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      A First Tentative for Simultaneous Detection of Fungicides in Model and Real Wines by Microwave Sensor Coupled to Molecularly Imprinted Sol-Gel Polymers

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          Abstract

          A molecularly imprinted silica (MIS) coupled to a microwave sensor was used to detect three fungicides (iprodione, procymidone and pyrimethanil) present in most French wines. Chemometric methods were applied to interpret the microwave spectra and to correlate microwave signals and fungicide concentrations in a model wine medium, and in white and red Burgundy wines. The developed microwave sensor coupled to an MIS and to its control, a nonimprinted silica (NIS), was successfully applied to detect the three fungicides present in trace levels (ng L −1) in a model wine. The MIS sensor discriminated the fungicide concentrations better than the NIS sensor. Partial Least Squares models were suitable for determining iprodione in white and red wines. A preliminary method validation was applied to iprodione in the white and red wines. It showed a limit of detection (LOD) lower than 30 ng L −1 and a recovery percentage between 90 and 110% when the iprodione concentration was higher than the LOD. The determined concentrations were below the authorized level by far.

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          PLS-regression: a basic tool of chemometrics

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            Review of multidimensional data processing approaches for Raman and infrared spectroscopy

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              Validation of chemometric models – A tutorial

              In this tutorial, we focus on validation both from a numerical and conceptual point of view. The often applied reported procedure in the literature of (repeatedly) dividing a dataset randomly into a calibration and test set must be applied with care. It can only be justified when there is no systematic stratification of the objects that will affect the validated estimates or figures of merits such as RMSE or R(2). The various levels of validation may, typically, be repeatability, reproducibility, and instrument and raw material variation. Examples of how one data set can be validated across this background information illustrate that it will affect the figures of merits as well as the dimensionality of the models. Even more important is the robustness of the models for predicting future samples. Another aspect that is brought to attention is validation in terms of the overall conclusions when observing a specific system. One example is to apply several methods for finding the significant variables and see if there is a consensus subset that also matches what is reported in the literature or based on the underlying chemistry.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                31 October 2020
                November 2020
                : 20
                : 21
                : 6224
                Affiliations
                [1 ]Laboratoire Interdisciplinaire Carnot de Bourgogne, CNRS UMR 6303, Departement Interface, GERM, University Bourgogne Franche-Comté, 21078 Dijon, France; jerome.rossignol@ 123456u-bourgogne.fr (J.R.); didier.stuerga@ 123456u-bourgogne.fr (D.S.)
                [2 ]Service d’Appui à la Recherche, AgroSup Dijon, F-21000 Dijon, France; laurence.dujourdy@ 123456agrosupdijon.fr
                [3 ]AgroSup Dijon, University Bourgogne Franche-Comté, PAM UMR A 02.102, Procédés Alimentaires et Microbiologiques, F-21000 Dijon, France; philippe.cayot@ 123456agrosupdijon.fr (P.C.); regis.gougeon@ 123456u-bourgogne.fr (R.D.G.)
                [4 ]Institut Universitaire de la Vigne et du Vin Jules Guyot, AgroSup Dijon, University Bourgogne Franche-Comté, PAM UMR A 02.102, Procédés Alimentaires et Microbiologiques, F-21000 Dijon, France
                Author notes
                [* ]Correspondence: elias.bou-maroun@ 123456agrosupdijon.fr ; Tel.: +33-3-80-77-40-80
                Author information
                https://orcid.org/0000-0001-8318-6979
                https://orcid.org/0000-0002-5371-5523
                https://orcid.org/0000-0003-0208-858X
                Article
                sensors-20-06224
                10.3390/s20216224
                7662697
                33142813
                b7013814-6f51-4c59-b260-68105f6ee9e5
                © 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
                : 28 September 2020
                : 29 October 2020
                Categories
                Article

                Biomedical engineering
                molecularly imprinted polymers,microwave sensor,chemometric methods,pesticides,wine,rapid detection

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