Multi-unit calibration rejects inherent device variability of chemical sensor arrays

Sensors and Actuators, B: Chemical

Volumn 265, Issue , 2018, Pages 142-154

  Solórzano, Ana ^a,b   Rodríguez Pérez, Raquel ^b   Padilla, Marta ^b   Graunke, Thorsten ^c   Fernandez, Luis ^a,b   Marco, Santiago ^a,b   Fonollosa, Jordi ^a,b,d,e  

^a UNIVERSITY OF BARCELONA   (Spain)

^b INSTITUTE FOR BIOENGINEERING OF CATALONIA IBEC   (Spain)

^c Ams Sensors Germany GmbH   (Germany)

^d UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^e Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu   (Spain)

Author keywords

Calibration transfer; Gas sensor array; Machine olfaction; MOX sensor; Robust calibration

Indexed keywords

GAS DETECTORS; METALS;

CALIBRATION TRANSFER; CLASSIFICATION ACCURACY; INDIVIDUAL CALIBRATIONS; MACHINE OLFACTION; METAL OXIDE GAS SENSORS; MOX SENSORS; ROBUST CALIBRATION; ROBUST CALIBRATION MODELS;

CHEMICAL SENSORS;

EID: 85043791644     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2018.02.188     Document Type: Article

References (70)

1. Potyrailo, R.A., Multivariable sensors for ubiquitous monitoring of gases in the era of internet of things and industrial internet. Chem. Rev., 116, 2016, 10.1021/acs.chemrev.6b00187.
2. Fine, G.F., Cavanagh, L.M., Afonja, A., Binions, R., Metal oxide semi-conductor gas sensors in environmental monitoring. Sensors 10 (2010), 5469–5502.
3. Gao, H., Jia, H., Bierer, B., Wöllenstein, J., Lu, Y., Palzer, S., Scalable gas sensors fabrication to integrate metal oxide nanoparticles with well-defined shape and size. Sens. Actuators B Chem. 249 (2017), 639–646, 10.1016/j.snb.2017.04.031.
4. Padilla, M., a. Perera, Montoliu, I., a. Chaudry, Persaud, K., Marco, S., Drift compensation of gas sensor array data by Orthogonal Signal Correction. Chemom. Intell. Lab. Syst. 100 (2010), 28–35, 10.1016/j.chemolab.2009.10.002.
5. Marco, S., Gutierrez-Galvez, A., Signal and data processing for machine olfaction and chemical sensing: a review. IEEE Sens. J. 12 (2012), 3189–3214, 10.1109/JSEN.2012.2192920.
6. Monereo, O., Prades, J.D., Cirera, A., Self-heating effects in large arrangements of randomly oriented carbon nanofibers: application to gas sensors. Sens. Actuators B Chem. 211 (2015), 489–497, 10.1016/j.snb.2015.01.095.
7. Herrero-Carrón, F., Yáñez, D.J., Rodríguez, F.D.B., Varona, P., An active, inverse temperature modulation strategy for single sensor odorant classification. Sens. Actuators B Chem. 206 (2015), 555–563, 10.1016/j.snb.2014.09.085.
8. Drobek, M., Kim, J.H., Bechelany, M., Vallicari, C., Julbe, A., Kim, S.S., MOF-Based membrane encapsulated ZnO nanowires for enhanced gas sensor selectivity. ACS Appl. Mater. Interfaces 8 (2016), 8323–8328, 10.1021/acsami.5b12062.
9. Korotcenkov, G., Cho, B.K., Metal oxide composites in conductometric gas sensors: achievements and challenges. Sens. Actuators B Chem. 244 (2017), 182–210, 10.1016/j.snb.2016.12.117.
10. Krivetskiy, V., Malkov, I., Garshev, A., Mordvinova, N., Lebedev, O.I., Dolenko, S., Efitorov, A., Grigoriev, T., Rumyantseva, M., Gaskov, A., Chemically modified nanocrystalline SnO2-based materials for nitrogen-containing gases detection using gas sensor array. J. Alloys Compd. 691 (2017), 514–523, 10.1016/j.jallcom.2016.08.275.
11. Krivetskiy, V., Efitorov, A., Arkhipenko, A., Vladimirova, S., Rumyantseva, M., Dolenko, S., Gaskov, A., Selective detection of individual gases and CO/H2mixture at low concentrations in air by single semiconductor metal oxide sensors working in dynamic temperature mode. Sens. Actuators B Chem. 254 (2018), 502–513, 10.1016/j.snb.2017.07.100.
12. Eranna, G., Joshi, B.C., Runthala, D.P., Gupta, R.P., Oxide materials for development of integrated gas sensors—a comprehensive review. Crit. Rev. Solid State Mater. Sci. 29 (2004), 111–188, 10.1080/10408430490888977.
13. Lee, A.P., Reedy, B.J., Temperature modulation in semiconductor gas sensing. Sens. Actuators B Chem. 60 (1999), 35–42, 10.1016/S0925-4005(99)00241-5.
14. Ortega, A., Marco, S., Perera, A., Šundic, T., Pardo, A., Samitier, J., An intelligent detector based on temperature modulation of a gas sensor with a digital signal processor. Sens. Actuators B Chem. 78 (2001), 32–39, 10.1016/S0925-4005(01)00788-2.
15. Zampolli, S., Elmi, I., Cozzani, E., Cardinali, G.C., Scorzoni, A., Cicioni, M., Marco, S., Palacio, F., Gómez-Cama, J.M., Sayhan, I., Becker, T., Ultra-low-power components for an RFID Tag with physical and chemical sensors. Microsyst. Technol. 14 (2008), 581–588, 10.1007/s00542-007-0444-8.
16. Sayhan, I., Helwig, A., Becker, T., Muller, G., Elmi, I., Zampolli, S., Padilla, M., Marco, S., Discontinuously operated metal oxide gas sensors for flexible tag microlab applications. IEEE Sens. J. 8 (2008), 176–181, 10.1109/JSEN.2007.912791.
17. Simon, I., Bârsan, N., Bauer, M., Weimar, U., Micromachined metal oxide gas sensors: opportunities to improve sensor performance. Sens. Actuators B Chem. 73 (2001), 1–26, 10.1016/S0925-4005(00)00639-0.
18. Marco, S., The need for external validation in machine olfaction: emphasis on health-related applications Chemosensors and Chemoreception. Anal. Bioanal. Chem. 406 (2014), 3941–3956, 10.1007/s00216-014-7807-7.
19. Rodriguez-Lujan, I., Fonollosa, J., Vergara, A., Homer, M., Huerta, R., On the calibration of sensor arrays for pattern recognition using the minimal number of experiments. Chemom. Intell. Lab. Syst., 130, 2014, 10.1016/j.chemolab.2013.10.012.
20. Gardner, J.W., Bartlett, P.N., Pattern recognition in odour sensing. Sensors Sens. Syst. an Electron. Nose, 1992, Kluwer Academic Publishers, Dordretch, 161–179.
21. Gutierrez-Osuna, R., Nagle, H.T., A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors. IEEE Trans. Syst. Man. Cybern. B Cybern. 29 (1999), 626–632.
22. Artursson, T., Eklöv, T., Lundström, I., Mårtensson, P., Sjöström, M., Holmberg, M., Eklo, T., Lundstro, I., Sjo, M., Drift correction for gas sensors using multivariate methods. J. Chemom. 14 (2000), 711–723 10.1002/1099-128X(200009/12)14:5/6<711:AID-CEM607>3.0.CO;2-4.
23. Ziyatdinov, A., Marco, S., Chaudry, A., Persaud, K., Caminal, P., Perera, A., Drift compensation of gas sensor array data by common principal component analysis. Sens. Actuators B Chem. 146 (2010), 460–465, 10.1016/j.snb.2009.11.034.
24. Perera, A., Sundic, T., Pardo, A., Gutierrez-Osuna, R., Marco, S., A portable electronic nose based on embedded PC technology and GNU/Linux: hardware, software and applications. IEEE Sens. J. 2 (2002), 235–246, 10.1109/JSEN.2002.800683.
25. Pardo, M., Kwong, L.G., Sberveglieri, G., Brubaker, K., Schneider, J.F., Penrose, W.R., Stetter, J.R., Data analysis for a hybrid sensor array. Sens. Actuators B Chem. 106 (2005), 136–143, 10.1016/j.snb.2004.05.045.
26. Pardo, A., Marco, S., Calaza, C., Ortega, A., Perera, A., Sundic, T., Samitier, J., Methods for sensors selection in pattern recognition. Gardner, J.W., Persaud, K.C., (eds.) Proc. Seventh Int. Symp. Olfaction Electron. Noses, 2001, CRC Press, Bristol, 83–88.
27. Vergara, A., Llobet, E., Sensor selection and chemo-sensory optimization toward an adaptable chemo-sensory system. Front. Neuroeng., 4, 2012, 19, 10.3389/fneng.2011.00019.
28. Raman, B., Meier, D.C., Evju, J.K., Semancik, S., Designing and optimizing microsensor arrays for recognizing chemical hazards in complex environments. Sens. Actuators B Chem. 137 (2009), 617–629, 10.1016/j.snb.2008.11.053.
29. Raman, B., Douglas Semancik, M.C.S., A Statistical Approach to Materials Evaluation and Selection for Chemical Sensor Arrays. 2009, Springer, New York, NY.
30. Gardner, J.W., Boilot, P., Hines, E.L., Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach. Sens. Actuators B Chem. 106 (2005), 114–121, 10.1016/j.snb.2004.05.043.
31. Shmilovici, G., Bakir, S., Finding the best calibration points for a gas sensor array with support vector regression. 2nd Int. IEEE Conf., 2004, 174–177.
32. Walczak, B., Bouveresse, E., Massart, D.L., Standardization of near-infrared spectra in the wavelet domain. Chemom. Intell. Lab. Syst. 36 (1997), 41–51, 10.1016/S0169-7439(96)00075-5.
33. Bouveresse, E., Massart, D.L., Improvement of the piecewise direct standardisation procedure for the transfer of NIR spectra for multivariate calibration. Chemom. Intell. Lab. Syst. 32 (1996), 201–213, 10.1016/0169-7439(95)00074-7.
34. Milanez, K.D.T.M., Araújo Nóbrega, T.C., Silva Nascimento, D., Galvão, R.K.H., Pontes, M.J.C., Selection of robust variables for transfer of classification models employing the successive projections algorithm. Anal. Chim. Acta. 984 (2017), 76–85, 10.1016/j.aca.2017.07.037.
35. Balaban, M.O., Korel, F., Odabasi, A.Z., Folkes, G., Transportability of data between electronic noses: mathematical methods. Sens. Actuators B Chem. 71 (2000), 203–211, 10.1016/S0925-4005(00)00617-1.
36. Zhang, L., Tian, F.C., Peng, X.W., Yin, X., A rapid discreteness correction scheme for reproducibility enhancement among a batch of MOS gas sensors. Sens. Actuators A Phys. 205 (2014), 170–176, 10.1016/j.sna.2013.11.015.
37. Zhang, L., Tian, F., Kadri, C., Xiao, B., Li, H., Pan, L., Zhou, H., Sensors and Actuators B. Chemical on-line sensor calibration transfer among electronic nose instruments for monitoring volatile organic chemicals in indoor air quality. Sens. Actuators B Chem. 160 (2011), 899–909, 10.1016/j.snb.2011.08.079.
38. Fernandez, L., Guney, S., Gutierrez-Galvez, A., Marco, S., Calibration transfer in temperature modulated gas sensor arrays. Sens. Actuators B Chem. 231 (2016), 276–284, 10.1016/j.snb.2016.02.131.
39. Fonollosa, J., Fernández, L., Gutiérrez-Gálvez, A., Huerta, R., Marco, S., Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization. Sens. Actuators B Chem., 2016, 10.1016/j.snb.2016.05.089.
40. Zuppa, M., Distante, C., Siciliano, P., Persaud, K.C., Drift counteraction with multiple self-organising maps for an electronic nose. Sens. Actuators B Chem. 98 (2004), 305–317, 10.1016/j.snb.2003.10.029.
41. Vergara, A., Vembu, S., Ayhan, T., Ryan, M.a., Homer, M.L., Huerta, R., Chemical gas sensor drift compensation using classifier ensembles. Sens. Actuators B Chem. 166–167 (2012), 320–329, 10.1016/j.snb.2012.01.074.
42. Yan, K., Zhang, D., Xu, Y., Correcting instrumental variation and time-varying drift using parallel and serial multitask learning. IEEE Trans. Instrum. Meas. 66 (2017), 2306–2316, 10.1109/TIM.2017.2707898.
43. Zhang, L., Liu, Y., He, Z., Liu, J., Deng, P., Zhou, X., Anti-drift in E-nose: a subspace projection approach with drift reduction. Sens. Actuators B Chem. 253 (2017), 407–417, 10.1016/j.snb.2017.06.156.
44. Zhang, L., Zhang, D., Domain adaptation extreme learning machines for drift compensation in E-nose systems. IEEE Trans. Instrum. Meas. 64 (2015), 1790–1801, 10.1109/TIM.2014.2367775.
45. Tomic, O., Eklöv, T., Kvaal, K., Haugen, J.-E., Recalibration of a gas-sensor array system related to sensor replacement. Anal. Chim. Acta. 512 (2004), 199–206, 10.1016/j.aca.2004.03.001.
46. Boeker, P., On electronic nose methodology. Sens. Actuators B Chem. 204 (2014), 2–17, 10.1016/j.snb.2014.07.087.
47. Tsujita, W., Yoshino, A., Ishida, H., Moriizumi, T., Gas sensor network for air-pollution monitoring. Sens. Actuators B Chem. 110 (2005), 304–311, 10.1016/j.snb.2005.02.008.
48. De Vito, S., Di Palma, P., Ambrosino, C., Massera, E., Burrasca, G., Miglietta, M.L., Di Francia, G., Wireless sensor networks for distributed chemical sensing: addressing power consumption limits with on-board intelligence. IEEE Sens. J. 11 (2011), 947–955, 10.1109/JSEN.2010.2077277.
49. Mead, M.I., Popoola, O.a.M., Stewart, G.B., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J.J., McLeod, M.W., Hodgson, T.F., Dicks, J., Lewis, a., Cohen, J., Baron, R., Saffell, J.R., Jones, R.L., The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos. Environ. 70 (2013), 186–203, 10.1016/j.atmosenv.2012.11.060.
50. Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y., Li, K., Dick, R.P., Lv, Q., Hannigan, M., Shang, L., The next generation of low-cost personal air quality sensors for quantitative exposure monitoring. Atmos. Meas. Technol. 7 (2014), 3325–3336, 10.5194/amt-7-3325-2014.
51. Moltchanov, S., Levy, I., Etzion, Y., Lerner, U., Broday, D.M., Fishbain, B., On the feasibility of measuring urban air pollution by wireless distributed sensor networks. Sci. Total Environ. 502 (2015), 537–547, 10.1016/j.scitotenv.2014.09.059.
52. Fishbain, B., Moreno-Centeno, E., Self calibrated wireless distributed environmental sensory networks. Sci. Rep. 6 (2016), 1–10, 10.1038/srep24382.
53. Fonollosa, J., Vergara, A., Huerta, R., Algorithmic mitigation of sensor failure: is sensor replacement really necessary?. Sens. Actuators B Chem., 183, 2013, 10.1016/j.snb.2013.03.034.
54. Martinelli, E., Magna, G., Vergara, A., Di Natale, C., Cooperative classifiers for reconfigurable sensor arrays. Sens. Actuators B Chem. 199 (2014), 83–92, 10.1016/j.snb.2014.03.070.
55. ams AG, Germany, http://ams.com/.
56. Fonollosa, J., Fernandez, L., Huerta, R., Gutierrez-Galvez, A., Marco, S., Temperature optimization of metal oxide sensor arrays using Mutual Information. Sens. Actuators B Chem. 187 (2013), 331–339.
57. Kneer, J., Wöllenstein, J., Palzer, S., Manipulating the gas-surface interaction between copper(II) oxide and mono-nitrogen oxides using temperature. Sens. Actuators B Chem. 229 (2016), 57–62, 10.1016/j.snb.2016.01.104.
58. Walden, P., Kneer, J., Knobelspies, S., Kronast, W., Mescheder, U., Palzer, S., Micromachined hotplate platform for the investigation of ink-jet printed, functionalized metal oxide nanoparticles. J. Microelectromech. Syst. 24 (2015), 1384–1390, 10.1109/JMEMS.2015.2399696.
59. Kneer, J., Eberhardt, A., Walden, P., Ortiz Pérez, A., Wöllenstein, J., Palzer, S., Apparatus to characterize gas sensor response under real-world conditions in the lab. Rev. Sci. Instrum., 85, 2014, 55006, 10.1063/1.4878717.
60. Ballabio, D., Consonni, V., Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal. Methods 5 (2013), 3790–3798, 10.1039/C3AY40582F.
61. David, D.E., Goldberg, E., Genetic Algorithms in Search, Optimization, and Machine Learning. 1989, Addison-Wesley Longman Publishing Co., Inc.
62. Leardi, R., Lupiáñez González, A., Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom. Intell. Lab. Syst. 41 (1998), 195–207, 10.1016/S0169-7439(98)00051-3.
63. Leardi, R., Genetic algorithms in chemistry. J. Chromatogr. A 1158 (2007), 226–233, 10.1016/j.chroma.2007.04.025.
64. Venables, W.N., Ripley, B.D., Modern Applied Statistics with S. 2002, Springer-Verlag, New York, 10.1007/978-0-387-21706-2.
65. Mevik, B.-H., Wehrens, R., The pls package: principal component and partial least. J. Stat. Softw., 18, 2007.
66. Scrucca, L., GA: a package for genetic algorithms in R. J. Stat. Softw. 53 (2013), 1–37, 10.1359/JBMR.0301229.
67. Hand, D.J., Till, R.J., A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach. Learn. 45 (2001), 171–186, 10.1023/A:1010920819831.
68. Agresti, A., A survey of exact inference for contingency tables. Stat. Sci. 7 (1992), 131–153, 10.1214/ss/1177011454.
69. Rose, J.J., Wang, L., Xu, Q., Mctiernan, C.F., Shiva, S., Tejero, J., Gladwin, M.T., Carbon monoxide poisoning: pathogenesis, management, and future directions of therapy. Am. J. Respir. Crit. Care Med. 195 (2017), 596–606, 10.1164/rccm.201606-1275CI.
70. Pearce, T.C., Sánchez-Montañés, M.A., Chemical sensor array optimization: geometric and information theoretic approaches. Handb. Mach. Olfaction Electron. Nose Technol., 2004, 347–375, 10.1002/3527601597.