WeChat Mini Program
Old Version Features

An Efficient HPLC–PDA Coupled with Supel™ Tox DON SPE Approach for the Analysis of Deoxynivalenol Contamination in Cereal Grains and Feedstuffs in Jiangxi Province

Lin Tan, Qian Li, Chao Sun,Weiqiang Li,Ninan Tang,Kaijie Tang

JOURNAL OF FOOD PROTECTION(2023)

Jiangxi Agr Univ

Cited 6|Views9
Abstract
Deoxynivalenol (DON) was commonly found in grains and feedstuffs, which can cause human chronic diseases. In this study, a quick and reliable method was developed for the determination of DON in grains and feedstuffs in Jiangxi Province market. The sample was extracted with acetonitrile-water (84:16, v/v), then purified by SupelTM Tox DON SPE column, and detected by high-performance liquid chromatography (HPLC). The results showed that the calibration curve of DON showed good linearity in the range of 0.01-10.0 mu g/mL, and the correlation coefficient R2 = 0.9999. The recovery of DON in the spiked maize sample was 94.8-98.5% by spiking with DON at 0.2 mu g/g, 1.0 mu g/g, and 2.0 mu g/g. The RSD was between 2.5 and 3.3%. This method was used to analyze 120 samples, including 90 grains and 30 feedstuffs, collected from the Jiangxi Province market. The results showed that 81 samples of grains were positive with 90.0% positive rates, and 30 samples of feedstuff were positive with 100% positive rates. Maximum concentration of DON was 0.7 mu g/g in oat and 6.9 mu g/g in wheat feedstuffs, respectively. Fortunately, the positive samples of grains were safe levels in comparison with National standards for food safety limits of mycotoxins in food (1.0 mu g/g), while, the feedstuff of oats was over the Maximum Guideline Level of 16.7% (the Maximum Residue Limit, MRL is 5.0 mu g/g). The results of this study about current DON pollution in the grains and feedstuffs on the Jiangxi Province market have not been previously reported.
More
Translated text
Key words
Deoxynivalenol,Feedstuff,Grain,HPLC–UV,Supel™ Tox DON SPE
求助PDF
上传PDF
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
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