Araştırma Makalesi
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Fuzzy Logic Modeling of Yoghurt Incubation

Yıl 2022, , 167 - 176, 28.06.2022
https://doi.org/10.25308/aduziraat.1119592

Öz

Yoghurt production was modeled in this study based on different incubation temperatures, inoculum ratio of starter culture and incubation times. Experimental yoghurts were produced in two replicates and incubation final pH values of 343 yoghurt samples were determined. Resultant pH values were used in fuzzy logic modeling system. Fuzzy logic modelling was conducted in two sections: fuzzy rules were set and membership function was generated in the first section and defuzzification was conducted in the second section. Three different fuzzy sets (triangle membership function) were used for fuzzification of incubation temperature, inoculum ratio of culture and incubation time values. Since there were 7 membership functions of input parameters, 343 (7 x 7 x 7) rows of rule were generated. Mamdani method was used to tabulate fuzzy rules. Three trapezoidal sections of membership functions generated for defuzzification were used and membership function values were determined with the use of weighted average method. Incubation final pH values of 343 samples were assessed in modeling study and model outputs were compared with the expert decisions. Matlab (R2016b) software was used to assess model performance and model general performance was calculated as 90.27%. Automated yoghurt production lines should be designed in the future and put into service of food industry for present model to be used in industrial scale

Destekleyen Kurum

Aydın Adnan Menderes Üniversitesi Bilimsel Araştırma Projeleri Başkanlığı

Proje Numarası

KOMYO-17001

Teşekkür

Authors express their thanks to ADU-BAP for financial support and also thanks to TARBİYOMER for facilities of laboratory.

Kaynakça

  • Abiyeva, R H., Uyarb, K., Ilhanc, U., 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. http://doi.org/10.1016/j.eswa.2011.04.005
  • Aliyeva, E., Rzayevab, I., Askerova, N (2017). Fuzzy cognitive model development for monitoring of results and reporting with in the UN FAO food security program. 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, 22–23 August, Budapest, Hungary. Procedia Computer Science 120: 430–437. http://doi.org/10.1016/j.procs.2017.11.260
  • Akgül, H N., Yıldız-Akgül, F., Tuna, D (2014). Modeling of Kefir Production with Fuzzy Logic. Turkish Journal of Agriculture - Food Science and Technology, 2(6): 251–255 (in Turkish with an abstract in English).
  • Akıllı, A., Atıl, H., Kesenkaş, H (2014). Fuzzy logic approach in the evaluation of raw milk quality. Kafkas Univ. Vet. Fak. Derg. 20 (2): 223–229. http://doi.org/10.9775/kvfd.2013.9894
  • Awasthi, A., Chauhan, S S., Omrani, H (2011). Application of Fuzzy TOPSIS in evaluating sustainable transportation systems. Expert System Appl. 38: 12270–12280. http://doi.org/10.1016/j.eswa.2011.04.005
  • Başak, 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. http://doi.org/10.1016/j.foodchem.2017.08.047
  • Cavero, D., Tölle, K H., Buxade, C., Krieter, J (2006). Mastitis detection in dairy cows by application of fuzzy logic. Livestock Science, 105: 207–213. http://doi.org/10.1016/j.livsci.2006.06.006
  • Chen, S., Roger, E G (1994). Evaluation of cabbage seedling quality by fuzzy logic. ASAE Paper No. 943028, St. Joseph, MI.
  • 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. http://doi.org/10.1016/j.jfoodeng.2009.09.004
  • Dirim, S N (2010) Adaptive Control Systems and Some Applications in Food Industry. Academic Food Journal, 8 (3): 43–46.
  • Djekic, I., Smigic, N., Glavan, R., Miocinovic, J., Tomasevic, I (2018). Transportation sustainability index in dairy industry-Fuzz logic approach. Journal of Cleaner Production, 180:107–115. http://doi.org/10.1016/j.jclepro.2018.01.185
  • Freitas, M (2017). Chapter 24: The Benefits of Yogurt, Cultures, and Fermentation. The Microbiota in Gastrointestinal Pathophysiology, 209-223. http://doi.org/10.1016/B978-0-12-804024-9.00024-0
  • Guillaumea, S., Charnomordic, B (2000). Knowledge discovery for control purposes in food industry data bases. Fuzzy Sets and Systems, 122:487–497.
  • Halavati, R., Shouraki, S. B (2005). Fuzzy learning in zamin artificial World. Fuzzy Sets and Systems, 152 (3): 603–615. http://doi.org/10.1016/j.fss.2004.09.013
  • Harris, J (1998). Raw milk grading using fuzzy logic. International Journal of Dairy Technology, 51 (2): 52–56. http://doi.org/10.1111/j.1471-0307.1998.tb02508.x
  • 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. http://doi.org/10.1016/S0168-1699(00)00171-X
  • Kartalopoulos, S V (1996). Understanding Neural Networks and Fuzzy Logic. IEEE Press Understanding Series, NY.
  • Kavdır, İ., Guyer, D E (2003). Apple grading using fuzzy logic. Turkish Journal of Agriculture and Foresty, 27: 375–382.
  • Kramer, E., Cavero, D., Stamer, E., Krieter, J (2009). Mastitis and lameness detection in dairy cows by application of fuzzy logic. Livestock Science, 125(1):92–96. http://doi.org/10.1016/j.livsci.2009.02.020
  • Lee, C. C (1990). Fuzzy logic in control systems: Fuzzy logic controller-part II. IEEE Trans. on Systems, Man and Cybernetics, 20 (2): 419–432.
  • Linko, S (1998). Expert systems-what can they do for the food industry? Trends in Food Science andTechnology, 9: 3–12.
  • Ma, W., Fan, J., Li, Q., Tang, Y (2018). A raw milk service platform using BP Neural Network and Fuzzy Inference. Informatıon Processıng in Agrıculture 5(3): 308–319. http://doi.org/10.1016/j.inpa.2018.04.001
  • Mehraban, S M., Mohebbi, M., Shahidi, F., Vahidian, K A., Qhods, R M (2012). Application of fuzzy logic to classify raw milk based on qualitative properties. International Journal of Agriscience, 2 (12): 1168–1178.
  • Niamsiri, N., Batt, C A (2009). Dairy Products. Encyclopedia of Microbiology (Third Edition), 34-44. http://doi.org/10.1016/B978-012373944-5.00120-6
  • Özer, B (2006). Yoğurt Bilimi ve Teknolojisi. Sidas Yayıncılık, 488 pp. İzmir.
  • Rywotycki, R (2003). Food frying process control system. Journal of Food Engineering 59: 339–342.
  • 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. http://doi.org/10.1006/jaer.2001.0705
  • Sharma, A K., Sawhney, I K., Lal, M (2014). Intelligent Modeling and Analysis of Moisture Sorption Isotherms in Milk and Pearl Millet–Based Weaning Food Fortified Nutrimix. Drying Technology, 32(6): 728–741. http://doi.org/10.1080/07373937.2013.858265
  • Sofu, A., Ekinci, 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., Ekinci, F Y (2007). Estimation of storage time of yoghurt with artificial neural network modeling. Journal of Dairy Science, 90(7): 3118–3125. http://doi.org/10.3168/jds.2006-591
  • Sun, D W., Brosnan, T (2003a). Pizza quality evaluation using computervision— Part 1 Pizza base and sauce spread. Journal of Food Engineering, 57(1): 81–89. http://doi.org/10.1016/s0260-8774(02)00275-3
  • Sun, D W., Brosnan, T (2003b). Pizza quality evaluation using omputervision— Part 2 Pizza topping analysis. Journal of Food Engineering, 57(1): 91–95. http://doi.org/10.1016/S0260-8774(02)00276-5
  • Tamime, A Y., Robinson, R K (1999). Yoghurt: Science and Technology, Second Edition, Woodhead Publishing Ltd. and CRS pres LLC, England, p 619.
  • Zadeh, L A (1965). Fuzzy sets. Information and Control, 8 (3): 338–353
  • Zaninelli, M., Rossi, L., Tangorra, F M., Costa, A., Agazzi, A., Savoini, G (2016). On-line monitoring of milk electrical conductivity by fuzzy logic technology to characterise health status in dairy goats. Italian Journal of Animal Science, 13(2): 340–347. http://doi.org/10.4081/ijas.2014.3170

Bulanık Mantık ile Yoğurt İnkübasyonunun Modellenmesi

Yıl 2022, , 167 - 176, 28.06.2022
https://doi.org/10.25308/aduziraat.1119592

Öz

Bu çalışmada farklı inkübasyon sıcaklıkları, starter kültür inokülasyon oranı ve inkübasyon süreleri baz alınarak yoğurt üretimi modellenmiştir. Deneme yoğurtları iki tekerrürlü olarak üretilmiş ve 343 yoğurt örneğinin inkübasyon sonundaki pH değerleri belirlenmiştir. Elde edilen pH değerleri bulanık mantık modelleme sisteminde kullanılmıştır. Bulanık mantık modellemesi iki bölümde gerçekleştirilmiştir: birinci bölümde bulanık kurallar belirlenmiş ve üyelik fonksiyonu oluşturulmuş, ikinci bölümde berraklaştırma yapılmıştır. İnkübasyon sıcaklığı, kültür inokülasyon oranı ve inkübasyon süresi değerlerinin bulanıklaştırılması için üç ayrı bulanık küme (üçgen üyelik fonksiyonu) kullanılmıştır. Girdi parametrelerinin 7 üyelik fonksiyonu olduğu için 343 (7 x 7 x 7) satırlık kural oluşturulmuştur. Bulanık kurallar tablosu için Mamdani yöntemi kullanılmıştır. Berraklaştırma için oluşturulan üyelik fonksiyonlarının üç yamuk alanı kullanılmış ve ağırlıklı ortalama yöntemi kullanılarak üyelik fonksiyonu değerleri belirlenmiştir.Modelleme çalışmasında 343 örneğin inkübasyon sonundaki pH’ları değerlendirilmiş ve model çıktıları uzman kararları ile karşılaştırılmıştır. Model performansını değerlendirmek için Matlab (R2016b) yazılımı kullanılmış ve model genel performansı %90.27 olarak hesaplanmıştır. Gelecekte otomatik yoğurt üretim hatlarının tasarlanarak gıda sektörünün hizmetine sunulması düşünülmektedir.

Proje Numarası

KOMYO-17001

Kaynakça

  • Abiyeva, R H., Uyarb, K., Ilhanc, U., 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. http://doi.org/10.1016/j.eswa.2011.04.005
  • Aliyeva, E., Rzayevab, I., Askerova, N (2017). Fuzzy cognitive model development for monitoring of results and reporting with in the UN FAO food security program. 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, 22–23 August, Budapest, Hungary. Procedia Computer Science 120: 430–437. http://doi.org/10.1016/j.procs.2017.11.260
  • Akgül, H N., Yıldız-Akgül, F., Tuna, D (2014). Modeling of Kefir Production with Fuzzy Logic. Turkish Journal of Agriculture - Food Science and Technology, 2(6): 251–255 (in Turkish with an abstract in English).
  • Akıllı, A., Atıl, H., Kesenkaş, H (2014). Fuzzy logic approach in the evaluation of raw milk quality. Kafkas Univ. Vet. Fak. Derg. 20 (2): 223–229. http://doi.org/10.9775/kvfd.2013.9894
  • Awasthi, A., Chauhan, S S., Omrani, H (2011). Application of Fuzzy TOPSIS in evaluating sustainable transportation systems. Expert System Appl. 38: 12270–12280. http://doi.org/10.1016/j.eswa.2011.04.005
  • Başak, 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. http://doi.org/10.1016/j.foodchem.2017.08.047
  • Cavero, D., Tölle, K H., Buxade, C., Krieter, J (2006). Mastitis detection in dairy cows by application of fuzzy logic. Livestock Science, 105: 207–213. http://doi.org/10.1016/j.livsci.2006.06.006
  • Chen, S., Roger, E G (1994). Evaluation of cabbage seedling quality by fuzzy logic. ASAE Paper No. 943028, St. Joseph, MI.
  • 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. http://doi.org/10.1016/j.jfoodeng.2009.09.004
  • Dirim, S N (2010) Adaptive Control Systems and Some Applications in Food Industry. Academic Food Journal, 8 (3): 43–46.
  • Djekic, I., Smigic, N., Glavan, R., Miocinovic, J., Tomasevic, I (2018). Transportation sustainability index in dairy industry-Fuzz logic approach. Journal of Cleaner Production, 180:107–115. http://doi.org/10.1016/j.jclepro.2018.01.185
  • Freitas, M (2017). Chapter 24: The Benefits of Yogurt, Cultures, and Fermentation. The Microbiota in Gastrointestinal Pathophysiology, 209-223. http://doi.org/10.1016/B978-0-12-804024-9.00024-0
  • Guillaumea, S., Charnomordic, B (2000). Knowledge discovery for control purposes in food industry data bases. Fuzzy Sets and Systems, 122:487–497.
  • Halavati, R., Shouraki, S. B (2005). Fuzzy learning in zamin artificial World. Fuzzy Sets and Systems, 152 (3): 603–615. http://doi.org/10.1016/j.fss.2004.09.013
  • Harris, J (1998). Raw milk grading using fuzzy logic. International Journal of Dairy Technology, 51 (2): 52–56. http://doi.org/10.1111/j.1471-0307.1998.tb02508.x
  • 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. http://doi.org/10.1016/S0168-1699(00)00171-X
  • Kartalopoulos, S V (1996). Understanding Neural Networks and Fuzzy Logic. IEEE Press Understanding Series, NY.
  • Kavdır, İ., Guyer, D E (2003). Apple grading using fuzzy logic. Turkish Journal of Agriculture and Foresty, 27: 375–382.
  • Kramer, E., Cavero, D., Stamer, E., Krieter, J (2009). Mastitis and lameness detection in dairy cows by application of fuzzy logic. Livestock Science, 125(1):92–96. http://doi.org/10.1016/j.livsci.2009.02.020
  • Lee, C. C (1990). Fuzzy logic in control systems: Fuzzy logic controller-part II. IEEE Trans. on Systems, Man and Cybernetics, 20 (2): 419–432.
  • Linko, S (1998). Expert systems-what can they do for the food industry? Trends in Food Science andTechnology, 9: 3–12.
  • Ma, W., Fan, J., Li, Q., Tang, Y (2018). A raw milk service platform using BP Neural Network and Fuzzy Inference. Informatıon Processıng in Agrıculture 5(3): 308–319. http://doi.org/10.1016/j.inpa.2018.04.001
  • Mehraban, S M., Mohebbi, M., Shahidi, F., Vahidian, K A., Qhods, R M (2012). Application of fuzzy logic to classify raw milk based on qualitative properties. International Journal of Agriscience, 2 (12): 1168–1178.
  • Niamsiri, N., Batt, C A (2009). Dairy Products. Encyclopedia of Microbiology (Third Edition), 34-44. http://doi.org/10.1016/B978-012373944-5.00120-6
  • Özer, B (2006). Yoğurt Bilimi ve Teknolojisi. Sidas Yayıncılık, 488 pp. İzmir.
  • Rywotycki, R (2003). Food frying process control system. Journal of Food Engineering 59: 339–342.
  • 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. http://doi.org/10.1006/jaer.2001.0705
  • Sharma, A K., Sawhney, I K., Lal, M (2014). Intelligent Modeling and Analysis of Moisture Sorption Isotherms in Milk and Pearl Millet–Based Weaning Food Fortified Nutrimix. Drying Technology, 32(6): 728–741. http://doi.org/10.1080/07373937.2013.858265
  • Sofu, A., Ekinci, 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., Ekinci, F Y (2007). Estimation of storage time of yoghurt with artificial neural network modeling. Journal of Dairy Science, 90(7): 3118–3125. http://doi.org/10.3168/jds.2006-591
  • Sun, D W., Brosnan, T (2003a). Pizza quality evaluation using computervision— Part 1 Pizza base and sauce spread. Journal of Food Engineering, 57(1): 81–89. http://doi.org/10.1016/s0260-8774(02)00275-3
  • Sun, D W., Brosnan, T (2003b). Pizza quality evaluation using omputervision— Part 2 Pizza topping analysis. Journal of Food Engineering, 57(1): 91–95. http://doi.org/10.1016/S0260-8774(02)00276-5
  • Tamime, A Y., Robinson, R K (1999). Yoghurt: Science and Technology, Second Edition, Woodhead Publishing Ltd. and CRS pres LLC, England, p 619.
  • Zadeh, L A (1965). Fuzzy sets. Information and Control, 8 (3): 338–353
  • Zaninelli, M., Rossi, L., Tangorra, F M., Costa, A., Agazzi, A., Savoini, G (2016). On-line monitoring of milk electrical conductivity by fuzzy logic technology to characterise health status in dairy goats. Italian Journal of Animal Science, 13(2): 340–347. http://doi.org/10.4081/ijas.2014.3170
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ziraat Mühendisliği (Diğer)
Bölüm Araştırma
Yazarlar

Hüseyin Nail Akgül 0000-0003-4185-4769

Filiz Yıldız-akgül 0000-0001-7894-6531

Ayşe Demet Karaman 0000-0001-9913-9763

Proje Numarası KOMYO-17001
Yayımlanma Tarihi 28 Haziran 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Akgül, H. N., Yıldız-akgül, F., & Karaman, A. D. (2022). Fuzzy Logic Modeling of Yoghurt Incubation. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi, 19(1), 167-176. https://doi.org/10.25308/aduziraat.1119592
AMA Akgül HN, Yıldız-akgül F, Karaman AD. Fuzzy Logic Modeling of Yoghurt Incubation. ADÜ ZİRAAT DERG. Haziran 2022;19(1):167-176. doi:10.25308/aduziraat.1119592
Chicago Akgül, Hüseyin Nail, Filiz Yıldız-akgül, ve Ayşe Demet Karaman. “Fuzzy Logic Modeling of Yoghurt Incubation”. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi 19, sy. 1 (Haziran 2022): 167-76. https://doi.org/10.25308/aduziraat.1119592.
EndNote Akgül HN, Yıldız-akgül F, Karaman AD (01 Haziran 2022) Fuzzy Logic Modeling of Yoghurt Incubation. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi 19 1 167–176.
IEEE H. N. Akgül, F. Yıldız-akgül, ve A. D. Karaman, “Fuzzy Logic Modeling of Yoghurt Incubation”, ADÜ ZİRAAT DERG, c. 19, sy. 1, ss. 167–176, 2022, doi: 10.25308/aduziraat.1119592.
ISNAD Akgül, Hüseyin Nail vd. “Fuzzy Logic Modeling of Yoghurt Incubation”. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi 19/1 (Haziran 2022), 167-176. https://doi.org/10.25308/aduziraat.1119592.
JAMA Akgül HN, Yıldız-akgül F, Karaman AD. Fuzzy Logic Modeling of Yoghurt Incubation. ADÜ ZİRAAT DERG. 2022;19:167–176.
MLA Akgül, Hüseyin Nail vd. “Fuzzy Logic Modeling of Yoghurt Incubation”. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi, c. 19, sy. 1, 2022, ss. 167-76, doi:10.25308/aduziraat.1119592.
Vancouver Akgül HN, Yıldız-akgül F, Karaman AD. Fuzzy Logic Modeling of Yoghurt Incubation. ADÜ ZİRAAT DERG. 2022;19(1):167-76.