پیش‌بینی ماندگاری فیلۀ میگوی سفیدغربی در شرایط انجماد براساس مدل آرنیوس و شبکۀ عصبی مصنوعی

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 گروه علوم و صنایع غذایی، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

2 گروه شیلات، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

چکیده

برای ارزیابی پیش‌بینی ماندگاری فیلۀ میگوی سفیدغربی با استفاده از مدل ریاضی آرنیوس و شبکۀ عصبی مصنوعی در دماهای متفاوت (15-، 25-، 35- و 45- درجۀ سانتی‌گراد)، تغییرات کیفی فیله شامل پروتئین‌های قابل استخراج از نمک (SEP)، شاخص K، میزان بازهای نیتروژن فرار کل (TVB-N)، شاخص پراکسید (PV)، مقدار اسید تیوباربیتوریک (TBARS)، هدایت الکتریکی (EC) و ارزیابی حسی (SA) بررسی شدند. برای مدل آرنیوس دامنۀ خطای نسبی بین مقادیر اندازه‌گیری‌شده و پیش‌بینی‌شده برای فاکتورهای کیفی TVB-N، SA، EC، TBARS، K و SEP به‌ترتیب 15/12-73/17-، 54/04-2/13-، 47/62-6/1-، 81/00-0/0-، 99/02-25/2- و 0/82-5/59- درصد قرار داشت. درمورد مدل شبکۀ عصبی مصنوعی، دامنۀ خطای نسبی بین مقادیر پیش‌بینی‌شده و اندازه‌گیری‌شده برای فاکتورهای کیفی TVB-N، SA، EC، TBARS، K و SEP به‌ترتیب 0/00، 0/00، 0/00-0/38-، 0/00، 0/00 و 0/30-0/80- درصد قرار داشت. مقادیر حداقل مربعات خطای مدل شبکۀ عصبی مصنوعی در اکثریت فاکتورهای کیفی کمتر از مدل آرنیوس بود. ضریب R2 مربوط به فاکتورهای کیفی فیلۀ میگوی منجمد به‌دست‌آمده از مدل شبکۀ عصبی مصنوعی به‌جزء درمورد فاکتور SA، بیشتر از مدل آرنیوس بود. مدل شبکۀ عصبی مصنوعی توانست روند تغییرات کیفیت میگوهای نگهداری‌شده طی 6 ماه دورۀ انجماد، در دماهای 15- تا 45- درجۀ سانتی‌گراد، در مقایسه با مدل آرنیوس را بهتر نشان دهد.

کلیدواژه‌ها

موضوعات

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Abu-Bakar, F., Salleh, A., Razak, C., Basri, M., Ching, M., & Son, R. (2008). Biochemical changes of fresh and preserved freshwater prawns (Macrobrachium rosenbergii) during storage. Int Food Res J, 15(2), 181-191.
Bhagya Raj, G., & Dash, K. K. (2022). Comprehensive study on applications of artificial neural network in food process modeling. Critical reviews in food science and nutrition, 62(10), 2756-2783. https://doi.org/10.1080/10408398.2020.1858398
Byrne, C., Troy, D., & Buckley, D. (2000). Postmortem changes in muscle electrical properties of bovine M. longissimus dorsi and their relationship to meat quality attributes and pH fall. Meat science, 54(1), 23-34. https://doi.org/10.1016/S0309-1740(99)00055-8
Cadun, A., Kışla, D., & Çaklı, Ş. (2008). Marination of deep-water pink shrimp with rosemary extract and the determination of its shelf-life. Food Chemistry, 109(1), 81-87. https://doi.org/10.1016/j.foodchem.2007.12.021
Choe, E., & Min, D. B. (2009). Mechanisms of antioxidants in the oxidation of foods. Comprehensive reviews in food science and food safety, 8(4), 345-358. https://doi.org/10.1111/j.1541-4337.2009.00085.x
De Abreu, D. P., Losada, P. P., Maroto, J., & Cruz, J. (2011). Natural antioxidant active packaging film and its effect on lipid damage in frozen blue shark (Prionace glauca). Innovative Food Science & Emerging Technologies, 12(1), 50-55. https://doi.org/10.1016/j.ifset.2010.12.006
Erkan, N., & Özden, Ö. (2008). Quality assessment of whole and gutted sardines (Sardina pilchardus) stored in ice. International Journal of Food Science & Technology, 43(9), 1549-1559. https://doi.org/10.1111/j.1365-2621.2007.01579.x
Estévez, M. (2011). Protein carbonyls in meat systems: A review. Meat science, 89(3), 259-279. https://doi.org/10.1016/j.meatsci.2011.04.025
Goncalves, A. A., & Santos, T. C. L. (2019). Improving quality and shelf-life of whole chilled Pacific white shrimp (Litopenaeus vannamei) by ozone technology combined with modified atmosphere packaging. LWT, 99, 568-575. https://doi.org/10.1016/j.lwt.2018.09.083
Haghshenas, M., Hosseini, H., Nayebzadeh, K., Khanghah, A., Kakesh, B., & Fonood, R. (2014). Production of prebiotic functional shrimp nuggets using ß-glucan and reduction of oil absorption by carboxymethyl cellulose: Impacts on sensory and physical properties. J. Aquacul. Res. Dev, 5, 245-248. https://doi.org/10.4172/2155-9546.1000245
Hultmann, L., & Rustad, T. (2004). Iced storage of Atlantic salmon (Salmo salar)–effects on endogenous enzymes and their impact on muscle proteins and texture. Food Chemistry, 87(1), 31-41. https://doi.org/10.1016/j.foodchem.2003.10.013
IMP, I. (1959). A new method for estimating the freshness of fish. Bulletin of the Japanese Society of Scientific Fisheries, 24(9), 749.
Jiang, Q., Gao, P., Liu, J., Yu, D., Xu, Y., Yang, F., . . . Xia, W. (2021). Endogenous proteases in giant freshwater prawn (Macrobrachium rosenbergii): Changes and its impacts on texture deterioration during frozen storage. International Journal of Food Science & Technology, 56(11), 5824-5832. https://doi.org/10.1111/ijfs.15197
Kalleda, R. K., Han, I. Y., Toler, J. E., Chen, F., Kim, H. J., & Dawson, P. L. (2013). Shelf life extension of shrimp (white) using modified atmosphere packaging. Polish journal of food and nutrition sciences, 63(2).
Kaymak-Ertekin, F., & Gedik, A. (2005). Kinetic modelling of quality deterioration in onions during drying and storage. Journal of Food Engineering, 68(4), 443-453. https://doi.org/10.1016/j.jfoodeng.2004.06.022
Lan, W., Yang, X., Gong, T., & Xie, J. (2023). Predicting the shelf life of Trachinotus ovatus during frozen storage using a back propagation (BP) neural network model. Aquaculture and Fisheries, 8(5), 544-550. https://doi.org/10.1016/j.aaf.2021.12.016
Lepetit, J., Salé, P., Favier, R., & Dalle, R. (2002). Electrical impedance and tenderisation in bovine meat. Meat science, 60(1), 51-62. https://doi.org/10.1016/S0309-1740(01)00104-8
Liu, X., Jiang, Y., Shen, S., Luo, Y., & Gao, L. (2015). Comparison of Arrhenius model and artificial neuronal network for the quality prediction of rainbow trout (Oncorhynchus mykiss) fillets during storage at different temperatures. LWT-Food Science and Technology, 60(1), 142-147. https://doi.org/10.1016/j.lwt.2014.09.030
Okpala, C. O. R. (2015). The physicochemical changes of farm-raised Pacific white shrimp (Litopenaeus vannamei) as influenced by iced storage. Food and Nutrition Sciences, 6(10), 906. https://doi.org/10.4236/fns.2015.610095
Pan, C., Chen, S., Hao, S., & Yang, X. (2019). Effect of low‐temperature preservation on quality changes in Pacific white shrimp, Litopenaeus vannamei: a review. Journal of the Science of Food and Agriculture, 99(14), 6121-6128. https://doi.org/10.1002/jsfa.9905
Shi, J., Zhang, L., Lu, H., Shen, H., Yu, X., & Luo, Y. (2017). Protein and lipid changes of mud shrimp (Solenocera melantho) during frozen storage: Chemical properties and their prediction. International Journal of Food Properties, 20(sup2), 2043-2056. https://doi.org/10.1080/10942912.2017.1361973
Song, Y., Luo, Y., You, J., Shen, H., & Hu, S. (2012). Biochemical, sensory and microbiological attributes of bream (Megalobrama amblycephala) during partial freezing and chilled storage. Journal of the Science of Food and Agriculture, 92(1), 197-202. https://doi.org/10.1002/jsfa.4572
Soyer, A., Özalp, B., Dalmış, Ü., & Bilgin, V. (2010). Effects of freezing temperature and duration of frozen storage on lipid and protein oxidation in chicken meat. Food Chemistry, 120(4), 1025-1030. https://doi.org/10.1016/j.foodchem.2009.11.042
Sun, K., Pan, C., Chen, S., Liu, S., Hao, S., Huang, H., . . . Xiang, H. (2023). Quality changes and indicator proteins of Litopenaeus vannamei based on label-free proteomics analysis during partial freezing storage. Current Research in Food Science, 6, 100415. https://doi.org/10.1016/j.crfs.2022.100415
Tsironi, T., Dermesonlouoglou, E., Giannakourou, M., & Taoukis, P. (2009). Shelf life modelling of frozen shrimp at variable temperature conditions. LWT-Food Science and Technology, 42(2), 664-671. https://doi.org/10.1016/j.lwt.2008.07.010
Tsironi, T. N., Stoforos, N. G., & Taoukis, P. S. (2020). Quality and Shelf-Life Modeling of Frozen Fish at Constant and Variable Temperature Conditions. Foods, 9(12), 1893. https://doi.org/10.3390/foods9121893
Van Boekel, M. (1996). Statistical aspects of kinetic modeling for food science problems. Journal of Food Science, 61(3), 477-486. https://doi.org/10.1111/j.1365-2621.1996.tb13138.x
Wang, H., Kong, C., Li, D., Qin, N., Fan, H., Hong, H., & Luo, Y. (2015). Modeling quality changes in brined bream (Megalobrama amblycephala) fillets during storage: comparison of the Arrhenius model, BP, and RBF neural network. Food and Bioprocess Technology, 8, 2429-2443. https://doi.org/10.1007/s11947-015-1595-8
Wang, H., Zheng, Y., Shi, W., & Wang, X. (2022). Comparison of Arrhenius model and artificial neuronal network for predicting quality changes of frozen tilapia (Oreochromis niloticus). Food Chemistry, 372, 131268. https://doi.org/10.1016/j.foodchem.2021.131268
Wang, L., Chen, Z., Yang, G., Sun, Q., & Ge, J. (2020). An interval uncertain optimization method using back-propagation neural network differentiation. Computer Methods in Applied Mechanics and Engineering, 366, 113065. https://doi.org/10.1016/j.cma.2020.113065
Wu, H., Wang, Z., Luo, Y., Hong, H., & Shen, H. (2014). Quality changes and establishment of predictive models for bighead carp (Aristichthys nobilis) fillets during frozen storage. Food and Bioprocess Technology, 7, 3381-3389. https://doi.org/10.1007/s11947-014-1340-8
Xu, Z., Liu, X., Wang, H., Hong, H., & Luo, Y. (2017). Comparison between the Arrhenius model and the radial basis function neural network (RBFNN) model for predicting quality changes of frozen shrimp (Solenocera melantho). International Journal of Food Properties, 20(11), 2711-2723. https://doi.org/10.1080/10942912.2016.1248292
Yin, C., Wang, J., Qian, J., Xiong, K., & Zhang, M. (2022). Quality changes of rainbow trout stored under different packaging conditions and mathematical modeling for predicting the shelf life. Food Packaging and Shelf Life, 32, 100824. https://doi.org/10.1016/j.fpsl.2022.100824
Zhang, L., Li, X., Lu, W., Shen, H., & Luo, Y. (2011). Quality predictive models of grass carp (Ctenopharyngodon idellus) at different temperatures during storage. Food control, 22(8), 1197-1202. https://doi.org/10.1016/j.foodcont.2011.01.017
Zhu, S., Luo, Y., Feng, L., & Bao, Y. (2015). Establishment of kinetic models based on electrical conductivity and global stability index for predicting the quality of allogynogenetic crucian carps (C arassius auratus gibelio) during chilling storage. Journal of Food Processing and Preservation, 39(2), 167-174. https://doi.org/10.1111/jfpp.12218
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دوره 12، شماره 3
آذر 1402
صفحه 369-384
  • تاریخ دریافت: 22 فروردین 1402
  • تاریخ بازنگری: 22 تیر 1402
  • تاریخ پذیرش: 05 مرداد 1402