Artificial senses and their fusion as a booming technique in food quality assessment—a review
Main Article Content
Keywords
artificial senses, e-eye, e-nose, e-tongue, fusion technique, sensory evaluation
Abstract
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.
References
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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
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Published
Jun 10, 2022
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How to Cite
Kiran, E., Kaur, K., & Aggarwal, P. (2022). Artificial senses and their fusion as a booming technique in food quality assessment—a review. Quality Assurance and Safety of Crops & Foods, 14(3), 9-18. https://doi.org/10.15586/qas.v14i3.1036
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Review Article
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How to Cite
Kiran, E., Kaur, K., & Aggarwal, P. (2022). Artificial senses and their fusion as a booming technique in food quality assessment—a review. Quality Assurance and Safety of Crops & Foods, 14(3), 9-18. https://doi.org/10.15586/qas.v14i3.1036