Evaluation of Dynamic Changes in the Bioactive Components in Citri Reticulatae Pericarpium (citrus Reticulata 'chachi') under Different Harvesting and Drying Conditions.
Journal of the Science of Food and Agriculture（2020）
Guangzhou Med Univ
Abstract
BACKGROUND The Citrus reticulata 'Chachi' pericarp (CRCP) is one cultivar of Citri Reticulatae Pericarpium (Chenpi), which is widely applied in medicine and food. To determine the potential value of CRCP harvested at different stages and subjected to different drying processes, the dynamic changes in the bioactive components were profiled and evaluated in this study. RESULTS The contents of all non-volatile components, i.e. synephrine, limonin, phenolic acids and flavonoids, decreased with delayed harvest time. The volatiles thujene, alpha-pinene, beta-pinene, d-citronellol, d-citronellal, decanal, linalool, geraniol, l-cis-carveol, terpinen-4-ol, alpha-terpineol, carvacrol, perillaldehyde, methyl 2-(methylamino)benzoate and d-limonene were considered the characteristic components for distinguishing CRCP harvested at different stages. Phenolic acids, synephrine and limonin were stable at different drying temperatures; however, high-temperature drying at 60 degrees C induced a significant transformation in the flavonoids (especially polymethoxyflavones) and volatile substances in CRCP. CONCLUSIONS The results suggested that most of the bioactive components declined with the growth of Citrus reticulata 'Chachi'. And it is believed that the fresh peel should be naturally sun-dried or dried at low temperature (30 or 45 degrees C) rather than at high temperature (60 degrees C) to prevent excessive loss of nutrients. (c) 2020 Society of Chemical Industry
MoreTranslated text
Key words
Citrus reticulata ‘,Chachi’,pericarp (CRCP),bioactive components,harvest period,drying process
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined