4fb6b8bee4e4c7fb499b3e4cfaa918f1 engproc_06_00050_v3.pdf 8d9cd664e752fad640b59692aff109b0712c682d engproc_06_00050_v3.pdf 6f4e35f7e15a5c84d4e84cfe0b0f368450c863305a8c68d3f465103af32e80d6 engproc_06_00050_v3.pdf Title: Coupling of Sensors and Machine Learning Algorithms in the Qualitative Analysis of Wine "2279 Subject: This work attempts the identification of the production year, the cultivar’s region and the aging method used in the elaboration of different Spanish red wines, all from the “tempranillo” grape variety. The identification of such characteristics relies on the use of a voltammetric electronic tongue (ET) system formed by modified graphite-epoxy electrodes (GEC) and metallic electrodes to collect a set of six voltammograms per sample, and different chemometric tools to accomplish the final identifications. A large sample set that included 199 different wine samples from commercial and own elaboration origin were analysed with the electronic tongue system, using the cyclic voltammetry technique and without any sample pre-treatment. To process the extremely complex and high-dimensionality generated data, a compression strategy was used for the acquired voltammograms, using discrete wavelet transform (DWT). This treatment reduced the information to ca. 10%, preserving significant features from the voltammetric signals. Compressed data was evaluated firstly by unsupervised methods, i.e., principal component analysis (PCA), without much success as it was found that such methods were unable to unravel the patterns contained within such complex data samples. Finally, the processed electrochemical information was evaluated by supervised methods to accomplish the proper identification; among those methods were linear discriminant analysis (LDA), supported vector machines (SVM) or artificial neural networks (ANN). The best results were obtained using artificial neural networks (ANNs), achieving 96.1% of correct classification for bottled year, 86.8% for elaboration region (protected designation of origin) and 98.6% for maturation type with or without use of wood barrel. Keywords: voltammetry; multisensor array; electronic tongue; artificial neural networks; wines Author: Anna Herrera-Chacon, Inmaculada Campos, Andreu González-Calabuig, Mireia Torres and Manel del Valle Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Sep 9 05:40:46 2021 CEST ModDate: Thu Sep 9 05:44:24 2021 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 1 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 193901 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- AIQCCL+VnURWPalladioL-Bold Type 1 Custom yes yes no 10 0 KPNHET+VnURWPalladioL Type 1 Custom yes yes no 15 0 IBFQPI+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 PTTIWP+URWPalladioL-Bold Type 1 Custom yes yes no 24 0 NNWRFC+URWPalladioL-Roma Type 1 Custom yes yes no 28 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 55 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 59 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 63 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2022-02-08 02:30:53 CET RepresentationInformation: engproc_06_00050_v3.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2022-01-23 04:56:51 CET Size: 193901 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 85 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Info: Title: Coupling of Sensors and Machine Learning Algorithms in the Qualitative Analysis of Wine "2279 Author: Anna Herrera-Chacon, Inmaculada Campos, Andreu González-Calabuig, Mireia Torres and Manel del Valle Subject: This work attempts the identification of the production year, the cultivar’s region and the aging method used in the elaboration of different Spanish red wines, all from the “tempranillo” grape variety. The identification of such characteristics relies on the use of a voltammetric electronic tongue (ET) system formed by modified graphite-epoxy electrodes (GEC) and metallic electrodes to collect a set of six voltammograms per sample, and different chemometric tools to accomplish the final identifications. A large sample set that included 199 different wine samples from commercial and own elaboration origin were analysed with the electronic tongue system, using the cyclic voltammetry technique and without any sample pre-treatment. To process the extremely complex and high-dimensionality generated data, a compression strategy was used for the acquired voltammograms, using discrete wavelet transform (DWT). This treatment reduced the information to ca. 10%, preserving significant features from the voltammetric signals. Compressed data was evaluated firstly by unsupervised methods, i.e., principal component analysis (PCA), without much success as it was found that such methods were unable to unravel the patterns contained within such complex data samples. Finally, the processed electrochemical information was evaluated by supervised methods to accomplish the proper identification; among those methods were linear discriminant analysis (LDA), supported vector machines (SVM) or artificial neural networks (ANN). The best results were obtained using artificial neural networks (ANNs), achieving 96.1% of correct classification for bottled year, 86.8% for elaboration region (protected designation of origin) and 98.6% for maturation type with or without use of wood barrel. 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