Skip to main content
SpringerLink
Log in

Deep learning-based gas identification and quantification with auto-tuning of hyper-parameters

  • Data analytics and machine learning
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In this work, we propose two deep learning-based architectures tailored for gas identification and quantification, which automatically tune hyper-parameters of the network for optimal performance. The immense success of deep learning in the field of computer vision and natural language processing inspired us to design deep learning-based gas identification and quantification network. The first architecture is proposed for gas quantification, which is based on 1D-CNN. It makes use of raw time-series gas sensor array data and provides the concentration of each gas in a mixture of gases. The second architecture is presented for gas quantification, which is based on a deep belief network combined with drift-aware feature adaptation strategy. The proposed models identify and quantify the gases with improved accuracy despite the presence of sensor drift. Additionally, hyper-parameters of both the networks are automatically tuned for optimal performance. Although several pattern recognition methods related to machine learning, fuzzy logic and hybrid models have been used to identify gas and quantify the gases in the mixture, the performances of these techniques enormously depend on the feature engineering and selection of hyper-parameters. Experimental results show that the proposed methods are an effective technique for identifying gases and quantifying the mixture of gases for e-nose data. We also present that the proposed methods outperforms various other methods and can provide higher identification and quantification accuracy in the pres-ence of sensor drift.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Akbar MA, Ali AAS, Amira A, Bensaali F, Benammar M, Hassan M, Bermak A (2016) An empirical study for PCA-and LDA-based feature reduction for gas identification. IEEE Sens J 16(14):5734–5746

    Article  Google Scholar 

  • Badura M, Szczurek A, Szecówka P (2013) Statistical assessment of quantification methods used in gas sensor system. Sens Actuators B Chem 188:815–823

    Article  Google Scholar 

  • Bahraminejad B, Basri S, Isa M, Hambli Z (2010) Real-time gas identification by analyzing the transient response of capillary-attached conductive gas sensor. Sensors 10(6):5359–5377

    Article  Google Scholar 

  • Barkó G, Abonyi J, Hlavay J (1999) Application of fuzzy clustering and piezoelectric chemical sensor array for investigation on organic compounds. Anal Chim Acta 398(2–3):219–226

    Article  Google Scholar 

  • Blatt R, Bonarini A, Calabro E, Torre MD, Matteucci M, Pastorino U (2007) Lung cancer identification by an electronic nose based on an array of MOS sensors. In: 2007 International joint conference on neural networks, pp 1423–1428. https://doi.org/10.1109/IJCNN.2007.4371167

  • Boilot P, Hines E, Gongora M, Folland R (2003) Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions. Sens Actuators B Chem 88(1):80–88

    Article  Google Scholar 

  • Buratti S, Benedetti S, Scampicchio M, Pangerod E (2004) Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue. Anal Chim Acta 525(1):133–139

    Article  Google Scholar 

  • Calandra R, Raiko T, Deisenroth MP, Pouzols FM (2012) Learning deep belief networks from non-stationary streams. In: International conference on artificial neural networks. Springer, Berlin, pp 379–386

  • Carey WP, Beebe KR, Kowalski BR (1987) Multicomponent analysis using an array of piezoelectric crystal sensors. Anal Chem 59(11):1529–1534

    Article  Google Scholar 

  • Ciaramella A, Tagliaferri R, Pedrycz W, Di Nola A (2006) Fuzzy relational neural network. Int J Approx Reason 41(2):146–163 (Advances in Fuzzy Sets and Rough Sets)

    Article  MathSciNet  Google Scholar 

  • Ciosek P, Brzózka Z, Wróblewski W, Martinelli E, Di Natale C, Damico A (2005) Direct and two-stage data analysis procedures based on PCA, PLS-DA and ANN for ISE-based electronic tongue—effect of supervised feature extraction. Talanta 67(3):590–596

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Daniel DAP, Thangavel K, Manavalan R, Boss RSC (2014) ELM-based ensemble classifier for gas sensor array drift dataset. In: Computational intelligence, cyber security and computational models. Springer, Berlin, pp 89–96

  • Di Natale C, Martinelli E, DAmico A (2002) Counteraction of environmental disturbances of electronic nose data by independent component analysis. Sens Actuators B Chem 82(2–3):158–165

    Article  Google Scholar 

  • Distante C, Leo M, Siciliano P, Persaud KC (2002) On the study of feature extraction methods for an electronic nose. Sens Actuators B Chem 87(2):274–288

    Article  Google Scholar 

  • Fonollosa J, Sheik S, Huerta R, Marco S (2015) Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring. Sens Actuators B Chem 215:618–629

    Article  Google Scholar 

  • Goschnick J, Koronczi I, Frietsch M, Kiselev I (2005) Water pollution recognition with the electronic nose KAMINA. Sens Actuators B Chem 106(1):182–186

    Article  Google Scholar 

  • Hinton GE (2012) A practical guide to training restricted Boltzmann machines. In: Neural networks: tricks of the trade. Springer, Berlin, pp 599–619

  • Kolk A, Hoelscher M, Maboko L, Jung J, Kuijper S, Cauchi M, Bessant C, van Beers S, Dutta R, Gibson T et al (2010) Electronic-nose technology using sputum samples in diagnosis of patients with tuberculosis. J Clin Microbiol 48(11):4235–4238

    Article  Google Scholar 

  • Kumar R, Das R, Mishra V, Dwivedi R (2009) A fuzzy logic based neural network classifier for qualitative classification of odors/gases. In: 2009 International conference on emerging trends in electronic and photonic devices systems, pp 185–188. https://doi.org/10.1109/ELECTRO.2009.5441140

  • Längkvist M, Loutfi A (2011) Unsupervised feature learning for electronic nose data applied to bacteria identification in blood. In: NIPS 2011 workshop on deep learning and unsupervised feature learning

  • Liu H, Chu R, Tang Z (2015) Metal oxide gas sensor drift compensation using a two-dimensional classifier ensemble. Sensors 15(5):10180–10193

    Article  Google Scholar 

  • Luo Y, Wei S, Chai Y, Sun X (2016) Electronic nose sensor drift compensation based on deep belief network. In: 2016 35th Chinese control conference (CCC). IEEE, pp 3951–3955

  • Luo Y, Ye W, Zhao X, Pan X, Cao Y (2017) Classification of data from electronic nose using gradient tree boosting algorithm. Sensors 17(10):2376

    Article  Google Scholar 

  • Ma Z, Luo G, Qin K, Wang N, Niu W (2018) Weighted domain transfer extreme learning machine and its online version for gas sensor drift compensation in e-nose systems. Wirel Commun Mob Comput 2018

  • Maziarz W, Potempa P, Sutor A, Pisarkiewicz T (2003) Dynamic response of a semiconductor gas sensor analysed with the help of fuzzy logic. Thin Solid Films 436(1):127–131

    Article  Google Scholar 

  • Men H, Fu S, Yang J, Cheng M, Shi Y, Liu J (2018) Comparison of SVM, RF and ELM on an electronic nose for the intelligent evaluation of paraffin samples. Sensors 18(1):285

    Article  Google Scholar 

  • Mitchell M (1998) An introduction to genetic algorithms. MIT press, Cambridge

    Book  Google Scholar 

  • Papadopoulou OS, Tassou CC, Schiavo L, Nychas GJE, Panagou EZ (2011) Rapid assessment of meat quality by means of an electronic nose and support vector machines. Procedia Food Sci 1:2003–2006

    Article  Google Scholar 

  • Pardo M, Faglia G, Sberveglieri G, Corte M, Masulli F, Riani M (2000) A time delay neural network for estimation of gas concentrations in a mixture. Sens Actuators B Chem 65(1–3):267–269

    Article  Google Scholar 

  • Parthasarathy R, Kalaichelvi V, Sundaram S (2015) A novel fuzzy logic model for multiple gas sensor array. In: 2015 International conference on communications and signal processing (ICCSP), pp 1143–1146. https://doi.org/10.1109/ICCSP.2015.7322683

  • Parvin H, Alinejad-Rokny H, Parvin S (2013) A classifier ensemble of binary classifier ensembles. Int J Learn Manag Syst 1(2):37–47

    Article  Google Scholar 

  • Peng P, Zhao X, Pan X, Ye W (2018) Gas classification using deep convolutional neural networks. Sensors 18(1):157

    Article  Google Scholar 

  • Persaud K, Dodd G (1982) Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299(5881):352

    Article  Google Scholar 

  • Ping W, Jun X (1996) A novel recognition method for electronic nose using artificial neural network and fuzzy recognition. Sens Actuators B Chem 37(3):169–174

    Article  Google Scholar 

  • Sabilla SI, Sarno R, Siswantoro J (2017) Estimating gas concentration using artificial neural network for electronic nose. Procedia Comput Sci 124:181–188

    Article  Google Scholar 

  • Saraoğlu HM, Selvi AO, Ebeoğlu MA, Taşaltin C (2013) Electronic nose system based on quartz crystal microbalance sensor for blood glucose and HbA1c levels from exhaled breath odor. IEEE Sens J 13(11):4229–4235

    Article  Google Scholar 

  • Schilling F (2016) The effect of batch normalization on deep convolutional neural networks

  • Singh S, Hines EL, Gardner JW (1996) Fuzzy neural computing of coffee and tainted-water data from an electronic nose. Sens Actuators B Chem 30(3):185–190

    Article  Google Scholar 

  • Sundgren H, Winquist F, Lundstrom I (1991) Artificial neural networks and statistical pattern recognition improve MOSFET gas sensor array calibration. In: TRANSDUCERS’91: 1991 international conference on solid-state sensors and actuators. Digest of technical papers. IEEE, pp 574–577

  • Szczurek A, Szecowka P, Licznerski B (1999) Application of sensor array and neural networks for quantification of organic solvent vapours in air. Sens Actuators B Chem 58(1–3):427–432

    Article  Google Scholar 

  • Vergara A, Vembu S, Ayhan T, Ryan MA, Homer ML, Huerta R (2012) Chemical gas sensor drift compensation using classifier ensembles. Sens Actuators B Chem 166:320–329

    Article  Google Scholar 

  • Verma M, Asmita S, Shukla K (2016) A regularized ensemble of classifiers for sensor drift compensation. IEEE Sens J 16(5):1310–1318

    Article  Google Scholar 

  • Wang XD, Zhang HR, Zhang CJ (2005) Signals recognition of electronic nose based on support vector machines. In: 2005 International conference on machine learning and cybernetics, vol 6. IEEE, pp 3394–3398

  • Wang Y, Yang A, Chen X, Wang P, Wang Y, Yang H (2017) A deep learning approach for blind drift calibration of sensor networks. IEEE Sens J 17(13):4158–4171. https://doi.org/10.1109/jsen.2017.2703885

    Article  Google Scholar 

  • Wei G, Li G, Zhao J, He A (2019) Development of a LeNet-5 gas identification CNN structure for electronic noses. Sensors 19(1):217

    Article  Google Scholar 

  • Zhai X, Ali AAS, Amira A, Bensaali F (2016) MLP neural network based gas classification system on Zynq SoC. IEEE Access 4:8138–8146

    Article  Google Scholar 

  • Zhang L, Zhang D (2014) Domain adaptation extreme learning machines for drift compensation in e-nose systems. IEEE Trans Instrum Meas 64(7):1790–1801

    Article  Google Scholar 

  • Zhang L, Zhang D (2015) Domain adaptation transfer extreme learning machines. In: Proceedings of ELM-2014, vol 1. Springer, Berlin, pp 103–119

Download references

Acknowledgements

Authors would like to acknowledge Director, CSIR-CEERI, for his encouragement and support.

Author information

Authors and Affiliations

  1. Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Electronics Engineering Research Institute (CSIR-CEERI), Pilani, Rajasthan, 333031, India

    Vishakha Pareek

  2. Indian Institute of Technology, Delhi, 110016, India

    Santanu Chaudhury

Authors
  1. Vishakha Pareek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santanu Chaudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vishakha Pareek.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pareek, V., Chaudhury, S. Deep learning-based gas identification and quantification with auto-tuning of hyper-parameters. Soft Comput 25, 14155–14170 (2021). https://doi.org/10.1007/s00500-021-06222-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06222-1

Keywords

Search

Navigation