Evaluation of the formation and stability of hydroxyalkylsulfonic acids in wines

Article abstract of DOI:10.1021/jf0709653

The presence of carbonyl compounds (CCs) in wines has sparked the interest of researchers in several countries. The quantification of some of these compounds has been used as a parameter of quality for many fermented beverages. Although present in minute quantities (except for acetaldehyde), they have a strong olfactory impact. In addition, the CCs found in wines have a strong affinity for bisulfite and can form stable adducts, which will also interfere in the characteristics of aroma. The greatest challenge, however, is to predict which CCs have the strongest affinity for S(IV) and what conditions favor this interaction. To better understand the reaction of CC-bisulfite adduct formation (HASA), this study has evaluated the profile of 22 CCs in a "synthetic wine" containing bisulfite and in 10 real samples of different wines from the Sao Francisco Valley, northeastern Brazil. On the basis of principal component analysis (PCA) and dissociation constants, the results revealed that aliphatic aldehydes form adducts with S(IV), whereas ketones, cyclic aldehydes, and trans-alkenes interact weakly and are found predominantly in their free form. These results revealed also that pH 10 and 11 were defined as the most appropriate for CC-SO₂ adduct dissociation, and the total CCs were quantified reliably.

Full text of DOI:10.1021/jf0709653

8670 J. Agric. Food Chem. 2007, 55, 8670–8680
Evaluation of the Formation and Stability of
Hydroxyalkylsulfonic Acids in Wines
†
,§
†
§,⊥
LUCIANA C. DE AZEVEDO,
MARINA M. REIS, LUIZ F. MOTTA,
#
†
,†
GISELE O. DA ROCHA, LUCIANA A. SILVA, AND JAILSON B. DE ANDRADE*
Instituto de Química, Universidade Federal da Bahia, Campus Universitário de Ondina, Salvador 40,
1
70-290 Bahia, Brazil; Centro Federal de Educação Tecnológica de Petrolina, Rod. BR 407, Km 08,
Jardim São Paulo, 56314-520 Petrolina, Pernambuco, Brazil; Instituto Multidisciplinar em Saúde,
Universidade Federal da Bahia, Campus Anísio Teixeira, Vitória da Conquista, 45055-201 Bahia,
Brazil; and Universidade Estadual de Campinas. Instituto de Química, Barão Geraldo, Campinas,
1
3083-970 São Paulo, Brazil
The presence of carbonyl compounds (CCs) in wines has sparked the interest of researchers in
several countries. The quantification of some of these compounds has been used as a parameter of
quality for many fermented beverages. Although present in minute quantities (except for acetaldehyde),
they have a strong olfactory impact. In addition, the CCs found in wines have a strong affinity for
bisulfite and can form stable adducts, which will also interfere in the characteristics of aroma. The
greatest challenge, however, is to predict which CCs have the strongest affinity for S(IV) and what
conditions favor this interaction. To better understand the reaction of CC–bisulfite adduct formation
(
HASA), this study has evaluated the profile of 22 CCs in a “synthetic wine” containing bisulfite and
in 10 real samples of different wines from the São Francisco Valley, northeastern Brazil. On the
basis of principal component analysis (PCA) and dissociation constants, the results revealed that
aliphatic aldehydes form adducts with S(IV), whereas ketones, cyclic aldehydes, and trans-alkenes
interact weakly and are found predominantly in their free form. These results revealed also that pH
10 and 11 were defined as the most appropriate for CC–SO
2
adduct dissociation, and the total CCs
were quantified reliably.
KEYWORDS: Hydroxyalkylsulfonic acids; carbonyl compounds; bisulfite; wine; São Francisco Valley,
Brazil
INTRODUCTION
Union) and the Organización Internacional de la Viña e del
Vino–International Grape and Wine Organization (OIV) for
Sulfur-based compounds [Na2S2O5 or K2S2O5 (sodium or
potassium metabisulfite) and/or KHSO3 (potassium bisulfite)]
have been used by wineries to solve problems related with
oxidation and microbial interactions in wine (1–3). In aqueous
solutions, these compounds produce various species of S(IV)
involved in the equilibrium depending mainly on the pH.
These S(IV) species compete with molecular oxygen for the
chemical groups susceptible to oxidation and then inhibit some
reactions caused by molecular oxygen. As the use of these
compounds became widespread, so did the concern about their
damaging effect on health, particularly because they are
associated with problems of asthma in people who consume
alcoholic beverages (4, 5). In terms of the SO2 concentration,
the levels of S(IV) compounds allowed by the EU (European
-
1
some types of wine vary from 160 to 260 mg L and from
1
1
75 to 360 mg L- , respectively (6). However, according to
-1
the literature, concentrations of <10 mg L suffice to trigger
asthma attacks (7).
There is the establishment of a chemical equilibrium
between “free SO2” and “bound SO2” forms in sulfited wines
(6). The concentrations of free SO2 found in different types
of wine may vary as a function of the composition of the
beverage, the quantity of bisulfite and/or metabisulfite added,
and the greater or lesser facility for interaction between S(IV)
and some components of the matrix. Several carbonyl
substances are present among these components, such as
formaldehyde, acetaldehyde, glyoxal, methylglyoxal, pyruvic
acid, xylose, R-ketoglutaric acid, galacturonic acid, glucu-
ronic acid, 2-ketogluconic acid, 2,5-diketogluconic acid,
*
Corresponding author (telephone 55-71-3263-6451; fax 55-71-
3
263-6435; e-mail jailsong@ufba.br).
5
-ketofructose, and hydroxypropanedial (6, 8, 9). Because
†
Instituto de Química, Universidade Federal da Bahia.
Centro Federal de Educação Tecnológica de Petrolina.
Universidade Estadual de Campinas.
§
musts and wines contain significant levels of these carbonyl
compounds (CCs), this combination should be considered,
because it might influence the reduction of the concentration
⊥
#
Instituto Multidisciplinar em Saúde, Universidade Federal da Bahia.
1
0.1021/jf0709653 CCC: $37.00  2007 American Chemical Society
Published on Web 09/19/2007

Hydroxyalkylsulfonic Acids in Wines
J. Agric. Food Chem., Vol. 55, No. 21, 2007 8671
of free SO2 (6). Another aspect to be taken into account is
the fact that the adduct formed between these compounds
and S(IV) is usually not very stable, except for the interaction
with acetaldehyde. The equilibrium of the adduct’s formation
depends on the temperature, the pH of the medium, and the
type of chemical constituents that combine with S(IV).
Moreover, any change in the composition of the wine
inevitably affects this equilibrium, which is demonstrated in
eq 1 (3, 6):
pk ) 1.81
pk ) 6.91
z
-
+
SO 23 -
(
SO + H O
y
z
HSO + H
y
2
2
3
(
predominant form in wine)
1)
Figure 1. Representation of different possibilities of combination of SO
2
in the wine (adapted from Ribereau-Gayon, 2003).
In a typical wine possessing pH from 3 to 4, the predominant
form among the S(IV) species is that of the bisulfite ion (6, 8, 10).
Table 1 shows the relative abundance of S(IV) species such as
free SO2 [active S(IV) species] and HSO3 , over pH from 3 to
. The data make evident that for higher pH values the amount
of free SO2 decreases, justifying a larger sulfiting when must
or wine is at weak acidic conditions. The different forms that
sulfur dioxide can assume in wine are shown in Figure 1.
Briefly, in the region ranging from (a) to (b), on the left, is
present the free SO2 form, which may be transformed into
Table 1. Relative Abundance of Two Free S(IV) Species as a Function of
pH at 20 °C in Aqueous Solution (6)
-
pH
free SO2 (%)
HSO3- (%)
4
3
.00
.10
.20
.30
6.06
4.88
3.91
3.13
2.51
2.00
1.60
1.27
1.01
0.81
0.64
94.94
95.12
96.09
96.87
97.49
98.00
98.40
98.73
98.99
99.19
99.36
3
3
3
3.40
3.50
3.60
-
HSO3 as a function of the pH. The line at (b) divides the free
3.70
3.80
3.90
SO2 form region from the SO2 bound to carbonyl compounds
from wine ranges, excluding acetaldehyde. This line may be
moved in either direction by changes of temperature and SO2
level available in wine. On the right, the range (c–d) represents
the SO2 bound to acetaldehyde. This is a fixed line because it
depends only on the acetaldehyde level of the wine (6).
In aqueous solutions, S(IV) compounds can bind to carbonyl
compounds in various ways, and one of these combinations may
lead to the formation of addition compounds known as hy-
droxyalkylsulfonic acids (HASA) (eq 2) (11, 12).
4.00
-6
) 1.4 × 10 ]; its dissociation constant value was too small to
allow acetaldehyde in the free form in solution (6, 17, 18).
According to Burroughs (13), at a free SO concentration of
2
6.4 mg L- , 98% of acetaldehyde in solution would be in the
1
bound form, whereas galacturonic acid was the least stable [K
d
-2
-2
(
pH 3) ) 1.6 × 10 and Kd (pH 4) ) 2.1 × 10 ].
Acetaldehyde, in particular, reacts rapidly with sulfite or
bisulfite ions, forming an addition product that is not volatile
and, thus, reducing its olfactory perception (4). This justifies
the use of S(IV) compounds to disguise the excess of this
aldehyde in wine. The adduct thus formed, R-hydroxyethane-
sulfonic acid, consists of a strong acid species involved in the
equilibrium. On the basis of the typical acid strengths of other
sulfonic acids, the first acid dissociation constant of R-hydro-
xyethanesulfonic acid is expected to be >0.1 and the second
acid dissociation constant has also been determined as 5.0 ×
(
2)
The affinity of a carbonyl compound for bisulfite is repre-
sented by the dissociation constant of the corresponding addition
compound defined according to the law of mass action. The
apparent equilibrium constant (Kd) is given by
-11
1
0
(19).
Carbonyl compounds of higher molar masses can also form
HASA. The stability of these acids also depends on the structure
of the bound CC, the pH, and the temperature of the medium
[
S][X - x]
K )
(3)
d
[
x]
where [S] ) concentration of free S(IV) species in any form,
X] ) total concentration of carbonyl compound (free + bound),
and [x] ) concentration of undissociated carbonyl bisulfite.
Burroughs and co-workers (13–16) studied intensively the
sulfite-binding power focused on the equilibrium constants for
dissociation of some carbonyl bisulfite compounds present in
apple juice, ciders, and wines. The carbonyl compounds
evaluated by these authors were acetaldehyde, pyruvic acid,
(
18). It is essential to know the concentrations of the acids
formed with more complex carbonyl compounds in order to
understand the equilibrium of the adduct formation reactions
and to allow the quantification of total CCs present in samples
of wine or of other fermented beverages.
[
Free aldehydes can be determined directly by high-perfor-
mance liquid chromatography (HPLC), after derivatization
reaction with 2,4-dinitrophenylhydrazine (2,4-DNPH) solution
to form the respective 2,4-dinitrophenylhydrazones, and then
quantifying them by comparison to standard solutions containing
known concentrations of the CCs under study (20–25). Deriva-
tization is necessary due to the relative instability of the carbonyl
chemical function in view of the complex medium of
wine (20–26). The formation of hydrazones must occur in acidic
conditions, because the acidic catalysis activates the carbon of
2
-ketoglutaric acid, L-xylosone, D-threo-2,5-hexodiulose, 2,5-
diketogluconic acid, 2-ketogluconic acid, and galacturonic acid.
The apparent equilibrium constants remain almost unchanged
-6
over pH 3 and 4 for each CC, ranging from 1.5 × 10 to 1.6
-2
×
10 . Acetaldehyde was the most stable carbonyl bisulfite
-
6
compound evaluated [Kd (pH 3) ) 1.5 × 10 and Kd (pH 4)

8672 J. Agric. Food Chem., Vol. 55, No. 21, 2007
de Azevedo et al.
Figure 2. Diagram of the preparation of a sample to obtain the HASA adduct in “synthetic wine”.
the carbonyl group by protonation of oxygen to yield a stronger
electrophile that can be attacked by a weak nucleophile, such
as 2,4-DNPH. According to Veloso and co-workers (24, 25, 27),
the pH influence is due to a competition between nucleophilicity
and basicity of 2,4-DNPH and the electrophilic nature of the
carbon of the carbonyl group. For too low pH values, 2,4-DNPH
acts as a base and it is protonated, decreasing its nucleophilic
action. For higher pH values, the reactivity of the carbonyl group
decreases. In this regard, it is necessary to establish the optimum
pH to equilibrate both factors. Veloso and co-workers found
the ideal range of pH to yield the best carbonyl compound
recovery to lie in the interval of 1.5–2.2.
Figure 3. Loadings diagram of the first two principal components (PC1
and PC2), indicating the variables responsible for the grouping of the
samples into subgrouups (FCC, free; BCC, bound; TCC, total).
A method to determine formaldehyde, acetaldehyde, hy-
droxymethanesulfonic acid (AHMS), and R-hydroxyethane-
sulfonic acid (AHES) in white wine samples based on 2,4-
DNPH derivatives was developed by de Oliveira and de Andrade
free aldehydes gives the bound aldehydes or the respective
AHMS and AHES contents in the wine samples.
(
21). Total formaldehyde and acetaldehyde were measured by
dissociating the AHMS and AHES into formaldehyde, acetal-
dehyde, and bisulfite with a strong base and reacting the
aldehydes with 2,4-DNPH. Free formaldehyde and acetaldehyde
are similarly determined in another aliquot, but without addition
of the alkaline solution. The difference between the total and
In the quantification of total CCs based on the decomposition
of the HASA adduct, the literature (21) suggests that the sample
first be acidified with HCl and slightly heated (50 °C) to
eliminate excess SO2. NaOH solution should then be added to
the sample to ensure the medium is sufficiently alkaline to

Hydroxyalkylsulfonic Acids in Wines
J. Agric. Food Chem., Vol. 55, No. 21, 2007 8673
Figure 4. Variation of the CC concentration as a function of pH in synthetic wine, using the quadratic equations obtained by regression.
decompose the HASA. This stage is followed by the derivati-
zation and quantification procedure, as described for free CCs.
The HASA concentrations are determined by the difference
between contents of total and free CCs, for each CC (21, 28).
Although some studies on the formation of HASA from main
high molar mass CCs found in wines, apple juice, and cider
are reported in the literature (8, 14–16), a better understanding
of CCs and SO2 interactions as well as the ideal pH for the
HASA dissociation is still needed. Therefore, the main focus
of this study was to evaluate the influence of the molecular
structure of aldehydes and ketones on the formation of HASA
and to define the best range of alkaline pH within which it is
possible to obtain the highest analytical signal for the determi-
nation of total CCs. To prevent interferences from the matrix,
the tests were carried out on “synthetic wine” and latter applied
to real samples of both white and red wines.
E-pent-2-en-1-al, E-hex-2-en-1-al, E-hept-2-en-1-al, E-oct-2-en-1-al,
benzaldehyde, salicylaldehyde, propanone, hexan-2-one, heptan-2-one,
octan-2-one, nonan-2-one, decan-2-one, ꢀ-ionone, and cyclopentanone.
All of these compounds have been cited as constituents of the volatile
fraction of wines (8, 9, 26, 29–32).
Standard solutions of each CC were prepared to obtain a concentra-
-1
tion of 1000 mg L . An aliquot of 1 mL of this solution was transferred
to a volumetric flask A and another to flask B with the same capacity
(
Figure 2). In both cases, the volumes were completed with 11%
-1
ethanol solution to give a final concentration 100 mg L for each
CC. Tartaric acid and sodium bisulfite (to favor the formation of the
AHAS adduct) were added to flask B, and it was dubbed “synthetic
wine” (SW).
One milliliter of solutions A and B was transferred to flasks A1 and
B1 and completed with 11% ethanol solution to give a final concentra-
-
1
tion of 10 mg L . Then, 1 mL from each flask was taken and
transferred to amber-colored bottles containing 5 mL of 2,4-DNPH at
0.4%, acidified with phosphoric acid, to enable the CC to be derivatized.
The mixtures were then sonicated for 15 min and injected into the HPLC
system. The free carbonyl compounds (FCC) contained in the solutions
of flasks A1 and B1 were then analyzed. The solution of flask A1 was
considered the bisulfite blank (BB) and was used as reference in the
calculation of the concentrations.
MATERIALS AND METHODS
Preparation of Standards and Samples. The reagents and standards
utilized were of analytical grade and the solvents of HPLC grade. The
solutions were prepared using deionized water obtained with the
Millipore system.
The profile of different aldehydes and ketones in the presence of
bisulfite was investigated in a solution that reproduced some of the
conditions found in wine, such as the alcohol content, bisulfite content,
and pH. This solution, which was dubbed “synthetic wine”, was
prepared by mixing a solution of CC standards (Cfinal ) 10 mg L ),
sodium bisulfite (Cfinal ) 50 mg L- ), and tartaric acid, which was
added to adjust the pH close to that of wine, that is, 3.5, and completed
with 11% ethanol (11:89, ethanol/deionized water, v/v).
The 22 carbonyl compounds studied here were formaldehyde,
acetaldehyde, propanal, pentanal, hexanal, octanal, nonanal, decanal,
Equal volumes (1 mL) of synthetic wine (B) were then transferred
to volumetric flasks C1, C2, C3, C4, and C5. The pH of the solutions
contained in the flasks was adjusted to 9, 10, 11, 12, and 13,
-
1
respectively, by the addition of 1 mol L of NaOH. All flasks then
were completed with 11% ethanol solution. An aliquot of 1 mL of the
solution contained in each flask was derivatized with 5 mL of 2,4-
DNPH 0.4% solution and used for the analysis of the total carbonyl
compounds (TCC).
-
1
1
To evaluate the role of both an individual CC by itself and all CCs
together with respect to adduct formation at different pH values, the
above-described sample procedure was initially prepared with each of

8674 J. Agric. Food Chem., Vol. 55, No. 21, 2007
de Azevedo et al.
Figure 5. Scores diagram of the first two principal components (PC1 and PC2), indicating the grouping of the samples into three subgroups: aldehydes,
ketones, and trans-alkenes.
Table 2. Contribution of the Autoscores of Variables to PC1 and PC2
The statistical treatment data of the “synthetic wine” was based on
a multivariate analysis of the data matrix. The original data matrix
consisted of 22 samples and 10 variables in different pH values (the
22 × 10 format). The samples correspond to the carbonyl compounds
evaluated, whereas the concentrations of both bound (BCC) and total
carbonyl compounds (TCC) represent the variables. The concentrations
of BCCs and TCCs were measured in synthetic wine at pH 9, 10, 11,
variable
PC1
PC2
BCC-pH11
BCC-pH10
BCC-pH10
0.599
0.588
0.544
-0.298
-0.467
0.832
1
2, and 13. FCC values were not used in the statistical analysis.
The method employed was principal component analysis (PCA),
Table 3. Accumulative Percentage Variance into PC1 and PC2
PC1 PC2
using the computational program Unscrambler, version 7.6. In this
method, the data are explored and compacted, grouping similar samples
and selecting the most relevant variables. First, a preprocessing of
autoscaling was performed in order to obtain the same loading for all
variables. In this way, a symmetric and quadratic correlation matrix
with diagonal elements with values equal to 1 was reached, confirming
high correlations among them. Second, by performing the PCA, these
variables are grouped to a new variable (the principal component),
directed to the axis of the greatest spreading of data and then decreasing
the variables dimension, which consequently provokes the reduction
of the number of variables. The diagrams of scores and loadings were
analyzed.
In the multivariate analysis the reduction processing of spatial
dimension provokes a consequent decrease of the number of variables.
The goal is to get parameters that are not highly correlated to each
other and then samples are grouped and distinguished themselves. The
grouping was performed as follow: (a) decomposition of singular values
variable
PCA
BCC-pH11
BCC-pH10
TCC-pH10
calibration
validation
calibration
Validation
90.00
96.78
93.20
80.01
83.30
94.62
88.65
66.05
99.60
99.34
99.48
99.98
98.46
97.50
97.98
99.95
the CCs individually and then analyzed. Next the same sample
procedure was repeated for the preparation of a solution containing all
of the CCs jointly, and it was also subjected to analysis. The latter
was prepared with all 22 CCs at a concentration of 1000 mg L
followed by the dilution steps as already described for each individual
CC solution.
-
1
,
Free S(IV) species determinations were made by titrating the
equilibrated solutions containing carbonyl bisulfite compounds with
iodine standard solution. The start solutions were prepared following
(
DSV); (b) diagonalizing of correlation matrix. First, the original data
-1
matrix (X) was decomposed into three matrices (A, B, and C), DSV
Method, according to eq 4; matrixes A and C are squared and
orthonormals, that is, the rows A and C are orthogonal to each other
and normalized.
the same procedure already described, with 100 mg L of each CC
-
1
-
-
3 3
and 500 mg L HSO , keeping the same ratio (CC:HSO ) of all
solutions used here. The pH was adjusted at 3.5 with tartaric acid.
For real sample analysis five bottles of white wine and five bottles
of red wine were acquired from wineries in the São Francisco Valley,
northeastern Brazil. Free and total CCs were determined in each bottle,
and all analyses were performed in triplicate, using the same procedure
described above. TCC determination was carried out at pH 11.
HPLC Analysis. The carbonyl compounds and their respective
hydroxyalkylsulfonic acids were analyzed by high-performance liquid
chromatography (HPLC) with a reversed-phase gradient system com-
posed of a Perkin-Elmer series 200 pump, a Rheodyne injector valve
with a 20 µL loop, a Perkin-Elmer series 200 UV–vis (model 5100)
detector equipped with a deuterium lamp (λ ) 365 nm), and an Intralab
T
X ) A · B · C
(4)
The B matrix is diagonal and rectangular, being of singular values
in the diagonal to all elements out of the zero diagonal. In this way,
the multiplication factor A · B represents the scores matrix T and C
corresponds to the loadings matrix. The rows from C matrix are
autovectors of matrix X · X, and the rows from matrix A are
autovectors of matrix X · X . Then the diagonalization of matrix
correlation was made, where the autovectors C and K were calculated
by using eq 5. The autoscores K are the diagonal and their elements
k
are the respective squared singular values (λ ) according to eq 6.
T
T
(
model 4290) integrator. The compounds were separated in a LiChro-
CART RP-18 (250 × 0.4 mm; 5 µm) column by passage of the mobile
phases A (74.5% methanol/0.5% acetonitrile/25% water, v/v/v) and B
T
X · X · C ) k · C
(5)
(6)
(
100% methanol), according to the following schedule: 12 min, 100%
A; 12 min, 100% AfB; 3 min, 100% B; 10 min, 100% BfA.
Statistical Treatment. CC quantification was made by applying the
external standardizing method, through standard analytical curves,
considering the integrated area of chromatographic peaks.
2
λ ) (S )
k
kk
k
Each element λ from the diagonal refers to variance of original data,
which is described by the kth principal component. The information

Hydroxyalkylsulfonic Acids in Wines
J. Agric. Food Chem., Vol. 55, No. 21, 2007 8675
a
Table 4. Concentrations (Milligrams per Liter) of Free, Bound, and Total CCs in “Synthetic Wine”, Analyzed Individually
CC
BB FCC TCC-pH9 TCC-pH10 TCC-pH11 TCC-pH12 TCC-pH13 BCC-pH9 BCC-pH10 BCC-pH11 BCC-pH12 BCC-pH13
formaldehyde
acetaldehyde
propanal
pentanal
hexanal
octanal
nonanal
decanal
E-pent-2-en-1-al 1.67 1.65
E-hex-2-en-1-al 1.67 1.78
E-hept-2-en-1-al 1.67 1.59
E-oct-2-en-1-al
benzaldehyde
salicylaldehyde
propanone
1.67 0.21
1.67 0.96
1.67 0.57
1.67 0.78
1.67 0.56
1.67 0.34
1.67 0.52
1.67 0.75
1.22
1.30
1.54
1.46
1.47
1.46
1.20
1.85
0.04
0.54
0.04
0.13
1.55
1.43
1.60
1.70
1.24
1.67
1.67
1.56
1.63
1.10
1.51
1.54
1.51
1.46
1.48
1.65
1.33
1.80
0.06
0.38
0.06
0.09
1.39
1.58
1.59
1.70
1.11
1.77
1.67
1.35
1.60
1.41
1.56
1.78
1.66
1.67
1.36
1.80
1.33
1.59
0.08
0.24
0.12
0.16
1.67
1.50
1.59
1.57
1.23
1.89
1.79
1.46
1.60
1.67
1.83
1.44
1.12
1.62
1.26
1.55
1.47
1.72
0.12
0.20
0.18
0.22
1.59
1.46
1.60
1.50
1.48
1.77
1.73
0.73
1.60
1.36
1.56
1.79
1.09
1.67
1.39
1.56
1.36
1.69
0.19
0.24
0.26
0.29
1.61
1.41
1.66
1.54
1.44
1.84
1.71
1.32
1.67
1.35
1.01
0.34
0.96
0.68
0.91
1.12
0.68
1.10
nd
nd
nd
nd
nd
1.30
0.58
0.93
0.68
0.92
1.31
0.81
1.04
nd
nd
nd
nd
nd
0.08
nd
0.12
nd
nd
nd
nd
1.35
0.82
1.08
1.04
0.80
1.46
0.81
0.83
nd
nd
nd
nd
nd
nd
nd
nd
nd
0.06
nd
nd
1.62
0.48
0.54
0.84
0.70
1.21
0.95
0.97
nd
nd
nd
nd
nd
nd
0.01
nd
0.19
nd
nd
nd
1.34
0.83
0.76
1.53
0.83
1.22
0.84
0.94
nd
nd
nd
nd
nd
nd
0.07
nd
0.14
0.01
nd
1.67 1.53
1.67 1.66
1.67 1.50
1.67 1.59
1.67 1.57
1.67 1.30
1.67 1.83
1.67 1.92
1.67 1.49
1.67 1.58
nd
0.01
0.12
nd
nd
nd
0.07
0.04
nd
hexan-2-one
heptan-2-one
octan-2-one
nonan-2-one
decan-2-one
nd
0.08
nd
ꢀ
-ionone
0.02
nd
0.02
0.28
0.02
nd
cyclopentanone 1.67 1.39
a
nd, not detected; BB, bisulfite blank; FCC, free; TCC, total; BCC, bound.
a
Table 5. Concentrations (Milligrams per Liter) of Free, Bound, and Total CCs in “Synthetic Wine”, Analyzed Jointly
CC
BB FCC TCC-pH9 TCC-pH10 TCC-pH11 TCC-pH12 TCC-pH13 BCC-pH9 BCC-pH10 BCC-pH11 BCC-pH12 BCC-pH13
formaldehyde
acetaldehyde
propanal
pentanal
hexanal
octanal
nonanal
decanal
E-pent-2-en-1-al
E-hex-2-en-1-al
benzaldehyde
salicylaldehyde
propanone
hexan-2-one + E-hept-2-en-1-al 1.67 1.47
heptan-2-one + E-oct-2-en-1-al 1.67 1.67
1.67 1.09
1.67 0.81
1.67 0.70
1.67 0.43
1.67 0.68
1.67 0.81
1.67 0.92
1.67 1.33
1.67 1.53
1.67 1.42
1.67 1.32
1.67 1.42
1.67 1.55
1.44
1.20
1.45
0.31
1.51
1.70
1.16
1.00
0.17
0.23
1.37
1.40
1.59
1.55
1.52
1.25
1.14
1.03
0.86
0.89
1.79
1.53
1.74
1.48
1.80
1.91
1.62
1.55
0.24
0.34
1.80
1.43
1.54
1.50
1.59
1.19
1.14
0.90
0.81
1.39
1.91
1.67
1.72
1.67
1.88
1.98
1.67
1.67
0.12
0.15
1.80
1.43
1.61
1.55
1.49
1.37
1.25
1.03
0.97
1.67
1.55
1.56
1.74
1.65
1.50
1.70
1.49
1.33
0.12
0.15
1.72
1.40
1.61
1.55
1.49
1.31
1.25
1.03
0.86
1.48
1.53
1.72
1.72
1.64
1.46
1.70
1.49
1.33
0.24
0.34
1.59
1.50
1.67
1.61
1.49
1.31
1.15
0.90
0.86
1.45
0.35
0.40
0.75
nd
0.83
0.89
0.26
nd
nd
nd
0.05
nd
0.70
0.74
1.04
1.04
1.11
1.10
0.71
0.22
nd
0.82
0.88
1.02
1.23
1.19
1.17
0.74
0.33
nd
0.46
0.76
1.04
1.22
0.81
0.89
0.58
nd
nd
nd
0.40
nd
0.06
0.08
nd
0.06
0.16
0.06
0.05
0.34
0.44
1.32
1.02
1.52
0.77
0.89
1.76
nd
nd
nd
nd
nd
0.48
0.02
nd
0.03
nd
nd
0.05
nd
0.48
0.02
0.06
0.08
nd
0.12
0.16
0.06
0.16
0.92
0.28
0.08
0.11
0.14
nd
0.06
0.05
nd
0.04
0.08
nd
octan-2-one
nonan-2-one
decan-2-one
ꢀ-ionone
cyclopentanone
1.67 1.25
1.67 1.09
1.67 0.96
1.67 0.81
1.67 1.16
nd
0.05
0.06
0.05
nd
nd
0.23
0.05
0.43
a
nd, not detected; BB, bisulfite blank; FCC, free; TCC, total; BCC, bound.
content in each principal component can be described by variance
percentage (%Var ). The total variance of the group is determined by
k
the role of concentration values of the studied CCs in synthetic wine
as a function of pH values changing in alkaline range during TCC
analysis and, in this way, to select the best pH (or pH range) for a
satisfactory response in the study. Through observation of critical points
in the obtained curves, we have defined the ideal range of pH to be
applied in the preparation of samples.
summation of variance of each principal component. Thus, the first
principal component always will contain the greatest percentage of
information, as already described. It is necessary to consider all principal
components that describe about 95% of the original information. In
this moment, there are the significant principal components, yet their
accumulated percentage variance (%Varaccumulated) can be calculate. After
the PCA treatment, a cross-validation was executed to obtain the
validated percentage of the analysis. The software Unscrambler, version
RESULTS AND DISCUSSION
The determination of free carbonyl compounds relies on the
assumption that carbonyl bisulfite compounds are stable in acidic
medium; thus, bound CCs in the HASA adducts do not react
with 2,4-DNPH to form hydrazone derivatives (21, 28, 36). Ang
and co-workers (36) studied hydroxymethanesulfonic acid
7
.6, does the PCA using DSV and diagonalizing of correlation matrix,
reaching a resulting modeling of calibration. This software also performs
the internal cross-validation method by taking samples one by one with
the goal of testing the prediction capacity of the proposed method. The
cross-validation method was randomly based.
(
HMSA) dissociation and 2,4-DNPH reaction at pH 2; because
Using the same data matrix, a regression analysis was also carried
2
no HMSA dissociation was observed by them under this pH
condition, no HCHO–2,4-DNPH derivatives were thus formed
out, selecting the mathematical model by the highest adjusted R value
and setting the significance of the regression coefficient at 5% by the
t test. For the majority of studied CCs, the quadratic model presented
the best fit. The regression analysis was carried out by the SAEG-
UFV, version 8.1, program (33), and its main objective was to predict
(36). On the other hand, the addition of a strong base to HMSA
solution (pH 13) dissociates the adduct into free formaldehyde
and bisulfite, allowing the derivatization reaction. In a previous

8676 J. Agric. Food Chem., Vol. 55, No. 21, 2007
de Azevedo et al.
Figure 6. Percentage of FCC and BCC-pH11 in samples of “synthetic wine”, analyzed individually (BCC, bound; FCC, free).
Figure 7. Percentage of FCC and BCC-pH11 in samples of “synthetic wine”, analyzed jointly (BCC, bound; FCC, free).
work (28), we observed similar results for HMSA. This was
also verified for acetaldehyde in which standard R-hydroxy-
ethanesulfonic acid (HESA) solutions were added to 2,4-DNPH
reagent in pH ranging from 2.53 to 5.50 and no H3CCHO–2,4-
DNPH derivatives were formed. Indeed, an alkaline pH is
necessary to ensure the complete dissociation of the HASA
adduct (the BCC form), thus allowing for the reliable quanti-
fication of the TCC present in the sample. Generally speaking,
pH 10 and 11 were considered to be the most significant to
obtain the best response in terms of TCC concentration. This
pH range was defined after PCA and regression results. The
loadings diagram (Figure 4) indicates that the variables TCC-
pH10, BCC-pH10, and BCC-pH11 were selected, justifying the
choice of pH 10 and 11 as the most important. This analysis
showed that the other pH values, because they present autoscores
smaller than those of pH values 10 and 11, were not selected;
therefore, they do not present a significant relevance in the
determination of concentrations of the studied CCs and their
discrimination. In the same way, in Figure 4, which was
obtained by regression, it is clear that these pH values are
considered to be critical points of the curves because before
pH 10 and after pH 11 there are changes in the curve trending
(response either increases or decreases). This is not desired
during a simultaneous analysis of all CCs in a real sample of
wine. On the other hand, by using quadratic modeling to
estimate the role of the response in the pH range 10–11, it could
be noted that there were not significant differences among
results. Therefore, this pH range could guarantee a complete
dissociation of the adduct and ensure reliable results in the TCC
analysis to determine CCs quantitatively.
The PCA revealed that the first two components were able
to explain 99.6% of the total variance of the data set. The first
principal component (PC1) explained 89.99% of the variance,
whereas the second (PC2) explained 9.61%. After PCA, three

Hydroxyalkylsulfonic Acids in Wines
J. Agric. Food Chem., Vol. 55, No. 21, 2007 8677
Figure 8. Resonance structures and resonance hybrid of R- and ꢀ-unsaturated aldehydes favorable to simple addition reaction (adapted from ref 28).
Figure 9. Conjugate addition, under Michael addition route, between an anion and an R,ꢀ-unsaturated carbonyl compound.
Table 6. Dissociation Constants (K ) of Carbonyl Bisulfite Compounds at
d
pH 3.5
derivatives, they were grouped in the upper region of the scores
diagram, which can be explained by analyzing the loadings
diagram, because the variable TCC-pH10 accounts for this
category of derivatives. Thus, the FCC-pH10 form is the one
that best justifies the grouping of the ketone compounds studied.
Table 2 shows selected autoscores and their respective contribu-
tions to PC1 and PC2. Table 3 indicates the accumulated
percentage variance of calibrations and validation for PC1 and
PC2 and how much each variable contributes to each principal
component.
CC
Kd
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
7
6
6
6
6
6
6
6
4
4
4
3
4
4
5
4
formaldehyde
acetaldehyde
propanal
pentanal
hexanal
octanal
nonanal
decanal
E-pent-2-en-1-al
E-hept-2-en-1-al
E-oct-2-en-1-al
benzaldehyde
hexan-2-one
heptan-2-one
decan-2-one
1.09 × 10
2.06 × 10
7.70 × 10
8.00 × 10
3.45 × 10
2.52 × 10
4.12 × 10
4.46 × 10
8.25 × 10
1.57 × 10
8.48 × 10
2.83 × 10
1.13 × 10
4.30 × 10
8.30 × 10
2.07 × 10
According to Table 2, components PC1 and PC2 can be
expressed by the following equations:
PC1 ) +0.599[BCC-pH11] +0.588[BCC-pH10] +
0
.544[TCC-pH10] (7)
ꢀ-ionone
PC2 ) –0.298[BCC-pH11] – 0.467[BCC-pH10]+
0
.832[TCC-pH10] (8)
parameters were selected, and neither outlier was detected. The
scores diagram (Figure 5) indicates that the samples were
grouped into three categories: aldehydes, ketones, and alkenals.
In PCA, scores situated close to each other present correlations
that are statistically significant (34). The scores diagram revealed
not only that the samples were separated into three groups but
also that the unsaturated derivatives (alkenals) were separated
from the other carbonyl compounds. The BCC-pH10 and BCC-
pH11 variables (loadings diagram) contribute positively to PC1,
which justifies that in the pH 10–11 the alkenals group (scores
diagram) is distinguished from the other CCs group (the
aldehydes and ketones groups) because the unsaturated alde-
hydes are located on the left quadrant with the least contribution
to PC1. Also, this grouping is lower on the PC1 axis,
characterized by a small negative contribution to PC2. Aldehyde
and ketone samples tend to be presented mainly in the bound
form in the pH range when compared to alkenals. Analyzing
the data matrix, one can see that at pH 11, these CCs are
practically in free form, which is congruent with the statistical
analysis.
In eq 7, the variables BCC-pH11 and BCC-pH10 are the ones
that most contribute to separate the saturated (aldehydes and
ketones) from the unsaturated carbonyl derivatives (alkenals). Of
the three selected variables, BCC-pH11 is the one that most
contributes to PC1. Indeed, this can be observed from the loadings
diagram in Figure 3 as well as from the fact all variables contribute
positively to PC1. By analyzing eq 8, variables BCC-pH11 and
BCC-pH10 are the ones that most contribute to the grouping of
the aldehyde carbonyls, whereas the variable TCC-pH10 is the one
that most contributes to the grouping of the ketone carbonyls. Thus,
a detailed analysis of the scores and loadings diagrams reveals that
the form bound at pH 11 is the one that is most interesting for the
aldehydes and that the variable TCC-pH10 is the most interesting
one for the ketones. Table 3 indicates accumulated percentage
variance. The proposed modeling of %Varaccumulated for calibration
and validation is 99.60 and 98.46%, respectively. The %Varaccu-
mulated for calibration indicates the model capacity to explain original
data, showing as well its fitting power, whereas %Varaccumulated
indicates the prediction capacity of proposed modeling. These
results reveal that the BCC-pH11 variable is best adjusted as well
as the most predictable in the model because it explains 96.78%
and validates 94.62% of original data set just using the first principal
component. The second principal component is necessary because
it complements the analysis, showing the division between alde-
hydes and ketones. This also can be proved by data from Table 3,
where the BCC-pH10 and TCC-pH10 variables should also be in
the modeling because they contribute to validation of PC2. In this
A joint analysis of the scores and loadings diagrams shows
an indication for the interpretation of the experimental data. It
was found that the aldehyde compounds group together in the
lower quadrant (scores), so the variable BCC-pH11 (loadings)
is responsible for explaining the aldehydes’ profile. Therefore,
the form bound at pH 11 is the one that best explains the
grouping of the aldehydes studied here. As for the ketone

8678 J. Agric. Food Chem., Vol. 55, No. 21, 2007
de Azevedo et al.
a
Table 7. Concentrations (Milligrams per Liter, ( Standard Deviation) of Free, Bound, and Total CCs Identified in Five Different White Wines
wine 1 wine 2 wine 3 wine 4
wine 5
CC
FCC
BCC
1.49 ( 0.85 5.23 ( 0.17 0.29 ( 0.15 2.45 ( 0.08
19.23 ( 0.37 2.14 ( 0.58 18.80 ( 0.44 5.68 ( 0.54 12.18 ( 0.61 1.12 ( 0.29 19.05 ( 1.05 4.60 ( 0.30
FCC
BCC
FCC
BCC
FCC
BCC
FCC
BCC
formaldehyde 3.50 ( 0,32
acetaldehyde 1.68 ( 0.50
furfural
butanal
benzaldehyde 1.72 ( 0.55
hexanal 1.44 ( 0.48
-ethylhexanal 0.93 ( 0.04
0.42 ( 0.22 5.61 ( 0.58 2.10 ( 0.46 14.13 ( 0.51
0.40 ( 0.17
16.15 ( 0.57
1.00 ( 0.22
0.27 ( 0.03
2.55 ( 0.80
0.41 ( 0.26
1.31 ( 0.86
1.87 ( 0.16
8.14 ( 0.63
1.74 ( 0.17 4.58 ( 0.63 3.56 ( 0.28 5.79 ( 0.09
3.58 ( 0.85 2.32 ( 0.23 2.00 ( 0.13 1.62 ( 0.04
1.01 ( 0.22 1.79 ( 0.55 3.56 ( 0.66 8.59 ( 0.61
0.90 ( 0.32 0.40 ( 0.14 1.60 ( 0.23 0.25 ( 0.12
0.58 ( 0.04 4.34 ( 0.52 12.02 ( 0.61 2.52 ( 0.12
1.78 ( 0.47 6.23 ( 0.29 8.29 ( 0.28 1.84 ( 0.18
1.77 ( 0.59 3.04 ( 0.31 3.31 ( 0.24 0.51 ( 0.14
1.30 ( 0.35 1.18 ( 0.40 0.59 ( 0.31 1.24 ( 0.29
2
1.00 ( 0.04 0.35 ( 0.03 1.60 ( 0.08 0.07 ( 0.003 1.50 ( 0.03 0.14 ( 0.06 1.20 ( 0.34 0.93 ( 024
a
FCC, free; BCC, bound.
a
Table 8. Concentrations (Milligrams per Liter, ( Standard Deviation) of Free, Bound, and Total CCs Identified in Five Different Red Wines
wine 1 wine 2 wine 3 wine 4
wine 5
CC
FCC
BCC
FCC
BCC
FCC
BCC
FCC
BCC
FCC
BCC
formaldehyde 12.80 ( 0,32
0.81 ( 0.22 2.76 ( 0.26 0.51 ( 0.23 2.61 ( 0.31 0.91 ( 0.43 13.91 ( 0.50 0.53 ( 0.05 4.20 ( 0.22 0.32 ( 0.11
acetaldehyde
furfural
butanal
1.55 ( 0.45
1.97 ( 0.30
0.90 ( 0.19
0.65 ( 0.08
0.16 ( 0.006
1.35 ( 0.94 1.99 ( 0.17 3.14 ( 0.56 1.45 ( 0.12 0.94 ( 0.48
1.15 ( 0.50 2.69 ( 0.42 0.62 ( 0.05 1.95 ( 0.22 0.84 ( 0.18
0.49 ( 0.04 1.81 ( 0.26 1.08 ( 0.49 0.83 ( 0.14 0.91 ( 0.10
0.60 ( 0.08 0.42 ( 0.11 0.10 ( 0.03 0.49 ( 0.06 1.38 ( 0.27
0.00 ( 0.00 5.25 ( 0.09 2.25 ( 0.66 5.44 ( 0.66
1.67 ( 0.18 1.08 ( 0.27 1.69 ( 0.34 5.65 ( 0.92
1.35 ( 0.04 1.15 ( 0.17 1.19 ( 0.14 6.15 ( 0.74
0.15 ( 0.008 0.07 ( 0.03 0.50 ( 0.08 0.80 ( 0.14
benzaldehyde
hexanal
0.08 ( 0.03 0.25 ( 0.11 0.14 ( 0.03 0.09 ( 0.03 0.04 ( 0.004 1.54 ( 0.37 0.36 ( 0.16 0.15 ( 0.05 1.34 ( 0.29
0.21 ( 0.02 0.17 ( 0.04 0.04 ( 0.004 0.09 ( 0.02 0.16 ( 0.06 0.36 ( 0.13 1.31 ( 0.37 0.18 ( 0.07 1.31 ( 0.17
2-ethylhexanal 0.15 ( 0.008
a
FCC, free; BCC, bound.
Figure 10. Average percentage of FCC and BCC compositions in 10 samples of real wines.
way, the BCC-pH11 variable is the one that best explains the
original data set and best divides the sample groupings and
discriminates categories, whereas BCC-pH10 and TCC-pH10 most
contribute to a good discrimination of categories by functional
groups of both aldehydes and ketones.
Tables 4 and 5 list the values of the concentrations of free,
bound, and total carbonyl compounds in “synthetic wine”,
analyzed individually and jointly, respectively. The values of
FCC are equivalent to the results found in the sample contained
in flask B1 (Figure 2), whereas the values of TCC-pH 9–13
are equivalent to the results found in flasks C1–C5, respectively.
Concentration values of samples from flask A1 (bisulfite blank,
BB) were used as reference in the calculations of FCC and TCC.
The data indicate that adduct formation does not appear to
be conditioned only on the presence of carbonyl in the organic
compounds studied. The values of the FCC and BCC concentra-
tions demonstrate that the position of the carbonyl group, the
size of the chain, and the trans character of the structure of the
stereoisomers of CCs can influence the formation of the BCC.
The graphs depicted in Figures 6 and 7 show the free and
bound fractions of the CCs after the reaction with bisulfite, in
synthetic wine. The data of FCC and BCC, expressed in percent,
were calculated on the basis of TCC, assumed as 100%, except
for the trans-alkenals, the FCC values of which needed to be
calculated on the basis of the value of the blank (A1); therefore,
the determination in the total form presented problems and will
be argued ahead.
Taking into account that in the preparation of A1 an aliquot of
1
1
mL of 10 mg L- of CC was diluted into 5 mL of 2,4-DNPH
All values of BCC refer to the analysis in which pH 11 was
used in the dissociation of the adduct, because this proved to
be the most appropriate pH. Figure 6 illustrates the profile of
for derivatization reaction, then in all cases the final concentra-
-
1
tion of CC in A1 was 1.67 mg L
.

Hydroxyalkylsulfonic Acids in Wines
J. Agric. Food Chem., Vol. 55, No. 21, 2007 8679
the CC studied individually, whereas Figure 7 represents the
profile of the 22 CCs mixed together. Both graphs indicate that
the stability of the BCC is greater for the saturated aliphatic
carbonyl compounds (formaldehyde, acetaldehyde, propanal,
pentanal, hexanal, octanal, nonanal, and decanal) and that, in
most cases, >50% of the concentration of these compounds is
present in bound form. In the individual study of these
compounds, the BCC values are even higher. Among them, the
one displaying the strongest interaction with bisulfite is form-
aldehyde, an aldehyde with a very short chain containing only
one carbon atom. Approximately 90% of the formaldehyde was
present in bound form. As for the benzene aldehydes (benzal-
dehyde and salicylaldehyde), they do not form adducts in
significant quantities.
86.21 and 59.53%, respectively, whereas acetaldehyde and
2-ethylhexanal prevail in bound form, 74.00 and 74.36%,
respectively. Butanal, benzaldehyde, and hexanal are found to
be uniformly distributed as free and bound forms. When
analyzed together in a “synthetic wine”, formaldehyde, acetal-
dehyde, benzaldehyde, and hexanal showed the same profile.
Although butanal was absent in synthetic wine, we could
identify this CC in real wine samples, and its profile is quite
similar to the aliphatic aldehyde with three to eight carbon atoms
in the synthetic wine. The same profile was observed for furfural,
which resembles benzaldehyde, an aromatic aldehyde. Both CCs
showed just a little affinity for bisulfite ion, being present in
higher concentrations in their FCC forms in real wines.
In conclusion, evaluation of the 22 carbonyl compounds in
sulfited synthetic wine and of several of them in real wine
samples, therefore, revealed that the molecular structure of these
aldehydes and ketones plays a relevant role in BCC formation,
because aliphatic aldehydes were found to constitute the CC
group with the highest affinity for bisulfite (∼50% of their
concentration is represented in bound form). On the other hand,
for the ketones, the percentage of the BCC fraction is very small
in relation to the aldehydes having chains that possess the same
number of carbon atoms. Both the aliphatic and cyclic ketones
were predominantly present in their free form.
When studied separately, the ketones also do not display a
strong affinity for the bisulfite ion, because significant contents
of the BCC formed from this group were not identified, except
for cyclopentatone, with 20% of its concentration in the
combined form. Surprisingly, when evaluated in the mixture,
the ketones were found to be, on average, 10% combined in
adduct form.
For the alkenals, the lack of graphic representation for BCC
values, shown in Figures 6 and 7, is due to the difficulty in
determining the total contents of these components by the
method proposed in this work. Two mechanisms are suggested
to explain this phenomenon. One of them, the alkaline medium
required in TCC quantification, becomes much easier than the
simple addition reactions between R- and ꢀ-unsaturated alde-
hydes or ketones and strong nucleophilic species such as
hydroxide ion (35). In this way, the hydroxide ion will be added
to unsaturated CC through the carbonyl double bond (Figure
ACKNOWLEDGMENT
We thank Prof. Dr. Cícero Antônio de Araújo (CEFET
Petrolina, Brazil) for his contribution to this work.
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8
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(
ꢀ
-unsaturated CCs, known as “Michael addition” (Figure 9).
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The dissociation constants (Kd) for several carbonyl bisulfite
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agreement with literature data for formaldehyde (28) and
acetaldehyde ( 15, 16, 37) and match the trend of adduct stability
foreseen by PCA; that is, the Kd values for aliphatic aldehyde
adducts are smaller than for ketones, cyclic aldehydes, and trans-
alkenes adducts.
(
(
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2
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Received for review April 3, 2007. Revised manuscript received August
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Brazilian agencies) for their financial support.
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