Analytica Chimica Acta 588 (2007) 224–230
Effect of temperature variation on the visible and near infrared spectra of
wine and the consequences on the partial least square calibrations
developed to measure chemical composition
D. Cozzolino a,b,∗ , L. Liu a,b,c,1 , W.U. Cynkar a,b,1 , R.G. Dambergs a,b,1 ,
L. Janik a,b,1 , C.B. Colby c , M. Gishen a,b,1
a
The Australian Wine Research Institute, Waite Road, Urrbrae, PO Box 197, Adelaide, SA 5064, Australia
b Cooperative Research Centre for Viticulture, PO Box 154, Adelaide, SA 5064, Australia.
c School of Chemical Engineering, Engineering North Building, The University of Adelaide, Adelaide, SA 5005, Australia
Received 29 November 2006; received in revised form 29 January 2007; accepted 31 January 2007
Available online 6 February 2007
Abstract
Many studies have reported the use of near infrared (NIR) spectroscopy to characterize wines or to predict wine chemical composition. However,
little is known about the effect of variation in temperature on the NIR spectrum of wine and the subsequent effect on the performance of calibrations
used to measure chemical composition. Several parameters influence the spectra of organic molecules in the NIR region, with temperature being
one of the most important factors affecting the vibration intensity and frequency of molecular bonds. Wine is a complex mixture of chemical
components (e.g. water, sugars, organic acids, and ethanol), and a simple ethanol and water model solution cannot be used to study the possible
effects of temperature variations in the NIR spectrum of wine. Ten red and 10 white wines were scanned in triplicate at six different temperatures
(25 ◦ C, 30 ◦ C, 35 ◦ C, 40 ◦ C, 45 ◦ C and 50 ◦ C) in the visible (vis) and NIR regions (400–2500 nm) in a monochromator instrument in transmission
mode (1 mm path length). Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using full cross
validation (leave-one-out). These models were used to interpret the spectra and to develop calibrations for alcohol, sugars (glucose + fructose)
and pH at different temperatures. The results showed that differences in the spectra around 970 nm and 1400 nm, related to O H bonding were
observed for both varieties. Additionally an effect of temperature on the vis region of red wine spectra was observed. The standard error of cross
validation (SECV) achieved for the PLS calibration models tended to inverse as the temperature increased. The practical implication of this study
it is recommended that the temperature of scanning for wine analysis using a 1 mm path length cuvette should be between 30 ◦ C and 35 ◦ C.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Near infrared spectra; Temperature; Wine; Principal components; Partial least squares; Spectral changes; Hydrogen bonding
1. Introduction
The near infrared (NIR) spectra of agricultural products arise
from overtones and combinations of vibrations of molecular
bonds of the organic components [1]. In order to relate spectral properties to chemical information, several chemometric
methods such as multiple linear regression (MLR), principal
component regression (PCR), partial least squares regression
∗
Corresponding author at: The Australian Wine Research Institute, Waite
Road, Urrbrae, PO Box 197, Adelaide, SA 5064, Australia.
Fax: +61 8 8303 4373.
E-mail address: [email protected] (D. Cozzolino).
1 Fax: +61 8 8303 6601.
0003-2670/$ – see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.aca.2007.01.079
(PLS), locally weighted regression (LWR) and neural networks
are frequently used [2–10]. In recent years, two-dimensional
(2D) correlation spectroscopy was used to examine the effect
of temperature induced changes in different materials [11–14].
In routine food analysis using NIR spectroscopy methods, the
main drawback of calibration models developed for a specific
food or beverage is their lack of robustness [6,10]. The lack of
robustness in a calibration can be due to several causes such as
variable sample temperature, spectrophotometer temperature or
ambient stray light, among others [1]. The external factor that has
been most widely reported as affecting calibration robustness is
sample temperature. Vibrational spectra of liquid samples are
not only primary molecular features such as chemical structure
and functional groups, but also intramolecular features such as
D. Cozzolino et al. / Analytica Chimica Acta 588 (2007) 224–230
hydrogen bonding. These weaker forces that influence molecular
bond and therefore their vibration modes are themselves affected
by conditions such as temperature and pressure [2]. Many
authors have demonstrated the sensitivity of NIR spectra to sample temperature [1–14]. It is well known that NIR spectra of alcohols are very sensitive to temperature because the self-associated
forms of the alcohols dissociated into small oligomers, dimers
and monomers as a function of temperature [5,6]. Hansen et
al. [8] demonstrated that temperature changes affect the vibration intensity of molecular bonds, so the spectrum will change
according to the temperature variation. Swierenga et al. [9]
reported that an increase in temperature results in a decrease in
the number of hydroxyl groups involved in hydrogen bonding,
and consequently the absorption band of free hydroxyl increases.
The same authors reported that the second overtone absorption
band of the hydroxyl group of ethanol and iso-propanol (around
970 nm) increases as the temperature increases. The hydroxyl
group gives a sharper band for the free O H group and a broader
one for the stretch mode of hydrogen-bonded OH groups [2,5,6].
With increasing temperature, the broad band, that can be seen as
an overlay of many bands that belong to different cluster sizes of
molecules formed by the hydrogen bonding, is shifted towards
lower energies (longer wavelengths) as the degree of hydrogen
bonding decreases [2]. The existence of temperature dependent behaviour of the bands due to rotational isomerism of the
monomer terminal free O H groups, weakly hydrogen bonded
O H groups and hydrogen bonded O H of the self-associated
species is evident from spectroscopic analysis [5,6]. Similar phenomena were described by various authors using mixtures of
water and ethanol, ethanol and iso-propanol, consequently, sample temperature can affect the result of either classification or
calibration models when a NIR spectrum is used [1–15].
Wine is a complex mixture of chemical components (e.g. sugars, ethanol, organic acids, metal ions). These components can
themselves affect hydrogen bonding, therefore a simple ethanol
and water model solution should not be used to study the effect of
temperature on the NIR spectrum of wine. Although some studies have been done using NIR spectroscopy to either classify
wines or to predict wine chemical composition, little is known
about the effect of different temperatures on the NIR spectrum of wine and subsequent effect on calibration performance
[16–19].
The objective of this study was to investigate the effect of
temperature on the visible (vis) and NIR spectra of wine and on
the predictive ability of calibration models for the measurement
of wine chemical composition.
225
The white wine sample (labelled as Wws) set was a composite of
Chardonnay (n = 4), Pinot Gris (n = 1), Riesling (n = 1), Semillon (n = 2), Sauvignon Blanc (n = 1) and Verdelho (n = 1) wines.
Each bottle was analysed for alcohol content, pH, titratable acidity (TA), and glucose plus fructose (G + F) using a mid-infrared
spectrophotometer (Foss WineScanTM FT 120; Foss, Hillerød,
Denmark). Note that the aim of this study was not to develop
NIR calibrations for wine compositional parameters but rather
to test the effect of the temperature on such calibration models.
Therefore, the calibrations developed in this study were only
used as an indication of the effect of the different temperature
treatments.
2.2. Spectroscopic measurements
Both MilliQ (deionised) water and wine samples were
scanned in the vis and NIR wavelength regions (400–2500 nm)
using a scanning monochromator FOSS NIRSystems6500
(FOSS NIRSystems, Silver Spring, MD, USA). Spectral data
collection was performed with Vision software (version 1.0,
FOSS NIRSystems, Silver Spring, USA). Samples were analysed in transmission mode using a 1 mm path length cuvette
after equilibration in the instrument at 30 ◦ C, 35 ◦ C, 40 ◦ C,
45 ◦ C, 50 ◦ C, and at room temperature (∼25 ± 1 ◦ C) for
2 min.
Spectral data were stored as the logarithm of the reciprocal
of transmittance [log (1/T)] at 2 nm intervals (1050 data points).
Each sample was scanned in triplicate (repack) and the spectra were averaged for chemometric analysis. Air was used as
reference (empty sample holder).
2.3. Data analysis and interpretation
2.1. Wine samples and chemical analysis
Spectra were exported from the Vision software in NSAS format to The Unscrambler software (Version 9.1, CAMO ASA,
Oslo, Norway) for chemometric analysis. Principal component
analysis (PCA) was performed to examine the dominant patterns in the spectral data. Calibration models between chemical
composition and NIR spectra were developed using partial least
square (PLS) with full cross validation [20]. The spectra were
transformed using the second derivative (Savitzky–Golay transformation, 10 point smoothing and second order filtering) before
calibration models were developed.
In order to evaluate the effect of the temperature on the NIR
calibration for alcohol, pH, TA and G + F, the resulting standard
error in cross validation (SECV) of the calibration was compared
using a Fisher’s test (F value) [21]. The F value was calculated
as
SECV2
F=
, where SECV1 < SECV2
SECV1
Ten white and 10 red wine samples were collected randomly
from the AWRI Analytical Service laboratory. All samples were
commercially available bottles of Australian wine. The red wine
sample (labelled as Rws) set was a composite of Cabernet
Sauvignon (n = 5), Shiraz (n = 2), Pinot Noir (n = 1), a blend of
Cabernet Sauvignon and Shiraz (n = 1) and Rose (n = 1) wines.
The calculated F value was compared with the confidence
limit F critical (1 − α, n1–1 , n2–2 ), obtained from the distribution F table, where α is the test significance level (α = 0.05 in this
experiment), n1 the sample number measured at the first temperature, n2 the sample number measured at the second temperature
(n1 , n2 , . . . = 10 in this experiment). The differences between the
SECV are significant when F > F limit.
2. Materials and methods
226
D. Cozzolino et al. / Analytica Chimica Acta 588 (2007) 224–230
Four wavelength regions were analysed in order to spectrally
evaluate the effect of the temperature on the NIR wine spectrum. These regions were, 950–1000 nm, 1410–1470 nm related
with O H overtones (water and ethanol), 1660–1706 nm and
2250–2360 nm related with C H combinations (ethanol, sugars,
organic compounds). The spectra in the region of 1800–2000 nm
were off scale, and therefore were not used in the analysis. The
heights of each absorption band were exported to Microsoft
Excel for linear regression analysis of the temperature change
and absorbance variation.
To quantify the proportions of the total spectral variability
explained by temperature and wine variety, scores from the PCA
were analysed statistically as follows. After PCA analysis, the
scores from the first four principal components (PCs), which
accounted for more than 95% of the total spectral variability in
the raw spectra, were analysed using ANOVA (Systat, USA).
The sum of variances of a specific factor (e.g. temperature and
variety) or an interaction term (e.g. temperature × variety) of
the PCs can be interpreted as the expected variance of future
samples taken from the whole population [20,22].
3. Results and discussion
A number of changes were observed in the vis and NIR
spectra of wines analysed at different temperatures. It is well
known that water shows typical NIR absorption bands around
970 nm (O H stretch second overtone), at 1450 nm (O H stretch
first overtone) and at 1900 nm (O H stretching and deformation vibrations) [11]. These bands are affected by sample
composition and temperature. Hydrogen bonding lowers the
frequency of water stretching vibrations while increasing the
frequency of bending vibrations, therefore at higher temperatures the stretching vibrations increase in frequency [11]. Fig. 1
shows the NIR spectra of both red and white wine samples
scanned between 1400 nm and 1500 nm at six different temperatures. It was observed that the absorbance at low frequency
(longer wavelengths) with respect to the position of band maxima decreased with the temperature increase. Opposite changes
were observed at the high frequency (shorter wavelengths),
where it was observed that the absorption is increased with the
temperature. A close examination of the isobestic point between
the two sub-ranges reveals that for white wine samples analysed
this absorbance is located at 1446 nm while for red wine samples
is located at 1448 nm. In addition, red wine samples have a distinctive absorption band that is affected by temperature changes
in the vis region, around 540 nm related to the absorption of
wine pigments, principally anthocyanins (data not shown) [23].
Water is the major component of wine, but ethanol also contributes to O H absorption bands [24–26]. Absorption bands
of alcohols in the region between 1390 nm and 1640 nm were
reported to be related to the first overtones of the stretching mode
of the free O H group of the monomer and of the terminal free
and hydrogen bonded O H groups of the self-associated species
[5,6,25,26]. This is also observed in the spectra of the wine
samples analysed. Between 2270 nm and 2300 nm (C H combinations), an increased absorbance in response to temperature
has been observed. According to other authors it is believed that
the amount of absorbed light in the region between 2200 nm and
2300 nm decreases significantly as sample temperature increases
[5,6,11]. This can be of particular importance for the glucose,
fructose and ethanol band in this wavelength region [11]. At
short wavelengths, both wine types had an absorption band at
978 nm when analysed at 30 ◦ C, following temperature increase,
shifting to 972 nm at 50 ◦ C. Additionally, it was observed that
the absorption bands for MilliQ water was shifted from 974 nm
to 970 nm from 30 ◦ C to 50 ◦ C. A close examination of the
lowest points of the second derivative spectra showed that the
absorption bands of all wine samples and water at six different
temperatures were at 966 nm. Plotting the height of the second
derivative absorption bands against the six temperatures of each
wine sample, a linear relationship was observed (Fig. 2). At
wavelengths around 1410–1470 nm a similar shifting trend was
observed, but the extent of the observed shifting was even larger
than that observed for the short wavelengths. Absorption bands
shifted from 1454 nm at 30 ◦ C to 1444 nm at 50 ◦ C. After second
derivative transformation, the 1420 nm absorption band shifted
from 1420 nm to 1418 nm as temperature increased (data not
presented) [24]. However, the absorption band at 1460 nm did
not shift.
The second derivative at 1420 nm decreased with temperature (increased absorbance), while at 1460 nm increased. These
phenomena might be explained by the increase of free hydroxyl
groups with temperature as reported by other authors [2,11].
The broad band, that can be seen as an overlay of many bands
that belong to different cluster sizes formed by the hydrogen
bonding, is shifted towards lower energies (longer wavelengths)
Fig. 1. Near infrared spectrum of red (A) and white (B) wine samples scanned at six different temperatures. Arrows indicate direction of temperature increase.
D. Cozzolino et al. / Analytica Chimica Acta 588 (2007) 224–230
227
Fig. 3. Linear relationship of the second derivative heights at 1460 nm for red
wine samples.
Table 1
Analysis of variance of the PCA scores of the visible and near infrared raw
spectra of red and white wines analysed together
PC1
PC2
PC3
PC4
Temperature
25 ◦ C
30 ◦ C
35 ◦ C
40 ◦ C
45 ◦ C
50 ◦ C
−0.03 a
−0.05 a
−0.04 a
−0.06 a
0.08 a
0.06 a
−0.50 a
−0.58 b
−0.26 c
0.16 d
0.39 e
0.75 f
0.22 a
0.05 b
0.008 bc
0.03 b
−0.13 cd
−0.17 d
0.19 a
−0.17 b
−0.10 c
0.22 a
−0.16 b
0.02 d
Fig. 2. Linear relationship of the second derivative heights at 962 nm for red
and white wine samples.
Type
Red
White
1.82 a
−1.85 b
−0.02 a
−0.04 a
0.004 a
−0.19 b
relative to the free O H stretch. Therefore, increasing the temperature of the sample, decreases the average cluster size and
increases the relative absorbance of free groups [2,5,6,11]. A
similar phenomenon was observed in this study. The existence
and temperature dependent behaviour of the bands due to rotational isomerism of the monomer, terminal free O H groups,
O H groups weakly hydrogen bonded and hydrogen bonded
O H of the self-associated species might explain the observable changes in the NIR spectra of the wines analysed. In this
study, the NIR spectra of the wine samples analysed did not
show any shifting as a result of temperature changes in both the
raw spectra and after the second derivative transformation in the
region between 1660 nm and 1710 nm [25] (Fig. 3).
The results from the one-way analysis of variance, in order to
test the effect of temperature and variety on the scores of the vis
and NIR spectra, are shown in Tables 1 and 2. When comparing the overall effect of temperature on the spectra of both wine
varieties, no statistically significant differences were observed in
PC1, while statistically significant differences were observed for
the other three PCs. When the overall effect of the variety was
analysed, statistically significant differences were observed in
PC1 and PC3 but none between the other PCs analysed. When
the effect of temperature was analysed in relation to an individual variety, it was observed that no statistically significant
Effect of temperature and type of wine. PC: principal component. Levels in the
column not connected by same letter (a–f) are significantly different (p < 0.05).
0.01 a
0.002 a
Table 2
Analysis of variance of the PCA scores of the visible and near infrared raw
spectra for red and white wine samples with respect to temperature analysed
separately
PC1
Red wine
25 ◦ C
30 ◦ C
35 ◦ C
40 ◦ C
45 ◦ C
50 ◦ C
White wine
25 ◦ C
30 ◦ C
35 ◦ C
40 ◦ C
45 ◦ C
50 ◦ C
PC2
PC3
PC4
1.75 a
2.01 a
1.75 a
1.70 a
1.90 a
1.84 a
−0.49 a
−0.57 b
−0.32 c
0.15 d
0.43 e
0.74 f
0.22 a
0.09 b
−0.10 b
0.04 c
−0.07 c
−0.15 d
0.25 a
−0.15 b
−0.22 b
0.23 a
−0.09 c
0.06 d
−1.69 a
−1.63 ab
−1.65 c
−1.64 bc
−1.53 d
−1.54 d
−0.56 a
−0.64 b
−0.26 c
0.12 d
0.32 e
0.72f
0.02 a
−0.19 b
−0.07 b
−0.15 bc
−0.37 d
−0.39d
0.15 a
−0.17 c
−0.02 d
0.25 b
−0.23 c
0.006 e
PC: principal component. Levels in the column not connected by same letter
(a–f) are significantly different (p < 0.05).
228
D. Cozzolino et al. / Analytica Chimica Acta 588 (2007) 224–230
differences were observed for PC1 in red wine samples, while
statistically significant differences were observed in PC1 for the
white wine samples analysed. It is interesting to highlight that
both wine types had different behaviour on the PCs in response
to temperature changes. The highest eigenvectors observed in
PC1 were related to pigments (visible region) for the red wine
samples and to O H vibration bands for white wine samples.
Generally, PC score distributions indicate the degree of similarities between the sample spectra. If the scores for some
components are spread irregularly as a function of a temperature, it is very likely that the PC is capturing only noise or
other non-deterministic variations and does not describe physically significant changes. It is interesting to note that the scores
were arranged in a linear way against temperature. When the
scores of the samples scanned at different temperatures were
plotted it was observed that samples scanned at the same temperature were clustered together (Fig. 4). Additionally, it was
observed that for the case of white wines, samples clustered
from left to right as temperature increased along PC1, while
for red wines the samples were clustered from bottom to top
along PC2, which seems to explain the effect of temperature. It
was observed that PC1 captures all the dominant changes in the
spectra related to temperature in the set of white wine samples
while PC2 did the same for the red wine samples. Comparing
Fig. 4. Score plot of the first two principal components for red wines showing
effect of temperature variation.
Fig. 5. Eigenvectors for the first two principal components for the effect of
temperature in red wine samples.
the eigenvectors from the PCA, it can be observed that PC2 of
the red wine is similar to PC1 of the white samples. On the other
hand, the PC1 in red wines is mainly related to the vis region,
around 540 nm corresponding to wine pigments (anthocyanins)
[23]. Both PC2 in the case of red wines and PC1 in the case
of white wine samples explain the spectra temperature related
changes, and are specifically related to the observed changes
in the NIR region around the O H bonds. However, the most
important finding of this study was that for red wines, PC1 was
also related to temperature changes in electronic transitions of
wine pigments, or to shifts in anthocyanin absorption related
with co-pigmentation [23,27]. Looking at the eigenvectors for
both varieties (Figs. 5 and 6), one can obtain the temperature
profile of the absorption changes developed in the system.
In order to quantify the effect of temperature on the spectra of
wine and on the accuracy of NIR calibrations models for alcohol, sugars (glucose plus fructose), pH and titratable acidity, the
SECV values for different NIR calibrations models for spectra
collected at different temperature and the chemical composition,
were statistically compared.
Fig. 6. Eigenvectors for the first two principal components for the effect of
temperature in white wine samples.
D. Cozzolino et al. / Analytica Chimica Acta 588 (2007) 224–230
229
Table 3
Sample characteristics and chemical composition of white wine samples analysed
Sample code
Variety
Alcohol (%)
pH
TA (g L−1 )
G + F (g L−1 )
Ww1
Ww2
Ww3
Ww4
Ww5
Ww6
Ww7
Ww8
Ww9
Ww10
CH
CH
CH
PG
R
SB
S
S
UCH
Verdelho
13.75
13.37
13.43
13.76
13.21
12.99
11.70
11.09
13.55
12.90
3.36
3.41
3.26
3.13
3.18
3.27
3.27
3.18
3.36
3.41
6.55
6.73
6.54
7.48
6.52
6.26
6.3
7.05
6.32
6.91
1.80
4.10
0.70
1.60
8.0
2.10
0.70
2.60
9.30
4.10
12.98
0.89
11.09
13.76
3.28
0.10
3.13
3.41
6.67
0.39
6.26
7.48
3.50
2.97
0.70
9.30
Mean
S.D.
Min
Max
S.D.: standard deviation; Min: minimum; Max: maximum; TA: titratable acidity; G + F: glucose plus fructose; CH: Chardonnay; PG: Pinot Gris; R: Riesling; UCH:
unwooded Chardonnay; S: Semillon; SB: Sauvignon Blanc.
Table 4
Sample characteristics and chemical composition of red wine samples analysed
Sample code
Variety
Alcohol (%)
pH
TA (g L−1 )
G + F (g L−1 )
Rw1
Rw2
Rw3
Rw4
Rw5
Rw6
Rw7
Rw8
Rw9
Rw10
CS
CS
CS
CS
CS
PN
Rośe
SH
SH
Blend of CS and SH
13.61
13.08
12.49
13.21
12.92
13.51
13.08
13.65
14.08
14.15
3.53
3.57
3.43
3.49
3.36
3.62
3.36
3.54
3.63
3.43
6.63
6.04
7.35
7.78
7.22
7.31
6.06
6.44
6.73
6.58
0.30
1.80
0.40
3.90
0.20
0.50
4.60
0.20
0.60
0.30
13.29
0.52
12.49
14.15
3.50
0.10
3.43
3.63
6.84
0.58
6.04
7.78
1.34
1.64
0.2
4.6
Mean
S.D.
Min
Max
S.D.: standard deviation; Min: minimum; Max: maximum; TA: titratable acidity; G + F: glucose plus fructose; CS: Cabernet Sauvignon; SH: Shiraz; PN: Pinot Noir.
Tables 3 and 4 show the chemical composition of both red
and white wine samples analysed. A wide range in composition was observed in the set of wines analysed. It was therefore
considered to be a representative set of samples on which NIR
calibration models could be developed in order to test the effect
of temperature on both the spectra and calibration robustness.
Note that the aim of this work was not to develop NIR calibrations for wine compositional parameters rather to test the effect
of the temperature on such calibration models.
Table 5 shows the standard error of cross validation (SECV)
for the chemical parameters evaluated for calibrations developed on samples scanned at six different temperatures. Firstly,
it was observed that the SECV obtained was different depending on the type of wine used (e.g. red or white). No statistically
significant differences for all parameters were observed for the
SECV obtained with calibrations developed on red wine samples
scanned at 30 ◦ C and 35 ◦ C. While same statistically significant
differences were observed for the SECV of red wine samples
scanned at ambient (∼25 ◦ C), 40 ◦ C, 45 ◦ C and 50 ◦ C, respectively, no statistically significant differences were observed on
Table 5
Standard error in cross validation (SECV) obtained using partial least squares
as calibration models for the determination of chemical composition in red and
white wines scanned at six different temperatures
Alcohol (%)
pH
TA (g L−1 )
G + F (g L−1 )
Red wine
25 ◦ C
30 ◦ C
35 ◦ C
40 ◦ C
45 ◦ C
50 ◦ C
0.084 a
0.059 a
0.062 a
0.14 b
0.30 c
0.097 a
0.038 a
0.013 b
0.017 b
0.029 a
0.059 c
0.027 a
0.18 a
0.12 b
0.071 b
0.18 a
0.11 b
0.17 a
0.54 a
0.18 b
0.27 b
0.51 a
0.43 a
0.59 a
White wine
25 ◦ C
30 ◦ C
35 ◦ C
40 ◦ C
45 ◦ C
50 ◦ C
0.077 a
0.070 a
0.074 a
0.12 b
0.069 a
0.23 b
0.056 a
0.058 a
0.059 a
0.065 a
0.040 a
0.08 b
0.19 a
0.17 a
0.22 a
0.23 a
0.17 a
0.24 a
0.64 a
0.66 a
0.80 a
1.04 b
0.58 a
2.58 b
Levels in the column not connected by same letter are significantly different
(p < 0.05); TA: titratable acidity; G + F: glucose plus fructose.
230
D. Cozzolino et al. / Analytica Chimica Acta 588 (2007) 224–230
the SECV of white wine samples scanned between laboratory
ambient temperature (∼25 ◦ C) up to 40 ◦ C. However, at 50 ◦ C,
the SECV values obtained for the calibrations for alcohol and
G + F were statistically significantly different. It was observed
that the NIR calibrations developed using red wine samples,
were more influenced by changes in temperature than those
developed using white wine samples, but for both wine types
a systematic trend of the error, increasing as the temperature
increased, was observed for the four parameters measured. Similar results were found by other authors where the effect of
temperature was studied on alcohol calibration in beverages [4].
It was suggested that some compositional characteristics of the
red wine matrix could be more affected than others (e.g. pigments, phenolic compounds) when perturbations in the vis and
NIR spectra are induced by temperature.
4. Conclusions
This study demonstrated the importance of the effect of temperature on both vis and NIR spectra of wine and the calibrations
developed for alcohol, sugars, pH and TA in red and white wine
samples. It was observed that the SECV obtained was dependent on the variety used to develop the NIR calibrations and
tended to be higher as the temperature of the sample increased.
The NIR spectra were affected by changes in the temperature
and these changes were also different depending on the variety of wine. The main changes observed in the NIR spectra of
the wine samples relating to temperature were observed around
970 nm and 1400 nm (O H bonds). It was also observed that
no changes occurred in the SECV when wine samples were
scanned between 30 ◦ C and 35 ◦ C. The practical implication of
this study is that the recommended temperature for scanning of
wines using a 1 mm path length cuvette is between 30 ◦ C and
35 ◦ C.
Acknowledgements
We wish to thank Dr. M. Herderich for the suggestions and
comments made in the manuscript. This project is supported by
Australia’s grapegrowers and winemakers through their investment body the Grape and Wine Research and Development
Corporation, with matching funds from the Australian government, and by the Commonwealth Cooperative Research Centres
Program. The work was conducted by The Australian Wine
Research Institute, and formed part of the research portfolio
of the Cooperative Research Centre for Viticulture.
References
[1] H. Abe, C. Iyo, S.J. Kawano, Near Infrared Spectrosc. 8 (2000) 209–214.
[2] F. Wulfert, W.T. Kok, A.K. Smilde, Anal. Chem. 7 (1998) 1761–1767.
[3] K. DebRaekeleer, F. Cuesta Sanchez, P.A. Hailey, D.C.A. Sharp, A.J.
Pettman, D.L. Massart, J. Pharm. Biomed. Anal. 17 (1998) 141.
[4] F.D. Barboza, R.J. Poppi, Anal. Bioanal. Chem. 377 (2003) 695–701.
[5] H. Maeda, Y. Ozaki, J. Near Infrared Spectrosc. 3 (1995) 191–201.
[6] H. Maeda, Y. Wang, Y. Ozaki, M. Suzuki, M.A. Czarnecki, M. Iwahashi,
Chem. Intell. Lab. Syst. 45 (1999) 121–130.
[7] F. Chauchard, J.M. Roger, V. Bello-Meurel, J. Near Infrared Spectrosc. 12
(2004) 199–205.
[8] W.G. Hansen, S.C.C. Wiedemann, M. Snieder, V.A.L. Wortel, J. Near
Infrared Spectrosc. 8 (2000) 125.
[9] H. Swierenga, F. Wülfert, O.E. De Noord, A.P. de Weijer, A.K. Smilde,
L.M.C. Buydens, Anal. Chim. Acta 411 (2000) 121.
[10] M. Blanco, D. Valdes, J. Near Infrared Spectrosc. 3 (2004) 121–126.
[11] M.A. Czarnecki, Y. Ozaki, Phys. Chem. Chem. Phys. 1 (1999) 797–800.
[12] Y. Ozaki, S. Sasic, J.H. Jiang, J. Near Infrared Spectrosc. 9 (2001) 63–95.
[13] B. Czarnik-Matusewicz, S. Pilorz, J.P. Hawranek, Anal. Chim. Acta 544
(2005) 15–25.
[14] B. Czarnik-Matusewicz, S. Pilorz, Vib. Spec. 40 (2006) 235–245.
[15] J.A. Hageman, J.A. Westerhuis, A.K. Smilde, J. Near Infrared Spectrosc.
13 (2005) 53–62.
[16] D. Cozzolino, H.E. Smyth, M. Gishen, J. Agric. Food Chem. 51 (2003)
7703–7708.
[17] D. Cozzolino, M. Kwiatkowski, M. Parker, M. Gishen, R.G. Dambergs, W.
Cynkar, M. Herderich, Anal. Chim. Acta 513 (2003) 73–80.
[18] M. Urbano Cuadrado, M.D. Luque de Castro, P.M. Pérez Juan, M.A.
Gómez-Nieto, Talanta 66 (2005) 218–224.
[19] M. Urbano-Cuadrado, M.D. Luque de Castro, P.M. Pérez-Juan, J. Garcı́aOlmo, M.A. Gómez-Nieto, Anal. Chim. Acta 527 (2004) 81–88.
[20] T. Naes, T. Isaksson, T. Fearn, T. Davies, A User-friendly Guide to Multivariate Calibration and Classification, NIR Publications, Chichester, UK,
2002, p. 420.
[21] M. Samuels, Statistics for the Life Sciences, Macmillan, Inc., 1989.
[22] R.G. Brereton, Chemometrics, in: Data Analysis for the Laboratory and
Chemical Plant, Wiley and Sons, 2003.
[23] C. Somers, The Wine Spectrum: An Approach towards Objective Definition
of Wine Quality, Winetitles, Adelaide, 1998, p. 50.
[24] W.R. Hruschka, Spectral reconstruction, in: D.A. Burns, E.W. Ciurczak
(Eds.), Handbook of Near-infrared Analysis, Marcel Dekker, New York,
1992, pp. 365–382.
[25] B.G. Osborne, T. Fearn, P.H. Hindle, Practical NIR Spectroscopy with
Applications in Food and Beverage Analysis, 2nd ed., Longman Scientific
and Technical, Harlow, Essex, UK, 1993, p. 227.
[26] R.G. Dambergs, A. Kambouris, I.L. Francis, M. Gishen, J. Agric. Food
Chem. 50 (2002) 3079–3084.
[27] R. Boulton, Am. J. Enol. Vitic. 52 (2001) 67–87.