Sensory assessors determine the result of sensory analysis; therefore, investigation of panel performance is inevitable to obtain well-established results. In the last few decades, numerous publications examine the performance of both panelists and panels. The initial point of any panelist measures are the applied selection methods, which are chosen according to the purpose (general suitability or product-specific skills). A practical overview is given on the available solutions, methods, protocols and software relating to all major panelist and panel measure indices (agreement, discrimination, repeatability, reproducibility and scale usage), with special focus on the utilized statistical methods. The novel approach of the presented methods is multi-faceted, concerning time factor (measuring performance at a given moment or over a period), the level of integration in the sensory testing procedure and the target of the measurements (panelist versus panel). The present paper supports the choice of the performance parameter and its related statistical procedure. Available software platforms, their accessibility (open-source status) and their functions are thoroughly analyzed concerning panelist or whole panel evaluation. The applied sensory test method strongly defines the applicable performance evaluation tools; therefore, these aspects are also discussed. A special field is related to proficiency testing. With the focus on special activities (product competitions, expert panels, food and horticultural goods), practical examples are given. In our research, special attention was given to sensory activity in companies and product experts or product-specific panels. Emerging future trends in this field will involve meta-analyses, application of AI and integration of psychophysics.

The rapid growth of the human population, the increase in consumer needs regarding food authenticity, and the sub-par synchronization between agricultural and food industry production necessitate the development of reliable track and tracing solutions for food commodities. The present research proposes a simple and affordable digital system that could be implemented in most production processes to improve transparency and productivity. The system combines non-destructive, rapid quality assessment methods, such as near infrared spectroscopy (NIRS) and computer/machine vision (CV/MV), with track and tracing functionalities revolving around the Internet of Things (IoT) and radio frequency identification (RFID). Meanwhile, authenticity is provided by a self-developed blockchain-based solution that validates all data and documentation “from farm to fork”. The system is introduced by taking certified Hungarian sweet potato production as a model scenario. Each element of the proposed system is discussed in detail individually and as a part of an integrated system, capable of automatizing most production flows while maintaining complete transparency and compliance with authority requirements. The results include the data and trust model of the system with sequence diagrams simulating the interactions between participants. The study lays the groundwork for future research and industrial applications combining digital tools to improve the productivity and authenticity of the agri-food industry, potentially increasing the level of trust between participants, most importantly for the consumers.

Fruit juice and wine are important beverages that are consumed all over the world. Due to their constantly increasing demand and high value, fruit juice and wine are one of the most frequent targets of adulteration. Since adulterated foods are proven to have harmful effects on health, several approaches have been utilized for the detection of fruit juice and wine adulteration. Based on the requirement for sample destruction, analytical techniques to assess food authenticity can be classified into 2 main categories, i.e., destructive and non-destructive techniques. This paper provides an overview on the principle of adulteration detection, its application and performance, and the advantages and limitations of various analytical techniques. Destructive approaches, such as physicochemical methods, isotope analysis, elemental analysis, chromatographic techniques, and DNA-based techniques, are reviewed. Furthermore, non-destructive approaches, including spectroscopic-based techniques, nuclear magnetic resonance spectroscopic technique, electronic techniques, and imaging-based techniques, are discussed.

The viability of probiotics is strictly influenced by the production, storage, and digestion, while microencapsulation is a technology that can protect them against harsh environments. In this study, the impact of different core-to-wall ratios and wall material formulations on physical properties and the cell number of the microcapsules were investigated. The samples with core-to-wall ratio 1:1 have a significantly higher cell number, encapsulation efficiency, and bulk density than samples with core-to-wall ratio 1:1.5. The yields of the encapsulation method were changes in the opposite direction. Meanwhile, core-to-wall ratios and formulation have a significant effect on the cell number of the microcapsules during the in vitro SGJ test, whereas time, core-to-wall ratios, and formulation have a similar influence in the in vitro SIJ test. Moreover, probiotic apple juices stored at 4 °C for 6 weeks kept the highest cell number at the end. Furthermore, probiotic apple juices fortified by microcapsules coated with WP:DWP 1:1 in core-to-wall ratio 1:1 and stored at 4 °C for 4–8 weeks exhibited a significantly lower pH value. In summary, both whey proteins and denatured whey proteins are as good as coating material for microencapsulation of probiotic bacteria Lactobacillus plantarum 299v strains. These microcapsules have high potential in the production of probiotic apple juice even by fermentation or fortification methods.

Microencapsulation is an up-and-coming technology for maintaining the viability of probiotics. However, the effect of core-to-wall ratios and ratios of polysaccharides on the protection of the Lactiplantibacillus plantarum 299v strain has not been deeply discussed. Lyophilization of the Lp. plantarum 299v strain was conducted, and different core-to-wall ratios and ratios of maltodextrin (MD) and resistant starch (RS) were applied. Results demonstrated that the content of MD and RS had an influence on the yield and bulk density in both core-to-wall ratios (1:1 and 1:1.5). In addition, samples coated with a core-to-wall ratio of 1:1.5 had significantly higher viability than those coated with a core-to-wall ratio of 1:1. Moreover, samples coated with core-to-wall ratios of 1:1 and MD:RS 1:1, as well as core-to-wall ratios of 1:1.5 and MD:RS 3:1, had the highest cell number after simulated gastric fluid and simulated intestinal fluid testing, respectively. Furthermore, the optimal formulation for the application of microencapsulated Lp. plantarum 299v in apple juice (serving as a functional beverage) is listed as follows: core-to-wall ratios of 1:1 and MD:RS 1:1, with the fortification method, and stored at 4 °C. After 11 weeks of storage, the cell count was 8.28 log (CFU/mL). This study provided a strategy for Lp. plantarum 299v to achieve high viability in long-term storage and provides an application in functional apple beverages.

Discrimination and species identification of meat has always been of paramount importance in the European meat market. This is often achieved using different conventional analytical methods but advanced sensor-based methods, such as the electronic tongue (e-tongue), are also gaining attention for rapid and reliable analysis. The aim of this study was to discriminate Angus, domestic buffalo, Hungarian Grey, Hungarian Spotted cattle, and Holstein beef meat samples from the chuck steak part of the animals, which mostly contained longissimus dorsi muscles, using e-tongue as a correlative technique with conventional methods for analysis of pH, color, texture, water activity, water-holding capacity, cooking yield, water binding activity, and descriptive sensory analysis. Analysis of variance (ANOVA) was used to determine significant differences between the measured quality traits of the five-meat species after analysis with conventional analytical methods. E-tongue data were visualized with principal component analysis (PCA) before classifying the five-meat species with linear discriminant analysis (LDA). Significant differences were observed among some of the investigated quality parameter. In most cases, Hungarian Grey was most different from the other species. Using e-tongue, separation patterns could be observed in the PCA that were confirmed with 100% recognition and 97.5% prediction of all the different meat species in LDA.