M. F. Adak and N. Yumusak, “Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network,” Sensors, vol. 16, no. 3, p. 304, Feb. 2016.
 M. Baietto and A. Wilson, “Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading,” Sensors, vol. 15, no. 1, pp. 899–931, Jan. 2015.
 A. Wilson, “Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry,” Sensors, vol. 13, no. 2, pp. 2295–2348, Feb. 2013.
 A. Versari, V. F. Laurie, A. Ricci, L. Laghi, and G. P. Parpinello, “Progress in authentication, typification and traceability of grapes and wines by chemometric approaches,” Food Res. Int., vol. 60, pp. 2–18, Jun. 2014.
 H. Men, Y. Shi, Y. Jiao, F. Gong, and J. Liu, “Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer,” Anal. Methods, vol. 10, no. 17, pp. 2016–2025, 2018.
 S. Murugan and N. Gala, “ELENA: A low-cost portable electronic nose for alcohol characterization,” in 2017 IEEE SENSORS, 2017, pp. 1–3.
 Q. Ameer and S. B. Adeloju, “Polypyrrole-based electronic noses for environmental and industrial analysis,” Sensors Actuators B Chem., vol. 106, no. 2, pp. 541–552, May 2005.
 C. Cevoli, L. Cerretani, A. Gori, M. F. Caboni, T. Gallina Toschi, and A. Fabbri,“Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC–MS analysis of volatile compounds,” Food Chem., vol. 129, no. 3, pp. 1315–1319, Dec. 2011.
 P. Hanafizadeh, A. Zare Ravasan, and H. R. Khaki, “An expert system for perfume selection using artificial neural network,” Expert Syst. Appl., vol. 37, no. 12, pp. 8879–8887, Dec. 2010.
 Y. Al-Bastaki, “An Artificial Neural Networks-Based on-Line Monitoring Odor Sensing System,” J. Comput. Sci., vol. 5, no. 11, pp. 878–882, Nov. 2009.
 C. Li, P. Heinemann, and R. Sherry, “Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection,” Sensors Actuators, B Chem., vol. 125, pp. 301–310, 2007.
 A. K. Pavlou, N. Magan, J. M. Jones, J. Brown, P. Klatser, and A. P. F. Turner, “Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system.,” Biosens. Bioelectron., vol. 20, no. 3, pp. 538–44, Oct. 2004.
 H. M. Saraoglu and B. Edin, “E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network,” J. Med. Syst., vol. 31, no. 6, pp. 475–482, Aug. 2007.
 C. M. Bishop, Pattern Recognition and Machine Learning, 1st ed. Singapore: Springer India, 2006.
 Sunny, V. Kumar, V. N. Mishra, R. Dwivedi, and R. R. Das, “Classification and Quantification of Binary Mixtures of Gases/Odors Using Thick-Film Gas Sensor Array Responses,” IEEE Sens. J., vol. 15, no. 2, pp. 1252–1260, Feb. 2015.
 M. F. Adak, M. Akpinar, and N. Yumusak, “Determination of the Gas Density in Binary Gas Mixtures Using Multivariate Data Analysis,” IEEE Sens. J., vol. 17, no. 11, pp. 3288–3297, Jun. 2017.
 S.-I. Choi, T. Eom, and G.-M. Jeong, “Gas Classification Using Combined Features Based on a Discriminant Analysis for an Electronic Nose,” J. Sensors, vol. 2016, pp. 1–9, 2016.
 H. Singh, V. B. Raj, J. Kumar, U. Mittal, M. Mishra, A. T. Nimal, M. U. Sharma, and V. Gupta, “Metal oxide SAW E-nose employing PCA and ANN for the identification of binary mixture of DMMP and methanol,” Sensors Actuators B Chem., vol. 200, pp. 147–156, Sep. 2014.
 A. Ziyatdinov, S. Marco, A. Chaudry, K. Persaud, P. Caminal, and A. Perera, “Drift compensation of gas sensor array data by common principal component analysis,” Sensors Actuators B Chem., vol. 146, no. 2, pp. 460–465, Apr. 2010.
 T. Artursson, T. Eklov, I. Lundstrom, P. Martensson, M. Sjostrom, and M. Holmberg, “Drift correction for gas sensors using multivariate methods,” J. Chemom., vol. 14, no. 5–6, pp. 711–723, Sep. 2000.
 A. Gulbag, F. Temurtas, and I. Yusubov, “Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks,” Sensors Actuators B Chem., vol. 131, no. 1, pp. 196–204, Apr. 2008.
 M. F. Adak and N. Yumusak, “Development of smart gas sensor system to classify binary gas mixtures,” in 17th IEEE International Conference on Smart Technologies, EUROCON 2017 - Conference Proceedings, 2017.
 E. Kim, S. Lee, J. Kim, C. Kim, Y. Byun, H. Kim, and T. Lee, “Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays,” Sensors, vol. 12, no. 12, pp. 16262–16273, Nov. 2012.
 V. K. Ojha, P. Dutta, A. Chaudhuri, and H. Saha, “Understating continuous ant colony optimization for neural network training: A case study on intelligent sensing of manhole gas components,” Int. J. Hybrid Intell. Syst., vol. 12, no. 4, pp. 185–202, Mar. 2016.
 L. Zhang, F. Tian, S. Liu, J. Guo, B. Hu, Q. Ye, L. Dang, X. Peng, C. Kadri, and J. Feng, “Chaos based neural network optimization for concentration estimation of indoor air contaminants by an electronic nose,” Sensors Actuators A Phys., vol. 189, pp. 161–167, Jan. 2013.
 D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, “A comprehensive survey: artificial bee colony (ABC) algorithm and applications,” Artificial Intelligence Review, vol. 42, no. 1. pp. 21–57, 11-Mar-2012.
 M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Trans. Syst. Man Cybern. Part B, vol. 26, no. 1, pp. 29–41, 1996.
 R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43.