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The dried petals of Echium amoenum Fisch. & C.A. Mey. are used for the treatment of many diseases but there are not any reports about the effects of drying conditions on their bioactive compounds. A complete 3 2 factorial design was used, where the independent variables were drying temperature (40°C-60°C) and air flow rate (0.5-1.0 m/s). The bioactive compounds studied were total phenolic, total flavonoid and anthocyanin contents, and antioxidant activity. The desirability index was used to predict the optimal drying conditions. Both independent variables were statistically significant. The optimal drying conditions were air velocity of 0.86 m/s at 60°C. The results show that the drying conditions play an important role in determining the final quality of the product, content of its bioactive compounds, and minimum drying time. ARTICLE HISTORY

BACKGROUND: Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first-order Takagi-Sugeno-type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed-drying process of Echium amoenum Fisch. & C. A. Mey. The moisture ratio evolution is calculated based on the drying temperature, airflow velocity and process time. Different ANN topologies are examined by evaluating the number of neurons (3 to 20), the activation functions and the addition of a second hidden layer. Different numbers (2 to 5) and shapes of membership functions are examined for the ANFIS, using the grid partitioning method. The models with the best performance in terms of prediction accuracy, as evaluated by the statistical indices, are compared with the best fit thin-layer model and the available data from the experimental cases of 40 °C, 50 °C and 60 °C temperatures at 0.5, 0.75 and 1 ms −1 airflow velocity. RESULTS:The best performed ANFIS model, comprised by 5-2-2 of π-shaped andtriangular membership functions for time, temperature and airflow velocityinputs respectively, was able to describe the moisture ratio evolution of E. amoenum more precisely than the best ANN topology, achieving higher values of coefficientof determination (R 2 ), root mean square error (RMSE) and sum of squared errors(SSE). The best thin-layer model involving six adjustable parameters, managedto describe experimental data most accurately with R 2 = 0.9996, RMSE = 0.0057and SSE = 7.3•10 −4 . CONCLUSION:The results of the comparative study indicate that empirical regression models with increased numbers of adjustable parameters, constitute a simpler and more accurate modeling approach for estimating the moisture ratio of E. amoenum Fisch. & C. A. Mey under fluidized bed drying.

A new semi-theoretical thin-layer model of the fluidized bed drying of Echium amoenum Fisch. & C.A. Mey. petals has been developed. Experiments were conducted under different conditions: temperatures of 40, 50 and 60 °C, and air velocities of 0.5, 0.75 and 1.0 m/s, until the moisture content decreased to 0.04.-0.06 kg of water per kg of dry matter. Drying processes in the fluidized bed were completed in between 55 and 465 min, with minimum drying time required at the maximum temperature of 60 °C and air velocity of 1 m/s. The comparison of the new model developed here with sixteen previously published theoretical, semi-empirical or empirical thin-layer drying equations shows that using the new model the highest coefficient of correlation, the lowest reduced chi-square and root mean square error were obtained. The highest retention of total phenolic compounds in E. amoenum petals was achieved when drying at 60 °C and 1.0 m/s.

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A novel multi-objective optimization algorithm was developed, which was successfully applied in the drying process. The effect of drying parameters (air velocity (vd), drying temperature (Td)) on the energy consumption (EC) and the quality parameters of Echium amoenum petals in fluidized drying were experimentally studied. The following quality parameters were examined: the color difference, the bioactive compounds as losses of total antioxidant capacity (TAC) and losses of phenolic (TPC), flavonoids (TFC) and anthocyanin (A). The six optimization objectives included simultaneous minimization of the quality parameters and energy consumption. The objective functions represent relationships between process variables and optimization objectives. The relations were approximated using an Artificial Neural Network (ANN). The Pareto optimal set with a nondominated sorting genetic algorithm was developed. No unequivocal solution to the optimization problem was found. Cannot be obtained E. amoenum petals characterized a low color change at low energy consumption due to its fluidized drying. Unique Pareto optimal solutions were found: Td = 54 °C and vd = 1.0 m/s–for the strategy in which the lower losses of TAC, TFC and A are most important, and Td = 59.8 °C and vd = 0.52 m/s–for the strategy in which the lower losses of TPC and TFC are important with accepted EC values. The results of this research are essential for the improvement of industrial dehydration of E. amoenum petals in order to maintain their high content of bioactive compounds with low energy consumption and low colour change

This research aims to conduct a comprehensive analysis of the drying conditions in a pilot-scale fluidized bed dryer by considering energetic, economic and global warming aspects. Echium amoenum Fisch. & C. A. Mey. E. amoenum petals were dried at a temperature range of 40-60 C and air velocities of 0.5 to 1 m s À1 . According to the results, the air velocity has a positive effect but the drying temperature has a negative impact on the specific energy consumption (SEC), the production cost and the GHG emissions. It was observed that the case of 0.5 m s À1 air velocity and 60 C drying temperature, the production cost, the SEC and the GHG emissions were minimized. However, considering the quality of the dried E. amoenum petals the optimal conditions were obtained at 60 C drying temperature and 0.91 m s À1 air velocity.Under these conditions, GHG emissions, production cost and SEC of dried E. amoenum petals would be 60.56 g CO 2 kg À1 , $38.87 kg À1 of dried E. amoenum petals and 29.87 MJ kg À1 of evaporated water, respectively.