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Yıl 2024, Cilt: 42 Sayı: 2, 438 - 449, 30.04.2024

Havva Polat Kaya Tuğba Koç Lütfiye Ekici Gülhan Toğa

Öz

Kaynakça

  • [1] Baytop T. Türkiye'nin Tibbi ve Zehirli Bitkileri. Istanbul, Turkey; Istanbul Üniversitesi Yayınları; 1963.
  • [2] Demirci M, Tomas M, Tekin-Cakmak ZH, Karasu S. Berberis crataegina DC. as a novel natural food colorant source: ultrasound-assisted extraction optimization using response surface methodology and thermal stability studies. Food Sci Technol 2021;2061:1–8.
  • [3] El-Merahbi R, Liu YN, Eid A, Daoud G, Hosry L, Monzer A, et al. Berberis libanotica ehrenb extract shows anti-neoplastic effects on prostate cancer stem/progenitor cells. PLoS One 2014;9:1–10.
  • [4] Singh J, Kakkar P. Antihyperglycemic and antioxidant effect of Berberis aristata root extract and its role in regulating carbohydrate metabolism in diabetic rats. J Ethnopharmacol 2009;123:22–26.
  • [5] Balasundram N, Sundram K, Samman S. Phenolic compounds in plants and agri-industrial by-products: Antioxidant activity, occurrence, and potential uses. Food Chem 2006;99:191–203.
  • [6] Ongkowijoyo P, Luna-Vital DA, Gonzalez de Mejia E. Extraction techniques and analysis of anthocyanins from food sources by mass spectrometry: An update. Food Chem 2018;250:113126.
  • [7] Longo L, Vasapollo G. Extraction and identification of anthocyanins from Smilax aspera L. berries. Food Chem 2006;94:226231.
  • [8] Kong JM, Chia LS, Goh NK, Chia TF, Brouillard R. Analysis and biological activities of anthocyanins. Phytochemistry 2003;64:923933.
  • [9] Sun J, Cao X, Bai weibin, Liao X, Hu X. Comparative analyses of copigmentation of cyanidin 3-glucoside and cyanidin 3-sophoroside from red raspberry fruits. Food Chem 2010;120:1131–1137.
  • [10] Hill T, Marquez L, O’Connor M, Remus W. Artificial neural network models for forecasting and decision making. Int J Forecast 1994;10:5–15.
  • [11] Goñi SM, Oddone S, Segura JA, Mascheroni RH, Salvadori VO. Prediction of foods freezing and thawing times: Artificial neural networks and genetic algorithm approach. J Food Eng 2008;84:164–178.
  • [12] Movagharnejad K, Nikzad M. Modeling of tomato drying using artificial neural network. Comput Electron Agric 2007;59:78–85.
  • [13] Huang Y, Kangas LJ, Rasco BA. Applications of Artificial Neural Networks (ANNs) in food science. Crit Rev Food Sci Nutr 2007;47:113–126.
  • [14] Balıkesir Univerity. Preparing buffer systems. Available at: http:// lisanskimya.balikesir.edu.tr/~f10501/tam.html 2008 Accessed on March 08, 2024.
  • [15] Ekici L, Simsek Z, Ozturk I, Sagdic O, Yetim H. Effects of temperature, time, and pH on the stability of anthocyanin extracts: Prediction of total anthocyanin content using nonlinear models. Food Anal Methods 2014;7:1328–1336.
  • [16] Mónica Giusti M, Wrolstad RE. Characterization and Measurement of Anthocyanins by UV-visible Spectroscopy. Handb Food Anal Chem 2005;2:1931.
  • [17] Hayta M, İşçimen EM. Optimization of ultrasound-assisted antioxidant compounds extraction from germinated chickpea using response surface methodology. LWT - Food Sci Technol 2017;77:208-216.
  • [18] Orhan I, Kartal M, Naz Q, Ejaz A, Yilmaz G, Kan Y, et al. Antioxidant and anticholinesterase evaluation of selected Turkish Salvia species. Food Chem 2007;103:1247–1254.
  • [19] Ekici L, Kafadar AD, Albayrak S. Physicochemical, sensory, and bioactive properties of some traditional Turkish sorbets. J Food Process Preserv 2018;42(6): e13664.
  • [20] Nawi NM, Ransing MR, Ransing RS. An improved learning algorithm based on the Broyden-Fletcher-GoldfarbShanno (BFGS) method for back propagation neural networks. Proc - ISDA 2006 Sixth Int Conf Intell Syst Des Appl 2006;1:152–157.
  • [21] Patras A, Brunton NP, O’Donnell C, Tiwari BK. Effect of thermal processing on anthocyanin stability in foods; mechanisms and kinetics of degradation. Trends Food Sci Technol 2010;21:3–11.
  • [22] Farhadi Chitgar M, Aalami M, Maghsoudlou Y, Milani E. Comparative Study on the Effect of Heat Treatment and Sonication on the Quality of Barberry (Berberis Vulgaris) Juice. J Food Process Preserv 2017;41.
  • [23] Kara Ş, Ercȩlebi EA. Thermal degradation kinetics of anthocyanins and visual colour of Urmu mulberry (Morus nigra L.). J Food Eng 2013;116:541–547.
  • [24] Aşkın B, Küçüköner E. Factors Affecting the Stability of Anthocyanins. Turkish J Agric - Food Sci Technol 2019;7:1759–65.
  • [25] Hou Z, Qin P, Zhang Y, Cui S, Ren G. Identification of anthocyanins isolated from black rice (Oryza sativa L.) and their degradation kinetics. Food Res Int 2013;50:691–697.
  • [26] Gradinaru G, Biliaderis CG, Kallithraka S, Kefalas P, Garcia-Viguera C. Thermal stability of Hibiscus sabdariffa L. anthocyanins in solution and in solid state: Effects of copigmentation and glass transition. Food Chem 2003;83:423–436.
  • [27] Nikkhah E, Khayamy M, Heidari R, Jamee R. Effect of sugar treatment on stability of anthocyanin pigments in berries. J Biol Sci 2007;7:1412–1417.
  • [28] Boyer J, Liu RH. Apple phytochemicals and their health benefits. Nutr J 2004;3:1–15.
  • [29] Karadeniz F, Ekşi A. Sugar composition of apple juices. Eur Food Res Technol 2002;215:145–148.
  • [30] Li J, Li XD, Zhang Y, Zheng ZD, Qu ZY, Liu M, et al. Identification and thermal stability of purple-fleshed sweet potato anthocyanins in aqueous solutions with various pH values and fruit juices. Food Chem 2013;136:1429–1434.
  • [31] Maillard MN, Soum MH, Boivin P, Berset C. Antioxidant activity of barley and malt: Relationship with phenolic content. LWT - Food Sci Technol 1996;29:238–244.
  • [32] Sólyom K, Solá R, Cocero MJ, Mato RB. Thermal degradation of grape marc polyphenols. Food Chem 2014;159:361–366.
  • [33] Nayak B, Berrios JDJ, Powers JR, Tang J. Thermal degradation of anthocyanins from purple potato (Cv. Purple Majesty) and impact on antioxidant capacity. J Agric Food Chem 2011;59:11040–11049.
  • [34] Turturicə M, Stənciuc N, Bahrim G, Râpeanu G. Effect of thermal treatment on phenolic compounds from plum (prunus domestica) extracts - A kinetic study. J Food Eng 2016;171:200–207.
  • [35] Sui X, Dong X, Zhou W. Combined effect of pH and high temperature on the stability and antioxidant capacity of two anthocyanins in aqueous solution. Food Chem 2014;163:163– 170.
  • [36] Lozano JE, Ibarz A. Colour changes in concentrated fruit pulp during heating at high temperatures. J Food Eng 1997;31:365–373.
  • [37] Pathare PB, Opara UL, Al-Said FAJ. Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Technol 2013;6:36–60.
  • [38] Fischer UA, Carle R, Kammerer DR. Thermal stability of anthocyanins and colourless phenolics in pomegranate (Punica granatum L.) juices and model solutions. Food Chem 2013;138:1800–1809.
  • [39] Kammerer DR, Schillmöller S, Maier O, Schieber A, Carle R. Colour stability of canned strawberries using black carrot and elderberry juice concentrates as natural colourants. Eur Food Res Technol 2007;224:667–679.
  • [40] Homma T, Saltelli A. Importance measures in global sensitivity analysis of model output. Reliab Eng Sys Saf 1996;52:1–17.
  • [41] Cabrera C, Lloris F, Giménez R, Olalla M, López MC. Mineral content in legumes and nuts: Contribution to the Spanish dietary intake. Sci Total Environ 2003;308:1–14.
  • [42] Cimpoiu C, Cristea VM, Hosu A, Sandru M, Seserman L. Antioxidant activity prediction and classification of some teas using artificial neural networks. Food Chem 2011;127:1323– 1328.
  • [43] Hosu A, Cristea VM, Cimpoiu C. Analysis of total phenolic, flavonoids, anthocyanins and tannins content in Romanian red wines: Prediction of antioxidant activities and classification of wines using artificial neural networks. Food Chem 2014;150:113–118.
  • [44] Taghadomi-Saberi S, Omid M, Emam-Djomeh Z, Ahmadi H. Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing. J Sci Food Agric 2014;94:95–101.
  • [45] Cheok CY, Chin NL, Yusof YA, Talib RA, Law CL. Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network. Ind Crops Prod 2012;40:247–253.

Anthocyanin and bioactivity properties of berberis crategina DC. In buffer system and apple juice: impact of temperature, time, and pH; Prediction using artificial neural network

Yıl 2024, Cilt: 42 Sayı: 2, 438 - 449, 30.04.2024

Havva Polat Kaya Tuğba Koç Lütfiye Ekici Gülhan Toğa

Öz

Our work contributes to investigate and estimate the degradation, altered bioactivity and color of Berberis crategina anthocyanins in different buffer systems and apple juice. Anthocyanins are glycosides of anthocyanidins, a subclass of flavonoids. These pigments impart red to blue coloration to fruits and flowers. Anthocyanins have antioxidant properties due to the positive-ly charged oxygen atoms they contain. Chemical structure, enzymes, temperature, light, pH, oxygen, ascorbic acid, sugars, metals, sulfur dioxide, and copigmentation affect the stability of anthocyanins. In this study, it was primarily aimed to investigate the effects of temperature, time and pH on total anthocyanin content (TAC), total phenolic content (TPC), antioxidant activity (AA) and color of Berberis crataegina. Another aim was to estimate the TAC, TPC, AA, and color of Berberis based on temperature, time, and pH with ANN modeling. An ar-tificial neural network (ANN) was used to predict the relationship between TAC, TPC, AA and color of Berberis crataegina and temperature, time, and pH for both apple juice and buffer solution. It was found that high temperature and low acidity increased anthocyanin degradation, while total phenolic content and antioxidant activity decreased. L* and h° were found to decrease and C* to increase due to anthocyanin degradation. The results indicate that pH is the most effective factor (73%) in prediction and that ANN performs better than a buffer solution for apple juice. The sum of square errors of the validation samples was 7.89 for buffer solution and 1.26 for apple juice. This study showed that the parameters studied can be successfully estimated using ANN.

Anahtar Kelimeler

Berberis Crategina Anthocyanin Degradation Heat Treatment Artificial Neural Network Bioactivity

Kaynakça

  • [1] Baytop T. Türkiye'nin Tibbi ve Zehirli Bitkileri. Istanbul, Turkey; Istanbul Üniversitesi Yayınları; 1963.
  • [2] Demirci M, Tomas M, Tekin-Cakmak ZH, Karasu S. Berberis crataegina DC. as a novel natural food colorant source: ultrasound-assisted extraction optimization using response surface methodology and thermal stability studies. Food Sci Technol 2021;2061:1–8.
  • [3] El-Merahbi R, Liu YN, Eid A, Daoud G, Hosry L, Monzer A, et al. Berberis libanotica ehrenb extract shows anti-neoplastic effects on prostate cancer stem/progenitor cells. PLoS One 2014;9:1–10.
  • [4] Singh J, Kakkar P. Antihyperglycemic and antioxidant effect of Berberis aristata root extract and its role in regulating carbohydrate metabolism in diabetic rats. J Ethnopharmacol 2009;123:22–26.
  • [5] Balasundram N, Sundram K, Samman S. Phenolic compounds in plants and agri-industrial by-products: Antioxidant activity, occurrence, and potential uses. Food Chem 2006;99:191–203.
  • [6] Ongkowijoyo P, Luna-Vital DA, Gonzalez de Mejia E. Extraction techniques and analysis of anthocyanins from food sources by mass spectrometry: An update. Food Chem 2018;250:113126.
  • [7] Longo L, Vasapollo G. Extraction and identification of anthocyanins from Smilax aspera L. berries. Food Chem 2006;94:226231.
  • [8] Kong JM, Chia LS, Goh NK, Chia TF, Brouillard R. Analysis and biological activities of anthocyanins. Phytochemistry 2003;64:923933.
  • [9] Sun J, Cao X, Bai weibin, Liao X, Hu X. Comparative analyses of copigmentation of cyanidin 3-glucoside and cyanidin 3-sophoroside from red raspberry fruits. Food Chem 2010;120:1131–1137.
  • [10] Hill T, Marquez L, O’Connor M, Remus W. Artificial neural network models for forecasting and decision making. Int J Forecast 1994;10:5–15.
  • [11] Goñi SM, Oddone S, Segura JA, Mascheroni RH, Salvadori VO. Prediction of foods freezing and thawing times: Artificial neural networks and genetic algorithm approach. J Food Eng 2008;84:164–178.
  • [12] Movagharnejad K, Nikzad M. Modeling of tomato drying using artificial neural network. Comput Electron Agric 2007;59:78–85.
  • [13] Huang Y, Kangas LJ, Rasco BA. Applications of Artificial Neural Networks (ANNs) in food science. Crit Rev Food Sci Nutr 2007;47:113–126.
  • [14] Balıkesir Univerity. Preparing buffer systems. Available at: http:// lisanskimya.balikesir.edu.tr/~f10501/tam.html 2008 Accessed on March 08, 2024.
  • [15] Ekici L, Simsek Z, Ozturk I, Sagdic O, Yetim H. Effects of temperature, time, and pH on the stability of anthocyanin extracts: Prediction of total anthocyanin content using nonlinear models. Food Anal Methods 2014;7:1328–1336.
  • [16] Mónica Giusti M, Wrolstad RE. Characterization and Measurement of Anthocyanins by UV-visible Spectroscopy. Handb Food Anal Chem 2005;2:1931.
  • [17] Hayta M, İşçimen EM. Optimization of ultrasound-assisted antioxidant compounds extraction from germinated chickpea using response surface methodology. LWT - Food Sci Technol 2017;77:208-216.
  • [18] Orhan I, Kartal M, Naz Q, Ejaz A, Yilmaz G, Kan Y, et al. Antioxidant and anticholinesterase evaluation of selected Turkish Salvia species. Food Chem 2007;103:1247–1254.
  • [19] Ekici L, Kafadar AD, Albayrak S. Physicochemical, sensory, and bioactive properties of some traditional Turkish sorbets. J Food Process Preserv 2018;42(6): e13664.
  • [20] Nawi NM, Ransing MR, Ransing RS. An improved learning algorithm based on the Broyden-Fletcher-GoldfarbShanno (BFGS) method for back propagation neural networks. Proc - ISDA 2006 Sixth Int Conf Intell Syst Des Appl 2006;1:152–157.
  • [21] Patras A, Brunton NP, O’Donnell C, Tiwari BK. Effect of thermal processing on anthocyanin stability in foods; mechanisms and kinetics of degradation. Trends Food Sci Technol 2010;21:3–11.
  • [22] Farhadi Chitgar M, Aalami M, Maghsoudlou Y, Milani E. Comparative Study on the Effect of Heat Treatment and Sonication on the Quality of Barberry (Berberis Vulgaris) Juice. J Food Process Preserv 2017;41.
  • [23] Kara Ş, Ercȩlebi EA. Thermal degradation kinetics of anthocyanins and visual colour of Urmu mulberry (Morus nigra L.). J Food Eng 2013;116:541–547.
  • [24] Aşkın B, Küçüköner E. Factors Affecting the Stability of Anthocyanins. Turkish J Agric - Food Sci Technol 2019;7:1759–65.
  • [25] Hou Z, Qin P, Zhang Y, Cui S, Ren G. Identification of anthocyanins isolated from black rice (Oryza sativa L.) and their degradation kinetics. Food Res Int 2013;50:691–697.
  • [26] Gradinaru G, Biliaderis CG, Kallithraka S, Kefalas P, Garcia-Viguera C. Thermal stability of Hibiscus sabdariffa L. anthocyanins in solution and in solid state: Effects of copigmentation and glass transition. Food Chem 2003;83:423–436.
  • [27] Nikkhah E, Khayamy M, Heidari R, Jamee R. Effect of sugar treatment on stability of anthocyanin pigments in berries. J Biol Sci 2007;7:1412–1417.
  • [28] Boyer J, Liu RH. Apple phytochemicals and their health benefits. Nutr J 2004;3:1–15.
  • [29] Karadeniz F, Ekşi A. Sugar composition of apple juices. Eur Food Res Technol 2002;215:145–148.
  • [30] Li J, Li XD, Zhang Y, Zheng ZD, Qu ZY, Liu M, et al. Identification and thermal stability of purple-fleshed sweet potato anthocyanins in aqueous solutions with various pH values and fruit juices. Food Chem 2013;136:1429–1434.
  • [31] Maillard MN, Soum MH, Boivin P, Berset C. Antioxidant activity of barley and malt: Relationship with phenolic content. LWT - Food Sci Technol 1996;29:238–244.
  • [32] Sólyom K, Solá R, Cocero MJ, Mato RB. Thermal degradation of grape marc polyphenols. Food Chem 2014;159:361–366.
  • [33] Nayak B, Berrios JDJ, Powers JR, Tang J. Thermal degradation of anthocyanins from purple potato (Cv. Purple Majesty) and impact on antioxidant capacity. J Agric Food Chem 2011;59:11040–11049.
  • [34] Turturicə M, Stənciuc N, Bahrim G, Râpeanu G. Effect of thermal treatment on phenolic compounds from plum (prunus domestica) extracts - A kinetic study. J Food Eng 2016;171:200–207.
  • [35] Sui X, Dong X, Zhou W. Combined effect of pH and high temperature on the stability and antioxidant capacity of two anthocyanins in aqueous solution. Food Chem 2014;163:163– 170.
  • [36] Lozano JE, Ibarz A. Colour changes in concentrated fruit pulp during heating at high temperatures. J Food Eng 1997;31:365–373.
  • [37] Pathare PB, Opara UL, Al-Said FAJ. Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Technol 2013;6:36–60.
  • [38] Fischer UA, Carle R, Kammerer DR. Thermal stability of anthocyanins and colourless phenolics in pomegranate (Punica granatum L.) juices and model solutions. Food Chem 2013;138:1800–1809.
  • [39] Kammerer DR, Schillmöller S, Maier O, Schieber A, Carle R. Colour stability of canned strawberries using black carrot and elderberry juice concentrates as natural colourants. Eur Food Res Technol 2007;224:667–679.
  • [40] Homma T, Saltelli A. Importance measures in global sensitivity analysis of model output. Reliab Eng Sys Saf 1996;52:1–17.
  • [41] Cabrera C, Lloris F, Giménez R, Olalla M, López MC. Mineral content in legumes and nuts: Contribution to the Spanish dietary intake. Sci Total Environ 2003;308:1–14.
  • [42] Cimpoiu C, Cristea VM, Hosu A, Sandru M, Seserman L. Antioxidant activity prediction and classification of some teas using artificial neural networks. Food Chem 2011;127:1323– 1328.
  • [43] Hosu A, Cristea VM, Cimpoiu C. Analysis of total phenolic, flavonoids, anthocyanins and tannins content in Romanian red wines: Prediction of antioxidant activities and classification of wines using artificial neural networks. Food Chem 2014;150:113–118.
  • [44] Taghadomi-Saberi S, Omid M, Emam-Djomeh Z, Ahmadi H. Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing. J Sci Food Agric 2014;94:95–101.
  • [45] Cheok CY, Chin NL, Yusof YA, Talib RA, Law CL. Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network. Ind Crops Prod 2012;40:247–253.
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği
Bölüm Research Articles
Yazarlar

Havva Polat Kaya

Tuğba Koç

Lütfiye Ekici

Gülhan Toğa 0000-0001-8835-1769

Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 15 Mart 2022
Yayımlandığı Sayı Yıl 2024 Cilt: 42 Sayı: 2

Kaynak Göster

Vancouver Polat Kaya H, Koç T, Ekici L, Toğa G. Anthocyanin and bioactivity properties of berberis crategina DC. In buffer system and apple juice: impact of temperature, time, and pH; Prediction using artificial neural network. SIGMA. 2024;42(2):438-49.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/