Kaveh Mohammad, Zomorodi Shahin, Mariusz Szymanek, Dziwulska-Hunek Agata
Agricultural Engineering Research Department, West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia 5716963963, Iran.
Department of Agricultural, Forest and Transport Machinery, University of Life Sciences in Lublin, Głęboka, 28, 20-612 Lublin, Poland.
Foods. 2024 Sep 10;13(18):2867. doi: 10.3390/foods13182867.
Drying is one of the most common and effective techniques for preserving the quantitative and qualitative characteristics of medicinal plants in the post-harvest phase. Therefore, in this research, the effect of the new refractance window (RW) technology on the kinetics, thermodynamics, greenhouse gasses, color indices, bioactive properties, and percentage of mint leaf essential oil was investigated in five different water temperatures in the form of a completely randomized design. This process was modeled by the methods of mathematical models and artificial neural networks (ANNs) with inputs (drying time and water temperature) and an output (moisture ratio). The results showed that with the increase in temperature, the rate of moisture removal from the samples increased and as a result, the drying time, specific energy consumption, CO, NO, enthalpy, and entropy decreased significantly ( < 0.05). In addition, the drying water temperature had a significant effect on the rehydration ratio, color indices, bioactive properties, and essential oil percentage of the samples ( < 0.05). The highest value of rehydration ratio was obtained at 80 °C. By increasing temperature, the main color indices such as b*, a*, L*, and Chroma decreased significantly compared to the control ( < 0.05). However, with the increase in temperature, the overall color changes (ΔE) and L* first had a decreasing trend and then an increasing trend, and this trend was the opposite for the rest of the indicators. The application of drying water temperature from 50 to 70 °C increased antioxidant, phenol content, and flavonoid content, and higher drying temperatures led to a significant decrease in these parameters ( < 0.05). On the other hand, the efficiency of the essential oil of the samples was in the range of 0.82 to 2.01%, and the highest value was obtained at the water temperature of 80 °C. Based on the analysis performed on the modeled data, a perceptron artificial neural network with 2-15-14-1 structure with explanation coefficient (0.9999) and mean square error (8.77 × 10) performs better than the mathematical methods for predicting the moisture ratio of mint leaves.
干燥是收获后阶段保存药用植物数量和质量特征最常见且有效的技术之一。因此，在本研究中，以完全随机设计的形式，研究了新型折射窗（RW）技术在五种不同水温下对薄荷叶片精油的动力学、热力学、温室气体、颜色指标、生物活性特性及含量百分比的影响。该过程通过数学模型和人工神经网络（ANN）方法进行建模，输入参数为（干燥时间和水温），输出参数为（含水率）。结果表明，随着温度升高，样品的水分去除速率增加，干燥时间、比能耗、一氧化碳、一氧化氮、焓和熵显著降低（<0.05）。此外，干燥水温对样品的复水率、颜色指标、生物活性特性和精油含量百分比有显著影响（<0.05）。在80℃时获得了最高的复水率值。与对照相比，随着温度升高，主要颜色指标如b*、a*、L和色度显著降低（<0.05）。然而，随着温度升高，总体颜色变化（ΔE）和L首先呈下降趋势，然后呈上升趋势，而其他指标则相反。干燥水温在50至70℃时，抗氧化剂、酚类含量和黄酮类含量增加，而较高的干燥温度导致这些参数显著降低（<0.05）。另一方面，样品精油的效率在0.82%至2.01%之间，在水温80℃时获得最高值。基于对建模数据的分析，具有2-15-14-1结构、解释系数为（0.9999）和均方误差为（8.77×10）的感知器人工神经网络在预测薄荷叶含水率方面比数学方法表现更好。
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