1
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Zülch F, Holle M, Hofmann A. Theoretical design of blockchain-based traceability for organic egg supply chains according to regulation (EU) 2018/848. PLoS One 2024; 19:e0304791. [PMID: 38861508 PMCID: PMC11166310 DOI: 10.1371/journal.pone.0304791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 05/13/2024] [Indexed: 06/13/2024] Open
Abstract
The use of blockchain technology to establish food traceability chains has the potential to provide transparent information of food stuffs along the entire supply chain and also aid in the documentation or even execution of official food control processes. Particularly in instances where analytical methodologies cannot provide definitive data for food control questions under study, the certificate-based approach of a traceability chain may offer a way of regulatory control for state authorities. Given the rising importance of organic produce and the high share of eggs among the organic produce in the European Union as well as the new EU regulation on organic products and labelling that came into force in 2022, we analyze here how the control of egg production type and marketing standards can be represented within a blockchain-based traceability chain such as to maximize the traceability in compliance with the current relevant EU regulations. Intended for the use by the official food control authorities, a traceability chain for organically produced eggs in the EU would need to be implemented as a permissioned blockchain, since only select entities are allowed to participate. By combining a proof of authority consensus mechanism with issuance of soulbound tokens, we effectively suggest a 'proof of soulbound authority' consensus process. The soulbound tokens are issued throughout the administrative chain from the European Commission down to the official food control authorities in individual member states that ultimately certify the control bodies for organic produce. Despite the general limitation of not providing unambiguous proof of the organic status of individual products, the concept discussed here offers advantages with respect to allocation of authority at EU level and therefore might have positive effects beyond the traceability chain.
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Affiliation(s)
- Florian Zülch
- Fakultät Life Sciences, Department Ökotrophologie, Hochschule für Angewandte Wissenschaften, Hamburg, Germany
| | - Martin Holle
- Fakultät Life Sciences, Department Ökotrophologie, Hochschule für Angewandte Wissenschaften, Hamburg, Germany
| | - Andreas Hofmann
- Nationales Referenzzentrum für Authentische Lebensmittel, Max Rubner-Institut, Kulmbach, Germany
- Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, Victoria, Australia
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2
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Bischof G, Januschewski E, Juadjur A. Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning. Foods 2024; 13:1098. [PMID: 38611402 PMCID: PMC11011716 DOI: 10.3390/foods13071098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024] Open
Abstract
(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.
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Affiliation(s)
- Greta Bischof
- Chemical Analytics, German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany (A.J.)
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3
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Gottstein V, Lachenmeier DW, Kuballa T, Bunzel M. 1H NMR-based approach to determine the geographical origin and cultivation method of roasted coffee. Food Chem 2024; 433:137278. [PMID: 37688828 DOI: 10.1016/j.foodchem.2023.137278] [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: 05/05/2023] [Revised: 08/04/2023] [Accepted: 08/23/2023] [Indexed: 09/11/2023]
Abstract
A comprehensive study of 603 roasted arabica coffee samples using NMR fingerprinting and multivariate data analysis was performed to differentiate coffee samples according to their geographical origin and cultivation method. Both lipophilic and hydrophilic coffee metabolites were recorded using 1H NMR spectroscopy, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was applied. Coffee samples were fist differentiated according to their continents of origin followed by discrimination of coffee samples from Brazil, Ethiopia, and Colombia from coffee samples originating from another continent. Discrimination of coffee samples according to their continent of origin and additional assignment to the countries Brazil and Ethiopia were successful. However, an unambiguous separation of Colombian coffee samples from coffee samples of another continent (other than South America) was not possible. Also, differentiation of organically and conventionally produced coffee samples by using 1H NMR and PCA-LDA was not achieved.
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Affiliation(s)
- Vera Gottstein
- Karlsruhe Institute of Technology (KIT), Department of Food Chemistry and Phytochemistry, Adenauerring 20A, D-76131 Karlsruhe, Germany; Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, D-76187 Karlsruhe, Germany
| | - Dirk W Lachenmeier
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, D-76187 Karlsruhe, Germany.
| | - Thomas Kuballa
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, D-76187 Karlsruhe, Germany.
| | - Mirko Bunzel
- Karlsruhe Institute of Technology (KIT), Department of Food Chemistry and Phytochemistry, Adenauerring 20A, D-76131 Karlsruhe, Germany.
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4
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Lösel H, Brockelt J, Gärber F, Teipel J, Kuballa T, Seifert S, Fischer M. Comparative Analysis of LC-ESI-IM-qToF-MS and FT-NIR Spectroscopy Approaches for the Authentication of Organic and Conventional Eggs. Metabolites 2023; 13:882. [PMID: 37623826 PMCID: PMC10456441 DOI: 10.3390/metabo13080882] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023] Open
Abstract
The importance of animal welfare and the organic production of chicken eggs has increased in the European Union in recent years. Legal regulation for organic husbandry makes the production of organic chicken eggs more expensive compared to conventional husbandry and thus increases the risk of food fraud. Therefore, the aim of this study was to develop a non-targeted lipidomic LC-ESI-IM-qToF-MS method based on 270 egg samples, which achieved a classification accuracy of 96.3%. Subsequently, surrogate minimal depth (SMD) was applied to select important variables identified as carotenoids and lipids based on their MS/MS spectra. The LC-MS results were compared with FT-NIR spectroscopy analysis as a low-resolution screening method and achieved 80.0% accuracy. Here, SMD selected parts of the spectrum which are associated with lipids and proteins. Furthermore, we used SMD for low-level data fusion to analyze relations between the variables of the LC-MS and the FT-NIR spectroscopy datasets. Thereby, lipid-associated bands of the FT-NIR spectrum were related to the identified lipids from the LC-MS analysis, demonstrating that FT-NIR spectroscopy partially provides similar information about the lipidome. In future applications, eggs can therefore be analyzed with FT-NIR spectroscopy to identify conspicuous samples that can subsequently be counter-tested by mass spectrometry.
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Affiliation(s)
- Henri Lösel
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany; (H.L.); (J.B.); (F.G.); (S.S.)
| | - Johannes Brockelt
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany; (H.L.); (J.B.); (F.G.); (S.S.)
| | - Florian Gärber
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany; (H.L.); (J.B.); (F.G.); (S.S.)
| | - Jan Teipel
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, 76187 Karlsruhe, Germany (T.K.)
| | - Thomas Kuballa
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weissenburger Strasse 3, 76187 Karlsruhe, Germany (T.K.)
| | - Stephan Seifert
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany; (H.L.); (J.B.); (F.G.); (S.S.)
| | - Markus Fischer
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany; (H.L.); (J.B.); (F.G.); (S.S.)
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5
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Chin ST, Hoerlendsberger G, Wong KW, Li S, Bong SH, Whiley L, Wist J, Masuda R, Greeff J, Holmes E, Nicholson JK, Loo RL. Targeted lipidomics coupled with machine learning for authenticating the provenance of chicken eggs. Food Chem 2023; 410:135366. [PMID: 36641906 DOI: 10.1016/j.foodchem.2022.135366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/17/2022] [Accepted: 12/29/2022] [Indexed: 12/31/2022]
Abstract
Free-range eggs are ethically desirable but as with all high-value commercial products, the establishment of provenance can be problematic. Here, we compared a simple one-step isopropanol method to a two-step methyl-tert-butyl ether method for extracting lipid species in chicken egg yolks before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The isopropanol method extracted 937 lipid species from 20 major lipid subclasses with high reproducibility (CV < 30 %). Machine learning techniques could differentiate conventional cage, barn, and free-range eggs using an external test dataset with an accuracy of 0.94, 0.82, and 0.82, respectively. Lipid species that differentiated cage eggs were predominantly phosphocholines and phosphoethanolamines whilst the free-range egg lipidomes were dominated by acylglycerides with up to three fatty acids. The lipid profiles were found to be characteristic of the cage, barns, and free-range eggs. The lipidomic analysis together with the statistical modeling approach thus provides an efficient tool for verifying the provenance of conventional chicken eggs.
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Affiliation(s)
- Sung-Tong Chin
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
| | - Gerhard Hoerlendsberger
- Discipline of Information Technology, Murdoch University, 90 South Street, Perth, WA 6150, Australia
| | - Kok Wai Wong
- Discipline of Information Technology, Murdoch University, 90 South Street, Perth, WA 6150, Australia
| | - Sirui Li
- Discipline of Information Technology, Murdoch University, 90 South Street, Perth, WA 6150, Australia
| | - Sze How Bong
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia; Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
| | - Julien Wist
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia; Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
| | - Reika Masuda
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia; Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
| | - Johan Greeff
- Department of Primary Industries and Regional Development, 3 Baron-Hay Court, South Perth, WA 6151, Australia
| | - Elaine Holmes
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia; Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia; Nutrition Research, Department of Metabolism, Nutrition and Reproduction, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, London SW7 2AZ, U.K
| | - Jeremy K Nicholson
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia; Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
| | - Ruey Leng Loo
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia; Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia.
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6
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Puertas G, Cazón P, Vázquez M. A quick method for fraud detection in egg labels based on egg centrifugation plasma. Food Chem 2023; 402:134507. [PMID: 36303393 DOI: 10.1016/j.foodchem.2022.134507] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/16/2022] [Accepted: 10/01/2022] [Indexed: 06/16/2023]
Abstract
The aim of this work was to develop a quick and cheap method for fraud detection in egg labels according to the four legal farming method of the EU. The plasma obtained from egg centrifugation was investigated for this purpose. Initial protein content in egg, plasma protein content, plasma colour parameters (L*, a* and b*) and plasma UV-VIS-NIR (Ultraviolet-Visible-Near-infrared) spectra were evaluated. The classification algorithms applied were SVM (Support-Vector-Machine), LDA (Linear-Discriminant-Analysis) and QDA (Quadratic-Discriminant-Analysis). The analysis of the protein content did not detect differences. Colour parameters and spectral measurements showed significant differences between eggs. Spectra analysis with QDA gave sensitivity of 100% in the calibration set. The validation set scored 87.5% sensitivity and 94.07% specificity using the visible spectra. This work established plasma spectral measurements combined with classification algorithms as a powerful tool to discriminate the four farming systems. This work presents a fast tool for the egg label control.
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Affiliation(s)
- Gema Puertas
- Department of Analytical Chemistry, Faculty of Veterinary, University of Santiago de Compostela, 27002 Lugo, Spain
| | - Patricia Cazón
- Department of Analytical Chemistry, Faculty of Veterinary, University of Santiago de Compostela, 27002 Lugo, Spain
| | - Manuel Vázquez
- Department of Analytical Chemistry, Faculty of Veterinary, University of Santiago de Compostela, 27002 Lugo, Spain.
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7
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A t-test ranking-based discriminant analysis for classification of free-range and barn-raised broiler chickens by 1H NMR spectroscopy. Food Chem 2023; 399:134004. [DOI: 10.1016/j.foodchem.2022.134004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/15/2022] [Accepted: 08/21/2022] [Indexed: 11/20/2022]
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8
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Decker C, Krapf R, Kuballa T, Bunzel M. Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis. Front Nutr 2022; 9:985797. [PMID: 36245505 PMCID: PMC9566576 DOI: 10.3389/fnut.2022.985797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
Meat species of raw meat and processed meat products were investigated by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. 1H NMR data was analyzed by using a non-targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published 1H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy.
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Affiliation(s)
- Christina Decker
- Karlsruhe Institute of Technology (KIT), Department of Food Chemistry and Phytochemistry, Karlsruhe, Germany
- Chemisches und Veterinäruntersuchungsamt Karlsruhe, Karlsruhe, Germany
| | - Reiner Krapf
- Bosch Power Tools, Leinfelden-Echterdingen, Germany
| | - Thomas Kuballa
- Chemisches und Veterinäruntersuchungsamt Karlsruhe, Karlsruhe, Germany
| | - Mirko Bunzel
- Karlsruhe Institute of Technology (KIT), Department of Food Chemistry and Phytochemistry, Karlsruhe, Germany
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9
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Chaudhary V, Kajla P, Dewan A, Pandiselvam R, Socol CT, Maerescu CM. Spectroscopic techniques for authentication of animal origin foods. Front Nutr 2022; 9:979205. [PMID: 36204380 PMCID: PMC9531581 DOI: 10.3389/fnut.2022.979205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.
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Affiliation(s)
- Vandana Chaudhary
- College of Dairy Science and Technology, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, India
| | - Priyanka Kajla
- Department of Food Technology, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Aastha Dewan
- Department of Food Technology, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - R. Pandiselvam
- Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR–Central Plantation Crops Research Institute, Kasaragod, India
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10
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An Easy and Reliable Method for the Mitigation of Deuterated Chloroform Decomposition to Stabilise Susceptible NMR Samples. CHEMISTRY 2022. [DOI: 10.3390/chemistry4030055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Highly reactive decomposition products of deuterated chloroform can deteriorate samples dissolved in this commonly used solvent for nuclear magnetic resonance (NMR) spectroscopy. Samples for metabolomics studies often contain a complex mixture of sensitive substances such as phospholipids, peptides, unsaturated fatty acids or vitamins. If these react with decomposition products (of chloroform), abnormal NMR spectra could result, e.g., signal shifts depending on pH, attenuation of signals over time due to chemical changes of analytes or new signals from reaction products. Such irreproducibly influenced spectra are especially problematic for non-targeted analysis methods using automated chemometrical data evaluation. To prevent these artefacts, chlorine, phosgene and hydrochloric acid need to be eliminated from deuterated chloroform before its use. Since the common stabilisation methods have proven to be insufficient for sensitive NMR samples, another purging method has been tested: Mitigation is easily and reliably achieved by washing the deuterated chloroform with concentrated disodium carbonate solution and subsequent desiccation with oven-dried disodium carbonate.
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11
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Cardoso PHS, de Oliveira ES, Lião LM, de Almeida Ribeiro Oliveira G. 1H NMR as a simple methodology for differentiating barn and free-range chicken eggs. Food Chem 2022; 396:133720. [PMID: 35870239 DOI: 10.1016/j.foodchem.2022.133720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/30/2022] [Accepted: 07/13/2022] [Indexed: 12/21/2022]
Abstract
The conventional intensive system produces cheap and safe chicken eggs, but exposes the animals to stress due to overcrowding on farms. This work compared the 1HNMR lipidic profile of chicken eggs produced in conventional and free-range systems. Sample preparation consisted of a single-step extraction and centrifugation, and the 1H NMR experimental time was just 3 min per sample. Eggs from free-range chickens had higher concentrations of ω-3 and ω-6 polyunsaturated fatty acids. The ratio between the signals at δ2.85 and 4.14 from bis-allylic polyunsaturated fatty acids and glycerol moiety, respectively, was able to correctly classify 93.8 % of the samples. These results were similar to those of PLS-DA, used for comparative purposes. Therefore, the proposed method could be easily used to assist quality control and fraud prevention in the egg industry. Free-range eggs had higher concentrations of cholesterol but, as they are smaller, similar amounts to conventional ones.
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Affiliation(s)
| | - Enya Silva de Oliveira
- LabRMN, Instituto de Química, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil
| | - Luciano Morais Lião
- LabRMN, Instituto de Química, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil.
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12
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Decker C, Krapf R, Kuballa T, Bunzel M. Nontargeted Analysis of Lipid Extracts Using 1H NMR Spectroscopy Combined with Multivariate Statistical Analysis to Discriminate between the Animal Species of Raw and Processed Meat. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:7230-7239. [PMID: 35648805 DOI: 10.1021/acs.jafc.2c01871] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The animal species of raw meat and processed meat products was determined by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on comprehensive lipid extraction to capture nonpolar and polar (amphiphilic) fat components of meat. A nontargeted approach was used to analyze the 1H NMR data, followed by a principal component analysis, linear discrimination analysis, and cross-validation embedded in a Monte Carlo re-sampling approach. A total of 437 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, and poultry) were collected to build and/or test the classification model. On average, 98% of the analyzed raw meat samples and 97% of the processed meat products were correctly classified with respect to meat species. Furthermore, relevant spectral regions to identify potential chemical markers such as linoleic acids, trans-fatty acids, and cholesterol for the meat species classification were described.
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Affiliation(s)
- Christina Decker
- Department of Food Chemistry and Phytochemistry, Karlsruhe Institute of Technology (KIT), Adenauerring 20A, D-76131 Karlsruhe, Germany
- Bosch Power Tools, Max-Lang-Straße 40-46, D-70771 Leinfelden-Echterdingen, Germany
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weißenburger Straße 3, D-76187 Karlsruhe, Germany
| | - Reiner Krapf
- Bosch Power Tools, Max-Lang-Straße 40-46, D-70771 Leinfelden-Echterdingen, Germany
| | - Thomas Kuballa
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weißenburger Straße 3, D-76187 Karlsruhe, Germany
| | - Mirko Bunzel
- Department of Food Chemistry and Phytochemistry, Karlsruhe Institute of Technology (KIT), Adenauerring 20A, D-76131 Karlsruhe, Germany
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13
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Strecker C, Ara V. Detecting Admixture to Mango Purée of the Alphonso Cultivar (Mangifera indica L. cv. Alphonso) by 1H-NMR Spectroscopy. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02116-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractFood authenticity is becoming increasingly important but challenges existing analytical methods. In this study, we analyze the mango cultivar Alphonso with regard to authenticity using 1H-NMR spectroscopy. This cultivar has been termed “the king of mangoes” due to its unique flavor. Regarding its metabolites however, little is known about unique constellations that allow for differentiation of the Alphonso cultivar. We find that the Alphonso cultivar is distinguished by high levels of niacin, trigonelline, and histidine but features relatively low levels of alanine. Furthermore, we develop a model based on the local outlier factor algorithm that effectively detects admixture of non-Alphonso cultivars to Alphonso purée. This task is highly challenging because we identified no metabolites that are unique or uniquely absent in the Alphonso cultivar compared to other mango cultivars analyzed in this study. Our model shows promising results on a test set: Admixtures consisting of 35% non-Alphonso and 65% Alphonso mango purée were uncovered with a sensitivity of 88%. At the same time, our model verified Alphonso samples with a good specificity of 86%.
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14
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Food synthetic biology-driven protein supply transition: From animal-derived production to microbial fermentation. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2020.11.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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15
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Bischof G, Witte F, Terjung N, Januschewski E, Heinz V, Juadjur A, Gibis M. Analysis of aging type- and aging time-related changes in the polar fraction of metabolome of beef by 1H NMR spectroscopy. Food Chem 2020; 342:128353. [PMID: 33092915 DOI: 10.1016/j.foodchem.2020.128353] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 10/06/2020] [Accepted: 10/07/2020] [Indexed: 12/20/2022]
Abstract
The tenderness and taste of beef is improved by either dry- or wet-aging or a combination of both. The objective was to develop a validated method for detecting differences in the polar fraction of metabolome in dry-aged and wet-aged beef over the aging time and quantifying the metabolites of interest by 1H NMR spectroscopy using beef. Sixty strip loin (M. longissimus dorsi) samples aged in different ways (wet-aging vs. dry-aging) and aging times (0, 7, 14, 21, 28 days) were analyzed. The aging type could be defined by linear discriminant analysis with an accuracy of 95%. Ten (lactic acid, alanine, methionine, fumaric acid, inosine, inosine monophosphate, creatine, betaine, carnosine and hypoxanthine) out of eighteen metabolites differ significantly (p < 0.05) in content depending on the aging type. Fifteen metabolites in dry-aged and ten in wet-aged beef correlate with the aging time (r > 0.7, <-0.7), which shows significant aging time-related effects on the polar fraction of metabolome.
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Affiliation(s)
- Greta Bischof
- German Institute of Food Technologies, Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany; Department of Food Material Science, Institute of Food Science and Biotechnology, University of Hohenheim, Garbenstr. 25, 70599 Stuttgart, Germany
| | - Franziska Witte
- German Institute of Food Technologies, Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Nino Terjung
- German Institute of Food Technologies, Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Edwin Januschewski
- German Institute of Food Technologies, Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Volker Heinz
- German Institute of Food Technologies, Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Andreas Juadjur
- German Institute of Food Technologies, Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany.
| | - Monika Gibis
- Department of Food Material Science, Institute of Food Science and Biotechnology, University of Hohenheim, Garbenstr. 25, 70599 Stuttgart, Germany.
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Hassoun A, Måge I, Schmidt WF, Temiz HT, Li L, Kim HY, Nilsen H, Biancolillo A, Aït-Kaddour A, Sikorski M, Sikorska E, Grassi S, Cozzolino D. Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years. Foods 2020; 9:E1069. [PMID: 32781687 PMCID: PMC7466239 DOI: 10.3390/foods9081069] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/29/2020] [Accepted: 08/01/2020] [Indexed: 12/27/2022] Open
Abstract
Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.
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Affiliation(s)
- Abdo Hassoun
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Ingrid Måge
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Walter F. Schmidt
- United States Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705-2325, USA;
| | - Havva Tümay Temiz
- Department of Food Engineering, Bingol University, 12000 Bingol, Turkey;
| | - Li Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China;
| | - Hae-Yeong Kim
- Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Korea;
| | - Heidi Nilsen
- Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; (I.M.); (H.N.)
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L’Aquila, 67100 Via Vetoio, Coppito, L’Aquila, Italy;
| | | | - Marek Sikorski
- Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland;
| | - Ewa Sikorska
- Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland;
| | - Silvia Grassi
- Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via Celoria, 2, 20133 Milano, Italy;
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, 39 Kessels Rd, Coopers Plains, QLD 4108, Australia;
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17
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Creydt M, Fischer M. Food authentication in real life: How to link nontargeted approaches with routine analytics? Electrophoresis 2020; 41:1665-1679. [PMID: 32249434 DOI: 10.1002/elps.202000030] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/19/2020] [Accepted: 03/23/2020] [Indexed: 12/20/2022]
Abstract
In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.
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Affiliation(s)
- Marina Creydt
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Hamburg, Germany
| | - Markus Fischer
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Hamburg, Germany
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