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.

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.

The main challenge of this scientific work was the implementation on an industrial olive oil extraction plant of an NIR device for the multispectral analysis of pomace to predict the percentage of humidity and oil contained in it. Subsequent to the implementation of the NIR device on the oil extraction line on the solid’s outlet from the decanter, NIRS interaction measurements in the 761–1081 nm region were used to probe the pomace. NIRS calibration models for the prediction of water and oil content in the pomace were obtained and successfully tested and validated. The correlations of calibration results for oil and water content were 0.700 and 0.829, while the correlations of validation were 0.773 and 0.676, respectively. Low values of root mean square error were found for both the prediction and validation set. The results highlight the good robustness of an NIR approach based on a PLS calibration model to monitor the industrial olive oil process. The results obtained are a first step toward the large-scale implementation of NIR devices for monitoring pomace in oil mills. The possibility of knowing the oil lost in the pomace, moment by moment, would open a new frontier towards system control and the sustainability of the olive oil extraction process.

In the agri-food sector, the olive oil mill boasts a consolidated process technology, supported by to in-depth research studies, for the extraction of olive oil. The market and consumers now demand continuous optimization of production processes, greater extractability of the product and superior quality of Extra Virgin Olive Oil (EVOO). Emerging technologies, based on physical treatment processes applicated to olive pastes, such as pulsed electric fields (PEF), microwave-assisted extraction and ultrasound-assisted extraction (UAE), integrated into conventional extraction processes, have demonstrated increased extractability, reduced production costs and time, and improved final product quality. Starting from the results of previous studies, this implemented an experimental test plan with a US plant installed before the malaxer phase, processing olive paste, performing tests on different olive cultivars: Coratina and Nocellara del Belice typical of two different areas of Italy: Puglia and Sicily. The results demonstrated a 3% increase in extractability and an improvement in the qualitative values of the EVOO phenolic composition.

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.

This study investigated the application of Finite Element Analysis (FEA) to optimize the design and material selection for the construction of the telescopic arm of an elevating work platform (EWP) used in agricultural environments. By comparing the structural performance of four materials—Aluminum Alloy (EN-AW 1200), Aluminum Alloy (EN-AW 2014), High-Strength Low-Alloy (HSLA) Steel Fe275JR, and HSLA Steel S700—under simulated operational conditions, this research identified the most suitable material for robust yet lightweight platforms. The results revealed that HSLA Steel S700 provides superior performance in terms of strength, low deformation, and high safety factors, making it ideal for scenarios requiring maximum durability and load-bearing capacity. Conversely, Aluminum Alloy (EN-AW 2014), while exhibiting lower strength compared with HSLA Steel S700, significantly reduces platform weight by approximately 60% and lowers the center of gravity, enhancing maneuverability and compatibility with smaller, less powerful tractors. These findings highlight the potential of FEA in optimizing EWP design by enabling precise adjustments to material selection and structural geometry. The outcomes of this research contribute to the development of safer, more efficient, and cost-effective EWPs, with a specific focus on improving productivity and safety in agricultural operations such as pruning and harvesting. Future work will explore advanced geometries and hybrid materials to further enhance the performance and versatility of these platforms.