Artificial senses and their fusion as a booming technique in food quality assessment—a review

Main Article Content

Ekaspreet Kiran
Kamaljit Kaur
Poonam Aggarwal


artificial senses, e-eye, e-nose, e-tongue, fusion technique, sensory evaluation


Sensory science has witnessed increased adoption of technological advancements in recent years. Food analysis using human senses severely impacted the evaluation responses due to errors and the complexity of the assessment methods. Hence, the adoption of tools capable of mimicking human senses is considered a more viable approach. This article provides a critical demonstration of the developments in sensory science detailing the technology behind the construction and working of the electronic tongue, electronic nose, and electronic eye. The paper also attempts to brief the industrial applications of artificial senses and the fusion technique in monitoring as well as assessment of food quality.

Abstract 585 | PDF Downloads 587 HTML Downloads 161 XML Downloads 11


Ajay Priya, V.S, Joseph, P., Kiruba Daniel, S.C.G., Lakshmanan, S., Kinoshita, T. and Muthusamy, S., 2017. Colorimetric sensors for rapid detection of various analytes. Materials Science and Engineering: C 78: 1231–1245. 10.1016/j.msec.2017.05.018

Amoli, V., Kim, S.Y., Kim, J.S., Choi, H., Koo, J. and Kim, D.H., 2019. Biomimetics for high-performance flexible tactile sensors and advanced artificial sensory systems. Journal of Materials Chemistry C 7(47): 14816–14844. 10.1039/C9TC05392A

Apetrei, C., Apetrei, I.M., Villanueva, S., De Sajab, J.A., Gutierrez-Rosalesc, F. and Rodriguez-Mendez, M.L., 2010. Combination of an e-nose, an e-tongue and an e-eye for the characterization of olive oils with different degree of bitterness. Analytica Chimica Acta 663: 91–97. 10.1016/j.aca.2010.01.034

Baldwin, E.A., Bai, J., Plotto, A. and Dea, S., 2011. Electronic noses and tongues: applications for the food and pharmaceutical industries. Sensors 11: 4744–4766. 10.3390/s110504744

Borras, E., Ferre, J., Boque, R., Mestres, M., Acena, L. and Busto, O., 2015. Data fusion methodologies for food and beverage authentication and quality assessment–a review. Analytica Chimica Acta 891: 1–14. 10.1016/j.aca.2015.04.042

Calvini, R. and Pigani, L., 2022. Toward the development of combined artificial sensing systems for food quality evaluation: a review on the application of data fusion of electronic noses, electronic tongues and electronic eyes. Sensors 22(2): 577. 10.3390/s22020577

Che Harun, F.K., Taylor, J.E., Covington, J.A. and Gardner, J.W., 2009. An electronic nose employing dual-channel odor separation columns with large chemosensor arrays for advanced odour discrimination. Sensors and Actuators B 141: 134–140. 10.1016/j.snb.2009.05.036

Chen, Q., Song, J., Bi, J., Meng, X. and Wu, X., 2018. Characterization of volatile profile from ten different varieties of Chinese jujubes by HS-SPME/GC–MS coupled with E-nose. Food Research International 105: 605–615. 10.1016/j.foodres.2017.11.054

Chouhan, S.S., Singh, U.P. and Jain, S., 2020. Applications of computer vision in plant pathology: a survey. Archives of Computational Methods in Engineering 27(2): 611–632. 10.1007/s11831-019-09324-0

Corona, P., Frangipane, M.T., Moscetti, R., Lo Feudo, G., Castellotti, T. and Massantini, R., 2021. Chestnut cultivar identification through the data fusion of sensory quality and FT-NIR spectral data. Foods 10: 2575. 10.3390/foods10112575

Di Rosa, A.R., Leone, F., Cheli, F. and 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. 10.1016/j.jfoodeng.2017.04.024

Di Rosa, A.R., Leone, F., Scattareggia, C. and Chiofalo, V., 2018. Botanical origin identification of Sicilian honeys based on artificial senses and multi-sensor data fusion. European Food Research and Technology 244: 117–125. 10.1007/s00217-017-2945-8

Du, C.J. and Sun, D.W., 2006. Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering 72: 39−55. 10.1016/j.jfoodeng.2004.11.017

Ghasemi-Varnamkhasti, M. and Aghbashlo, M., 2014. Electronic nose and electronic mucosa as innovative instruments for real-time monitoring of food dryers. Trends in Food Science and Technology 38: 158–166. 10.1016/j.tifs.2014.05.004

Ghasemi-Varnamkhasti, M., Mohtasebi, S.S. and Siadat, M., 2010. Biomimetic-based odor and taste sensing systems to food quality and safety characterization: an overview on basic principles and recent achievements. Journal of Food Engineering 100: 377–387. 10.1016/j.jfoodeng.2010.04.032

Go, D.B., Atashbar, M.Z., Ramshani, Z. and Chang, H.C., 2017. Surface acoustic wave devices for chemical sensing and microfluidics: a review and perspective. Analytical Methods 9(28): 4112–4134. 10.1039/C7AY00690J

Gomez, A.H., Wang, J., Hu, G. and Pereira, A.G., 2007. Discrimination of storage shelf-life for mandarin by electronic nose technique. LWT-Food Science and Technology 40: 681–689. 10.1016/j.lwt.2006.03.010

Goncalves, T.R., Rosa, L.N., Torquato, A.S., Da Silva, L.F., Março, P.H., Gomes, S.T.M., Matsushita, M. and Valderrama, P., 2020. Assessment of Brazilian monovarietal olive oil in two different package systems by using data fusion and chemometrics. Food Analytical Methods 13: 86–96. 10.1007/s12161-019-01511-w

Gowen, A.A., Tiwari, B.K., Cullen, P.J., McDonnell, K. and O’Donnell, C.P., 2010. Applications of thermal imaging in food quality and safety assessment. Trends in Food Science and Technology 21: 190–200. 10.1016/j.tifs.2009.12.002

Haddi, Z., Amari, Z., Bouchikhi, B., Gutierrez, J.M., Cetoc, X., Mimendia, A. and Vallem, M.D., 2011. Data fusion from voltammetric and potentiometric sensors to build a hybrid electronic tongue applied in classification of beers. Proceedings of the 14th International Symposium on Olfaction and Electronic Nose. AIP Conference Proceedings 1362: 189–190. 10.1063/1.3626353

Hong, H., Yang, X., You, Z. and Cheng, F., 2014. Visual quality detection of aquatic products using machine vision. Aquacultural Engineering. 63: 62–71. 10.1016/j.aquaeng.2014.10.003

Huang, L., Zhao, J., Chen, Q. and 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. 10.1016/j.foodchem.2013.06.073

Huang, Q., Chen, Q., Li, H., Huang, G., Ouyang, Q. and Zhao, J., 2015. Non-destructively sensing pork’s freshness indicator using near infrared multispectral imaging technique. Journal of Food Engineering 154: 69–75.

Jain, H., Panchal, R., Pradhan, P., Patel, H. and Pasha, T.Y., 2010. Electronic tongue: a new taste sensor. International Journal of Pharmaceutical Sciences Review and Research 5: 91–96.

Jiang, H., Zhang, M., Bhandari, B. and Adhikari, B., 2018. Application of electronic tongue for fresh foods quality evaluation: a review. Food Reviews International 34(8): 746–769. 10.1080/87559129.2018.1424184

Jin, W., Ho, H.L., Cao, Y.C., Ju, J. and Qi, L.F., 2013. Gas detection with micro-and nano-engineered optical fibers. Optical Fiber Technology 19: 741–759. 10.1016/j.yofte.2013.08.004

Kakani, V., Nguyen, V.H., Kumar, B.P., Kim, H. and Pasupuleti, V.R., 2020. A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research 2: 100033. 10.1016/j.jafr.2020.100033

Karakaya, D., Ulucan, O. and Turkan, M., 2019. Electronic nose and its applications: a survey. International Journal of Automation and Computing 17(2): 179–209. 10.1007/s11633-019-1212-9

Khulal, U., Zhao, J., Hu, W. and Chen, Q., 2017. Intelligent evaluation of total volatile basic nitrogen (TVB-N) content in chicken meat by an improved multiple level data fusion model. Sensors and Actuators B 238: 337–345. 10.1016/j.snb.2016.07.074

Kiani, S., Minaei, S. and Ghasemi-Varnamkhasti, M., 2016. Fusion of artificial senses as a robust approach to food quality assessment. Journal of Food Engineering 171: 230–239. 10.1016/j.jfoodeng.2015.10.007

Kodagali, J.A. and Balaji, S., 2012. Computer vision and image analysis based techniques for automatic characterization of fruits—a review. IJCA—International Journal of Computer Applications 50: 6–12. 10.5120/7773-0856

Koyama, K., Tanaka, M., Cho, B.H., Yoshikawa, Y. and Koseki, S., 2021. Predicting sensory evaluation of spinach freshness using machine learning model and digital images. PLoS One 16(3): e0248769. 10.1371/journal.pone.0248769

Kutsanedzie, F.Y., Hao, L., Yan, S., Ouyang, Q. and Chen, Q., 2018. Near infrared chemo-responsive dye intermediaries spectra-based in-situ quantification of volatile organic compounds. Sensors and Actuators B 254: 597–602. 10.1016/j.snb.2017.07.134

Lawless, H.T. and Heymann, H., 2010. Introduction. In: Lawless H.T., Heymann H. (eds.) Sensory evaluation of food: principles and practices. 2nd ed. Springer Science and Business Media, New York, NY, pp. 1–2.

Lee, W.H. and Lee, W., 2014. Food inspection system using terahertz imaging. Microwave and Optical Technology Letters 56: 1211–1214. 10.1002/mop.28303

Leon-Medina, J.X., Cardenas-Flechas, L.J. and Tibaduiza, D.A., 2019. A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue. International Journal of Distributed Sensor Networks 15(10): 1550147719881601. 10.1177/1550147719881601

Li, H., Chen, Q., Zhao, J. and Wu, M., 2015. Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion. LWT-Food Science and Technology 63: 268–274. 10.1016/j.lwt.2015.03.052

Li, H., Kutsanedzie, F., Zhao, J. and Chen, Q., 2016. Quantifying total viable count in pork meat using combined hyperspectral imaging and artificial olfaction techniques. Food Analytical Methods 9: 3015–3024. 10.1007/s12161-016-0475-9

Magdalena, S., Paulina, W., Tomasz, D., Jacek, N. and Waldemar, W., 2014. Food analysis using artificial senses. Journal of Agricultural and Food Chemistry 62: 1423–1448. 10.1021/jf403215y

Marques, C., Correia, E., Dinis, L.T. and Vilela, A., 2022. An overview of sensory characterization techniques: from classical descriptive analysis to the emergence of novel profiling methods. Foods 11(3): 255. 10.3390/foods11030255

Mattes, R.D. and Popkin, B.M., 2009. Nonnutritive sweetener consumption in humans: effects on appetite and food intake and their putative mechanisms. The American Journal of Clinical Nutrition 89: 1–14. 10.3945/ajcn.2008.26792

Miguel, P. and Laura, E.G., 2009. A 21st century technique for food control: electronic noses. Analytical Chimica Acta 638: 1–15. 10.1016/j.aca.2009.02.009

Milna, T.K., Ksenija, M., Samir, K., Nada, V. and Jasmina, H., 2014. Application of electronic nose and electronic tongue in the dairy industry. Mljekarstvo 64: 228–244. 10.15567/mljekarstvo.2014.0402

Ouyang, Q., Zhao, J. and Chen, Q., 2014. Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion. Analytica Chimica Acta 841: 68–76. 10.1016/j.aca.2014.06.001

Patel, K.K., Kar, A., Jha, S.N. and Khan, M.A., 2011. Machine vision system: a tool for quality inspection of food and agricultural products. Journal of Food Science and Technology 42: 123–141. 10.1007/s13197-011-0321-4

Pereira, P.F., de Sousa Picciani, P.H., Calado, V. and Tonon, R.V., 2021. Electrical gas sensors for meat freshness assessment and quality monitoring: a review. Trends in Food Science and Technology 118: 36–44. 10.1016/j.tifs.2021.08.036

Qiu, S., Wang, J., Tang, C. and Du, D., 2015. Comparison of ELM, RF, and SVM on E-nose and E-tongue to trace the quality status of mandarin (Citrus unshiu Marc.). Journal of Food Engineering 166: 193–203. 10.1016/j.jfoodeng.2015.06.007

Rodríguez-Méndez, M.L., Apetrei, C. and De Saja, J.A., 2010. Electronic tongues purposely designed for the organoleptic characterization of olive oils. In: Olives and olive oil in health and disease prevention. Elsevier: Amsterdam, The Netherlands, pp. 525–532.

Rodríguez-Mendez, M.L. and Preddy, V., 2016. Electronic noses and tongues in food science. Academic Press, London.

Smyth, H. and Cozzolino, D., 2013. Instrumental methods (spectroscopy, electronic nose, and tongue) as tools to predict taste and aroma in beverages: advantages and limitations. Chemical Reviews 113: 1429–1440. 10.1021/cr300076c

Tan, J., Balasubramanian, B., Sukha, D., Ramkissoon, S. and Umaharan, P., 2019. Sensing fermentation degree of cocoa (Theobroma cacao L.) beans by machine learning classification models based electronic nose system. Journal of Food Process Engineering 42(6): e13175. 10.1111/jfpe.13175

Tan, J. and Xu, J., 2020. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: a review. Artificial Intelligence in Agriculture 4: 104–115. 10.1016/j.aiia.2020.06.003

Tretola, M., Di Rosa, A.R., Tirloni, E., Ottoboni, M., Giromini, C., Leone, F., Bernardi, C.E.M., Dell’Orto, V., Chiofalo, V. and Pinotti, L., 2017a. Former food products safety: microbiological quality and computer vision evaluation of packaging remnants contaminations. Food Additives and Contaminants Part A 34: 1427–1435. 10.1080/19440049.2017.1325012

Tretola, M., Ottoboni, M., Di Rosa, A.R., Giromini, C., Fusi, E., Rebucci, R., Leone, F., Dell’Orto, V., Chiofalo, V. and Pinotti, L., 2017b. Former food products safety evaluation: computer vision as an innovative approach for packaging remnants detection. Journal of Food Quality. 2017, 1–6. 10.1155/2017/1064580

Tuorila, H. and Monteleone, E., 2009. Sensory food science in the changing society: opportunities, needs, and challenges. Trends Food Science Technology 20: 54–62. 10.1016/j.tifs.2008.10.007

Yakubu, H.G., Kovacs, Z., Toth, T. and Bazar, G., 2021. Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy production—a review. Critical Reviews in Food Science and Nutrition. 10.1080/10408398.2021.1945533

Yang, J. and Lee, J., 2019. Application of sensory descriptive analysis and consumer studies to investigate traditional and authentic foods: a review. Foods 8(2): 54. 10.3390/foods8020054