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Food and Bioprocess Technology (2022) 15:1040–1054
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ORIGINAL RESEARCH
A Chemometric Approach toAssess theRheological Properties
ofDurum Wheat Dough byIndirect FTIR Measurements
FabioFanari1 · GianlucaCarboni2 · FrancescoDesogus1 · MassimilianoGrosso1 · ManfredWilhelm3
Received: 12 November 2021 / Accepted: 14 March 2022 / Published online: 23 March 2022
© The Author(s) 2022, corrected publication 2022
Abstract
Rheological measurements and FTIR spectroscopy were used to characterize different doughs, obtained by commercial and
monovarietal durum wheat flours (Cappelli and Karalis). Rheological frequency sweep tests were carried out, and the Weak
Gel model, whose parameters may be related to gluten network extension and strength, was applied. IR analysis mainly
focused on the Amide III band, revealing significant variations in the gluten network. Compared to the other varieties, Karalis
semolina showed a higher amount of α-helices and a lower amount of β-sheets and random structures. Spectroscopic and
rheological data were then correlated using Partial Least Squares regression (PLS) coupled with the Variable Importance in
Projection (VIP) technique. The combined use of the techniques provided useful insights into the interplay among protein
structures, gluten network features, and rheological properties. In detail, β-sheets and α-helices protein conformations were
shown to significantly affect the gluten network's mechanical strength.
Keywords FTIR measurements· Dough rheology· Rheological model· Durum wheat· Chemometric methods· Variable
selection methods
Introduction
Durum wheat is a staple food for human nutrition since
it is the main ingredient of pasta, couscous, bulgur, and
some breads in Mediterranean areas. In Italy, a consistent
part of the durum wheat is consumed in several forms of
bread. Such breads may have very different characteristics,
are obtained through various methods, although they share
some features in common. This frequently results in breads
with a yellowish colour, a characteristic taste, smell, and
aroma, a fine crumb structure, and a prolonged shelf-life,
all of which give these breads high appeal to consumers
(Ficco etal., 2017). This has raised the consumers’ inter-
est in durum wheat-based bread. So, producers are trying
to set up standardized industrial processes that enhance
state-of-the-art production, still based on an artisanal
approach in most cases. Despite that, contrary to ordinary
wheat flour, the studies about semolina dough characteri-
zation are less numerous in literature. Although there are
several studies concerning the dough rheological proper-
ties (Martín-Esparza etal., 2018; Meerts etal., 2017a, b;
Mironeasa etal., 2019), very few studies analyze the rela-
tionship between these properties and the microstructure of
the material which is responsible for the dough mechanical
properties.
In this sense, Fourier transform infrared spectroscopy
(FTIR) is an up-and-coming technique regarding the dough
characterization field because the measurements are easy
to perform but at the same time can give both qualitative
and quantitative chemical information about the micro-
structure. Throughout this practice, it is possible to identify
molecular functional groups by detecting the absorption of
infrared light in the wavenumber range 400–4000 cm–1. In
this regard, the dough’s main components (gluten protein,
water, fat, and starch) can be easily identified due to known
infrared absorption frequencies (Karoui etal., 2010). Glu-
ten proteins represent the protein part of the dough, mainly
consisting of two types of macromolecules, the monomeric
* Massimiliano Grosso
massimiliano.grosso@unica.it
1 Department ofMechanical, Chemical andMaterials
Engineering, University ofCagliari, 09123Cagliari, Italy
2 Agris Sardegna, Agricultural Research Agency ofSardinia,
09123Cagliari, Italy
3 Institute forChemical Technology andPolymer Chemistry,
Karlsruhe Institute ofTechnology, 76131Karlsruhe,
Germany
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1041Food and Bioprocess Technology (2022) 15:1040–1054
1 3
soluble gliadins and the glutenins, aggregates of insoluble
proteins linked by interchain disulphide bonds (Wieser,
2007). Glutenins are able to create a three-dimensional net-
work in which they interact with gliadin by non-covalent
forces, mainly hydrogen bonds (Tuhumury etal., 2014).
The characteristics of this network are of great importance
in determining the quality of the final product. The water
absorption capacity and water mobility in flour products
greatly depend on the distribution of polar groups, on the
accessibility of these groups to water, on the relative strength
of water-water and water-macromolecule interactions, on
the degree of crystallinity of the matrix, and on the rela-
tive humidity conditions (Wang etal., 2014). Spectroscopic
methods, like infrared spectroscopy, are regarded as a valu-
able tool to study changes in the gluten network structure
(Nawrocka etal., 2018a). In particular, the secondary struc-
ture of the proteins is studied by analysis of the characteristic
Amide I band (1570–1720 cm−1) (Rumińska etal., 2020).
However, this band is strongly dominated by the presence of
the water OH deformation peak at approximately 1640 cm−1
(Kong & Yu, 2007), which in most cases overlaps with it
(van Velzen etal., 2003). For this reason, the Amide III
band (1200–1340 cm−1) is utilized since it is less affected
by water oscillations (Nawrocka etal., 2020).
As regards secondary structures, the unhydrated gluten
is mainly characterized by β-sheet (39%) and random (30%)
conformations that are converted into β-turn one when glu-
ten is hydrated, as it occurs in dough development (Dong
etal., 1990). The α-helix content, instead, increases as
humidity increases, so its destruction might be related to
the reduction of the hydrogen bond in the gluten (Jia etal.,
2018).
Another advantage of FTIR is its capability to pro-
vide quantitative information about water populations in
the bread dough (Bock & Damodaran, 2013). Free water
availability is one of the essential properties affecting the
dough structure building, and its amount should be carefully
dosed (Fanari etal., 2020). Water has characteristic infra-
red resonances at wavenumbers of 3350 cm–1 (OH stretch-
ing), 1640 cm–1 (OH bending), and in the region beneath
800 cm–1 (OH deformation) (van Velzen etal., 2003), being
the latter one sometimes related to the excess water (Warren
etal., 2016). The water populations analysis through IR band
investigation can reveal a decrease or increase in the number
of strong and weak hydrogen bonds between gluten pro-
teins and water molecules, and lack or excess of free water
in the gluten network, with respect to the dough formula-
tion or wheat characteristics (Nawrocka etal., 2017). The
dehydration of the gluten matrix is regarded as an effect of
competition for water between gluten proteins and polysac-
charides that is connected with a redistribution of the water
in the wheat dough (Xuan etal., 2017). This phenomenon is
strongly influenced by the gluten content of the dough since
a higher amount of gluten proteins is usually connected with
a higher water absorption capacity (Fanari etal., 2019).
The connection between rheological properties and IR
measurements can be an effective tool in the material char-
acterization field, as demonstrated in the recent literature
(Chen & Zhen, 2021; Öztürk, 2021; Radebe etal., 2021).
This work correlates the possible gluten protein structures
on a molecular level to the macroscopic rheological proper-
ties of the dough. The IR spectrum analysis has a specific
focus on the Amide III band. A Partial Least Squares (PLS)
model is proposed here to relate FTIR measurements and
rheological model parameters. Chemometric approaches
are recently been used to relate food properties and meas-
urements obtained by different spectroscopic techniques
(Carbas etal., 2020; Cortés etal., 2019; Cueto etal., 2018;
Liu etal., 2014a, b; Wu etal., 2014).
In this work, in order to estimate the rheological proper-
ties of durum wheat dough from FTIR measurements, the
Amide III band was investigated, and a data-driven model
was built by considering different semolina species that
significantly differ in gluten, gluten index, and proteins, to
cover a wide variety of possible semolina features(see
details inTable1). Dough composition is further varied by
considering different water/flour ratios.
Partial least squares regression allows the estimation
of the Variable Importance in Projection (VIP) scores for
the regressor model (Liu etal., 2014a, b). This technique
enables identifying the most significant wavenumbers, and
thus the corresponding protein structures and their role in
defining rheological properties. The purpose is to find if this
technique is effective to detect the changes in the dough
microstructure, in view of a possible implementation of an
on-line monitoring system based on rapid and non-invasive
FTIR measurements.
Materials andMethods
For each sample, 300g of semolina and distilled water in
different amounts were kneaded using a measuring mixer
type 350 (Brabender). The mixing time was determined as
the one required to reach the dough maximum strength point
as observed in previous investigations (Fanari etal., 2021),
Table 1 Semolina varieties characteristics (means ± STD); values
with different letters in the same column are significantly different
(p-value ≤ 0.05)
Proteins (%) Gluten (%) Gluten Index (%)
Cappelli (CAP) 14.0 ± 0.17 a12.3 ± 0.30 a16.5 ± 2.35 a
Karalis (KAR) 11.0 ± 0.05 b7 ± 0.34 b98.4 ± 0.79 b
Commercial
(COM) 12.0 ± 0.12 c8.3 ± 0.27 c90.3 ± 2.12 c
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1042 Food and Bioprocess Technology (2022) 15:1040–1054
1 3
and it was found to be 5–8min, depending on the sample.
Values of the mixing time are reported in Table2. The rota-
tional mixing speed was set to 20rpm to avoid structure
breaks in the dough network. Three different types of semo-
lina were investigated. Two are of non-commercial mono-
varietal species cultivated in the “S. Michele” experimen-
tal farm of the Agricultural Research Agency of Sardinia
(AGRIS) in Ussana (Italy), resulting from the milling of
Karalis and Cappelli grains. The third kind is a commercial
blend acquired in the German retail stores (Gold Puder-Hart-
weizen Grieß, Aurora Mühlen GmbH, Hamburg, Germany).
It should be remarked that their properties are quite different
in terms of protein, gluten percentages, and gluten index,
as can be seen in Table1. Gluten index (G.I.), a parameter
providing information on both gluten quality and gluten
quantity, was determined by the AACC Int. 38-12A Stand-
ard method (AACC International, 2000). Table2 reports
the water content in the dough samples. The percentage of
water is based on the semolina's total weight. The semolina
protein content was determined through the nitrogen com-
bustion method (ISO 16634–2:2016, 2016) using a Leco
FP528 nitrogen analyzer (LECO, Stockport, U.K.). Gluten
content and G.I. of semolina were determined following the
ICC standard method No. 158 (ICC, 1995) by using the Glu-
tomatic 2200 system (Perten Instruments AB, Huddinge,
Sweden).
FTIR Measurements
The IR Spectrometer used for the measurements was a Ver-
tex 70 spectrometer (Bruker, Ettlingen, Germany), equipped
with a Universal ATR (attenuated total reflectance) sampling
device containing diamond crystal. Spectra were collected in
the 600–4000 cm−1 infrared spectral range at room tempera-
ture. Each spectrum was an average of 32 scans at 2 cm−1 reso-
lution. Data were processed by the OPUS software (Bruker,
Ettlingen, Germany). Each spectrum was manually corrected
with a linear baseline using ORIGIN (v.9.0 PRO, OriginLab
Corporation, USA). All experiments were repeated three times
on three different portions of the sample. In order to reach
quantitative data, the height of the peaks was considered and
compared after normalizing all the spectra dividing them by
the intensity of the CO/CC peak. Consequently, this peak can-
not be used for quantification purposes of the CO/CC band.
The second derivative of the processed spectra was calculated
to identify the protein secondary structure in the Amide III
band. Local minima of the second derivative, assuming only
negative values, were used to identify and assign each second-
ary structure to its correspondent wavelength range (Seabourn
etal., 2008). Each Amide III peak was then integrated into
the corresponding band interval. This interval was chosen
based on the literature (intervals and references are reported in
Table3) to estimate the percentage content of each structure,
dividing the integral of the interval by the integral of the entire
band. Mean values and standard deviations for each sample
triplet were calculated.
Rheological Measurements
Rheological experiments were performed with an ARES-G2
strain-controlled rheometer (TA Instruments, New Castle,
USA) equipped with a 25-mm parallel plate geometry. Imme-
diately after the kneading process, a piece of dough was loaded
on the rheometer, compressed to a gap of 2mm, and then left
at rest for 15min to allow material relaxation, as suggested in
the literature (Phan-Thien & Safari-Ardi, 1998). A layer of sili-
con oil was applied to the edge of the parallel plate geometry
to prevent water evaporation from the sample. The measure-
ment temperature in the rheometer was kept constant at 25°C
using a Peltier temperature control system. Frequency sweep
tests were performed with frequencies ranging from 0.1 to
100rad·s−1with a constant strain of γo = 0.1%, that is the upper
limit of the linear viscoelastic regime as evaluated through
preliminary amplitude sweep tests. Complex module data were
modelled as a function of the deformation frequency using the
Weak Gel model (Gabriele etal., 2001), reported in Eq. (1):
where
G∗
is the viscoelastic modulus,
𝜔
is the angular
frequency,
AF
is a model parameter that is related to the
strength of the network structure, and
z
is a model parameter
linked to the extension of the three-dimensional network.
(1)
G
∗(𝜔)=
√
G�(𝜔)2+G��(𝜔)2=A
F
𝜔
1∕
z
Table 2 Samples water content based on semolina weight
Sample Semolina variety Water (wt.%) Mixing
time
(min)
CAP40 Cappelli 40 4.9
CAP50 Cappelli 50 6.2
CAP60 Cappelli 60 7.8
KAR40 Karalis 40 4.9
KAR50 Karalis 50 6.2
KAR60 Karalis 60 7.8
COM40 Commercial 40 4.9
COM50 Commercial 50 6.2
COM60 Commercial 60 7.8
Table 3 Secondary structures frequency range in Amide III region,
with reference to the literature (Cai & Singh, 1999; Singh, 1999)
β-sheets Random coils β-turns α-helices
1220–1250 cm−1 1250–1270 cm−1 1270–1295 cm−1 1295–1330 cm−1
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1043Food and Bioprocess Technology (2022) 15:1040–1054
1 3
In the literature, several examples of the application of the
Weak Gel model on doughs can be found (Angioloni &
Collar, 2013; Baldino etal., 2014; Lucas etal., 2019; Meeus
etal., 2020). Each test was repeated three times for each
sample, and the mean value was taken into account for the
study.
Statistical Analysis
The spectral data were related to the rheological parameters
of the samples by means of a PLS model (Geladi & Kowalski,
1986), which links the dependence of the multivariate data
matrix X (for the case at hand, the IR spectra) to the rheologi-
cal properties measured on the same samples. The PLS model
is reported in Eq. (2), where the reference values y are the
parameters AF and z estimated through the Weak Gel model.
where E (n × p) and f (n × 1) are error matrices containing
the part of X (n × p) and y (n × 1), respectively, which the
model does not explain, n and p are the number of samples
(rows) and variables (columns), respectively. In the present
study, n = 36, and p = 74. The vector ti is the i-th column
vector that composes the score matrix T (n × m), pi and qi
are the loadings that compose the loading matrices P (p × m)
and q (1 × m), where m is the number of latent variables
chosen to explain the significative variance of the data. The
matrix W (p × m) is the weight matrix obtained by the PLS
regression.
After PLS regression had been accomplished, the interest
was focused on discriminating wavelengths in correspond-
ence of which the absorption signal is the most influential on
the changes in y (i.e., the rheological parameters) from the
ones having no discrimination power. The relative impor-
tance of wavelengths in the model could be described by the
VIP scores. For the j-th variable, the VIP scores in a PLS
model with m principal components can be calculated as:
where
ta
is the a-th column vector of the score matrix T,
qa
is the a-th element of the regression coefficient vector q
of T,
wa
is the a-th column vector of the weighting matrix
W (Mehmood etal., 2012). Wavelengths at which the VIP
scores were above a threshold value of 1.0 were considered
significant (Eriksson etal., 2006).
(2)
X
=TP
T
+E=
m
i=1ti𝐩T
i+
E
y=TqT+f=m
i=1ti𝐪T
i+f
T=XW
PTW
−1
(3)
VIP
j=
pm
a=1
q2
atT
ata
wja
wa
2
m
a=
1q2
a
tT
a
ta
Results
FTIR Spectra, Peak Assignments, andComparison
In Fig.1, the FTIR spectrum of the CAP50 sample, con-
sidered the reference for the whole series of IR spectra, is
reported. Six main peaks were detected.
Going from 4000 to 600 cm−1, the first high-intensity
peak was identified as the stretching of the OH in polymers,
OH-H hydrogen bond, and free OH− (Socrates, 2001). The
center of this peak is located in the range 3250–3400 cm−1,
varying from one sample to the other. Unfortunately, it is not
possible to distinguish which part of OH is hydrogen-bonded
or not. This region is followed towards lower wavenumbers
by a very small peak, typical of the C-H stretching (CH)
phenomenon of CH and CH2 groups (Alvarez & Vázquez,
2006); this band, approximately situated between 2800 and
3000 cm−1, suggests the presence of aliphatic groups like
methyl or methylene, and in general hydrophobic groups.
Thus, it can be linked to the presence of unsaturated lipids
and the minor contribution of small carbohydrates. This
reading may explain its limited intensity since it combines
the concentration and the specific dipole moment of the
bond under investigation. Furthermore, going to lower
wavenumbers, the next identified band is in the region
1600–1710 cm−1, usually associated with C = O stretch-
ing of the amide group (Fevzioglu etal., 2020) and called
“Amide I” ( A1) in combination with the OH bending of
water (1640 cm−1) (Kong & Yu, 2007). This area contains
several peaks, and, as already stated, it is frequently sub-
jected to deconvolution because this procedure is able to
Fig. 1 FTIR absorbance spectrum of CAP50 sample, normalized with
respect to CO/CC peak intensity
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1044 Food and Bioprocess Technology (2022) 15:1040–1054
1 3
give information on the amount of the different protein struc-
tures. In all the samples here investigated, the peak centered
at around 1635 cm−1, and linked to the -OH in-plane bend-
ing of water was visible (Wang etal., 2001). Another peak
region linked to proteins was identified at 1400–1550 cm−1.
This is the typical localization of the so-called “Amide II”
(A2) band associated with the N–H bending and to gluten
protein C-N and C–C stretching (van Velzen etal., 2003).
Also, the “Amide III” band (A3), at 1200–1340 cm−1, which
mainly arises from N–H bending and C-N stretching vibra-
tions (Fevzioglu etal., 2020), was identified. Although its
intensity is limited, a more detailed analysis of this band can
be informative about the protein structure characterization,
as already discussed in the introduction section. Another
spectral region (CO/CC), located at 900–1200 cm−1, is asso-
ciable with the coupled C-O and C–C stretching vibrations
of polysaccharide molecules (Sivam etal., 2012), mainly
starch in this case. In particular, two interesting peaks are
distinguishable: the first one, at about 1020 cm−1, is related
to amorphous regions of starch, and the second one, at
about 1080 cm−1, is linked to crystalline starch, according
to Almeida and Chang (Almeida & Chang, 2013). Finally,
the last peak, at around 700 cm−1 (COH), was assigned to
the out-of-plane bending of the hydroxyl groups, sometimes
linked to free water molecules (Célino etal., 2014). A sum-
mary of identified peaks, localization and assignment is
reported in Table4.
Figure2 addresses the comparison among the FTIR spec-
tra of samples achieved from the same semolina but with
different water amounts. Figure2a shows the FTIR spectra
measured from the samples with Karalis (KAR), Fig.2b
the samples with Cappelli (CAP), and Fig.2c the samples
obtained with the commercial flour (COM). All of them
were obtained at different water amounts. In Table5, it is
possible to observe the peak intensities and integral values
for all the samples. As a general statement, it can be seen
that water affects mainly the peak height and area, which
increase with water content. The peak position is affected
to a lesser extent.
Concerning the water content impact, the most influ-
enced peaks are OH, COH, the peak in the Amide I band
at about 1640 cm−1, which is linked to OH bending, and, to
a lesser extent, the peak at about 1550 cm−1 in the Amide
II band. KAR and COM samples present the most signifi-
cant changes when the water quantity is increased from 40
to 50%: OH peak intensity undergoes an increase of about
70% and 25%, respectively, while COH intensity of about
50% and 15%, respectively. On the contrary, CAP samples
peak heights show changes of 10–15% maximum when the
water quantity increases from 40 to 50%, and the most sig-
nificant variations occur when water goes from 50 to 60%,
with an increase of 40–45% for OH and COH peaks. KAR
samples, on the contrary, show very low changes (less than
5%) in the peak intensity when water goes from 50 to 60%.
Figure3 compares three samples prepared with the same
water amount (50%) but different semolina varieties. This
figure shows a similar spectrum for KAR50 and COM50
samples, especially in the water-linked peaks, while CAP50
presents quite different intensities, particularly for OH, CH,
A2, and COH peaks. In more detail, CAP50 sample shows
significantly lower intensities in the OH and COH peaks and
slightly higher ones regarding CH and A2 peaks.
Amide III Band Analysis
Amide III band analysis was performed to identify the pro-
tein structure bands by means of the second derivative of
the spectra. The analysis of the spectra second derivative
is shown in Fig.4 for one of the CAP50 samples. However,
comparable results in terms of peak positions were obtained
also for the other samples. The negative peaks of the second
derivative were associated with hidden peaks in the band,
representative of the protein structures conformations. From
Fig.4, it can be seen that seven peaks are detectable. The
peaks at 1242, 1263, and 1284 cm−1 can be assigned to
β-sheet (β-S), random coil (RC), and β-turn (β-T) structures
respectively, while peaks at 1302, 1315, and 1335 cm−1 can
be associated with α-helix (α-H) structure, according to sev-
eral previous works (Cai & Singh, 1999; Nawrocka etal.,
2017, 2018b; Wang etal., 2015). The peak at 1302 cm−1 is
not always clearly visible, as it presents very small values
in the second derivative. The peak at 1207 cm−1 is often
assigned to tyrosine (Ngarize etal., 2005) or the associ-
ated conformation of phenylalanine (phe) and tyrosine (tyr)
(Frushour & Koenig, 1975; Xie etal., 2004). Phenylalanine
is one of the most abundant aminoacids in ω-gliadins frac-
tion (Seilmeier etal., 2001). Tyrosine is mainly contained in
high molecular weight glutenins (Peña etal., 2006). It might
be involved in forming covalent interactions like tyr-tyr
crosslinks between gluten polypeptide chains, influencing
the gluten network structure (Kłosok etal., 2021). Moreo-
ver, tyrosine residues are able to create hydrogen bonds that
Table 4 Summary of the detected peaks, band wavenumbers, and
assignments
Peak name Band
(cm−1)Corresponding phenomena
OH 3850–2980 -OH stretching vibration
CH 2980–2865 C-H stretching vibration of –CH and -CH2
A11800–1575 C = O stretching of the gluten protein and
-OH in plain bending
A21575–1485 N–H bending and C-N, C–C stretching
A31340–1200 N–H bending and C-N stretching
CO/CC 1200–905 C-O and C–C stretching vibrations
COH 905–615 C–OH out of plane bending
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1045Food and Bioprocess Technology (2022) 15:1040–1054
1 3
improve the stability of the dough during the kneading pro-
cess (Nawrocka etal., 2016). The involvement of tyrosine
residues in hydrogen bond formation possibly accounts for
intermolecular hydrogen bond formation between β-sheets
acting as junction zones in stabilizing the gel network
(Wang & Damodaran, 1991). This can explain the position
of this peak inside the β-sheet region, confirming that the
1207 cm−1 peak can be related to aminoacid, tyrosine in
particular, capacity to create intermolecular bonds between
protein chains.
Concerning the analysis of the protein structures distribu-
tion, results of integration, in terms of percentual contribu-
tion of each structure and total peak area of Amide III band
are reported for all samples in Fig.5. As a general statement,
it can be seen that water influence reflects a slight increase of
β-sheets and a slight decrease of α-helices in CAP and KAR
samples. However, comparing the absolute values among the
samples prepared with different durum wheat varieties, KAR
has a significantly higher amount of α-helices compared to
the other varieties. Moreover, regarding β-sheets and random
structures, KAR is the variety with the lowest percentage
amount of these conformations. On the other hand, CAP and
COM varieties have similar percentage amounts of the four
structures, with no significant differences, especially when
the added water amount is 40–50%. Instead, the β-turn frac-
tion does not show substantial differences in the comparison
among the three varieties.
Rheological Measurements
The rheological measurement results and the Weak Gel
model function representation are reported in Figs.6, 7, and
8. In order to show the typical response to sinusoidal defor-
mation of dough, storage, and loss modulus as a function
of frequency are reported in Fig.6 together with tanδ data
for the CAP50 sample chosen as representative of the entire
Fig. 2 FTIR absorbance spectra of CAP (a), KAR (b), and COM (c) samples with different water amounts, 40% (blue), 50% (black), and 60%
(red). All spectra were normalized with respect to CO/CC peak intensity
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1046 Food and Bioprocess Technology (2022) 15:1040–1054
1 3
data set. The results do not qualitatively change for the other
samples. It is possible to notice that the trends of the modules
are almost linear in the logarithmic scale. Concerning tanδ, a
slight increase with frequency can be appreciated. The tanδ
parameter is smaller than 1 in the frequency range investi-
gated, implying a rheological response mainly dominated by
the elastic contribution. Furthermore, the two modules vary
similarly following two almost parallel linear trends, justify-
ing the use of the Weak Gel model (Gabriele etal., 2001).
Figure7 shows |G*| as a function of frequency for
each variety and at different water amounts. KAR, CAP,
and COM samples are reported in Fig.7a–c, respectively.
Figure8, instead, shows the semolina variety influence on
|G*|. Table6, in the Appendix, reports the values of the
Weak Gel model parameters, their confidence interval, and
the adjusted R2 value for the regression. In Fig.9, instead,
these values were reported as means with confidence inter-
vals. From Fig.9, one should appreciate that rheological
Table 5 Center (cm−1) and height of peaks and band area for spectra normalized with respect to CO/CC peak intensity (means ± std)
OH CH A1
Center
(cm−1)Height Area
(cm−1)Center
(cm−1)Height Area
(cm−1)Center
(cm−1)Height Area
(cm−1)
CAP40 3284.7
± 0.0
0.989
± 0.110
431.0
± 46.1
2930.4
± 1.1
0.266
± 0.011
26.3
± 1.0
1641.4
± 0.0
0.801
± 0.073
78.5
± 6.7
CAP50 3282.1
± 1.113
1.071
± 0.034
464.5
± 12.4
2925.3
± 1.1
0.304
± 0.019
28.6
± 1.1
1641.4
± 0.0
0.908
± 0.061
88.6
± 5.6
CAP60 3280.8
± 0.0
1.558
± 0.084
661.5
± 37.3
2939.4
± 6.7
0.259
± 0.010
25.8
± 1.1
1640.7
± 1.1
1.214
± 0.084
114.7
± 6.8
KAR40 3282.1
± 4.0
0.835
± 0.04
378.6
± 10.0
2928.5
± 2.2
0.237
± 0.020
23.3
± 1.5
1641.4
± 0.0
0.549
± 0.035
55.2
± 6.2
KAR50 3283.4
± 2.2
1.417
± 0.022
611.2
± 7.2
2935.5
± 0.0
0.240
± 0.012
24.2
± 1.3
1640.1
± 1.1
0.821
± 0.021
82.5
± 1.6
KAR60 3282.7
± 0.0
1.483
± 0.065
632.0
± 26.7
2947.8
± 1.1
0.230
± 0.006
23.1
± 0.6
1639.4
± 0.0
0.862
± 0.050
85.8
± 5.6
COM40 3281.4
± 2.9
1.169
± 0.125
511.8
± 62.0
2932.3
± 1.1
0.242
± 0.012
24.2
± 1.4
1640.1
± 1.1
0.886
± 0.096
88.6
± 8.0
COM50 3307.8
± 7.2
1.458
± 0.070
627.8
± 28.2
2929.8
± 1.9
0.276
± 0.013
23.5
± 0.9
1640.1
± 1.1
0.967
± 0.055
93.8
± 5.3
COM60 3282.7
± 3.3
1.582
± 0.118
689.4
± 50.2
2947.1
± 0.0
0.237
± 0.010
27.0
± 1.1
1637.5
± 0.0
0.996
± 0.072
100.5
± 7.2
A2A3COH
Center
(cm−1)Height Area
(cm−1)Center
(cm−1)Height Area
(cm−1)Center
(cm−1)Height Area
(cm−1)
CAP40 1546.9
± 0.0
0.446
± 0.030
31.1
± 2.1
1334.7
± 0.0
0.313
± 0.013
40.1
± 2.0
661.6
± 0.0
1.261
± 0.103
257.2
± 20.1
CAP50 1546.9
± 0.0
0.519
± 0.039
35.7
± 2.4
1334.7
± 0.0
0.340
± 0.0144
44.4
± 2.3
661.6
± 0.0
1.399
± 0.004
285.3
± 2.3
CAP60 1547.5
± 1.1
0.611
± 0.066
41.7
± 3.8
1336.6
± 0.0
0.360
± 0.020
47.1
± 2.6
661.6
± 0.0
1.980
± 0.080
390.6
± 16.3
KAR40 1548.8
± 0.0
0.263
± 0.018
17.9
± 3.1
1334.0
± 1.1
0.267
± 0.010
32.5
± 1.7
661.6
± 0.0
1.161
± 0.068
240.3
± 12.6
KAR50 1550.1
± 1.1
0.355
± 0.026
26.0
± 1.4
1334.7
± 1.1
0.312
± 0.009
39.0
± 1.4
659.0
± 1.1
1.747
± 0.052
353.5
± 9.6
KAR60 1550.7
± 0.0
0.364
± 0.026
27.4
± 1.8
1335.3
± 1.1
0.308
± 0.010
38.5
± 1.6
660.9
± 1.1
1.813
± 0.079
362.9
± 14.8
COM40 1548.1
± 1.1
0.466
± 0.061
32.9
± 3.4
1336.6
± 0.0
0.326
± 0.017
42.0
± 2.4
660.3
± 2.2
1.511
± 0.113
307.7
± 25.6
COM50 1548.8
± 0.0
0.451
± 0.038
33.4
± 2.3
1335.3
± 2.2
0.333
± 0.009
43.3
± 1.5
661.6
± 1.9
1.710
± 0.026
345.6
± 5.7
COM60 1550.1
± 1.1
0.448
± 0.032
32.2
± 3.1
1336.6
± 0.0
0.343
± 0.015
44.3
± 2.3
661.6
± 0.0
2.010
± 0.163
405.7
± 31.2
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1047Food and Bioprocess Technology (2022) 15:1040–1054
1 3
properties of CAP samples show lower differences when
water amount changes, whereas COM samples are the most
sensitive.
In more detail, when water goes from 40 to 50%, all
samples registered a decrease in the network strength of
about 25–30%, but when water is increased from 50 to 60%,
KAR and COM samples show a decrease of AF parameter
of about 55–60%. In comparison, CAP samples decrease
their network strength by about 35%, showing an almost
linear dependence. Moreover, the influence of water on the
extension of the network, observable in Fig.9, is clearly vis-
ible only for KAR samples. Regarding Fig.8, it is possible
to notice that the CAP50 sample has the highest G' and G"
values, so the Cappelli semolina variety is the strongest one,
followed by KAR. At the same time, COM turns out to be
the weakest, in rheological terms, with the lowest strength
of the network (Table6). Concerning the network extension,
explained by the parameter z, as reported in Fig.9, KAR has
a considerably higher network extension than the other two
semolina doughs.
Fig. 3 FTIR absorbance spectra, comparison among CAP50 (black),
KAR50 (blue), and COM50 (red). All spectra were normalized with
respect to CO/CC peak intensity
Fig. 4 Amide III band second derivative for the CAP50 sample
Fig. 5 Secondary structures
percentage contribution
(means ± STD) in Amide III
Region for CAP (black), KAR
(blue), and COM (red) samples
as a function of water content
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1048 Food and Bioprocess Technology (2022) 15:1040–1054
1 3
Relationship Between Rheological Measurements
andAmide III Band
A PLS regression model relating the rheological AF and z
parameters with the spectra of the A3 band was developed
to assess a possible correlation between rheological proper-
ties and changes in the Amide III band composition. The
number of latent variables chosen for the models was 5. The
regression results for AF are reported in Fig.10, where the
parameter values predicted by the PLS regression model
are shown as a function of the Weak Gel model regression
ones. A good correlation between the AF parameter, pre-
dicted by the PLS model, and the experimental rheological
data was found, as confirmed by the high value of R2 for the
regression, equal to 0.832. On the other hand, the PLS tech-
nique failed to establish a solid correlation between z and
the Amide III spectra, as seen from the low value of R2 for
the regression (0.550). Therefore, it is likely to assume that
Fig. 6 Storage and loss modulus (red squares and blue triangles
respectively), and tan(δ) values measured for CAP50 sample, reported
as a function of frequency
Fig. 7 Comparison among |G*| of samples with an amount of water of 40% (blue), 50% (black), and 60% (red) for each variety investigated,
CAP (a), KAR (b), COM (c). The black lines represent the Weak Gel model fitting curve
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1049Food and Bioprocess Technology (2022) 15:1040–1054
1 3
the extension of the network, which is strictly related to the
z-value, is not clearly influenced by the protein structure's
conformation and its quantities, at least for the experimental
conditions here investigated.
Since the PLS regression revealed to be effective in AF
prediction, VIP scores calculation was used to establish the
secondary structures in the Amide III that are more relevant
in defining the rheological properties. Wavenumbers show-
ing VIP scores higher than 1 were considered more signifi-
cant in the definition of AF parameter in this case.
VIP scores as a function of the wavenumbers are reported
in Fig.11a and compared to the second derivative (Fig.11b)
to analyze which intervals of the Amide III band are the
most important. The local highest VIP score positions are
pointed with an arrow in Fig.11a. As previously asserted
(Cai & Singh, 1999), the amide III band can be divided
into four spectral regions, corresponding to 1200–1250,
1250–1270, 1270–1295, and 1295–1330 cm−1, assigned to
β-sheets, random coils, β-turns, and α-helices, respectively.
In this case, it is possible to see that VIP scores are higher
than 1 in correspondence to the second derivative minima
occurring at 1207 cm−1 and 1242 cm−1 that are well within
the β-sheet interval and in correspondence to the minimum
at 1335 cm−1, belonging to the α-helix interval. Thus, one
can conclude that these latter protein configurations have
a higher importance in defining the network rheologi-
cal strength. On the other hand, random coils and β-turns
regions show lower significance in this sense. For the sake
of completeness, the calculation of VIP scores concern-
ing the z parameter has also been reported in the appendix
(Figure12). The graph, where the comparison between VIP
scores and the second derivative is reported, shows that the
most influencing areas in the case of z are comparable to
those identified for AF.
Discussion
The spectra analysis reveals that OH and COH peaks are
positively related to the water amount. Indeed, looking at
the differences in the OH peaks in the spectra of CAP50,
KAR50, and COM50, it is possible to hypothesize that
CAP50 has a lower amount of free water compared to the
other two semolina samples. This aspect is supported by
the higher amount of proteins and gluten of this variety,
which confers it the capacity to bond higher quantities of
water. Moreover, it was observed that CAP50 samples show
more pronounced peaks (in terms of height and area) in the
regions of CH and Amide II. Thus, one can hypothesize that
these spectral bands might be related to the bound water in
the network.
Since KAR has a higher G.I. value and a lower gluten
quantity, it can absorb a lower amount of water (Dhaka
& Khatkar, 2015). So, when water is added at 40%, the
dough has limited water content, and probably most of this
is bonded in the network. When the added water quantity
increases to 50% and then to 60%, the network is not able
to completely absorb such large amounts of water. Thus, a
consistent part of the latter stays in the dough as free water,
Fig. 8 |G*| comparison among CAP50 (black), KAR50 (blue), and
COM50 (red); the black lines represent the Weak Gel model fitting
curve
Fig. 9 Weak Gel model param-
eters (means and confidence
intervals) reported for CAP
(black), KAR (blue), and COM
(red) samples as a function of
water content
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1050 Food and Bioprocess Technology (2022) 15:1040–1054
1 3
significantly enhancing both OH and COH peaks. CAP, on
the contrary, has a higher gluten content and a low G.I.,
which means that it can absorb larger amounts of water in
the network and requires lower mixing times. Consequently,
the free water amount in the dough significantly accumu-
lates only starting from a content of added water of 60%, for
which OH and COH peaks are enhanced.
The differences in the water binding are also confirmed
by the changes in the network strength, more pronounced for
KAR and COM samples when water is added up to 60%. The
quantity of free water is an important parameter to predict
the final rheological properties of the dough since CAP50
with a lower amount of free water results to be the strong-
est dough with the strongest gluten network, as previously
stated. This finding originates from the composition of its
gluten network. As previously discussed, CAP has the high-
est percentage amount of β-sheets, which are considered the
main component contributing to the formation of the net-
work structure and, consequently, to the elastic properties
of gluten proteins (Li etal., 2006). COM and CAP semolina
varieties have a similar protein structure amount regarding
β-sheets and α-helices. Still, the COM one, due to the lower
amount of total gluten, shows less resistance to deforma-
tion, as can be observed by the rheological curves. KAR
dough, on the contrary, despite its low percentage content
of β-sheets and its low water absorption capacity, still pre-
sents higher consistency with respect to COM. This leads to
the hypothesis that the high amount of α-helices, which is
related to the high content of glutenins (Shewry etal., 2002),
is able to counterbalance the lower amount of β-sheets. This
assumption is supported by the results of the VIP scores
analysis.
Concerning the influence of water, it results in a slight
increase of β-sheets and a slight decrease of α-helices in
CAP and KAR samples but, since these differences are very
small, it is difficult to relate them to the changes in rheologi-
cal properties as a function of water amount. More likely, the
Fig. 10 AF data (a) and z data (b) predicted by the PLS regression reported as a function of the ones estimated by the Weak Gel model (Gabriele
etal., 2001)
Fig. 11 Comparison between VIP scores of the PLS regression (a) and
second derivative of the Amide III spectrum (b) for the AF parameter.
Figure 11a is a representative example corresponding to the sample
CAP50-3
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1051Food and Bioprocess Technology (2022) 15:1040–1054
1 3
water distribution in the dough is the dominant factor that
controls rheological response.
Regarding the chemometric technique proposed in this
work, the PLS-VIP method combined with the analysis of
the second-derivative spectra appears as a promising tool
to understand the role of the protein configuration in the
strength and extension of the gluten network, based on the
information provided by the rheological measurements.
In detail, the parameter AF of the Weak Gel model (link-
able to the strength of the network) might be estimated
from the Amide III band spectra. Moreover, from the VIP
scores analysis, it emerged that β-sheet and α-helix struc-
tures have a higher importance in defining gluten network
strength. This statement is also consistent with results
recently obtained by Wang etal. (2022) through a differ-
ent approach, which related the ratio of β-sheet to α-helix
to the dough consistency. Instead, the extension of the
network represented by the z parameter did not depend on
the changes in the protein structure contribution. The low
variability shown by the z parameter among the different
samples and the results obtained with VIP scores analysis,
which identified that the same regions are more significant
both for AF and z, suggest that more in deep analysis would
be necessary to better investigate the link between exten-
sion of the network and protein conformations.
Conclusions
In the present work, durum wheat doughs were studied by
exploiting the combined use of rheological and infrared
spectral analysis. The goal was to infer rheological infor-
mation from indirect spectral measurements by resorting
to a PLS data-driven model. For the scope, different kinds
of semolina were tested on samples with different water
amounts in order to cover a wide range of dough consistency.
Satisfactory results were found regarding the prediction of
the gluten network strength. Additionally, the methodol-
ogy was capable of giving valuable insights on the protein
conformations that mainly affect the rheological behavior.
Availability in the future of larger datasets, including also
other semolina varieties, may be helpful for a better estima-
tion of the dependence of the rheological parameters on IR
spectra. This can also allow implementing a model valida-
tion, by exploiting experimental points not used in the PLS
calibration step.
In conclusion, the method could be promising for future
development of FTIR-based prediction systems for the on-
line monitoring of rheological and structural properties of
dough in the baking industry, considering the fast response
times guaranteed by FTIR measurements.
Appendix
Acknowledgements The authors would like to acknowledge Cristopher
Klein and Carlo Botha (Karlsruhe Institute of Technology) for their
support in the experimental activities.
Table 6 Weak Gel model parameters, confidence interval (CI), and
adjusted R2 of the regression
Sample AF [Pa ∙ s1/z]AF, CI z [-] z, CI Adj. R2
KAR40 51,433.365 ± 842.468 4.746 ± 0.112 0.998
KAR50 36,935.217 ± 287.937 5.323 ± 0.100 0.996
KAR60 16,677.441 ± 1164.505 4.399 ± 0.122 0.998
CAP40 62,086.174 ± 811.298 4.632 ± 0.118 0.997
CAP50 45,475.164 ± 670.735 4.676 ± 0.158 0.997
CAP60 29,972.824 ± 482.805 4.444 ± 0.136 0.998
COM40 34,829.502 ± 490.980 4.697 ± 0.097 0.997
COM50 26,141.236 ± 605.530 4.917 ± 0.116 0.997
COM60 10,356.511 ± 182.968 4.957 ± 0.133 0.997
Fig. 12 Comparison between VIP scores of the PLS regression (a)
and second derivative of the Amide III spectrum (b) for the z parame-
ter. Figure12b is a representative example corresponding to the sam-
ple CAP50-3
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1052 Food and Bioprocess Technology (2022) 15:1040–1054
1 3
Funding Open access funding provided by Università degli Studi di
Cagliari within the CRUI-CARE Agreement. Partial financial support
was received from Italian Government (Ministero dello Sviluppo Eco-
nomico), Fondo per la Crescita Sostenibile – Sportello “Agrifood” PON
I&C 2014–2020, Prog. n. F/200133/01–03/X45.
Data Availability The datasets generated during and/or analyzed dur-
ing the current study are available from the corresponding author on
reasonable request.
Declarations
Conflict of Interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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