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Banana is a really chilling injury sensitive product. Its sensitivity to cold temperatures generates serious practical, economical and commercial problems. Chilling injury related physiological responses of Cavendish type green banana samples stored at 2.5, 5, 10 °C and near optimal (15 °C) cold storage temperature were investigated by nondestructive optical methods (surface color and chlorophyll fluorescence measurement, DA-index ® evaluation) and by the determination of the physiological reactions (respiration, ethylene production, symptom manifestation) during cold storage and the 8-day long subsequent shelf-life. The positive effects of low temperature storage were proven on mass loss, respiration and ethylene production. In case of bananas stored at 2.5-10 °C, the chilling injury related changes in chlorophyll content related DA-index ® , IR-values; F m and F v chlorophyll fluorescence values, the L*, a*, b*, C* and hue angle color characteristics suggested clearly from day 3 the onset of chilling injury several days before the visible signs of chilling injury appeared.

The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species. In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R2 during the validation except for one attribute, which was much higher compared to the independent models built for instruments.

Tomato, and its concentrate are important food ingredients with outstanding gastronomic and industrial importance due to their unique organoleptic, dietary, and compositional properties. Various forms of food adulteration are often suspected in the different tomato-based products causing major economic and sometimes even health problems for the farmers, food industry and consumers. Near infrared (NIR) spectroscopy and electronic tongue (e-tongue) have been lauded as advanced, high sensitivity techniques for quality control. The aim of the present research was to detect and predict relatively low concentration of adulterants, such as paprika seed and corn starch (0.5, 1, 2, 5, 10%), sucrose and salt (0.5, 1, 2, 5%), in tomato paste using conventional (soluble solid content, consistency) and advanced analytical techniques (NIR spectroscopy, e-tongue). The results obtained with the conventional methods were analyzed with univariate statistics (ANOVA), while the data obtained with advanced analytical methods were analyzed with multivariate methods (Principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR). The conventional methods were only able to detect adulteration at higher concentrations (5–10%). For NIRS and e-tongue, good accuracies were obtained, even in identifying minimal adulterant concentrations (0.5%). Comparatively, NIR spectroscopy proved to be easier to implement and more accurate during our evaluations, when the adulterant contents were estimated with R2 above 0.96 and root mean square error (RMSE) below 1%.

1-Methylcyclopropene (1-MCP) is the active component of the SmartFresh Quality System. By the application of the 1-MCP compound, quality of the harvested pears can be preserved longer during the normal cold storage. In our work, the effectiveness of the SmartFresh Quality System was investigated on 'Bosc Kobak' pears (Pyruscommunis L.) harvested at different times. The rheological changes and storage losses were measured. The effectiveness of 1-MCP depends on many variables, but our results show that the optimal harvest date and the condition of the harvested fruit are the most infl uential factors.

The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740–1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.

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.