Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 7 Sayı: 15, 622 - 632, 29.12.2024

Öz

Kaynakça

  • Abiyeva, R.H., Uyarb, K., Ilhanc, Ü., Imanovd, E. (2016). Assessment of food security risk level using type 2 fuzzy system. 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29-30 August, Vienna, Austria. Procedia Computer Science 102: 547 – 554.
  • Addanki, M., Patra, P., Kandra, P. (2022). Recent advances and applications of artificial intelligence and related technologies in the food industry. Applied Food Research, 2(2), Article 100126.
  • Akgül, N.A., Akgül, F.Y., Tuna, D. (2014). Bulanık Mantık ile Kefir Üretiminin Modellenmesi. Türk Tarım Gıda Bilim ve Teknoloji Dergisi, 2(6): 251-255.
  • Akoka, J., Comynfi Wattiau, I., Laoufi, N. (2017). Research on Big Data—A systematic mapping study. Comput. Stand. Interfaces, 54, 105–115.
  • Amore, A., Philip, S. (2023) Artificial intelligence in food biotechnology: trends and perspectives. Front. Ind. Microbiology, 1,1255505.
  • Basak, S. (2018). The use of fuzzylogic to determine the concentration of betel leaf essential oil and itspotency as a juice preservative. FoodChemistry 240,1113–1120.
  • Bouzembrak, Y., Marvin, H. J. P. (2016). Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling. Food Control, 61, 180–187.
  • Chang, W. T., Yeh, Y. P., Wu, H. Y., Lin, Y. F., Dinh, T. S., Lian, I. B. (2020). An automated alarm system for food safety by using electronic invoices. PloS One 24, 15(1), e0228035.
  • Chidinma-Mary-Agbai. (2020). Application of artificial intelligence (AI) in food industry. GSC Biological and Pharmaceutical Sciences, 13(1), 171–178.
  • Chung, C., Chen, H.,Tıng, C. (2010). Grey prediction fuzzy control for pH processes in the food industry. Journal of Food Engineering, 96, 575–582.
  • Cimander, C., Carlssn,M., Mandenıus, C.F. (2002). Sensor fusion for online monitoring of yoghurt fermentation. Journal of Biotechnology, 99(3), 237 – 48.
  • Cimpoiu, C., Cristea, V.M., Hosu, A., Sandru M., Seserman L. (2011). Antioxidant activity prediction and classification of some teas using artificial neural networks. Food Chemistry 127: 1323–1328.
  • Cote, M., Lamarche, B. (2021). Artificial intelligence in nutrition research: Perspectives on current and future applications. Appl. Physiol. Nutr. Metabol. 1(15), 1–8.
  • Danneskiold-Samsøe, N. B., de Freitas Queiroz Barros, H. D., Santos, R., Bicas, J. L., Cazarin, C. B. B., Madsen, L. (2019). Interplay between food and gut microbiota in health and disease. Food Res. Int. 115, 23–31.
  • Demir, N., Balaban, M.O., Brecht, J.K., Sıms, C.A., Gülay, M.S. (2002). Objective Quality Assessment of Modified Atmosphere Stored Zucchini Slices Using Electronic Nose, Machine Vision, and Instron, 15th-19th June, pp181.
  • Demiral, M.F. (2013). Bulanık Doğrusal Programlama İle Süt Endüstrisinde Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 18(2), 373-397.
  • Ding, H., Tian, J., Yu, W., Wilson, D.I., Young, B.R., Cui, X., Xin, X., Wang, Z., Li, W. (2023). The Application of Artificial Intelligence and Big Data in the Food Industry. Foods, 12, 4511.
  • FDA. (2022). Import screening pilot unleashes the power of data and leverages artificial intelligence. US Food and Drug Administration. Available at: https://www. fda.gov/news-events/fda-voices/import-screening-pilot-unleashes-power-data-andleverages-artificial- intelligence (Accessed 22 October, 2024).
  • Gevrekçi, Y.E., Yeğenoğlu, D., Akbağ,Y., Sesli, M. (2011). Yapay sinir ağlarının tarımsal alanda kullanımı. E.Ü. Ziraat Fak. Dergi, 48(1), 71-76.
  • Gobbi, E., Falasconi, M., Zambotti, G., Sberveglieri, V., Pulvirenti, A., and Sberveglieri, G. (2015). Rapid diagnosis of Enterobacteriaceae in vegetable soups by a metal oxide sensor based electronic nose. Sensors Actuators B: Chemistry, 207 (B), 1104– 1113.
  • Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass.
  • Gonçalves, E.C., Minim, L.A., Coimbra, J.S.R., Minim,V.P.R. (2005). Modeling sterilization process of canned foods using artificial neural networks. Chemical Engineering and Processing, 44,1269-1276. 1   1. m.scirp.org m.scirp.org
  • Gowen, A., O'Donnell, C. P., Cullen, P. J., Downey, G., Frias, J. M. (2007). Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci. Tech., 18 (12), 590–598.
  • Gr´ac, S., Beno, P., Duchon, F., Dekan, M., T¨olgyessy, M. (2020). “Automated detection of multifirotor UAVs using a machinelearning approach,” Applied System Innovation, 3, 29.
  • Grzesıak, W., LacroiX, R., WojcıK, J., Blaszczyk, P. (2003). A comparison of neural network and multipleregressionpredictions for 305-day lactation yield using partiallactation records. Can. J. Anim. Sci., 83 (2): 307-310.
  • Guıllaume, S., Charnomordic, B. (2000). Knowledge discovery for control purposes in food industry data bases. Fuzzy Sets and Systems, 122,487–497.
  • Hussıan, M.A., Shafıur,M., Rahman, Ng, C.W. (2002). Prediction of poresformation (porosity) in foods during drying: generic models by the use of hybrid neural network. Journal of Food Engineering, 51, 239-248.
  • Jahns, G., Nielsen,H.M., Paul, W. (2001). Measuring İmage Analysis Attributes and Modelling Fuzzy Consumer Aspects For Tomato Quality Grading. Computers and Electronics in Agriculture, 31,17–29.
  • Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, e100033.
  • Kavas, G. (2012). Gıdalarda yapay sinir ağları ve bulanık mantık.http://www.dunyagida. com.tr/kose-yazisi/gidalarda-yapay-sinir-aglari-ve-bulanikmantik/1043.
  • Khan, R. (2021). Artificial Intelligence and Machine Learning in Food Industries: A Study. J Food Chem Nanotechnol, 7(3), 60-67.
  • Kler, R. (2023). Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach (Retraction of Vol 2022, art no 8521236, ).
  • Kler, R., Elkady, G., Rane, K., Singh, A., Hossain, M. S., Malhotra, D. 2022. Machine learning and artificial intelligence in the food industry: A sustainable approach. Journal of Food Quality. 2022(1), 8521236.
  • Kumar, I., Rawat, J., Mohd, N.,  Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021(1), 4535567.
  • Liao, K., Paulsen, M.R., Reid, J.F., Ni B.C, Bonıfacio-Maghirang, E.P. (1993). Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of the ASAE, 36(6), 1949–1953.
  • Linko, S. (1998). Expert systems - what can they do for the food industry? Trends in Food Science and Technology, 9(1), 3–12.
  • Livingstone, K. M., Ramos-Lopez, O., Pé russe, L., Kato, H., Ordovas, J. M., do Martınez, J. A. (2022). Precision nutrition: A review of current approaches and future ́ endeavors. Trends Food Science and Technology,128 (10184).
  • Lupolova, N., Dallman, T. J., Holden, N. J.,  Gally, D. L. (2017). Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli. Microbial genomics, 3(10), e000135.
  • Luzuriaga D., Balaban M., Yeralan S. 1997. Analysis of Visual Quality Attributes of White Shrimp by Machine Vision, Journal of Food Science, 62(1), 113-118.
  • Mıttal, G.S., Zhang, J. (2000). Prediction of freezing time for food products using a neural network. Food Research International, 33, 557-562.
  • Movagharnejad, K., Nıkzad, M. (2007). Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59,78-85.
  • Nayak, J., Vakula, K., Dinesh, P., Naik, B.,  Pelusi, D. (2020). Intelligent food processing: Journey from artificial neural network to deep learning. Computer Science Review, 38, Article 100297.
  • Nikolola-Alexieva, V., Valeva, K., Pashev, S. (2024). Artificial intelligence in the food industry. In BIO Web of Conferences (Vol. 102, p. 04002). EDP Sciences.
  • Oztemel, E., 2006. Artificial neural networks. Papatya Publishing, Istanbul.
  • P´erez-Correa, J. R., Zaror, C. A. (1993). Recent advances in process control and their potential applications to food processing. Food Control, 4(4), 202–209.
  • Razmi-Rad, E., Ghanbarzadeh, B., Mousavi, S.M, Emam-Djomeh, Z., Khazaei, J. (2007). Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. Journal of Food Engineering, 81, 728-734.  
  • Ropodi, A. I., Panagou, E. Z., Nychas, G. J. E. (2016). Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends in Food Science and Technology, 50, 11–25.
  • Ruan, R., Almaer, S., Zhang, J. (1995). Prediction of Dough Rheological Properties Using Neural Networks. Cereal Chem., 72(3), 308-311.
  • Rywotycki, R. (2003). Food frying process control system. Journal of Food Engineering, 59, 339–342.
  • Saha, D., Manickavasagan, A. (2021). Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science, 4, 28–44.
  • Samani, B.H., HouriJafari, H., Zareiforoush, H. (2017). Artificial neural networks, genetic algorithm and response surface methods: the energy consumption of food and beverage industries in Iran. Journal of AI and Data Mining, 5(1), 79-88.
  • Schroeder, R. (2016).Big data business models: Challenges and opportunities. Cogent Soc. Sci., 2, 1166924.
  • Shahin, M.A., Tollner, E.W., Mcclendon, R.W. (2001). Artificial intelligence classifiers for sorting apples based on water core. Journal of Agricultural Engineering Research, 79(3),: 265–274.
  • Sofu, A., Ekinci Kitiş, F.Y. (2005). Predicting survival rate of Yersinia Enterocolitica in Turkish feta cheese during maturation and storage by using fuzzy logic modelling. 1st International Food and Nutrition Congress- Food Safety, İstanbul.
  • Sofu, A. (2006). Yoğurtların depolama esnasında mikrobiyal ve kimyasal değişimlerinin bilgisayarlı görüntüleme sistemiyle belirlenmesi ve elde edilen verilerin yapay sinir ağlarıyla değerlendirilmesi. Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Gıda Mühendisliği Anabilim Dalı, yüksek lisans tezi, Isparta, 98s.
  • Sofu, B.A., Demir, N., Ekinci, F.Y. (2007). Gıda Bilimi Ve Teknolojisi Alanında Yapay Zeka Uygulamaları. GIDA 32 (2), 93-99.
  • Sun, D.W., Brosnan, T. (2003). Pizza quality evaluation using computervision—Part 1 Pizza base and sauce spread. Journal of Food Engineering, 57, 81–89.
  • Tan, J.,  Xu, J. (2020). Applications of electronic nose (e-nose) and electronic tongue (etongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture, 4, 104–115.
  • Taner, A., Tekgüler,A., Sauk, H., Demirel, B. (2012). Yulaf Çeşitlerinin Yapay Sinir Ağları Kullanılarak Sınıflandırılması. 27. Tarımsal Mekanizasyon Ulusal Kongresi, 5-7 Eylül 2012, Samsun, 519-529s.
  • Tarallo, E., Akabane, G. K., Shimabukuro, C. I., Mello, J., Amancio, D. (2019). Machine learning in predicting demand for fast-moving consumer goods: An exploratory research. IFAC-PapersOnLine, 52(13), 737–742.
  • Tollner, E.W., Shahin, M.A., Maw, B.W., Gitaitis, R.D., Summer, D.R. (1999). Classification of onions based on internal defects using imaging processing and neural network techniques, In 1999 ASAE Annual International Meeting, Paper No, 993165, St, Joseph, Michigan, USA: ASAE.
  • Torrecilla, J.S, Otero L, Sanz, P.D. (2005). Artificial neural networks: a promising tool to design and optimize high-pressure food processes. Journal of Food Engineering, 69,299-306.  
  • Trıpathy, P.P., Kumar,S. (2009). Neural network approach for food Temperature prediction during solar drying. International Journal of ThermalSciences, 48,1452-1459.
  • Wang, H., Ceylan, K. H., Qiu, Y., Bai, B., Zhang, Y., Jin, Y. (2020). Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light Sci. Appl. 9, 118.
  • Wang, X., Bouzembrak, Y., Lansink, A.G.J.M.O., van der Fels-Klerx, H.J. (2022). Application of machine learning to the monitoring and prediction of food safety: A review. Comprehensive Reviews in Food Science and Food Safety, 21(1), 416–434.
  • Wang, S. S., Lin, P., Wang, C. C., Lin, Y. C., and Tung, C. W. (2023). Machine learning for predicting chemical migration from food packaging materials to foods. Food and Chemical Toxicology, 178, 113942.
  • Yin Y-G, Ding, Y. (2009). A close to real-time prediction method of total coliform bacteria in foods based on image identification technology and artificial neural network. Food Research International, 42,191-199.
  • Yongnıan, N., Chao, L.(1999). Artificial neural networks and multivariate calibration for spectrophotometric differential kinetic determinations of food antioxidants. Analytica Chimica Acta, 396,: 221–230.
  • Zhu, L., Spachos, P., Pensini, E., Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science, 4, 233–249.
  • Zhu, N., Wang, K., Zhang, S., Zhao, B., Yang, J., Wang, S.W. (2021). Application of artificial neural networks to predict multiple quality of dry-cured ham based on protein degradation. Food Chemistry, 344, 128586.

ARTIFICIAL INTELLIGENCE IN THE FOOD INDUSTRY

Yıl 2024, Cilt: 7 Sayı: 15, 622 - 632, 29.12.2024

Öz

Nutrition is vital for human survival. It is essential to reduce food waste, streamline the supply chain, and improve food logistics, delivery, and safety. Artificial intelligence and machine learning significantly contribute to achieving these objectives. Artificial Intelligence (AI) refers to the development of intelligent systems capable of doing activities that typically require human intelligence. In the food industry, it is seen that solution tools such as ANN (Neural Network), Fuzzy Logic and Genetic Algorithm are widely used in solving problems and performing their analyses. Artificial intelligence has been employed in food science and technology for classification, process modeling and optimization, quality control of food, prediction of dough rheological properties, classification of wine based on anthocyanin content, forecasting the maximum or minimum temperature attained in a sample post-pressurization, determining the time required for thermal re-equilibration in high-pressure food processing systems, and classifying fruits and vegetables according to their morphological characteristics. This article discusses artificial intelligence applications in the food industry and manufacturing.

Kaynakça

  • Abiyeva, R.H., Uyarb, K., Ilhanc, Ü., Imanovd, E. (2016). Assessment of food security risk level using type 2 fuzzy system. 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29-30 August, Vienna, Austria. Procedia Computer Science 102: 547 – 554.
  • Addanki, M., Patra, P., Kandra, P. (2022). Recent advances and applications of artificial intelligence and related technologies in the food industry. Applied Food Research, 2(2), Article 100126.
  • Akgül, N.A., Akgül, F.Y., Tuna, D. (2014). Bulanık Mantık ile Kefir Üretiminin Modellenmesi. Türk Tarım Gıda Bilim ve Teknoloji Dergisi, 2(6): 251-255.
  • Akoka, J., Comynfi Wattiau, I., Laoufi, N. (2017). Research on Big Data—A systematic mapping study. Comput. Stand. Interfaces, 54, 105–115.
  • Amore, A., Philip, S. (2023) Artificial intelligence in food biotechnology: trends and perspectives. Front. Ind. Microbiology, 1,1255505.
  • Basak, S. (2018). The use of fuzzylogic to determine the concentration of betel leaf essential oil and itspotency as a juice preservative. FoodChemistry 240,1113–1120.
  • Bouzembrak, Y., Marvin, H. J. P. (2016). Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling. Food Control, 61, 180–187.
  • Chang, W. T., Yeh, Y. P., Wu, H. Y., Lin, Y. F., Dinh, T. S., Lian, I. B. (2020). An automated alarm system for food safety by using electronic invoices. PloS One 24, 15(1), e0228035.
  • Chidinma-Mary-Agbai. (2020). Application of artificial intelligence (AI) in food industry. GSC Biological and Pharmaceutical Sciences, 13(1), 171–178.
  • Chung, C., Chen, H.,Tıng, C. (2010). Grey prediction fuzzy control for pH processes in the food industry. Journal of Food Engineering, 96, 575–582.
  • Cimander, C., Carlssn,M., Mandenıus, C.F. (2002). Sensor fusion for online monitoring of yoghurt fermentation. Journal of Biotechnology, 99(3), 237 – 48.
  • Cimpoiu, C., Cristea, V.M., Hosu, A., Sandru M., Seserman L. (2011). Antioxidant activity prediction and classification of some teas using artificial neural networks. Food Chemistry 127: 1323–1328.
  • Cote, M., Lamarche, B. (2021). Artificial intelligence in nutrition research: Perspectives on current and future applications. Appl. Physiol. Nutr. Metabol. 1(15), 1–8.
  • Danneskiold-Samsøe, N. B., de Freitas Queiroz Barros, H. D., Santos, R., Bicas, J. L., Cazarin, C. B. B., Madsen, L. (2019). Interplay between food and gut microbiota in health and disease. Food Res. Int. 115, 23–31.
  • Demir, N., Balaban, M.O., Brecht, J.K., Sıms, C.A., Gülay, M.S. (2002). Objective Quality Assessment of Modified Atmosphere Stored Zucchini Slices Using Electronic Nose, Machine Vision, and Instron, 15th-19th June, pp181.
  • Demiral, M.F. (2013). Bulanık Doğrusal Programlama İle Süt Endüstrisinde Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 18(2), 373-397.
  • Ding, H., Tian, J., Yu, W., Wilson, D.I., Young, B.R., Cui, X., Xin, X., Wang, Z., Li, W. (2023). The Application of Artificial Intelligence and Big Data in the Food Industry. Foods, 12, 4511.
  • FDA. (2022). Import screening pilot unleashes the power of data and leverages artificial intelligence. US Food and Drug Administration. Available at: https://www. fda.gov/news-events/fda-voices/import-screening-pilot-unleashes-power-data-andleverages-artificial- intelligence (Accessed 22 October, 2024).
  • Gevrekçi, Y.E., Yeğenoğlu, D., Akbağ,Y., Sesli, M. (2011). Yapay sinir ağlarının tarımsal alanda kullanımı. E.Ü. Ziraat Fak. Dergi, 48(1), 71-76.
  • Gobbi, E., Falasconi, M., Zambotti, G., Sberveglieri, V., Pulvirenti, A., and Sberveglieri, G. (2015). Rapid diagnosis of Enterobacteriaceae in vegetable soups by a metal oxide sensor based electronic nose. Sensors Actuators B: Chemistry, 207 (B), 1104– 1113.
  • Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass.
  • Gonçalves, E.C., Minim, L.A., Coimbra, J.S.R., Minim,V.P.R. (2005). Modeling sterilization process of canned foods using artificial neural networks. Chemical Engineering and Processing, 44,1269-1276. 1   1. m.scirp.org m.scirp.org
  • Gowen, A., O'Donnell, C. P., Cullen, P. J., Downey, G., Frias, J. M. (2007). Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci. Tech., 18 (12), 590–598.
  • Gr´ac, S., Beno, P., Duchon, F., Dekan, M., T¨olgyessy, M. (2020). “Automated detection of multifirotor UAVs using a machinelearning approach,” Applied System Innovation, 3, 29.
  • Grzesıak, W., LacroiX, R., WojcıK, J., Blaszczyk, P. (2003). A comparison of neural network and multipleregressionpredictions for 305-day lactation yield using partiallactation records. Can. J. Anim. Sci., 83 (2): 307-310.
  • Guıllaume, S., Charnomordic, B. (2000). Knowledge discovery for control purposes in food industry data bases. Fuzzy Sets and Systems, 122,487–497.
  • Hussıan, M.A., Shafıur,M., Rahman, Ng, C.W. (2002). Prediction of poresformation (porosity) in foods during drying: generic models by the use of hybrid neural network. Journal of Food Engineering, 51, 239-248.
  • Jahns, G., Nielsen,H.M., Paul, W. (2001). Measuring İmage Analysis Attributes and Modelling Fuzzy Consumer Aspects For Tomato Quality Grading. Computers and Electronics in Agriculture, 31,17–29.
  • Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, e100033.
  • Kavas, G. (2012). Gıdalarda yapay sinir ağları ve bulanık mantık.http://www.dunyagida. com.tr/kose-yazisi/gidalarda-yapay-sinir-aglari-ve-bulanikmantik/1043.
  • Khan, R. (2021). Artificial Intelligence and Machine Learning in Food Industries: A Study. J Food Chem Nanotechnol, 7(3), 60-67.
  • Kler, R. (2023). Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach (Retraction of Vol 2022, art no 8521236, ).
  • Kler, R., Elkady, G., Rane, K., Singh, A., Hossain, M. S., Malhotra, D. 2022. Machine learning and artificial intelligence in the food industry: A sustainable approach. Journal of Food Quality. 2022(1), 8521236.
  • Kumar, I., Rawat, J., Mohd, N.,  Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021(1), 4535567.
  • Liao, K., Paulsen, M.R., Reid, J.F., Ni B.C, Bonıfacio-Maghirang, E.P. (1993). Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of the ASAE, 36(6), 1949–1953.
  • Linko, S. (1998). Expert systems - what can they do for the food industry? Trends in Food Science and Technology, 9(1), 3–12.
  • Livingstone, K. M., Ramos-Lopez, O., Pé russe, L., Kato, H., Ordovas, J. M., do Martınez, J. A. (2022). Precision nutrition: A review of current approaches and future ́ endeavors. Trends Food Science and Technology,128 (10184).
  • Lupolova, N., Dallman, T. J., Holden, N. J.,  Gally, D. L. (2017). Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli. Microbial genomics, 3(10), e000135.
  • Luzuriaga D., Balaban M., Yeralan S. 1997. Analysis of Visual Quality Attributes of White Shrimp by Machine Vision, Journal of Food Science, 62(1), 113-118.
  • Mıttal, G.S., Zhang, J. (2000). Prediction of freezing time for food products using a neural network. Food Research International, 33, 557-562.
  • Movagharnejad, K., Nıkzad, M. (2007). Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59,78-85.
  • Nayak, J., Vakula, K., Dinesh, P., Naik, B.,  Pelusi, D. (2020). Intelligent food processing: Journey from artificial neural network to deep learning. Computer Science Review, 38, Article 100297.
  • Nikolola-Alexieva, V., Valeva, K., Pashev, S. (2024). Artificial intelligence in the food industry. In BIO Web of Conferences (Vol. 102, p. 04002). EDP Sciences.
  • Oztemel, E., 2006. Artificial neural networks. Papatya Publishing, Istanbul.
  • P´erez-Correa, J. R., Zaror, C. A. (1993). Recent advances in process control and their potential applications to food processing. Food Control, 4(4), 202–209.
  • Razmi-Rad, E., Ghanbarzadeh, B., Mousavi, S.M, Emam-Djomeh, Z., Khazaei, J. (2007). Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. Journal of Food Engineering, 81, 728-734.  
  • Ropodi, A. I., Panagou, E. Z., Nychas, G. J. E. (2016). Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends in Food Science and Technology, 50, 11–25.
  • Ruan, R., Almaer, S., Zhang, J. (1995). Prediction of Dough Rheological Properties Using Neural Networks. Cereal Chem., 72(3), 308-311.
  • Rywotycki, R. (2003). Food frying process control system. Journal of Food Engineering, 59, 339–342.
  • Saha, D., Manickavasagan, A. (2021). Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science, 4, 28–44.
  • Samani, B.H., HouriJafari, H., Zareiforoush, H. (2017). Artificial neural networks, genetic algorithm and response surface methods: the energy consumption of food and beverage industries in Iran. Journal of AI and Data Mining, 5(1), 79-88.
  • Schroeder, R. (2016).Big data business models: Challenges and opportunities. Cogent Soc. Sci., 2, 1166924.
  • Shahin, M.A., Tollner, E.W., Mcclendon, R.W. (2001). Artificial intelligence classifiers for sorting apples based on water core. Journal of Agricultural Engineering Research, 79(3),: 265–274.
  • Sofu, A., Ekinci Kitiş, F.Y. (2005). Predicting survival rate of Yersinia Enterocolitica in Turkish feta cheese during maturation and storage by using fuzzy logic modelling. 1st International Food and Nutrition Congress- Food Safety, İstanbul.
  • Sofu, A. (2006). Yoğurtların depolama esnasında mikrobiyal ve kimyasal değişimlerinin bilgisayarlı görüntüleme sistemiyle belirlenmesi ve elde edilen verilerin yapay sinir ağlarıyla değerlendirilmesi. Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Gıda Mühendisliği Anabilim Dalı, yüksek lisans tezi, Isparta, 98s.
  • Sofu, B.A., Demir, N., Ekinci, F.Y. (2007). Gıda Bilimi Ve Teknolojisi Alanında Yapay Zeka Uygulamaları. GIDA 32 (2), 93-99.
  • Sun, D.W., Brosnan, T. (2003). Pizza quality evaluation using computervision—Part 1 Pizza base and sauce spread. Journal of Food Engineering, 57, 81–89.
  • Tan, J.,  Xu, J. (2020). Applications of electronic nose (e-nose) and electronic tongue (etongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture, 4, 104–115.
  • Taner, A., Tekgüler,A., Sauk, H., Demirel, B. (2012). Yulaf Çeşitlerinin Yapay Sinir Ağları Kullanılarak Sınıflandırılması. 27. Tarımsal Mekanizasyon Ulusal Kongresi, 5-7 Eylül 2012, Samsun, 519-529s.
  • Tarallo, E., Akabane, G. K., Shimabukuro, C. I., Mello, J., Amancio, D. (2019). Machine learning in predicting demand for fast-moving consumer goods: An exploratory research. IFAC-PapersOnLine, 52(13), 737–742.
  • Tollner, E.W., Shahin, M.A., Maw, B.W., Gitaitis, R.D., Summer, D.R. (1999). Classification of onions based on internal defects using imaging processing and neural network techniques, In 1999 ASAE Annual International Meeting, Paper No, 993165, St, Joseph, Michigan, USA: ASAE.
  • Torrecilla, J.S, Otero L, Sanz, P.D. (2005). Artificial neural networks: a promising tool to design and optimize high-pressure food processes. Journal of Food Engineering, 69,299-306.  
  • Trıpathy, P.P., Kumar,S. (2009). Neural network approach for food Temperature prediction during solar drying. International Journal of ThermalSciences, 48,1452-1459.
  • Wang, H., Ceylan, K. H., Qiu, Y., Bai, B., Zhang, Y., Jin, Y. (2020). Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light Sci. Appl. 9, 118.
  • Wang, X., Bouzembrak, Y., Lansink, A.G.J.M.O., van der Fels-Klerx, H.J. (2022). Application of machine learning to the monitoring and prediction of food safety: A review. Comprehensive Reviews in Food Science and Food Safety, 21(1), 416–434.
  • Wang, S. S., Lin, P., Wang, C. C., Lin, Y. C., and Tung, C. W. (2023). Machine learning for predicting chemical migration from food packaging materials to foods. Food and Chemical Toxicology, 178, 113942.
  • Yin Y-G, Ding, Y. (2009). A close to real-time prediction method of total coliform bacteria in foods based on image identification technology and artificial neural network. Food Research International, 42,191-199.
  • Yongnıan, N., Chao, L.(1999). Artificial neural networks and multivariate calibration for spectrophotometric differential kinetic determinations of food antioxidants. Analytica Chimica Acta, 396,: 221–230.
  • Zhu, L., Spachos, P., Pensini, E., Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science, 4, 233–249.
  • Zhu, N., Wang, K., Zhang, S., Zhao, B., Yang, J., Wang, S.W. (2021). Application of artificial neural networks to predict multiple quality of dry-cured ham based on protein degradation. Food Chemistry, 344, 128586.
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Gıda Teknolojileri
Bölüm Food Health and Technology Innovations
Yazarlar

Seval Seval Kırdar

Yayımlanma Tarihi 29 Aralık 2024
Gönderilme Tarihi 2 Aralık 2024
Kabul Tarihi 10 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 7 Sayı: 15

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