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The EU Chocolate Directive 2000/36/EC allows the use of the vegetable fats CBEs and CBIs up to a maximum of 5% in chocolate. Manufacturers and users must know how this has an influence on the properties of chocolate. The objective of the work reported here was to find out by systematic investigations, which effect CBEs/CBIs have on the quality parameters, hardness and heat resistance of chocolate. The influence on the hardness was tested also under extreme practical storage conditions. The quality monitoring was performed up to one year. For the determination of the heat resistance the penetrometric method was used in the temperature range 25–32 °C measuring the maximum loading force, occurring during the penetration of a cylindrical probe of 2 mm diameter with 4 mm penetration. The correlation between the average maximum loading force, relevant to the hardness of chocolate, and the temperature can be described by a linear regression at 95% confidence level. Statistical analyses (correlation analysis, residual analysis, Durban-Watson statistic) showed that it is possible to define the heat resistance of solid chocolate in the temperature range of 25–32 °C by the slope and the ordinate intercept of the regression line of the loading force vs. temperature for given parameters (composition, storage, experimental layout, etc.). For the determination of the hardness of the chocolate also the penetrometric method was used to measure the maximum loading force occurring during the penetration of a needle probe with 2 mm deformation. The hardness of the chocolate samples determined with the penetrometric method and statistical analysis (One-Way, Two-Way Analysis of Variance, Dunnett’s comparisons) is significantly dependent on the composition and storage conditions, where the storage conditions are the dominant factor. The results show that the differences in hardness between the chocolate samples with CBE/CBI and those without CBE/CBI, both stored in the cellar (cold storage), are marginal. After one week of storage the sample with CBI has nearly the same hardness as the standard sample with CB, whereas the sample with CBE was slightly softer. The differences are slightly clearer for the northern storage room (moderate temperature) and for the southern room (warm temperature). After a definite storage time the hardness of all samples increased and was in the case of the southern storage room (warm temperature) up to twice as high. The quality monitoring up to one year showed that the reason for this increase in hardness is not a special storage time but the increasing temperatures with the beginning of the warm season and the cyclic change of the temperature during day and night. So an explanation for this unexpected increase in hardness can be a thermocyclic hardening of the chocolate samples under these storage conditions.

Abstract

This study aims to predict drought periods affecting the Tokaj-Hegyalja wine region and the application of this in crop protection. The Tokaj-Hegyalja wine region is the only closed wine region in Hungary with a specific mesoclimate and a corresponding wine grape variety composition, in which climate change strongly threatens cultivation. The probability that a randomly selected day in the vegetation period will fall into a drought period in the future was estimated using the daily precipitation amount and daily maximum temperature data from the Hungarian Meteorological Service for the period 2002–2020. The Markov model, a relatively new mathematical method for the statistical investigation of weather phenomena, was used for this. Markov chains can, therefore, be a valuable tool for organizing integrated pest management. This can be used to plan irrigation, control fungal pathogens infecting the vines, and plan the success of a given vintage.

Efforts have been made to predict the sensory profile of coffee samples by instrumental measurement results. The objective of the work was to evaluate the most important sensory attributes of coffee samples prepared from ground roasted coffee by electronic tongue and by sensory panel. Further aim was to predict the Arabica concentration and the main sensory attributes of the different coffee blends by electronic tongue and to analyze the sensitivity of the electronic tongue to the detection of poor quality coffee samples. Five coffee blends with known Arabica and Robusta concentration ratio, five commercially available coffee blends and a poor quality coffee were analyzed. The electronic tongue distinguished the coffee samples according to the Arabica and Robusta content. The sensory panel was able to discriminate the samples based on global aroma, bitterness and coffee aroma intensity (p < 0.01). The Arabica concentration was predicted from the electronic tongue results by PLS with close correlation and low prediction error. Models were developed to predict sensory attributes of the tested coffee samples from the results obtained by the electronic instrument.