Luis Mayor

The food processing industry is a vital sector in Europe contributing significantly to the economy and ensuring food safety and security for consumers. Identifying and addressing the gaps between occupations, qualifications, and certification of professionals is essential for policymakers, educators, and industry stakeholders to develop effective strategies for training and recruiting qualified workers in the food processing sector. This review aims to provide a comprehensive overview of the occupations, qualifications, and certifications of professionals in the European food processing sector, and to identify gaps or inconsistencies in the existing requirements. This study used desk research, and standardized classification systems (ESCO, ISCO, ISCED, EQF) were used to analyze food processing occupations, qualifications, and certifications in Europe. Occupational data were collected from ESCO, while vocational and academic qualifications were collected from national agencies and the ISEKI-Food Association. The Jaccard Similarity Index was used to assess skill overlap between occupations, and Pearson’s correlation was used to examine relationships between qualifications, industry focus, and economic indicators. A frequency analysis was conducted to identify common academic degree titles. Regulated professions were acquired from the European Commission’s database, and certifications were recognized by searching for them online. This review confirmed that there are significant variations in the qualifications and certifications required for food processing occupations across different European countries. These variations are likely influenced by factors such as industry size, national specificities, and regulatory requirements. Additionally, there is no full correspondence between occupations and qualifications. The regulation of professions is limited to a few countries, specifically for Food Technologists and Food Inspectors, with heterogeneous requirements.

The agri-food and forestry sectors are in transition towards more sustainable, green, and innovative systems tackling several challenges posed by globalization, governance, and consumers’ demands. This transition to novel processes, markets, and businesses requires skills and competences to prepare the new generations and upskill the actual workforce. The purpose of this paper was to assess the skills and knowledge needs of future professionals in the agri-food and forestry sectors, from European stakeholders’ perspectives, by using a European questionnaire. Overall, respondents highlighted the importance of improving sustainability and soft and digital skills. In particular, food safety management and control; quality management and assurance of processes and product; efficient use of resources and organization; and planning, visioning, and strategic thinking skills ranked higher. In almost all countries, respondents had the perception that neither formal nor non-formal training covered training needs, though formal training was more suited to address education requirements. Both for organizations and individuals, it is far more relevant to have skills to perform than to have training recognition. The outcomes also provide findings that can be used to help develop updated curricula that meet the sector’s needs.

Artichoke wastes after processing represent 60–70% of the raw material and are a potential source of inulin and polyphenols, bioactive compounds that can be valorized as food ingredients or nutraceutical products. The aim of this work was to assess and optimize the extraction of these compounds from artichoke wastes using water or water–ethanol mixtures as extracting agents. For simultaneous inulin and polyphenol extraction and to achieve high antioxidant activity in extracts, the best process conditions using water as an extracting agent were T = 89 °C and t = 139 min, where 80% of the inulin content, 60% of the total phenolic content (TPC) and 56% of the antioxidant activity (Aox) were obtained. For water–ethanol extractions, the best results were obtained with EtOH = 22.4%, T = 81 °C and t = 217 min, leading to extraction yields of 90% of TPC, 38% of Aox and 58% of inulin content. From these results, we recommend the use of water for the recovery of inulin and polyphenols from artichoke wastes. Although the extraction yield of polyphenols is lower in water treatments, the amount extracted is considerable and it is a greener option when compared with water–ethanol mixtures.

The agri-food and forestry sectors are under increasing pressure to adapt to climate change, consumer concern, technological and economic change, and complex global value chains. In turn, such challenges require that the necessary skills and competences are identified at various levels and within specific areas of the sectors. For that purpose, eleven focus groups in nine different EU-countries and two at EU-level were organized within the ERASMUS+ project “FIELDS” with the participation of farmers, cooperatives, agri-food companies, foresters, forest industries, advisors, and education providers to identify the skills needed in the agri-food and forestry sectors. The focus group participants identified business and strategic management skills, communication skills, and other skills related to sustainability, entrepreneurship, digital and soft skills to be most important for the agri-food and forestry sectors as a whole.

Training can better meet user needs by involving the potential users early in the process. Here, development of open science training for food science students and professionals began with 11 guided interviews of 15 beneficiaries in a European project aiming to build a ‘food cloud’ of datasets and services (FNS-Cloud, H2020 No. 863059). Discussions covered what partners want to learn, how they prefer to learn, and who are their ideal trainers. Inductive coding of interview transcripts with NVivo 12 qualitative analysis software revealed an inclination for technical training with a focus on data. Face-to-face learning and on-demand elearning offered by younger scientists were preferred methods and trainers. Most interviewees also talked about ‘food cloud’-specific fears and desires. These interviews are now the foundation of three well received elearning courses and two workshop series supporting the value of user input in early course development decisions.

Food wastes have traditionally been considered as dead-end materials; however, recent international, national, and regional policies strongly promote the use of these wastes as potential resources. This change of perception considers the need for sustainable exploitation of natural resources. In this context, artichoke processing produces high amounts of wastewaters, and specifically, wastewaters from the artichoke blanching step present a high content of valuable biocomponents such as carbohydrates and phenolic compounds (e.g., chlorogenic acid, 1700 ppm). In this work, the recovery of chlorogenic acid by applying sorption processes was studied. Five resins were tested, and it was found that the resin XAD 7 HP presented the best total adsorption-desorption yield (72.8%) at 20 °C. It was also found that there was a strong variation of the adsorption ratio depending on solution pH (3–7). Four models of isotherms were applied to the adsorption processes, and the Langmuir isotherm better explained the adsorption behavior. The kinetic study showed that the adsorption and desorption of chlorogenic acid followed a pseudo-second order model where the kinetic constant depended on the adsorbate concentration. In addition, it was found that the sorption process was controlled by more than just the intraparticle diffusion mechanism.

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

Chesnut and pumpkin fruits were dehydrated with osmotic solutions of sucrose and NaCl at 25°C. These food materials have different structure, composition and porosity. Water loss and solids gain kinetics were experimentally determined and modeled using a diffusional model. In spite of the several mass transfer mechanisms taking place along with diffusion during osmotic dehydration, the modeling was satisfactory and involved effective coefficients of diffusion useful to quantify the different mass transfer fluxes. Water and sucrose transfer rates during osmotic dehydration with sucrose solutions are independent on the initial food material characteristics; however they seem to be related with the permeability of these components to a sucrose layer formed in the surface of the samples. In the case of osmotic dehydration with sodium chloride solutions, the coefficients of diffusion show a dependence on food material characteristic and higher values of these coefficients for pumpkin (more porous material) were found.