The transition toward more sustainable food systems, which already represents a central element of the European Farm to Fork and Green Deal strategies, could be an effective measure to contribute to global decarbonization and greenhouse gas (GHGs) reduction goals; concurrently, it could improve the health status and nutrition of the global population. In this context, the Mediterranean diet (MD) could play a considerable role, as it is generally recognized as a more balanced, healthy, and sustainable eating pattern than Western consumption patterns, which are characterized by excess food and high energy content, thus causing undesirable effects on both human health and the environment. Although traditionally linked to MD, Italy sees relatively moderate adherence by its citizens, as they consume about +75% of the daily caloric intake recommended by MD. Therefore, this study aims to quantitatively assess the potential environmental, economic, and health impacts of this lower adherence to MD by Italians. Current Italian Food Patterns (CIFP) in 2019 were analyzed and compared to the MD recommended amounts through a Life Cycle Thinking (LCT) approach (LCA) and carbon footprint (CF) analysis. The results show that CIFP, compared to MD, has +133% greater impacts on the environmental macro-area, +100% greater impacts on the human health macro-area, and +59% greater impacts on the economic macro-area (with annual fossil and mineral resource savings of $53.35 per person, $3.2 billion per year). The analysis also shows that CIFP has a CF of 6.54 × 10¹ kg CO₂ eq, +142% over MD (2.7 × 10¹ kg CO₂ eq), resulting in a lower environmental impact of the Mediterranean diet.

Nowadays, the growing interest in a healthy lifestyle, to compensate for modern stressful habits, has led to an increased demand for wholesome products with quick preparation times. Fresh and ready-to-eat leafy green vegetables are generally perceived as salutary and safe, although they have been recognized as a source of food poisoning outbreaks worldwide. The reason is that these products retain much of their indigenous microflora after minimal industrial processing, and are expected to be consumed without any additional treatment by consumers. Microbiological safety requires a systematic approach that encompasses all aspects of production, processing and distribution. Nevertheless, the most common laboratory techniques used for the detection of pathogens are expensive, time consuming, need laboratory professionals and are not able to provide prompt results, required to undertake effective corrective actions. In this context, the solution proposed in this work is a novel electrochemical sensing system, able to provide real-time information on microbiological risk, which is also potentially embeddable in an industrial production line. The results showed the sensor ability to detect leafy green salad bacterial contaminations with adequate sensibility, even at a low concentration.

The need for accurate information and the availability of novel tool and technological advances in agriculture have given rise to innovative autonomous systems. The aim is to monitor key parameters for optimal water and fertilizer management. A key issue in precision agriculture is the in situ monitoring of soil macronutrients. Here, a proof-of-concept study was conducted that tested two types of sensors capable of capturing both the electrochemical response of the soil and the electrical potential generated by the interaction between the soil and plants. These two sensors can be used to monitor large areas using a network approach, due to their small size and low power consumption. The voltammetric sensor (BIONOTE-L) proved to be able to characterize different soil samples. It was able, indeed, to provide a reproducible voltammetric fingerprint specific for each soil type, and to monitor the concentration of CaCl₂ and NaCl in the soil. BIONOTE-L can be coupled to a device capable of capturing the energy produced by interactions between plants and soil. As a consequence, the functionality of the microsystem node when applied in a large-area monitoring network can be extended. Additional calibrations will be performed to fully characterize the instrument node, to implement the network, and to specialize it for a particular application in the field.

Quality control is mandatory in the food industry and chemical sensors play a crucial role in this field. Coffee is one of the most consumed and commercialized food products globally, and its quality is of the utmost importance. Many scientific papers have analyzed coffee quality using different approaches, such as analytical and sensor analyses, which, despite their good performance, are limited to structured lab implementation. This study aims to evaluate the capability of a smart electrochemical sensor to discriminate among different beverages prepared using coffee beans with different moisture content (0%, 2%, >4%) and ground in three sizes (fine, medium and coarse). These parameters reflect real scenarios where coffee is produced and its quality influenced. The possibility of optimizing coffee quality in real time by tuning these parameters could open the way to intelligent coffee machines. A specific experimental setup has been designed, and the data has been analyzed using machine learning techniques. The results obtained from Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) show the sensor’s capability to distinguish between samples of different quality, with a percentage of correct classification of 86.6%. This performance underscores the potential benefits of this sensor for coffee quality assessment, enabling time and resource savings, while facilitating the development of analytical methods based on smart electrochemical sensors.

A plant biological system is exposed to external influences. In general, each plant has its characteristics and needs with specific interaction mechanisms adapted to its survival. Interactions between systems can be examined and modeled as energy exchanges of mechanical, chemical or electrical variables. Thus, each specific interaction can be examined by triggering the system via a specific stimulus. The objective of this work was to study a specific stimulus (mechanical stimulation) as a driver of plants and their interaction with the environment. In particular, the experimental design concerns the setting up and testing of an automatic source of mechanical stimuli at different wavelengths, generated by an electromechanical transducer, to induce a micro-interaction in plants (or in parts of them) that produces a specific behavior (hypothesis) of plants. Four different experimental setups were developed for this work, each pursuing the same objective: the analysis of the germination process induced by stimulation by sound waves in the audible range. It can be said that the introduction of sound waves as a stimulant or a brake for the growth of plants can offer significant advantages when used on a large scale in the primary sector, since these effects can be used instead of polluting chemical solutions.