Optimisation of an Industrial Optical Sorter of Legumes for Gluten-Free Production Using Hyperspectral Imaging Techniques.

The market demand for gluten-free food is increasing due to the growing gluten sensitivity and coeliac disease (CD) in the population. The market requires grass-free cereals to produce gluten-free food. This requires sorting methods that guarantee the perfect separation of gluten contaminants from the legumes. The objective of the research was the development of an optical sorting system based on hyperspectral image processing, capable of identifying the spectral characteristics of the products under investigation to obtain a statistical classifier capable of enabling the total elimination of contaminants. The construction of the statistical classifier yielded excellent results, with a 100% correct classification rate of the contaminants. Tests conducted subsequently on an industrial optical sorter validated the result of the preliminary tests. In fact, the application of the developed classifier was able to correctly select the contaminants from the mass of legumes with a correct classification percentage of 100%. A small proportion of legumes was misclassified as contaminants, but this did not affect the scope of the work. Further studies will aim to reduce even this small share of waste with investigations into optimising the seed transport systems of the optical sorter.

Modelling the Rheology of Olive Paste for Oil Extraction Plant Automation: Effects of the Crushing Process on the Rheology of Olive Pastes.

In extra virgin olive oil production, it is essential to obtain a well-prepared olive paste which allows not only the extraction of the oil drops from the olives, but also the achievement of a high-quality oil while maintaining high yields. This work addresses the problem of determining the effect of three crushing machines on the viscosity of the olive paste: a hammer crusher, a disk crusher and a de-stoner were tested. The tests were repeated on both the paste leaving each machine and the paste to which water was added; this was done with the main aim of considering the different dilutions of the paste while entering the decanter. A power law and the Zhang and Evans model were used to analyse the rheological behaviour of the paste. The experimental results allow validation of the two models with a high (more than 0.9) coefficient of determination between experimental and numerical data. The results also show that the pastes obtained with the two classic crushing methods (hammers and disks) are almost identical, with a packing factor of about 17.9% and 18.6%, respectively. Conversely, the paste obtained with the de-stoner entails higher viscosity values and a smaller solid packing factor, of about 2.8%. At 30% dilution with water, the volume of the solid concentration dropped to about 11.6% for the hammer and disc crushers, while for the de-stoner it only reached 1.8%. This behaviour is also reflected in the evaluation of yields, which were 6% lower with the de-stoner. No significant differences regarding the legal parameters of oil quality were found using the three different crushing systems. Finally, this paper establishes some fundamental pillars in the research for an optimal model for identifying the rheological behaviour of the paste as a function of the crusher used. Indeed, since there is an increasing need for automation in the oil extraction process, these models can be of great help in optimizing this process.

Experimental Investigation of a New Modular Crusher Machine Developed for Olive Oil Extraction Plants.

The crushing system is crucial in the virgin olive oil (VOO) mechanical extraction process. The use of different crusher machines can highly influence the quality of the final product, mainly due to the phenolic and volatile content responsible for VOO sensory and health properties. An experimental investigation was conducted to evaluate the effect of the geometric features of a new model of crusher machine for olives. The crusher machine consists of interchangeable rotors: a rotor with hammers and a rotor with knives. The evaluation was carried out with the same fixed grid in stainless steel with 6 mm diameter circular holes. An evaluation was carried out on the impact of the crusher tools on the pit particle size and on the distribution of energy and temperature. The performance of the plant was also assessed in terms of process efficiency and olive oil quality. The results showed that the specific energy released by the tool per unit of product, calculated through both energy conservation and comminution theory, is about 25-27% higher in the case of hammers. Since the impact energy is mainly dissipated in the product as heat, the temperature reached during milling operations with the hammer crusher was also higher by the same percentage with respect to the knife crusher. This has important consequences on the quality of the product: the new knife rotor used in the crushing phase produced an improvement in VOO quality, relating mainly to sensory attributes and the health-enhancing properties of the final product. The ability of the crusher to break cell walls and vacuoles, thus releasing the oil contained therein, is comparable for the two different rotors.

Investigation on the Effects of a Pulsed Electric Field (PEF) Continuous System Implemented in an Industrial Olive Oil Plant.

The aim of this study was to investigate how the treatment of olive paste of the Picholine variety with pulsed electric fields (PEF) under real operating conditions in a large-scale olive oil extraction plant affects the extractability, chemical composition and sensory profile of the oils. The application of pulsed electric fields (PEF) as a non-thermal food processing technology is interesting for many food extraction processes. The results of this study show that pulsed electric fields can be used as a pretreatment before oil separation to increase the extractability of the process and improve the content of functional components. The application of pulsed electric field (PEF) treatment (2.4 kV/cm, 4 kJ/kg, 6 µs pulse width) to olive paste through a continuous system significantly increased the extractability and total concentration of phenols (especially oleuropein derivatives) compared to the control. In addition, the volatile compounds, α-tocopherol, the fatty acid profile and the main legal quality parameters of extra virgin olive oil (EVOO), including free acidity, peroxide values, extinction indices and sensory analysis, were evaluated. The pulsed electric fields (PEF) treatment did not modify these EVOO quality parameters, neither the α-tocopherol content nor the volatile profile. The sensory properties of EVOO were not affected as well as the PEF treatment showed a similar intensity of fruity and pungent attributes without any off-flavor according to the European Union legal standards. An increase in the bitter taste attribute was observed in the PEF oils. Consequently, this study demonstrates that pulsed electric fields (PEF) processing could be implemented in olive oil processing as pretreatment for improving the efficiency of the process.

Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness.

The digital transformation of the defence sector is not exempt from innovative requirements and challenges, with the lack of availability of reliable, unbiased and consistent data for training automatisms (machine learning algorithms, decision-making, what-if recreation of operational conditions, support the human understanding of the hybrid operational picture, personnel training/education, etc.) being one of the most relevant gaps. In the context of cyber defence, the state-of-the-art provides a plethora of data network collections that tend to lack presenting the information of all communication layers (physical to application). They are synthetically generated in scenarios far from the singularities of cyber defence operations. None of these data network collections took into consideration usage profiles and specific environments directly related to acquiring a cyber situational awareness, typically missing the relationship between incidents registered at the hardware/software level and their impact on the military mission assets and objectives, which consequently bypasses the entire chain of dependencies between strategic, operational, tactical and technical domains. In order to contribute to the mitigation of these gaps, this paper introduces CYSAS-S3, a novel dataset designed and created as a result of a joint research action that explores the principal needs for datasets by cyber defence centres, resulting in the generation of a collection of samples that correlate the impact of selected Advanced Persistent Threats (APT) with each phase of their cyber kill chain, regarding mission-level operations and goals.