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<title>Easy Nitrite Analysis of Processed Meat with Colorimetric Polymer Sensors and a Smartphone App</title>
<creator>Guembe García, Marta</creator>
<creator>González Ceballos, Lara</creator>
<creator>Arnáiz Alonso, Ana</creator>
<creator>Fernández Muiño, Miguel Ángel</creator>
<creator>Sancho Ortiz, María Teresa</creator>
<creator>Osés Gómez, Sandra María</creator>
<creator>Ibeas Cortes, Saturnino</creator>
<creator>Rovira Carballido, Jordi</creator>
<creator>Melero Gil, Beatriz</creator>
<creator>Represa Pérez, César</creator>
<creator>García Pérez, José Miguel</creator>
<creator>Vallejos Calzada, Saúl</creator>
<subject>Polymer chemosensory films</subject>
<subject>Sensors</subject>
<subject>Nitrite in meat</subject>
<subject>Colorimetry</subject>
<subject>RGB</subject>
<subject>HSV</subject>
<subject>Color space</subject>
<description>We have developed an in situ methodology for determining nitrite concentration in processed meats that can also be used by unskilled personnel. It is based on a colorimetric film-shaped sensory polymer that changes its color upon contacting the meat and a mobile app that automatically calculates the manufacturing and residual nitrite concentration by only taking digital photographs of sensory films and analyzing digital color parameters. The film-shaped polymer sensor detects nitrite anions by an azo-coupling reaction, since they activate this reaction between two of the four monomers that the copolymer is based on. The sensory polymer is complemented with an app, which analyzes the color in two different digital color spaces (RGB and HSV) and performs a set of 32 data fittings representing the concentration of nitrite versus eight different variables, finally providing the nitrite concentration of the test samples using the best fitting curve. The calculated concentration of nitrite correlates with a validated method (ISO 2918: 1975) usually used to determine nitrite, and no statistically significant difference between these methods and our proposed one has been found in our study (26 meat samples, 8 prepared, and 18 commercial). Our method represents a great advance in terms of analysis time, simplicity, and orientation to use by average citizens.</description>
<date>2022-10-05</date>
<date>2022-10-05</date>
<date>2022-08</date>
<type>info:eu-repo/semantics/article</type>
<identifier>1944-8244</identifier>
<identifier>http://hdl.handle.net/10259/7083</identifier>
<identifier>10.1021/acsami.2c09467</identifier>
<identifier>1944-8252</identifier>
<language>eng</language>
<relation>ACS Applied Materials &amp; Interfaces. 2022, V. 14, n. 32, p. 37051–37058</relation>
<relation>https://doi.org/10.1021/acsami.2c09467</relation>
<relation>info:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//LCF%2FPR%2FPR18%2F51130007</relation>
<relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113264RB-I00/ES/DISEÑO, SINTESIS Y CARACTERIZACION DE POLIMEROS INTELIGENTES. APLICACION DE SENSORES QUIMICOS POLIMERICOS EN CONTROL Y SEGURIDAD ALIMENTARIA Y EN EL AMBITO SANITARIO</relation>
<rights>http://creativecommons.org/licenses/by/4.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Atribución 4.0 Internacional</rights>
<publisher>American Chemical Society</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>