Hot Air Drying of Red Peppers: Enrichment of Drying Characteristics by Different Ethanol Concentrations and Immersion Time and Energy Consumption

The study investigates the effects of ethanol pretreatment on drying characteristics and energy consumption, thin layer and artificial neural network (ANN) modelling, colour and shrinkage properties and principal component analysis (PCA) of red peppers. Ethanol pretreatment had a positive contribution to drying time and rate of red peppers. High concentration and long pretreatment time were found to be highly effective on the increment of drying rate. Thus, drying time was shortened. Additionally, SMER value increased, and SEC value decreased upon the shortening in the drying time. The highest effective pretreatment was revealed as 100% ethanol concentration and 20 min. pretreatment time. On the other hand, Midilli and Kucuk model gave the better results among the thin layer models, while, ANN modelling showed the best prediction performance of the drying of red peppers. Ethanol pretreatments reduced the L* value in the inner part of red peppers, with the most pronounced decrease observed in samples treated with 50% ethanol for 10 minutes. In the outer part, samples 50ET20 had the highest L* value, while 100ET20 had the lowest. There was no significant change in the a* value of the inner part, but the a* value of the outer part decreased the most in samples 100ET20. The b* value increased in the inner part with 10-minute treatment in 50% ethanol, while no notable change was in the outer part. All samples showed shrinkage tendance in both width and thickness after drying. The highest shrinkage ratios were obtained from 50ET20 and 100ET20. PCA revealed that fresh samples were positioned significantly farther from the pretreated samples. 50ET20, 100ET20 and C showed a similar arrangement in the same plane. In conclusion, ethanol pretreatment can be considered a highly promising approach. In addition, employing ANN modelling is advisable for enhancing the accuracy of the drying process optimization.