Citrus spp. are spread mainly in the Mediterranean basin and represent the largest fruit source for human consumption. Postharvest losses, mainly due to diseases and metabolic disorders of fruits, can cause severe wastage, reaching 30 to 50% of the total production. Preserving quality and extending shelf life are essential objectives for postharvest technological innovation, determined by the proper handling, treatment, storage and transport of harvested produce. Moreover, the application of novel sustainable strategies is critical for the reduction of synthetic fungicide residues on fruit surfaces and the impact on the environment caused by waste disposal of fungicides. In this article, the current knowledge about the safest and more sustainable strategies, as well as advanced postharvest handling and storage technologies, will be critically reviewed.

The production of biogas from anaerobic digestion (AD) of residual agro-food biomasses represents an opportunity for alternative production of energy from renewable sources, according to the European Union legislation on renewable energy. This review provides an overview of the various aspects involved in this process with a focus on the best process conditions to be used for AD-based biogas production from residual agro-food biomasses. After a schematic description of the AD phases, the biogas plants with advanced technologies were described, pointing out the strengths and the weaknesses of the different digester technologies and indicating the main parameters and operating conditions to be monitored. Subsequently, a brief analysis of the factors affecting methane yield from manure AD was conducted and the AD of fruit and vegetables waste was examined. Particular attention was given to studies on co-digestion and pre-treatments as strategies to improve biogas yield. Finally, the selection of specific microorganisms and the genetic manipulation of anaerobic bacteria to speed up the AD process was illustrated. The open challenges concern the achievement of the highest renewable energy yields reusing agro-food waste with the lowest environmental impact and an increment of competitiveness of the agricultural sector in the perspective of a circular economy.

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied.