3acd121d60273ed880b4cebae5368fec engproc_06_00041_v2.pdf 6fc936a9f1a323a6e436f542d93b72426330a306 engproc_06_00041_v2.pdf 40940905496fa2f56e7ad0b47b691ab7aeaea87e6cdbd0fffc8b0dd329c583b1 engproc_06_00041_v2.pdf Title: Detection of Biogenic Amines in Canned Tuna Using a Voltammetric Electronic Tongue "2279 Subject: Biogenic amines (BAs), which are produced naturally due to the decomposition of amino acids, are crucial for the food industry because its formation is directly related to improper storage and the presence of bacteria. High concentrations of BAs can be easily related with the quality and spoilage of the products of this sector. The necessity to quickly and efficiently quantify these targets makes mandatory the use of alternatives to conventional analytical methods used up to now. For example, the combination of sensors with chemometric tools (known as electronic tongue) are a promising alternative for quick and informative analysis in the food sector. Chemometric tools allow us to develop models for the quantification of specific compounds in a complex matrix, making it a feasible tool for the development of more user-friendly methods than the traditional ones. In this context, the work presents an electronic tongue created for the detection of histamine, cadaverine and tyramine using a set of five modified GEC (graphite epoxy composite) electrodes: ZnO NPs, CuO NPs, SnO2 NPs, Bi2O3 NPs, and polypyrrole, as the voltammetric multisensor array. The chemometric model was obtained with an Artificial Neural Network (ANN) with 51 input neurons, five neurons in the hidden layer and three neurons in the output layer. The functions used for the hidden and output layers were tansig and purelin, respectively. The results show slopes near to 1 and intercepts close to 0, indicating the feasibility of the model. Keywords: artificial neural networks; electronic tongue; biogenic amines; histamine; voltammetry Author: Marta Bonet-San-Emeterio, Maria Bruguera-Jané, Xavier Cetó and Manel del Valle Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Sep 8 07:36:50 2021 CEST ModDate: Thu Jan 20 17:37:02 2022 CET 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: 191619 bytes Optimized: yes PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HJOMHV+VnURWPalladioL-Bold Type 1 Custom yes yes no 65 0 MIKRSI+VnURWPalladioL Type 1 Custom yes yes no 68 0 IBFQPI+URWPalladioL-Ital Type 1 Custom yes yes no 72 0 BYQWLL+URWPalladioL-Bold Type 1 Custom yes yes no 75 0 AQTXXZ+URWPalladioL-Roma Type 1 Custom yes yes no 78 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 92 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 95 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 98 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2022-03-10 02:50:21 CET RepresentationInformation: engproc_06_00041_v2.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2022-01-23 04:56:27 CET Size: 191619 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf Profile: Linearized PDF PDFMetadata: Objects: 0 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: HJOMHV+VnURWPalladioL-Bold FontSubset: true FirstChar: 178 LastChar: 226 FontDescriptor: FontName: HJOMHV+VnURWPalladioL-Bold Flags: Symbolic FontBBox: -152, -301, 1000, 935 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: MIKRSI+VnURWPalladioL FontSubset: true FirstChar: 225 LastChar: 225 FontDescriptor: FontName: MIKRSI+VnURWPalladioL Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: IBFQPI+URWPalladioL-Ital FontSubset: true FirstChar: 46 LastChar: 116 FontDescriptor: FontName: IBFQPI+URWPalladioL-Ital Flags: Symbolic FontBBox: -170, -305, 1010, 941 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: BYQWLL+URWPalladioL-Bold FontSubset: true FirstChar: 39 LastChar: 134 FontDescriptor: FontName: BYQWLL+URWPalladioL-Bold Flags: Symbolic FontBBox: -152, -301, 1000, 935 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: AQTXXZ+URWPalladioL-Roma FontSubset: true FirstChar: 2 LastChar: 243 FontDescriptor: FontName: AQTXXZ+URWPalladioL-Roma Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true XMP: application/pdf Marta Bonet-San-Emeterio, Maria Bruguera-Jané, Xavier Cetó and Manel del Valle Biogenic amines (BAs), which are produced naturally due to the decomposition of amino acids, are crucial for the food industry because its formation is directly related to improper storage and the presence of bacteria. High concentrations of BAs can be easily related with the quality and spoilage of the products of this sector. The necessity to quickly and efficiently quantify these targets makes mandatory the use of alternatives to conventional analytical methods used up to now. For example, the combination of sensors with chemometric tools (known as electronic tongue) are a promising alternative for quick and informative analysis in the food sector. Chemometric tools allow us to develop models for the quantification of specific compounds in a complex matrix, making it a feasible tool for the development of more user-friendly methods than the traditional ones. In this context, the work presents an electronic tongue created for the detection of histamine, cadaverine and tyramine using a set of five modified GEC (graphite epoxy composite) electrodes: ZnO NPs, CuO NPs, SnO2 NPs, Bi2O3 NPs, and polypyrrole, as the voltammetric multisensor array. The chemometric model was obtained with an Artificial Neural Network (ANN) with 51 input neurons, five neurons in the hidden layer and three neurons in the output layer. The functions used for the hidden and output layers were tansig and purelin, respectively. The results show slopes near to 1 and intercepts close to 0, indicating the feasibility of the model. 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