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Abedini A, Salimi M, Mazaheri Y, Sadighara P, Alizadeh Sani M, Assadpour E, Jafari SM. Assessment of cheese frauds, and relevant detection methods: A systematic review. Food Chem X 2023; 19:100825. [PMID: 37780280 PMCID: PMC10534187 DOI: 10.1016/j.fochx.2023.100825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/28/2023] [Accepted: 08/02/2023] [Indexed: 10/03/2023] Open
Abstract
Dairy products are widely consumed in the world due to their nutritional and functional characteristics. This group of food products are consumed by all age groups due to their health-giving properties. One of these products is cheese which has a high price compared to other dairy products. Because of this, it can be prone to fraud all over the world. Fraud in food products threatens the world's food safety and can cause serious damage to human health. There are many concerns among food authorities in the world about the fraud of food products. FDA, WHO, and the European Commission provide different legislations and definitions for fraud. The purpose of this review is to identify the most susceptible cheese type for fraud and effective methods for evaluating fraud in all types of cheeses. For this, we examined the Web of Science, Scopus, PubMed, and ScienceDirect databases. Mozzarella cheese had the largest share among all cheeses in terms of adulteration due to its many uses. Also, the methods used to evaluate different types of cheese frauds were PCR, Spectrometry, stable isotope, image analysis, electrophoretic, ELISA, sensors, sensory analysis, near-infrared and NMR. The methods that were most used in detecting fraud were PCR and spectrometry methods. Also, the least used method was sensory evaluation.
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Affiliation(s)
- Amirhossein Abedini
- Students Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahla Salimi
- Student Research Committee, Department of Food Science and Technology, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Science and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Yeganeh Mazaheri
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Parisa Sadighara
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahmood Alizadeh Sani
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Elham Assadpour
- Food Industry Research Co., Gorgan, Iran
- Food and Bio-Nanotech International Research Center (Fabiano), Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
| | - Seid Mahdi Jafari
- Department of Food Materials and Process Design Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
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Zhang J, Sun M, Elmaidomy AH, Youssif KA, Zaki AMM, Hassan Kamal H, Sayed AM, Abdelmohsen UR. Emerging trends and applications of metabolomics in food science and nutrition. Food Funct 2023; 14:9050-9082. [PMID: 37740352 DOI: 10.1039/d3fo01770b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.
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Affiliation(s)
- Jianye Zhang
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511436, China
| | - Mingna Sun
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511436, China
| | - Abeer H Elmaidomy
- Department of Pharmacognosy, Faculty of Pharmacy, Beni-Suef University, Beni-Suef 62511, Egypt
| | - Khayrya A Youssif
- Department of Pharmacognosy, Faculty of Pharmacy, El-Saleheya El Gadida University, Cairo, Egypt
| | - Adham M M Zaki
- Faculty of Pharmacy, Minia University, Minia 61519, Egypt
| | - Hossam Hassan Kamal
- Faculty of Pharmacy, Deraya University, 7 Universities Zone, New Minia 61111, Egypt
| | - Ahmed M Sayed
- Department of Pharmacognosy, Faculty of Pharmacy, Nahda University, 62513 Beni-Suef, Egypt.
- Department of Pharmacognosy, Faculty of Pharmacy, Almaaqal University, 61014 Basra, Iraq
| | - Usama Ramadan Abdelmohsen
- Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia 61519, Egypt.
- Department of Pharmacognosy, Faculty of Pharmacy, Deraya University, 7 Universities Zone, New Minia 61111, Egypt
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Food Quality, Drug Safety, and Increasing Public Health Measures in Supply Chain Management. Processes (Basel) 2022. [DOI: 10.3390/pr10091715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Over the last decade, there has been an increased interest in public health measures concerning food quality and drug safety in supply chains and logistics operations. Against this backdrop, this study systematically reviewed the extant literature to identify gaps in studying food quality and drug safety, the proposed solutions to these issues, and potential future research directions. This study utilized content analysis. The objectives of the review were to (1) identify the factors affecting food quality and possible solutions to improve results, (2) analyze the factors that affect drug safety and identify ways to mitigate them through proper management; and (3) establish integrated supply chains for food and drugs by implementing modern technologies, followed by one another to ensure a multi-layered cross-verification cascade and resource management at the different phases to ensure quality, safety, and sustainability for the benefit of public health. This review investigated and identified the most recent trends and technologies used for successfully integrated supply chains that can guarantee food quality and drug safety. Using appropriate keywords, 298 articles were identified, and 205 were shortlisted for the analysis. All analysis and conclusions are based on the available literature. The outcomes of this paper identify new research directions in public health and supply chain management.
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Yang Z, Zhou Q, Wu W, Zhang D, Mo L, Liu J, Yang X. Food fraud vulnerability assessment in the edible vegetable oil supply chain: A perspective of Chinese enterprises. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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An Integrative Glycomic Approach for Quantitative Meat Species Profiling. Foods 2022; 11:foods11131952. [PMID: 35804766 PMCID: PMC9265272 DOI: 10.3390/foods11131952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/16/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023] Open
Abstract
It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost the global food industry around USD 6.2 to USD 40 billion annually. To overcome this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic approach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication.
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Abstract
The wine sector is one of the most ‘amazing’ and significant agri-food sectors worldwide since ancient times, considering revenue or employment as well as health aspects. This article aims to describe the impact of the implementation of blockchain technology (BCT) in the wine supply chain. After the literature review, the study is based on Agent Based Models (ABMs) and carried out by the GAMA program. Then, the model and simulation of BCT wine supply chain is designed. Finally, the paper compares traditional and BCT-based supply chains, and the advantages of the last one are evident. Blockchain is a useful tool to ensure a traceability system and to protect the production from any type of fraud and contamination.
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Ríos-Rodríguez D, Sahi VP, Nick P. Authentication of holy basil using markers relating to a toxicology-relevant compound. Eur Food Res Technol 2021. [DOI: 10.1007/s00217-021-03812-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractHoly Basil—Ocimumtenuiflorum—is one of the popular new “superfoods” thought to act as an antioxidant and to reduce stress and anxiety. However, it is often surrogated with other Ocimum species differing in their chemical profiles that may even pose health risks to the consumers. Moreover, even specific chemotypes of Holy Basil itself can be toxicologically relevant, because they sometimes contain the carcinogen compound methyleugenol. Using DNA barcoding based on plastidic markers, O.tenuiflorum can be differentiated from other species of Ocimum. However, this approach is still suboptimal in handling larger sample numbers and in tracing chemotypes that accumulate methyleugenol. We have, therefore, designed a trait-related DNA barcode based on the enzyme eugenol O-methyltransferase (EOMT), responsible for the synthesis of methyleugenol. We show that a multiplex PCR combining trait-related and trait-independent markers can differentiate O.tenuiflorum from other Ocimum species and identify methyleugenol chemotypes of O.tenuiflorum, even in dried material sold as mixtures.
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Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics. Foods 2021; 10:foods10081803. [PMID: 34441579 PMCID: PMC8392494 DOI: 10.3390/foods10081803] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/30/2021] [Accepted: 08/02/2021] [Indexed: 01/18/2023] Open
Abstract
Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.
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9
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Provenance and Uniqueness in the Emerging Botanical and Natural Food Industries—Definition, Issues and Tools. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02079-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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11
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López-Maestresalas A, Insausti K, Jarén C, Pérez-Roncal C, Urrutia O, Beriain MJ, Arazuri S. Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control 2019. [DOI: 10.1016/j.foodcont.2018.12.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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12
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Jiménez-Carvelo AM, González-Casado A, Bagur-González MG, Cuadros-Rodríguez L. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review. Food Res Int 2019; 122:25-39. [PMID: 31229078 DOI: 10.1016/j.foodres.2019.03.063] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/31/2022]
Abstract
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.
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Affiliation(s)
- Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain.
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - M Gracia Bagur-González
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - Luis Cuadros-Rodríguez
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
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Fornal E, Montowska M. Species-specific peptide-based liquid chromatography-mass spectrometry monitoring of three poultry species in processed meat products. Food Chem 2019; 283:489-498. [PMID: 30722903 DOI: 10.1016/j.foodchem.2019.01.074] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 12/28/2018] [Accepted: 01/08/2019] [Indexed: 12/23/2022]
Abstract
The detection of adulteration and mislabeling of food products, including intensively processed meat, is a challenge which needs urgent solutions to protect consumers' rights. The aim of the study was to demonstrate the feasibility of species-specific peptide-based LC-MS methods for monitoring duck, goose and chicken in processed meat products. Food commodities of various compositions, subjected to various treatments, including homogenization, cooking, roasting, drying, and sterilization during production, were examined to ensure that MS-based methods are resistant to matrix composition changes. A qualitative LC-QQQ multiple reaction monitoring (MRM) method was developed which allows high-confidence monitoring of duck, goose and chicken meat (ten specific peptides), simultaneously with beef and pork (seven peptides), in the presence of turkey meat, in highly processed food. The developed LC-MS methods can be used for food authentication, monitoring of the food composition conformity with label statements and detection of adulteration of poultry-containing food products.
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Affiliation(s)
- Emilia Fornal
- Department of Pathophysiology, Medical University of Lublin, ul Jaczewskiego 8b, 20-090 Lublin, Poland.
| | - Magdalena Montowska
- Department of Meat Technology, Poznan University of Life Sciences, ul Wojska Polskiego 31, 60-624 Poznan, Poland.
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Esteki M, Regueiro J, Simal-Gándara J. Tackling Fraudsters with Global Strategies to Expose Fraud in the Food Chain. Compr Rev Food Sci Food Saf 2019; 18:425-440. [PMID: 33336950 DOI: 10.1111/1541-4337.12419] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/24/2018] [Accepted: 12/02/2018] [Indexed: 12/30/2022]
Abstract
Deliberate adulteration of food products is as old as food processing and production systems. Food adulteration is occurring increasingly often today. With globalization and complex distribution systems, adulteration may have a far-reaching impact and even adverse consequences on well-being. The means of the international community to confront and solve food fraud today are scattered and largely ineffective. A collective approach is needed to identify all stakeholders in the food supply chain, certify and qualify them, exclude those failing to meet applicable standards, and track food in a real time. This review provides some background into the drivers of fraudulent practices (economically motivated adulteration, food-industry perspectives, and consumers' perceptions of fraud) and discusses a wide range of the currently available technologies for detecting food adulteration followed by multivariate pattern recognition tools. Food chain integrity policies are discussed. Future directions in research, concerned not only with food adulterers but also with food safety and climate change, may be useful for researchers in developing interdisciplinary approaches to contemporary problems.
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Affiliation(s)
- M Esteki
- Dept. of Chemistry, Univ. of Zanjan, Zanjan, 45195-313, Iran
| | - J Regueiro
- Nutrition and Bromatology Group, Dept. of Analytical and Food Chemistry, Food Science and Technology Faculty, Univ. of Vigo - Ourense Campus, E-32004, Ourense, Spain
| | - J Simal-Gándara
- Nutrition and Bromatology Group, Dept. of Analytical and Food Chemistry, Food Science and Technology Faculty, Univ. of Vigo - Ourense Campus, E-32004, Ourense, Spain
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Djekic I, Jambrak AR, Djugum J, Rajkovic A. How the food industry experiences and perceives food fraud. QUALITY ASSURANCE AND SAFETY OF CROPS & FOODS 2018. [DOI: 10.3920/qas2018.1365] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- I. Djekic
- Department of Food Safety and Quality Management, Faculty of Agriculture, University of Belgrade, E, Nemanjina 8, 11080 Zemun, Serbia
| | - A. Režek Jambrak
- Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia
| | - J. Djugum
- Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia
- Ministry of Agriculture, Ulica grada Vukovara 78, 10000 Zagreb, Croatia
| | - A. Rajkovic
- Department of Food Safety and Quality Management, Faculty of Agriculture, University of Belgrade, E, Nemanjina 8, 11080 Zemun, Serbia
- Department of Food Safety and Food Quality, Food2Know, Faculty of Bioscience Engineering, Ghent University, Campus Coupure, A, Coupure Links 653, 9000 Ghent, Belgium
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Pallone JAL, Caramês ETDS, Alamar PD. Green analytical chemistry applied in food analysis: alternative techniques. Curr Opin Food Sci 2018. [DOI: 10.1016/j.cofs.2018.01.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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17
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Handheld NIR device: A non-targeted approach to assess authenticity of fish fillets and patties. Food Chem 2018; 243:382-388. [DOI: 10.1016/j.foodchem.2017.09.145] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 09/28/2017] [Accepted: 09/28/2017] [Indexed: 11/20/2022]
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18
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Huck CW. Advances of Vibrational Spectroscopic Technologies in Life Sciences. Molecules 2017; 22:molecules22020278. [PMID: 28208823 PMCID: PMC6155783 DOI: 10.3390/molecules22020278] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 02/10/2017] [Accepted: 02/11/2017] [Indexed: 11/22/2022] Open
Affiliation(s)
- Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, CCB-Center for Chemistry and Biomedicine, Innrain 80/82, 6020 Innsbruck, Austria.
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Josić D, Peršurić Ž, Rešetar D, Martinović T, Saftić L, Kraljević Pavelić S. Use of Foodomics for Control of Food Processing and Assessing of Food Safety. ADVANCES IN FOOD AND NUTRITION RESEARCH 2017; 81:187-229. [PMID: 28317605 DOI: 10.1016/bs.afnr.2016.12.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Food chain, food safety, and food-processing sectors face new challenges due to globalization of food chain and changes in the modern consumer preferences. In addition, gradually increasing microbial resistance, changes in climate, and human errors in food handling remain a pending barrier for the efficient global food safety management. Consequently, a need for development, validation, and implementation of rapid, sensitive, and accurate methods for assessment of food safety often termed as foodomics methods is required. Even though, the growing role of these high-throughput foodomic methods based on genomic, transcriptomic, proteomic, and metabolomic techniques has yet to be completely acknowledged by the regulatory agencies and bodies. The sensitivity and accuracy of these methods are superior to previously used standard analytical procedures and new methods are suitable to address a number of novel requirements posed by the food production sector and global food market.
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Affiliation(s)
- D Josić
- University of Rijeka, Centre for High-Throughput Technologies, Radmile Matejčić 2, Rijeka, Croatia.
| | - Ž Peršurić
- University of Rijeka, Centre for High-Throughput Technologies, Radmile Matejčić 2, Rijeka, Croatia
| | - D Rešetar
- University of Rijeka, Centre for High-Throughput Technologies, Radmile Matejčić 2, Rijeka, Croatia
| | - T Martinović
- University of Rijeka, Centre for High-Throughput Technologies, Radmile Matejčić 2, Rijeka, Croatia
| | - L Saftić
- University of Rijeka, Centre for High-Throughput Technologies, Radmile Matejčić 2, Rijeka, Croatia
| | - S Kraljević Pavelić
- University of Rijeka, Centre for High-Throughput Technologies, Radmile Matejčić 2, Rijeka, Croatia
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