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A Comparative Assessment of Agronomic and Baking Qualities of Modern/Old Varieties and Landraces of Wheat Grown in Calabria (Italy)

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  • Università degli Studi "Mediterranea" di Reggio Calabria - Italia

Abstract

The cultivation of wheat has been part of the evolution of human civilisation since ancient times. Wheat breeding has modified some of its characteristics to obtain improved varieties with high production potential that better meet the demands of the bread and pasta industry. Even today, there are still old varieties, landraces, adapted to particular environments. They are still cultivated in some areas because of the interest shown by the market in typical bakery products expressing the cultural heritage of local communities. The aim of this work was to evaluate the bio-agronomic and bakery characteristics of four modern genotypes, one old cultivar and two landraces of wheat typically grown in Calabria (Southern Italy). The experiment was carried out over two years in two different locations, during which the main bio-agronomic and quality traits related to bread making aptitude were detected. A marked difference was found between the landraces and the other genotypes in both agronomic and technological characteristics. Despite the higher protein and gluten content, landraces were found to have a significantly lower gluten index.
Citation: Preiti, G.; Calvi, A.; Giuffrè,
A.M.; Badagliacca, G.; Virzì, N.;
Bacchi, M. A Comparative
Assessment of Agronomic and
Baking Qualities of Modern/Old
Varieties and Landraces of Wheat
Grown in Calabria (Italy). Foods 2022,
11, 2359. https://doi.org/10.3390/
foods11152359
Academic Editor: Laura Gazza
Received: 13 July 2022
Accepted: 2 August 2022
Published: 6 August 2022
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4.0/).
foods
Article
A Comparative Assessment of Agronomic and Baking
Qualities of Modern/Old Varieties and Landraces of Wheat
Grown in Calabria (Italy)
Giovanni Preiti 1, * , Antonio Calvi 1, Angelo Maria Giuffrè1, Giuseppe Badagliacca 1, Nino Virzì2
and Monica Bacchi 1
1Department of AGRARIA, University Mediterranea of Reggio Calabria, 89122 Reggio Calabria, Italy
2CREA–Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops,
95024 Acireale, Italy
*Correspondence: giovanni.preiti@unirc.it
Abstract:
The cultivation of wheat has been part of the evolution of human civilisation since ancient
times. Wheat breeding has modified some of its characteristics to obtain improved varieties with high
production potential that better meet the demands of the bread and pasta industry. Even today, there
are still old varieties, landraces, adapted to particular environments. They are still cultivated in some
areas because of the interest shown by the market in typical bakery products expressing the cultural
heritage of local communities. The aim of this work was to evaluate the bio-agronomic and bakery
characteristics of four modern genotypes, one old cultivar and two landraces of wheat typically
grown in Calabria (Southern Italy). The experiment was carried out over two years in two different
locations, during which the main bio-agronomic and quality traits related to bread making aptitude
were detected. A marked difference was found between the landraces and the other genotypes in
both agronomic and technological characteristics. Despite the higher protein and gluten content,
landraces were found to have a significantly lower gluten index.
Keywords: wheat; Triticum aestivum;Triticum turgidum; landraces; gluten index; baking quality
1. Introduction
Wheat represents a staple crop in temperate areas since prehistoric times, providing a
basic resource of nourishment for the human population and as cattle feed. Well-known
for its high nutritional qualities, due to its content in starch, nitrogen compounds, micronu-
trients and dietary fiber, but also for its adverse effects on sensitive individuals suffering
from celiac disease or allergies, it provides the basis for the production of diversified food-
stuffs [
1
]. Global wheat production in 2020 was estimated at roughly 761 million tons from
an area of approximately 219 million hectares [2].
The two most widely cultivated wheat species in the world are: common wheat
(
Triticum aestivum L.
) also known as “bread” or “soft”, and durum wheat (
Triticum turgidum L.
var. durum Desf.) also referred to as “pasta” or “hard” wheat [
3
]. The former accounts for
95% of global wheat production [
4
]. With a variety of products more diverse than any other
commodity crop, food made from wheat can provide up to 20% of human energy require-
ments [
5
]. Although durum wheat is also used as an ingredient in typical breads, particularly
in some areas of Southern Italy, it represents the main cereal for pasta production [
6
,
7
].
Common wheat is used in a wide variety of food and beverage applications, from the
production of bread, pasta and oriental noodles [
8
] to malting and brewing beer [
9
]. Since
its domestication, it has been subjected to a range of breeding techniques in order to im-
prove certain traits useful for human purposes [
10
]. Improvements in terms of yield and
breadmaking quality were mainly achieved through a conventional farming management
based on high agronomic inputs with increased use of material and energy resources
Foods 2022,11, 2359. https://doi.org/10.3390/foods11152359 https://www.mdpi.com/journal/foods
Foods 2022,11, 2359 2 of 16
and, nevertheless, pollution-related concerns [
11
]. Indeed, contemporary society has to
cope with a variety of issues related to environment, population growth, food security
and safety. It is well known that modern wheat varieties possess better agronomic and
baking characteristics than older cultivars as a result of genetic improvement. The latter
may have a higher mineral content, depending on the growing area, and for whose flour
consumers are more interested and accept higher prices [
12
]. Landraces were maintained
by farmers through the decades. They were the most widespread varieties in the northwest
of the Old Continent until before the Green Revolution, adapting themselves to certain
cultivation environments [
13
]. As reported in Zeven [
14
], the term landrace designates a
variety characterized by high yield stability and tolerance to biotic and abiotic stress condi-
tions, capable of giving intermediate production yields under low agronomic input. Some
outcomes of the transition from landraces to improved varieties have been a decrease in
the protein percentage of the caryopsis [
15
] along with an increased gluten content [
16
] and
genetic homogeneity among genotypes [
17
]. Significant differences are reported comparing
the agronomic characteristics between old and modern varieties, the former presenting
generally a lower yield [
18
], higher plant height and hectoliter weight [
19
] and increased
1000 kernel weight [
20
]. Variability occurs as well with regard to the quality characteristics
of flours derived from old wheat varieties. As reported in Boukid et al. [
21
], bread made
from the old wheat variety Abbondanza showed the highest specific volume while achiev-
ing good consumer acceptance. The ability of old wheat populations to give consistent
or even higher yields than commercial varieties, under low input and in marginal areas,
could be an initial response to some matters mentioned above [
22
], as a genetic resource
and for the preservation of biodiversity [
23
]. Finally, they would provide a valuable food
production resource for farmers and traders targeting niche markets [24].
The present work involved the evaluation of the agronomic traits of old and modern
wheat genotypes together with the characterization of the rheological and technological
properties of their respective flours and breads, with the aim of enhancing local genotypes
for the production of traditional baked products.
2. Materials and Methods
2.1. Site Characteristics, Experimental Design and Raw Materials
Two field experiments were conducted during the growing seasons 2014/15 and
2015/16 in two different locations in the countryside of the municipalities of Rombiolo
(38
36
0
N to 15
58
0
E; m 568 a.s.l.) and Maierato (38
42
0
N to 16
10
0
E; m 362 a.s.l.) in Vibo
Valentia province, Calabria (Italy). The first location is comprised within the perimeter of
the Monte Poro Plateau, the main cereal-growing area of the province. The location of the
sites that hosted the experiments are shown in Figure 1.
Foods 2022, 11, x FOR PEER REVIEW 3 of 18
Figure 1. Study areas in the province of Vibo Valentia.
The study areas are characterized by a typical Mediterranean climate with a rainy
season from October to March and a dry summer during which occasional thunderstorms
may occur. The localities fell within a thermo-climatic zone between 14 °C and 15 °C of
mean annual isotherms with annual precipitation of approximately 1000 mm [25]. The
soils that hosted trials were characterized by a sandy-loam texture with almost no skele-
ton, rich in organic substance, with a sub-acid reaction in Rombiolo and sub-alkaline in
Maierato, with a medium-high cation exchange capacity. The field experiment was based
on a randomized block design with three replications. Raw materials included two wheat
landraces (Rosia and Mazzancoio), one old wheat cultivar (Abbondanza) and four re-
cently developed wheat varieties (Bologna, Altamira, Solehio, PR22R58). Rosia and Maz-
zancoio are two local landraces of high size, widely cultivated in the hinterland of Vibo
Valentia, where they are appreciated for bread making purposes and valued for the large
amount of straw destined to livestock use. Abbondanza is an old variety (registered as
conservation variety) established in the 1950s, that has gradually been replaced in culti-
vation. The latter have established themselves since the 2000s, conventionally classified
on the basis of their qualitative and technological characteristics, so as to indicate their
prevalent use. An Italian method known as ISQ (Synthetic Index of Quality) is used to
classify common wheat in five quality classes with different end uses [26]. Altamira,
PR22R58 and Solehio (included in the national register of varieties in 2003, 2002 and 2008)
are catalogued as ordinary “bread making wheat” (FP), while Bologna variety (entered in
the variety register in 2002) is classified as “improver wheat” (FF) for its excellent techno-
logical properties (Table 1). As shown by the officially certified seed quantities (20162020
average), Bologna is still the most widely cultivated soft wheat cultivar in Italy [27]. All
the genotypes belong to the Triticum aestivum L. species, with the exception of Mazzan-
coio, a peculiar species of wheat (Triticum turgidum L. spp.) characterized by a floury en-
dosperm.
Figure 1. Study areas in the province of Vibo Valentia.
Foods 2022,11, 2359 3 of 16
The study areas are characterized by a typical Mediterranean climate with a rainy
season from October to March and a dry summer during which occasional thunderstorms
may occur. The localities fell within a thermo-climatic zone between 14
C and 15
C of
mean annual isotherms with annual precipitation of approximately 1000 mm [
25
]. The soils
that hosted trials were characterized by a sandy-loam texture with almost no skeleton, rich
in organic substance, with a sub-acid reaction in Rombiolo and sub-alkaline in Maierato,
with a medium-high cation exchange capacity. The field experiment was based on a
randomized block design with three replications. Raw materials included two wheat
landraces (Rosia and Mazzancoio), one old wheat cultivar (Abbondanza) and four recently
developed wheat varieties (Bologna, Altamira, Solehio, PR22R58). Rosia and Mazzancoio
are two local landraces of high size, widely cultivated in the hinterland of Vibo Valentia,
where they are appreciated for bread making purposes and valued for the large amount of
straw destined to livestock use. Abbondanza is an old variety (registered as conservation
variety) established in the 1950s, that has gradually been replaced in cultivation. The latter
have established themselves since the 2000s, conventionally classified on the basis of their
qualitative and technological characteristics, so as to indicate their prevalent use. An Italian
method known as ISQ (Synthetic Index of Quality) is used to classify common wheat in
five quality classes with different end uses [
26
]. Altamira, PR22R58 and Solehio (included
in the national register of varieties in 2003, 2002 and 2008) are catalogued as ordinary
“bread making wheat” (FP), while Bologna variety (entered in the variety register in 2002)
is classified as “improver wheat” (FF) for its excellent technological properties (Table 1). As
shown by the officially certified seed quantities (2016–2020 average), Bologna is still the
most widely cultivated soft wheat cultivar in Italy [
27
]. All the genotypes belong to the
Triticum aestivum L. species, with the exception of Mazzancoio, a peculiar species of wheat
(Triticum turgidum L. spp.) characterized by a floury endosperm.
Table 1. Characteristics of the wheat selected for the experiment.
Genotype ISQ Genealogy Maintenance Manager
Bologna (FF) (H89092 ×H89136)
×Soissons
356-ETS Claude Camille Benoist,
159-Venturoli Sementi S.R.L.
Altamira (FP) 96,248 ×Isengrain
1168-Nickerson International
Research Geie, 1242-Limagrain
Italia S.P.A
Solehio (FP) Isengrain ×Ornicar 441-Kws Momont SAS
PR22R58 (FP) (Victo ×FVP0040)
×XXC31
1057-Pioneer Genetique S.A.R.L.,
681-Pioneer Hi-bred Int. Inc.,
53-Pioneer Hi-Bred Italia servizi
agronomici SRL
Abbondanza NA Autonomia x
Fontarronco
1674-Molini Cicogni SRL,
1471-Arcoiris SRL
Rosia NA NA Local farmers
Mazzancoio NA NA Local farmers
ISQ: Synthetic Index of Quality; FF (frumento di forza): improver wheat; FP (frumento panificabile): ordinary bread
making wheat; NA: not available. Maintenance manager information was obtained at [28].
Sowing was carried out in 14.4 m
2
plots (1.44
×
10 m) with 18 cm spacing between
rows, using a plot seeder (Vignoli) between the second and third decade of November.
Fertilisation was carried out with 92 kg ha
1
of P
2
O
5
and 36 kg ha
1
of N at the same
time as sowing, and 50 units ha
1
of N in urea form were applied during coverage (stages
25–29 [
29
]). A mixture of pinoxaden, clopyralid, florasulam (Axial 60) and fluroxypyr
meptil (Columbus) was applied to control weed growth. Grains were harvested at maturity
(stages 93–97) with a “Wintersteiger” plot combine. The main bio-agronomic traits of the
tested genotypes and quality traits on harvested grains and their respective flours and
breads were evaluated.
Foods 2022,11, 2359 4 of 16
2.2. Field Measurements
A number of bio-agronomic parameters were measured during the growing season:
the plant heading period (HP), expressed in number of days from the first of April, and the
plant height (PH) (stages 83–87) were measured close to harvest.
2.3. Quality Assessment and Grain Milling
After harvest, grains for each plot were weighted, and the yield (GY), reported
in
t ha1
, was determined and converted into a standard moisture of 13%. Hectolitre weight
(HW) and hardness (H) were determined for each sample using an Infratec
1241 Grain
Analyzer (FOSS Analytical A/S, Hillerød, Denmark) following the manufacturer’s guide-
lines. Thousand kernel weight (TKW) was determined with a seed counter (Sadkiewicz
Instruments, Polonia) by counting the number of seeds contained in 25 g sample and
relating to 1000 seeds by means of the proportion. The grain nitrogen content (Kjeldahl
method) as well as the protein content (GP) were determined using the conversion factor
of 6.25 [30].
2.4. Flour Quality Evaluation
A 10 kg grain sample for each genotype, year and location (pooled sample of the
3 replications) was conditioned for 24 h at 20
C, until a 14% moisture content of the grains
was reached, using an experimental Bona cylinder mill (Bona Labormill 4RB, Monza, Italy)
and separating fine particles (<250
µ
m), with an extraction rate of about 55–60%. This
percentage was considered optimal to perform the quality analyses, due to a larger grain
particle size and a lower degree of damaged starch. Quality analyses of the grain, dough
and bread were performed in duplicate.
The protein content of the flour (FP) was determined using an Infratec 1241 Grain
Analyzer. Wet (WGC), and dry gluten (DGC) and gluten index (GI) were determined using
a Glutomatic System (Glutomatic 2200, Centrifuge 2015, Glutork 2020; Perten Instruments
AB, Huddinge, Sweden) according to the AACC method 38–11 [
31
]. The color parameter b*,
referred as yellow index (YI), was determined by Chroma meter CR-300 (Minolta, Osaka,
Japan), under the illuminant, D65.
2.5. Dough Rheological Measurements
Alveograph parameters, i.e., deformation energy W (DEW) and alveograph ratio P/L
(ARPL), were determined using an alveograph equipped with the software Alveolink NG
(Chopin MA 87 model, Tripette and Renaud, Villeneuve-la-Garenne, France), according
to the AACC method 54–30A [
31
]. Water absorption of the flour (WAC) at a maximum
consistency of 500 FU (Farinograph Units), as well as the development time of the dough
(FDDT) and dough stability (FS), degree of softening (FSD) and farinographic quality
number (FQN), were determined using a Farinograph-E (Brabender, Germany) according
to AACC 54–21 [30,31].
2.6. Bread-Making and Bread Quality Evaluation
The baking procedure was carried out according to the AACC method 10.10 [
31
].
In the experimental baking laboratory (temperature, 25
±
2
C), each loaf of bread was
obtained adding 100 g of flour (14% moisture basis), compressed yeast (3%), sugar (6%),
NaCl (2%), ascorbic acid (80 p.p.m) and shortening (3%) to distilled water. The amount
of water required has been calculated as the difference between the water absorption,
determined by farinographic analysis, and the water added with the solutions. The doughs
were leavened at 29
±
1.41
C and at 82.5%
±
3.54% relative humidity in a thermostatic
proofing cabinet equipped with a steam humidifier for 1.75 h. The dough was sheeted in a
roller, then it was proofed for another 50 min, and a second sheeting roll was performed.
The doughs were sheeted through the sheeting rolls, and after an additional 25 min, they
were manually rounded and placed into individual metal bread moulds. The doughs were
then proofed for another 50 min, for a total fermentation time of 3.83 h. They were then
Foods 2022,11, 2359 5 of 16
baked in a humidified, ventilated and thermostated electric oven for 18 min at 215–220
C.
Loaf bread volume (LBV) was determined in a loaf volume metre, according to the AACC
method 10.05.01 [
31
]. Bread height (BH) was measured by using a digital calliper (Digi-
MaxTM, Scienceware, N.J., USA). Bread weight (BW) was also assessed. The CIELAB space
L*,a*,b* colour parameters were measured for bread crumb and crust. The corresponding
results are reported in the Supplementary Materials, (Tables S1 and S2). Crumb porosity
(PO) was expressed based on the Mohs scale, ranging from 1 to 8, for higher and lower
porosity, respectively. Crust texture (CT) was indicated on a scale varying from 1 to 4
(smooth to rough, respectively).
2.7. Statistical Analysis
Data analysis was realised using R programming language and environment [
32
],
software version 4.1.0 (18 May 2021). The full list of R packages utilised [
32
41
] is available
in the Supplementary Material section [Table S3]. The Bartlett test indicated homogeneity
of variance between years, and therefore, the ‘year’ factor has not been considered as a
source of variation; consequently, a two-way analysis of variance with interaction (location
and genotype) was performed. The statistical significance of the effects was analysed using
F-tests, whereas the differences between means were tested using the Tukey’s HSD Test
at a p
0.05 significance. In order to investigate the relationship between each pair of
variables, correlation coefficients were estimated based on Pearson’s correlation method.
Holm’s method was used for p-values adjustment for multiple comparisons. R package
ggstatsplot [
35
] was used to compute the correlation between variables and to graphically
display the correlation matrix. Principal component analysis was computed to reduce the
dimensionality of the dataset so as to make it more interpretable. Variables were scaled
before computing PCA to allow comparison between variables expressed by different units
of measure. The crumb and crust data were not considered to perform well in estimating
the source of variability and were not included in the PCA analysis. The first three principal
components explained a variance of 83.3% (Table S4). Only the first two components were
used in this study to ensure a more effective representation and interpretation of the data.
R package FactoMineR [
36
] was used to compute PCA and the package factoextra [
37
] to
extract and visualize graphically PCA results.
3. Results
3.1. Bio-Agronomic and Grain Quality Traits
Modern varieties were characterized by high yields, ranging from 4.98 to 5.82 t ha
1
,
while the other genotypes presented lower values (<4 t ha
1
), with Rosia resulting the least
productive (2.93 t ha
1
). The genotype–environment interaction term resulted significant
(data not shown). In this regard, it is interesting to note that two modern varieties (Altamira
and Bologna) showed significantly different GY between the two locations. On the contrary,
GY remained almost identical for the old variety and the landraces between the two
experimental areas (Table 2).
A considerable difference was found for the plant height between the different geno-
types, as expected. The modern varieties presented shorter culms, ranging from 70 to 81 cm
for PR22R58 and Altamira, respectively, while Abbondanza showed an intermediate PH
(103 cm). Landraces were the tallest, with a maximum PH of 180 cm for Mazzancoio. The
heading period varied from 32 days for Altamira to 49 days for Rosia. Modern genotypes
therefore presented shorter HD, while no statistically significant difference has been found
for Abbondanza and Mazzancoio. Only the genotype exerted a significant effect on the HP.
All the genotypes showed ideal values for the hectolitre weight (
76 kg hL
1
) with the
exception of Abbondanza. In particular, Mazzancoio presented the highest HW among all
genotypes under study (Table 2). Thousand kernel weight was influenced by both genotype
and location, as well as their interaction (data not shown). A clear discrepancy was found
between Abbondanza and Mazzancoio (32.8 and 61.7 g, respectively). TKW was found to
vary between modern genotypes, with a maximum value of 47.1 g for Altamira. Landraces
Foods 2022,11, 2359 6 of 16
were characterized by higher hardness values (65% for Rosia and 75% for Mazzancoio),
while variability was detected among modern varieties, with values ranging from 17 to 61%
for Solehio and Bologna, respectively. Grain protein content ranged from 10.4 for Solehio
to 15.1% d.m. for Rosia. GP was significantly affected by both genotype and location.
Landraces were characterized by the highest GP (Table 2), while PR22R58 and Solehio
values were below 11% (d.m.).
Table 2. Bio-agronomic and grain quality traits.
GY PH HP HW TKW H GP
Genotype *** *** *** *** *** *** ***
Altamira 5.82 ±0.62 a81 ±4.26 d32 ±2.35 e77.6 ±2.25 c47.1 ±1.70 b37 ±15.94 c11.6 ±1.05 d
Bologna 5.65 ±0.72 ab 78 ±3.41 d35 ±1.70 cd 80.2 ±1.46 b34.3 ±1.96 d61 ±9.49 b12.7 ±0.81 bc
Solehio 5.17 ±0.51 bc 77 ±2.47 d36 ±2.01 c76.7 ±2.28 cd 44.4 ±3.80 c17 ±4.23 d10.4 ±0.43 e
PR22R58 4.98 ±0.45 c70 ±2.78 d34 ±1.90 de 76.6 ±2.07 e42.8 ±2.07 c34 ±9.75 c10.5 ±1.03 e
Abbondanza 3.51 ±0.33 d103 ±4.61 c41 ±1.41 b74.8 ±0.74 d32.8 ±0.69 d41 ±5.51 c12.1 ±0.76 cd
Mazzancoio 3.57 ±0.16 d180 ±5.94 a41 ±1.51 b84.5 ±2.88 a61.7 ±2.19 a75 ±1.38 a13.2 ±0.12 b
Rosia 2.93 ±0.16 e160 ±6.50 b49 ±1.61 a77.9 ±1.13 bc 48.1 ±2.72 b65 ±11.52 ab 15.1 ±1.52 a
Location ** ** ns ** * ** ***
Rombiolo 4.40 ±0.97 b106 ±42.30 b38 ±5.16 a78.2 ±3.29 a45.0 ±8.85 a50 ±20.10 a12.6 ±1.70 a
Maierato 4.64 ±1.34 a108 ±41.95 a38 ±6.06 a78.5 ±3.74 a43.9 ±9.76 b44 ±22.33 b11.8 ±1.76 b
Variety
Modern 5.41 ±0.66 76 ±5.22 34 ±2.40 77.8 ±2.47 42.1 ±5.43 37 ±19.21 11.3 ±1.28
Old 3.51 ±0.33 103 ±4.61 41 ±1.41 74.8 ±0.74 32.8 ±0.69 41 ±5.51 12.1 ±0.76
Landrace 3.25 ±0.36 170 ±12.08 45 ±4.01 81.2 ±3.99 54.9 ±7.38 70 ±9.44 14.1 ±1.44
GY, grain yield (t ha
1
); PH, plant height (cm); HD, heading period (d); HW, hectolitre weight (kg hL
1
); TKW,
thousand kernel weight (g); H, hardness (%); GP, grain protein (% d.m.). Mean values
±
standard deviation are
reported for each variable. Different letters in the same column indicate significant differences according to Tukey
HSD test at p
0.05, while ***, **, *, ns, indicate the following levels of significance for each ANOVA model: 0.001,
0.01, 0.5, not significant. Only the mean values
±
standard deviation are shown for the variety component for
descriptive purposes. Sources of variability are indicated in bold in the first column of the table.
3.2. Flour and Dough Quality Evaluation
As well as for the GP, Rosia presented the highest flour protein content (14.5% d.m.),
while Solehio showed the lowest one (9.0% d.m.). Location and genotype, as well as
their interaction (data not shown), exerted a strong influence on this quality trait. The
yellow index ranged from 5.36 for Abbondanza to 11.89 b* for Mazzancoio. Landraces
were characterized by the highest values for the wet and dry gluten content but lower
gluten indexes (34 and 8 for Rosia and Mazzancoio, respectively, as reported in Table 3).
On the contrary, the other genotypes presented an inverse trend, with WGC ranging from
12.0 to 22.5%, DGC between 3.9 and 8.2% and higher GI. In particular, modern genotypes
presented GI values close to 100%.
Landraces were characterized by lower values of DEW when compared with the old
and modern genotypes. No significant difference was recorded for Abbondanza, PR22R58
and Altamira. The highest value, as expected, was recorded for Bologna (Table 4).
The modern varieties were characterized by similar ARPL, ranging from 1.13 for
PR22R58 to 1.46 for Bologna. Mazzancoio showed a significantly different result (2.30)
compared to all the other genotypes. On the contrary, Abbondanza and Rosia did not signif-
icantly differentiate, presenting the lowest ARPL (Table 4). The farinograph development
time ranged from 1.53 to 2.35 min for PR22R58 and Rosia, respectively. As well as for the
ARPL, Rosia and Abbondanza presented similar outcomes (2.24 min on average). FDDT
of less than 2 min were recorded for all other varieties. The farinograph stability ranged
Foods 2022,11, 2359 7 of 16
from 0.54 min for Mazzancoio to 14.80 min for Bologna, a distinctive high value associated
with a strong dough. All the other varieties presented no significant difference, with the
exception of Abbondanza (Table 4). The farinograph softening degree varied in the range
54–177 BU for Bologna and Mazzancoio, respectively. Overall, landraces showed higher
FSD (Table 4). Moreover, there was a significant difference among the modern varieties,
with Solehio, Altamira and PR22R58 forming a different group from Bologna. The old
variety presented a low FSD as well (Table 4). The farinograph water absorption ranged
from 48.4 for Abbondanza to 66.5% for Rosia. Modern genotypes were characterized by
intermediate WAC values, in the range 50.3–55.6% for Solehio and Bologna, respectively.
The farinograph quality number ranged from 20.7 for Mazzancoio to 47.8 for Bologna.
Location, variety and their interaction (data not shown) exerted a significant effect on
FQN, overall.
Table 3. Flour quality characteristics.
FP YI WGC DGC GI
Genotype *** *** *** *** ***
Altamira 10.7 ±1.08 cd 7.77 ±0.77 d12.9 ±1.36 d4.1 ±0.42 d99 ±1.38 a
Bologna 12.5 ±0.83 b7.79 ±0.25 d22.5 ±0.73 b8.2 ±0.25 b99 ±1.04 a
Solehio 9.0 ±0.93 e8.85 ±0.40 c12.0 ±0.64 d4.2 ±0.46 d100 ±0.67 a
PR22R58 9.9 ±1.37 de 9.66 ±0.71 b12.6 ±1.75 d3.9 ±0.70 c99 ±1.34 a
Abbondanza 11.6 ±0.78 bc 5.36 ±0.79 e19.4 ±2.75 c6.4 ±2.07 c70 ±3.19 b
Mazzancoio 12.5 ±0.25 b
11.89
±
0.39
a34.1 ±1.94 a10.7 ±0.88 a8±0.52 d
Rosia 14.5 ±1.40 a7.54 ±0.36 d33.3 ±2.79 a11.1 ±0.55 a34 ±1.71 c
Location *** ns ** ** ns
Rombiolo 11.9 ±1.74 a8.48 ±1.94 a21.5 ±8.85 a7.2 ±3.07 a73 ±35.08 a
Maierato 11.1 ±2.16 b8.33 ±2.02 a20.4 ±9.32 b6.7 ±3.05 b73 ±35.07 a
Variety
Modern 10.5 ±1.69 8.52 ±0.97 15 ±4.55 5.1 ±1.86 99 ±1.00
Old 11.6 ±0.78 5.36 ±0.79 19.4 ±2.75 6.4 ±2.07 70 ±3.00
Landrace 13.5 ±1.45 9.71 ±2.25 33.7 ±2.38 10.9 ±0.75 21 ±13.00
FP, flour protein (% d.m.); YI, yellow index (b*); WGC, wet gluten content (%); DGC, dry gluten content (%);
GI, gluten index (%). Mean values
±
standard deviation are reported for each variable. Different letters in the
same column indicate significant differences according to Tukey HSD test at p
0.05, while ***, **, ns indicate
the following levels of significance for each ANOVA model: 0.001, 0.01, not significant. Only the mean values
±
standard deviation are shown for the variety component for descriptive purposes. Sources of variability are
indicated in bold in the first column of the table.
Table 4. Alveograph and farinograph results.
DEW ARPL FDDT FS FSD WAC FQN
Genotype *** *** *** *** *** *** ***
Altamira 120.75 ±26.67 bc 1.43 ±0.39 bc 1.83 ±0.32 bcd 1.88 ±0.69 c105 ±13 c51.7 ±3.13 c22.0 ±4.60 d
Bologna 260.80 ±57.63 a1.46 ±0.50 b1.75 ±0.49 cd 14.80 ±3.76 a54 ±13 e55.6 ±2.08 b47.8 ±9.50 a
Solehio 99.75 ±29.55 cd 1.39 ±0.17 bc 1.95 ±0.45 bc 1.83 ±0.93 c113 ±17 c50.3 ±1.55 cd 27.8 ±8.88 c
PR22R58 128.50 ±53.93 bc 1.13 ±0.32 c1.53 ±0.41 d1.65 ±0.71 c101 ±29 c51.1 ±1.69 c23.8 ±6.65 c
Abbondanza 145.50 ±6.06 b0.49 ±0.21 d2.13 ±0.14 ab 3.23 ±1.38 b85 ±9d48.4 ±1.02 d38.2 ±13.13 b
Mazzancoio 30.91 ±3.88 e2.30 ±0.11 a1.91 ±0.11 bc 0.54 ±0.05 d177 ±5a66.5 ±3.34 a20.7 ±1.59 e
Rosia 81.25 ±15.21 d0.35 ±0.06 d2.35 ±0.12 a1.83 ±0.34 c145 ±14 b56.6 ±1.91 b34.3 ±1.89 c
Foods 2022,11, 2359 8 of 16
Table 4. Cont.
DEW ARPL FDDT FS FSD WAC FQN
Location ** ** ns *** *** ns ***
Rombiolo 135.46 ±73.16 a1.14 ±0.66 b1.95 ±0.34 a2.13 ±1.16 a121 ±38 a54.3 ±5.18 a34.1 ±13.77 a
Maierato 112.38 ±73.57 b1.31 ±0.69 a1.89 ±0.46 a1.41 ±0.74 b102 ±40 b54.3 ±6.96 a27.2 ±8.45 b
Variety
Modern 152 ±77.1 1.35 ±0.38 1.76 ±0.44 5.04 ±6.02 93 ±30 52.2 ±2.98 30.3 ±12.79
Old 145 ±6.06 0.49 ±0.21 2.13 ±0.14 3.23 ±1.38 85 ±9 48.4 ±1.02 38.2 ±13.13
Landrace 56.1 ±27.9 1.32 ±1 2.13 ±0.25 1.18 ±0.7 161 ±19 61.6 ±5.73 27.5 ±7.14
DEW, deformation energy W (
×
10
4
J); ARPL, alveograph ratio (P/L); FDDT, farinograph dough development
time (min); FS, farinograph stability (min); FSD, farinograph softening degree (BU); WAC, water absorption
capacity 500 BU (%); FQN, farinograph quality number (mm). Mean values
±
standard deviation are reported
for each variable. Different letters in the same column indicate significant differences according to Tukey HSD
test at p
0.05, while ***, **, ns indicate the following levels of significance for each ANOVA model: 0.001, 0.01,
not significant. Only the mean values
±
standard deviation are shown for the variety component for descriptive
purposes. Sources of variability are indicated in bold in the first column of the table.
3.3. Bread Quality Evaluation
The bread made from Abbondanza flour resulted in being the most voluminous
(
485.0 cm3
). Solehio, PR22R58 and Mazzancoio did not exhibit any significant difference. In
particular, Solehio and Mazzanocio had the lowest LBV (Table 5). The LBV of the landrace
Rosia was comparable to that of some modern genotypes (PR22R58, Bologna and Altamira).
Table 5. Quality characteristics of bread obtained from the different genotypes.
LBV BH BW PO CT
Genotype *** *** *** *** ***
Altamira 436.3 ±53.9 b79.5 ±6.07 bc 135.9 ±4.40 c7.0 ±0.06 a1.0 ±0.00 a
Bologna 442.5 ±44.9 ab 81.7 ±5.68 b139.7 ±4.32 bc 6.0 ±0.00 b1.0 ±0.00 a
Solehio 385.0 ±22.7 c75.5 ±3.35 c136.7 ±3.26 c7.0 ±0.06 a1.0 ±0.00 a
PR22R58 406.3 ±54.1 bc 78.9 ±7.32 bc 137.0 ±2.51 c7.0 ±0.00 a1.0 ±0.00 a
Abbondanza 485.0 ±58.2 a88.3 ±4.35 a130.9 ±2.54 d6.0 ±0.05 b2.0 ±0.00 b
Mazzancoio 385.6 ±18.9 c58.1 ±2.57 d150.8 ±6.84 a6.0 ±0.05 b2.0 ±0.00 b
Rosia 440.0 ±45.7 b79.9 ±6.73 bc 143.7 ±3.49 b6.0 ±0.00 b2.0 ±0.00 b
Location *** *** *** ns ns
Rombiolo 448.7 ±58.1 a
80.0
±
10.91
a137.7 ±5.66 b6.4 ±0.50 a1.4 ±0.50 a
Maierato 402.9 ±40.3 b74.9 ±8.77 b140.8 ±8.17 a6.4 ±0.50 a1.4 ±0.50 a
Variety
Modern 418 ±50.1 78.9 ±6.02 137 ±3.87 6.8 ±0.44 1.0 ±0.00
Old 485 ±58.2 88.3 ±4.35 130.9 ±2.54 6.0 ±0.05 2.0 ±0.00
Landrace 413 ±44.1 69 ±12.21 147 ±6.42 6.0 ±0.04 2.0 ±0.00
LBV, loaf bread volume (cm
3
); BH, bread height (mm); BW, bread weight (g); PO, porosity; CT, crust texture.
Mean values
±
standard deviation are reported for each variable. Different letters in the same column indicate
significant differences according to Tukey HSD test at p
0.05, while ***, ns indicate the following levels of
significance for each ANOVA model: 0.001, not significant. Only the mean values
±
standard deviation are shown
for the variety component for descriptive purposes. Sources of variability are indicated in bold in the first column
of the table.
Bread height ranged from 58.1 for Mazzancoio to 88.3 mm for Abbondanza. No
significant difference was found in terms of BH comparing Rosia with modern genotypes.
The bread made from Mazzancoio was the heaviest (150.8 g), while the one baked with
Abbondanza flour resulted in being the lightest (130.9 g). No significant difference was
Foods 2022,11, 2359 9 of 16
reported for the modern genotypes. Rosia presented a BW not statistically different from
Bologna (Table 5). Crumb porosity resulted the same for the old genotype and the landraces
(Table 5). The modern genotypes presented a value equal to 7, with the exception of
Bologna, which presented the same value as the old and landraces genotypes. The crumb
colour characteristics are reported in Table S1. The L* coordinate ranged from 69.4 to 75.9
for Mazzancoio and Abbondanza, respectively. All the genotypes presented negative values
for the a* coordinate, except for Mazzancoio, that also presented the highest value (18.5)
for the b* coordinate. Bread crust texture was expressed with a scale ranging from 1 to 4.
The modern genotypes showed the lowest value, while the old genotype and the landraces
were characterized by a value equal to 2. The crust colour characteristics are reported in
the Table S2. L* coordinate for the crust ranged between 36.6 for Mazzancoio to 47.8 for
Abbondanza (Table S2), showing the same trend for the same qualitative trait referred to
the crumb. Mazzancoio and Rosia presented the lowest value for the a* coordinate when
compared to all the other genotypes (Table S2). The b* coordinate for the crust was higher
for the modern genotypes, while the landraces presented lower values. Abbondanza had
the greatest and significantly different outcome (Table S2).
4. Discussion
A marked difference in yield was observed in this study, in accordance with the
results reported by Frankin et al. [
42
]. It is well known that technological, biological
and environmental factors affect this important crop trait [
43
]. A historic contribution to
the increase in wheat yield can be attributed to the Italian agronomist and plant breeder
Nazareno Strampelli, whose research work during the “Battle of Wheat” (after 1925) led to
doubling of national yields over a decade [
44
]. That approach aimed at enhancing the crop
performance in high-input systems has continued over time with the gradual replacement
of old, low-yielding varieties with improved ones [
45
]. In this study, the modern genotypes
resulted in being more productive than the old one and the landraces. The genetic difference
and the pedo-climatic conditions exerted a significant influence on some modern varieties
(Altamira and Bologna). Landraces did not exhibit significant fluctuations between years
and different locations, confirming what is reported in Zeven [
14
] with reference to their
production stability. Along with increased yields, another consequence of the “Green
Revolution” was the introduction of dwarfing traits with the aim of reduce plant height and,
consequently, the risk of lodging [
46
]. As expected, indeed, modern genotypes presented
shorter culms than the old variety and the landraces. A similar trend was observed with
regard to the heading period, which was generally faster for modern genotypes. This
represents a further consequence of the breeding improvement on wheat, which allows
improved varieties to avoid dry periods in the late season when caryopsis filling occurs [
44
].
Grain quality is crucial in terms of flour yield at the milling stage. Nevertheless, it
influences the final quality of the derived bakery products. For this reason, the hectolitre
weight, thousand kernel weight, hardness and protein content were determined as well.
The hectolitre weight is defined as the ratio between the mass of a cereal sample and the
volume occupied when placed in a container under defined conditions [
47
]. Stress factors
during cereal growth and genetic differences affect this physical quality trait. Measure-
ments above 76 kg hL
1
are associated with high quality wheat for the milling industry [
48
].
Abbondanza was the only genotype showing a HW below the optimal threshold men-
tioned above, while some variability was found between modern genotypes and landraces.
Thousand kernel weight is an indicator of seed weight and size related to yield and milling
quality traits [49]. There has been a gradual increase from the old variety to the landraces,
with intermediate values for the modern genotypes, as also reported by Ruiz et al. [
50
].
Hardness is related to kernel texture, and it is used to distinguish wheat into categories
such as “hard” or “soft” [
51
]. As expected, modern genotypes presented lower H values,
which reflected their suitability to obtain flours for bread production. Taking into consider-
ation the variability of environmental and genetic factors, a difference of 1–1.5% in protein
content is common between landraces and modern varieties [
45
]. With the exception of
Foods 2022,11, 2359 10 of 16
Bologna, which presented the highest GP among the modern genotypes, a statistically
significant difference was detected between the latter and the landraces. Rosia had the
highest GP, which was found to be constant among years and locations. As confirmed by
correlation analysis and consequently depicted in the correlogram (Figure 2), GY showed
an opposite trend to that of HP (r:
0.84), PH (r:
0.73) and GP (r:
0.53), in accordance
with Iqbal et al. [52].
Foods 2022, 11, x FOR PEER REVIEW 11 of 18
Figure 2. Correlation matrix of all pairs of variables. Correlation coefficients on the principal diag-
onal are not shown. Flagged coefficients are not significant (at 5% significance level). Negative and
positive correlations are displayed in red and steelblue, respectively, while white colour represents
no association between pairs of variables. Colour intensity is proportional to the correlation coeffi-
cients values, as reported in the legend. GY, grain yield; HP, heading period; PH, plant height; HW,
hectolitre weight; TKW, thousand kernel weight; H, hardness; GP, grain protein; YI, yellow index;
FP, flour protein; WGC, wet gluten content; DGC, dry gluten content; GI, gluten index; DEW, de-
formation energy W; ARPL, alveograph ratio P/L; FDDT, farinograph dough development time; FS,
farinograph stability, FSD, farinograph softening degree; WAC, water absorption capacity; FQN,
farinograph quality number; LBV, loaf bread volume; BH, bread height; BW, bread weight.
The resulting biplot from the PCA (Figure 3) clearly shows the negative correlation
between the variables located in the opposite quadrants from the origin of the graph. This
confirms that higher GY, shorter HP and lower PH are typical attributes of modern vari-
eties.
Figure 2.
Correlation matrix of all pairs of variables. Correlation coefficients on the principal diagonal
are not shown. Flagged coefficients are not significant (at 5% significance level). Negative and
positive correlations are displayed in red and steelblue, respectively, while white colour represents no
association between pairs of variables. Colour intensity is proportional to the correlation coefficients
values, as reported in the legend. GY, grain yield; HP, heading period; PH, plant height; HW, hectolitre
weight; TKW, thousand kernel weight; H, hardness; GP, grain protein; YI, yellow index; FP, flour
protein; WGC, wet gluten content; DGC, dry gluten content; GI, gluten index; DEW, deformation
energy W; ARPL, alveograph ratio P/L; FDDT, farinograph dough development time; FS, farinograph
stability, FSD, farinograph softening degree; WAC, water absorption capacity; FQN, farinograph
quality number; LBV, loaf bread volume; BH, bread height; BW, bread weight.
The resulting biplot from the PCA (Figure 3) clearly shows the negative correlation
between the variables located in the opposite quadrants from the origin of the graph. This
confirms that higher GY, shorter HP and lower PH are typical attributes of modern varieties.
Furthermore, a negative but not statistically significant correlation was found between
GY, HW and TKW (Figure 2), in contrast to the findings of Assefa [
53
], in which a positive
correlation has been reported. The two landraces formed two distinct groups in the opposite
direction of GY, with Mazzancoio individuals (observations) located on the same side of
HW and TKW (Figure 3), thus presenting high values for these variables. As depicted in
Figure 2, H resulted strongly correlated with GP (r: 0.79), WGC (r: 0.83) and DGC (r: 0.81).
Foods 2022,11, 2359 11 of 16
Foods 2022, 11, x FOR PEER REVIEW 12 of 18
Figure 3. Biplot of principal component scores and loading vectors. Observations are shown as
points with different shapes as described by the legend. Variables are shown as vectors. The first
component (PC1) accounts for 44.8% of the variance, while the second component (PC2) for the
24.1%. Point concentration ellipses are drawn assuming a multivariate t-distribution with a confi-
dence interval of 95%. GY, grain yield; HP, heading period; PH, plant height; HW, hectolitre weight;
TKW, thousand kernel weight; H, hardness; GP, grain protein; YI, yellow index; FP, flour protein;
WGC, wet gluten content; DGC, dry gluten content; GI, gluten index; DEW, deformation energy W;
ARPL, alveograph ratio P/L; FDDT, farinograph dough development time; FS, farinograph stability,
FSD, farinograph softening degree; WAC, water absorption capacity; FQN, farinograph quality
number; LBV, loaf bread volume; BH, bread height; BW, bread weight.
Furthermore, a negative but not statistically significant correlation was found be-
tween GY, HW and TKW (Figure 2), in contrast to the findings of Assefa [53], in which a
positive correlation has been reported. The two landraces formed two distinct groups in
the opposite direction of GY, with Mazzancoio individuals (observations) located on the
same side of HW and TKW (Figure 3), thus presenting high values for these variables. As
depicted in Figure 2, H resulted strongly correlated with GP (r: 0.79), WGC (r: 0.83) and
DGC (r: 0.81).
Flours obtained by landraces were characterized by high FP (Table 3). Except for
Solehio, all genotypes exceeded the threshold of 9.0% under the relevant Italian law, DPR
09/02/2001 n. 187 [54] for ‘type 00 wheat flour. Carotenoids and anthocyanins are the main
pigments that determine the colour of flour and semolina. Their presence is strongly re-
quested in bakery products thanks to their nutritional, technological and hedonistic ben-
efits [55]. The quantity of intrinsic pigments in the flour influences the measurement of
the yellow index. Flours with low b* are suitable and preferred for white bread production
[56]. From all the genotypes under study, it was possible to produce flours with an aver-
age YI of less than 10 b*. The high and significantly different value for Mazzancoio could
be attributed to the fact that it belongs to Triticum turgidum L. spp., although its caryopsis
has a peculiar floury fracture; for such features, it was used in the past by local communi-
ties to make a traditional bread mixed with other soft wheat flours. The quantity and qual-
ity of gluten were significantly affected by genetic differences among genotypes. Indeed,
genetic improvement has also been directed towards enhancing technological character-
istics for the bakery industry. Consequently, there has been an increase in the gluten index
Figure 3.
Biplot of principal component scores and loading vectors. Observations are shown as points
with different shapes as described by the legend. Variables are shown as vectors. The first component
(PC1) accounts for 44.8% of the variance, while the second component (PC2) for the 24.1%. Point
concentration ellipses are drawn assuming a multivariate t-distribution with a confidence interval of
95%. GY, grain yield; HP, heading period; PH, plant height; HW, hectolitre weight; TKW, thousand
kernel weight; H, hardness; GP, grain protein; YI, yellow index; FP, flour protein; WGC, wet gluten
content; DGC, dry gluten content; GI, gluten index; DEW, deformation energy W; ARPL, alveograph
ratio P/L; FDDT, farinograph dough development time; FS, farinograph stability, FSD, farinograph
softening degree; WAC, water absorption capacity; FQN, farinograph quality number; LBV, loaf
bread volume; BH, bread height; BW, bread weight.
Flours obtained by landraces were characterized by high FP (Table 3). Except for
Solehio, all genotypes exceeded the threshold of 9.0% under the relevant Italian law,
DPR 09/02/2001 n. 187 [
54
] for ‘type 00’ wheat flour. Carotenoids and anthocyanins
are the main pigments that determine the colour of flour and semolina. Their presence
is strongly requested in bakery products thanks to their nutritional, technological and
hedonistic benefits [
55
]. The quantity of intrinsic pigments in the flour influences the
measurement of the yellow index. Flours with low b* are suitable and preferred for white
bread production [
56
]. From all the genotypes under study, it was possible to produce
flours with an average YI of less than 10 b*. The high and significantly different value
for Mazzancoio could be attributed to the fact that it belongs to Triticum turgidum L. spp.,
although its caryopsis has a peculiar floury fracture; for such features, it was used in
the past by local communities to make a traditional bread mixed with other soft wheat
flours. The quantity and quality of gluten were significantly affected by genetic differences
among genotypes. Indeed, genetic improvement has also been directed towards enhancing
technological characteristics for the bakery industry. Consequently, there has been an
increase in the gluten index from landraces to modern varieties [
45
]. Gluten is defined as a
visco-elastic protein mass obtained during the kneading of certain flours (made of wheat,
barley, rye, oats and their crossbred varieties) and water, which gives the flour a high
water-absorption capacity and the ability to retain the gas formed during leavening [
57
].
Gluten strength is a complex trait influenced by various quali-quantitative properties of
gluten, such as the type and ratio of glutenin and gliadin subunits. The consequences of
genetic improvement have also resulted in an increase in gluten strength from landraces
Foods 2022,11, 2359 12 of 16
to modern varieties. The former are generally characterised by GI values ranging from
6 to 32%; the latter by higher levels varying from 55 to 87% [
45
]. Indeed, a clear difference
between the genotypes was detected regarding quantity and quality of gluten (Table 3). The
debate on the forms of disease related to gluten quantity and quality is still open; however,
a protein fragment related to coeliac disease appears to be more frequent in modern than in
old wheat genotypes and landraces [58,59].
A higher gluten content was quantified in landraces, which, however, corresponded to
a medium or poor quality by reason of low GI (8–34%). On the contrary, the other genotypes
showed a completely opposite trend, with exceptionally higher GI for modern genotypes
(70–100%). Among them, Bologna presented the highest WGC with a value similar to
that reported in Bosi et al. [
60
]. WGC and DGC were highly negatively correlated with GI
(r:
0.89,
0.82, respectively). Nevertheless, WGC was positively correlated to GP (r: 0.82),
in accordance with the findings of Bonfil and Posner [
61
]. Furthermore, a negative and
significant correlation between GI and GP was detected (Figure 2), contrary to the results
reported by Bonfil and Posner [
61
], where a weak positive correlation has been reported.
GI and GP presented completely divergent orientations on the PCA biplot (Figure 3). In
addition, they contributed most to the construction of the first two PCs (
Figure S1
). Based
on specific gluten index ranges [
62
], modern genotypes were characterized by a strong
gluten (GI > 80%) of excellent quality. Rosia and Abbondanza presented a normal gluten
(30% < GI > 80%). On the other hand, Mazzancoio was characterized by a weak gluten of
poor quality (GI < 30%). If compared to GI values reported in another study on durum
wheat landraces, Mazzancoio recorded a lower result overall [
63
]. The deformation energy
W represents the energy required to inflate a dough sample until it breaks [
64
]. In this
study, DEW was influenced by both variety and location. Moving from landraces to
modern genotypes, there has been an increase in DEW, with the maximum value recorded
for Bologna. In particular, Mazzancoio was characterized by the lowest value among
genotypes, while Rosia and Solehio showed a value not significantly different. The sole
use of flour obtained from Mazzancoio grains would then not provide an ideal glutinic
network. It would therefore be preferable to use it in blends with flours with better baking
features. DEW was negatively strongly correlated with FSD (r:
0.89), while no significant
correlation was detected with traits related to GP, FP and gluten content (related vectors
on the PCA biplot form approximately an angle of 90
, Figure 3). A positive correlation
between DEW and GI (r: 0.59) was also found, in accordance with Bornhofen et al. [
64
].
The modern genotypes under study are generally characterized by high DEW, such as to
receive the denomination of improver wheat (Bologna) or ordinary bread making wheat
(Altamira, Solehio, PR22R58). In the present study, they showed lower DEW, overall. This
variability could be attributed to the particular pedoclimatic conditions of the study areas.
Indeed, the effects of year and locality, as well as their interaction, play a preponderant
role in defining the agronomic and qualitative characteristics of wheat [
65
]. The ratio
between tenacity and extensibility of dough (P/L), also known as configuration ratio, is
commonly used together with the deformation energy as a quality flour indicator [
64
].
Modern genotypes showed similar ARPL values. Mazzancoio showed the highest value,
typical of a dough that is hard to process. On the contrary, Abbondanza and Rosia showed
a low ARPL, indicative of an extensible and weak dough. The farinograph development
time represents the kneading interval necessary to obtain the optimal development of a
dough [
47
]. With exception of Rosia and Abbondanza, all genotypes presented FDDT less
than 2 min. High values make it possible to obtain dough that can withstand long kneading
and rising times. FDDT was positively correlated with GP (r: 0.49). The time interval
during which a dough remains at its maximum consistency is known as farinograph
stability [
47
]. Most genotypes were characterized by very low FS, in particular Mazzancoio,
which maintained maximum consistency for an average time of less than 1 min. Bologna
presented the highest value, significantly different from all the other genotypes. FS resulted
in being highly and positively correlated with DEW (r: 0.80). The farinograph softening
degree represents the decrease in the dough consistency after a set interval compared to
Foods 2022,11, 2359 13 of 16
the standard consistency of 500 BU (Brabender Unit) [
47
]. High values were associated
with landraces, while lower ones have characterized the old and the modern genotypes.
The high FS and low FSD shown at the same time by Bologna confirmed that this genotype
presents excellent breadmaking aptitude. FSD was negatively correlated with GI (r:
0.76),
DEW (r:
0.89) and FS (r:
0.64). The farinograph water absorption is defined as the
amount of water to add to flour to reach the optimal consistency of 500 BU [
47
]. Starch and
gluten influence this parameter, with high values commonly preferred [
66
]. Mazzancoio
required the highest amount of water to develop an optimal dough consistency, which
combined with the highest FSD, indicated a flour of poor quality [
67
]. As specified in the
PCA biplot, in fact, the vectors of these two variables are close to Mazzancoio individuals.
Overall, WAC was positively correlated with GP (r: 0.56), YI (r: 0.69), WGC (r: 0.77),
ARPL (r: 0.47) and FSD (r: 0.55) and negatively correlated with GI (r:
0.73). FQN was
positively correlated with FDDT (r: 0.4) and FS (r: 0.63), but an inverse correlation was
found in relation to FSD (r:
0.56), as also reported in Ja´nczak-Pieni ˛a˙
zek et al. [
68
], where
a stronger negative correlation was found. Breads made from the flour obtained from the
two landraces were characterized by a significant difference. Moreover, Rosia presented
similar values when compared with the modern genotypes under study. Unexpectedly,
Solehio presented the lowest volume, as well as Mazzancoio (Table 5). LBV was negatively
correlated with ARPL (r:
0.43) and FSD (r:
0.46), while a positive correlation with DEW
(r: 0.46) and FQN (r: 0.56) was found. BH was positively correlated with GI (r: 0.51), DEW
(r: 0.59), FQN (0.52) and LBV (0.77), while a negative correlation with ARPL (r:
0.62), FSD
(r:
0.71) and WAC (r:
0.67) was detected. Breads obtained from the landraces were the
heaviest. There was a strong correlation between BW and WAC (r: 0.92), while an inverse
relationship was found with BH (r:
0.67) and GI (r:
0.62). There was no difference for
the crumb porosity between the old genotype and the two landraces, while the modern
genotypes presented a higher value overall. No statical difference has been detected for
the L*Rosia and Mazzancoio presented a similar value. In particular, Mazzancoio had
the lowest value for the L* coordinate, but the greater for the a* and b* coordinates, thus
presenting a more pronounced yellowish colour than the common wheat genotypes, as
expected. The crust of the breads obtained from modern genotypes were characterised by
a softer texture than the old genotype and landraces. Statistically significant differences
were found for all colour parameters measured on the crust between the three genotype
categories under study. Mazzancoio generally presented a more yellowish colour even in
this case, with intermediate yellow index for Abbondanza and Rosia.
5. Conclusions
In this study, the quality traits of grains, flours, doughs and breads obtained from
different wheat genotypes were evaluated. The agronomic study on selected genotypes
confirmed the better performance of modern varieties compared to unimproved ones. The
landraces, with lower yields, were characterised by a higher protein content and a low
gluten index. The cultivation of old grains allows the use of less impactful technologies and
the reduction in waiting times for raw materials in short supply chains, with consequent
advantages in terms of quality and wholesomeness. Furthermore, the productive stability
of these wheats in different environments and years, their rusticity and competitiveness
against weeds is of particular importance in low-input and organic farming systems.
As was to be expected, with regard to the technological quality parameters, a clear
discrepancy was found; the breadmaking properties of modern varieties were found to
be better, with significant differences compared to old varieties. However, it would be
interesting to study the best combination of flours from different genotypes. Flours from
modern varieties and landraces could be used in different mixtures to obtain traditional
bakery products that are very closely related to the cultivation area. The exploitation of the
latter would also make it possible to keep in cultivation genotypes otherwise destined to
disappear, contributing to the loss of genetic diversity.
Foods 2022,11, 2359 14 of 16
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/foods11152359/s1, Table S1: Crumb colour characteristics;
Table S2: Crust colour characteristics, Table S3: Full list of R packages for data analysis and visualiza-
tion; Table S4: Details on principal components and variance; Figure S1: Contribution of variables to
PC1 and PC2; Figure S2: Representation of the different breads obtained using the flours from the
varieties under study in the two localities.
Author Contributions:
Conceptualization, G.P., N.V. and M.B.; methodology, G.P., N.V. and M.B.;
software, A.C.; validation, G.P., N.V. and M.B.; formal analysis, A.C. and A.M.G.; investigation,
G.P., A.M.G. and G.B.; resources, G.P.; data curation, G.P.; writing—original draft preparation, G.B.;
writing—review and editing, G.P., A.C. and A.M.G.; visualization, A.C. and A.M.G.; supervision,
G.P., A.C., N.V. and M.B.; project administration, G.P. and N.V.; funding acquisition, G.P. All authors
have read and agreed to the published version of the manuscript.
Funding:
This work was financially supported for 90% by the PSR Calabria 2007–2013 (Rural Devel-
opment Programme of Calabria) within Project Action 124: “Cooperation for the development of new
products, processes and technologies in the agri-food and forestry sector” and for the remaining 10%
by PON03PE_00090_1 fund: “Process and product innovations in baked products and confectionary
food chain”.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data that supported the results of this study are available from the
corresponding author upon request.
Acknowledgments:
We would like to thank Domenico Petrolo, project leader “The added value of
territoriality in the Vibonese area: from wheat to local bread”, and Saveria Giannini for technical
support in the management of the field experiment. Antonino Denami and Maria Arcidiaco for their
technical advice and support during sampling and analysis.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Shewry, P.R. Wheat. J. Exp. Bot. 2009,60, 1537–1553. [CrossRef] [PubMed]
2. FAOSTAT. FAOSTAT Data. 2022. Available online: https://www.fao.org/faostat/en/#data/QCL (accessed on 15 June 2022).
3. Shewry, P.R.; Hey, S.J. The Contribution of Wheat to Human Diet and Health. Food Energy Secur. 2015,4, 178–202. [CrossRef]
4. de Sousa, T.; Ribeiro, M.; Sabença, C.; Igrejas, G. The 10,000-Year Success Story of Wheat! Foods 2021,10, 2124. [CrossRef]
5. Wieser, H.; Koehler, P.; Scherf, K.A. The Two Faces of Wheat. Front. Nutr. 2020,7, 517313. [CrossRef] [PubMed]
6.
Pasqualone, A. Italian Durum Wheat Breads. In Bread Consumption and Health; Pedrosa Silva, M.T.C., Ed.; Nova Science Publisher
Inc.: Hauppauge, NY, USA, 2012; pp. 57–79.
7. Sissons, M. Role of Durum Wheat Composition on the Quality of Pasta and Bread. Food 2008,2, 75–90.
8.
Goel, S.; Singh, M.; Grewal, S.; Razzaq, A.; Wani, S.H. Wheat Proteins: A Valuable Resources to Improve Nutritional Value of
Bread. Front. Sustain. Food Syst. 2021,5, 769681. [CrossRef]
9.
Faltermaier, A.; Waters, D.; Becker, T.; Arendt, E.; Gastl, M. Common Wheat (Triticum aestivum L.) and Its Use as a Brewing
Cereal—A Review: Common Wheat and Its Use as a Brewing Cereal. J. Inst. Brew. 2014,120, 1–15. [CrossRef]
10.
Venske, E.; dos Santos, R.S.; Busanello, C.; Gustafson, P.; Costa de Oliveira, A. Bread wheat: A Role Model for Plant Domestication
and Breeding. Hereditas 2019,156, 16. [CrossRef] [PubMed]
11.
Guarda, G.; Padovan, S.; Delogu, G. Grain Yield, Nitrogen-Use Efficiency and Baking Quality of Old and Modern Italian
Bread-Wheat Cultivars Grown at Different Nitrogen Levels. Eur. J. Agron. 2004,21, 181–192. [CrossRef]
12.
Løes, A.-K.; Frøseth, R.B.; Dieseth, J.A.; Skaret, J.; Lindö, C. What Should Organic Farmers Grow: Heritage or Modern Spring
Wheat Cultivars? Org. Agric. 2020,10 (Suppl. 1), 93–108. [CrossRef]
13. Belderok, B. Developments in Bread-Making Processes. Plant Foods Hum. Nutr. 2000,55, 1–14. [CrossRef]
14. Zeven, A. Landraces: A Review of Definitions and Classifications. Euphytica 1998,104, 127–139. [CrossRef]
15.
Laino, P.; Limonta, M.; Gerna, D.; Vaccino, P. Morpho-Physiolological and Qualitative Traits of a Bread Wheat Collection Spanning
a Century of Breeding in Italy. Biodivers. Data J. 2015,3, e4760. [CrossRef]
16.
Carranza-Gallego, G.; Guzmán Casado, G.I.; González de Molina, M. Wheat Landraces in the Andalusian Agri-Food Chain Final
Report. 2021. Available online: https://lha.es/file/documentos/final_report_wheat_landraces_in_the_andalusian_agrifood_
chain.pdf (accessed on 5 June 2022).
17.
Newton, A.C.; Akar, T.; Baresel, J.P.; Bebeli, P.J.; Bettencourt, E.; Bladenopoulos, K.V.; Czembor, J.H.; Fasoula, D.A.; Katsiotis, A.;
Koutis, K.; et al. Cereal Landraces for Sustainable Agriculture. A Review. Agron. Sustain. Dev. 2010,30, 237–269. [CrossRef]
Foods 2022,11, 2359 15 of 16
18.
De Vita, P.; Mastrangelo, M.; Codianni, P.; Fornara, M.; Palumbo, M.; Cattivelli, L. Bio-Agronomic Evaluation of Old and Modern
Wheat, Spelt and Emmer Genotypes for Low-Input Farming in Mediterranean Environment. Ital. J. Agron.
2007
,3, 291–302.
[CrossRef]
19.
Migliorini, P.; Spagnolo, S.; Torri, L.; Arnoulet, M.; Lazzerini, G.; Ceccarelli, S. Agronomic and Quality Characteristics of Old,
Modern and Mixture Wheat Varieties and Landraces for Organic Bread Chain in Diverse Environments of Northern Italy. Eur. J.
Agron. 2016,79, 131–141. [CrossRef]
20.
Piergiovanni, A.R. Evaluation of Genetic Variation and Grain Quality of Old Bread Wheat Varieties Introduced in North-Western
Italian Environments. Genet. Resour. Crop Evol. 2013,60, 325–333. [CrossRef]
21.
Boukid, F.; Gentilucci, V.; Vittadini, E.; De Montis, A.; Rosta, R.; Bosi, S.; Dinelli, G.; Carini, E. Rediscovering Bread Quality of
“Old” Italian Wheat (Triticum aestivum L. ssp. aestivum.) through an Integrated Approach: Physicochemical Evaluation and
Consumers’ Perception. LWT 2020,122, 109043. [CrossRef]
22.
Bocci, R.; Bussi, B.; Petitti, M.; Franciolini, R.; Altavilla, V.; Galluzzi, G.; Di Luzio, P.; Migliorini, P.; Spagnolo, S.; Floriddia, R.; et al.
Yield, Yield Stability and Farmers’ Preferences of Evolutionary Populations of Bread Wheat: A Dynamic Solution to Climate
Change. Eur. J. Agron. 2020,121, 126156. [CrossRef]
23.
Boggini, G.; Palumbo, M.; Calcagno, F. Characterization and Utilization of Sicilian Landraces of Durum Wheat in Breeding
Programmes. In Wheat Genetic Resources: Meeting Diverse Needs; Srivastava, J.P., Damania, A.B., Eds.; John Wiley and Sons:
Chichester, UK, 1990; pp. 223–234.
24.
Astrid, J.; Chrissie, M. How Do Older Wheat Cultivars Compare to Modern Wheat Cultivars Currently on the Market in South
Africa? J. Hortic. Sci. 2017,1, 42–47. [CrossRef]
25. ARPACAL. Available online: https://www.cfd.calabria.it/index.php/dati-stazioni/dati-storici (accessed on 6 May 2022).
26.
Foca, G.; Ulrici, A.; Corbellini, M.; Pagani, M.A.; Lucisano, M.; Franchini, G.C.; Tassi, L. Reproducibility of the Italian ISQ Method
for Quality Classification of Bread Wheats: An Evaluation by Expert Assessors. J. Sci. Food Agric. 2007,87, 839–846. [CrossRef]
27. CREA. Available online: https://www.crea.gov.it/web/difesa- e-certificazione/- /statistiche (accessed on 23 May 2022).
28. MIPAAF. Available online: https://www.sian.it/mivmPubb/autenticazione.do (accessed on 3 May 2022).
29.
Zadoks, J.C.; Chang, T.T.; Knozak, C.F. A Decimal Code for the Growth Stages of Cereals. Weed Res.
1974
,14, 415–421. [CrossRef]
30. D’Egidio, M.G.; Carcea, M. I Metodi Analitici per la Misura della Qualitàdei Cereali. Molini d’Italia 2013,6, 16–21.
31.
AACC Approved Methods of Analysis, 11th Edition. Cereals & Grains Association. Available online: https://www.cerealsgrains.
org/resources/Methods/Pages/54PhysicalDoughTests.aspx (accessed on 23 June 2022).
32.
R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria,
2021; Available online: https://www.R-project.org/ (accessed on 12 June 2022).
33.
Wickham, H.; François, R.; Henry, L.; Müller, K. dplyr: A Grammar of Data Manipulation. R Package Version 1.0.8. 2022.
Available online: https://CRAN.R- project.org/package=dplyr (accessed on 12 June 2022).
34.
Graves, S.; Piepho, H.-P.; Selzer, L.; Dorai-Raj, S. multcompView: Visualizations of Paired Comparisons. R Package Version 0.1-8.
2019. Available online: https://CRAN.R-project.org/package=multcompView (accessed on 12 June 2022).
35. Patil, I. Visualizations with Statistical Details: The ‘ggstatsplot’ Approach. J. Open Source Softw. 2021,6, 3167. [CrossRef]
36. Le, S.; Josse, J.; Husson, F. FactoMineR: An R Package for Multivariate Analysis. J. Stat. Soft. 2008,25, 1–18. [CrossRef]
37.
Kassambara, A.; Mundt, F. Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R Package Version 1.0.7.
2020. Available online: https://CRAN.R-project.org/package=factoextra (accessed on 12 June 2022).
38. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016; ISBN 978-3-319-24277-4. Available
online: https://ggplot2.tidyverse.org (accessed on 12 June 2022).
39.
Arnold, J.B. ggthemes: Extra Themes, Scales and Geoms for ’ggplot2’. R Package Version 4.2.4. 2021. Available online:
https://CRAN.R-project.org/package=ggthemes (accessed on 12 June 2022).
40.
Xiao, N. ggsci: Scientific Journal and Sci-Fi Themed Color Palettes for ‘ggplot2’. R Package Version 2.9. 2018. Available online:
https://CRAN.R-project.org/package=ggsci (accessed on 12 June 2022).
41.
Chan, C.; Chan, G.C.; Leeper, T.J.; Becker, J. rio: A Swiss-Army Knife for Data File I/O. R Package Version 0.5.29. 2021. Available
online: https://CRAN.R-project.org/package=rio (accessed on 12 June 2022).
42.
Frankin, S.; Roychowdhury, R.; Nashef, K.; Abbo, S.; Bonfil, D.J.; Ben-David, R. In-Field Comparative Study of Landraces vs.
Modern Wheat Genotypes under a Mediterranean Climate. Plants 2021,10, 2612. [CrossRef]
43.
Ngoune, L.T.; Shelton, C.M. Factors Affecting Yield of Crops. In Agronomy—Climate Change and Food Security; Amanullah, Ed.;
IntechOpen: London, UK, 2020. [CrossRef]
44.
Salvi, S.; Porfiri, O.; Ceccarelli, S. Nazareno Strampelli, the ‘Prophet’ of the Green Revolution. J. Agric. Sci.
2013
,151, 1–5.
[CrossRef]
45.
Mefleh, M.; Conte, P.; Fadda, C.; Giunta, F.; Piga, A.; Hassoun, G.; Motzo, R. From Ancient to Old and Modern Durum Wheat
Varieties: Interaction among Cultivar Traits, Management, and Technological Quality: From Ancient to Old and Modern Durum
Wheat Varieties. J. Sci. Food Agric. 2019,99, 2059–2067. [CrossRef]
46.
Vergauwen, D.; De Smet, I. From Early Farmers to Norman Borlaug—The Making of Modern Wheat. Curr. Biol.
2017
,27,
R858–R862. [CrossRef] [PubMed]
47. Cabras, P.; Tuberoso, I.G.C. Analisi dei Prodotti Alimentari; Piccin Nuova Libraria S.p.A.: Padova, Italy, 2014.
Foods 2022,11, 2359 16 of 16
48.
Lauro, A.O.; Paulo, H.C.; Man, M.K. New Microchondrometer to Measure Hectoliter Weight in Small Samples of Wheat. Afr. J.
Agric. Res. 2020,15, 524–530. [CrossRef]
49.
Wu, W.; Zhou, L.; Chen, J.; Qiu, Z.; He, Y. GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android
Platform. Agronomy 2018,8, 178. [CrossRef]
50.
Ruiz, M.; Zambrana, E.; Fite, R.; Sole, A.; Tenorio, J.L.; Benavente, E. Yield and Quality Performance of Traditional and Improved
Bread and Durum Wheat Varieties under Two Conservation Tillage Systems. Sustainability 2019,11, 4522. [CrossRef]
51.
Pasha, I.; Anjum, F.M.; Morris, C.F. Grain Hardness: A Major Determinant of Wheat Quality. Food Sci. Technol. Int.
2010
,16,
511–522. [CrossRef]
52. Iqbal, M.; Moakhar, N.P.; Strenzke, K.; Haile, T.; Pozniak, C.; Hucl, P.; Spaner, D. Genetic Improvement in Grain Yield and other
Traits of Wheat Grown in Western Canada. Crop Sci. 2016,56, 613–624. [CrossRef]
53.
Assefa, E. Correlation and Path Coefficient Studies of Yield and Yield Associated Traits in Bread Wheat (Triticum aestivum L.)
Genotypes. Adv. Plants Agric. Res. 2017,6, 128–136. [CrossRef]
54.
Normattiva, il Portale Della Legge Vigente. Decreto del Presidente della Repubblica 9 Febbraio 2001, n. 187. Available online:
https://www.normattiva.it/uri-res/N2Ls?urn:nir:presidente.repubblica:decreto:2001-02-09;187 (accessed on 30 May 2022).
55.
Ficco, D.B.M.; Mastrangelo, A.M.; Trono, D.; Borrelli, G.M.; De Vita, P.; Fares, C.; Beleggia, R.; Platani, C.; Papa, R. The Colours of
Durum Wheat: A Review. Crop Pasture Sci. 2014,65, 1–15. [CrossRef]
56. Oliver, J.R.; Blakeney, A.B.; Allen, H.M. Measurement of Flour Color in Color Space Parameters. Cereal Chem. 1992,69, 546–551.
57. Scherf, A.; Köhler, P. Wheat and Gluten: Technological and Health Aspects. Ernährungs Umschau 2016,63, 166–175. [CrossRef]
58.
van den Broeck, H.C.; de Jong, H.C.; Salentijn, E.M.J.; Dekking, L.; Bosch, D.; Hamer, R.J.; Gilissen, L.J.W.J.; van der Meer, I.M.;
Smulders, M.J.M. Presence of Celiac Disease Epitopes in Modern and Old Hexaploid Wheat Varieties: Wheat Breeding May Have
Contributed to Increased Prevalence of Celiac Disease. Theor. Appl. Genet. 2010,121, 1527–1539. [CrossRef]
59.
Fiore, M.C.; Mercati, F.; Spina, A.; Blangiforti, S.; Venora, G.; Dell’Acqua, M.; Lupini, A.; Preiti, G.; Monti, M.; Pè, M.E.; et al.
High-Throughput Genotype, Morphology, and Quality Traits Evaluation for the Assessment of Genetic Diversity of Wheat
Landraces from Sicily. Plants 2019,8, 116. [CrossRef] [PubMed]
60.
Bosi, S.; Negri, L.; Fakaros, A.; Oliveti, G.; Whittaker, A.; Dinelli, G. GGE Biplot Analysis to Explore the Adaption Potential of
Italian Common Wheat Genotypes. Sustainability 2022,14, 897. [CrossRef]
61. Bonfil, D.J.; Posner, E.S. Can Bread Wheat Quality be Determined by Gluten Index? J. Cereal Sci. 2012,56, 115–118. [CrossRef]
62.
Oikonomou, N.A.; Bakalis, S.; Rahman, M.S.; Krokida, M.K. Gluten Index for Wheat Products: Main Variables in Affecting the
Value and Nonlinear Regression Model. Int. J. Food Prop. 2015,18, 1–11. [CrossRef]
63.
Spina, A.; Dinelli, G.; Palumbo, M.; Whittaker, A.; Cambrea, M.; Negri, L.; Bosi, S. Evaluation of Standard Physico-Chemical and
Rheological Parameters in Predicting Bread-Making Quality of Durum Wheat (Triticum turgidum L. ssp. durum [Desf.] Husn.).
Int. J. Food Sci. 2021,56, 3278–3288. [CrossRef]
64.
Jødal, A.-S.S.; Larsen, K.L. Investigation of the Relationships Between the Alveograph Parameters. Sci. Rep.
2021
,11, 5349.
[CrossRef] [PubMed]
65.
Bornhofen, E.; Woyann, L.G.; Bozi, A.H.; Stoco, M.G.; Marchioro, V.S.; Benin, G. Associations Between Agronomic and Bread-
Making Quality Traits in Wheat: Location and Crop-Year Effects. Científica 2018,46, 38. [CrossRef]
66.
Aydo˘gan, S.; ¸Sahin, M.; Akçacık, A. Relationships between Farinograph Parameters and Bread Volume, Physicochemical Traits in
Bread Wheat Flours. Crop Sci. 2015,3, 14–18.
67.
Dapcevic, T.; Pojic, M.; Hadnaev, M.; Torbic, A. The Role of Empirical Rheology in Flour Quality Control. In Wide Spectra of
Quality Control; Akyar, I., Ed.; InTechOpen: London, UK, 2011; ISBN 978-953-307-683-6. [CrossRef]
68.
Ja ´nczak-Pieni ˛a ˙
zek, M.; Buczek, J.; Kaszuba, J.; Szpunar-Krok, E.; Bobrecka-Jamro, D.; Jaworska, G. A Comparative Assessment of
the Baking Quality of Hybrid and Population Wheat Cultivars. Appl. Sci. 2020,10, 7104. [CrossRef]
SUPPLEMENTARY MATERIALS
Table S1. Crumb colour characteristics.
L*
b*
Genotype
***
***
Altamira
74.6 ± 1.39 ab
12.8 ± 1.11 cd
Bologna
73.4 ± 1.85 bc
12.3 ± 1.26 d
Solehio
75.3 ± 0.74 ab
14.0 ± 1.48 bc
PR22R58
73.4 ± 1.69 bc
15.5 ± 2.58 b
Abbondanza
75.9 ± 2.25 a
10.0 ± 0.33 e
Rosia
71.6 ± 0.92 cd
10.5 ± 1.39 e
Mazzancoio
69.4 ± 4.72 d
18.5 ± 1.01 a
Location
***
ns
Rombiolo
72.6 ± b
13.1 ± 2.81 a
Maierato
74.1 ± a
13.6 ± 3.35 a
Variety
Modern
74.1 ± 1.65
13.6 ± 2.07
Old
75.9 ± 2.25
10.0 ± 0.33
Landrace
70.5 ± 3.51
14.5 ± 4.24
L*, lightness; a*, red to green coordinate; b*, yellow to blue coordinate. Mean values are reported for each variable. Different
letters in the same column indicate significant differences according to Tukey HSD test at p ≤ 0.05. Different letters in the
same column indicate significant differences according to Tukey HSD test at p ≤ 0.05, while ***, ns, indicate the following
levels of significance for each ANOVA model: 0.001, not significant Only the mean values ± standard deviation are shown
for the variety component for descriptive purposes.
Table S2. Crust colour characteristics.
L*
a*
b*
Genotype
***
***
***
Altamira
42.5 ± 6.57 ab
13.7 ± 3.03 a
21.6 ± 7.89 abc
Bologna
44.8 ± 6.76 ab
14.2 ± 1.94 a
23.7 ± 6.93 ab
Solehio
43.0 ± 4.56 ab
14.9 ± 1.11 a
22.7 ± 4.78 ab
PR22R58
44.3 ± 3.93 ab
15.2 ± 1.12 a
24.1 ± 4.70 ab
Abbondanza
47.8 ± 2.50 a
14.9 ± 1.04 a
27.2 ± 2.47 a
Rosia
39.3 ± 3.20 bc
13.4 ± 2.32 ab
18.5 ± 5.09 bc
Mazzancoio
36.6 ± 2.23 c
11.2 ± 0.76 b
15.0 ± 1.07 c
Location
ns
ns
ns
Rombiolo
42.5 ± 5.80 a
13.8 ± 1.88 a
21.6 ± 6.27 a
Maierato
42.8 ± 5.46 a
14.0 ± 2.40 a
22.0 ± 6.25 a
Variety
Modern
43.7 ± 5.49
14.5 ± 1.99
23.0 ± 6.11
Old
47.8 ± 2.50
14.9 ± 1.04
27.2 ± 2.47
Landrace
37.9 ± 3.04
12.3 ± 2.04
16.7 ± 4.00
L*, lightness; a*, red to green coordinate; b*, yellow to blue coordinate. Mean values are reported for each variable. Different
letters in the same column indicate significant differences according to Tukey HSD test at p ≤ 0.05. Different letters in the
same column indicate significant differences according to Tukey HSD test at p ≤ 0.05, while ***, ns, indicate the following
levels of significance for each ANOVA model: 0.001, not significant Only the mean values ± standard deviation are shown
for the variety component for descriptive purposes.
Table S3. Full list of R packages used for data analysis, manipulation and graphic representation.
Function
R package
Reference
Base function
“Base functions of R”
[32]
Data wrangling
“dplyr”
[33]
Multiple paired comparisons
“multcompView”
[34]
Correlation computation and
representation
“ggstatsplot”
[35]
PCA
“FactoMineR”
[36]
PCA
“factoextra”
[37]
Graphic visualization
“ggplot2”
[38]
Graphic visualization
“ggthemes”
[39]
Graphic visualization
“ggsci”
[40]
Data exportation
“rio”
[41]
Table S4. Details on principal components values and variance.
Principal component
Eigenvalue
% of variance
Cumulative % of
variance
1
9.861363838
44.82438108
44.82438108
2
5.311577534
24.14353424
68.96791533
3
3.150107433
14.31867015
83.28658548
4
0.898434333
4.083792424
87.3703779
5
0.752893903
3.422245015
90.79262292
6
0.466842849
2.122012951
92.91463587
7
0.321507338
1.461396992
94.37603286
8
0.262023458
1.19101572
95.56704858
9
0.213776555
0.971711612
96.53876019
10
0.20371589
0.925981319
97.46474151
11
0.128778232
0.585355602
98.05009711
12
0.122624863
0.557385741
98.60748285
13
0.074637497
0.33926135
98.9467442
14
0.071806507
0.326393213
99.27313742
15
0.0641448
0.291567271
99.56470469
16
0.033322182
0.151464463
99.71616915
17
0.027324791
0.124203597
99.84037275
18
0.015204307
0.069110487
99.90948323
19
0.012063103
0.054832286
99.96431552
20
0.003922335
0.017828795
99.98214432
21
0.00231914
0.010541545
99.99268586
22
0.001609111
0.00731414
100
Figure S1. Contribution of variables to PC1 and PC2.
Figure S2. Representation of the different breads obtained using the flours from the varieties under study in
the two localities. (a) Abbondanza; (b) PR22R58; (c) Altamira; (d) Rosia; (e) Bologna; (f) Solehio; (g)
Mazzancoio.
... Interestingly, a strongly negative correlation was found between wet gluten and GI for both emmer and einkorn (Table 5), confirming the suggestion that higher protein quantity is not related to the quality. Preiti et al. [42] also confirmed a negative correlation between WG and GI for common wheat. The impact of the growing year on total variation was the lowest (4.9%) of all the quality evaluation parameters, while the impact of the cultivar × growing year interaction was the highest (35.1%), indicating the different responses of cultivars to the growing conditions. ...
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Given the substantial variation in global wheat yield, insufficient research in cultivar selection for climate change, and the lack of suitable germplasm in sustainable agroecosystems, there is a requisite for soft wheat genotypes, with stable grain yield as well as quality parameters. The present study was aimed at genotype evaluation (GGE biplot for “mean performance versus stability”) not only for yield, but also for technological, phytosanitary and functional quality parameters of 24 Triticum aestivum L. genotypes (eight landraces, old and modern varieties, respectively) within a single organic farm location (Argelato, Emilia-Romagna, Italy) over three consecutive years. Overall, high yield stability was shown for the landraces and old varieties. In particular, the landraces Piave and Gamba di Ferro, as well as the old variety Verna, showed high stability with above-average means for numerous quality parameters of interest. Additionally, relative stability combined with above-average mean for quality parameters was also demonstrated for the high-yielding Gentil Bianco and Guà 113. Aside from Verna, these “unrecognized” resilient genotypes were also shown to meet the requisites for suitable germplasm in sustainable agroecosystems. Future potential utilization of these more stable landraces in addressing climate change would also ultimately facilitate the survival of valuable genetic resources.
Article
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The Near East climate ranges from arid to a Mediterranean, under which local wheat landraces have been grown for over millennia, assumingly accumulating a unique repertoire of genetic adaptations. In the current study, we subjected a subset of the Israeli Palestinian Landraces (IPLR) collection (n = 19: durum and bread wheat landraces, modern wheat cultivars, and landraces mixtures) to full-field evaluation. The multifield experiment included a semiarid site (2018–2019, 2019–2020) under low (L) and high (H) supplementary irrigation, and a Mediterranean site (2019–2020). Water availability had a major impact on crop performance. This was reflected in a strong discrimination between environments for biomass productivity and yield components. Compared to landraces, modern cultivars exhibited significantly higher grain yield (GY) across environments (+102%) reflecting the effect of the Green Revolution. However, under the Gilat19 (L) environment, this productivity gap was significantly reduced (only +39%). Five excelling landraces and the durum mix exhibited good agronomic potential across all trails. This was expressed in relatively high GY (2.3–2.85 t ha−1), early phenology (86–96 days to heading) and lodging resistance. Given the growing interest of stakeholders and consumers, these might be considered future candidates for the local artisanal wheat grain market. Yet, this step should be taken only after establishing an adjustable field management protocol.
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Triticum aestivum, commonly known as bread wheat, is one of the most cultivated crops globally. Due to its increasing demand, wheat is the source of many nutritious products including bread, pasta, and noodles containing different types of seed storage proteins. Wheat seed storage proteins largely control the type and quality of any wheat product. Among various unique wheat products, bread is the most consumed product around the world due to its fast availability as compared to other traditional food commodities. The production of highly nutritious and superior quality bread is always a matter of concern because of its increasing industrial demand. Therefore, new and more advanced technologies are currently being applied to improve and enrich the bread, having increased fortified nutrients, gluten-free, highly stable with enhanced shelf-life, and long-lasting. This review focused on bread proteins with improving wheat qualities and nutritional properties using modern technologies. We also describe the recent innovations in processing technologies to improve various quality traits of wheat bread. We also highlight some modern forms of bread that are utilized in different industries for various purposes and future directions.
Article
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Wheat is one of the most important cereal crops in the world as it is used in the production of a diverse range of traditional and modern processed foods. The ancient varieties einkorn, emmer, and spelt not only played an important role as a source of food but became the ancestors of the modern varieties currently grown worldwide. Hexaploid wheat (Triticum aestivum L.) and tetraploid wheat (Triticum durum Desf.) now account for around 95% and 5% of the world production, respectively. The success of this cereal is inextricably associated with the capacity of its grain proteins, the gluten, to form a viscoelastic dough that allows the transformation of wheat flour into a wide variety of staple forms of food in the human diet. This review aims to give a holistic view of the temporal and proteogenomic evolution of wheat from its domestication to the massively produced high-yield crop of our day.
Technical Report
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Final report with the objectives, actions carried out and results of the transfer project "Transfer to Andalusian agro-industry of traditional wheat cultivars with potential for insertion in niche markets with high added value".