Research Article
BibTex RIS Cite

Modeling acrylamide formation in oven-baked French fries using optimized gaussian process regression

Year 2025, Volume: 14 Issue: 3, 1088 - 1099, 15.07.2025
https://doi.org/10.28948/ngumuh.1696315

Abstract

High levels of acrylamide exposure exhibit genotoxic and potentially carcinogenic effects and are considered a serious risk factor for human health. Therefore, controlling acrylamide formation in food products is of great importance for ensuring food safety. In this study, the effects of cooking temperature and time on acrylamide formation in oven-baked French fries were experimentally investigated, and the relationship between these parameters and acrylamide formation was modeled. Acrylamide levels measured under different cooking conditions ranged from 94.78 µg/kg to 1070.67 µg/kg, and it was observed that increases in temperature and time led to higher acrylamide formation. In the modeling process, a Gaussian process regression model, with hyperparameters optimized using a proportional–integral–derivative-based search algorithm, was proposed. The performance of the proposed model was compared with multivariate linear, interaction, simple quadratic, quadratic, Gaussian, logarithmic, and exponential regression models. The findings revealed that the proposed model represented the experimental data with the highest accuracy. This model demonstrated superior performance compared to other models, with a root mean square error (RMSE) value of 56.90 and a coefficient of determination (R²) value of 0.97. Additionally, based on the reference value of 500 µg/kg established by the European Union, the potential health risk was mapped according to the combinations of temperature and time. This study contributes to the development of effective strategies to reduce acrylamide formation and provides important insights into ensuring food safety and protecting public health.

References

  • V. Gökmen, T. K. Palazoğlu, and H. Z. Şenyuva, Relation between the acrylamide formation and time–temperature history of surface and core regions of French fries. Journal of Food Engineering, 77 (4), 972-976, 2006. https://doi.org/10.1016/j.jfoodeng.2005.08 .030.
  •   G. Arusoğlu, Akrilamid oluşumu ve insan sağlığına etkileri. Akademik Gıda, 13 (1), 61-71, 2015.
  •   H. A. Deribew and A. Z. Woldegiorgis, Acrylamide levels in coffee powder, potato chips and French fries in Addis Ababa city of Ethiopia. Food Control, 123, 107727, 2021. https://doi.org/10.1016/j.foodcont.202 0.107727.
  •   L. Peivasteh-Roudsari, M. Karami, R. Barzegar-Bafrouei, S. Samiee, H. Karami, B. Tajdar-Oranj, V. Mahdavi, A. M. Alizadeh, P. Sadighara, G. O. Conti, and A. Mousavi Khaneghah, Toxicity, metabolism, and mitigation strategies of acrylamide: a comprehensive review. International Journal of Environmental Health Research, 34 (1), 1–29, 2022. https://doi.org/10.1080/09603123.2022.2123907.
  •   T. K. Tepe, Patates kızartmada uygulanan farklı işlemlerin akrilamid oluşumu üzerine etkisi. Yüksek Lisans Tezi, Pamukkale Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2018.
  •   N. M. Nizamlıoğlu ve S. Nas, Gıdalarda Akrilamid Oluşum Mekanizmaları, Gıdaların Akrilamid İçeriği Ve Sağlık Üzerine Etkileri. Akademik Gıda, 17 (2), 232-242, 2019. https://doi.org/10.24323/akademik-gida.613588.
  •   N. M. Nizamlıoğlu, Kavurma ve depolama koşullarının Bademin bazı fiziksel, kimyasal ve duyusal özellikleri üzerine etkisi. Doktora Tezi, Pamukkale Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2015.
  •   A. Karagöz, Akrilamid ve Gıdalarda Bulunuşu. TAF Preventive Medicine Bulletin, 8 (2), 187-192, 2009.
  •   M. Gölükçü and H. Tokgöz, Gıdalarda akrilamid oluşum mekanizması ve insan sağlığı üzerine etkileri. Derim, 22 (1), 41-48, 2005.
  • F. Ş. Bayraktar, Isıl ışlemin yağ oranı kısmen azaltılmış fındık üzerine etkisinin belirlenmesi. Yüksek Lisans Tezi, Ankara Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2014.
  • International Agency for Research on Cancer (IARC), Some industrial chemicals. IARC monographs on the evaluation of carcinogenic risks to humans. Vol. 60, 1994, https://publications.iarc.fr/78, Accessed 29 April 2025. 
  • European Commission, Proposal for a Directive of the European Parliament and of the Council Amending Directive 2004/37/EC on the Protection of Workers From the Risks Related to Exposure to Carcinogens or Mutagens at Work. Brussels, 2016.
  • V. Matoso, P. Bargi-Souza, F. Ivanski, M. A. Romano, and R. M. Romano, Acrylamide: A review about its toxic effects in the light of Developmental Origin of Health and Disease (DOHaD) concept. Food Chemistry, 283, 422-430, 2019. https://doi.org/10.101 6/j.foodchem.2019.01.054.
  • M. Bušová, V. Bencko, K. V. Laktičová, I. Holcátová, and M. Vargová, Risk of exposure to acrylamide. Central European Journal of Public Health, 28, 43-46, 2020. https://doi.org/10.21101/ cejph.a6177.
  • M. Fan, X. Xu, W. Lang, W. Wang, X. Wang, A. Xin, F. Zhou, Z. Ding, X. Ye, and B. Zhu, Toxicity, formation, contamination, determination and mitigation of acrylamide in thermally processed plant-based foods and herbal medicines: A review. Ecotoxicology and Environmental Safety, 260, 115059, 2023. https://doi.org/10.1016/ j.ecoenv.2023.115059.
  • V. Gokmen and B. A. Mogol, Acrylamide in food. Elsevier, 2023.
  • B. Asar, Ev Tipi Fırında Sıcak Hava İle Kızartma İşleminin, Taze Ve Dondurulmuş Parmak Patateslerin Kalite Parametrelerine Etkisinin İncelenmesi. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2014.
  • F. Pedreschi, K. Kaack, K. Granby, and E. Troncoso, Acrylamide reduction under different pre-treatments in French fries. Journal of Food Engineering, 79 (4), 1287-1294, 2007. https://doi.org/10.1016/j.jfoodeng. 2006.04.014.
  • F. Mestdagh, T. De Wilde, S. Fraselle, Y. Govaert, W. Ooghe, J. M. Degroodt, R. Verhé, C. V. Peteghem, and B. De Meulenaer, Optimization of the blanching process to reduce acrylamide in fried potatoes. LWT-Food Science and Technology, 41 (9), 1648-1654, 2008. https://doi.org/10.1016/j.lwt.2007.10.007.
  • D. K. Bedade, Y. B. Sutar, and R. S. Singhal, Chitosan coated calcium alginate beads for covalent immobilization of acrylamidase: Process parameters and removal of acrylamide from coffee. Food Chemistry, 275, 95-104, 2009. https://doi.org/10. 1016/j.foodchem.2018.09.090.
  • S. Wang, J. Yu, Q. Xin, S. Wang, and L. Copeland, Effects of starch damage and yeast fermentation on acrylamide formation in bread. Food Control, 73, 230-236, 2017. https://doi.org/10.1016/j.foodcont.2016. 08.002.
  • A. Antunes-Rohling, S. Ciudad-Hidalgo, J. Mir-Bel, J. Raso, G. Cebrián, and I. Álvarez, Ultrasound as a pretreatment to reduce acrylamide formation in fried potatoes. Innovative Food Science & Emerging Technologies, 49, 158-169, 2018. https://doi.org/10. 1016/j.ifset.2018.08.010.
  • J. Genovese, S. Tappi, W. Luo, U. Tylewicz, S. Marzocchi, S. Marziali, S. Romani, L. Ragni, and P. Rocculi, Important factors to consider for acrylamide mitigation in potato crisps using pulsed electric fields. Innovative Food Science & Emerging Technologies, 55, 18-26, 2019. https://doi.org/10. 1016/j.ifset.2019.05.008.
  • Y. Narita and K. Inouye, Decrease in the acrylamide content in canned coffee by heat treatment with the addition of cysteine. Journal of Agricultural and Food Chemistry, 62 (50), 12218-12222, 2014. https://doi.org/10.1021/jf5035288.
  • M. Z. Mulla, V. R. Bharadwaj, U. S. Annapure, P. S. Variyar, A. Sharma, and R. S. Singhal, Acrylamide content in fried chips prepared from irradiated and non-irradiated stored potatoes. Food Chemistry, 127 (4), 1668-1672, 2011. https://doi.org/10.1016/j. foodchem.2011.02.034.
  • A. A. Maan, M. A. Anjum, M. K. I. Khan, A. Nazir, F. Saeed, M. Afzaal, and R. M. Aadil, Acrylamide Formation and Different Mitigation Strategies during Food Processing – A Review. Food Reviews International, 38 (1), 70-87, 2022. https://doi.org /10.1080/87559129.2020.1719505.
  • X. Zhang, M. Zhang, and B. Adhikari, Recent developments in frying technologies applied to fresh foods. Trends in Food Science & Technology, 98, 68-81, 2020. https://doi.org/10.1016/j.tifs.2020.02.007.
  • C. Rannou, D. Laroque, E. Renault, C. Prost, and T. Sérot, Mitigation strategies of acrylamide, furans, heterocyclic amines and browning during the Maillard reaction in foods. Food Research International, 90, 154-176, 2016. https://doi.org/10.1016/j.foodres. 2016.10.037.
  • R. Pandiselvam, Ö. Süfer, Z. T. Özaslan, N. A. N. Gowda, M. K. Pulivarthi, A. P. R. Charles, B. Ramesh, S. Ramniwas, S. Rustagi, Z. Jafari, and G. Jeevarathinam, Acrylamide in food products: Formation, technological strategies for mitigation, and future outlook. Food Frontiers, 5 (3), 1063-1095, 2024. https://doi.org/10.1002/fft2.368.
  • L. Rifai and F. A. Saleh, A review on acrylamide in food: Occurrence, toxicity, and mitigation strategies. International Journal of Toxicology, 39 (2), 93-102, 2020. https://doi.org/10.1177/1091581820 902.
  • M. A. Adimas, B. D. Abera, Z. T. Adimas, H. W. Woldemariam, and M. A. Delele, Traditional food processing and Acrylamide formation: A review. Heliyon, 10 (9), e30258, 2024. https://doi.org/ 10.1016/j.heliyon.2024.e30258.
  • I. Govindaraju, M. Sana, I. Chakraborty, M. H. Rahman, R. Biswas, and N. Mazumder, Dietary Acrylamide: A Detailed Review on Formation, Detection, Mitigation, and Its Health Impacts. Foods, 13 (4), 556, 2024. https://doi.org/10.3390/foods 13040556.
  • V. Gökmen and H. Z. Şenyuva, Acrylamide formation is prevented by divalent cations during the Maillard reaction. Food Chemistry, 103 (1), 196-203, 2007. https://doi.org/10.1016/j.foodchem.2006.08.011.
  • G. Mutlu, S. Uğur ve B. Şimşek, Farklı tatlandırıcılar ve kaymak kullanımı ile üretilen Höşmerim tatlısının akrilamid içeriği ve bazı özellikleri. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14 (2), 588-596, 2025. https://doi.org/10. 28948/ngumuh.1587053.
  • B. Matthäus, N. U. Haase, and K. Vosmann, Factors affecting the concentration of acrylamide during deep‐fat frying of potatoes. European Journal of Lipid Science and Technology, 106 (11), 793-801, 2004.  https://doi.org/10.1002/ejlt.200400992.
  • F. Pedreschi, P. Moyano, K. Kaack, and K. Granby, Color changes and acrylamide formation in fried potato slices. Food Research International, 38 (1), 1-9, 2005. https://doi.org/10.1016/j.foodres.2004.07.002.
  • D. M. H. Farah and A. H. Zaibunnisa, Optimization of cocoa beans roasting process using Response Surface Methodology based on concentration of pyrazine and acrylamide. International Food Research Journal, 19 (4), 2012.
  • O. Lasekan and K. Abbas, Investigation of the roasting conditions with minimal acrylamide generation in tropical almond (Terminalia catappa) nuts by response surface methodology. Food Chemistry, 125 (2), 713-718, 2011. https://doi.org/10.1016/j.foodchem.2010. 09.073.
  • E. Akwagiobe, M. Ekpenyong, A. Asitok, A. Amenaghawon, D. Ubi, E. Ikharia, H. Kusuma, and S. Antai, Strain improvement, artificial intelligence optimization, and sensitivity analysis of asparaginase-mediated acrylamide reduction in sweet potato chips. Journal of Food Science and Technology, 60 (9), 2358-2369, 2023. https://doi.org/10.1007/s13197-023-05757-5.
  • H. Liu, X. Li, and Y. Yuan, Mitigation effect of sodium alginate on acrylamide formation in fried potato chips system based on response surface methodology. Journal of Food Science, 85 (8), 2615-2621, 2020. https://doi.org/10.1111/1750-3841. 15343.
  • M. J. Chen, H. T. Hsu, C. L. Lin, and W. Y. Ju, A statistical regression model for the estimation of acrylamide concentrations in French fries for excess lifetime cancer risk assessment. Food and Chemical Toxicology, 50 (10), 3867-3876, 2012. https://doi.org/ 10.1016/j.fct.2012.07.010.
  • Y. Zhang and Y. Wu, Introducing Machine Learning Models to Response Surface Methodologies. IntechOpen, 2021.
  • E. Alpaydin, Introduction to machine learning. MIT press, 2020.
  • X. Liu, G. Sun, R. Ju, J. Li, Z. Li, Y. Jiang, K. Zhao, Y. Zhang, Y. Jing, and G. Yang, Prediction of load-bearing capacity of FRP-steel composite tubed concrete columns: Using explainable machine learning model with limited data. Structures, 71, 10789, 2025. https://doi.org/10.1016/j.istruc.2024. 107890.
  • Q. Chen and C. Yang, Hybrid algorithm for multi-objective optimization design of parallel manipulators. Applied Mathematical Modelling, 98, 245-265, 2021. https://doi.org/10.1016/j.apm.2021. 05.009.
  • F. Zhu, C. Xu, and G. Dui, Particle swarm hybridize with Gaussian process regression for displacement prediction. 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 522-525, Changsha, China, 2010. https://doi.org/10.1109/BICTA.2010.5645179.
  • B. K. Isamura and P. L. Popelier, Metaheuristic optimisation of Gaussian process regression model hyperparameters: Insights from FEREBUS. Artificial Intelligence Chemistry, 1 (2), 100021, 2023. https://doi.org/10.1016/j.aichem.2023.100021.
  • L. Kang, R. S. Chen, N. Xiong, Y. C. Chen, Y. X. Hu and C. M. Chen, Selecting hyper-parameters of Gaussian process regression based on non-inertial particle swarm optimization in Internet of Things. IEEE Access, 7, 59504-59513, 2019. https://doi.org/10.1109/ACCESS.2019.2913757.
  • J. A. Rufián-Henares and F. J. Morales, Determination of acrylamide in potato chips by a reversed-phase LC–MS method based on a stable isotope dilution assay. Food Chemistry, 97 (3), 555-562, 2006. https://doi.org/10.1016/j.foodchem.2005.06.007.
  • J. Q. Shi and T. Choi, Gaussian process regression analysis for functional data. CRC press, 2011.
  • T. Hai, A. Basem, A. A. Alizadeh, K. Sharma, D. J. Jasim, H. Rajab, M. Ahmed, M. Kassim, N. S. S. Singh, and H. Maleki, Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs. Scientific Reports, 14 (1), 20271, 2024. https://doi.org/10.1038/s41598-024-71027-9.
  • F. Gisperg, R. Klausser, M. Elshazly, J. Kopp, E. P. Brichtová, and O. Spadiut, Bayesian Optimization in Bioprocess Engineering—Where Do We Stand Today?. Biotechnology and Bioengineering, 2025.  
  • /doi.org/10.1002/bit.28960.
  • M. A. Ulaş, Gauss Süreç Regresyonu ve Destek Vektör Makineleri Kullanılarak Değerlendirilen Kendiliğinden Yerleşen Beton Davranışının Deneysel Veri İle Doğrulanması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35 (1), 379-388, 2023. https://doi.org/10.35234/fumbd.1237839.
  • M. Ehteram, A. N. Ahmed, Z. Sheikh Khozani, and A. El-Shafie, Convolutional neural network-support vector machine model-gaussian process regression: a new machine model for predicting monthly and daily rainfall. Water Resources Management, 37 (9), 3631-3655, 2023. https://doi.org/10.1007/s11269-023- 03519-8.
  • J. Yuan, K. Wang, T. Yu, and M. Fang, Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression. International Journal of Machine Tools and Manufacture, 48 (1), 47-60, 2008. https://doi.org/10.1016/j.ijmachtools.2007. 07.011.
  • B. Likar and J. Kocijan, Predictive control of a gas–liquid separation plant based on a Gaussian process model. Computers & Chemical Engineering, 31 (3), 142-152, 2007. https://doi.org/10.1016/j.compc hemeng.2006.05.011.
  • L. L. T. Chan, Y. Liu, and J. Chen, Nonlinear System Identification with Selective Recursive Gaussian Process Models. Industrial & Engineering Chemistry Research, 52 (51), 18276-18286, 2013. https://doi.org/ 10.1021/ie4031538.
  • T. Gil-Díaz and M. Trumm, Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry. Ecological Informatics, 84, 102877, 2024. https://doi.org/10.1016 /j.ecoinf.2024.102877.
  • O. Ozbayram, A. Olivier, and L. Graham-Brady, Heteroscedastic Gaussian Process Regression for material structure–property relationship modeling. Computer Methods in Applied Mechanics and Engineering, 431, 117326, 2024. https://doi.org/ 10.1016/j.cma.2024.117326.
  • N. Gültekin ve A. Doğan, Kohezyonlu zeminlerde net limit basınç ve deformasyon modülünün makine öğrenimi temelli modeller kullanılarak tahmin edilmesi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11 (4), 1025-1033, 2022. https://doi.org/10.28948/ngumuh.1155568.
  • C. E. Rasmussen, Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning, ML 2003, Lecture Notes in Computer Science, 3176. Springer, Berlin, Heidelberg, 2003. https://doi.org/10 .1007/978-3-540-28650-9_4.
  • A. Kumar, S. Patil, A. Kovacevic, and S. A. Ponnusami, Performance prediction and Bayesian optimization of screw compressors using Gaussian Process Regression. Engineering Applications of Artificial Intelligence, 133, 108270, 2024. https://doi.org/10.1016/j.engappai.2024.108270.
  • Y. Gao, PID-based search algorithm: A novel metaheuristic algorithm based on PID algorithm. Expert Systems with Applications, 232, 120886, 2023. https://doi.org/10.1016/j.eswa.2023.120886.
  • M. A. Azad, A. Sarwar, M. Tariq, F. I. Bakhsh, S. Ahmad, A. S. N. Mohamed, and M. R. Islam, Global maximum power point tracking for photovoltaic systems under partial and complex shading conditions using a PID based search algorithm (PSA). IET Renewable Power Generation, 19 (1), e70005, 2025. https://doi.org/10.1049/rpg2.70005.
  • W. Jiang, H. Han, D. Feng, L. Qian, Q. Wang, and X. G. Xia, Energy-efficient and Accuracy-aware DNN Inference with IoT Device-edge Collaboration. IEEE Transactions on Services Computing, 1–14, 2025. https://doi.org/10.1109/TSC.2025.3536311.
  • Z. You, Y. Bian, Y. Zhang, and L. Chen, An intelligent optimization algorithm with novel fitness function for high-performance PMSM FOC. Alexandria Engineering Journal, 115, 286–296, 2025. https://doi.org/10.1016/j.aej.2024.12.029.
  • F. Li, D. Wang, N. Zhang, R. Zheng and Y. Zhu, Autocalibration for MEMS triaxial accelerometer based on a PID-based search algorithm. 2024 6th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), pp. 470-473, Xi'an, China, 2024. https://doi.org/10.1109 /ICMSP64464.2024.10866298.
  • E. Bouguenna, B. Lekouaghet, and M. Haddad, Improved Fractional PIαDβ Controller for AVR System via a New Optimization Algorithm. 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC), 1-6, Algeria, 2024. https://doi.org/10.1109/ICEEAC61226.2024.10576215.
  • S. Tang, Z. Chai, X. Wang, H. Chang, and X. Guo, Adaptive impedance control method for manipulator based on radial basis function. Industrial Robot, 2024. https://doi.org/10.1108/IR-07-2024-0327.
  • B. Ghojogh and M. Crowley, The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial. arXiv preprint arXiv:1905.12787, 2019. https://doi.org/10.48550/ arXiv.1905.12787.
  • Y. A. Üncü, T. Danışman, and H. Özdoğan, Predicting (n, 3n) nuclear reaction cross-sections using XGBoost and Leave-One-Out Cross-Validation. Applied Radiation and Isotopes, 219, 111714, 2025. https://doi.org/10.1016/j.apradiso.2025.111714.
  • T. K. Tepe and Ç. Kadakal, Temperature and slice size dependences of acrylamide in potato fries. Journal of Food Processing and Preservation, 43 (12), e14270, 2019. https://doi.org/10.1111/jfpp.14270.
  • J. Michalak, E. Gujska, and J. Klepacka, The Effect of Domestic Preparation of Some Potato Products on Acrylamide Content. Plant Foods for Human Nutrition, 66, 307-312, 2011. https://doi.org/10.1007/s11130-011-0252-2.
  • R. Lu, Z. Yang, H. Song, Y. Zhang, S. Zheng, Y. Chen, and N. Zhou, The aroma‐active compound, acrylamide and ascorbic acid contents of pan‐fried potato slices cooked by different temperature and time. Journal of Food Processing and Preservation, 40 (2), 183-191, 2016. https://doi.org/10.1111/jfpp.12595.
  • L. El Hosry, V. Elias, V. Chamoun, M. Halawi, P. Cayot, A. Nehme, and E. Bou-Maroun, Maillard Reaction: Mechanism, Influencing Parameters, Advantages, Disadvantages, and Food Industrial Applications: A Review. Foods, 14 (11), 1881, 2025. https://doi.org/10.3390/foods14111881.
  • A. Serpen and V. Gökmen, Modeling of acrylamide formation and browning ratio in potato chips by artificial neural network. Molecular Nutrition & Food Research, 51 (4), 383-389, 2007. https://doi.org/ 10.1002/mnfr.200600121.
  • S. Romani, M. Bacchiocca, P. Rocculi, and M. Dalla Rosa, Effect of frying time on acrylamide content and quality aspects of French fries. European Food Research and Technology, 226, 555-560, 2008. https://doi.org/10.1007/s00217-007-0570-7.
  • M. G. Corradini and M. Peleg, Linear and non-linear kinetics in the synthesis and degradation of acrylamide in foods and model systems. Critical Reviews in Food Science and Nutrition, 46 (6), 489-517, 2006. https://doi.org/10.1080/10408390600758280.
  • D. Chicco, M. J. Warrens, and G. Jurman, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623, 2021. https://doi.org/ 10.7717/peerj-cs.623.
  • P. Chaurasia, K. Younis, O. S. Qadri, G. Srivastava, and K. Osama, Comparison of Gaussian process regression, artificial neural network, and response surface methodology modeling approaches for predicting drying time of mosambi (Citrus limetta) peel. Journal of Food Process Engineering, 42 (2), e12966, 2019. https://doi.org/10.1111/jfpe.12966.
  • X. Chen, J. Xue, X. Chen, X. Zhao, S. Ali, and G. Huang, Gaussian process regression for prediction and confidence analysis of fruit traits by near-infrared spectroscopy. Food Quality and Safety, 7, fyac068, 2023. https://doi.org/10.1093/fqsafe/fyac068.
  • R. R. Pullanagari and M. Li, Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. Journal of Food Engineering, 289, 110177, 2021. https://doi.org/10.1016/j.jfoodeng.2020.110 177.
  • European Commission, Commission Regulation (EU) 2017/2158 establishing mitigation measures and benchmark levels for the reduction of the presence of acrylamide in food. Official Journal of the European Union, L304, 24–44, 2017, http://data.europa.eu/eli/ reg/2017/2158/oj, Accessed 08 May 2025.

Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi

Year 2025, Volume: 14 Issue: 3, 1088 - 1099, 15.07.2025
https://doi.org/10.28948/ngumuh.1696315

Abstract

Yüksek düzeyde akrilamid maruziyeti, genotoksik ve potansiyel olarak kanserojen etkiler göstermekte olup, insan sağlığı açısından ciddi bir risk unsuru olarak değerlendirilmektedir. Bu nedenle, gıda ürünlerinde akrilamid oluşumunun kontrol altına alınması, gıda güvenliği açısından büyük önem taşımaktadır. Bu çalışmada, ev tipi fırında pişirilen patates kızartmalarında pişirme sıcaklığı ve süresinin akrilamid oluşumu üzerindeki etkileri deneysel olarak incelenmiş ve bu parametrelerin akrilamid oluşumu ile ilişkisi modellenmiştir. Farklı pişirme koşullarında ölçülen akrilamid düzeyleri 94.78 µg/kg ile 1070.67 µg/kg arasında değişmiş, sıcaklık ve süredeki artışın akrilamid oluşumunu artırdığı gözlemlenmiştir. Modelleme sürecinde, hiperparametre optimizasyonu oransal–integral–türevsel tabanlı arama algoritmasıyla gerçekleştirilen Gauss süreç regresyon modeli önerilmiştir. Önerilen modelin performansı, çok değişkenli doğrusal, etkileşimli, basit karesel, karesel, Gauss, logaritmik ve üstel regresyon modelleriyle karşılaştırılmıştır. Elde edilen bulgular, önerilen modelin deneysel verileri en yüksek doğrulukla temsil ettiğini ortaya koymuştur. Bu model, kök ortalama kare hata (RMSE) değeri 56.90 ve determinasyon katsayısı (R²) değeri 0.97 ile diğer modellere kıyasla üstün bir performans sergilemiştir. Ayrıca, Avrupa Birliği tarafından belirlenen 500 µg/kg'lık referans değer esas alınarak, sıcaklık ve süre kombinasyonlarına bağlı potansiyel sağlık riski haritalandırılmıştır. Bu çalışma, akrilamid oluşumunu azaltmaya yönelik etkili stratejilerin geliştirilmesine katkı sağlamakta ve gıda güvenliği ile halk sağlığının korunmasına yönelik önemli bilgiler sunmaktadır.

Thanks

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 2209-B Üniversite Öğrencileri Sanayiye Yönelik Araştırma Projeleri Desteği Programı kapsamında desteklenmiştir. Bu destek için TÜBİTAK’a ve sanayi destekçisi olarak katkılarından dolayı BSH Ev Aletleri Sanayi ve Ticaret A.Ş.'ye teşekkür ederiz. Ayrıca, çalışma sürecinde sağladığı değerli bilgi ve katkılarından dolayı Prof. Dr. Ahmet KOLUMAN’a teşekkür ederiz.

References

  • V. Gökmen, T. K. Palazoğlu, and H. Z. Şenyuva, Relation between the acrylamide formation and time–temperature history of surface and core regions of French fries. Journal of Food Engineering, 77 (4), 972-976, 2006. https://doi.org/10.1016/j.jfoodeng.2005.08 .030.
  •   G. Arusoğlu, Akrilamid oluşumu ve insan sağlığına etkileri. Akademik Gıda, 13 (1), 61-71, 2015.
  •   H. A. Deribew and A. Z. Woldegiorgis, Acrylamide levels in coffee powder, potato chips and French fries in Addis Ababa city of Ethiopia. Food Control, 123, 107727, 2021. https://doi.org/10.1016/j.foodcont.202 0.107727.
  •   L. Peivasteh-Roudsari, M. Karami, R. Barzegar-Bafrouei, S. Samiee, H. Karami, B. Tajdar-Oranj, V. Mahdavi, A. M. Alizadeh, P. Sadighara, G. O. Conti, and A. Mousavi Khaneghah, Toxicity, metabolism, and mitigation strategies of acrylamide: a comprehensive review. International Journal of Environmental Health Research, 34 (1), 1–29, 2022. https://doi.org/10.1080/09603123.2022.2123907.
  •   T. K. Tepe, Patates kızartmada uygulanan farklı işlemlerin akrilamid oluşumu üzerine etkisi. Yüksek Lisans Tezi, Pamukkale Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2018.
  •   N. M. Nizamlıoğlu ve S. Nas, Gıdalarda Akrilamid Oluşum Mekanizmaları, Gıdaların Akrilamid İçeriği Ve Sağlık Üzerine Etkileri. Akademik Gıda, 17 (2), 232-242, 2019. https://doi.org/10.24323/akademik-gida.613588.
  •   N. M. Nizamlıoğlu, Kavurma ve depolama koşullarının Bademin bazı fiziksel, kimyasal ve duyusal özellikleri üzerine etkisi. Doktora Tezi, Pamukkale Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2015.
  •   A. Karagöz, Akrilamid ve Gıdalarda Bulunuşu. TAF Preventive Medicine Bulletin, 8 (2), 187-192, 2009.
  •   M. Gölükçü and H. Tokgöz, Gıdalarda akrilamid oluşum mekanizması ve insan sağlığı üzerine etkileri. Derim, 22 (1), 41-48, 2005.
  • F. Ş. Bayraktar, Isıl ışlemin yağ oranı kısmen azaltılmış fındık üzerine etkisinin belirlenmesi. Yüksek Lisans Tezi, Ankara Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2014.
  • International Agency for Research on Cancer (IARC), Some industrial chemicals. IARC monographs on the evaluation of carcinogenic risks to humans. Vol. 60, 1994, https://publications.iarc.fr/78, Accessed 29 April 2025. 
  • European Commission, Proposal for a Directive of the European Parliament and of the Council Amending Directive 2004/37/EC on the Protection of Workers From the Risks Related to Exposure to Carcinogens or Mutagens at Work. Brussels, 2016.
  • V. Matoso, P. Bargi-Souza, F. Ivanski, M. A. Romano, and R. M. Romano, Acrylamide: A review about its toxic effects in the light of Developmental Origin of Health and Disease (DOHaD) concept. Food Chemistry, 283, 422-430, 2019. https://doi.org/10.101 6/j.foodchem.2019.01.054.
  • M. Bušová, V. Bencko, K. V. Laktičová, I. Holcátová, and M. Vargová, Risk of exposure to acrylamide. Central European Journal of Public Health, 28, 43-46, 2020. https://doi.org/10.21101/ cejph.a6177.
  • M. Fan, X. Xu, W. Lang, W. Wang, X. Wang, A. Xin, F. Zhou, Z. Ding, X. Ye, and B. Zhu, Toxicity, formation, contamination, determination and mitigation of acrylamide in thermally processed plant-based foods and herbal medicines: A review. Ecotoxicology and Environmental Safety, 260, 115059, 2023. https://doi.org/10.1016/ j.ecoenv.2023.115059.
  • V. Gokmen and B. A. Mogol, Acrylamide in food. Elsevier, 2023.
  • B. Asar, Ev Tipi Fırında Sıcak Hava İle Kızartma İşleminin, Taze Ve Dondurulmuş Parmak Patateslerin Kalite Parametrelerine Etkisinin İncelenmesi. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2014.
  • F. Pedreschi, K. Kaack, K. Granby, and E. Troncoso, Acrylamide reduction under different pre-treatments in French fries. Journal of Food Engineering, 79 (4), 1287-1294, 2007. https://doi.org/10.1016/j.jfoodeng. 2006.04.014.
  • F. Mestdagh, T. De Wilde, S. Fraselle, Y. Govaert, W. Ooghe, J. M. Degroodt, R. Verhé, C. V. Peteghem, and B. De Meulenaer, Optimization of the blanching process to reduce acrylamide in fried potatoes. LWT-Food Science and Technology, 41 (9), 1648-1654, 2008. https://doi.org/10.1016/j.lwt.2007.10.007.
  • D. K. Bedade, Y. B. Sutar, and R. S. Singhal, Chitosan coated calcium alginate beads for covalent immobilization of acrylamidase: Process parameters and removal of acrylamide from coffee. Food Chemistry, 275, 95-104, 2009. https://doi.org/10. 1016/j.foodchem.2018.09.090.
  • S. Wang, J. Yu, Q. Xin, S. Wang, and L. Copeland, Effects of starch damage and yeast fermentation on acrylamide formation in bread. Food Control, 73, 230-236, 2017. https://doi.org/10.1016/j.foodcont.2016. 08.002.
  • A. Antunes-Rohling, S. Ciudad-Hidalgo, J. Mir-Bel, J. Raso, G. Cebrián, and I. Álvarez, Ultrasound as a pretreatment to reduce acrylamide formation in fried potatoes. Innovative Food Science & Emerging Technologies, 49, 158-169, 2018. https://doi.org/10. 1016/j.ifset.2018.08.010.
  • J. Genovese, S. Tappi, W. Luo, U. Tylewicz, S. Marzocchi, S. Marziali, S. Romani, L. Ragni, and P. Rocculi, Important factors to consider for acrylamide mitigation in potato crisps using pulsed electric fields. Innovative Food Science & Emerging Technologies, 55, 18-26, 2019. https://doi.org/10. 1016/j.ifset.2019.05.008.
  • Y. Narita and K. Inouye, Decrease in the acrylamide content in canned coffee by heat treatment with the addition of cysteine. Journal of Agricultural and Food Chemistry, 62 (50), 12218-12222, 2014. https://doi.org/10.1021/jf5035288.
  • M. Z. Mulla, V. R. Bharadwaj, U. S. Annapure, P. S. Variyar, A. Sharma, and R. S. Singhal, Acrylamide content in fried chips prepared from irradiated and non-irradiated stored potatoes. Food Chemistry, 127 (4), 1668-1672, 2011. https://doi.org/10.1016/j. foodchem.2011.02.034.
  • A. A. Maan, M. A. Anjum, M. K. I. Khan, A. Nazir, F. Saeed, M. Afzaal, and R. M. Aadil, Acrylamide Formation and Different Mitigation Strategies during Food Processing – A Review. Food Reviews International, 38 (1), 70-87, 2022. https://doi.org /10.1080/87559129.2020.1719505.
  • X. Zhang, M. Zhang, and B. Adhikari, Recent developments in frying technologies applied to fresh foods. Trends in Food Science & Technology, 98, 68-81, 2020. https://doi.org/10.1016/j.tifs.2020.02.007.
  • C. Rannou, D. Laroque, E. Renault, C. Prost, and T. Sérot, Mitigation strategies of acrylamide, furans, heterocyclic amines and browning during the Maillard reaction in foods. Food Research International, 90, 154-176, 2016. https://doi.org/10.1016/j.foodres. 2016.10.037.
  • R. Pandiselvam, Ö. Süfer, Z. T. Özaslan, N. A. N. Gowda, M. K. Pulivarthi, A. P. R. Charles, B. Ramesh, S. Ramniwas, S. Rustagi, Z. Jafari, and G. Jeevarathinam, Acrylamide in food products: Formation, technological strategies for mitigation, and future outlook. Food Frontiers, 5 (3), 1063-1095, 2024. https://doi.org/10.1002/fft2.368.
  • L. Rifai and F. A. Saleh, A review on acrylamide in food: Occurrence, toxicity, and mitigation strategies. International Journal of Toxicology, 39 (2), 93-102, 2020. https://doi.org/10.1177/1091581820 902.
  • M. A. Adimas, B. D. Abera, Z. T. Adimas, H. W. Woldemariam, and M. A. Delele, Traditional food processing and Acrylamide formation: A review. Heliyon, 10 (9), e30258, 2024. https://doi.org/ 10.1016/j.heliyon.2024.e30258.
  • I. Govindaraju, M. Sana, I. Chakraborty, M. H. Rahman, R. Biswas, and N. Mazumder, Dietary Acrylamide: A Detailed Review on Formation, Detection, Mitigation, and Its Health Impacts. Foods, 13 (4), 556, 2024. https://doi.org/10.3390/foods 13040556.
  • V. Gökmen and H. Z. Şenyuva, Acrylamide formation is prevented by divalent cations during the Maillard reaction. Food Chemistry, 103 (1), 196-203, 2007. https://doi.org/10.1016/j.foodchem.2006.08.011.
  • G. Mutlu, S. Uğur ve B. Şimşek, Farklı tatlandırıcılar ve kaymak kullanımı ile üretilen Höşmerim tatlısının akrilamid içeriği ve bazı özellikleri. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14 (2), 588-596, 2025. https://doi.org/10. 28948/ngumuh.1587053.
  • B. Matthäus, N. U. Haase, and K. Vosmann, Factors affecting the concentration of acrylamide during deep‐fat frying of potatoes. European Journal of Lipid Science and Technology, 106 (11), 793-801, 2004.  https://doi.org/10.1002/ejlt.200400992.
  • F. Pedreschi, P. Moyano, K. Kaack, and K. Granby, Color changes and acrylamide formation in fried potato slices. Food Research International, 38 (1), 1-9, 2005. https://doi.org/10.1016/j.foodres.2004.07.002.
  • D. M. H. Farah and A. H. Zaibunnisa, Optimization of cocoa beans roasting process using Response Surface Methodology based on concentration of pyrazine and acrylamide. International Food Research Journal, 19 (4), 2012.
  • O. Lasekan and K. Abbas, Investigation of the roasting conditions with minimal acrylamide generation in tropical almond (Terminalia catappa) nuts by response surface methodology. Food Chemistry, 125 (2), 713-718, 2011. https://doi.org/10.1016/j.foodchem.2010. 09.073.
  • E. Akwagiobe, M. Ekpenyong, A. Asitok, A. Amenaghawon, D. Ubi, E. Ikharia, H. Kusuma, and S. Antai, Strain improvement, artificial intelligence optimization, and sensitivity analysis of asparaginase-mediated acrylamide reduction in sweet potato chips. Journal of Food Science and Technology, 60 (9), 2358-2369, 2023. https://doi.org/10.1007/s13197-023-05757-5.
  • H. Liu, X. Li, and Y. Yuan, Mitigation effect of sodium alginate on acrylamide formation in fried potato chips system based on response surface methodology. Journal of Food Science, 85 (8), 2615-2621, 2020. https://doi.org/10.1111/1750-3841. 15343.
  • M. J. Chen, H. T. Hsu, C. L. Lin, and W. Y. Ju, A statistical regression model for the estimation of acrylamide concentrations in French fries for excess lifetime cancer risk assessment. Food and Chemical Toxicology, 50 (10), 3867-3876, 2012. https://doi.org/ 10.1016/j.fct.2012.07.010.
  • Y. Zhang and Y. Wu, Introducing Machine Learning Models to Response Surface Methodologies. IntechOpen, 2021.
  • E. Alpaydin, Introduction to machine learning. MIT press, 2020.
  • X. Liu, G. Sun, R. Ju, J. Li, Z. Li, Y. Jiang, K. Zhao, Y. Zhang, Y. Jing, and G. Yang, Prediction of load-bearing capacity of FRP-steel composite tubed concrete columns: Using explainable machine learning model with limited data. Structures, 71, 10789, 2025. https://doi.org/10.1016/j.istruc.2024. 107890.
  • Q. Chen and C. Yang, Hybrid algorithm for multi-objective optimization design of parallel manipulators. Applied Mathematical Modelling, 98, 245-265, 2021. https://doi.org/10.1016/j.apm.2021. 05.009.
  • F. Zhu, C. Xu, and G. Dui, Particle swarm hybridize with Gaussian process regression for displacement prediction. 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 522-525, Changsha, China, 2010. https://doi.org/10.1109/BICTA.2010.5645179.
  • B. K. Isamura and P. L. Popelier, Metaheuristic optimisation of Gaussian process regression model hyperparameters: Insights from FEREBUS. Artificial Intelligence Chemistry, 1 (2), 100021, 2023. https://doi.org/10.1016/j.aichem.2023.100021.
  • L. Kang, R. S. Chen, N. Xiong, Y. C. Chen, Y. X. Hu and C. M. Chen, Selecting hyper-parameters of Gaussian process regression based on non-inertial particle swarm optimization in Internet of Things. IEEE Access, 7, 59504-59513, 2019. https://doi.org/10.1109/ACCESS.2019.2913757.
  • J. A. Rufián-Henares and F. J. Morales, Determination of acrylamide in potato chips by a reversed-phase LC–MS method based on a stable isotope dilution assay. Food Chemistry, 97 (3), 555-562, 2006. https://doi.org/10.1016/j.foodchem.2005.06.007.
  • J. Q. Shi and T. Choi, Gaussian process regression analysis for functional data. CRC press, 2011.
  • T. Hai, A. Basem, A. A. Alizadeh, K. Sharma, D. J. Jasim, H. Rajab, M. Ahmed, M. Kassim, N. S. S. Singh, and H. Maleki, Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs. Scientific Reports, 14 (1), 20271, 2024. https://doi.org/10.1038/s41598-024-71027-9.
  • F. Gisperg, R. Klausser, M. Elshazly, J. Kopp, E. P. Brichtová, and O. Spadiut, Bayesian Optimization in Bioprocess Engineering—Where Do We Stand Today?. Biotechnology and Bioengineering, 2025.  
  • /doi.org/10.1002/bit.28960.
  • M. A. Ulaş, Gauss Süreç Regresyonu ve Destek Vektör Makineleri Kullanılarak Değerlendirilen Kendiliğinden Yerleşen Beton Davranışının Deneysel Veri İle Doğrulanması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35 (1), 379-388, 2023. https://doi.org/10.35234/fumbd.1237839.
  • M. Ehteram, A. N. Ahmed, Z. Sheikh Khozani, and A. El-Shafie, Convolutional neural network-support vector machine model-gaussian process regression: a new machine model for predicting monthly and daily rainfall. Water Resources Management, 37 (9), 3631-3655, 2023. https://doi.org/10.1007/s11269-023- 03519-8.
  • J. Yuan, K. Wang, T. Yu, and M. Fang, Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression. International Journal of Machine Tools and Manufacture, 48 (1), 47-60, 2008. https://doi.org/10.1016/j.ijmachtools.2007. 07.011.
  • B. Likar and J. Kocijan, Predictive control of a gas–liquid separation plant based on a Gaussian process model. Computers & Chemical Engineering, 31 (3), 142-152, 2007. https://doi.org/10.1016/j.compc hemeng.2006.05.011.
  • L. L. T. Chan, Y. Liu, and J. Chen, Nonlinear System Identification with Selective Recursive Gaussian Process Models. Industrial & Engineering Chemistry Research, 52 (51), 18276-18286, 2013. https://doi.org/ 10.1021/ie4031538.
  • T. Gil-Díaz and M. Trumm, Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry. Ecological Informatics, 84, 102877, 2024. https://doi.org/10.1016 /j.ecoinf.2024.102877.
  • O. Ozbayram, A. Olivier, and L. Graham-Brady, Heteroscedastic Gaussian Process Regression for material structure–property relationship modeling. Computer Methods in Applied Mechanics and Engineering, 431, 117326, 2024. https://doi.org/ 10.1016/j.cma.2024.117326.
  • N. Gültekin ve A. Doğan, Kohezyonlu zeminlerde net limit basınç ve deformasyon modülünün makine öğrenimi temelli modeller kullanılarak tahmin edilmesi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11 (4), 1025-1033, 2022. https://doi.org/10.28948/ngumuh.1155568.
  • C. E. Rasmussen, Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning, ML 2003, Lecture Notes in Computer Science, 3176. Springer, Berlin, Heidelberg, 2003. https://doi.org/10 .1007/978-3-540-28650-9_4.
  • A. Kumar, S. Patil, A. Kovacevic, and S. A. Ponnusami, Performance prediction and Bayesian optimization of screw compressors using Gaussian Process Regression. Engineering Applications of Artificial Intelligence, 133, 108270, 2024. https://doi.org/10.1016/j.engappai.2024.108270.
  • Y. Gao, PID-based search algorithm: A novel metaheuristic algorithm based on PID algorithm. Expert Systems with Applications, 232, 120886, 2023. https://doi.org/10.1016/j.eswa.2023.120886.
  • M. A. Azad, A. Sarwar, M. Tariq, F. I. Bakhsh, S. Ahmad, A. S. N. Mohamed, and M. R. Islam, Global maximum power point tracking for photovoltaic systems under partial and complex shading conditions using a PID based search algorithm (PSA). IET Renewable Power Generation, 19 (1), e70005, 2025. https://doi.org/10.1049/rpg2.70005.
  • W. Jiang, H. Han, D. Feng, L. Qian, Q. Wang, and X. G. Xia, Energy-efficient and Accuracy-aware DNN Inference with IoT Device-edge Collaboration. IEEE Transactions on Services Computing, 1–14, 2025. https://doi.org/10.1109/TSC.2025.3536311.
  • Z. You, Y. Bian, Y. Zhang, and L. Chen, An intelligent optimization algorithm with novel fitness function for high-performance PMSM FOC. Alexandria Engineering Journal, 115, 286–296, 2025. https://doi.org/10.1016/j.aej.2024.12.029.
  • F. Li, D. Wang, N. Zhang, R. Zheng and Y. Zhu, Autocalibration for MEMS triaxial accelerometer based on a PID-based search algorithm. 2024 6th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), pp. 470-473, Xi'an, China, 2024. https://doi.org/10.1109 /ICMSP64464.2024.10866298.
  • E. Bouguenna, B. Lekouaghet, and M. Haddad, Improved Fractional PIαDβ Controller for AVR System via a New Optimization Algorithm. 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC), 1-6, Algeria, 2024. https://doi.org/10.1109/ICEEAC61226.2024.10576215.
  • S. Tang, Z. Chai, X. Wang, H. Chang, and X. Guo, Adaptive impedance control method for manipulator based on radial basis function. Industrial Robot, 2024. https://doi.org/10.1108/IR-07-2024-0327.
  • B. Ghojogh and M. Crowley, The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial. arXiv preprint arXiv:1905.12787, 2019. https://doi.org/10.48550/ arXiv.1905.12787.
  • Y. A. Üncü, T. Danışman, and H. Özdoğan, Predicting (n, 3n) nuclear reaction cross-sections using XGBoost and Leave-One-Out Cross-Validation. Applied Radiation and Isotopes, 219, 111714, 2025. https://doi.org/10.1016/j.apradiso.2025.111714.
  • T. K. Tepe and Ç. Kadakal, Temperature and slice size dependences of acrylamide in potato fries. Journal of Food Processing and Preservation, 43 (12), e14270, 2019. https://doi.org/10.1111/jfpp.14270.
  • J. Michalak, E. Gujska, and J. Klepacka, The Effect of Domestic Preparation of Some Potato Products on Acrylamide Content. Plant Foods for Human Nutrition, 66, 307-312, 2011. https://doi.org/10.1007/s11130-011-0252-2.
  • R. Lu, Z. Yang, H. Song, Y. Zhang, S. Zheng, Y. Chen, and N. Zhou, The aroma‐active compound, acrylamide and ascorbic acid contents of pan‐fried potato slices cooked by different temperature and time. Journal of Food Processing and Preservation, 40 (2), 183-191, 2016. https://doi.org/10.1111/jfpp.12595.
  • L. El Hosry, V. Elias, V. Chamoun, M. Halawi, P. Cayot, A. Nehme, and E. Bou-Maroun, Maillard Reaction: Mechanism, Influencing Parameters, Advantages, Disadvantages, and Food Industrial Applications: A Review. Foods, 14 (11), 1881, 2025. https://doi.org/10.3390/foods14111881.
  • A. Serpen and V. Gökmen, Modeling of acrylamide formation and browning ratio in potato chips by artificial neural network. Molecular Nutrition & Food Research, 51 (4), 383-389, 2007. https://doi.org/ 10.1002/mnfr.200600121.
  • S. Romani, M. Bacchiocca, P. Rocculi, and M. Dalla Rosa, Effect of frying time on acrylamide content and quality aspects of French fries. European Food Research and Technology, 226, 555-560, 2008. https://doi.org/10.1007/s00217-007-0570-7.
  • M. G. Corradini and M. Peleg, Linear and non-linear kinetics in the synthesis and degradation of acrylamide in foods and model systems. Critical Reviews in Food Science and Nutrition, 46 (6), 489-517, 2006. https://doi.org/10.1080/10408390600758280.
  • D. Chicco, M. J. Warrens, and G. Jurman, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623, 2021. https://doi.org/ 10.7717/peerj-cs.623.
  • P. Chaurasia, K. Younis, O. S. Qadri, G. Srivastava, and K. Osama, Comparison of Gaussian process regression, artificial neural network, and response surface methodology modeling approaches for predicting drying time of mosambi (Citrus limetta) peel. Journal of Food Process Engineering, 42 (2), e12966, 2019. https://doi.org/10.1111/jfpe.12966.
  • X. Chen, J. Xue, X. Chen, X. Zhao, S. Ali, and G. Huang, Gaussian process regression for prediction and confidence analysis of fruit traits by near-infrared spectroscopy. Food Quality and Safety, 7, fyac068, 2023. https://doi.org/10.1093/fqsafe/fyac068.
  • R. R. Pullanagari and M. Li, Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. Journal of Food Engineering, 289, 110177, 2021. https://doi.org/10.1016/j.jfoodeng.2020.110 177.
  • European Commission, Commission Regulation (EU) 2017/2158 establishing mitigation measures and benchmark levels for the reduction of the presence of acrylamide in food. Official Journal of the European Union, L304, 24–44, 2017, http://data.europa.eu/eli/ reg/2017/2158/oj, Accessed 08 May 2025.
There are 84 citations in total.

Details

Primary Language Turkish
Subjects Machine Learning (Other), Biomedical Engineering (Other), Food Engineering
Journal Section Research Articles
Authors

Eşref Boğar 0000-0003-3640-363X

Damla Nur Kaya 0009-0000-9376-3880

Deniz Kaynar 0009-0001-2115-5568

Büşra Braho This is me 0009-0003-4218-2703

Melek Harmancı 0009-0006-0460-863X

Early Pub Date July 8, 2025
Publication Date July 15, 2025
Submission Date May 9, 2025
Acceptance Date June 17, 2025
Published in Issue Year 2025 Volume: 14 Issue: 3

Cite

APA Boğar, E., Kaya, D. N., Kaynar, D., Braho, B., et al. (2025). Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14(3), 1088-1099. https://doi.org/10.28948/ngumuh.1696315
AMA Boğar E, Kaya DN, Kaynar D, Braho B, Harmancı M. Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi. NOHU J. Eng. Sci. July 2025;14(3):1088-1099. doi:10.28948/ngumuh.1696315
Chicago Boğar, Eşref, Damla Nur Kaya, Deniz Kaynar, Büşra Braho, and Melek Harmancı. “Fırında pişirilen Patates kızartmalarında Akrilamid oluşumunun Optimize Edilmiş Gauss süreç Regresyonu Ile Modellenmesi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14, no. 3 (July 2025): 1088-99. https://doi.org/10.28948/ngumuh.1696315.
EndNote Boğar E, Kaya DN, Kaynar D, Braho B, Harmancı M (July 1, 2025) Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14 3 1088–1099.
IEEE E. Boğar, D. N. Kaya, D. Kaynar, B. Braho, and M. Harmancı, “Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi”, NOHU J. Eng. Sci., vol. 14, no. 3, pp. 1088–1099, 2025, doi: 10.28948/ngumuh.1696315.
ISNAD Boğar, Eşref et al. “Fırında pişirilen Patates kızartmalarında Akrilamid oluşumunun Optimize Edilmiş Gauss süreç Regresyonu Ile Modellenmesi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14/3 (July 2025), 1088-1099. https://doi.org/10.28948/ngumuh.1696315.
JAMA Boğar E, Kaya DN, Kaynar D, Braho B, Harmancı M. Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi. NOHU J. Eng. Sci. 2025;14:1088–1099.
MLA Boğar, Eşref et al. “Fırında pişirilen Patates kızartmalarında Akrilamid oluşumunun Optimize Edilmiş Gauss süreç Regresyonu Ile Modellenmesi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 14, no. 3, 2025, pp. 1088-99, doi:10.28948/ngumuh.1696315.
Vancouver Boğar E, Kaya DN, Kaynar D, Braho B, Harmancı M. Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi. NOHU J. Eng. Sci. 2025;14(3):1088-99.

download