Research Article
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Comparative analysis of raw milk samples in Amasya region

Year 2024, Volume: 8 Issue: 4, 752 - 759
https://doi.org/10.31015/jaefs.2024.4.3

Abstract

Milk is a complete and incredibly nutritious food supply for humans. Millions of tons of raw milk have been processed by the dairy industry to meet huge public demand. Therefore, there are studies needed related to the classification of raw milks in the supply chain. The aim of this study is to evaluate the quality of raw milk samples in Amasya region. Firstly, raw cow's milk is classified according to its protein and fat values. In the first period, the mean fat value of raw milk collected from three lines was found to be 3.86, 3.89, and 3.87 as (%) the percent value, respectively, while the mean protein value of raw milk collected from three lines was found to be 3.29, 3.28, and 3.25 as (%) the percent value, respectively. In the second period, the mean fat value of raw milk collected from three lines was found to be 3.93, 3.99, and 4.03 as (%) the percent value, respectively, while the mean protein value of raw milk collected from three lines was found to be 3.34, 3.35, and 3.34 as (%) the percent value, respectively. The findings indicated that during both periods, the daily raw milk collected from three lines is class A, where protein value (%) is 3.1 and above while fat value (%) is 3.5 and above. Since the quality of raw milk is important not only for milk consumers but also for the quality of the corresponding dairy products, the quality of raw milk must be controlled correctly. Consumer requirements for high-quality milk and dairy are of importance on dairy products.

Supporting Institution

Dumlupınar Üniversitesi

Project Number

2024-05

References

  • Ahmad, I., Komolavanij, S. & Chanvarasuth, P. (2010). Prediction of raw milk microbial quality using data mining techniques. Agricultural Information Research, 19(3), 64-70. https://doi.org/10.3173/air.19.64
  • AIDSYB (2024). Amasya İli Damızlık Sığır Yetiştiricileri Birliği. Retrieved in June, 09, 2024 from https://www.amasyadsyb.org/birlik/hak (in Turkish)
  • Anonymous (2019). Çiğ inek sütünün sınıflandırılmasına ilişkin Tebliğ (TEBLİĞ NO: 2019/64). Retrieved in September, 30, 2024 from https://www.resmigazete.gov.tr/eskiler/2020/01/20200125-41.htm (in Turkish)
  • Anonymous (2024). Karayolları genel müdürlüğü. Retrieved in October, 01, 2024 from https://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Bolgeler/7Bolge/Iller/IlAmasya.aspx (in Turkish)
  • Berhilevych, O., Kasianchuk, V., Chernetskyi, I., Konieva, A., Dimitrijevich, L. & Marenkova, T. (2019). Construction of a method for predicting the number of enterobacteria in milk using artifical neural networks. Eastern-European Journal of Enterprise Technologies, 2(11), 6-13. https://doi.org/10.15587/1729-4061.2019.160021
  • Cebeci, T. (2019). A survey of raw milk for microbiological quality and typing of foodborne pathogens by MALDI-TOF MS. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi, 16(2), 185-191. https://doi.org/10.25308/aduziraat.575681
  • Demirbaş, N., Tosun, D., Cukur, F., & Gölge, E. (2009). Practices in milk collection centres for quality milk production: a case from the Aegean Region of Turkey. New Medit, 8(3), 21-27.
  • Guetouache, M., Guessas, B., & Medjekal, S. (2014). Composition and nutritional value of raw milk. Issues in Biological Sciences and Pharmaceutical Research, 2(10), 115-122. http://dx.doi.org/10.15739/ibspr.005
  • Mu, L., Dawande, M., Geng, X., ve Mookerjee, V. (2016). Milking the quality test: Improving the milk supply chain under competing collection intermediaries. Management Science, 62(5), 1259-1277. https://doi.org/10.1287/mnsc.2015.2171
  • Ndahetuye, J. B., Artursson, K., Båge, R., Ingabire, A., Karege, C., Djangwani, J., Nyman, A.-K., Ongol, M.-P., Tukei, M., & Persson, Y. (2020). Milk Symposium review: Microbiological quality and safety of milk from farm to milk collection centers in Rwanda. Journal of Dairy Science, 103(11), 9730-9739. https://doi.org/10.3168/jds.2020-18302
  • Neware, S. (2023). Cow milk quality grading using machine learning methods. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1005
  • Özdamar, K. (2013). Paket programlar ile istatistiksel veri analizi, Cilt 1. Nisan Kitapevi. (in Turkish)
  • Preka, J., & Bekteshi, A. (2016). Evaluation of the physicochemical parameters of cow’s fresh milk in Shkodra. Journal of Agricultural Science and Technology B, 6, 274-280. https://doi.org/10.17265/2161-6264/2016.04.008
  • Reguillo, L., Hernández, M., Barrientos, E., Perez-Rodriguez, F., & Valero, A. (2018). Evaluation of the influence of frequency of milk collection and milking dayshift on the microbiological quality of raw milk. Journal of Food Quality, 2018. https://doi.org/10.1155/2018/1306107
  • Sangatash, M. M., Mohebbi, M., Shahidi, F., Kamyad, A. V., & Rohani, M. Q. (2012). Application of fuzzy logic to classify raw milk based on qualitative properties. International journal of AgriScience, 2(12), 1168-1178.
  • Sayin, C., Mencet, M. N., ve Karaman, S. (2011). Milk distribution channels in the roles of milk collection centers in Turkey: A case study of Antalya. African Journal of Agricultural Research, 6(1), 174-180.
  • Sel, Ç., ve Bilgen, B. (2015). Quantitative models for supply chain management within dairy industry: a review and discussion. European Journal of Industrial Engineering, 9(5), 561-594. https://doi.org/10.1504/EJIE.2015.071772
  • Sigmaxl. (2024). How do i perform equal variance tests (Barlett, Levene and Welch's ANOVA) in excel using SigmaXL? Retrieved in September, 30, 2024 from https://www.sigmaxl.com/EqualVarianceTests.shtml
  • Tohidi, M., Ghasemi-Varnamkhasti, M., Ghafarinia, V., Mohtasebi, S. S., & Bonyadian, M. (2018). Identification of trace amounts of detergent powder in raw milk using a customized low-cost artificial olfactory system: A novel method. Measurement, 124, 120-129. https://doi.org/10.1016/j.measurement.2018.04.006
  • TÜİK, (2023). Raw milk production statistics. Retrieved in September, 30, 2024 from https://data.tuik.gov.tr/Bulten/Index?p=Cig-Sut-Uretim-Istatistikleri-2023-53542#:~:text=Buna%20g%C3%B6re%2C%202022%20y%C4%B1l%C4%B1nda%2021,%C3%BCretimi%20%251%2C3%20azald%C4%B1
  • Uzun, B. & Allahverdi, N. (2021). Fuzzy control system for predicting the quality of thermally processed raw milk. International Journal of Applied Mathematics Electronics and Computers, 9(3), 79-84. https://doi.org/10.18100/ijamec.980645
Year 2024, Volume: 8 Issue: 4, 752 - 759
https://doi.org/10.31015/jaefs.2024.4.3

Abstract

Project Number

2024-05

References

  • Ahmad, I., Komolavanij, S. & Chanvarasuth, P. (2010). Prediction of raw milk microbial quality using data mining techniques. Agricultural Information Research, 19(3), 64-70. https://doi.org/10.3173/air.19.64
  • AIDSYB (2024). Amasya İli Damızlık Sığır Yetiştiricileri Birliği. Retrieved in June, 09, 2024 from https://www.amasyadsyb.org/birlik/hak (in Turkish)
  • Anonymous (2019). Çiğ inek sütünün sınıflandırılmasına ilişkin Tebliğ (TEBLİĞ NO: 2019/64). Retrieved in September, 30, 2024 from https://www.resmigazete.gov.tr/eskiler/2020/01/20200125-41.htm (in Turkish)
  • Anonymous (2024). Karayolları genel müdürlüğü. Retrieved in October, 01, 2024 from https://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Bolgeler/7Bolge/Iller/IlAmasya.aspx (in Turkish)
  • Berhilevych, O., Kasianchuk, V., Chernetskyi, I., Konieva, A., Dimitrijevich, L. & Marenkova, T. (2019). Construction of a method for predicting the number of enterobacteria in milk using artifical neural networks. Eastern-European Journal of Enterprise Technologies, 2(11), 6-13. https://doi.org/10.15587/1729-4061.2019.160021
  • Cebeci, T. (2019). A survey of raw milk for microbiological quality and typing of foodborne pathogens by MALDI-TOF MS. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi, 16(2), 185-191. https://doi.org/10.25308/aduziraat.575681
  • Demirbaş, N., Tosun, D., Cukur, F., & Gölge, E. (2009). Practices in milk collection centres for quality milk production: a case from the Aegean Region of Turkey. New Medit, 8(3), 21-27.
  • Guetouache, M., Guessas, B., & Medjekal, S. (2014). Composition and nutritional value of raw milk. Issues in Biological Sciences and Pharmaceutical Research, 2(10), 115-122. http://dx.doi.org/10.15739/ibspr.005
  • Mu, L., Dawande, M., Geng, X., ve Mookerjee, V. (2016). Milking the quality test: Improving the milk supply chain under competing collection intermediaries. Management Science, 62(5), 1259-1277. https://doi.org/10.1287/mnsc.2015.2171
  • Ndahetuye, J. B., Artursson, K., Båge, R., Ingabire, A., Karege, C., Djangwani, J., Nyman, A.-K., Ongol, M.-P., Tukei, M., & Persson, Y. (2020). Milk Symposium review: Microbiological quality and safety of milk from farm to milk collection centers in Rwanda. Journal of Dairy Science, 103(11), 9730-9739. https://doi.org/10.3168/jds.2020-18302
  • Neware, S. (2023). Cow milk quality grading using machine learning methods. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1005
  • Özdamar, K. (2013). Paket programlar ile istatistiksel veri analizi, Cilt 1. Nisan Kitapevi. (in Turkish)
  • Preka, J., & Bekteshi, A. (2016). Evaluation of the physicochemical parameters of cow’s fresh milk in Shkodra. Journal of Agricultural Science and Technology B, 6, 274-280. https://doi.org/10.17265/2161-6264/2016.04.008
  • Reguillo, L., Hernández, M., Barrientos, E., Perez-Rodriguez, F., & Valero, A. (2018). Evaluation of the influence of frequency of milk collection and milking dayshift on the microbiological quality of raw milk. Journal of Food Quality, 2018. https://doi.org/10.1155/2018/1306107
  • Sangatash, M. M., Mohebbi, M., Shahidi, F., Kamyad, A. V., & Rohani, M. Q. (2012). Application of fuzzy logic to classify raw milk based on qualitative properties. International journal of AgriScience, 2(12), 1168-1178.
  • Sayin, C., Mencet, M. N., ve Karaman, S. (2011). Milk distribution channels in the roles of milk collection centers in Turkey: A case study of Antalya. African Journal of Agricultural Research, 6(1), 174-180.
  • Sel, Ç., ve Bilgen, B. (2015). Quantitative models for supply chain management within dairy industry: a review and discussion. European Journal of Industrial Engineering, 9(5), 561-594. https://doi.org/10.1504/EJIE.2015.071772
  • Sigmaxl. (2024). How do i perform equal variance tests (Barlett, Levene and Welch's ANOVA) in excel using SigmaXL? Retrieved in September, 30, 2024 from https://www.sigmaxl.com/EqualVarianceTests.shtml
  • Tohidi, M., Ghasemi-Varnamkhasti, M., Ghafarinia, V., Mohtasebi, S. S., & Bonyadian, M. (2018). Identification of trace amounts of detergent powder in raw milk using a customized low-cost artificial olfactory system: A novel method. Measurement, 124, 120-129. https://doi.org/10.1016/j.measurement.2018.04.006
  • TÜİK, (2023). Raw milk production statistics. Retrieved in September, 30, 2024 from https://data.tuik.gov.tr/Bulten/Index?p=Cig-Sut-Uretim-Istatistikleri-2023-53542#:~:text=Buna%20g%C3%B6re%2C%202022%20y%C4%B1l%C4%B1nda%2021,%C3%BCretimi%20%251%2C3%20azald%C4%B1
  • Uzun, B. & Allahverdi, N. (2021). Fuzzy control system for predicting the quality of thermally processed raw milk. International Journal of Applied Mathematics Electronics and Computers, 9(3), 79-84. https://doi.org/10.18100/ijamec.980645
There are 21 citations in total.

Details

Primary Language English
Subjects Dairy Technology
Journal Section Research Articles
Authors

Semih Latif İpek 0000-0002-4661-7765

Project Number 2024-05
Early Pub Date November 16, 2024
Publication Date
Submission Date August 5, 2024
Acceptance Date October 17, 2024
Published in Issue Year 2024 Volume: 8 Issue: 4

Cite

APA İpek, S. L. (2024). Comparative analysis of raw milk samples in Amasya region. International Journal of Agriculture Environment and Food Sciences, 8(4), 752-759. https://doi.org/10.31015/jaefs.2024.4.3


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