The Mediterranean area is one of the major global producers of agricultural food. However, along the entire supply chain—from farming to food distribution and consumption—food waste represents a significant fraction. Additionally, plant waste residues generated during the cultivation of specific fruits and vegetables must also be considered. This heterogeneous biomass is a valuable source of bioactive compounds and materials that can be transformed into high-performance functional products. By analyzing technical and scientific literature, this review identifies extraction, composite production, and bioconversion as the main strategies for valorizing agricultural by-products and waste. The advantages of these approaches as well as efficiency gains through digitalization are discussed, along with their potential applications in the Mediterranean region to support new research activities and bioeconomic initiatives. Moreover, the review highlights the challenges and disadvantages associated with waste valorization, providing a critical comparison of different studies to offer a comprehensive perspective on the topic. The objective of this review is to evaluate the potential of agricultural waste valorization, identifying effective strategies while also considering their limitations, to contribute to the development of sustainable and innovative solutions in Mediterranean bioeconomy.

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.

Intelligent packaging can assist with reducing food waste and improving food safety. While indicators already provide the current food quality, sensors combined with machine learning techniques could predict the remaining shelf life and provide recommendations to maintain food quality. Thus, information from intelligent packaging must be transmitted and evaluated. However, successfully deploying such a concept requires the consumers’ acceptance. Therefore, we conducted a consumer survey, investigating the attitudes and preferences of 242 participants regarding the use and connectivity of smarter technologies to evaluate food quality. The results revealed that 85% of the participants perceive a need for technological innovations to reduce food waste. Subsequently, we propose three approaches. Regarding information display, consumers prefer to show information directly on the packaging, via integrated displays in refrigerators, or a smartphone application. The information should provide the remaining shelf life, a scale, or a color indication.

Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been established to track the ripeness, spoilage, and quality of fresh produce. Our study developed a freshness monitoring system for tomatoes, combining E-Nose technology with storage condition monitoring, color analysis, and weight-loss tracking. Different post-purchase scenarios were investigated, focusing on the influence of temperature and mechanical damage on shelf life. Support Vector Classifier (SVC) and k-Nearest Neighbor (kNN) were applied to classify storage scenarios and storage days, while Support Vector Regression (SVR) and kNN regression were used for predicting storage days. By using a data fusion approach with Linear Discriminant Analysis (LDA), the SVC achieved an accuracy of 72.91% in predicting storage days and an accuracy of 86.73% in distinguishing between storage scenarios. The kNN yielded the best regression results, with a Mean Absolute Error (MAE) of 0.841 days and a coefficient of determination of 0.867. The results highlight the method’s potential to predict storage scenarios and storage days, providing insight into the product’s remaining shelf life.