A novel duplex SYBR Green real-time PCR with melting curve analysis method for beef adulteration detection

Li, Jiapeng; Wei, Yixuan; Li, Jinchun; Liu, Ruixi; Xu, Suigen; Xiong, Suyue; Guo, Ya; Qiao, Xiaoling; Wang, Shouwei

doi:10.1016/j.foodchem.2020.127932

Cited by 19 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To date, the technologies that have been used in the detection of meat adulteration include biology-based, chemistry-based, and spectroscopy-based methods. Biology-based technologies have included polymerase chain reaction (PCR) [ 6 ], polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) [ 7 ], loop-mediated isothermal amplification (LAMP) [ 8 ], and enzyme-linked immunosorbent assay (ELISA) [ 9 ]. Although these methods have proven to be reliable, specific, and sensitive, biology-based technologies are time-consuming, expensive, and require complex laboratory procedures to be performed by skilled personnel [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Huang

2022

Foods

View full text Add to dashboard Cite

Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named 1DCNN-RFR, is proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. The RFR improved the regression performance due to its strong prediction ability. The effectiveness of the 1DCNN-RFR framework was verified by comparing it with four other models (support vector regression model (SVR), RFR, backpropagation neural network (BPNN), and 1DCNN). The proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration.

show abstract

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Huang

2022

Foods

View full text Add to dashboard Cite

show abstract

“…With the increase in temperature, the duplex of DNA denatures in a gradient, which causes the release of fluorescent dye or the departure of a fluorescent reporter probe, thus leading to a change in fluorescence. HRM is divided into two types based on the source of the fluorescence signal, including saturating DNA dyes (such as SYBR Green, LC green, and Eva Green) , and dual-labeled probes (such as hairpin probe and dual-hybridization probes). , HRM based on dyes takes intercalating fluorescent dyes to monitor the transition of DNA products during heating. HRM based on probes uses fluorescent probes to hybridize with DNA products to monitor the melting profit during heating.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Detection of Three Genotypes of Gene Methylene Tetrahydrofolate Reductase and Methionine Synthase Reductase Based on Multiplex Asymmetric Real-Time PCR-HRM Biosensing

Yao

Zhang

2022

Anal. Chem.

View full text Add to dashboard Cite

Genotyping of folate metabolism genes is of great importance in disease diagnosis and prevention. However, most current detection methods used for folate metabolism gene genotyping are based on sequencing and chips, which suffer from a high cost and laborious and time-consuming procedures. Herein, we reported a multiplex asymmetric PCR-HRM strategy for identifying genotypes of folate metabolism genes in a single tube. The proposed multiplex PCR-HRM assay has been successfully applied to identify the genotypes of folate metabolism genes, methylene tetrahydrofolate reductase (C677T, A1298C) and methionine synthase reductase A66G, on 1 μL of genomic DNA (gDNA) samples directly released from blood specimens, and the genotyping results were 100% consistent with the results of sequencing. The assay allows us to accurately detect the genotypes of gDNA with the detection limit down to 1 ng, which meets the clinical requirement. What is more, the capacity of resistance to aerosol pollution of the multiplex asymmetric PCR-HRM biosensing was first addressed and has been evaluated as it can withstand contamination of roughly 12.5–25% interfering nucleic acids. Because of the advantages of multiplex detection, high accuracy, and resistance to aerosol pollution and having no open tube procedure, this approach would pave the way for establishing a fast and cost-effective platform for folate metabolism gene genotyping and other SNP genotyping in clinical diagnostics.

show abstract

“…Among them, most of the meat used for adulteration is pork and duck meat, which may be accompanied by minor ingredients such as sheep tail oil, butter and starch, which are usually processed into fake "mutton rolls" and "beef rolls" sold to the catering link (Li et al, 2021;Bai et al, 2019); a small amount of beef jerky and beef granules are also made from pork jerky and added with beef avor essence; some businesses use pig blood mixed with formaldehyde to replace duck blood for sale (duck blood is more expensive in the market, and is poultry by-product with certain nutritional value) (Li et al, 2021); some unscrupulous traders will also buy fox, mink, mule and other dead livestock and poultry meat that have not been inspected and quarantined, added with gelatin or nitrite for "bonding" and "color-changing" to be sold as beef and mutton (Li et…”

Section: Involving Non-edible Substancesmentioning

confidence: 99%

A study on fraud of meat foods in China market: based on 1987 supervision and sampling inspection, import inspection and media reports

Zang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Economically motivated adulteration and food fraud (EMA / FF) is intentional conduct for economic gain, which have aroused great concern all over the world. Meat adulteration or fraud is one of the problems with the highest proportion in relevant cases, many food fraud problems have been found to occur frequently in meat products in China and abroad in recent ten years. Our research compilated 1,987 pieces of information on meat fraud and adulteration recorded by official circular information and media reports in China since 2012 to 2021, based on which we created a meat food fraud risk information database. The data covered fresh and frozen livestock, poultry, and various processed products. We conducted a summary analysis of EMA adulteration and fraud incidents by researching on fraudulent methods, types of contaminants, adulterated ingredients, involved foods and categories, risk links and sites, etc. The findings can be used not only to analyze meat food safety situations and study the burden of food fraud, but also help to promote the efficiency of detection and rapid screening, along with improving prevention and regulation of adulteration in the meat supply chain markets.

show abstract

A novel duplex SYBR Green real-time PCR with melting curve analysis method for beef adulteration detection

Cited by 19 publications

References 29 publications

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Simultaneous Detection of Three Genotypes of Gene Methylene Tetrahydrofolate Reductase and Methionine Synthase Reductase Based on Multiplex Asymmetric Real-Time PCR-HRM Biosensing

A study on fraud of meat foods in China market: based on 1987 supervision and sampling inspection, import inspection and media reports

Contact Info

Product

Resources

About