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      Data fusion methodologies for food and beverage authentication and quality assessment - a review.

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          Abstract

          The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.

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          Author and article information

          Journal
          Anal. Chim. Acta
          Analytica chimica acta
          Elsevier BV
          1873-4324
          0003-2670
          Sep 03 2015
          : 891
          Affiliations
          [1 ] iSens Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
          [2 ] Chemometrics, Qualimetrics and Nanosensors Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain. Electronic address: joan.ferre@urv.cat.
          [3 ] Chemometrics, Qualimetrics and Nanosensors Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
          Article
          S0003-2670(15)00552-8
          10.1016/j.aca.2015.04.042
          26388360
          5716893f-32c8-4e03-862a-189721e04c48
          History

          Food,Data fusion,Classification,Chemometrics,Beverage,Authentication

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