Purpose: Consumer confidence in the European food industry has been shaken by a number of recent scandals due to food fraud and accidental contamination, reminding the authors that deliberate incidents can occur. Food defence methods aim to prevent or mitigate deliberate attacks on the food supply chain but are not a legal requirement. The purpose of this paper is to discuss how proactive and reactive food defence practices can help prevent or mitigate malicious attacks on the food chain and also food fraud, food crime and food safety. The authors look at how food defence differs from food safety and how it contributes to food supply chain integrity. Design/methodology/approach: Food defence has been the focus of two different EU FP7 security projects, EDEN and SNIFFER. Food industry stakeholders participated in workshops and demonstrations on food defence and relevant technology was tested in different food production scenarios. Findings: Food industry end-users reported a lack of knowledge regarding food defence practices. They wished for further guidelines and training on risk assessment as well as access to validated test methods. Novel detection tools and methods showed promise with authentication, identification, measurement, assessment and control at multiple levels of the food supply chain prior to distribution and retail. Practical implications: The prevention of a contamination incident, prior to retail, costs less than dealing with a large foodborne disease outbreak. Food defence should therefore be integral to food supply chain integrity and not just an afterthought in the wake of an incident. Originality/value: It is argued that food defence practices have a vital role to play across the board in unintentional and intentional food contamination incidents. The application of these methods can help ensure food supply chain integrity.

A highly sensitive and specific SPR-based competitive immunoassay for the detection of Thiabendazole (TBZ) has been developed. An indirect format where a TBZ–protein conjugate is immobilized onto gold surfaces has been selected. Under the optimal conditions, a LOD of 0.67 nM (0.13 μg L⁻¹) and an IC₅₀ of 3.2 nM (0.64 μg L⁻¹) have been achieved which are comparable to the values obtained by conventional ELISA. Analysis of real samples has been attempted by first evaluating the influence of complex matrix samples coming from whole oranges and secondly measuring samples containing TBZ previously evaluated by chromatographic methods. A methanolic extraction procedure followed by a simple dilution in assay buffer has proven to be sufficient to measure orange samples using the developed immunoassay with an excellent recovery percentage. The sensitivity and the feasibility of measuring whole orange samples demonstrate the effectiveness and robustness of the SPR biosensor, which can be useful for the determination of TBZ in food at concentrations below the Maximum Residue Levels (MRLs) established by the European legislation.

Hyperspectral imaging is a powerful technique that combines information of spatial distribution and of the chemical composition. In this study a hyperspectral system has been used to asses the feasibility of this technology to detect defects and properties related to the composition of meat products. Foreign bodies were added to pork tenderloin samples, which were processed to obtain the corresponding hyperspectral datacubes. Multivariate analysis was applied to process the datasets and artificial images of each sample were created to display the results. The obtained results show the great potential of this technique that combines the advantages of traditional machine vision and spectroscopy

Agroforestry systems (AFSs) enhance long-term carbon sequestration through tree biomass accumulation. As the European Union’s Carbon Farming Certification (CRCF) Regulation now recognizes AFSs in carbon farming (CF) schemes, accurate tree biomass estimation becomes essential for certification. This review examines field-destructive and remote sensing methods for estimating tree aboveground biomass (AGB) in AFSs, with a specific focus on their advantages, limitations, timing, and associated costs. Destructive methods, although accurate and necessary for developing and validating allometric equations, are time-consuming, costly, and labour-intensive. Conversely, satellite- and drone-based remote sensing offer scalable and non-invasive alternatives, increasingly supported by advances in machine learning and high-resolution imagery. Using data from the INNO4CFIs project, which conducted parallel destructive and remote measurements in an AFS in Tuscany (Italy), this study provides a novel quantitative comparison of the resources each method requires. The findings highlight that while destructive measurements remain indispensable for model calibration and new species assessment, their feasibility is limited by practical constraints. Meanwhile, remote sensing approaches, despite some accuracy challenges in heterogeneous AFSs, offer a promising path forward for cost-effective, repeatable biomass monitoring but in turn require reliable field data. The integration of both approaches might represent a valid strategy to optimize precision and resource efficiency in carbon farming applications.