- -

Optimization of electronic nose drift correction applied to tomato volatile profiling

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Optimization of electronic nose drift correction applied to tomato volatile profiling

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Valcarcel-GermesI ...
Tamaño: 1.548Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: ABC 2021 Electronic ...
Tamaño: 1.414Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Valcárcel-Germes, Mercedes es_ES
dc.contributor.author Ibañez, Gines es_ES
dc.contributor.author Martí-Renau, Raul es_ES
dc.contributor.author Beltran, Joaquim es_ES
dc.contributor.author Cebolla Cornejo, Jaime es_ES
dc.contributor.author Rosello Ripolles, Salvador es_ES
dc.date.accessioned 2022-09-16T18:04:09Z
dc.date.available 2022-09-16T18:04:09Z
dc.date.issued 2021-06 es_ES
dc.identifier.issn 1618-2642 es_ES
dc.identifier.uri http://hdl.handle.net/10251/186214
dc.description.abstract [EN] E-noses can be routinely used to evaluate the volatile profile of tomato samples once the sensor drift and standardization issues are adequately solved. Short-term drift can be corrected using a strategy based on a multiplicative drift correction procedure coupled with a PLS adaptation of the component correction. It must be performed specifically for each sequence, using all sequence signals data. With this procedure, a drastic reduction of sensor signal %RSD can be obtained, ranging between 91.5 and 99.7% for long sequences and between 75.7 and 98.8% for short sequences. On the other hand, long-term drift can be fixed up using a synthetic reference standard mix (with a representation of main aroma volatiles of the species) to be included in each sequence that would enable sequence standardization. With this integral strategy, a high number of samples can be analyzed in different sequences, with a 94.4% success in the aggrupation of the same materials in PLS-DA two-dimensional graphical representations. Using this graphical interface, e-noses can be used to developed expandable maps of volatile profile similitudes, which will be useful to select the materials that most resemble breeding objectives or to analyze which preharvest and postharvest procedures have a lower impact on the volatile profile, avoiding the costs and sample limitations of gas chromatography. es_ES
dc.description.sponsorship This research was partially funded by Jaume I University with projects P1-1B2011-41 and COGRUP/2016/04. G. Ibanez also thanks Universitat Jaume I for funding his pre-doctoral grant (PREDOC/2015/45). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Analytical and Bioanalytical Chemistry es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Electronic nose es_ES
dc.subject Drift correction es_ES
dc.subject Chemometrics es_ES
dc.subject Sequence standardization es_ES
dc.subject Tomato es_ES
dc.subject.classification GENETICA es_ES
dc.title Optimization of electronic nose drift correction applied to tomato volatile profiling es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00216-021-03340-5 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UJI//P1-1B2011-41/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UJI//COGRUP%2F2016%2F04/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UJI//PREDOC%2F2015%2F45/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana - Institut Universitari de Conservació i Millora de l'Agrodiversitat Valenciana es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Biotecnología - Departament de Biotecnologia es_ES
dc.description.bibliographicCitation Valcárcel-Germes, M.; Ibañez, G.; Martí-Renau, R.; Beltran, J.; Cebolla Cornejo, J.; Rosello Ripolles, S. (2021). Optimization of electronic nose drift correction applied to tomato volatile profiling. Analytical and Bioanalytical Chemistry. 413(15):3893-3907. https://doi.org/10.1007/s00216-021-03340-5 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00216-021-03340-5 es_ES
dc.description.upvformatpinicio 3893 es_ES
dc.description.upvformatpfin 3907 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 413 es_ES
dc.description.issue 15 es_ES
dc.identifier.pmid 33893513 es_ES
dc.relation.pasarela S\434675 es_ES
dc.contributor.funder Universitat Jaume I es_ES
dc.description.references Tieman D, Zhu G, Resende MFR, Lin T, Nguyen C, Bies D, et al. A chemical genetic roadmap to improved tomato flavor. Science. 2017;355(6323):391–4. https://doi.org/10.1126/science.aal1556. es_ES
dc.description.references Baldwin EA, Scott J, Shewmaker CK, Schuh W. Flavor trivia and tomato aroma: biochemistry and possible mechanism for control of important aroma components. HortScience. 2000;35(6):1013–22. https://doi.org/10.21273/HORTSCI.35.6.1013. es_ES
dc.description.references Davies JN, Hobson GE. The constituents of tomato fruit — the influence of environment, nutrition, and genotype. CRC Crit Rev Food Sci Nutr. 1981;15(3):205–80. https://doi.org/10.1080/10408398109527317. es_ES
dc.description.references Lahoz I, Pérez-de-Castro A, Valcárcel M, Macua JI, Beltrán J, Roselló S, et al. Effect of water deficit on the agronomical performance and quality of processing tomato. Sci Hortic (Amsterdam). 2016;200:55–65. https://doi.org/10.1016/j.scienta.2015.12.051. es_ES
dc.description.references Schouten RE, Woltering EJ, Tijskens LMM. Sugar and acid interconversion in tomato fruits based on biopsy sampling of locule gel and pericarp tissue. Postharvest Biol Technol. 2016;111:83–92. https://doi.org/10.1016/j.postharvbio.2015.07.032. es_ES
dc.description.references Boukobza F, Taylor AJ. Effect of postharvest treatment on flavour volatiles of tomatoes. Postharvest Biol Technol. 2002;25(3):321–31. https://doi.org/10.1016/S0925-5214(02)00037-6. es_ES
dc.description.references Zhao J, Sauvage C, Zhao J, Bitton F, Bauchet G, Liu D, et al. Meta-analysis of genome-wide association studies provides insights into genetic control of tomato flavor. Nat Commun. 2019;10(1):1–12. https://doi.org/10.1038/s41467-019-09462-w. es_ES
dc.description.references Loutfi A, Coradeschi S, Mani GK, Shankar P, Rayappan JBB. Electronic noses for food quality: a review. J Food Eng. 2015;144:103–11. https://doi.org/10.1016/j.jfoodeng.2014.07.019. es_ES
dc.description.references Kiani S, Minaei S, Ghasemi-Varnamkhasti M. Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: a review. J Appl Res Med Aromat Plants. 2016;3(1):1–9. https://doi.org/10.1016/j.jarmap.2015.12.002. es_ES
dc.description.references Sun Y, Wang J, Cheng S. Discrimination among tea plants either with different invasive severities or different invasive times using MOS electronic nose combined with a new feature extraction method. Comput Electron Agric. 2017;143:293–301. https://doi.org/10.1016/j.compag.2017.11.007. es_ES
dc.description.references Majchrzak T, Wojnowski W, Dymerski T, Gębicki J, Namieśnik J. Electronic noses in classification and quality control of edible oils: a review. Food Chem. 2018;246:192–201. https://doi.org/10.1016/j.foodchem.2017.11.013. es_ES
dc.description.references Jia W, Liang G, Jiang Z, Wang J. Advances in electronic nose development for application to agricultural products. Food Anal Methods. 2019;12(10):2226–40. https://doi.org/10.1007/s12161-019-01552-1. es_ES
dc.description.references Marco S, Gutierrez-Galvez A. Signal and data processing for machine olfaction and chemical sensing: a review. IEEE Sensors J. 2012;12(11):3189–214. https://doi.org/10.1109/JSEN.2012.2192920. es_ES
dc.description.references Rudnitskaya A. Calibration update and drift correction for electronic noses and tongues. Front Chem. 2018;6:433. https://doi.org/10.3389/fchem.2018.00433/full. es_ES
dc.description.references Gutierrez-Osuna R. Pattern analysis for machine olfaction: a review. IEEE Sensors J. 2002;2(3):189–202. https://doi.org/10.1109/JSEN.2002.800688. es_ES
dc.description.references Beltran J, Serrano E, López FJ, Peruga A, Valcarcel M, Rosello S. Comparison of two quantitative GC-MS methods for analysis of tomato aroma based on purge-and-trap and on solid-phase microextraction. Anal Bioanal Chem. 2006;385(7):1255–64. https://doi.org/10.1007/s00216-006-0410-9. es_ES
dc.description.references Casals J, Pascual L, Cañizares J, Cebolla-Cornejo J, Casañas F, Nuez F. Genetic basis of long shelf life and variability into Penjar tomato. Genet Resour Crop Evol. 2012;59(2):219–29. https://doi.org/10.1007/s10722-011-9677-6. es_ES
dc.description.references Roselló S, Nuez F, Casals J, Beltrán J, Casañas F, Cebolla-Cornejo J. Long-term postharvest aroma evolution of tomatoes with the alcobaça (alc) mutation. Eur Food Res Technol. 2011;233:331–42. https://doi.org/10.1007/s00217-011-1517-6. es_ES
dc.description.references Cebolla-Cornejo J, Roselló S, Nuez F. Selection of tomato rich in nutritional terpenes. Natural Products: Phytochemistry, Botany and Metabolism of Alkaloids, Phenolics and Terpenes. 2013. es_ES
dc.description.references Skov T, Bro R. A new approach for modelling sensor based data. Sensors Actuators B Chem. 2005;106(2):719–29. https://doi.org/10.1016/j.snb.2004.09.023. es_ES
dc.description.references Salit ML, Turk GC. A drift correction procedure. Anal Chem. 1998;70(15):3184–90. https://doi.org/10.1021/ac980095b. es_ES
dc.description.references Artursson T, Eklo T, Lundstro I, Sjo M. Drift correction for gas sensors using multivariate methods. J Chemom. 2000;14(5–6):711–23. https://doi.org/10.1002/1099-128X(200009/12)14:5/6<711::AID-CEM607>3.0.CO;2-4. es_ES
dc.description.references Ballabio D, Consonni V. Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal Methods. 2013;5(16):3790. https://doi.org/10.1039/C3AY40582F. es_ES
dc.description.references Krzanowski W. Principles of multivariate analysis: a user’s perspective: Oxford University Press; 2000. es_ES
dc.description.references Di Natale C, Martinelli E, D’Amico A. Counteraction of environmental disturbances of electronic nose data by independent component analysis. Sensors Actuators B Chem. 2002;82(2–3):158–65. https://doi.org/10.1016/S0925-4005(01)01001-2. es_ES
dc.description.references Hyvärinen a, Oja E. Independent component analysis: algorithms and applications. Neural Netw 2000;13(4–5):411–430; https://doi.org/10.1016/S0893-6080(00)00026-5. es_ES
dc.description.references Xu M, Yang SL, Peng W, Liu YJ, Xie DS, Li XY, et al. A novel method for the discrimination of semen arecae and its processed products by using computer vision, electronic nose, and electronic tongue. Evid Based Complement Altern Med. 2015;2015. https://doi.org/10.1155/2015/753942. es_ES
dc.description.references Gromski PS, Correa E, Vaughan AA, Wedge DC, Turner ML, Goodacre R. A comparison of different chemometrics approaches for the robust classification of electronic nose data. Anal Bioanal Chem. 2014;406(29):7581–90. https://doi.org/10.1007/s00216-014-8216-7. es_ES
dc.description.references Shi Y, Gong F, Wang M, Liu J, Wu Y, Men H. A deep feature mining method of electronic nose sensor data for identification identifying beer olfactory information. J Food Eng. 2019;263:437–45. https://doi.org/10.1016/j.jfoodeng.2019.07.023. es_ES
dc.description.references Song J, Bi J, Chen Q, Wu X, Lyu Y, Meng X. Assessment of sugar content, fatty acids, free amino acids, and volatile profiles in jujube fruits at different ripening stages. Food Chem. 2019;270:344–52. https://doi.org/10.1016/j.foodchem.2018.07.102. es_ES
dc.description.references Hong X, Wang J, Qi G. E-nose combined with chemometrics to trace tomato-juice quality. J Food Eng. 2015;149:38–43. https://doi.org/10.1016/j.jfoodeng.2014.10.003. es_ES
dc.description.references Buttery R, Teranishi R, Ling LC. Fresh tomato aroma volatiles: a quantitative study. J Agric Food Chem. 1987;35(4):540–4. es_ES
dc.description.references Tieman D, Bliss P, McIntyre LM, Blandon-Ubeda A, Bies D, Odabasi AZ, et al. The chemical interactions underlying tomato flavor preferences. Curr Biol. 2012;22(11):1035–9. https://doi.org/10.1016/j.cub.2012.04.016. es_ES
dc.description.references Padilla M, Perera A, Montoliu I, Chaudry A, Persaud K, Marco S. Drift compensation of gas sensor array data by orthogonal signal correction. Chemom Intell Lab Syst. 2010;100(1):28–35. https://doi.org/10.1016/j.chemolab.2009.10.002. es_ES
dc.description.references Tomic O, Ulmer H, Haugen JE. Standardization methods for handling instrument related signal shift in gas-sensor array measurement data. Anal Chim Acta. 2002;472(1–2):99–111. https://doi.org/10.1016/S0003-2670(02)00936-4. es_ES
dc.description.references Tomic O, Eklöv T, Kvaal K, Haugen JE. Recalibration of a gas-sensor array system related to sensor replacement. Anal Chim Acta. 2004;512(2):199–206. https://doi.org/10.1016/j.aca.2004.03.001. es_ES
dc.description.references Zhang L, Zhang D. Domain adaptation extreme learning machines for drift compensation in e-nose systems. IEEE Trans Instrum Meas. 2015;64(7):1790–801. https://doi.org/10.1109/TIM.2014.2367775. es_ES
dc.description.references Yan K, Zhang D. Calibration transfer and drift compensation of e-noses via coupled task learning. Sensors Actuators B Chem. 2016;225:288–97. https://doi.org/10.1016/j.snb.2015.11.058. es_ES
dc.description.references Solórzano A, Rodríguez-Pérez R, Padilla M, Graunke T, Fernandez L, Marco S, et al. Multi-unit calibration rejects inherent device variability of chemical sensor arrays. Sensors Actuators B Chem. 2018;265:142–54. https://doi.org/10.1016/j.snb.2018.02.188. es_ES
dc.description.references Ziyatdinov A, Marco S, Chaudry A, Persaud K, Caminal P, Perera A. Drift compensation of gas sensor array data by common principal component analysis. Sensors Actuators B Chem. 2010;146(2):460–5. https://doi.org/10.1016/j.snb.2009.11.034. es_ES
dc.description.references Fernandez L, Guney S, Gutierrez-Galvez A, Marco S. Calibration transfer in temperature modulated gas sensor arrays. Sensors Actuators B Chem. 2016;231:276–84. https://doi.org/10.1016/j.snb.2016.02.131. es_ES
dc.description.references Gutierrez-Osuna R. Drift reduction for metal-oxide sensor arrays using canonical correlation regression and partial least squares. Proc 7th Int Symp Olfaction Electron Nose. 2000;1–7. es_ES
dc.description.references Vergara A, Vembu S, Ayhan T, Ryan MA, Homer ML, Huerta R. Chemical gas sensor drift compensation using classifier ensembles. Sensors Actuators B Chem. 2012;166–167:320–9. https://doi.org/10.1016/j.snb.2012.01.074. es_ES
dc.description.references Cebolla-Cornejo J, Roselló S, Valcárcel M, Serrano E, Beltrán J, Nuez F. Evaluation of genotype and environment effects on taste and aroma flavour components of Spanish fresh tomato. J Agric Food Chem. 2011;59:2440–50. https://doi.org/10.1021/jf1045427. es_ES
dc.description.references Cebolla-Cornejo J, Soler S, Nuez F. Genetic erosion of traditional varieties of vegetable crops in Europe: tomato cultivation in Valencia (Spain) as a case study. Int J Plant Prod. 2007;1(2):113–28. es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem