Skip to main content
SpringerLink
Log in

Detection of rice type and its storage duration via an improved particle swarm optimization algorithm

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Due to the non-selective behavior of gas sensors in electronic nose (e-nose) systems, the provided signals in exposure to target analytes contain un-needed information. These are considered as noise reducing the detection accuracy. Feature selection, as a pre-processing step in data analysis, removes extra information from the sensors’ signals and provides a more relevant data matrix with lower dimensionality to enhance the system selectivity. In the high-dimensional sensor array response spaces, it is however essential to improve the conventional algorithms to be able to cope with complicated feature selection problem. In this study, in order to acquire optimal responses from the gas sensor array of an e-nose system and increase its selectivity for detection of rice type and its storage duration (freshness), the feature selection problem was formulated in an optimization framework. For this reason, a particle swarm optimization (PSO) with an automatic stagnation detecting system was enhanced by genetic operators of differential evolution. This helped acquiring more exploration ability through providing oriented jumps. It was revealed that the system’s detection accuracy was improved when smaller subset of features was utilized instead of the whole response, indicating that the sensor array signals included large amount of irrelevant information. The improved PSO could significantly present lower error values than the standard PSO and other examined conventional algorithms. It was concluded the developed algorithm has the potential to be applied as a promising feature selection algorithm in high-dimensional signals of the e-nose systems.

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
Algorithm 1
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The data supporting the findings of this study are available from the corresponding author on reasonable request.

References

  1. Shi J, Wu M, Quan M (2017) Effects of protein oxidation on gelatinization characteristics during rice storage. J Cereal Sci 75:228–233

    Article  Google Scholar 

  2. Avian C, Mahali MI, Putro NAS, Prakosa SW, Leu JS (2022) Fx-Net and PureNet: convolutional neural network architecture for discrimination of chronic obstructive pulmonary disease from smokers and healthy subjects through electronic nose signals. Comput Biol Med 148:105913

    Article  Google Scholar 

  3. Chen K, Liu L, Nie B, Lu B, Fu L, He Z, Li W, Pi X, Liu H (2021) Recognizing lung cancer and stages using a self-developed electronic nose system. Comput Biol Med 131:104294

    Article  Google Scholar 

  4. Herman-Saffar O, Boger Z, Libson S, Lieberman D, Gonen R, Zeiri Y (2018) Early non-invasive detection of breast cancer using exhaled breath and urine analysis. Comput Biol Med 96:227–232

    Article  Google Scholar 

  5. Li W, Jia Z, Xie D, Chen K, Cui J, Liu H (2020) Recognizing lung cancer using a homemade e-nose: A comprehensive study. Comput Biol Med 120:103706

    Article  Google Scholar 

  6. Rahimzadeh H, Sadeghi M, Mireei SA, Ghasemi-Varnamkhasti M (2021) Modifying genetic algorithm by dynamic memory and solution reconstructing mechanism for selectivity control of chemical sensors. Chemom Intell Lab Syst 214:104332

    Article  Google Scholar 

  7. Rahimzadeh H, Sadeghi M, Mireei SA, Ghasemi-Varnamkhasti M (2022) Unsupervised modelling of rice aroma change during ageing based on electronic nose coupled with bio-inspired algorithms. Biosyst Eng 216:132–146

    Article  Google Scholar 

  8. Vergara A, Llobet E (2011) Sensor selection and chemo-sensory optimization: Toward an adaptable chemo-sensory system. Front Neuroeng 4:1–21

    Google Scholar 

  9. Gardner JW, Boilot P, Hines EL (2005) Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach. Sensors Actuators, B Chem 106:114–121

    Article  Google Scholar 

  10. Kalinichenko A, Arseniyeva L (2020) Electronic nose combined with chemometric approaches to assess authenticity and adulteration of sausages by soy protein. Sensors Actuators, B Chem 303:127250

    Article  Google Scholar 

  11. El Barbri N, Duran C, Brezmes J, Cañellas N, Ramírez JL, Bouchikhi B, Llobet E (2008) Selectivity enhancement in multisensor systems using flow modulation techniques. Sensors 8:7369–7379

    Article  Google Scholar 

  12. Rahimzadeh H, Sadeghi M, Ghasemi-Varnamkhasti M, Mireei SA, Tohidi M (2019) On the feasibility of metal oxide gas sensor based electronic nose software modification to characterize rice ageing during storage. J Food Eng 245:1–10

    Article  Google Scholar 

  13. La Hoz De, Emiro DL, Hoz E, Ortiz A, Ortega J, Martínez-Álvarez A (2014) Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps. Knowledge-Based Syst 71:322–338

    Article  Google Scholar 

  14. Martarelli NJ, Nagano MS (2020) Unsupervised feature selection based on bio-inspired approaches. Swarm Evol Comput 52:1–13

    Article  Google Scholar 

  15. Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Biol Med 112:103375

    Article  Google Scholar 

  16. Zhang R, Nie F, Li X, Wei X (2019) Feature selection with multi-view data: A survey. Inf Fusion 50:158–167

    Article  Google Scholar 

  17. Aggarwal CC (2014) Educational and software resources for data classification. In: Aggrawal CC (ed) Data classification: algorithms and applications. Taylor & Francis Group, Chapman and Hall/CRC, New York, pp 657–665

  18. Bennasar M, Hicks Y, Setchi R (2015) Feature selection using Joint Mutual Information Maximisation. Expert Syst Appl 42:8520–8532

    Article  Google Scholar 

  19. Tan P, Wang X, Wang Y (2020) Dimensionality reduction in evolutionary algorithms-based feature selection for motor imagery brain-computer interface. Swarm Evol Comput 52:100597

    Article  Google Scholar 

  20. Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20:606–626

    Article  Google Scholar 

  21. Zhang J, Xiong Y, Min S (2019) A new hybrid filter/wrapper algorithm for feature selection in classification. Anal Chim Acta 1080:43–54

    Article  Google Scholar 

  22. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Comput Stat Data Anal 143:106839

    Article  MathSciNet  Google Scholar 

  23. Viegas F, Rocha L, Gonçalves M, Mourão F, Sá G, Salles T, Andrade G, Sandin I (2018) A Genetic programming approach for feature selection in highly dimensional skewed data. Neurocomputing 273:554–569

    Article  Google Scholar 

  24. Chohra A, Shirani P, Karbab EB, Debbabi M (2022) Chameleon: Optimized feature selection using particle swarm optimization and ensemble methods for network anomaly detection. Comput Secur 117:102684

    Article  Google Scholar 

  25. Hu P, Pan J-S, Chu S-C, Sun C (2022) Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection. Appl Soft Comput 121:108736

    Article  Google Scholar 

  26. Rashno A, Shafipour M, Fadaei S (2022) Particle ranking: An efficient method for multi-objective particle swarm optimization feature selection. Knowledge-Based Syst 245:108640

    Article  Google Scholar 

  27. Thaher T, Chantar H, Too J, Mafarja M, Turabieh H, Houssein EH (2022) Boolean particle swarm optimization with various evolutionary population dynamics approaches for feature selection problems. Expert Syst Appl 195:116550

    Article  Google Scholar 

  28. Rarità L (2022) A genetic algorithm to optimize dynamics of supply chains. In: Amorosi L, Dell’Olmo P, Lari I (eds) Optimization in artificial intelligence and data sciences. AIRO Springer Series 8, pp 107–115

  29. Al-Yaseen WL, Idrees AK, Almasoudy FH (2022) Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system. Pattern Recognit 1325:108912

    Article  Google Scholar 

  30. Pan J-S, Liu N, Chu S-C (2022) A competitive mechanism based multi-objective differential evolution algorithm and its application in feature selection. Knowledge-Based Syst 245:108582

    Article  Google Scholar 

  31. Wang X, Wang Y, Wong K-C, Li X (2022) A self-adaptive weighted differential evolution approach for large-scale feature selection. Knowledge-Based Syst 235:107633

    Article  Google Scholar 

  32. Abdel-Basset M, El-Shahat D, El-henawy I, de Albuquerque VHC, Mirjalili S (2020) A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst Appl 139:112824

    Article  Google Scholar 

  33. Engelbrecht AP, Grobler J, Langeveld J (2019) Set based particle swarm optimization for the feature selection problem. Eng Appl Artif Intell 85:324–336

    Article  Google Scholar 

  34. Zhang Y, Gong DW, Gao XZ, Tian T, Sun XY (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf. Sci. 507:67–85

    Article  MathSciNet  Google Scholar 

  35. Fearn T (2014) Particle swarm optimisation. NIR News 25:27–27

    Google Scholar 

  36. Agarwal A, Nanavati N (2016) Association rule mining using hybrid GA-PSO for multi-objective optimisation. Proc IEEE Int Con on Comput Intl and Comput Res (ICCIC), Chennai, India, pp 1–7

  37. Galvez A, Iglesias A (2013) A new iterative mutually coupled hybrid GA-PSO approach for curve fitting in manufacturing. Appl Soft Comput J 13:1491–1504

    Article  Google Scholar 

  38. Storn R, Price K (1997) Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  Google Scholar 

  39. Andrade CE, Silva T, Pessoa LS (2019) Minimizing flowtime in a flowshop scheduling problem with a biased random-key genetic algorithm. Expert Syst Appl 128:67–80

    Article  Google Scholar 

  40. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Hassan Rahimzadeh, Morteza Sadeghi & Seyed Ahmad Mireei

  2. Department of Biosystems Engineering, Faculty of Agriculture, Shahrekord University, Shahrekond, Iran

    Mahdi Ghasemi-Varnamkhasti

Authors
  1. Hassan Rahimzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Morteza Sadeghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyed Ahmad Mireei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahdi Ghasemi-Varnamkhasti
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors provided critical feedback and helped shape the research, manuscript, and conception or design of the work. H.R. carried out the experiments and wrote the draft manuscript. M.S. and H.R. conceived of the presented idea and contributed to sample preparation. M.S. encouraged H.R. to investigate on the optimization task and supervised the project. M.G. made substantial contributions to the creation of the hardware used in the work. S.A.M. worked and commented on the manuscript.

Corresponding author

Correspondence to Morteza Sadeghi.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahimzadeh, H., Sadeghi, M., Mireei, S.A. et al. Detection of rice type and its storage duration via an improved particle swarm optimization algorithm. Evol. Intel. (2024). https://doi.org/10.1007/s12065-024-00933-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12065-024-00933-8

Keywords

Search

Navigation