeasts, chemical additives or SO
2
(or minimal doses of it) dur-
ing the process. A French winemakers' union managed to
officially regulate the Vin Méthode Nature logo in 2020, although
several NW associations with their own respective internal regula-
tions had operated for decades in Europe.
9,10
There is heated debate about the organoleptic quality of NWs.
For NW supporters, it represents more closely the ‘terroir’where it
comes from, being more ‘expressive’,‘pure’or ‘authentic’.
11
Another controversy regarding NW production is that they may
be perceived as healthier products. There is growing public atten-
tion to health issues and so the consumer is likely to associate the
concepts of NWwith a healthier product because it has fewer addi-
tives than a conventional wine (CW). However, it should also be
noted that the restricted use of sulphur dioxide as a preservative,
as well as the fact that no selected yeasts or lactic acid bacteria
are added during production, could lead to greater undesired
microbial alterations in wine. This lack of microbiological control
could generate higher levels of toxic products, such as biogenic
amines from the nitrogen nutrition of lactic bacteria,
12
as well as
ethyl carbamate produced by reaction of ethanol with compounds
having a carbamyl group
13
or ochratoxins. Moreover, because nat-
ural winemakers are more likely to continue fermentation on grape
skins even in white wines, higher levels of the toxic methanol can
be expected in natural over conventional wines.
14
Despite growing interest in NWs, specific scientific information
on them is scarce. Indeed, knowledge of their physicochemical
composition (including conventional oenological parameters
and toxic-related compounds) and sensory profile compared to
their conventional counterparts is practically non-existent, thus
requiring in-depth analysis. The general objective of the present
study was to explore the sensory, oenological and toxicological
profiles of a set of natural Spanish white wines compared to their
conventionally produced counterparts. Implementation of both
instrumental and sensory analyses was aimed at increasing the
validity of results, in line with recent calls to reduce the subjective
rhetoric that prevails in wine assessment by experts.
15
The first starting hypothesis was that self-defined NWs differ
from CWs in their chemical composition regarding both oenolog-
ical and toxicological parameters [e.g. level of total sulphur diox-
ide, biogenic amines, ochratoxin A (OTA), ethyl carbamate,
certain heavy metals, methanol, chlorides and sulphates]. The sec-
ond hypothesis was that NWs differ from CWs in their sensory pro-
file, given their different self-declared production methods.
MATERIALS AND METHODS
Wines
A balanced sample set of NW and CW was selected from the mar-
ket. In total, 28 commercial white wines, half NW and half CW,
were included in the study (Table 1). For each NW, a CW sharing
vintage, origin and variety was selected to obtain a balanced sam-
ple. It is important to consider that NW is not a regulated Apella-
tion or Denomination in wine production; thus, the samples
included in the experiment were from wines for which producers
declared they intervene minimally during grape and wine produc-
tion, including the principles proposed by the French vin méthode
nature regulations. These include: (i) production of organic or bio-
dynamic grapes; (ii) handpicking; (iii) use of indigenous yeasts;
(iv) no added external oenological products; (v) no intentional
modification of grape composition; (vi) no use of aggressive phys-
ical practices (reverse osmosis, filtration, tangential flow filtration,
flash pasteurisation, thermovinification, centrifuging); and (vii) no
added sulphite either before or after fermentation (maximal
30 mg L
−1
of total SO
2
in final wines). The selected wines were
all commercial wines from 2018–2019 vintages.
The sample set included different varieties (Macabeo-Mac,
Godello-God, Verdejo-Ver, Xarelo-Xar, Garnacha Blanca-Gar, Mal-
vasía-Mal, Parellada-Par, Zalema-Zal or Airén-Air) and origin
(Catalonia-Cat, Castilla La Mancha-CM, Andalucía-And, Castilla y
León-CL, Bierzo-Bie, Galicia-Gal and North-Central Spanish
region-NC). All wines were fermented in stainless steel tanks and
had no or limited contact with oak barrels (Table 1).
Physicochemical characterisation of wines
Conventional oenological analysis
Alcohol content was determined by a near infrared technique
(Spectraalyzer 2.0; Zeutec, Rendsburg, Germany), volatile acidity
and reducing sugars by a QuAAtro 39 segmented flow autoanaly-
ser (Bran+Luebbe, Norderstedt, Germany), pH and total acidity by
potentiometry (ATP 3000; Tecnológia Difusión Ibérica, S.L., Barce-
lona, Spain), and malic acid following the method proposed by
the International Organisation of Vine and Wine (OIV) (OIV-MA-
AS313-10). These parameters were analysed by accredited proce-
dures at Estación Enológica de Haro (La Rioja, Spain) following the
UNE-EN ISO/IEC 17025 standards.
Colour of wines was determined by calculating CIELAB coordi-
nates. For this, the spectra of centrifuged and filtered samples
(0.45 μm, recorded every 1 nm between 380 and 780 nm) were
acquired in a UV-1800 spectrophotometer (Shimadzu, Milan,
Italy) using 0.2-cm path-length quartz cuvettes. From the spectra,
colour coordinates were calculated using the CIE method with the
CIE 1964 10°standard observer and the illuminant D65, according
to the OIV. Turbidity of wines was measured by a portable turbi-
dimeter (HI 93703-11; Hanna Instruments, Woonsocket, RI, USA).
Analysis of OTA and other toxics
Ethyl carbamate was quantified as described by Alberts et al.
16
using an HPLC instrument (model 1200 Infinity; Agilent,
Santa Clara, CA, USA) coupled to a triple Quadrupole mass spec-
trometer detector (model 6490; Agilent) (limit of quantification,
LOQ =10 μgL
−1
). Ochratoxin A (LOQ =0.1 μgL
−1
) was quantified
by HPLC-tandem mass spectrometry (MS) (model 1200 Infinity
HPLC coupled to a 6490 triple quadrupole mass detector, using
n-propylcarbamate as internal standard), and biogenic amines
(LOQ =1.0 mg L
−1
) by HPLC with o-phthaldialdehyde precolumn
derivatisation and fluorescence detection (Nexera; Shimadzu) as
described previously,
16,17
respectively.
Methanol content was measured by gas chromatography-flame
ionisation detection (GC-FID) (Clarus 500; Perkin Elmer, Waltham,
MA, USA), total and free SO
2
levels by the titration/aspiration
method, and chlorides and sulphates by potentiometry
Natural versus conventional production of Spanish white wines www.soci.org
J Sci Food Agric 2023; 103: 3540–3549 © 2023 The Authors.
Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
wileyonlinelibrary.com/jsfa
3541
(Ag/AgCl) and gravimetry, respectively. The following heavy
metals were also quantified by atomic absorption spectroscopy:
lead, copper, zinc, arsenic and iron.
All analyses were carried out by accredited procedures follow-
ing the UNE-EN ISO/IEC 17025 standards at the laboratories of
Estación Enológica de Haro (La Rioja, Spain).
Quantification of volatile compounds with sensory activity
Major volatile compounds present in the concentration range of
10 to 200 mg L
−1
including higher alcohols, acetates and ethyl
esters, and volatile fatty acids were quantified by solid phase
extraction-GC-FID as described previously.
18
Minor compounds present between 0.1 and 1000 μgL
−1
consisted of acetate and ethyl esters, vanillin derivatives, vola-
tile phenols, terpenes, norisoprenoids and lactones. These
were quantified by GC-MS as described previously
19
with some
modifications.
20
Concentrations of major and minor compounds were obtained
from the relative response factor of each compound related to its
corresponding internal standard.
The total concentration of most relevant Strecker aldehydes
(isobutyraldehyde, 2 methylbutanal, 3-methylbutanal, methional
and phenylacetaldehyde) were quantified by GC-MS as described
by Castejón-Musulén and collegues.
21
Their amounts in free forms
were estimated using the apparent equilibrium constants (Ka) for
the sulphur dioxide-carbonyl adducts, as reported elsewhere.
22
The reagents used for volatile analysis are specified in the
Supporting information Appendix S1.
Data analysis
To evaluate the effect of the type of production (CW or NW) on the
sample set studied (28 wines), one-way analysis of variance
(ANOVA) for oenological parameters, mycotoxins and other
toxics, turbidimetric measurements, and volatile compounds
was conducted, considering the type of production (CW versus
NW) as a fixed factor.
To identify volatile compounds inducing sensory differences
between NW and CW and among the clusters of wines generated
in the sorting task, the volatile compounds were grouped in vec-
tors as described in the Supporting information (Appendices S1
and S2). Furthermore, one-way ANOVA was calculated with aroma
vectors considering the type of wine production (NW or CW) or
the cluster for each sorting task as fixed factors. A Fisher's post-
hoc means comparison test was carried out whenever the ANOVA
was significant.
P<0.05 was considered statistically significant. Analyses were
carried out using XLSTAT, version 19.03 (https://www.xlstat.
com/en).
Sensory characterisation of wines
Participants
In total, 16 established winemakers from Rioja area took part in
the study (11 women, and five men, ranging in age from 26 to
Table 1. Twenty-eight commercial white wines [14 natural (NW) and 14 conventional (CW)] sampled in the present study
Code Sorting Variety Origin Type Vintage Oak
Mac1_Cat_N S1 Macabeo Catalonia NW 2018 No
Mac1_Cat_C S1 Macabeo Catalonia CW 2018 No
Xar2_Cat_N S1 Xarel-lo Catalonia NW 2019 No
Xar2_Cat_C S1 Xarel-lo Catalonia CW 2019 No
Par_Cat_N S1 Parellada Catalonia NW 2019 No
Par_Cat_C S1 Parellada Catalonia CW 2019 No
Xar1_Cat_N S1 Xarel-lo Catalonia NW 2018 9-month acacia barrel
Xar1_Cat_C S1 Xarel-lo Catalonia CW 2018 6-month oak barrel
Gar_Cat_N S1/S2 Garnacha Blanca Catalonia NW 2019
Gar_Cat_C S1/S2 Garnacha Blanca Catalonia CW 2019 Short period oak barrel
God_Bie_N S2 Godello Castilla y León NW 2019 No
God_Bie_C S2 Godello Castilla y León CW 2019 No
God_Rib_N S2 Godello Galicia NW 2018 No
God_Rib_C S2 Godello Galicia CW 2018 No
Ver1_CL_N S2 Verdejo Castilla y León NW 2019 No
Ver1_CL_C S2 Verdejo Castilla y León CW 2019 No
Gar_NC_N S2 Garnacha Blanca North-Central region NW 2019 No
Gar_NC_C S2 Garnacha Blanca North-Central region CW 2019 No
Ver2_CL_N S2/S3 Verdejo Castilla y León NW 2019 No
Ver2_CL_C S2/S3 Verdejo Castilla y León CW 2019 No
Mal_CL_N S3 Malvasía, Palomino Castilla y León NW 2019 No
Mal_CL_C S3 Malvasía Castilla y León CW 2019 No
Zal_An_N S3 Zalema Andalucía NW 2019 No
Zal_An_C S3 Zalema Andalucía CW 2019 5-month oak barrel
Air_CM_N S3 Airén Castilla La Mancha NW 2019 Oak barrel
Air_CM_C S3 Airén Castilla La Mancha CW 2019 No
Mac2_Cat_N S3 Macabeo Catalonia NW 2019 No
Mac2_Cat_C S3 Macabeo Catalonia CW 2019 12% fermented in oak barrels
www.soci.org M-P Sáenz-Navajas et al.
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Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
J Sci Food Agric 2023; 103: 3540–3549
3542
45 years, average of 34 years). On average, they had 17 years
of experience (range 10–30 years) in wine production and
tasting.
Procedures
The 28 wines studied were selected to constitute three sets of
samples to be evaluated in three free sorting tasks followed by
free description of the groups. Each sample set included wines
with a priori different sensory variability, from minimal (Sorting
1) to maximal variability (Sorting 3), as indicated in Table 1. Sorting
1 included 10 samples from the same region and four grape
varieties, Sorting 2 was constituted of 12 wines from five different
origins and three different grape varieties, and Sorting 3 was
formed by 10 samples from four different origins and five differ-
ent varieties.
Each participant carried out the three sorting tasks on the same
day, each lasting about 20 min separated by at least 10 min. The
three tasks were performed in different order for each expert in
order to avoid priming effects. Water and unsalted crackers were
available during each session for rinsing purposes. Participants
were provided with the samples of each sorting task simulta-
neously (20 mL) in dark wine glasses coded with different three-
digit numbers and arranged in random order. Participants were
asked to taste and group the wines on the basis of similarity,
attending to olfactory and gustatory stimuli. Participants could
make as many groups as they wished. Upon completion, they
recorded the three-digit codes of the samples of each group on
a paper sheet, and they were further asked to describe each of
the groups formed with a maximum of three attributes. All wines
were served at room temperature and evaluated in individual
booths. The sessions took place in a ventilated and air-
conditioned tasting room (at around 20 °C). No information was
given about the wines or the purpose of the study.
Ethical approval for the involvement of human subjects in the
present study was granted by the Research Ethics Committee of
the Consejo Superior de Investigaciones Científicas (CSIC), Ref.
211/2020, in February 2021.
Data analysis for sensory characterisation
Raw data were encoded in 16 individual matrices (one by subject),
each consisting of a wines ×wines matrix. These individual
matrices were summed across subjects and the resulting
co-occurrence matrix was submitted to a multidimensional
scaling (MDS) analysis with a non-parametric scaling algorithm
(absolute method). For significant dimensions (stress value >0.2)
derived from each MDS, one way-ANOVA was performed on the
scores of wines considering the type of wine production (NW or
CW) as fixed factor, aiming to identify sensory dimensions linked
to the type of production. The stress value (varies from 0 =perfect
fitto1=worst possible fit) is based on the sum of the squares of
distances between objects observed in the raw data and objects
observed in the p-dimensional MDS space and measures the
quality of the fitoftheMDSconfiguration.
Finally, all dimensions derived from the MDS configuration were
analysed using hierarchical cluster analysis with the Ward crite-
rion. All analyses were carried out with XLSTAT, version 19.03.
The descriptions of groups formed in the sorting tasks were ana-
lysed following as described in the Supporting information
(Appendix S1).
RESULTS AND DISCUSSION
Physicochemical characterisation of wines
Conventional oenological parameters
Table 2shows that white NWs presented significantly higher pH
levels and volatile acidity than CWs, whereas the opposite was
observed for malic acid. The higher pH value in NWs may be a
result of their lower levels of malic acid or the correction of pH
levels in CW by adding tartaric acid. The latter is a usual and per-
mitted practice in conventional production in warm climate
areas,
23
such as Spain. The higher levels of volatile acidity in
NWs may be a result of the fact that alcoholic fermentation takes
place with indigenous yeasts and lactic acid bacteria, which are
linked to higher levels of acetic acid compared to CWs made with
selected microorganisms.
24
In addition, by not using sulphur
Table 2. Mean ±SD and range of occurrence of conventional oenological parameters and colour coordinates of the 14 conventional (CW) and 14
natural (NW) wines studied
Parameter
CW NW
PMean ±SD Range Mean ±SD Range
pH 3.29 ±0.11 3.10–3.50 3.42 ±0.18 3.20–3.91 *
Volatile acidity (g L
−1
)
a
0.41 ±0.13 0.22–0.78 0.72 ±0.44 0.25–2.00 *
Total acidity (g L
−1
)
b
5.66 ±0.56 4.80–6.50 5.99 ±1.28 4.6–10.00 NS
Reducing sugars (g L
−1
)1.96 ±1.42 1.00–5.20 1.87 ±1.43 1.00–4.80 NS
Malic acid (g L
−1
)1.09 ±0.76 0.10–2.40 0.33 ±0.51 0.10–1.60 **
Alcohol content (% v/v) 12.87 ±0.57 12.04–13.56 12.79 ±1.29 9.28–14.74 NS
Free SO
2
(mg L
−1
)10.29 ±6.27 <5.00–23.00 6.93 ±4.01 <5.00–17.00 NS
a*−1.46 ±1.26 −4.01 –(−0.31) −1.83 ±2.56 −4.88 –5.20 NS
b*3.51 ±4.13 0.55–13.62 11.87 ±11.15 0.65–41.66 *
L*99.86 ±0.29 99.00–100.00 97.99 ±3.75 86.40–100.00 NS
C*3.81 ±4.31 0.63–14.11 12.31 ±11.09 0.74–41.98 *
H117.31 ±4.62 105.20–122.20 108.55 ±12.35 82.88–121.60 *
Significance (P) was calculated according to one-way-ANOVA with the type of wine (CW, NW) as main fixed factor (*P<0.05; **P<0.01; NS, no sig-
nificant difference).
a
Expressed as g L
−1
of acetic acid.
b
Expressed as g L
−1
of tartaric acid.
Natural versus conventional production of Spanish white wines www.soci.org
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Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
wileyonlinelibrary.com/jsfa
3543
dioxide after alcoholic fermentation or in only very small quanti-
ties, Acetobacter bacteria can develop, transforming ethanol into
acetic acid. Finally, NWs showed lower malic acid content, which
may be a result of the presence of lactic acid bacteria in NW pro-
duction, transforming malic acid into lactic acid.
25
Indeed, NW
makers often seek malolactic fermentation (MLF) for greater wine
stabilisation, whereas generally Spanish white wines do not follow
MLF given their relatively low acid contents, which is the case for
the wine samples investigated in the present study.
Regarding the CIELAB coordinates (Table 2), NWs showed
higher values of b* and C* (Chroma) coordinates, which indicates
that they present a yellower, stronger or more saturated colour
than the CWs. The NWs showed lower values of h*(=arctg a
10
*/
b
10
*), which is related to their higher levels of yellow colour
(or b
10
* coordinate). This could result from the non-use of SO
2
in
NWs, leading to oxidation of musts and wine, particularly of phe-
nolic compounds through formation of yellow species (i.e. o-qui-
nones) with varying degrees of polymerisation.
26,27
Maceration
promoted in NW could explain the yellow–brownish colour of
NW, as well as their significantly (10×) higher (F=10.7; P<0.05)
levels of turbidity, also explained by lack of filtration and
clarification.
These results confirm our initial hypothesis that NWs can be dif-
ferentiated from CWs according to their conventional oenological
parameters, including pH, malic acid, volatile acidity, turbidity and
colour.
Toxicological parameters
Regarding the mycotoxin analysis, in the determination of bio-
genic amines, OTA and ethyl carbamate (Table 3), NWs showed
only significant higher levels of putrescine than CWs. This could
be explained by the presence of endogenous lactic acid bacteria
in NWs, promoted by the lower levels of SO
2
. Moreover, higher
amounts of amino acids extracted during maceration of NWs,
which are precursors of biogenic amines, could explain the higher
levels of putrescine in NWs.
28
This biogenic amine has been
reported to lower blood pressure, enhancing the negative effects
on human health.
29
Besides its effect on health, the presence of
10–15 mg L
−1
of this amine in white wines produces an unpleas-
ant taste and, at a presence >30 mg L
−1
, this becomes foul-
smelling and putrid.
30
Only one of these NWs (with 14.1 mg L
−1
)
exceeds the sensory threshold and could therefore be affected
by its negative aroma. By contrast, none of the CWs exceed
7.8 mg L
−1
. Significant correlations have been found between
low levels of total SO
2
and higher content of biogenic amines,
31
which could explain the higher levels of putrescine in NW. The
OTA and ethyl carbamate contents were both below the LOQ
(0.1 and 10 μgL
−1
, respectively) for both wine types.
The effect of the production method related to the use of pesti-
cides, fungicides or fertilisers in the vineyard, as well as to the
winemaking practices and the use of additives in the cellar on
the presence of other toxic compounds, such as methanol,
sulphates, chlorides, total sulphur dioxide and heavy metals, was
also evaluated. Interpreting the ANOVA, significant differences
between the two wine categories were only observed for total sul-
phur dioxide content (Table 3). It is noteworthy that 12 out of the
14 NWs studied present levels of total sulphur dioxide lower than
the LOQ (<10 mg L
−1
). However, there are two samples that con-
tain levels as high as 82 mg L
−1
(Xar2_Cat_N) and 120 mg L
−1
(Gar_NC_N), which contradicts declarations by these two NW pro-
ducers. One would expect certain sulphur dioxide produced nat-
urally by the yeasts, but such high levels are undoubtedly a
result of the addition of this compound. The question of possible
fraud arises because of the absence of monitoring institutions and
certifications. This raises doubts as to whether the wines are as
natural as they are claimed to be, calling for the need of
certification.
10
Finally, the determination of metals, sulphates and chlorides
was in all cases within the established legal limits and did not
show any significant difference between the two wine types
(Table 4).
These results partially confirm our initial hypothesis regarding
the possible differences between NWs and CWs in terms of toxico-
logical parameters. However, the lower levels of toxicological
products in NWs could not be confirmed because, although they
present lower levels of total SO
2
content, higher contents of some
biogenic amines were detected.
Sensory characterisation
The flavour of the 28 wine samples was characterised using a
labelled sorting task. To evaluate the generalisability of our
results, three sorting tasks were performed on three sets of wines,
with different expected sensory variability. The results obtained
for the three sets were similar, with three dimensional solutions
showing stress values of 0.150, 0.169 and 0.150 for set 1, set
2 and set 3 respectively (data not shown). Figure 1shows the
Table 3. Mean ±SD and range of occurrence of biogenic amines, ochratoxin A, ethyl carbamate and total SO
2
in the 14 conventional (CW) and 14
natural (NW) wines studied
CW NW
PLOD Mean ±SD Range Mean ±SD Range
Histamine (mg L
−1
)1.0 <1.00 ±0.00 <1.00 1.44 ±1.04 <1.00–4.50 NS
Tyramine (mg L
−1
)1.0 <1.00 ±0.00 <1.00 2.22 ±3.13 <1.00–12.20 NS
Phenylethylamine (mg L
−1
)1.0 <1.00 ±0.00 <1.00 <1.00 ±0.00 <1.00 NS
Putrescine (mg L
−1
)1.0 2.80 ±1.89 <1.00–7.80 5.36 ±4.20 <1.00–14.10 *
Cadaverine (mg L
−1
)1.0 <1.00 ±0.00 1.00 1.00 ±0.00 <1.00 NS
Ochratoxin-A (∼gL
−1
)0.1 <0.1 ±0.00 <0.1 <0.1 ±0.00 <0.1 NS
Ethyl carbamate (∼gL
−1
)0.1 <0.1 ±0.00 <0.1 <0.1 ±0.00 <0.1 NS
Total SO
2
(mg L
−1
)20 86.71 ±36.31 <10.00–133.0 37.07 ±32.56 <10.00–120.0 **
Significance (P) was calculated according to one-way-ANOVA with the type of wine (CW, NW) as main fixed factor (*P<0.05; **P<0.01; NS, no sig-
nificant difference).
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J Sci Food Agric 2023; 103: 3540–3549
3544
dendrograms derived from the cluster analysis calculated with all
the MDS dimensions obtained from the three sorting tasks.
Figure 1(a) shows the results for Sorting Task 1 (S1), which con-
sisted of grouping a total of 10 wines (half NWs and half CWs),
with all sharing their origin (Catalonia). The hierarchical cluster
analysis shows the presence of three main groups of wines differ-
ing in their sensory profile. The first group, or cluster 1, consisted
of three natural wines (Mac1_Cat_N, Par_Cat_N and Xar1_Cat_N),
elaborated with three different varieties (Macabeo, Parellada and
Xarel.lo). The attributes that defined this cluster were default-
related including: ‘faulty’,‘vinegar’and ‘animal’. This sensory pro-
file is consistent with their significant higher levels of ethyl acetate
[odour activity values (OAV) =10] and ethylphenol (OAV =25.6)
aroma vectors shown in Table 5. Cluster 2 consisted of two CWs
and one NW (Xar1_Cat_C, Mac1_Cat_C and Xar2_Cat_N), two of
the Xarel.lo variety and one Macabeo. The attributes that defined
this group were: ‘fruity’,‘floral’,‘woody/toasty’and ‘balanced’. This
cluster presents the highest levels in the fatty acid (OAV =51.9)
and vinylphenol (OAV =2.2) vectors, which could explain the fru-
ity and floral character of this cluster, respectively. Although, in
isolation, fatty acids may present some unpleasant rancid
cheese-like aroma, they have been reported to be involved in
the formation of positive fruity aroma of wines.
32
Furthermore,
this cluster presents the highest levels of the whisky lactone vec-
tor. However, the levels are quite discrete with OAV of 0.4, which
could explain the subtle woody/toasted profile of these wines.
Finally, Cluster 3 comprised four wines from three different varie-
ties (Garnacha, Parellada and Xarel.lo). Here, three NWs and only
one CW are characterised by the terms ‘fruity’,‘vegetable’and
‘sour’. Although the fruity character could be related to their high
levels of damascenone (OAV =177.7) and fatty acid (as high as in
cluster 2: OAV =41.8) vectors, we cannot rule out other aroma
compounds not quantified in the present study, responsible for
the ‘vegetable’aroma notes in this cluster. Despite the fact that
60% of NWs were grouped together in the first cluster sharing
faulty aromas, our initial hypothesis was not confirmed by the
ANOVA results because no significant effect of wine production
type (NW or CW) was observed for any of the dimensions derived
from this sorting task.
Sorting Task 2 (Fig. 1b) consisted of a total of 12 wines, compris-
ing six NWs and six CWs from different regions and three grape
varieties (Garnacha, Verdejo and Godello). Cluster 1 consisted of
five NWs from fibe different regions and three grape varieties.
The attributes that defined this group were: ‘dried/candied fruit’,
‘oxidation/evolved’and ‘vegetable’. Low levels of fruitiness and
the appearance of these typical notes of oxidation are usually
related with higher levels of Strecker aldehydes.
33
However, no
significant differences were observed in comparison with the
other two clusters, neither in total amount, nor in that of free
forms (data not shown). This lack of significant differences could
be explained by the formation of aldehydes over time at different
production stages, depending on the levels of SO
2
. On the one
hand, it has been observed that fermentative formation of
Strecker aldehydes is positively influenced by total SO
234
because
this molecule prevents a fraction of the Strecker aldehydes pro-
duced within the Ehrlich pathway from being enzymatically
reduced or oxidised by the corresponding dehydrogenases. This
would suggest that CWs would have higher levels of Strecker
aldehydes because they contain higher levels of SO
2
during alco-
holic fermentation. On the other hand, Strecker aldehydes forma-
tion during ageing at low SO
2
levels
35
would be consistent with
development of the Fenton reaction once SO
2
cannot prevent
H
2
O
2
accumulation
36
and thus with higher Strecker aldehyde
levels in NWs. It is possible that, in the present study, a lower
amount of aldehydes was formed during fermentation in NW as
a result of its low SO
2
content; however, this same low amount
of total SO
2
during bottle ageing would produce more aldehydes
at this stage, finally leaving a balanced and similar content of
Strecker aldehydes to that in CW. From the sensory point of view,
the oxidation notes of this Cluster 1 may be explained by percep-
tual interactions because it has a significantly lower level of the
fruity isoamyl acetate vector and of the floral-like cinnamate vec-
tor compared to the other two clusters, which could lead to a
clearer perception of the oxidation-related aldehyde vector than
in the other two clusters. Second, Cluster 2 consisted of two
CWs from Castilla y León and Catalonia and two different varieties
(Garnacha and Verdejo). The attributes that defined this group
were ‘tropical fruit’,‘woody/toasted’and ‘sour’. This group of
wines presented high levels of isoamyl acetate that could
be responsible for a generic fruity aroma to these wines. None-
theless, this tropical fruit character detected for the group,
particularly Verdejo, could be induced by the presence of
Table 4. Mean ±SD and range of occurrence of methanol, metals, chlorides and sulphates in the 14 conventional (CW) and the 14 natural (NW)
wines of the study
CW NW
PMean ±SD Range Mean ±SD Range
Methanol (mg L
−1
)49.79 ±15.18 26.00–91.00 62.43 ±63.19 29.00–271.00 NS
Lead (∼gL
−1
)9.79 ±6.47 <5.00–19.60 7.85 ±6.36 <5.00–28.00 NS
Copper (mg L
−1
)0.08 ±0.03 0.05–0.13 0.09 ±0.06 0.03–0.20 NS
Zinc (mg L
−1
)0.47 ±0.29 <0.10–1.13 0.36 ±0.24 <0.10–0.90 NS
Arsenic (∼gL
−1
)<10.00 ±0.00 <10.00 <10.00 ±0.00 <10.00 NS
Iron (mg L
−1
)0.70 ±0.37 0.27–1.40 1.90 ±2.66 0.16–9.48 NS
Chlorides
a
128.43 ±345.23 13.00–1320.00 22.21 ±22.97 3.00–77.00 NS
Sulphates
b
0.31 ±0.11 0.20–0.50 0.32 ±0.14 <0.10–0.50 NS
Significance (P) was calculated according to one-way-ANOVA with the type of wine (CW, NW) as main fixed factor (*P<0.05; **P<0.01; NS, no sig-
nificant difference).
a
Expressed as mg L
−1
of sodium chloride.
b
Expressed as g L
−1
of potassium sulphate.
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3545
polyfunctional mercaptans, such as 3-mercaptohexyl acetate,
37
not quantified in the present study. Finally, the third cluster con-
sisted of five wines: four conventional and one natural. It was
mainly described as ‘fruity’(high levels in isoamyl acetate vector)
(Table 5), ‘floral’,‘silky’and ‘sour’. Once again, based on the
ANOVA (fixed factor wine production) of the scores, there was
no significant effect of production type (NW or CW) on the dimen-
sions derived from the MDS. However, it cannot be neglected that
83% of NWs are grouped together in the cluster described with
oxidation-related aroma notes.
Finally, Fig. 1(c) shows the tree diagram derived from Sorting
Task 3. The test consisted in grouping 10 wines (five natural and
five conventional) from different regions and varieties, and thus
maximal sensory variability according to their sensory similarity.
Cluster 1 comprised four wines, three conventional and one natu-
ral, each from a different variety (Macabeo, Verdejo, Airén and
Malvasía) and from the regions: Castilla y León, Castilla La Mancha
and Catalonia. The perceived attributes of the group were ‘fruity’,
‘floral’and ‘sour’. Cluster 2 consisted of three wines (two conven-
tional and one natural). Again, a NW is positioned within a group
of CWs indicating sensory similarity between them. This cluster
included negative descriptors such as: ‘oxidation/evolved’and
‘animal’, associated with their higher levels in isoaldehydes
(OAV =4.5) and ethylphenol (OAV =20.5) vectors (Table 5), and
‘woody/roasted’. Moreover, two of them belong to the same
region (Huelva) and variety (Zalema) but one is conventional
(A)
(B)
(C)
Figure 1. Dendrogram showing the groups of wines derived from the hierarchical cluster analysis calculated on all dimensions of the MDS derived from:
(a) Sorting Task 1 with 10 wines (five conventional and five natural) from the same region; (b) Sorting Task 2 with 12 wines (six conventional and six nat-
ural) sharing variety; and (c) Sorting Task 3 with 10 wines (five conventional and five natural) mixing variety and region. The attributes describing each
group are those with significantly higher scores within each.
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J Sci Food Agric 2023; 103: 3540–3549
3546
and the other natural. In this case, the production process does
not appear to be responsible for their sensory profile. Finally, clus-
ter 3 was formed by three NWs, each from a different variety
(Airén, Verdejo and Malvasía), two from Castilla y León and the
third from Castilla La Mancha. The attributes that defined this
group of NWs were: ‘vinegar’, consistent with the highest levels
of ethyl acetate vector (OAV =21.5) (Table 5), ‘faulty’and ‘vegeta-
ble’. Once again, none of the three MDS dimensions discriminated
significantly NWs from CWs.
In sum, no significant effect of the type of wine production
(NW or CW) was found for any of the dimensions derived from
any of the three sorting tasks. Notwithstanding these results, it
cannot be neglected that 70% of the NW were characterised by
aroma ‘defects’or non-positive attributes such as ‘vinegar’,‘oxida-
tion’,‘evolved’and ‘animal’. Instead, 30% of NWs could not be dif-
ferentiated from CWs and were grouped on the basis of their
sensory similarity with positive attributes such as ‘fruity’and/or
‘floral’. Regarding their profile of sensory-active compounds (see
Supporting information, Appendix S2), NWs tend to present sys-
tematically higher levels of animal-like ethyl phenols and volatile
acidity compared to their conventional counterparts, supporting
the evidence from their differential sensory characterisation. No
further significant effects of other fault-related volatile com-
pounds were detected.
CONCLUSIONS
The present study advances knowledge on the rather unex-
plored issue of NWs, an expanding concept and approach
to winemaking among the also growing market of wines with
sustainability attributes. Supporting the commonly held
assumption that NWs present distinct oenological, toxico-
logical and sensory profiles, the present study highlights var-
ious parameters where differences are clear with regard
to CWs.
First, oenological composition of NWs differed from their con-
ventional counterparts, NW presenting higher pH and volatile
acidity values and lower malic acid content. Regarding toxic-
related composition, the results only partially confirm our first
hypothesis because, although NW presented lower overall total
sulphur dioxide levels, they had higher biogenic amine content.
Therefore, we could not confirm the commonly-held belief that
they are healthier wines, at least regarding the compounds quan-
tified in the present study.
The present study also confirms our second hypothesis that
NWs differ from CWs in their sensory profile, showing significantly
higher levels of 4-ethylphenols and volatile acidity in NWs, noted
as animal and vinegar-like aromas. Nonetheless, although there
was a high percentage of NWs with olfactory defects, 30% of them
Table 5. Odour activity values (OAVs) of aroma vectors significantly differing among clusters derived from Sorting Tasks 1, 2 and 3, according to
one-way ANOVA with cluster as fixed factor (*P<0.05; **P<0.01; ***P<0.001)
Clusters derived from sorting tasks
Aroma vectors
Sorting Task 1 (S1)
Significance
Cluster 1 (3 NW) Cluster 2 (1 NW/2 CW) Cluster 3 (1 NW/3 CW)
Faulty, animal, vinegar Fruity, floral, woody/roasted, balanced Fruity, vegetable, sour
Ethyl acetate * 10.0 a 4.3 b 4.4 b
Fatty acids * 24.5 b 51.9 a 41.8 a
⊎-damascenone * 79.2 b 106.7 b 177.7 a
Whisky lactones * 0.0 b 0.4 a 0.1 b
Ethylphenols *** 25.6 a 0.1 b 0.0 b
Vinylphenols ** 0.6 b 2.2 a 1.8 ab
Sorting Task 2 (S2)
Significance
Cluster 1 (5 NW) Cluster 2 (2 CW) Cluster 3 (1 NW/4 CW)
Dried/candied fruit, oxidation/evolved, vegetable Tropical fruit, woody/roasted, sour Fruity, floral, silky, sour
Isoamyl acetate * 11.3 b 23.8 a 31.6 a
Acetic acid *** 1.9 a 0.6 b 0.8 b
Cinnamates ** 1.0 b 5.1 a 2.5 ab
Sorting Task 3 (S3)
Significance
Cluster 1(1 NW/3 CW) Cluster 2 (1 NW/2 CW) Cluster 3 (1 NW)
Floral, fruity, sour Oxidation/evolution, animal, woody/roasted Faulty, vinegar, vegetable
Isoaldehydes * 2.6 ab 4.5 a 1.3 b
Cinnamates * 3.1 a 2.1 ab 0.7 b
Spicy phenols ** 0.7 b 2.6 a 1.9 ab
Ethylphenols * 0.03 b 20.3 a 5.8 b
Ethyl acetate * 4.2 b 6.8 b 21.5 a
OAVs calculated as averages for wines belonging to the same cluster. Different lowercase letters after a given aroma vector indicate significant dif-
ferences (Fisher's test). The highest OAV value for given aroma vector (and sorting task) is indicated in bold. The number of NW and CW ineach cluster
is given in brackets.
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reached similar quality standards to CWs, showing that low-
sulphite wines can potentially achieve top quality results.
This prospective work, limited to 28 commercial white wines,
shows very interesting preliminary results, offering data for
policy-makers to regulate the NW market. It suggests that NWs
do not present toxicological profiles calling for a specific monitor-
ing strategy. Furthermore, these results open the door to further
studies to identify the key points in producing wines with minimal
intervention (e.g. not adding sulphur dioxide). These can show
high organoleptic quality, as in some of those rated in the present
study. Further research should also explore other chemical con-
tents such as pesticides and other heavy metals, and incorporate
a wider variety of international wines and experts beyond the
Spanish context.
ACKNOWLEDGEMENTS
This work was supported by the Spanish Plan of Innovation, Tech-
nical and Scientific Research 2017–2020 –through the programs
funded by AEI and MICIU: Ramón y Cajal (RYC2018-024025-I for
EPD and RYC2019-027995-I for MPSN) and Juan de la Cierva-Incor-
poración (IJC2018-037830-I for MB), and the Spanish Ministry of
Science and Innovation, the Spanish Research Agency and FEDER
(projects: PID2021-126272OA-I00, PID2021-126031OB-C21 and
PID2021-126031OB-C22). The manuscript was proof-edited by
Guido Jones, currently funded by the Cabildo de Tenerife, under
the TFinnova Programme supported by MEDI and FDCAN funds.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from
the corresponding author upon reasonable request.
SUPPORTING INFORMATION
Supporting information may be found in the online version of this
article.
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