Italy is one of Europe’s largest consumers of wood pellets, while domestic production remains comparatively limited. In parallel, wood chips (WC) represent a strategic biofuel for power generation, where particle size distribution (PSD) affects handling and storage. Conventional PSD assessment relies on time-consuming methodology. This study proposes a patent-pending image-processing approach (Shadow Size Distribution—SSD analysis) for PSD classification of WC under bulk conditions. One hundred samples were characterized via both standard analysis and SSD. PSD data were aggregated into fine and coarse macro-fractions and used to define binary class labels. Multivariate analyses (PERMANOVA, PCA) and Support Vector Classifier (SVC) models were employed to evaluate the discriminative capability of SSD features. PCA revealed coherent relationships between PSD macro-variables and key shadow descriptors, particularly shadow number and area. The best SVC configuration achieved 0.77 test accuracy, with strong recall for coarse samples. Although overall performance was constrained by dataset size and imbalance, the results demonstrate that SSD features retain meaningful granulometric information, supporting further development toward automated, in-line PSD monitoring systems. From a sustainability perspective, the proposed SSD-based approach enables faster and potentially in-line monitoring of biomass quality, supporting more efficient combustion processes, reduced emissions, and improved resource management in bioenergy systems.

The growing importance of wood pellets in renewable energy highlights the need for efficient, scalable methods of quality assessment. Current standards rely on manual caliper measurements, which are time-consuming and poorly capture variability across samples. This study introduces a patent-pending imaging approach that classifies pellet dimensions from shadow features. A prototype system was developed combining controlled lighting, a camera, and computational processing. Shadow characteristics were analyzed statistically and linked to pellet dimensions using machine learning models. Results show that illumination geometry strongly influences classification, with the best performance reaching 71 % accuracy. These findings demonstrate the feasibility of shadow-based imaging as a rapid, non-invasive alternative to manual measurement, with promising applications in pellet production, storage, and combustion systems.

Lignocellulosic (LC) biomass, the most abundant renewable biopolymer on Earth, holds significant promise as a sustainable feedstock for biofuels and high-value bioproducts. However, its structural complexity and high resistance to enzymatic degradation - mainly due to the presence of lignin - present major challenges for industrial bioconversion. This study aims to address these challenges by characterizing the composition of diverse LC materials using near- and mid-infrared spectroscopy combined with multivariate analysis. The focus is on exploring the variation in LC materials based on their cellulose, hemicellulose, and lignin content using Principal Component Analysis. Additionally, selected samples were subjected to liquid-submerged fermentation with the ligninolytic fungus Pleurotus ostreatus to assess its potential for ecofriendly pretreatment. Infrared spectroscopy was used to monitor compositional changes during fermentation, with the goal of evaluating its suitability as a rapid, non-destructive tool for real-time process control. The findings support the integration of spectroscopy and fungal biotechnology for sustainable LC residues valorization.

The management of agro-food waste (AFW) is a pressing environmental, social, and economic issue. Owing to the large and heterogeneous production in Italy, which is one of the EU's leading agricultural producers and food processors, it is crucial to effectively manage the AFW in terms of reduced emissions, costs, and lower resource consumption. The management of these wastes could be carried out by considering them as a resource for high-value compounds and (bio)energy. This review offers an overview of AFW management processes according to circular economy principles, by exploring available technologies at different Technology Readiness Levels (TRLs), focusing on the Italian context. The review provides descriptions of industrial-scale and pilot-scale plants, as well as emerging biological approaches for converting AFW into high-added valuable compounds and energy. This approach promotes the inclusion of AFW within a circular economy framework, increasing the sustainability of waste management by considering them as valuable resources.

Strawberry is a valuable crop produced in the EU, with an upward consumption trend due to its health-promoting and sensory attributes. The European strawberry market is estimated to be USD 3.8 billion. Thus, to foster sustainable and competitive production, efforts are underway to develop new cultivars that address the demand for innovative cultivation systems and high-quality produce. This initiative is crucial given the challenges climate change poses and the need for environmental preservation. Although breeding programs predominantly emphasize factors such as yield and overall nutritional quality parameters, there is a gap in their consideration of environmental performance. This gap must be addressed to help the strawberry industry adopt environmentally sustainable agricultural practices and contribute to global efforts to mitigate climate change. Using life cycle assessment (LCA), we evaluated the environmental performance of tunnel strawberry production in soilless-based field trials. Primary data regarding different cultivars were collected from partners in Italy and the UK. A cradle-to-farm gate assessment was conducted using a functional unit (FU) of 1 kg fresh strawberry. Environmental impacts were assessed using the Environmental Footprint 3.1 method. Results showed varying environmental performance of the systems across various impact categories. The climate change scores ranged between 0.47 to 0.56 kg CO2 eq./FU. Infrastructure and the soilless substrate (coir and peat) were the greatest drivers of the impacts, which could be mitigated by lifespan extension, increased recycling, and substrate reuse. This study underscores the importance of including environmental sustainability in multi-criteria decision-making for breeding materials.

This paper explores the integration of Machine Learning techniques in assessing the quality of wood chips, a key biomass source for sustainable energy production. Biomass, specifically wood chips, plays a critical role in transitioning from fossil fuels, which are the primary contributors to global carbon emissions. Traditional methods of evaluating wood chip quality, such as laboratory analysis for moisture content, ash content, nitrogen levels, and heating value, face limitations due to time constraints and variability in material composition. Machine Learning offers a solution by providing real-time, accurate predictions that can optimize combustion efficiency and reduce environmental impact. This study reviews various Machine Learning models like support vector machines, decision trees, artificial neural networks, and partial least squares regression, which have demonstrated high predictive accuracy for parameters like moisture content and heating value. However, challenges remain, particularly in predicting nitrogen and trace elements like chlorine and sulfur, due to biomass heterogeneity. The integration of Machine Learning with remote sensing technologies is proposed as a promising avenue for enhancing real-time quality monitoring throughout the wood chip production chain. Future advancements in model refinement and data acquisition are essential for further optimizing biomass as a renewable energy source.

In the context of climate change and the increasing demand for innovative solutions in agriculture and energy, agrivoltaic systems (AVSs) have emerged as promising technologies. These systems integrate photovoltaic panels with agricultural practices, optimizing both food and energy production. This study provides a comprehensive review focused on monitoring techniques applicable to AVS, including fixed sensors and remote monitoring tools. Bibliographic analysis revealed a significant increase in scientific interest in AVSs since 2019, with most publications focusing on technological, agronomic, and environmental aspects. Key findings highlight environmental benefits such as reduced greenhouse gas emissions, improved water efficiency, and enhanced soil quality. Otherwise, challenges including high initial costs and the persistence of technical complexities. Innovative configurations such as semi-transparent or vertically bifacial panels enable resource optimization and improved agricultural yields if combined with advanced monitoring systems. This study highlights the importance of incentive policies and further research to maximize the potential of AVSs in promoting sustainable land management.

Biomass continues to play a key role as an alternative to fossil fuels. Woody biomass produces lower greenhouse gas emissions than fossil fuels. However, in order to consider biomass as ‘green energy’, a number of factors should be taken into account, including the characterization of the quality of the resource. Therefore, monitoring quality parameters, such as moisture, ash, N content, is essential to assess the sustainability of biomass for energy production. This paper presents the results of laboratory analyses performed on wood chip samples from four Italian regions over a five-year period (2019–2023). In particular, all quality parameters defined by ISO 17225-9 for industrial wood chips were assessed. Data were analyzed using descriptive, parametric, non-parametric statistics, and multivariate analysis. An interest in quality monitoring has been observed, indicated by an increase in the number of samples received from suppliers and an enhancement in the average values of quality parameters. Moreover, an overall decrease in moisture and N content has been observed, while ash content and heating value have undergone non-linear variations. Statistically significant quality differences between samples from different regions may be the result of different practices, such as outdoor or indoor storage, climate differences, different biomass growth conditions.

The relevant growth of the wood pellet market in Europe in the last decade led to an increased focus on solid biofuel as a necessary and available renewable resource for energy production. Among biofuels, wooden pellets are among the most widespread for domestic heating. Therefore, monitoring the qualitative properties of commercialized pellets is crucial in order to minimize the amount of harmful emissions in residential areas. Standard ISO 17225 sets threshold values for the chemical and physical properties that commercialized biofuels must fulfil. Specifically, ISO 17225-2 defines that pellets for residential use must be produced from virgin wood, but no method is proposed to assess the actual origin of the material, leading to the risk of the commercialization of pellets made up from chemically treated materials. This study proposes a model obtained via near infrared spectroscopy analyses and chemometrics methods, such as classification, to rapidly assess whether pellets are made up of virgin or chemically treated wood. The result suggests the effectiveness of NIRs for the detection of non-virgin pellets with an accuracy greater than 99%. Furthermore, the model appeared to be accurate in the assessment of both milled and intact pellets, making it a potential in-line instrument for assessments of pellets’ quality.

Hydrothermal carbonization (HTC) is a promising method for the conversion of agricultural and agro-industrial residues into valuable products. HTC processes biomass through chemical reactions that produce hydrochar, a carbon-rich solid similar to lignite. Unlike other thermochemical processes, HTC can handle high-moisture biomass without pre-drying. This article evaluates the efficiency of HTC on wood chips, wheat straw, and grape pomace, examining their chemical and structural characteristics and critical operational parameters such as the temperature, pressure, biomass/water ratio, and reaction time. The obtained results highlight that the two key process parameters are the temperature and the ratio between the solid biomass and liquid phase. Increasing the first parameter increases the energy content by 20% and increases the carbon concentration by up to 50%, while reducing the oxygen content by 30% in the hydrochar. Varying the second parameter leads to the alternating reduction of the ash content but simultaneously reduces the energy content. The reaction time seems to have a limited influence on the quality parameters of the biochar produced. Lastly, HTC appears to successfully enhance the overall quality of widely available agricultural wastes, such as grape pomace.

The European Green Deal, together with the current political and economic background, aims at improving the use of renewable energy resources rather than fossil fuel dependence. Woodchip could be considered as a valid and widely available alternative, but the high heterogeneity of its properties must be evaluated according to the important influence in each step of the supply chain to combustion process. Moisture content is the main parameter involved in the quality characterization because it influences the energetic (it reduces the biomass calorific value in combustion), the management (during harvesting, handling, and storage steps), and the economic (price based on the load, affected by bulk density) aspects of woodchip utilization. Continuous monitoring of moisture content results essential, but laboratory analyses are time-consuming, destructive, and need economic efforts in terms of instruments and skilled personnel. Near infrared Spectroscopy (NIRS) is a fast, friendlyuse, and alternative method to qualitative characterization, based on non-destructive interaction between wavelength and chemical components. So, the aim of this project is to evaluate the prediction performance of moisture content using a portable NIRS adapted to in-line utilization. Then, the results have been compared with the already implemented prediction models of a different portable NIRS, applying statistics to assess the industrial woodchip quality, but also the Tukey's Test method to evaluate the replicates effect. Considering the general consistency of industrial woodchip quality and the use of three different replicates approach, results show good performance ranging from 0.64 to 0.86 as determination coefficient, with errors of prediction from 4.44 to 2.88%, confirmed by a prediction bias below the 2.5% for more than 50% of the samples. Tukey's Test shows that replicates adequately described samples variability thanks to constant and uniform analysis that dilutes the error. Furthermore, accepting a minimum error threshold, no investigation on replicates feasibility is needed, reducing time and economic efforts. In conclusion, the crucial demand of moisture control meets the several advantages of NIRS technique, with the potential in-line application to obtain real-time quality tracing and high-performance energy production.

Coffee is one of the most widely consumed beverages in the world; the European Union alone consumes about 2.5 million tons of coffee per year. Yearly, millions of tons of coffee residues are generated, becoming an attractive material for circular economy flows. This study explores the potential of utilizing pelletized coffee residues as sustainable bioenergy sources within the framework of a circular economy. The coffee residues, obtained from damaged capsules and pods from factories, were utilized in pure form or blended with sawdust at different percentages, then analyzed with respect to their physical and thermochemical parameters. The results indicate that unblended coffee residues exhibit favorable combustion properties with respect to heating value (18.84 MJ kg−1), but also high concentrations of N (4.14%) compared to the conventional pellets obtained from other agricultural residues. The blending with woody material negatively affects both durability and bulk density, but simultaneously promotes a reduction in ash content (3.09%) and N content (1.94%). In general, this study confirmed the findings of previous scientific reports, highlighting that at least 50% blending with low-nitrogen biomasses is necessary to reach the marketability of the product. In addition, this study highlighted the criticality in terms of durability that these mixtures confer to the final product, emphasizing that future research should focus on optimizing the combination of these factors to improve the properties of the pellet.

The combustion efficiency of wood pellets is partly affected by their average length. The ISO 17829 standard defines the methodology for assessing the average length of sample pellets, but the method does not always lead to representative data. Furthermore, a standard analysis is time-consuming as it requires manual measurement of the pellets using a caliper. This paper, whilst evaluating the effect of pellet length on combustion efficiency, proposes a pending-patented dimensional image processing method (DIP) for assessing pellet length. DIP allows the dimensional data of grouped and stacked pellets to be obtained by exploiting the shadows produced by pellets when exposed to a light source, assuming that different-sized pellets produce different shadows. Thus, the proposed method allows for the extraction of dimensional information from non-distinct objects, overcoming the reliance of classical image processing methods on object distance for effective segmentation. Combustion tests, carried out using pellets varying only in length, confirmed the influence of length on combustion efficiency. Shorter pellets, compared to longer ones, significantly reduced CO emissions by up to 94% (mg/MJ). However, they exhibited a higher fuel mass consumption rate (kg/h), with an increase of up to 22.8% compared to the longest sample. In addition, longer pellets produced fewer but larger shadows than shorter ones. Further studies are needed to correlate the number and size of shadows with samples’ average length so that DIP could be implemented in stoves and programmed to communicate with the control unit and automatically optimize the setting in order to improve combustion efficiency.

Strawberry is the most cultivated berry fruit globally and it is really appreciated by consumers because of its characteristics, mainly bioactive compounds with antioxidant properties. During the breeding process, it is important to assess the quality characteristics of the fruits for a better selection of the material, but the conventional approaches involve long and destructive lab techniques. Near infrared spectroscopy (NIR) could be considered a valid alternative for speeding up the breeding process and is not destructive. In this study, a total of 216 strawberry fruits belonging to four different cultivars have been collected and analyzed with conventional lab analysis and NIR spectroscopy. In detail, soluble solid content, acidity, vitamin C, anthocyanin, and phenolic acid have been determined. Partial least squares discriminant analysis (PLS-DA) models have been developed to classify strawberry fruits belonging to the four genotypes according to their quality and nutritional properties. NIR spectroscopy could be considered a valid non-destructive phenotyping method for monitoring the nutritional parameters of the fruit and ensuring the fruit quality, speeding up the breeding program.

Global market developments of wooden pellets have led to an increased attention towards pellet quality. ISO 17829 defines the procedure to assess pellets’ geometrical parameters, which play a key role in pellet overall quality. For instance, pellet length influences the spatial arrangement within the stove brazier, affecting the interaction between combustion air and solid biofuel, thus affecting CO emissions. The ISO 17829 method is time-consuming and affected by the operator’s accuracy. Recent studies have investigated the application of new methods, such as image processing, for monitoring the aforementioned parameter. While also assessing the representativeness of ISO 17829’s method, this paper proposes an alternative measuring tool based on image processing named Pellet Length Detector (PLD). Samples were obtained from Italian pellet suppliers and subjected to a multiple dimensional analysis via PLD and caliper. The PLD’s overall performance led to satisfactory results, with only 10% of the samples having a bias between replicates of >2 mm. Compared to caliper, PLD led to an average bias of 0.5 mm. Moreover, a one-way ANOVA highlighted that increasing the sample size between caliper and PLD leads to a greater statistical similarity of the data obtained for different replicates. Given the prototype status of the device, a further performance upgrade is possible, especially through error modeling.

Strawberry fruits are particularly appreciated by consumers for their sweet taste related to their soluble solids content (SSC). However, strawberries are characterized by a short shelf-life and high susceptibility to tissue infection, mainly by Botrytis cinerea. The SSC determination of strawberry fruit through traditional destructive techniques has some limitations related to the applicability, timing, and number of samples. The aims of this study are (i) to verify if any relation between SSC and B. cinerea susceptibility in the fruits of five strawberry cultivars occurs and (ii) to determine the SSC of strawberry fruits through near infrared spectroscopy (NIR). Principal component analysis was used to search for spectral differences among the strawberry genotypes. The partial least squares regression technique was computed in order to predict the SSC of the fruits collected during two harvesting seasons. Moreover, variable selection methods were tested in order to improve the models and get better predictions. The results demonstrated that there was a high correlation between SSC and B. cinerea susceptibility (R2 up to 0.87). The SSC was predicted with a standard error of 0.84 °Brix and R2p 0.75 (for the best model), which indicated the possibility to use the models for screening applications. NIR spectroscopy represents an important non-destructive alternative and finds remarkable applications in the agro-food market.

The EU common effort to reduce fossil fuel consumption paves the way to enhancing interest towards renewable resources, as solid biofuel. Its properties and origin are fundamental issues affecting energy conversion efficiency and supply chain sustainability. Laboratory conventional analyses are time consuming and require huge economic efforts, so it is essential to obtain rapid and real-time quality information along all the supply chain. Near Infrared Spectroscopy (NIRS) is able to satisfy these requirements, minimizing the time delay and promoting on site analysis along the whole process. The aim of the present work is to evaluate the performance of two different portable MEMS (Micro Electro Mechanical System) NIR spectrophotometers for identifying the raw biomass of which pellet is made. Thus, samples of different categories have been collected, particularly Italian softwood and hardwood species, wood processing industry residues and bioenergy crops. Ten replicates for each sample have been performed and different spectral pretreatments have been tested to reduce noise and scattering effects. Principal Component Analysis (PCA) has been used to investigate the spectral variability and look for groupings among the categories. According to the spectral range, the C12 and C13 spectrophotometers revealed different performance in groups separation due to the detection of different chemical bonds, for example glue presence. More interesting results could be obtained by widening the biomass samples to manage stronger dataset for chemometrics analysis. Nevertheless, results of the research aim at introducing NIR sensor, coupled with chemometrics, onboard pellet combustion devices to improve process efficiency and environmental performance.

Biomass materials play a key role in the renewable energy market as they can serve as a suitable alternative to fossil fuels. However, the quality of the material entering bioenergy plants is often a cause of technical concern. Biomass quality assessment is crucial not only for energy characterization but also for environmental and operational aspects. The goal of this study is to characterize and classify the biomasses used by Italian power plants with reference to the quality classes stated by the ISO standard 17225:2021. A further objective is to verify the ability of the standard to classify heterogeneous and specific biomasses. In this study, more than 900 biomass samples were analyzed. The samples were collected from several Italian power plants with >5 MWe between 2010 and 2020, and the most important physical and chemical parameters were analyzed according to the international standards of reference. The results of the analyses were collected in a large dataset used for subsequent statistical analyses. Statistical analyses applied are Principal Component Analysis and Pearson correlation maps, which showed that the ash content is a fundamental and ideal parameter to assess the biomass quality. Results obtained demonstrate that herbaceous biomasses are of low quality mainly due to the high ash content; a relatively low ash content was found for woody biomasses.

Food losses are responsible for noteworthy economic costs and negative environmental impacts. Within all the food-processing activities, seafood industry is of high importance, especially in coastal regions. Most seafood wastage is currently removed and discarded without recovering the valuable materials that it contains. However, seafood waste can be used as raw matter in biorefineries. Biorefineries refer to facilities where different conversion processes are integrated to obtain multiple bioproducts from organic feedstock. To this aim, different physical, chemical, thermochemical, and biological processes can be implemented to obtain biofertilizers, biostimulants, biofuels, and other value-added biocompounds. This chapter focuses on the theoretical implementation of biorefineries fed by wastes coming from the seafood industry. Some processes and technologies are reviewed, as well as some biocompounds that can be obtained from seafood wastes.

The chemical composition of woody biomass directly influences its thermal degradation and, subsequently, the selection of processes and technologies used for its conversion into energy or value-added products. Thus, the present study aimed to evaluate the thermal behavior and chemical-physical characteristics of three different woody biomass species (hardwood, softwood and chemically-treated wood) using thermogravimetric and characterization analysis based on ISO 16948, ISO 18125 and ISO 18122 methods. The main findings show that the most significant trend of mass loss, around 70%, in the thermal degradation of the different species of woody biomass occurred between 150 °C and 500 °C and that the residual mass at 650 °C was between 13% and 24%. Although the three species of woody biomass showed a high average energy content (19.60 MJ/kg), softwood samples had a more stable thermal degradation than hardwoods and chemically-treated woods.