___

• [1] Rasooly, A. (2005). Biosensor technologies. Methods, 37(1), 1–3.
• [2] Loutfi, A., Coradeschi, S., Mani, G.K., Shankar, P., Rayappan, J.B. (2015). Electronic noses for food quality: A review. Journal of Food Engineering, 144, 103–111.
• [3] Schaller, E., Bosset, J.O., Escher, F. (1998). Electronic noses and their application to food. Food Science and Technology, 31(4), 305–316.
• [4] Persaud, K.C., Payne, P.A., Pelosi, P.S.V. (1993). Design strategies for gas and odour sensors which mimic the olfactory system. Robots and Biological Systems: Towards a New Bionics, 102, 579-602.
• [5] Handa, P., Sıngh, B. (2016). Electronic nose and their application in food industries. Food Science Research Journal, 7(2), 314–318.
• [6] Wilson, A.D., Baietto, M. (2009). Applications and advances in electronic-nose technologies. Sensors, 9, 5099–5148.
• [7] Shurmer, H., Gardner, J.W. (1992). Odour discrimination with an electric nose. Sensors and Actuators, B: Chemical, 8, 1–11.
• [8] Kasnak, C., Palamutoglu, R. (2018). Gıda endüstrisinde elektronik burun kullanımı. Mühendislik ve Mimarlık Çalışmaları, 1-188.
• [9] Anukunprasert, T., Saiwan, C., Traversa, E. (2005). The development of gas sensor for carbon monoxide monitoring using nanostructure of Nb-TiO2. Science and Technology of Advanced Materials, 6, 359–363.
• [10] Eranna, G., Joshi, B.C., Runthala, D.P., Gupta, R.P. (2004). Oxide materials for development of integrated gas sensors-A comprehensive review. Critical Reviews in Solid State and Materials Sciences, 29, 111–188.
• [11] Dey, A. (2018). Semiconductor metal oxide gas sensors: A review. Materials Science and Engineering B, 206–217.
• [12] Korotcenkov, G. (2007). Metal oxides for solid-state gas sensors: What determines our choice? Materials Science and Engineering B, 139, 1–23.
• [13] Hooker, S.A. (2002). Nanotechnology advantages applied to gas sensor development. The Nanoparticles 2002 Conference Proceedings, 1-8.
• [14] Devi, G.S. (1998). SnO2∕Bi2O3: A suitable system for selective carbon monoxide detection. Journal of the Electrochemical Society, 145(3), 1039-1044.
• [15] Rella, R., Serra, A., Siciliano, P., Vasanelli, L., De, G., Licciulli, A. (1997). Tin oxide-based gas sensors prepared by the sol-gel process. Sensors and Actuators B: Chemical, 44, 462–467.
• [16] Costello, B.P., Ewen, R.J., Ratcliffe, N.M., Sivanand, P.S. (2003). Thick film organic vapour sensors based on binary mixtures of metal oxides. Sensors and Actuators B: Chemical, 92, 159–166.
• [17] Vaishanv, V.S., Patel, P.D., Patel, N.G. (2006). Indium tin oxide thin-film sensor for detection of volatile organic compounds (VOCs). Materials and Manufacturing Processes, 21, 257–261.
• [18] Fine, G.F., Cavanagh, L.M., Afonja, A., Binions, R. (2010). Metal oxide semi-conductor gas sensors in environmental monitoring. Sensors, 10, 5469–5502.
• [19] Yamazoe, N., Kurokawa, Y., Seiyama, T. (1983). Effects of additives on semiconductor gas sensors. Sensors and Actuators, B: Chemical, 4, 283–289.
• [20] Moseley, P.T. (1992). Materials selection for semiconductor gas sensors. Sensors and Actuators B: Chemical, 6, 149–156.
• [21] Jing, B.Z., Zhan, J. (2008). Fabrication and gas-sensing properties of porous ZnO nanoplates. Advanced Materials, 20, 4547–4551.
• [22] Moore, E., Lyons, G. M., Harris, J., Clifford, S. (2004). A review of gas sensors employed in electronic nose applications. Sensor Review, 24(2), 181–198.
• [23] Gao, X., Zhang, T. (2018). An overview: Facet-dependent metal oxide semiconductor gas sensors. Sensors and Actuators, B: Chemical, 277, 604–633.
• [24] Wang, C., Yin, L., Zhang, L., Xiang, D., Gao, R. (2010). Metal oxide gas sensors: sensitivity and influencing factors. Sensors, 10, 2088–2106.
• [25] Di Rosa, A.R., Leone, F., Cheli, F., Chiofalo, V. (2017). Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment – A review. Journal of Food Engineering, 210, 62–75.
• [26] Majchrzak, T., Wojnowski, W., Dymerski, T., Gębicki, J., Namieśnik, J. (2018a). Electronic noses in classification and quality control of edible oils: A review. Food Chemistry, 246, 192–201.
• [27] Cordero, C., Kiefl, J., Reichenbach, S.E. (2015). Comprehensive two-dimensional gas chromatography and food sensory properties: Potential and challenges. Analytical and Bioanalytical Chemistry, 407(1), 169–191.
• [28] Majchrzak, T., Wojnowski, W., Dymerski, T., Jacek, G. (2018). Electronic noses in classification and quality control of edible oils: A review. Food Chemistry, 246, 192–201.
• [29] Dymerski, T.M., Chmiel, T.M., Wardencki, W. (2011). Invited Review Article: An odor-sensing system—powerful technique for foodstuff studies. Review of Scientific Instruments, 82(11), 1-32.
• [30] Majchrzak, T., Wojnowski, W., Dymerski, T., Gębicki, J., Namieśnik, J. (2018b). Electronic noses in classification and quality control of edible oils: A review. Food Chemistry, 246, 192–201.
• [31] Mottram, T.T., Gardner, J.W., Hobbs, P.J., Bartlett, P.N. (1997). Preliminary investigation of breath sampling as a monitor of health in dairy cattle. Journal Agricultural Engineering Research, 67, 267–275.
• [32] Sivalingam, D., Rayappan, J.B. (2014). Development of e-nose prototype for raw milk quality discrimination. Milchwissenschaft, 67(4), 381–385.
• [33] Labreche, S., Bazzo, S., Cade, S., Chanie, E. (2005). Shelf life determination by electronic nose: Application to milk. Sensors and Actuators, B: Chemical, 106, 199–206.
• [34] Waller, K.P., Svennersten-Sjaunja, K., Haugen, J. (2005). Detection of mastitic milk using a gas-sensor array system (electronic nose). International Dairy Journal, 15, 1193–1201.
• [35] Capone, S., Siciliano, P., Quaranta, F., Rella, R., Epifani, M., Vasanelli, L. (2000). Analysis of vapours and foods by means of an electronic nose based on a sol–gel metal oxide sensors array. Sensors and Actuators, B: Chemical, 69, 230–235.
• [36] Capone, S., Epifani, M., Quaranta, F., Siciliano, P. (2001). Monitoring of rancidity of milk by means of an electronic nose and a dynamic PCA analysis. Sensors and Actuators, B: Chemical, 78, 174–179.
• [37] Brambilla, M., Navarotto, P. (2010). Application of E-NOSE technology for ultra-high temperature processed partly skimmed milk production batches monitoring. Chemical Engineering Transactions, 23, 171–176.
• [38] Brudzewski, K., Osowski, S., Markiewicz, T. (2004). Classification of milk by means of an electronic nose and SVM neural network. Sensors and Actuators, B: Chemical, 98, 291–298.
• [39] Collier, W.A., Baird, D.B., Park, Z.A., More, N., Hart, A.L. (2003). Discrimination among milks and cultured dairy products using screen-printed electrochemical arrays and an electronic nose. Sensors and Actuators, B: Chemical, 92, 232–239.
• [40] Wang, B., Xu, S., Sun, D.W. (2010). Application of the electronic nose to the identification of different milk flavorings. Food Research International, 43(1), 255–262.
• [41] Lorenzen, P., Walte, H., Bosse, B. (2013). Chemical development of a method for butter type differentiation by electronic nose technology. Sensors and Actuators, B: Chemical, 181, 690–693.
• [42] Haddi, Z., Annanouch, F., Amari, A., Hadoune, A., Bouchikhi, B. (2010). Application of a portable electronic nose device to discriminate and identify cheeses with known percentages of cow’s and goat’s milk. Kona, Hawaii. Proceedings of the IEEE Sensors Conference, 771-774.
• [43] Trihaas, J., Vognsen, L., Nielsen, P.V. (2005). Electronic nose: New tool in modelling the ripening of Danish blue cheese. International Dairy Journal, 15, 679–691.
• [44] O’Riordan, P.J., Delahunty, C.M. (2003). Characterisation of commercial Cheddar cheese flavour. 1 :traditional and electronic nose approach to quality assessment and market classification. International Dairy Journal, 13(5), 355–370.
• [45] Cevoli, C., Cerretani, L., Gori, A., Caboni, M.F., Toschi, T.G., Fabbri, A. (2011). Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC – MS analysis of volatile compounds. Food Chemistry, 129(3), 1315–1319.
• [46] Branciari, R., Valiani, A., Trabalza-Marinucci, M., Miraglia, D.R., Acuti, G., Esopsto, S. (2012). Consumer acceptability of ovine cheeses from ewes fed extruded linseed-enriched diets. Small Rumininanat Research, 106, S43–S48.
• [47] El Barbri, N., Mirhisse, J., Ionescu, R., El Bari, N., Correig, X., Bouchikhi, B. (2009). An electronic nose system based on a micro-machined gas sensor array to assess the freshness of sardines. Sensors and Actuators, B: Chemical, 141(2), 538-543.
• [48] Zhang, S., Xie, C., Bai, Z., Hu, M., Li, H., Z.D. (2009). Spoiling and formaldehyde-containing detections in octopus with an E-nose. Food Chemistry, 113(4), 1346–1350.
• [49] Musatov, V.Y., Sysoev, V.V., Sommer, M., Kiselev, I. (2010). Assessment of meat freshness with metal oxide sensor microarray electronic nose: A practical approach. Sensors and Actuators, B: Chemical, 144, 99–103.
• [50] Hong, X., Wang, J., Hai, Z. (2012). Chemical discrimination and prediction of multiple beef freshness indexes based on electronic nose. Sensors and Actuators, B: Chemical, 161(1), 381–389.
• [51] Wang, D., Wang, X., Liu, T., Liu, Y. (2012). Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine. Meat Science, 90(2), 373–377.
• [52] Tian, X., Wang, J., Cui, S. (2013). Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors. Journal of Food Engineering, 119(4), 744–749.
• [53] Huang, L., Zhao, J., Chen, Q., Zhang, Y. (2014). Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chemistry, 145, 228–236.
• [54] Timsorn, K., Thoopboochagorn, T. (2016). Evaluation of bacterial population on chicken meats using a briefcase electronic nose. Biosystems Engineering, 151, 116–125.
• [55] Lippolis, V.F. (2016). Rapid prediction of ochratoxin A-producing strains of Penicillium on dry-cured meat by MOS-based electronic nose. International Journal of Food Microbiology, 218, 71–77.
• [56] Wijaya, D.R., Sarno, R., Zulaika, E. (2018). Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions. Data in Brief, 21, 2414–2420.
• [57] Wang, Q., Li, L., Ding, W., Zhang, D., Wang, J., Reed, K. (2019). Adulterant identification in mutton by electronic nose and gas chromatography-mass spectrometer. Food Control, 98, 431–438.
• [58] Shi, J., Nian, Y., Da, D., Xu, X., Zhou, G., Zhao, D. (2020). Characterization of flavor volatile compounds in sauce spareribs by gas chromatography – mass spectrometry and electronic nose. Food Science and Technology, 124, 109182.
• [59] Yurdakoş, Ö.B. (2019). Detection of degradation levels in food industry by using E-Nose sensors. M.Sc. Thesis, Dokuz Eylül University, 69.
• [60] Zhang, J., Cao, J., Pei, Z., Wei, P., Xiang, D., Cao, X. (2019). Volatile flavour components and the mechanisms underlying their production in golden pompano (Trachinotus blochii) fillets subjected to different drying methods: A comparative study using an electronic nose, an electronic tongue and SDE-GC-MS. Food Research International, 123, 217–225.
• [61] González Martín, Y., Luis Pérez Pavón, J., Moreno Cordero, B., García Pinto, C. (1999). Classification of vegetable oils by linear discriminant analysis of Electronic Nose data. Analytica Chimica Acta, 384, 83–94.
• [62] Cosio, M.S., Ballabio, D., Benedetti, S., Gigliotti, C. (2007). Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue. Food Chemistry, 101(2), 485-491.
• [63] Buratti, S., Benedetti, S., Cosio, M.S. (2005). An electronic nose to evaluate olive oil oxidation during storage. Italian Journal of Food Science, 17(2), 203–210.
• [64] Mildner-Szkudlarz, S., Jeleń, H.H., Zawirska-Wojtasiak, R. (2008). The use of electronic and human nose for monitoring rapeseed oil autoxidation. European Journal of Lipid Science and Technology, 110(1), 61–72.
• [65] Xu, L., Yu, X., Liu, L., Zhang, R. (2016). A novel method for qualitative analysis of edible oil oxidation using an electronic nose. Food Chemistry, 202, 229–235.
• [66] Upadhyay, R., Sehwag, S., Mishra, H.N. (2017a). Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis. Food Chemistry, 221, 379–385.
• [67] Upadhyay, R., Sehwag, S., Mishra, H.N. (2017b). Frying disposal time of sunflower oil using hybrid electronic nose-fuzzy logic approach. Food Science and Technology, 78, 332–339.
• [68] Cosio, M.S., Ballabio, D., Benedetti, S., Gigliotti, C. (2006). Geographical origin and authentication of extra virgin olive oils by an electronic nose in combination with artificial neural networks. Analytica Chimica Acta, 567(2), 202–210.
• [69] Ballabio, D., Cosio, M.S., Mannino, S., Todeschini, R. (2006). A chemometric approach based on a novel similarity/diversity measure for the characterisation and selection of electronic nose sensors. Analytica Chimica Acta, 578(2), 170–177.
• [70] Park, M.H., Jeong, M.K., Yeo, J.D., Son, H.J., Lim, C.L., Hong, E.J. (2011). Application of solid phase-microextraction (SPME) and electronic nose techniques to differentiate volatiles of sesame oils prepared with diverse roasting conditions. Journal of Food Science, 76(1), 80–88.
• [71] Haddi, Z., Alami, H., El Bari, N., Tounsi, M., Barhoumi, H., Maaref, A. (2013). Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International, 54(2), 1488-1498.
• [72] Haddi, Z., Amari, A., Ould Ali, A., El Bari, N., Barhoumi, H., Maaref, A. (2011). Discrimination and identification of geographical origin virgin olive oil by an e-nose based on MOS sensors and pattern recognition techniques. Procedia Engineering, 25, 1137–1140.
• [73] Cerrato Oliveros, M.C., Pérez Pavón, J.L., García Pinto, C., Fernández Laespada, M.E., Moreno Cordero, B., Forina, M. (2002). Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils. Analytica Chimica Acta, 459, 219–228.
• [74] Mildner-Szkudlarz, S., Jeleń, H.H. (2008). The potential of different techniques for volatile compounds analysis coupled with PCA for the detection of the adulteration of olive oil with hazelnut oil. Food Chemistry, 110(3), 751–761.
• [75] Mildner-Szkudlarz, S., Jeleń, H.H. (2010). Detection of olive oil adulteration with rapeseed and sunflower oils using mos electronic nose and smpe-ms. Journal of Food Quality, 33(1), 21–41.
• [76] Bougrini, M., Tahri, K., Haddi, Z., Saidi, T., El Bari, N., Bouchikhi, B. (2014). Detection of adulteration in argan oil by using an electronic nose and a voltammetric electronic tongue. Journal of Sensors, 1–10.
• [77] Hai, Z., W.J. (2006). Detection of adulteration in camellia seed oil and sesame oil using an electronic nose. European Journal of Lipid Science and Technology, 108(2), 116–124.
• [78] Lerma-García, M.J., Cerretani, L., Cevoli, C., Simó-Alfonso, E.F., Bendini, A., Toschi, T.G. (2010). Use of electronic nose to determine defect percentage in oils. Comparison with sensory panel results. Sensors and Actuators, B: Chemical, 147(1), 283–289.
• [79] Ghosh, P.K., Chatterjee, S., Bhattacharjee, P., Bhattacharyya, N. (2016). Removal of rancid-acid odor of expeller-pressed virgin coconut oil by gamma irradiation: evaluation by sensory and electronic nose technology. Food and Bioprocess Technology, 9(10), 1724-1734.
• [80] Torri, L., Migliorini, P., Masoero, G. (2013). Sensory test vs. electronic nose and/or image analysis of whole bread produced with old and modern wheat varieties adjuvanted by means of the mycorrhizal factor. Food Research International, 54, 1400–1408.
• [81] Sapirstein, H.D., Siddhu, S., Aliani, M. (2012). Discrimination of volatiles of refined and whole wheat bread containing red and white wheat bran using an electronic nose. Journal of Food Science, 77, 399–406.
• [82] Romani, S., Rocculi, P., Dalla Rosa, M. (2006). Electronic nose approach to differentiate biscuits on the base of their recipe. Industrie Alimentari, 45, 1273–1276.
• [83] Li, W., Friel, J., Beta, T. (2010). An evaluation of the antioxidant properties and aroma quality of infant cereals. Food Chemistry, 121(4), 1095–1102.
• [84] Romani, S., Balestra, F., Angioloni, A., Rocculi, P., Dalla Rosa, M. (2012). Physico-chemical and electronic nose measurements on the study of biscuit baking kinetics. Italian Journal of Food Science, 24, 32–40.
• [85] Ponzoni, A., Depari, A., Falasconi, M., Comini, E., Flammini, A., Marioli, D. (2008). Bread baking aromas detection by low-cost electronic nose. Sensors and Actuators, B:Chemical, 130, 100–104.
• [86] Adams, K., Suchy, J., Lukow, O.M. (2011). Detection of wheat variety flour aromas using an electronic nose. Electronic Journal of Environmental, Agricultural and Food Chemistry., 10(9), 2861–2873.
• [87] Zhang, H., Wang, J., Tian, X., Yu, H., Yu, Y. (2007). Optimization of sensor array and detection of stored duration of wheat by electronic nose. Journal of Food Engineering, 82, 403–408.
• [88] Presicce, D.S., Forleo, A., Taurino, A.M., Zuppa, M., Siciliano, P., Laddomada, B. (2006). Response evaluation of an E-nose towards contaminated wheat by Fusarium poae fungi. Sensors and Actuators, B: Chemical, 118(1), 433–438.
• [89] Wang, M., Du, J., Zhao, M. (2014). Paddy and maize moldy status characterization using electronic nose. Food Measurement, 8, 54-60.
• [90] Abramson, D., Hulasare, R., York, R.K., White, N.D., Jayas, D.S. (2005). Mycotoxins, ergosterol, and odor volatiles in durum wheat during granary storage at 16% and 20% moisture content. Journal of Stored Products Research, 41, 67–76.
• [91] Gobbi, E., Falasconi, M., Torelli, E., Sberveglieri, G. (2011). Electronic nose predicts high and low fumonisin contamination in maize cultures. Food Research International, 44(4), 992–999.
• [92] Campagnoli, A., Cheli, F., Savoini, G., Crotti, A., Pastori, A.G., Dell’Orto, V. (2009). Application of an electronic nose to detection of aflatoxins in corn. Veterinary Research Communications, 33(1), 273–275.
• [93] Lippolis, V., Pascale, M., Cervellieri, S., Damascelli, A., Visconti, A. (2014). Screening of deoxynivalenol contamination in durum wheat by MOS-based electronic nose and identification of the relevant pattern of volatile compounds. Food Control, 37, 263–271.
• [94] Wessling, C., Nielsen, T., Giacin, J.R. (2001). Antioxidant ability of BHT- and α-tocopherol- impregnated LDPE film in packaging of oatmeal. Journal of the Science of Food and Agriculture, 81, 194–201.
• [95] Xu, S., Zhou, Z., Lu, H., Luo, X., Lan, Y., Zhang, Y. (2014). Estimation of the age and amount of brown rice plant hoppers based on bionic electronic nose use. Sensors, 14(10), 18114–18130.
• [96] Olunloyo, V.O., Ibidapo, T.A., Dinrifo, R.R. (2011). Neural network-based electronic nose for cocoa beans quality assessment. Agricultural Engineering International: CIGR Journal, 13(4), 1–17.
• [97] Gu, S., Wang, J., Wang, Y. (2019). Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose. Food Chemistry, 292, 325–335.
• [98] Bai, J., Baldwin, E.A., Soliva Fortuny, R.C., Mattheis, J.P., Stanley, R., Perera, C. (2004). Effect of pretreatment of intact “Gala” apple with ethanol vapor, heat, or 1-methylcyclopropene on quality and shelf life of fresh-cut slices. Journal of the American Society for Horticultural Science, 129(4), 583–593.
• [99] Brezmes, J., Llobet, E., Vilanova, X., Saiz, G., Correig, X. (2000). Fruit ripeness monitoring using an Electronic Nose. Sensors and Actuators, B: Chemical, 69(3), 223–229.
• [100] Brezmes, J., Llobet, E., Vilanova, X., Orts, J., Saiz, G., Correig, X. (2001). Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples. Sensors and Actuators, B: Chemical, 80(1), 41–50.
• [101] Brezmes, J., Fructuoso, M.L., Llobet, E., Vilanova, X., Recasens, I., Orts, J. (2005). Evaluation of an electronic nose to assess fruit ripeness. IEEE Sensors Journal, 5(1), 97–108.
• [102] Guohua, H., Yuling, W., Dandan, Y., Wenwen, D. (2013). Fuji apple storage time predictive method using electronic nose. Food Analytical Methods, 6(1), 82–88.
• [103] Duta, D., Hıncu, F., Calu, M., Pruteanu, E. (2009). Electronic nose for discrimination of Romanian apples. Proceeding of 2 International Symposium New Research In Biotechnology, 398–403.
• [104] Young, H., Rossiter, K., Wang, M., Miller, M. (1999). Characterization of royal gala apple aroma using electronic nose technology - Potential maturity indicator. Journal of Agricultural and Food Chemistry, 47(12), 5173–5177.
• [105] Defilippi, B.G., Juan, W.S., Valdés, H., Moya-León, M.A., Infante, R., Campos-Vargas, R. (2009). The aroma development during storage of Castlebrite apricots as evaluated by gas chromatography, electronic nose, and sensory analysis. Postharvest Biology and Technology, 51(2), 212-219.
• [106] Paola Parpinello, G., Fabbri, A., Domenichelli, S., Mesisca, V., Cavicchi, L., Versari, A. (2007). Discrimination of apricot cultivars by gas multisensor array using an artificial neural network. Biosystems Engineering, 97(3), 371–378.
• [107] Solis-Solis, H.M., Calderon-Santoyo, M., Gutierrez-Martinez, P., Schorr-Galindo, S., Ragazzo-Sanchez, J.A. (2007). Discrimination of eight varieties of apricot (Prunus armeniaca) by electronic nose, LLE and SPME using GC-MS and multivariate analysis. Sensors and Actuators, B: Chemical, 125(2), 415–421.
• [108] Russo, M., Serra, D., Suraci, F., Postorino, S. (2012). Effectiveness of electronic nose systems to detect bergamot (Citrus bergamia Risso et Poiteau) essential oil quality and genuineness. Journal of Essential Oil Research, 24(2), 137–151.
• [109] Bernal, L.J., Melo, L.A., Díaz Moreno, C. (2014). Evaluation of the antioxidant properties and aromatic profile during maturation of the blackberry (Rubus glaucus Benth) and the bilberry (Vaccinium meridionale Swartz). Revista Facultad Nacional de Agronomía Medellín, 67(1), 7209–7218.
• [110] Zawirska-Wojtasiak, R., Gośliński, M., Szwacka, M., Gajc-Wolska, J., Mildner-Szkudlarz, S. (2009). Aroma evaluation of transgenic, thaumatin II-producing cucumber fruits. Journal of Food Science, 74(3), 204–210.
• [111] Lebrun, M., Bıllot, C., Harrak, H., Self, G. (2007). The electronic nose: A fast and efficient tool for characterizing dates. Fruits, 60(6), 377–382.
• [112] Alasalvar, C., Pelvan, E., Bahar, B., Korel, F., Ölmez, H. (2012). Flavour of natural and roasted Turkish hazelnut varieties (Corylus avellana L.) by descriptive sensory analysis, electronic nose and chemometrics. International Journal of Food Science and Technology, 47(1), 122–131.
• [113] Hernández Gómez, A., Wang, J., Hu, G., & García Pereira, A. (2007). Discrimination of storage shelf-life for mandarin by electronic nose technique. Food Science and Technology, 40(4), 681–689.
• [114] Lebrun, M., Ducamp-Collin, M.N., Plotto, A., Goodner, K.L., Baldwin, E. (2004). Development of electronic nose measurements for mango (Mangifera indica) homogenate and whole fruit. Proceedings of The Annual Meeting of The Florida State Horticultural Society, 117, 421–425.
• [115] Lebrun, M., Plotto, A., Goodner, K., Ducamp, M.N., Baldwin, E. (2008). Discrimination of mango fruit maturity by volatiles using the electronic nose and gas chromatography. Postharvest Biology and Technology, 48(1), 122–131.
• [116] Russo, M., Di Sanzo, R., Cefaly, V., Carabetta, S., Serra, D., Fuda, S. (2013). Non-destructive flavour evaluation of red onion (Allium cepa L.) Ecotypes: An electronic-nose-based approach. Food Chemistry, 141(2), 896–899.
• [117] Zhang, H., Wang, J., Ye, S. (2008). Predictions of acidity, soluble solids and firmness of pear using electronic nose technique. Journal of Food Engineering, 86(3), 370–378.
• [118] S Benedetti, S., Buratti, S., Spinardi, A., Mannino, S., Mignani, I. (2008). Electronic nose as a non-destructive tool to characterise peach cultivars and to monitor their ripening stage during shelf-life. Postharvest Biology and Technology, 47(2), 181–188.
• [119] Infante, R., Rubio, P., Meneses, C., Contador, L. (2011). Ripe nectarines segregated through sensory quality evaluation and electronic nose assessment. Fruits, 66(2), 109–119.
• [120] Rizzolo, A., Bianchi, G., Vanoli, M., Lurie, S., Spinelli, L., Torricelli, A. (2013). Electronic nose to detect volatile compound profile and quality changes in “spring belle” peach (Prunus persica L.) during cold storage in relation to fruit optical properties measured by time-resolved reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 61(8), 1671-1685.
• [121] Su, M., Zhang, B., Ye, Z., Chen, K., Guo, J., Gu, X. (2013). Pulp volatiles measured by an electronic nose are related to harvest season, TSS concentration and TSS/TA ratio among 39 peaches and nectarines. Scientia Horticulturae, 150, 146-153.
• [122] Liu, H., Zeng, F.K., Wang, Q.H., Wu, H.S., Tan, L.H. (2013). Studies on the chemical and flavor qualities of white pepper (Piper nigrum L.) derived from five new genotypes. European Food Research and Technology, 237(2), 245–251.
• [123] Torri, L., Sinelli, N., Limbo, S. (2010). Shelf life evaluation of fresh-cut pineapple by using an electronic nose. Postharvest Biology and Technology, 56(3), 239–245.
• [124] Du, X., Baı, J., Platto, A., Baldwing, E.,Whitaker, V. (2010). Electronic nose for detecting strawberry fruit maturity. Proceedings of the Florida State Horticultural Society, 123, 259–263.
• [125] Qiu, S., Wang, J., Gao, L. (2014). Discrimination and characterization of strawberry juice based on electronic nose and tongue: Comparison of different juice processing approaches by LDA, PLSR, RF, and SVM. Journal of Agricultural and Food Chemistry, 62(27), 6426-6434.
• [126] Cheng, S., Wang, J., Ma, Y., Wang, Y. (2012). Discrimination of different types damage of tomato seedling by electronic nose. Transactions of the Chinese Society of Agricultural Engineering, 28, 102-106.
• [127] Gómez, A.H., Wang, J., Hu, G., Pereira, A.G. (2008). Monitoring storage shelf life of tomato using electronic nose technique. Journal of Food Engineering, 85(4), 625-631.
• [128] Wang, J., Zhou, Y. (2007). Electronic-nose technique: Potential for monitoring maturity and shelf life of tomatoes. New Zealand Journal of Agricultural Research, 50(5), 1219–1228.
• [129] Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M., Ahmadi, H. (2016). Application of MOS based electronic nose for the prediction of banana quality properties. Measurement, 82, 105–114.
• [130] Guohua, H., Yuling, W., Dandan, Y., Wenwen, D., Linshan, Z., Lvye, W. (2012). Study of peach freshness predictive method based on electronic nose. Food Control, 28, 25–32.
• [131] Huang, L., Meng, L., Zhu, N., Wu, D. (2017). A primary study on forecasting the days before decay of peach fruit using near-infrared spectroscopy and electronic nose techniques. Postharvest Biology and Technology, 133, 104-112.
• [132] Liu, Q., Zhao, N., Zhou, D., Sun, Y., Sun, K., Pan, L. (2018). Discrimination and growth tracking of fungi contamination in peaches using electronic nose. Food Chemistry, 262, 226-234.
• [133] Voss, H.G., Stevan, S.L., Ayub, R.A. (2019). .Peach growth cycle monitoring using an electronic nose. Computers and Electronics in Agriculture, 163, 104858.
• [134] Wang, L., Wang, P., Deng, W., Cai, J., Chen, J. (2019). Evaluation of aroma characteristics of sugarcane (Saccharum officinarum L.) juice using gas chromatography-mass spectrometry and electronic nose. Food Science and Technology, 108, 400–406.
• [135] Dou, T.X., Shi, J.F., Li, Y., Bi, F.C., Gao, H.J., Hu, C.H. (2020). Influence of harvest season on volatile aroma constituents of two banana cultivars by electronic nose and HS-SPME coupled with GC-MS. Scientia Horticulturae, 265, 109214.
• [136] Zhang, X., Li, M., Cheng, Z., Ma, L., Zhao, L., Li, J. (2019). A comparison of electronic nose and gas chromatography–mass spectrometry on discrimination and prediction of ochratoxin A content in Aspergillus carbonarius cultured grape-based medium. Food Chemistry, 297, 124850.
• [137] Chen, H.Z., Zhang, M., Bhandari, B., Guo, Z. (2018). Evaluation of the freshness of fresh-cut green bell pepper (Capsicum annuum var. grossum) using electronic nose. Food Science and Technology, 87, 77–84.
• [138] Chen, H.Z., Guo, Z. (2019). Discrimination of fresh-cut broccoli freshness by volatiles using electronic nose and gas chromatography-mass spectrometry. Postharvest Biology and Technology, 148, 168–175.
• [139] [139] Martínez Gila, D.M., Gámez García, J., Bellincontro, A., Mencarelli, F., Gómez Ortega, J. (2020). Fast tool based on electronic nose to predict olive fruit quality after harvest. Postharvest Biology and Technology, 160, 111058.
• [140] Dutta, R., Hines, E.L., Gardner, J.W., Kashwan, K.R., Bhuyan, M. (2003). Tea quality prediction using a tin oxide-based electronic nose : an artificial intelligence approach. Sensors and Actuators, B: Chemical, 94, 228–237.
• [141] Kashwan, K.R., Bhuyan, M. (2005). Robust electronic-nose system with temperature and humidity drift compensation for tea and spice flavour discrimination. Asian Conference on Sensors and the International Conference on New Techniques in Pharmaceutical and Biomedical Research–Proceedings, 154–158.
• [142] Yu, H., Wang, J. (2007). Discrimination of LongJing green-tea grade by electronic nose. Sensors and Actuators, B: Chemical, 122, 134–140.
• [143] Bhattacharyya, N., Seth, S., Tudu, B., Tamuly, P., Jana, A., Ghosh, D. (2007). Monitoring of black tea fermentation process using electronic nose. Journal of Food Engineering, 80, 1146–1156.
• [144] Bhattacharyya, N., Seth, S., Tudu, B., Tamuly, P., Jana, A., Ghosh, D. (2007). Detection of optimum fermentation time for black tea manufacturing using electronic nose. Sensors and Actuators: B. Chemical, 122, 627–634.
• [145] Bhattacharya, N., Tudu, B., Jana, A., Ghosh, D., Bandhopadhyaya, R., Bhuyan, M. (2008). Preemptive identification of optimum fermentation time for black tea using electronic nose. Sensors and Actuators, B: Chemical, 131, 110–116.
• [146] Yu, H., Wang, J., Zhang, H., Yu, Y., Yao, C. (2008). Identification of green tea grade using different feature of response signal from E-nose sensors. Sensors and Actuators, B: Chemical, 128, 455–461.
• [147] Bhattacharyya, N., Bandyopadhyay, R., Bhuyan, M., Tudu, B., Ghosh, D., Jana, A. (2008). Electronic nose for black tea classification and correlation of measurements with “ tea taster ” marks. IEEE Transactions on Instrumentation and Measurement, 57(7), 1313-1321.
• [148] Bhattacharya, N., Tudu, B., Jana, A., Ghosh, D., Bandhopadhyaya, R., Baran, A. (2008). Illumination heating and physical raking for increasing sensitivity of electronic nose measurements with black tea. Sensors and Actuators, B: Chemical, 131, 37–42.
• [149] Tudu, B., Jana, A., Metla, A., Ghosh, D., Bhattacharyya, N., Bandyopadhyay, R. (2009). Chemical electronic nose for black tea quality evaluation by an incremental RBF network. Sensors and Actuators, B: Chemical, 138, 90–95.
• [150] Tudu, B., Metla, A., Das, B., Bhattacharyya, N., Jana, A. (2009). Classifier for black tea quality evaluation: An incremental fuzzy approach. IEEE Transactions on Instrumentation and Measurement, 58(9), 3069–3078.
• [151] Yu, H., Wang, Y., Wang, J. (2009). Identification of tea storage times by linear discrimination analysis and back-propagation neural network techniques based on the eigenvalues of principal components analysis of e-nose sensor signals. Sensors, 8073-8082.
• [152] Yang, Z., Dong, F., Shimizu, K., Kinoshita, T., Kanamori, M., Morita, A. (2009). Identification of coumarin-enriched Japanese green teas and their particular flavor using electronic nose. Journal of Food Engineering, 92(3), 312–316.
• [153] Yu, H., Wang, J., Xiao, H., Liu, M. (2009). Chemical quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals. Sensors and Actuators, B: Chemical , 140, 378–382.
• [154] Sipos, L., Kovacs, Z., Szöllosi, D., Kokai, Z. (2011). Comparison of novel sensory panel performance evaluation techniques with e-nose analysis integration. Journal of Chemometrics, 25(5), 275–286.
• [155] Bag, A., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N., Roy, J. (2011). Optimization of sensor array in electronic nose: A rough set-based approach. Smart Sensors, Measurement and Instrumentation, 11(11), 3001–3008.
• [156] Chen, Q., Zhao, J., Chen, Z., Lin, H., Zhao, D.A. (2011). Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools. Sensors and Actuators, B: Chemical, 159(1), 294-300.
• [157] Kaur, R., Kumar, R., Gulati, A., Ghanshyam, C., Kapur, P., Bhondekar, A.P. (2012). Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis L., O. Kuntze). Sensors and Actuators, B: Chemical, 166, 309-319.
• [158] Qin, Z., Pang, X., Chen, D., Cheng, H., Hu, X., Wu, J. (2013). Evaluation of Chinese tea by the electronic nose and gas chromatography-mass spectrometry: Correlation with sensory properties and classification according to grade level. Food Research International, 53(2), 864-674.
• [159] Mirasoli, M., Gotti, R., Di Fusco, M., Leoni, A., Colliva, C., Roda, A. (2014). Electronic nose and chiral-capillary electrophoresis in evaluation of the quality changes in commercial green tea leaves during a long-term storage. Talanta, 129, 32–38.
• [160] Torri, L., Rinaldi, M., Chiavaro, E. (2014). Electronic nose evaluation of volatile emission of Chinese teas: From leaves to infusions. International Journal of Food Science and Technology, 49(5), 1315–1323.
• [161] Kablan, Y. (2018). Elektronik Burun ile Farklı Kalitedeki Doğu Karadeniz Çaylarının Sınıflandırılması. Yüksek Lisans Tezi, Karadeniz Teknik Üniversitesi, 1-98.
• [162] Zhu, J.C., Chen, F., Wang, L.Y., Niu, Y.W., Xiao, Z.B. (2017). Evaluation of the synergism among volatile compounds in Oolong tea infusion by odour threshold with sensory analysis and E-nose. Food Chemistry, 221, 1484–1490.
• [163] Xu, M., Wang, J., Gu, S. (2019). Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy. Journal of Food Engineering, 241, 10–17.
• [164] Banerjee, M.B., Roy, R.B., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N. (2019). Black tea classification employing feature fusion of E-Nose and E-Tongue responses. Journal of Food Engineering, 244, 55-63.
• [165] Xu, M., Wang, J., Zhu, L. (2019). The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food Chemistry, 289, 482–489.
• [166] Wang, J., Chang, M., He, W., Lu, X., Fei, S., Lu, G. (2020). Optimization of electronic nose sensor array for tea aroma detecting based on correlation coefficient and cluster analysis. Sensors and Actuators, B: Chemical, 128068.
• [167] Romani, S., Cevoli, C., Fabbri, A., Alessandrini, L., Rosa, M.D. (2012). Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control. Food Chemistry, 77(9), 960–965.
• [168] Gardner, J., Shurmer, H., Tan, T.T. (1992). Application of an electronic nose to the discrimination of coffees. Sensors and Actuators, B: Chemical, 6, 71-75.
• [169] Falasconi, M., Pardo, M., Sberveglieri, G., Riccò, I., Bresciani, A. (2005). The novel EOS835 electronic nose and data analysis for evaluating coffee ripening. Sensors and Actuators, B: Chemical, 110(1), 73-80.
• [170] Aishima, T. (1991). Aroma discrimination by pattern recognition analysis of responses from semiconductor gas sensor array. Journal of Agriculture and Food Chemistry, 39, 752–756.
• [171] Kermani, B.G., Schiffman, S.S., Nagle, H.T. (2005). Performance of the Levenberg – Marquardt neural network training method in electronic nose applications. Sensor and Actuators, B: Chemical, 110, 13-22.
• [172] Pardo, M., Sberveglieri, G. (2002). Coffee analysis with an electronic nose. IEEE Transactions on Instrumentation and Measurement, 51(6), 1334–1339.
• [173] Brudzewski, K., Osowski, S., Member, S., Dwulit, A. (2012). Recognition of coffee using differential electronic nose. IEEE Transactions on Instrumentation and Measurement, 61(6), 1803–1810.
• [174] Rodríguez, J., Durán, C., Reyes, A. (2010). Electronic nose for quality control of Colombian coffee through the detection of defects in " Cup Tests ". Sensors, 10, 36–46.
• [175] Dong, W., Zhao, J., Hu, R., Dong, Y., Tan, L. (2017). Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics. Food Chemistry, 229, 743–751.
• [176] Dong, W., Hu, R., Long, Y., Li, H., Zhang, Y., Zhu, K. (2019). Comparative evaluation of the volatile profiles and taste properties of roasted coffee beans as affected by drying method and detected by electronic nose, electronic tongue, and HS-SPME-GC-MS. Food Chemistry, 272, 723–731.
• [177] Mamatha, B.S., Prakash, A.T. (2011). Studies on Pepper (Piper nigrum L.) cultivars by sensory and instrumental techniques. Zeitschrift fur Arznei and Gewurzpflanzen, 16(4), 176–180.
• [178] Carmona, M., Martínez, J., Zalacain, A., Rodríguez-Méndez, M.L., De Saja, J.A., Alonso, G.L. (2006). Analysis of saffron volatile fraction by TD-GC-MS and e-nose. European Food Research and Technology, 223(1), 96–101.
• [179] Tahri, K., Tiebe, C., Bougrini, M., Saidi, T., El Alami El Hassani, N., El Bari, N. (2015). Characterization and discrimination of saffron by multisensory systems, SPME-GC-MS and UV-Vis spectrophotometry. Analytical Method, 7(24), 10328–10338.
• [180] Ravi, R., Prakash, M., Bhat, K.K. (2013). Characterization of aroma active compounds of cumin (Cuminum cyminum L.) by GC-MS, E-Nose, and sensory techniques. International Journal of Food Properties, 16(5), 1048–1058.
• [181] Ravi, R., Prakash, M., Bhat, K.K. (2007). Aroma characterization of coriander (Coriandrum sativum L.) oil samples. European Food Research and Technology, 225(3), 367–374.
• [182] Tamaki, K., Sonoki, S., Tamaki, T., Ehara, K. (2008). Measurement of odour after in vitro or in vivo ingestion of raw or heated garlic, using electronic nose, gas chromatography and sensory analysis. International Journal of Food Science and Technology, 43(1), 130–139.
• [183] Liu, H., Zeng, F., Wang, Q., Ou, S., Tan, L., Gu, F. (2013). The effect of cryogenic grinding and hammer milling on the flavour quality of ground pepper (Piper nigrum L.). Food Chemistry, 141(4), 3402–3408.
• [184] Lee, J.H., Sung, T.H., Lee, K.T., Kim, M.R. (2004). Effect of gamma-irradiation on color, pungency, and volatiles of Korean red pepper powder. Journal of Food Science, 69(8), 585–592.
• [185] García, M., Aleixandre, M., Gutiérrez, J., Horrillo, M.C. (2006). Electronic nose for wine discrimination. Sensors and Actuators, B: Chemical, 113, 911–916.
• [186] Rodriguez-Mendez, M.L., Apetrei, C., Gay, M., Medina-plaza, C., Saja, J.A., Aagaard, O. (2014). Evaluation of oxygen exposure levels and polyphenolic content of red wines using an electronic panel by an electronic nose and an electronic tongue. Food Chemistry, 155, 91–97.
• [187] Wongchoosuk, C., Choopun, S., Tuantranont, A., Kerdcharoen, T., Wongchoosuk, C., Choopun, S. (2009). Au-doped zinc oxide nanostructure sensors for detection and discrimination of volatile organic compounds. Materials Research Innovations, 13, 185–187.
• [188] Wei, Y., Yang, L., Liang, Y., Li, J.M. (2014). Application of electronic nose for detection of wine-aging methods. Advanced Materials Research, 875–877, 2206-2213.
• [189] Prieto, N., Dugan, M.E., López-Campos, O., Mcallister, T.A., Aalhus, J.L., Uttaro, B. (2012). Near infrared reflectance spectroscopy predicts the content of polyunsaturated fatty acids and biohydrogenation products in the subcutaneous fat of beef cows fed flaxseed. International Journal of Modern Education and Computer Science, 90(1), 43–51.
• [190] Apetrei, I.M., Rodríguez-Méndez, M.L., Apetrei, C., Nevares, I., Alamo, M., Saja, J.A. (2012). Monitoring of evolution during red wine aging in oak barrels and alternative method by means of an electronic panel test. Food Research International, 45(1), 244–249.
• [191] Lozano, J., Arroyo, T., Santos, J.P., Cabellos, J.M., Horrillo, M.C. (2008). Electronic nose for wine ageing detection. Sensors and Actuators B:Chemical, 133, 180–186.
• [192] Macías, M., Manso, A.G., Orellana, C.J., Velasco, H.M., Caballero, R.G., Chamizo, J.C. (2013). Acetic acid detection threshold in synthetic wine samples of a portable electronic nose. Sensors, 13, 208–220.
• [193] Lozano, J., Álvarez, F., Santos, J.P., Horrillo, C. (2011). Detection of acetic acid in wine by means of an electronic nose. AIP Conference Proceedings, 1362, 176–177.
• [194] Prieto, N., Gay, M., Vidal, S., Aagaard, O., Saja, J.A. (2011). Analysis of the influence of the type of closure in the organoleptic characteristics of a red wine by using an electronic panel. Food Chemistry, 129(2), 58.
• [195] Cabañes, F.J., Sahgal, N., Bragulat, M.R., Magan, N. (2009). Early discrimination of fungal species responsible of ochratoxin a contamination of wine and other grape products using an electronic nose. Mycotox Research, 25, 187–192.
• [196] Ragazzo-Sanchez, J.A., Chalier, P., Chevalier-Lucia, D., Calderon-Santoyo, M., Ghommidh, C. (2009). Chemical off-flavours detection in alcoholic beverages by electronic nose coupled to GC. Sensors and Actuators, B: Chemical, 140, 29–34.
• [197] Berna, A.M., Rowell, S.T., Ynkar, W.I. (2008). Comparison of metal oxide-based electronic nose and mass spectrometry-based electronic nose for the prediction of red wine spoilage. Journal of Agricultural Food Chemistry, 56, 3238–3244.
• [198] Buratti, S., Ballabio, D., Giovanelli, G., Dominguez, C.M., Moles, A., Benedetti, S. (2011). Monitoring of alcoholic fermentation using near infrared and mid infrared spectroscopies combined with electronic nose and electronic tongue. Analytica Chimica Acta, 697, 67–74.
• [199] Santos, J.P., Arroyo, T., Aleixandre, M., Lozano, J., Sayago, I., Garc, M. (2004). A comparative study of sensor array and GC – MS: application to Madrid wines characterization. Sensors and Actuators, B: Chemical, 102, 299–307.
• [200] Marti, M.P., Busto, O., Guasch, J. (2004). Application of a headspace mass spectrometry system to the differentiation and classification of wines according to their origin, variety and ageing. Journal of Chromatography A, 1057, 211–217.
• [201] Capone, S., Tufariello, M., Francioso, L., Montagna, G., Casino, F., Leone, A. (2013). Chemical aroma analysis by GC/MS and electronic nose dedicated to Negroamaro and Primitivo typical Italian Apulian wines. Sensors and Actuators B:Chemical, 179, 259-269.
• [202] Fu, J., Huang, C., Xing, J., Zheng, J. (2012). Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application. Sensors, 12(3), 2818–2830.
• [203] Santos, J.P., Lozano, J., Aleixandre, M., Arroyo, T., Mariano, J., Gil, M. (2010). Threshold detection of aromatic compounds in wine with an electronic nose and a human sensory panel. Talanta, 80, 1899–1906.
• [204] Arroyo, T., Lozano, J.E., Cabellos, J.M., Gıl-Diaz, M. (2009). Evaluation of wine aromatic compounds by a sensory human panel and an electronic nose. Journal of Agricultural Food Chemistry, 57, 11543–11549.
• [205] Berna, A.Z., Trowell, S., Clifford, D., Cynkar, W., Cozzolino, D. (2009). Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose. Analytica Chimica Acta, 648, 146–152.
• [206] Buratti, S., Benedetti, S., Scampicchio, M., & Pangerod, E.C. (2004). Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue. Analytica Chimica Acta, 525, 133–139.
• [207] Rodríguez-Méndez, M.L., Arrieta, A.A., Parra, V., Bernal, A., Vegas, A., Villanueva, S. (2004). Fusion of three sensory modalities for the multimodal characterization of red wines. IEEE Sensors Journal, 4(3), 348–354.
• [208] Lozano, J., Santos, J.P., Suárez, J.I., Cabellos, M., Arroyo, T., Horrillo, C. (2014). Automatic sensor system for the continuous analysis of the evolution of wine. American Journal of Enology and Viticulture, 66, 148–155.
• [209] Aleixandre, M., González, J.A., Sayago, I., Fernández, M.J., Gutiérrez, J., Horrillo, M.C. (2009). Analysis of grape variety and denomination of origin of several wines with an artificial nose. Spanish Conference on Electron Devices, 309-311.
• [210] Lozano, J., Santos, J.P., Arroyo, T., Aznar, M., Mariano, J., Gil, M. (2007). Correlating e-nose responses to wine sensorial descriptors and gas chromatography–mass spectrometry profiles using partial least squares regression analysis. Sensors and Actuators B:Chemical, 127, 267–276.
• [211] Aleixandre, M., Lozano, J., Gutierrez, J., Sayago, I., Fernandez, M.J., Horrillo, M.C. (2008). Portable e-nose to classify different kinds of wine. Sensors and Actuators, B: Chemical, 131, 71–76.
• [212] Lozano, J., Santos, J.P., Guti, J., Horrillo, M.C. (2007). Comparative study of sampling systems combined with gas sensors for wine discrimination. Sensors and Actuators, B: Chemical, 126, 616–623.
• [213] Villanueva, S., Vegas, A., Fernandez-Escudero, J.A., Rodr, M.L., Saja, J.A. (2006). SPME coupled to an array of MOS sensors reduction of the interferences caused by water and ethanol during the analysis of red wines. Sensors and Actuators, B: Chemical, 120, 278-287.
• [214] Aguilera, T., Lozano, J., Paredes, J., Alvarez, F., Suarez, J. (2012). Electronic nose based on independent component analysis combined with partial least squares and artificial neural networks for wine prediction. Sensors, 12, 8055–8072.
• [215] Lozano, J., Santos, J.P., Aleixandre, M., Sayago, I., Gutiérrez, J., Horrillo, M.C. (2006). Identification of typical wine aromas by means of an electronic nose. IEEE Sensors Journal, 6(1), 173–178.
• [216] Lozano, J., Santos, J.P., Horrillo, M.C. (2005). Classification of white wine aromas with an electronic nose. Talanta, 67, 610–616.
• [217] Buratti, S., Ballabio, D., Benedetti, S., Cosio, M.S. (2007). Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithm regression models. Food Chemistry, 100, 211–218.