Durum Wheat: Uses, Quality Characteristics, and Applied Tests

Durum wheat (Triticum durum) is a commercial and most important cultivated tetraploid species of wheat. Most of the durum wheat is used for pasta-making. It is also used for bread-making. In the last decades, the popularity of bread made from durum wheat is increasing. The majority of people from the milling and baking industry are not very familiar with this special wheat. The purpose of this work is to present the quality characteristics of durum wheat and the tests applied. A better understanding will have a positive influence on the final quality of the product. It will also increase the commercial appeal of the durum wheat and create alternative markets, especially in years of over production. The durum wheat has harder grain texture and stiffer gluten compared to common wheat (Triticum aestivum). Most of the tests applied are common for both wheat species and describe to a greater extent, the quality characteristics of the durum wheat. Although there is a developed and recognized international bread-making procedure, there is a need to develop the same specifically for durum bread

Effect of cysteine on different durum wheat flour fractions. Relationships between descriptive rheology and baking properties

The effect of cysteine on different durum wheat flour fractions was studied. Also, the relationships between descriptive rheology and breadmaking performance were examined. An industrial vertical vibro-sifter was used to sieve the Straight run durum flour and four fractions were obtained. In addition an industrial roller mill was used to regrind the coarse flour fraction in order to examine the impact of severe grinding. The sieve analysis showed a wide particle size distribution. The finer particles were related to higher starch damage content, lower wet gluten, lower Zeleny sedimentation volume and lower Falling Number. Analysis showed that flour particle size affected the dough behavior during fermentation but the impact on loaf volume was not critical. Two improver formulations were used in the baking tests, called Improver A and improver B, the only difference between them was that the improver B contained 12ppm of L-cysteine. At the fermentation stage the addition of L-cysteine had a positive impact on dough expansion only on coarse flour (200μm>), while the rest fractions were not affected. The L-cysteine had a positive impact on gas formation rate on a fraction of medium size particle size (between 110μm and 200μm). Also the L-cysteine had a positive impact on loaf volume on the finest flour fraction (110<μm), on the coarse and on the reground flour, while the loaf volume of rest fractions was not affected. A number of descriptive rheology tests were used in order to assess their predictive power on dough breadmaking performance. The results showed that the predictive ability of Farinograph on loaf volume was limited. The Consistogarph constant hydration protocol was significantly correlated with baking quality, while Consistograph adapted hydration protocol showed poor relationship with breadmaking properties. In order to assess the effect of dough consistency and the mixing procedure on biaxial extension profile as it is measured by the Alveograph four different dough mixing preparations were adopted. Results have shown that all the Alveographic modifications adopted in this study were poorly related with breadmaking performance. In contrast Rhermentometer test parameters were strongly related with loaf volume

Multidisciplinary shallow underwater geophysical prospecting at Delos island

Geophysical imaging methods have been applied to reconstruct the cultural dynamics in the two different submerged sites in Delos island. The geophysical results provided useful information for understanding the complexity of the submerged archaeological sites

Intrusion Detection System for Platooning Connected Autonomous Vehicles

The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks