Persona: Monago Maraña, Olga
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Monago Maraña
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Publicación Cost-effective fully 3D-printed on-drop electrochemical sensor based on carbon black/polylactic acid: a comparative study with screen-printed sensors in food analysis(Springer, 2024) Monago Maraña, Olga; Aouladtayib-Boulakjar, Nadia; Zapardiel Palenzuela, Antonio; García Domínguez, Amabel; Ayllón Pérez, Jorge; Rodríguez Prieto, Álvaro; Claver Gil, Juan; Camacho López, Ana María; González Crevillén, Agustín3D-printing technology allows scientist to fabricate easily electrochemical sensors. Until now, these sensors were designed employing a large amount of material, which increases the cost and decreases manufacturing throughput. In this work, a low-cost 3D-printed on-drop electrochemical sensor (3D-PES) was fully manufactured by fused filament fabrication, minimizing the number of printing layers. Carbon black/polylactic acid filament was employed, and the design and several printing parameters were optimized to yield the maximum electroanalytical performance using the minimal amount of material. Print speed and extrusion width showed a critical influence on the electroanalytical performance of 3D-PES. Under optimized conditions, the fabrication procedure offered excellent reproducibility (RSD 1.3% in working electrode diameter), speed (< 3 min/unit), and costs (< 0.01 $ in material cost). The 3D-PES was successfully applied to the determination of phloridzin in apple juice. The analytical performance of 3D-PES was compared with an equivalent commercial on-drop screen-printed electrode, yielding similar precision and accuracy but lower sensitivity. However, 3D-PES provides interesting features such as recyclability, biodegradability, low-cost, and the possibility of being manufactured near the point of need, some of which meets several demands of Green Chemistry. This cost-effective printing approach is a green and promising alternative for manufacturing disposable and portable electroanalytical devices, opening new possibilities not only in on-site food analysis but also in point-of-care testing.Publicación Raman spectroscopy as a tool for characterisation of quality parameters in Norwegian grown apples during ripening(ELSEVIER, 2024) Monago Maraña, Olga; Wold, Jens Petter; Remberg, Siv Fagertun; Sanden, Karen Wahlstrøm; Afseth, Nils KristianThis study shows for the first time the feasibility of Raman spectroscopy as a non-destructive method to follow the ripening process of apple fruits. Two different varieties of apples were studied: ‘Aroma’ and ‘Elstar’. By visual inspection, Raman spectra showed that the starch content was higher in ‘Elstar’ apples compared to ‘Aroma’. The degradation of starch over time could be detected in the Raman spectra, indicating that the method can be used to monitor the ripening process. The ripeness markers starch index, soluble solids content (SSC), and the sugars glucose, fructose and sucrose were determined with traditional destructive methods. Cross validated calibration models based on Raman spectroscopy were obtained for all quality parameters, and test set validation offered good results, with R2 in the range 0.4–0.86 for ‘Aroma’ and 0.4–0.95 for ‘Elstar’, respectively. The regression coefficients showed that the calibrations relied on Raman bands associated with starch and different sugars. The results suggest that Raman spectroscopy in the future could be used to determine the optimal time of harvesting and to sort apples into different degrees of ripeness.Publicación Untargeted authentication of fruit juices based on electrochemical fingerprints combined with chemometrics. Adulteration of orange juice as case of study(ELSEVIER, 2024) Monago Maraña, Olga; Zapardiel Palenzuela, Antonio; González Crevillén, AgustínThis work presents a novel strategy for authentication of fruit juices. The methodology is based on an electrochemical method combined with chemometrics. In addition, the case of orange juice adulteration with grapefruit juices was studied using this methodology. The electrochemical fingerprint of different juices showed the influence from different polyphenols according to the type of fruit. First, Principal Component Analysis (PCA) was able to differentiate clusters of different juices, being the higher group distance within apple, orange, and grape juices samples. On the other hand, partial least-squares – discriminant analysis (PLS-DA) and PCA-linear discriminant analysis (LDA) were used as classification methods, obtaining better results for orange and apple models. Furthermore, good results were obtained for the authentication of orange juices, compared to other juices, with an error rate of 0.04 for cross-validation. In the case of adulteration of orange juice was detected using PLS-DA at an adulteration level as low as 1%. Regression vectors for all models highlighted identifiable potential values that could be related to the main polyphenols in each type of fruit. This electrochemical method is rapid, low-cost, and compatible with on-site analysis compared to other laborious analysis described in the literature.