Identification and quantification of fox meat in meat products by liquid chromatography–tandem mass spectrometry

Zhang, Yingying; Liu, Mengyao; Wang, Shouwei; Kang, Chaodi; Zhang, Mingyue; Li, Yingying

doi:10.1016/j.foodchem.2021.131336

Cited by 18 publications

(9 citation statements)

References 24 publications

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“…The parameters searched for by the SVR model were the penalty factor (1,5,10,20,50,100,200,500) and gamma (0.01, 0.1, 1, 5, 10, 20). The parameters searched for by the RFR model were the max depth (3,5,7,9,11,13) and the minimum number of samples required to split an internal node (min. samples split : 7, 14, 21, 28, 35, 42).…”

Section: Experiments Amentioning

confidence: 99%

“…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 ]. Chemistry-based technologies, such as gas chromatography mass spectrometer (GCMS) [ 12 ] and liquid chromatography-tandem mass spectrometer (LC-MS/MS) [ 13 ], have been proposed for halal verification and the accurate identification of meat products. Chemistry-based technologies are reliable and precise in the identification of adulterated meat.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

2022

Foods

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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.

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Section: Experiments Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

2022

Foods

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“…Among them, the meat used for adulteration is mostly pork and duck meat, possibly 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 51 ; a small amount of beef jerky and beef granules are also made from pork jerky and added with beef flavor 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) 52 ; some unscrupulous traders will also buy fox, mink, mule and other dead livestock and poultry meat, which has not been inspected and quarantined, added with gelatin or nitrite for "bonding" and "color-changing" to be sold as beef and mutton 53,54 The above behaviors are all motivated economic interests, replacing high-priced animalderived products with other animal-derived ingredients of cheap or even unknown origin and then selling them at a price lower than the average price of normal products in the market 55,56 . These adulterated meats involve food fraud, which may lead to epidemic risk.…”

Section: Involving Adulterants and Categoriesmentioning

confidence: 99%

Meat food fraud risk in Chinese markets 2012–2021

Zang

et al. 2023

npj Sci Food

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Food fraud is a major concern worldwide, and the majority of cases include meat adulteration or fraud. Many incidences of food fraud have been identified for meat products both in China and abroad over the last decade. We created a meat food fraud risk database compiled from 1987 pieces of information recorded by official circular information and media reports in China from 2012 to 2021. The data covered livestock, poultry, by-products, and various processed meat products. We conducted a summary analysis of meat food fraud incidents by researching fraud types, regional distribution, adulterants and categories involved, categories and sub-categories of foods, risk links and locations, 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.

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“…The popular methods include PCA, PLS-DA, and OPLS-DA, which are capable of distinguishing, grouping, and classifying meats such as pork, chicken, and beef [ 78 ]. PCA is the most widely used method in which it reduces the number of metabolites to the main metabolite to determine the halalness of the product [ 79 ]. From LC-MS results, PCA could detect other animal species in a sample [ 80 ].…”

Section: Chemometrics Applications In Metabolomic Studies For Halal A...mentioning

confidence: 99%

The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products

et al. 2022

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The halal status of meat products is an important factor being considered by many parties, especially Muslims. Analytical methods that have good specificity for the authentication of halal meat products are important as quality assurance to consumers. Metabolomic and lipidomic are two useful strategies in distinguishing halal and non-halal meat. Metabolomic and lipidomic analysis produce a large amount of data, thus chemometrics are needed to interpret and simplify the analytical data to ease understanding. This review explored the published literature indexed in PubMed, Scopus, and Google Scholar on the application of chemometrics as a tool in handling the large amount of data generated from metabolomic and lipidomic studies specifically in the halal authentication of meat products. The type of chemometric methods used is described and the efficiency of time in distinguishing the halal and non-halal meat products using chemometrics methods such as PCA, HCA, PLS-DA, and OPLS-DA is discussed.

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Identification and quantification of fox meat in meat products by liquid chromatography–tandem mass spectrometry

Cited by 18 publications

References 24 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

Meat food fraud risk in Chinese markets 2012–2021

The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products

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