Application of artificial intelligence in the food industry Application of artificial intelligence in the food industry
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Abstract
The article examines innovative automated methods and approaches based on algorithms for artificial intelligence, data analysis, machine learning and deep learning in the food industry, which, in combination with modernized intelligent equipment, lead to sales growth, reduce product counterfeiting, increase the quality of the produced production, identify and track food products, evaluate their characteristics and qualities, improve health and customer satisfaction, etc. With the development of automation, computer-aided design, integration and management, the food industry reaches a peak and exponential progress. The paper aims to show verified practical applications of artificial intelligence (AI) in the food industry. In support of the thesis, developed and implemented technological solutions in sustainable industrial applications such as electronic noses, electronic palates, computer vision systems, the Internet of Things, and tools for identifying and distinguishing individual qualities of food products are presented.
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References
Ali A., Mansol A.S., Khan A.A., Muthoosamy K., Siddiqui Y. Electronic nose as a tool for early detection of diseases and quality monitoring in fresh postharvest produce: A comprehensive review. Comprehensive Reviews in Food Science and Food Safety, 2023, 22(3): 2408-2432. https://doi.org/10.1111/1541-4337.13151
Ali M.M., Hashim N., Aziz S.A., Lasekan O. Principles and recent advances in electronic nose for quality inspection of agricultural and food products. Trends in Food Science & Technology, 2020, 99(5): 1-10. https://doi.org/10.1016/j.tifs.2020.02.028
Allegra D., Battiato S., Ortis A., Urso S., Polosa R. A review on food recognition technology for health applications. Health Psychology Research, 2020, 8(3): 9297. https://doi.org/10.4081/hpr.2020.9297
Amugongo L.M., Kriebitz A., Boch A., Lütge C. Mobile computer vision-based applications for food recognition and volume and calorific estimation: A systematic review. Healthcare, 2023, 11(1): 59. https://doi.org/10.3390/healthcare11010059
Attallah O., Morsi I. An electronic nose for identifying multiple combustible/harmful gases and their concentration levels via artificial intelligence. Measurement, 2022, 199(8): 111458. https://doi.org/10.1016/j.measurement.2022.111458
Aung M.M., Chang Y.S. Traceability in a food supply chain: Safety and quality perspectives. Food Control, 2014, 39(5): 172-184. https://doi.org/10.1016/j.foodcont.2013.11.007
Baietto M., Wilson A.D. Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors, 2015, 15(1): 899-931. https://doi.org/10.3390/s150100899
Bouzembrak Y., Klüche M., Gavai A., Marvin H.J.P. Internet of Things in food safety: Literature review and a bibliometric analysis. Trends in Food Science & Technology, 2019, 94(12): 54-64. https://doi.org/10.1016/j.tifs.2019.11.002
Buratti S., Malegori C., Benedetti S., Oliveri P., Giovanelli G. E-nose, e-tongue and e-eye for edible olive oil characterization and shelf life assessment: A powerful data fusion approach. Talanta, 2018, 182(5): 131-141. https://doi.org/10.1016/j.talanta.2018.01.096
Chen H., Huo D., Zhang J. Gas recognition in e-nose system: A review. IEEE Transactions on Biomedical Circuits and Systems, 2022, 16(2): 169-184. https://doi.org/10.1109/TBCAS.2022.3166530
Chmiel M., Słowiński M., Dasiewicz K., Application of computer vision systems for estimation of fat content in poultry meat. Food Control, 2011, 22(8): 1424-1427. https://doi.org/10.1016/j.foodcont.2011.03.002
Di Rosa A.R., Leone F. Application of Electronic Nose Systems on Animal-Source Food: An Overview. In: Electronic Nose Technologies and Advances in Machine Olfaction, (Y.Albastaki, F. Albalooshi Eds.), IGI Global, 2018, pp. 151-174. ISBN 13: 978-1522-53862-2; ISBN 10: 152-253-862-3; EISBN 13: 978-15225-3863-9 https://doi.org/10.4018/978-1-5225-3862-2.ch008
Dutta G. and Goswami P. Application of drone in agriculture: A review. International Journal of Chemical Studies, 2020, 8(5): 181-187. https://doi.org/10.22271/chemi.2020.v8.i5d.10529
Fracarolli J.A., Pavarin F.F.A., Castro W., Blasco J. Computer vision applied to food and agricultural products. Revista Ciência Agronômica, 2021, 51(1): e20207749. https://doi.org/10.5935/1806-6690.20200087
Gonzalez Viejo C., Fuentes S., Godbole A.M., Widdicombe B. Unnithan R.R. Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality. Sensors and Actuators B-Chemical, 2020, 308(4): 127688. https://doi.org/10.1016/j.snb.2020.127688
Gupta K., Rakesh N. IoT-based Solution for Food Adulteration. In: Proceedings of First International Conference on Smart System, Innovations and Computing (A.K. Somani, S. Srivastava, A. Mundra, S.Rawat Eds.). Springer Singapore, SSIC 2017, Jaipur, India. 2018, рр. 9-18, Print Hardcover ISBN: 978-981-10-5827-1; Softcover ISBN: 978-981-13-5502-8; eBook ISBN: 978-981-10-5828-8, Series Title: Smart Innovation, Systems and Technologies Series. E-ISSN: 2190-3026, https://doi.org/10.1007/978-981-10-5828-8
Ibrahim S., Deb S.R., Ahmed M.F., Ahmed F. Impact of Using Electricity for Rural Warehouse Moisture Control System, Proc. 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2022, pp. 495-500. https://doi.org/10.1109/ICCMC53470.2022.9753736
Imankul M.N. (2019). RFID Systems Applications: Current Issues and Trends. In Innovative Development of Science and Education (G.Y. Gulyaev Ed.). Science and Education, Ural Federal University, Penza. 2019, рр. 158-166, Print ISBN: 978-5-907160-55-2 [in Russian]
Kakani V., Nguyen V.H., Kumar B.P., Kim H., Pasupuleti V.R. A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2020, 2(12): 100033. https://doi.org/10.1016/j.jafr.2020.100033
Kassanuk T., Phasinam K. A comprehensive review on the implementation of technological systems, standards, and interfaces used in the food and agriculture industries. Materials Today: Proceedings, 2022, 51(8): 2268-2271. https://doi.org/10.1016/j.matpr.2021.11.396
Kassanuk T., Phasinam K. Application of data mining and machine learning in food and agriculture industry towards precision agriculture. AIP Conference Proceedings, 2023, 2587(1): 050013. https://doi.org/10.1063/5.0150472
Kong Y.G., Hu X.F., Chen T.J., You Z.T. Research and development of quality traceability system based on intelligent services for bee products. Applied Mechanics and Materials, 2013, 302(2): 694-699. https://doi.org/10.4028/www.scientific.net/AMM.302.694
Kumar I., Rawat J., Mohd N., Husain S. Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021(7). https://doi.org/10.1155/2021%2F4535567
Lee C.H., Chen I.T., Yang H.C., Chen Y.J. An AI-powered electronic nose system with fingerprint extraction for aroma recognition of coffee beans. Micromachines, 2022, 13(8): 1313. https://doi.org/10.3390/mi13081313
Lee J. How to Establish Industrial AI Technology and Capability. In: Industrial AI. Applications with Sustainable Performance (J. Lee Ed.). Springer Singapore, Shanghai Jiao Tong University Press, 2020, pp. 119-159. eBook ISBN: 978-981-15-2144-7. https://doi.org/10.1007/978-981-15-2144-7
Lee J., Singh J., Azamfar M. Industrial artificial intelligence, Cornell University arXiv:1908.02150 Computer Science - Computers and Society, 2019, [v3] Tue, 22 Oct: 2150. https://doi.org/10.48550/arXiv.1908.02150
Li H., Wang Y., Zhang J., Li X., Wang J., Yi S., Zhu W., Xu Y., Li J. Prediction of the freshness of horse mackerel (Trachurus japonicus) using E-nose, E-tongue, and colorimeter based on biochemical indexes analyzed during frozen storage of whole fish. Food Chemistry, 2023, 402(2): 134325. https://doi.org/10.1016/j.foodchem.2022.134325
Li Z., Liu G., Liu L., Lai X., Xu G. IoT-based tracking and tracing platform for prepackaged food supply chain. Industrial Management & Data Systems, 2017, 117(9): 1906-1916. https://doi.org/10.1108/IMDS-11-2016-0489
Liu Y., Han W., Zhang Y., Li L., Wang J., Zheng L. An Internet-of-Things solution for food safety and quality control: A pilot project in China. Journal of Industrial Information Integration, 2016, 3(9): 1-7. https://doi.org/10.1016/j.jii.2016.06.001
Loutfi A., Coradeschi S., Kumar G., Shankar P., Bosco J., Rayappan B. Electronic noses for food quality: A review. Journal of Food Engineering, 2015, 144(1): 103-111. https://doi.org/10.1016/j.jfoodeng.2014.07.019
Ma T., Chang Z., Zhang N. Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review. Journal of Cancer Research and Clinical Oncology, 2024, 150(8): 401. https://doi.org/10.1007/s00432-024-05925-w
Mahanti N.K., Shivashankar S., Chhetri K.B., Kumar A., Rao B.B., Aravind J., Swami D. Enhancing food authentication through e-nose and e-tongue technologies: Current trends and future directions. Trends in Food Science & Technology, 2024, 150(8): 104574. https://doi.org/10.1016/j.tifs.2024.104574
Martı́nez-Máñez R., Soto J., Garcia-Breijo E., Gil L., Ibáñez J., Llobet E. An “electronic tongue” design for the qualitative analysis of natural waters. Sensors and Actuators B: Chemical, 2005, 104(2): 302-307. https://doi.org/10.1016/j.snb.2004.05.022
Maryam, Khero R., Ali I., Zareef U., Haque A., Faizan A. A systematic review on the early detection of oral cancer using artificial intelligence and electronic tongue technology. International Annals of Health Sciences, 2024, 1(2): 28-33. https://doi.org/10.69491/1gc2xx82
Mironov K. Certify your identity in two steps for Web applications via RFID and SMS, Scientific Work at Ruse University, 2013, 52(6.1): 98-102. Available at: https://conf.uni-ruse.bg/bg/docs/cp13/6.1/6.1-17.pdf [in Bulgarian]
Mohammad-Razdari A., Rousseau D., Bakhshipour A., Taylor S., Poveda J., Kiani H. Recent advances in E-monitoring of plant diseases. Biosensors and Bioelectronics, 2022, 201(4): 113953. https://doi.org/10.1016/j.bios.2021.113953
Momtaz M., Bubli S.Y., Khan M.S. Mechanisms and health aspects of food adulteration: A comprehensive review. Foods, 2023, 12(1): 199. https://doi.org/10.3390/foods12010199
Nirenjena S., BalaSubramanian D.L., Monisha M. Advancement in monitoring the food supply chain management using IoT. International Journal of Pure and Applied Mathematics, 2018, 119(15b): 3123-3131. Available at: https://acadpubl.eu/hub/2018-119-15/2/331.pdf
Peris M., Escuder-Gilabert L., Electronic noses and tongues to assess food authenticity and adulteration. Trends in Food Science & Technology, 2016, 58(12): 40-54. https://doi.org/10.1016/j.tifs.2016.10.014
Rajakumar G., Kumar T.A., Samuel T.A., Kumaran E.M. IoT based milk monitoring system for detection of milk adulteration. International Journal of Pure and Applied Mathematics, 2018, 118(9), 21-32. Available at: https://acadpubl.eu/jsi/2018-118-7-9/articles/9/4.pdf
Raviya K.H., Chavda M.K. Importance of artificial intelligence in industry. Turkish Online Journal of Qualitative Inquiry (TOJQI), 2021, 12(7): 14113-14118. Available at: https://www.tojqi.net/index.php/journal/article/view/8061/5731
Reddy P., Babu M. Energy efficient cluster head selection for internet of things. New Review of Information Networking, 2017, 22(1): 54-70. https://doi.org/10.1080/13614576.2017.1297734
Sobrino-Gregorio L., Bataller R., Soto J., Escriche I. Monitoring honey adulteration with sugar syrups using an automatic pulse voltametric electronic tongue. Food Control, 2018, 91(9): 254-260. https://doi.org/10.1016/j.foodcont.2018.04.003
Wang M., Chen Y. Electronic nose and its application in the food industry: a review. European Food Research and Technology, 2024, 250(10): 21-67. https://doi.org/10.1007/s00217-023-04381-z
Wang S., Zhang Q., Liu C., Wang Z., Gao J., Yang X., Lan Y. Synergetic application of an E-tongue, E-nose and E-eye combined with CNN models and an attention mechanism to detect the origin of black pepper. Sensors and Actuators A: Physical, 2023, 357(8): 114417. https://doi.org/10.1016/j.sna.2023.114417
Xu M., Wang J., Zhu L. The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food Chemistry, 2019, 289(8): 482-489. https://doi.org/10.1016/j.foodchem.2019.03.080
Zniber M., Vahdatiyekta P., Huynh T.P. Analysis of urine using electronic tongue towards non-invasive cancer diagnosis. Biosensors and Bioelectronics, 2023, 219(1): 114810. https://doi.org/10.1016/j.bios.2022.114810
Ali M.M., Hashim N., Aziz S.A., Lasekan O. Principles and recent advances in electronic nose for quality inspection of agricultural and food products. Trends in Food Science & Technology, 2020, 99(5): 1-10. https://doi.org/10.1016/j.tifs.2020.02.028
Allegra D., Battiato S., Ortis A., Urso S., Polosa R. A review on food recognition technology for health applications. Health Psychology Research, 2020, 8(3): 9297. https://doi.org/10.4081/hpr.2020.9297
Amugongo L.M., Kriebitz A., Boch A., Lütge C. Mobile computer vision-based applications for food recognition and volume and calorific estimation: A systematic review. Healthcare, 2023, 11(1): 59. https://doi.org/10.3390/healthcare11010059
Attallah O., Morsi I. An electronic nose for identifying multiple combustible/harmful gases and their concentration levels via artificial intelligence. Measurement, 2022, 199(8): 111458. https://doi.org/10.1016/j.measurement.2022.111458
Aung M.M., Chang Y.S. Traceability in a food supply chain: Safety and quality perspectives. Food Control, 2014, 39(5): 172-184. https://doi.org/10.1016/j.foodcont.2013.11.007
Baietto M., Wilson A.D. Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors, 2015, 15(1): 899-931. https://doi.org/10.3390/s150100899
Bouzembrak Y., Klüche M., Gavai A., Marvin H.J.P. Internet of Things in food safety: Literature review and a bibliometric analysis. Trends in Food Science & Technology, 2019, 94(12): 54-64. https://doi.org/10.1016/j.tifs.2019.11.002
Buratti S., Malegori C., Benedetti S., Oliveri P., Giovanelli G. E-nose, e-tongue and e-eye for edible olive oil characterization and shelf life assessment: A powerful data fusion approach. Talanta, 2018, 182(5): 131-141. https://doi.org/10.1016/j.talanta.2018.01.096
Chen H., Huo D., Zhang J. Gas recognition in e-nose system: A review. IEEE Transactions on Biomedical Circuits and Systems, 2022, 16(2): 169-184. https://doi.org/10.1109/TBCAS.2022.3166530
Chmiel M., Słowiński M., Dasiewicz K., Application of computer vision systems for estimation of fat content in poultry meat. Food Control, 2011, 22(8): 1424-1427. https://doi.org/10.1016/j.foodcont.2011.03.002
Di Rosa A.R., Leone F. Application of Electronic Nose Systems on Animal-Source Food: An Overview. In: Electronic Nose Technologies and Advances in Machine Olfaction, (Y.Albastaki, F. Albalooshi Eds.), IGI Global, 2018, pp. 151-174. ISBN 13: 978-1522-53862-2; ISBN 10: 152-253-862-3; EISBN 13: 978-15225-3863-9 https://doi.org/10.4018/978-1-5225-3862-2.ch008
Dutta G. and Goswami P. Application of drone in agriculture: A review. International Journal of Chemical Studies, 2020, 8(5): 181-187. https://doi.org/10.22271/chemi.2020.v8.i5d.10529
Fracarolli J.A., Pavarin F.F.A., Castro W., Blasco J. Computer vision applied to food and agricultural products. Revista Ciência Agronômica, 2021, 51(1): e20207749. https://doi.org/10.5935/1806-6690.20200087
Gonzalez Viejo C., Fuentes S., Godbole A.M., Widdicombe B. Unnithan R.R. Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality. Sensors and Actuators B-Chemical, 2020, 308(4): 127688. https://doi.org/10.1016/j.snb.2020.127688
Gupta K., Rakesh N. IoT-based Solution for Food Adulteration. In: Proceedings of First International Conference on Smart System, Innovations and Computing (A.K. Somani, S. Srivastava, A. Mundra, S.Rawat Eds.). Springer Singapore, SSIC 2017, Jaipur, India. 2018, рр. 9-18, Print Hardcover ISBN: 978-981-10-5827-1; Softcover ISBN: 978-981-13-5502-8; eBook ISBN: 978-981-10-5828-8, Series Title: Smart Innovation, Systems and Technologies Series. E-ISSN: 2190-3026, https://doi.org/10.1007/978-981-10-5828-8
Ibrahim S., Deb S.R., Ahmed M.F., Ahmed F. Impact of Using Electricity for Rural Warehouse Moisture Control System, Proc. 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2022, pp. 495-500. https://doi.org/10.1109/ICCMC53470.2022.9753736
Imankul M.N. (2019). RFID Systems Applications: Current Issues and Trends. In Innovative Development of Science and Education (G.Y. Gulyaev Ed.). Science and Education, Ural Federal University, Penza. 2019, рр. 158-166, Print ISBN: 978-5-907160-55-2 [in Russian]
Kakani V., Nguyen V.H., Kumar B.P., Kim H., Pasupuleti V.R. A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2020, 2(12): 100033. https://doi.org/10.1016/j.jafr.2020.100033
Kassanuk T., Phasinam K. A comprehensive review on the implementation of technological systems, standards, and interfaces used in the food and agriculture industries. Materials Today: Proceedings, 2022, 51(8): 2268-2271. https://doi.org/10.1016/j.matpr.2021.11.396
Kassanuk T., Phasinam K. Application of data mining and machine learning in food and agriculture industry towards precision agriculture. AIP Conference Proceedings, 2023, 2587(1): 050013. https://doi.org/10.1063/5.0150472
Kong Y.G., Hu X.F., Chen T.J., You Z.T. Research and development of quality traceability system based on intelligent services for bee products. Applied Mechanics and Materials, 2013, 302(2): 694-699. https://doi.org/10.4028/www.scientific.net/AMM.302.694
Kumar I., Rawat J., Mohd N., Husain S. Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021(7). https://doi.org/10.1155/2021%2F4535567
Lee C.H., Chen I.T., Yang H.C., Chen Y.J. An AI-powered electronic nose system with fingerprint extraction for aroma recognition of coffee beans. Micromachines, 2022, 13(8): 1313. https://doi.org/10.3390/mi13081313
Lee J. How to Establish Industrial AI Technology and Capability. In: Industrial AI. Applications with Sustainable Performance (J. Lee Ed.). Springer Singapore, Shanghai Jiao Tong University Press, 2020, pp. 119-159. eBook ISBN: 978-981-15-2144-7. https://doi.org/10.1007/978-981-15-2144-7
Lee J., Singh J., Azamfar M. Industrial artificial intelligence, Cornell University arXiv:1908.02150 Computer Science - Computers and Society, 2019, [v3] Tue, 22 Oct: 2150. https://doi.org/10.48550/arXiv.1908.02150
Li H., Wang Y., Zhang J., Li X., Wang J., Yi S., Zhu W., Xu Y., Li J. Prediction of the freshness of horse mackerel (Trachurus japonicus) using E-nose, E-tongue, and colorimeter based on biochemical indexes analyzed during frozen storage of whole fish. Food Chemistry, 2023, 402(2): 134325. https://doi.org/10.1016/j.foodchem.2022.134325
Li Z., Liu G., Liu L., Lai X., Xu G. IoT-based tracking and tracing platform for prepackaged food supply chain. Industrial Management & Data Systems, 2017, 117(9): 1906-1916. https://doi.org/10.1108/IMDS-11-2016-0489
Liu Y., Han W., Zhang Y., Li L., Wang J., Zheng L. An Internet-of-Things solution for food safety and quality control: A pilot project in China. Journal of Industrial Information Integration, 2016, 3(9): 1-7. https://doi.org/10.1016/j.jii.2016.06.001
Loutfi A., Coradeschi S., Kumar G., Shankar P., Bosco J., Rayappan B. Electronic noses for food quality: A review. Journal of Food Engineering, 2015, 144(1): 103-111. https://doi.org/10.1016/j.jfoodeng.2014.07.019
Ma T., Chang Z., Zhang N. Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review. Journal of Cancer Research and Clinical Oncology, 2024, 150(8): 401. https://doi.org/10.1007/s00432-024-05925-w
Mahanti N.K., Shivashankar S., Chhetri K.B., Kumar A., Rao B.B., Aravind J., Swami D. Enhancing food authentication through e-nose and e-tongue technologies: Current trends and future directions. Trends in Food Science & Technology, 2024, 150(8): 104574. https://doi.org/10.1016/j.tifs.2024.104574
Martı́nez-Máñez R., Soto J., Garcia-Breijo E., Gil L., Ibáñez J., Llobet E. An “electronic tongue” design for the qualitative analysis of natural waters. Sensors and Actuators B: Chemical, 2005, 104(2): 302-307. https://doi.org/10.1016/j.snb.2004.05.022
Maryam, Khero R., Ali I., Zareef U., Haque A., Faizan A. A systematic review on the early detection of oral cancer using artificial intelligence and electronic tongue technology. International Annals of Health Sciences, 2024, 1(2): 28-33. https://doi.org/10.69491/1gc2xx82
Mironov K. Certify your identity in two steps for Web applications via RFID and SMS, Scientific Work at Ruse University, 2013, 52(6.1): 98-102. Available at: https://conf.uni-ruse.bg/bg/docs/cp13/6.1/6.1-17.pdf [in Bulgarian]
Mohammad-Razdari A., Rousseau D., Bakhshipour A., Taylor S., Poveda J., Kiani H. Recent advances in E-monitoring of plant diseases. Biosensors and Bioelectronics, 2022, 201(4): 113953. https://doi.org/10.1016/j.bios.2021.113953
Momtaz M., Bubli S.Y., Khan M.S. Mechanisms and health aspects of food adulteration: A comprehensive review. Foods, 2023, 12(1): 199. https://doi.org/10.3390/foods12010199
Nirenjena S., BalaSubramanian D.L., Monisha M. Advancement in monitoring the food supply chain management using IoT. International Journal of Pure and Applied Mathematics, 2018, 119(15b): 3123-3131. Available at: https://acadpubl.eu/hub/2018-119-15/2/331.pdf
Peris M., Escuder-Gilabert L., Electronic noses and tongues to assess food authenticity and adulteration. Trends in Food Science & Technology, 2016, 58(12): 40-54. https://doi.org/10.1016/j.tifs.2016.10.014
Rajakumar G., Kumar T.A., Samuel T.A., Kumaran E.M. IoT based milk monitoring system for detection of milk adulteration. International Journal of Pure and Applied Mathematics, 2018, 118(9), 21-32. Available at: https://acadpubl.eu/jsi/2018-118-7-9/articles/9/4.pdf
Raviya K.H., Chavda M.K. Importance of artificial intelligence in industry. Turkish Online Journal of Qualitative Inquiry (TOJQI), 2021, 12(7): 14113-14118. Available at: https://www.tojqi.net/index.php/journal/article/view/8061/5731
Reddy P., Babu M. Energy efficient cluster head selection for internet of things. New Review of Information Networking, 2017, 22(1): 54-70. https://doi.org/10.1080/13614576.2017.1297734
Sobrino-Gregorio L., Bataller R., Soto J., Escriche I. Monitoring honey adulteration with sugar syrups using an automatic pulse voltametric electronic tongue. Food Control, 2018, 91(9): 254-260. https://doi.org/10.1016/j.foodcont.2018.04.003
Wang M., Chen Y. Electronic nose and its application in the food industry: a review. European Food Research and Technology, 2024, 250(10): 21-67. https://doi.org/10.1007/s00217-023-04381-z
Wang S., Zhang Q., Liu C., Wang Z., Gao J., Yang X., Lan Y. Synergetic application of an E-tongue, E-nose and E-eye combined with CNN models and an attention mechanism to detect the origin of black pepper. Sensors and Actuators A: Physical, 2023, 357(8): 114417. https://doi.org/10.1016/j.sna.2023.114417
Xu M., Wang J., Zhu L. The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food Chemistry, 2019, 289(8): 482-489. https://doi.org/10.1016/j.foodchem.2019.03.080
Zniber M., Vahdatiyekta P., Huynh T.P. Analysis of urine using electronic tongue towards non-invasive cancer diagnosis. Biosensors and Bioelectronics, 2023, 219(1): 114810. https://doi.org/10.1016/j.bios.2022.114810
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How to Cite
ZHEKOVA, Mariya.
Application of artificial intelligence in the food industry.
Food Science and Applied Biotechnology, [S.l.], v. 8, n. 1, p. 112-122, mar. 2025.
ISSN 2603-3380.
Available at: <https://www.ijfsab.com/index.php/fsab/article/view/477>. Date accessed: 20 apr. 2025.
doi: https://doi.org/10.30721/fsab2025.v8.i1.477.