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Aït-Kaddour A, Loudiyi M, Boukria O, Safarov J, Sultanova S, Andueza D, Listrat A, Cahyana Y. Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging. Meat Sci 2024; 214:109533. [PMID: 38735067 DOI: 10.1016/j.meatsci.2024.109533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/29/2024] [Accepted: 05/05/2024] [Indexed: 05/14/2024]
Abstract
The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).
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Affiliation(s)
- Abderrahmane Aït-Kaddour
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMRF, Lempdes F-63370, France; Laboratory of Food Chemistry, Department of Food Technology, Universitas Padjadjaran, Bandung, Indonesia.
| | - Mohammed Loudiyi
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMRF, Lempdes F-63370, France
| | - Oumayma Boukria
- Applied Organic Chemistry Laboratory, Sciences and Techniques Faculty, Sidi Mohamed Ben Abedallah University, BP 2202 route d'Immouzer, Fès, Morocco
| | - Jasur Safarov
- Department of Food Engineering, Faculty of Mechanical Building, Tashkent State Technical University named after Islam Karimov, University Str. 2, Tashkent 100095, Uzbekistan
| | - Shaxnoza Sultanova
- Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications in Tashkent, 111200, Tashkent region, Kibray district, Koramurt street, 1, Uzbekistan
| | - Donato Andueza
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, Saint-Genès-Champanelle F-63122, France
| | - Anne Listrat
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, Saint-Genès-Champanelle F-63122, France
| | - Yana Cahyana
- Laboratory of Food Chemistry, Department of Food Technology, Universitas Padjadjaran, Bandung, Indonesia
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Food forensics: techniques for authenticity determination of food products. Forensic Sci Int 2022; 333:111243. [DOI: 10.1016/j.forsciint.2022.111243] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/21/2022]
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Li J, Feng YW, Huang LJ, Jiang R, Shen XF. Strand-displacement DNA polymerase induced isothermal circular amplification fluorescence sensor for identification of pork component. FOOD BIOSCI 2021. [DOI: 10.1016/j.fbio.2021.101189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Hendrickson OD, Zvereva EA, Dzantiev BB, Zherdev AV. Sensitive lateral flow immunoassay for the detection of pork additives in raw and cooked meat products. Food Chem 2021; 359:129927. [PMID: 33945986 DOI: 10.1016/j.foodchem.2021.129927] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 12/23/2022]
Abstract
In this study, we developed a lateral flow immunoassay (LFIA) for the detection of pork additives in meat products. LFIA of porcine immunoglobulins (IgG) as a molecular biomarker was carried out in a sandwich format for species identification. Gold nanoparticles as a nano-dispersed label were conjugated to secondary antibodies specific to anti-porcine IgG. The test system was characterized by high specificity, which was confirmed by the absence of cross-reactivity with any other species tested. A short technique of sample preparation was proposed aimed at the effective extraction of IgG from meat samples. The developed LFIA enabled the detection of a pork ingredient at a level as low as 0.063% (w/w) in raw meat within 35 min including sample preparation. A large panel of real meat samples was analyzed by the LFIA. The results showed that porcine IgG can be reliably recognized both in raw meat products and processed meat foodstuffs.
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Affiliation(s)
- Olga D Hendrickson
- A.N. Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Leninsky prospect 33, Moscow 119071, Russia
| | - Elena A Zvereva
- A.N. Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Leninsky prospect 33, Moscow 119071, Russia
| | - Boris B Dzantiev
- A.N. Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Leninsky prospect 33, Moscow 119071, Russia
| | - Anatoly V Zherdev
- A.N. Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Leninsky prospect 33, Moscow 119071, Russia.
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Al-Sarayreh M, Reis MM, Yan WQ, Klette R. Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107332] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Ross A, Brunius C, Chevallier O, Dervilly G, Elliott C, Guitton Y, Prenni JE, Savolainen O, Hemeryck L, Vidkjær NH, Scollan N, Stead SL, Zhang R, Vanhaecke L. Making complex measurements of meat composition fast: Application of rapid evaporative ionisation mass spectrometry to measuring meat quality and fraud. Meat Sci 2020; 181:108333. [PMID: 33067082 DOI: 10.1016/j.meatsci.2020.108333] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/01/2020] [Accepted: 10/05/2020] [Indexed: 12/31/2022]
Abstract
Increasing demands are being placed on meat producers to verify more about their product with regards to safety, quality and authenticity. There are many methods that can detect aspects of these parameters in meat, yet most are too slow to keep up with the demands of modern meat processing plants and supply chains. A new technology, Rapid Evaporative Ionisation Mass Spectrometry (REIMS), has the potential to bridge the gap between advanced laboratory measurements and technology that can screen for quality, safety and authenticity parameters in a single measurement. Analysis with REIMS generates a detailed mass spectral fingerprint representative of a meat sample without the need for sample processing. REIMS has successfully been used to detect species fraud, detect use of hormones in meat animals, monitor meat processing and to detect off flavours such as boar taint. The aim of this review is to summarize these and other applications to highlight the potential of REIMS for meat analysis. Sampling methods and important considerations for data analysis are discussed as well as limitations of the technology and remaining challenges for practical adoption.
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Affiliation(s)
- Alastair Ross
- Food and Biobased Products Group, AgResearch, Lincoln, New Zealand.
| | - Carl Brunius
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Sweden.
| | | | | | | | | | - Jessica E Prenni
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, USA.
| | - Otto Savolainen
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science and Chalmers Mass Spectrometry Infrastructure, Chalmers University of Technology, Sweden.
| | | | - Nanna Hjort Vidkjær
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Sweden.
| | - Nigel Scollan
- Queen's University Belfast, Belfast, United Kingdom.
| | - Sara L Stead
- Scientific Operations, Waters Corporation, Wilmslow, UK.
| | - Renyu Zhang
- Food & Bio-based Products, AgResearch, Palmerston North, New Zealand.
| | - Lynn Vanhaecke
- Ghent University, Laboratory of Chemical Analysis, Belgium.
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Flesh ID: Nanopore Sequencing Combined with Offline BLAST Search for the Identification of Meat Source. Foods 2020; 9:foods9101392. [PMID: 33019679 PMCID: PMC7600754 DOI: 10.3390/foods9101392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/18/2020] [Accepted: 09/28/2020] [Indexed: 11/17/2022] Open
Abstract
Detection of animal species in meat product is crucial to prevent adulterated and unnecessary contamination during processing, in addition to avoid allergy and religious consequences. Gold standard is the real-time PCR assays, which has a limited target capability. In this study, we have established a rapid sequencing protocol to identify animal species within hours. Sequencing was achieved by nanopore sequencing and data analysis via offline BLAST search. The whole procedure was conducted in a mobile suitcase lab. As per national and international regulations, the developed assay detected adulteration of pork meat with 0.1% of horse, chicken, turkey, cattle, sheep, duck, rabbit, goat, and donkey. The developed test could be used on-site as a rapid and mobile detection system to determine contamination of meat products.
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