Determining the Effects of LFHV-PEF Treatment on the Water Distribution and Vigor of Aged Rice Seeds Using LF-NMR

Ensi Cheng,Ping Song,Tiangang Hou,Liyan Wu,Benhua Zhang

International Journal of Agricultural and Biological Engineering（2022）SCI 2区

Cited 2|Views11

Abstract

This study aimed to investigate the effect of low-frequency high-voltage pulsed electric field (LFHV-PEF) treatment on the germination of aged rice seeds. Aged rice seeds were subjected to LFHV-PEF treatment with different electric field strengths, and low-field nuclear magnetic resonance (LF-NMR) was performed to acquire the LF-NMR data of rice seeds at different germination periods during a standard seed germination test to analyze their internal patterns of water state and distribution. Optimal treatment conditions were determined based on the physicochemical data collected during germination, and the improvements in seed vigor were verified. The findings indicated that during germination, the contents of bound and semi-bound water within the aged rice seeds initially increased and then decreased, whereas free water and total water contents increased continuously and rapidly. Side peaks were also observed within the seeds. Under the LFHV-PEF treatment, the semi-bound water within the seeds was more easily converted to free water, and the water absorption rate, germination potential, germination rate, germination index, and vigor index of these seeds improved. Further, the optimal electrical field strength was 12 kV. By analyzing the internal patterns of water state and distribution in seeds, the mechanism by which electric field treatment improved seed vigor was elucidated, thus, providing theoretical support and data evidence for research on water absorption during the germination of rice seeds, and methods for improving seed vigor.

Translated text

Key words

aged rice seeds,germination,water phase,seed vigor,LF-NMR,LFHV-PEF

Bibtex

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

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper