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Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm

Yıl 2024, Cilt: 22 Sayı: 1, 23 - 33, 29.03.2024

Esra Uca Hacı Ali Güleç

https://doi.org/10.24323/akademik-gida.1460968

Öz

Valorization of agricultural wastes is ongoing topic in industry. Determining the best conditions by artificial neural networks based optimization techniques is the key step to extract valuable compounds efficiently and to obtain high quality extracts. In this study, the response surface methodology (RSM)-desirability function (DF) and artificial neural network (ANN)-genetic algorithm (GA) approaches were compared in modeling and optimization the extraction parameters (temperature, time and ethanol concentration (ratio of ethanol to water, % v/v)) of phenolic compounds in pomegranate peels. The ANN-GA approach providing higher coefficient of determination and lower root mean square deviation showed better predictive capability than the RSM. The optimum time (81.4 min) and ethanol concentration (15.7%) of RSM-DF approach shifted to the lower levels (78.8 min and 15.3%) with the ANN-GA approach while the optimum temperature (54.0°C) shifted to a higher level (59.3°C). The use of these values provided total phenolic content of >1000 mg GAE L-1 and the corresponding antioxidant activity was 11 mmol TE L-1. As a result, increasing temperature up to a critical level decreased the extraction time and ethanol concentration, and it was determined that higher time-temperature combinations must be used for the complete water-based extraction of phenolic compounds from plant wastes in comparison to ethanol-water based extraction.

Anahtar Kelimeler

Valorization pomegranate peel Extraction Response surface methodology Artificial neural network Genetic algorithm

Proje Numarası

2017/37

Kaynakça

  • [1] Pan, W., Xu, H., Cui, Y., Song, D., Feng, Y.-Q. (2008). Improved liquid-liquid-liquid microextraction method and its application to analysis of four phenolic compounds in water samples. Journal of Chromatography A, 1203 (1), 7-12.
  • [2] Singh, M., Jha, A., Kumar, A., Hettiarachchy, N., Rai, A.K., Sharma, D. (2014). Influence of the solvents on the extraction of major phenolic compounds (punicalagin, ellagic acid and gallic acid) and their antioxidant activities in pomegranate aril. Journal of Food Science and Technology, 51(9), 2070-2077.
  • [3] Amyrgialaki, E., Makris, D.P., Mauromoustakos, A., Kefalas, P. (2014). Optimisation of the extraction of pomegranate (Punica granatum) husk phenolics using water/ethanol solvent systems and response surface methodology. Industrial Crops and Products, 59, 216-222.
  • [4] Sood, A., Gupta, M. (2015). Extraction process optimization for bioactive compounds in pomegranate peel. Food Bioscience, 12, 100-106.
  • [5] Queimada, A.J., Mota, F.L., Pinho, S.P., Macedo, E.A. (2009). Solubilities of biologically active phenolic compounds: measurements and modeling. The Journal of Physical Chemistry B, 113 (11), 3469-3476.
  • [6] Oreopoulou, V., Russ, W. (2007). Utilization of by-products and treatment of waste in the food industry. Springer.
  • [7] Markom, M., Hasan, M., Daud, W.R.W., Singh, H., Jahim, J.M. (2007). Extraction of hydrolysable tannins from Phyllanthus niruri Linn.: Effects of solvents and extraction methods. Separation and Purification Technology, 52(3), 487-496.
  • [8] Rababah, T.M., Banat, F., Rababah, A., Ereifej, K., Yang, W. (2010). Optimization of extraction conditions of total phenolics, antioxidant activities, and anthocyanin of oregano, thyme, terebinth, and pomegranate. Journal of Food Science, 75(7), C626-C632.
  • [9] Cacace, J., Mazza, G. (2003). Mass transfer process during extraction of phenolic compounds from milled berries. Journal of Food Engineering, 59(4), 379-389.
  • [10] Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M. (2016). Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons.
  • [11] Haaland, P.D. (1989). Experimental design in biotechnology. CRC press: Vol. 105.
  • [12] Choudhury, A.K.R. (2014). Principles of colour and appearance measurement: visual measurement of colour, colour comparison and management. Woodhead Publishing.
  • [13] Dheenamma, M., Soman, D.P., Muthamizhi, K., Kalaichelvi, P. (2019). In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger. Fuel, 239, 461-470.
  • [14] He, Z., Xi, H., Ding, T., Wang, J., Li, Z. (2021). Energy efficiency optimization of an integrated heat pipe cooling system in data center based on genetic algorithm. Applied Thermal Engineering, 182, 115800.
  • [15] Anastácio, A., Silva, R., Carvalho, I.S. (2016). Phenolics extraction from sweet potato peels: modelling and optimization by response surface modelling and artificial neural network. Journal of Food Science and Technology, 53(12), 4117-4125.
  • [16] Kashyap, P., Riar, C. S., Jindal, N. (2021). Optimization of ultrasound assisted extraction of polyphenols from Meghalayan cherry fruit (Prunus nepalensis) using response surface methodology (RSM) and artificial neural network (ANN) approach. Journal of Food Measurement and Characterization, 15(1), 119-133.
  • [17] Marić, L., Malešić, E., Tušek, A.J., Benković, M., Valinger, D., Jurina, T., Kljusurić, J. G. (2020). Effects of drying on physical and chemical properties of root vegetables: Artificial neural network modelling. Food and Bioproducts Processing, 119, 148-160.
  • [18] Abdullah, S., Pradhan, R. C., Pradhan, D., Mishra, S. (2021). Modeling and optimization of pectinase-assisted low-temperature extraction of cashew apple juice using artificial neural network coupled with genetic algorithm. Food Chemistry, 339, 127862.
  • [19] Raj, G.B., Dash, K.K. (2020). Microwave vacuum drying of dragon fruit slice: Artificial neural network modelling, genetic algorithm optimization, and kinetics study. Computers and Electronics in Agriculture, 178, 105814.
  • [20] Tarafdar, A., Kaur, B.P., Nema, P.K., Babar, O.A., Kumar, D. (2020). Using a combined neural network- genetic algorithm approach for predicting the complex rheological characteristics of microfluidized sugarcane juice. LWT, 123, 109058.
  • [21] Singleton, V.L., Orthofer, R., Lamuela-Raventós, R.M. (1999). [14] Analysis of total phenols and other oxidation substrates and antioxidants by means of folin-ciocalteu reagent. Methods in Enzymology, 299, 152-178.
  • [22] Re, R., Pellegrini, N., Proteggente, A., Pannala, A., Yang, M., Rice-Evans, C. (1999). Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free Radical Biology and Medicine, 26(9-10), 1231-1237.
  • [23] Wang, C., Shi, L., Fan, L., Ding, Y., Zhao, S., Liu, Y., Ma, C. (2013). Optimization of extraction and enrichment of phenolics from pomegranate (Punica granatum L.) leaves. Industrial Crops and Products, 42, 587-594.
  • [24] Baş, D., Boyacı, İ.H. (2007). Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering, 78(3), 836-845.
  • [25] Boateng, I.D. (2023). Application of graphical optimization, desirability, and multiple response functions in the extraction of food bioactive compounds. Food Engineering Reviews, 1-20.
  • [26] Saleem, A., Hussain, A., Chaudhary, A., Ahmad, Q.-u.-A., Iqtedar, M., Javid, A., Akram, A.M. (2020). Acid hydrolysis optimization of pomegranate peels waste using response surface methodology for ethanol production. Biomass Conversion and Biorefinery, 1-12.
  • [27] Rosa, P.A., Azevedo, A.M., Aires-Barros, M.R. (2007). Application of central composite design to the optimisation of aqueous two-phase extraction of human antibodies. Journal of Chromatography A, 1141 (1), 50-60.
  • [28] Liazid, A., Palma, M., Brigui, J., Barroso, C.G. (2007). Investigation on phenolic compounds stability during microwave-assisted extraction. Journal of Chromatography A, 1140 (1-2), 29-34.
  • [29] Demir, T., Akpınar, Ö., Haki, K., Güngör, H. (2019). Nar kabuğundan antimikrobiyal ve antioksidan aktiviteye sahip fenolik bileşiklerin ekstraksiyon koşullarinin optimizasyonu. Gıda, 44 (2), 369-382.
  • [30] Fourati, M., Smaoui, S., Ennouri, K., Ben Hlima, H., Elhadef, K., Chakchouk-Mtibaa, A., Sellem, I., Mellouli, L. (2019). Multiresponse optimization of pomegranate peel extraction by statistical versus artificial intelligence: predictive approach for foodborne bacterial pathogen inactivation. Evidence-Based Complementary and Alternative Medicine, 2019.
  • [31] Tsakona, S., Galanakis, C.M., Gekas, V. (2012). Hydro-ethanolic mixtures for the recovery of phenols from Mediterranean plant materials. Food and Bioprocess Technology, 5(4), 1384-1393.
  • [32] Jahongir, H., Miansong, Z., Amankeldi, I., Yu, Z., Changheng, L. (2019). The influence of particle size on supercritical extraction of dog rose (Rosa canina) seed oil. Journal of King Saud University-Engineering Sciences, 31(2), 140-143.
  • [33] Rebollo-Hernanz, M., Cañas, S., Taladrid, D., Segovia, Á., Bartolomé, B., Aguilera, Y., Martín-Cabrejas, M.A. (2021). Extraction of phenolic compounds from cocoa shell: Modeling using response surface methodology and artificial neural networks. Separation and Purification Technology, 118779.
  • [34] Said, F.M., Gan, J.Y., Sulaiman, J. (2020). Correlation between response surface methodology and artificial neural network in the prediction of bioactive compounds of unripe Musa acuminata peel. Engineering Science and Technology, an International Journal, 23(4), 781-787.
  • [35] Yang, Q.-Q., Gan, R.-Y., Zhang, D., Ge, Y.-Y., Cheng, L.-Z., Corke, H. (2019). Optimization of kidney bean antioxidants using RSM & ANN and characterization of antioxidant profile by UPLC-QTOF-MS. LWT, 114, 108321.

Nar Kabuklarından Fenolik Bileşiklerin Ekstraksiyonunda Proses Optimizasyonu: Yanıt Yüzeyi Yöntemi-İstenebilirlik Fonksiyonu ve Yapay Sinir Ağı-Genetik Algoritma

Yıl 2024, Cilt: 22 Sayı: 1, 23 - 33, 29.03.2024

Esra Uca Hacı Ali Güleç

https://doi.org/10.24323/akademik-gida.1460968

Öz

Tarımsal atıkların değerlendirilmesi endüstride güncelliğini sürdüren bir konudur. Değerli bileşikleri verimli bir şekilde ekstrakte etmek ve yüksek kaliteli ekstraktlar elde etmek için en iyi koşulların yapay sinir ağları tabanlı optimizasyon teknikleri ile belirlenmesi önemli bir adımdır. Bu çalışmada, nar kabuklarındaki fenolik bileşiklerin ekstraksiyon parametrelerinin (sıcaklık, süre ve etanol konsantrasyonu (etanol/su oranı, % v/v)) modellenmesinde ve optimizasyonunda yanıt yüzeyi yöntemi (RSM)-istenebilirlik fonksiyonu (DF) ve yapay sinir ağı (YSA)-genetik algoritma (GA) yaklaşımları karşılaştırılmıştır. ANN-GA yaklaşımı daha yüksek determinasyon katsayısı ve daha düşük ortalama karekök sapması sağlayarak RSM'den daha iyi bir tahmin yeteneği göstermiştir. RSM-DF yaklaşımının optimum süresi (81.4 dakika) ve etanol konsantrasyonu (%15.7) ANN-GA yaklaşımı ile daha düşük seviyelere (78.8 dakika ve %15.3) kayarken optimum sıcaklık (54.0°C) ise daha yüksek bir seviyeye kaymıştır (59.3°C). Bu değerlerin kullanımı >1000 mg GAE L-1 toplam fenolik içerik ve 11 mmol TE L-1 antioksidan aktivite sağlamıştır. Sonuç olarak, sıcaklığın kritik bir seviyeye çıkarılması ekstraksiyon süresini ve etanol konsantrasyonunu azaltmıştır ve bitki atıklarından fenolik bileşiklerin tamamen su bazlı ekstraksiyonunda etanol-su bazlı ekstraksiyonuna göre daha yüksek zaman-sıcaklık kombinasyonlarının kullanılması gerektiği belirlenmiştir.

Anahtar Kelimeler

Nar kabuklarının değerlendirilmesi Ekstraksiyon Yanıt yüzey yöntemi Yapay sinir ağı Genetik algoritma

Destekleyen Kurum

Trakya University Scientific Research Projects Coordination Unit

Proje Numarası

2017/37

Teşekkür

This study was supported by Trakya University Scientific Research Projects Coordination Unit. Project Number: 2017/37

Kaynakça

  • [1] Pan, W., Xu, H., Cui, Y., Song, D., Feng, Y.-Q. (2008). Improved liquid-liquid-liquid microextraction method and its application to analysis of four phenolic compounds in water samples. Journal of Chromatography A, 1203 (1), 7-12.
  • [2] Singh, M., Jha, A., Kumar, A., Hettiarachchy, N., Rai, A.K., Sharma, D. (2014). Influence of the solvents on the extraction of major phenolic compounds (punicalagin, ellagic acid and gallic acid) and their antioxidant activities in pomegranate aril. Journal of Food Science and Technology, 51(9), 2070-2077.
  • [3] Amyrgialaki, E., Makris, D.P., Mauromoustakos, A., Kefalas, P. (2014). Optimisation of the extraction of pomegranate (Punica granatum) husk phenolics using water/ethanol solvent systems and response surface methodology. Industrial Crops and Products, 59, 216-222.
  • [4] Sood, A., Gupta, M. (2015). Extraction process optimization for bioactive compounds in pomegranate peel. Food Bioscience, 12, 100-106.
  • [5] Queimada, A.J., Mota, F.L., Pinho, S.P., Macedo, E.A. (2009). Solubilities of biologically active phenolic compounds: measurements and modeling. The Journal of Physical Chemistry B, 113 (11), 3469-3476.
  • [6] Oreopoulou, V., Russ, W. (2007). Utilization of by-products and treatment of waste in the food industry. Springer.
  • [7] Markom, M., Hasan, M., Daud, W.R.W., Singh, H., Jahim, J.M. (2007). Extraction of hydrolysable tannins from Phyllanthus niruri Linn.: Effects of solvents and extraction methods. Separation and Purification Technology, 52(3), 487-496.
  • [8] Rababah, T.M., Banat, F., Rababah, A., Ereifej, K., Yang, W. (2010). Optimization of extraction conditions of total phenolics, antioxidant activities, and anthocyanin of oregano, thyme, terebinth, and pomegranate. Journal of Food Science, 75(7), C626-C632.
  • [9] Cacace, J., Mazza, G. (2003). Mass transfer process during extraction of phenolic compounds from milled berries. Journal of Food Engineering, 59(4), 379-389.
  • [10] Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M. (2016). Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons.
  • [11] Haaland, P.D. (1989). Experimental design in biotechnology. CRC press: Vol. 105.
  • [12] Choudhury, A.K.R. (2014). Principles of colour and appearance measurement: visual measurement of colour, colour comparison and management. Woodhead Publishing.
  • [13] Dheenamma, M., Soman, D.P., Muthamizhi, K., Kalaichelvi, P. (2019). In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger. Fuel, 239, 461-470.
  • [14] He, Z., Xi, H., Ding, T., Wang, J., Li, Z. (2021). Energy efficiency optimization of an integrated heat pipe cooling system in data center based on genetic algorithm. Applied Thermal Engineering, 182, 115800.
  • [15] Anastácio, A., Silva, R., Carvalho, I.S. (2016). Phenolics extraction from sweet potato peels: modelling and optimization by response surface modelling and artificial neural network. Journal of Food Science and Technology, 53(12), 4117-4125.
  • [16] Kashyap, P., Riar, C. S., Jindal, N. (2021). Optimization of ultrasound assisted extraction of polyphenols from Meghalayan cherry fruit (Prunus nepalensis) using response surface methodology (RSM) and artificial neural network (ANN) approach. Journal of Food Measurement and Characterization, 15(1), 119-133.
  • [17] Marić, L., Malešić, E., Tušek, A.J., Benković, M., Valinger, D., Jurina, T., Kljusurić, J. G. (2020). Effects of drying on physical and chemical properties of root vegetables: Artificial neural network modelling. Food and Bioproducts Processing, 119, 148-160.
  • [18] Abdullah, S., Pradhan, R. C., Pradhan, D., Mishra, S. (2021). Modeling and optimization of pectinase-assisted low-temperature extraction of cashew apple juice using artificial neural network coupled with genetic algorithm. Food Chemistry, 339, 127862.
  • [19] Raj, G.B., Dash, K.K. (2020). Microwave vacuum drying of dragon fruit slice: Artificial neural network modelling, genetic algorithm optimization, and kinetics study. Computers and Electronics in Agriculture, 178, 105814.
  • [20] Tarafdar, A., Kaur, B.P., Nema, P.K., Babar, O.A., Kumar, D. (2020). Using a combined neural network- genetic algorithm approach for predicting the complex rheological characteristics of microfluidized sugarcane juice. LWT, 123, 109058.
  • [21] Singleton, V.L., Orthofer, R., Lamuela-Raventós, R.M. (1999). [14] Analysis of total phenols and other oxidation substrates and antioxidants by means of folin-ciocalteu reagent. Methods in Enzymology, 299, 152-178.
  • [22] Re, R., Pellegrini, N., Proteggente, A., Pannala, A., Yang, M., Rice-Evans, C. (1999). Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free Radical Biology and Medicine, 26(9-10), 1231-1237.
  • [23] Wang, C., Shi, L., Fan, L., Ding, Y., Zhao, S., Liu, Y., Ma, C. (2013). Optimization of extraction and enrichment of phenolics from pomegranate (Punica granatum L.) leaves. Industrial Crops and Products, 42, 587-594.
  • [24] Baş, D., Boyacı, İ.H. (2007). Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering, 78(3), 836-845.
  • [25] Boateng, I.D. (2023). Application of graphical optimization, desirability, and multiple response functions in the extraction of food bioactive compounds. Food Engineering Reviews, 1-20.
  • [26] Saleem, A., Hussain, A., Chaudhary, A., Ahmad, Q.-u.-A., Iqtedar, M., Javid, A., Akram, A.M. (2020). Acid hydrolysis optimization of pomegranate peels waste using response surface methodology for ethanol production. Biomass Conversion and Biorefinery, 1-12.
  • [27] Rosa, P.A., Azevedo, A.M., Aires-Barros, M.R. (2007). Application of central composite design to the optimisation of aqueous two-phase extraction of human antibodies. Journal of Chromatography A, 1141 (1), 50-60.
  • [28] Liazid, A., Palma, M., Brigui, J., Barroso, C.G. (2007). Investigation on phenolic compounds stability during microwave-assisted extraction. Journal of Chromatography A, 1140 (1-2), 29-34.
  • [29] Demir, T., Akpınar, Ö., Haki, K., Güngör, H. (2019). Nar kabuğundan antimikrobiyal ve antioksidan aktiviteye sahip fenolik bileşiklerin ekstraksiyon koşullarinin optimizasyonu. Gıda, 44 (2), 369-382.
  • [30] Fourati, M., Smaoui, S., Ennouri, K., Ben Hlima, H., Elhadef, K., Chakchouk-Mtibaa, A., Sellem, I., Mellouli, L. (2019). Multiresponse optimization of pomegranate peel extraction by statistical versus artificial intelligence: predictive approach for foodborne bacterial pathogen inactivation. Evidence-Based Complementary and Alternative Medicine, 2019.
  • [31] Tsakona, S., Galanakis, C.M., Gekas, V. (2012). Hydro-ethanolic mixtures for the recovery of phenols from Mediterranean plant materials. Food and Bioprocess Technology, 5(4), 1384-1393.
  • [32] Jahongir, H., Miansong, Z., Amankeldi, I., Yu, Z., Changheng, L. (2019). The influence of particle size on supercritical extraction of dog rose (Rosa canina) seed oil. Journal of King Saud University-Engineering Sciences, 31(2), 140-143.
  • [33] Rebollo-Hernanz, M., Cañas, S., Taladrid, D., Segovia, Á., Bartolomé, B., Aguilera, Y., Martín-Cabrejas, M.A. (2021). Extraction of phenolic compounds from cocoa shell: Modeling using response surface methodology and artificial neural networks. Separation and Purification Technology, 118779.
  • [34] Said, F.M., Gan, J.Y., Sulaiman, J. (2020). Correlation between response surface methodology and artificial neural network in the prediction of bioactive compounds of unripe Musa acuminata peel. Engineering Science and Technology, an International Journal, 23(4), 781-787.
  • [35] Yang, Q.-Q., Gan, R.-Y., Zhang, D., Ge, Y.-Y., Cheng, L.-Z., Corke, H. (2019). Optimization of kidney bean antioxidants using RSM & ANN and characterization of antioxidant profile by UPLC-QTOF-MS. LWT, 114, 108321.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Gıda Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Esra Uca Bu kişi benim 0000-0002-2952-1091

Hacı Ali Güleç 0000-0002-9525-6206

Proje Numarası 2017/37
Yayımlanma Tarihi 29 Mart 2024
Gönderilme Tarihi 18 Nisan 2022
Yayımlandığı Sayı Yıl 2024 Cilt: 22 Sayı: 1

Kaynak Göster

APA Uca, E., & Güleç, H. A. (2024). Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm. Akademik Gıda, 22(1), 23-33. https://doi.org/10.24323/akademik-gida.1460968
AMA Uca E, Güleç HA. Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm. Akademik Gıda. Mart 2024;22(1):23-33. doi:10.24323/akademik-gida.1460968
Chicago Uca, Esra, ve Hacı Ali Güleç. “Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm”. Akademik Gıda 22, sy. 1 (Mart 2024): 23-33. https://doi.org/10.24323/akademik-gida.1460968.
EndNote Uca E, Güleç HA (01 Mart 2024) Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm. Akademik Gıda 22 1 23–33.
IEEE E. Uca ve H. A. Güleç, “Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm”, Akademik Gıda, c. 22, sy. 1, ss. 23–33, 2024, doi: 10.24323/akademik-gida.1460968.
ISNAD Uca, Esra - Güleç, Hacı Ali. “Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm”. Akademik Gıda 22/1 (Mart 2024), 23-33. https://doi.org/10.24323/akademik-gida.1460968.
JAMA Uca E, Güleç HA. Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm. Akademik Gıda. 2024;22:23–33.
MLA Uca, Esra ve Hacı Ali Güleç. “Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm”. Akademik Gıda, c. 22, sy. 1, 2024, ss. 23-33, doi:10.24323/akademik-gida.1460968.
Vancouver Uca E, Güleç HA. Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm. Akademik Gıda. 2024;22(1):23-3.
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