Amplification-free Quantitative Detection of Genomic DNA Using Lateral Flow Strips for Milk Authentication.
BIOSENSORS & BIOELECTRONICS(2024)
Chinese Acad Agr Sci
Abstract
With the globalization and complexity of the food supply chain, the market is becoming increasingly competitive and food fraudulent activities are intensifying. The current state of food detection faced two primary challenges. Firstly, existing testing methods were predominantly laboratory-based, requiring complex procedures and precision instruments. Secondly, there was a lack of accurate and efficient quantitative detection methods. Taking cow's milk as an example, this study introduced a novel method for nucleic acid quantification in dairy products, based on lateral flow strips (LFS). The core idea of this method is to design single-stranded DNA (ssDNA) probes to hybridize with mitochondrial genes, which are abundant, stable, and species-specific in dairy products, as detection targets. Drawing inspiration from the principles of nucleic acid amplification, this research innovatively established a new DNA hybridization method, named LAMP-Like Hybridization (HybLAMP-Like). Leveraging the denaturation and DNA polymerization functions of the bst enzyme, efficient binding of the probe and template strand was achieved. This method eliminated the need for nucleic acid amplification, simplifying the procedure and mitigating aerosol contamination, thereby ensuring the accuracy of the detection results. The method exhibited exceptional sensitivity, capable of detecting extremely low to 12.5 ng in visual inspection and 3.125 ng when using a reader. In terms of practicality, it could achieve visual detection of cow's milk content as low as 1% in adulterated dairy products. When combined with a portable LFS reader, it also enabled precise quantitative analysis of milk adulteration.
MoreTranslated text
Key words
Food authenticity,Free-amplification,DNA hybridization,Lateral flow strips,Quantitative detection
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

