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Effect of Drying Kinetics, Volatile Components, Flavor Changes and Final Quality Attributes of Moslae herba during the Hot Air Thin-Layer Drying Process.

Min XieYing ChenYong SunYarou GaoZhenfeng WuRuiyu WuRui LiShixi HongMinyan WangYiping ZouHua ZhangYaokun Xiong
Published in: Molecules (Basel, Switzerland) (2023)
Moslae herba is considered to be a functional food ingredient or nutraceutical due to its rich bioactive components. The present research was carried out to investigate the effects of different temperatures (40 °C, 50 °C and 60 °C) on the drying characteristics, textural properties, bioactive compounds, flavor changes and final quality attributes of Moslae herba during the hot air-drying process. The results showed that the Midilli model could effectively simulate the drying process of Moslae herba . The effective moisture diffusivity ranged from 3.14 × 10 -5 m 2 /s to 7.39 × 10 -5 m 2 /s, and the activation energy was estimated to be 37.29 kJ/mol. Additionally, scanning electron microscopy (SEM) images of Moslae herba samples showed the shrinkage of the underlying epidermal layers and glandular trichomes. In total, 23 volatile compounds were detected in Moslae herba . Among them, the content of thymol increased from 28.29% in fresh samples to 56.75%, 55.86% and 55.62% in samples dried at temperatures of 40 °C, 50 °C and 60 °C, respectively, while the other two components, p-cymene and γ-terpinene, decreased with an increase in the temperature. Furthermore, both radar fingerprinting and principal component analysis (PCA) of the electronic nose (E-nose) showed that the flavor substances significantly altered during the drying process. Eventually, drying Moslae herba at 60 °C positively affected the retention of total phenolics, total flavonoids and the antioxidant capacity as compared with drying at 40 °C and 50 °C. The overall results elucidated that drying Moslae herba at the temperature of 60 °C efficiently enhanced the final quality by significantly reducing the drying time and maintaining the bioactive compounds.
Keyphrases
  • electron microscopy
  • quality improvement
  • machine learning
  • high resolution
  • mass spectrometry
  • convolutional neural network