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      Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

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          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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          Food authentication: Techniques, trends & emerging approaches

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            Convolutional neural networks for vibrational spectroscopic data analysis

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              Breaking with trends in pre-processing?

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                Author and article information

                Journal
                Foods
                Foods
                foods
                Foods
                MDPI
                2304-8158
                06 August 2020
                August 2020
                : 9
                : 8
                : 1069
                Affiliations
                [1 ]Nofima AS, Norwegian Institute of Food, Fisheries, and Aquaculture Research, Muninbakken 9-13, 9291 Tromsø, Norway; ingrid.mage@ 123456Nofima.no (I.M.); heidi.nilsen@ 123456nofima.no (H.N.)
                [2 ]United States Department of Agriculture, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705-2325, USA; walter.schmidt@ 123456usda.gov
                [3 ]Department of Food Engineering, Bingol University, 12000 Bingol, Turkey; tumaytemiz89@ 123456gmail.com
                [4 ]Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China; l_li@ 123456ouc.edu.cn
                [5 ]Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Korea; hykim@ 123456khu.ac.kr
                [6 ]Department of Physical and Chemical Sciences, University of L’Aquila, 67100 Via Vetoio, Coppito, L’Aquila, Italy; alessandra.biancolillo@ 123456univaq.it
                [7 ]Université Clermont-Auvergne, INRAE, VetAgro Sup, UMR F, 63370 Lempdes, France; abderrahmane.aitkaddour@ 123456vetagro-sup.fr
                [8 ]Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland; sikorski@ 123456amu.edu.pl
                [9 ]Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland; ewa.sikorska@ 123456ue.poznan.pl
                [10 ]Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via Celoria, 2, 20133 Milano, Italy; silvia.grassi@ 123456unimi.it
                [11 ]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; d.cozzolino@ 123456uq.edu.au
                Author notes
                [* ]Correspondence: abdo.hassoun@ 123456nofima.no ; Tel.: +47-930-73-906
                Author information
                https://orcid.org/0000-0002-3059-292X
                https://orcid.org/0000-0003-0364-0225
                https://orcid.org/0000-0003-3409-0932
                https://orcid.org/0000-0002-9461-9597
                https://orcid.org/0000-0003-4132-539X
                https://orcid.org/0000-0002-9527-7322
                https://orcid.org/0000-0002-0425-5807
                https://orcid.org/0000-0002-2102-9713
                https://orcid.org/0000-0001-6247-8817
                Article
                foods-09-01069
                10.3390/foods9081069
                7466239
                32781687
                568f0162-7482-49f1-be06-2d4dd2582d14
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 July 2020
                : 01 August 2020
                Categories
                Review

                authentication,authenticity,chemometric,fish,origin,honey,meat,milk,spectroscopy,species

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