The objective of this study was to investigate the textural changes of apple slices undergoing convective drying. Texture profile analysis (TPA) measurements at 20% compression clearly showed three periods in texture development: softening, uniform hardness, and hardening. In the period of softening, the initial hardness of 2,260 g exponentially decreased to 40–90 g, remained low in the moisture content range of 3.0–0.5 g/g, and rapidly increased below 0.5 g/g. Cohesiveness, springiness, resilience, and chewiness demonstrated similar three-phase behavior with the dependence on moisture content in the period of softening, constant values in the period of uniform hardness, and an inversion point below 0.5 g/g. In the period of hardening, the texture parameters were dependent on temperature.

In this study, an efficient optimisation method by combining response surface methodology (RSM) and genetic algorithm (GA) is introduced to find the optimal topology of artificial neural networks (ANNs) for predicting colour changes in rehydrated apple cubes. A multi-layered feed-forward backpropagation ANN model of algorithms was developed to correlate one output (colour change) to four input variables (drying air temperature, drying air velocity, temperature of distilled water and rehydration time). A predictive model for ANN topology in terms of the best mean squared error (MSE) performance on validation samples was created using RSM. RSM model was integrated with an effective GA to find the optimum topology of ANN. The optimum ANN had minimum MSE when the number of hidden neurons, learning rate, momentum constant, number of epochs and number of training runs were 13, 0.33, 0.89, 3869 and 3, respectively. MSE of optimal ANN topology on validation samples was 0.0072095. It turned out that the optimal ANN topology can be considered as more precise for predicting colour change in the rehydrated apple cubes. Mean absolute error and regression coefficient (R) of the optimal ANN topology were determined as 0.0259 and 0.96475 for training, 0.0399 and 0.95243 for testing and 0.0264 and 0.95151 for validation data sets. The results of the testing model on new samples showed excellent agreement between the actual and predicted data with coefficient of determination R ² = 0.97.

The aim of this study was to assess the possibilities to use brewer’s spent grains (BSGs) left over from beer production for energy purposes, and to determine its calorific value and chemical composition. The research materials were samples of wet spent grain from a brewery in Poland. Three samples, that are different in ingredient composition, were examined. The examined samples of BSGs were characterised by humidity that is typical for this product (approx. 77–80%). Convective drying of the spent grain contributed to a reduction in the water content in the biomass to below 10%. Samples of dry spent grain that were examined contained a similar amount of ash (3.8–4.1% d.m.) and organic matter (91.0–91.9% d.m.). All the examined spent grain samples demonstrated similar volatile matter content—approx. 77.8–78.7% d.m. and calorific value—approx. 15.6–15.9 MJ/kg. The estimated calorific value for wet samples (approx. 1.4–2.0 MJ/kg) indicated that it is necessary to lower water content in the biomass in order to improve its energy properties.

Enhancing plant productivity and mitigating the impact of environmental stressors require a thorough understanding of phytomonitoring and physiological features indicative of plant health. This study delves into the response of cucumber plants...