{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:55:04Z","timestamp":1792788904705,"version":"4.5.0"},"reference-count":73,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science, Technological Development, and Innovation of the Republic of Serbia","award":["451-03-33\/2026-03\/200161"],"award-info":[{"award-number":["451-03-33\/2026-03\/200161"]}],"id":[{"id":"https:\/\/ror.org\/01znas443","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Ministry of Science, Technological Development, and Innovation of the Republic of Serbia","award":["451-03-34\/2026-03\/200161"],"award-info":[{"award-number":["451-03-34\/2026-03\/200161"]}],"id":[{"id":"https:\/\/ror.org\/01znas443","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Ministry of Science, Technological Development, and Innovation of the Republic of Serbia","award":["451-03-33\/2026-03\/200134"],"award-info":[{"award-number":["451-03-33\/2026-03\/200134"]}],"id":[{"id":"https:\/\/ror.org\/01znas443","id-type":"ROR","asserted-by":"crossref"}]},{"award":["451-03-33\/2026-03\/200161"],"award-info":[{"award-number":["451-03-33\/2026-03\/200161"]}],"id":[{"id":"https:\/\/ror.org\/01znas443","id-type":"ROR","asserted-by":"publisher"}]},{"award":["451-03-34\/2026-03\/200161"],"award-info":[{"award-number":["451-03-34\/2026-03\/200161"]}],"id":[{"id":"https:\/\/ror.org\/01znas443","id-type":"ROR","asserted-by":"publisher"}]},{"award":["451-03-33\/2026-03\/200134"],"award-info":[{"award-number":["451-03-33\/2026-03\/200134"]}],"id":[{"id":"https:\/\/ror.org\/01znas443","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Science Fund of the Republic of Serbia","award":["#GRANTNo 7464"],"award-info":[{"award-number":["#GRANTNo 7464"]}],"id":[{"id":"https:\/\/ror.org\/02r531a15","id-type":"ROR","asserted-by":"crossref"}]},{"award":["#GRANTNo 7464"],"award-info":[{"award-number":["#GRANTNo 7464"]}],"id":[{"id":"https:\/\/ror.org\/02r531a15","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antioxidants"],"abstract":"<jats:p>Apple pomace represents a sustainable source of phenolic compounds with significant antioxidant potential. This study investigated the recovery of water-extractable bioactive constituents from apple pomace using aqueous ultrasound-assisted extraction (UAE) combined with biochemical profiling and machine learning-based modelling. Extraction conditions were varied according to time (10\u201330 min), temperature (25\u201375 \u00b0C), and solvent-to-solid ratio (10\u201320 mL\/g). The obtained extracts were evaluated for total phenolic content (TP), total flavonoid content (TF), antioxidant capacity (DPPH, ABTS, CUPRAC, FRAP, metal chelating, and phosphomolybdenum assays), and enzyme inhibitory activities against acetylcholinesterase, butyrylcholinesterase, tyrosinase, \u03b1-amylase, and \u03b1-glucosidase. TP and TF ranged from 4.70 to 9.97 mg GAE\/g and 0.21\u20130.79 mg RE\/g, respectively, while antioxidant assays demonstrated substantial variation depending on extraction conditions. Strong correlations (r = 0.827\u20130.945, p &lt; 0.001) were observed between phenolic content and antioxidant activity. Artificial neural networks, random forests, support vector machines, and a hybrid ensemble model were applied to predict extraction outcomes, with predictive performance varying substantially among biochemical responses and modelling approaches. Multi-objective optimization using the NSGA-II formulation identified a representative Pareto compromise at approximately 15.1 min, 25.7 \u00b0C, and a solvent-to-solid ratio of 13.7 mL\/g, balancing desirable biochemical responses with processing requirements.<\/jats:p>","DOI":"10.3390\/antiox15091215","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:12:09Z","timestamp":1789999929000},"page":"1215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bioactive Compound Recovery from Apple Pomace by Aqueous Ultrasound-Assisted Extraction: Machine Learning Modelling and Multi-Objective Optimization"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2994-6871","authenticated-orcid":false,"given":"Biljana","family":"Lon\u010dar","sequence":"first","affiliation":[{"name":"Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7192-4962","authenticated-orcid":false,"given":"Milena","family":"Terzi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5621-1788","authenticated-orcid":false,"given":"Aleksandra Cvetanovi\u0107","family":"Kljaki\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirjana","family":"Petronijevi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8438-634X","authenticated-orcid":false,"given":"Sanja","family":"Pani\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6080-0526","authenticated-orcid":false,"given":"Jelena","family":"Arsenijevi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6548-7823","authenticated-orcid":false,"given":"Gokhan","family":"Zengin","sequence":"additional","affiliation":[{"name":"Science Faculty, Selcuk University, Campus Konya, Konya 42130, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4884-040X","authenticated-orcid":false,"given":"Slavica","family":"Ra\u017ei\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Patocka, J., Bhardwaj, K., Klimova, B., Nepovimova, E., Wu, Q., Landi, M., and Wu, W. (2020). Malus domestica: A review on nutritional features, chemical composition, traditional and medicinal value. Plants, 9.","DOI":"10.3390\/plants9111408"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hussain, S.Z., Naseer, B., Qadri, T., Fatima, T., and Bhat, T.A. (2021). Apples (Pyrus malus)\u2014Morphology, taxonomy, composition and health benefits. Fruits Grown in Highland Regions of the Himalayas: Nutritional and Health Bene-Fits, Springer International Publishing.","DOI":"10.1007\/978-3-030-75502-7_2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Korban, S.S. (2023). Apples: Role of nutraceutical compounds. Compendium of Crop Genome Designing for Nutraceuti-Cals, Springer Nature.","DOI":"10.1007\/978-981-19-4169-6_34"},{"key":"ref_4","unstructured":"Market Growth Reports (2025). Apple Market Size, Share, Growth, and Industry Analysis, by Type (Fresh Apples, Processed Apples), by Application (Households, Food & Beverage Industry, Retail, Exports), Regional Insights and Forecast to 2035, Market Growth Reports. Available online: https:\/\/www.marketgrowthreports.com\/market-reports\/apple-market-112927."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"817","DOI":"10.5937\/ekoPolj2003817P","article-title":"Profitability analysis of apple production in the Republic of Serbia","volume":"67","year":"2020","journal-title":"Ekon. Poljopr."},{"key":"ref_6","first-page":"1","article-title":"Analysis and prediction of apple production in the Republic of Serbia and Bosnia and Herzegovina","volume":"12","year":"2019","journal-title":"Ekon. Teor. Praks"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"41","DOI":"10.5937\/ekonomika2304041K","article-title":"Economic aspects of fruit production in Serbia on the example of apple orchards","volume":"69","year":"2023","journal-title":"Ekonomika"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Go\u0142\u0119biewska, E., Kalinowska, M., and Yildiz, G. (2022). Sustainable use of apple pomace (AP) in different industrial sectors. Materials, 15.","DOI":"10.3390\/ma15051788"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tulej, W., and G\u0142owacki, S. (2022). Analysis of material-characterization properties of post-production waste\u2014The case of apple pomace. Materials, 15.","DOI":"10.3390\/ma15103532"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zaky, A.A., Witrowa-Rajchert, D., and Nowacka, M. (2024). Turning apple pomace into value: Sustainable recycling in food production\u2014A narrative review. Sustainability, 16.","DOI":"10.3390\/su16167001"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tsoupras, A., Gkika, D.A., Markopoulos, T., Curran, R., Scallon, C., Karali, M., and Kyzas, G.Z. (2024). Apple products (apple juice and cider) and by-products (apple pomace): Bioactive compounds and biological properties. Natural Products in Beverages: Botany, Phytochemistry, Pharmacology and Processing, Springer International Publishing.","DOI":"10.1007\/978-3-031-04195-2_214-1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kagueyam, S.S., dos Santos Filho, J.R., Contato, A.G., de Souza, C.G.M., Castoldi, R., Corr\u00eaa, R.C.G., Junior, C.A.C., Yamaguchi, N.U., Bracht, A., and Peralta, R.M. (2025). Green Extraction of Bioactive Compounds from Plant-Based Agri-Food Residues: Advances Toward Sustainable Valorization. Plants, 14.","DOI":"10.3390\/plants14233597"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106646","DOI":"10.1016\/j.ultsonch.2023.106646","article-title":"A comprehensive review of ultrasonic assisted extraction (UAE) for bioactive components: Principles, advantages, equipment, and combined technologies","volume":"101","author":"Shen","year":"2023","journal-title":"Ultrason. Sonochem."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pando Bedri\u00f1ana, R., Rodr\u00edguez Madrera, R., Loureiro Rodr\u00edguez, M.D., L\u00f3pez-Ben\u00edtez, K., and Picinelli Lobo, A. (2024). Green extraction of bioactive compounds from apple pomace from the cider industry. Antioxidants, 13.","DOI":"10.3390\/antiox13101230"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Eg\u00fc\u00e9s, I., Hernandez-Ramos, F., Rivilla, I., and Labidi, J. (2021). Optimization of ultrasound assisted extraction of bioactive compounds from apple pomace. Molecules, 26.","DOI":"10.3390\/molecules26133783"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e17736","DOI":"10.1016\/j.heliyon.2023.e17736","article-title":"Exploring the effect of utilising organic acid solutions in ultrasound-assisted extraction of pectin from apple pomace, and its potential for biomedical purposes","volume":"9","author":"Pazmino","year":"2023","journal-title":"Heliyon"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dranca, F., and Oroian, M. (2019). Ultrasound-assisted extraction of pectin from Malus domestica \u2018F\u0103lticeni\u2019apple pomace. Processes, 7.","DOI":"10.3390\/pr7080488"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gurev, A., Cesko, T., Dragancea, V., Ghendov-Mosanu, A., Pintea, A., and Sturza, R. (2023). Ultrasound-and microwave-assisted extraction of pectin from apple pomace and its effect on the quality of fruit bars. Foods, 12.","DOI":"10.3390\/foods12142773"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"27","DOI":"10.2478\/auoc-2024-0004","article-title":"Optimization of ultrasound-assisted extraction (UAE) of (poly) phenolic compounds from blueberry (Vaccinium myrtillus) leaves using full-factorial design","volume":"35","year":"2024","journal-title":"Ovidius Univ. Ann. Chem."},{"key":"ref_20","first-page":"100387","article-title":"Optimization of ultrasound-assisted extraction of phenolic antioxidants from turkey berry (Solanum torvum Sw) fruits using response surface methodology","volume":"30","author":"Weremfo","year":"2022","journal-title":"J. Appl. Res. Med. Aromat. Plants"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s44211-024-00681-w","article-title":"Artificial neural network in optimization of bioactive compound extraction: Recent trends and performance comparison with response surface methodology","volume":"41","author":"Subramani","year":"2025","journal-title":"Anal. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fern\u00e1ndez, R., Fern\u00e1ndez-G\u00f3mez, \u00c1., Mejuto, J.C., and Astray, G. (2023). Modelling polyphenol extraction through ultrasound-assisted extraction by machine learning in Olea europaea leaves. Foods, 12.","DOI":"10.3390\/foods12244483"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Terzi\u0107, M., Lon\u010dar, B., Petronijevi\u0107, M., Pani\u0107, S., Kljaki\u0107, A.C., Arsenijevi\u0107, J., Zengin, G., and Ra\u017ei\u0107, S. (2026). Ultrasound-Assisted Extraction of Bioactive Compounds from Strawberry Pomace: Optimization and Bioactivity Assessment. Antioxidants, 15.","DOI":"10.3390\/antiox15091215"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116530","DOI":"10.1016\/j.trac.2022.116530","article-title":"The ten principles of green sample preparation","volume":"148","author":"Zuin","year":"2022","journal-title":"Trends Anal. Chem."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116553","DOI":"10.1016\/j.trac.2022.116553","article-title":"AGREEprep\u2014Analytical Greenness Metric for Sample Preparation","volume":"149","author":"Wojnowski","year":"2022","journal-title":"Trends Anal. Chem."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Uysal, S., Zengin, G., Locatelli, M., Bahadori, M.B., Mocan, A., Bellagamba, G., De Luca, E., Mollica, A., and Aktumsek, A. (2017). Cytotoxic and enzyme inhibitory potential of two Potentilla species (P. speciose L. and P. reptans Willd.) and their chemical composition. Front. Pharmacol., 8.","DOI":"10.3389\/fphar.2017.00290"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.phytol.2017.03.005","article-title":"In vitro enzyme inhibitory properties, antioxidant activities, and phytochemical profile of Potentilla thuringiaca","volume":"20","author":"Grochowski","year":"2017","journal-title":"Phytochem. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105020","DOI":"10.1016\/j.jfca.2022.105020","article-title":"Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed","volume":"115","author":"Jeromela","year":"2023","journal-title":"J. Food Compos. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1080\/10408398.2020.1858398","article-title":"Comprehensive study on applications of artificial neural network in food process modeling","volume":"62","author":"Dash","year":"2022","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.2298\/TSCI210422285F","article-title":"Modeling convective thin-layer drying of carrot slices and quality parameters","volume":"26","author":"Lukyanov","year":"2022","journal-title":"Therm. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105352","DOI":"10.1016\/j.engappai.2022.105352","article-title":"Risk prediction model for food safety based on improved random forest integrating virtual sample","volume":"116","author":"Geng","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.cofs.2020.03.002","article-title":"Digital twins of food process operations: The next step for food process models?","volume":"35","author":"Verboven","year":"2020","journal-title":"Curr. Opin. Food Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"765","DOI":"10.32604\/csse.2023.034324","article-title":"Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model","volume":"46","author":"Elshewey","year":"2023","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3385656","article-title":"Better classifier calibration for small datasets","volume":"14","author":"Tuomo","year":"2020","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s44230-023-00046-y","article-title":"Future food production prediction using AROA based hybrid deep learning model in agri-sector","volume":"3","author":"Baswaraju","year":"2023","journal-title":"Hum.-Centric Intell. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11077","DOI":"10.1007\/s11042-023-15725-y","article-title":"SD-IIDS: Intelligent intrusion detection system for software-defined networks","volume":"83","author":"Shaji","year":"2024","journal-title":"Multim. Tools Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"116189","DOI":"10.1016\/j.eswa.2021.116189","article-title":"Food security prediction from heterogeneous data combining machine and deep learning methods","volume":"190","author":"Interdonato","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Uzer, M.S. (2025). Deep Learning-Based Classification Consisting of Pre-Trained Models and Proposed Model Using K-Fold Cross-Validation for Pistachio Species. Appl. Sci., 15.","DOI":"10.3390\/app15084516"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lon\u010dar, B., Pezo, L., Kne\u017eevi\u0107, V., Ni\u0107etin, M., Filipovi\u0107, J., Petkovi\u0107, M., and Filipovi\u0107, V. (2024). Enhancing cookie formulations with combined dehydrated peach: A machine learning approach for technological quality assessment and optimization. Foods, 13.","DOI":"10.3390\/foods13050782"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"Peer J. Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"36928","DOI":"10.1038\/s41598-025-20846-5","article-title":"Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning","volume":"15","author":"Zhang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_43","unstructured":"Mersmann, O., Trautmann, H., Steuer, D., Bischl, B., and Deb, K. (mco: Multiple Criteria Optimization Algorithms and Related Functions, 2024). mco: Multiple Criteria Optimization Algorithms and Related Functions, R Package Version 1.17."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hobbi, P., Okoro, O.V., Hajiabbas, M., Hamidi, M., Nie, L., Megalizzi, V., and Shavandi, A. (2023). Chemical composition, antioxidant activity and cytocompatibility of polyphenolic compounds extracted from food industry apple waste: Potential in biomedical application. Molecules, 28.","DOI":"10.3390\/molecules28020675"},{"key":"ref_45","first-page":"27915","article-title":"Comparative study of polyphenol extraction using physical techniques and water as a solvent: A sustainable approach for the valorization of apple pomace","volume":"32","author":"Demichelis","year":"2025","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0021-9673(00)01217-6","article-title":"Determination of phenolic acids and flavonoids of apple and pear by high-performance liquid chromatography","volume":"910","author":"Schieber","year":"2001","journal-title":"J. Chromatogr. A"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.ultsonch.2016.06.035","article-title":"Ultrasound assisted extraction of food and natural products. Mechanisms, techniques, combinations, protocols and applications. A review","volume":"34","author":"Chemat","year":"2017","journal-title":"Ultrason. Sonochem."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1016\/j.foodchem.2012.06.068","article-title":"Water at room temperature as a solvent for the extraction of apple pomace phenolic compounds","volume":"135","author":"Reis","year":"2012","journal-title":"Food Chem."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/S0260-8774(02)00497-1","article-title":"Mass transfer process during extraction of phenolic compounds from milled berries","volume":"59","author":"Cacace","year":"2003","journal-title":"J. Food Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s00217-020-03432-z","article-title":"Significance of FRAP, DPPH, and CUPRAC assays for antioxidant activity determination in apple fruit extracts","volume":"246","author":"Sethi","year":"2020","journal-title":"Eur. Food Res. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1002\/jsfa.6066","article-title":"Antioxidant activity and HPLC analysis of polyphenol-enriched extracts from industrial apple pomace","volume":"93","author":"Bai","year":"2013","journal-title":"J. Sci. Food Agric."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7970","DOI":"10.1021\/jf048741x","article-title":"Novel total antioxidant capacity index for dietary polyphenols and vitamins C and E, using their cupric ion reducing capability in the presence of neocuproine: CUPRAC method","volume":"52","author":"Apak","year":"2004","journal-title":"J. Agric. Food Chem."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/19476337.2014.971344","article-title":"Optimisation of the phenolic content and antioxidant activity of apple pomace aqueous extracts","volume":"13","author":"Candrawinata","year":"2015","journal-title":"CyTA J. Food"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6520","DOI":"10.1021\/jf800510j","article-title":"Polyphenolic compounds and antioxidant activity of new and old apple varieties","volume":"56","author":"Laskowski","year":"2008","journal-title":"J. Agric. Food Chem."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.foodchem.2005.05.044","article-title":"Iron-chelation properties of phenolic acids bearing catechol and galloyl groups","volume":"98","author":"Andjelkovic","year":"2006","journal-title":"Food Chem."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1006\/abio.1999.4019","article-title":"Spectrophotometric quantitation of antioxidant capacity through the formation of a phosphomolybdenum complex: Specific application to the determination of vitamin E","volume":"269","author":"Prieto","year":"1999","journal-title":"Anal. Biochem."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"11795735211029113","DOI":"10.1177\/11795735211029113","article-title":"The state of the art on acetylcholinesterase inhibitors in the treatment of Alzheimer\u2019s disease","volume":"13","author":"Vecchio","year":"2021","journal-title":"J. Cent. Nerv. Syst. Dis."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Cichon, N., Grabowska, W., Gorniak, L., Stela, M., Harmata, P., Ceremuga, M., and Bijak, M. (2025). Mechanistic and therapeutic insights into flavonoid-based inhibition of acetylcholinesterase: Implications for neurodegenerative diseases. Nutrients, 17.","DOI":"10.3390\/nu17010078"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Nasso, R., Pagliara, V., D\u2019Angelo, S., Rullo, R., Masullo, M., and Arcone, R. (2021). Annurca apple polyphenol extract affects acetylcholinesterase and mono-amine oxidase in vitro enzyme activity. Pharmaceuticals, 14.","DOI":"10.3390\/ph14010062"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"\u0106orkovi\u0107, I., Ga\u0161o-Soka\u010d, D., Pichler, A., \u0160imunovi\u0107, J., and Kopjar, M. (2022). Dietary polyphenols as natural inhibitors of \u03b1-amylase and \u03b1-glucosidase. Life, 12.","DOI":"10.3390\/life12111692"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1002\/mnfr.201000019","article-title":"Polyphenols and phenolic acids from strawberry and apple decrease glucose uptake and transport by human intestinal Caco-2 cells","volume":"54","author":"Manzano","year":"2010","journal-title":"Mol. Nutr. Food Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"110563","DOI":"10.1016\/j.fct.2019.110563","article-title":"Exploitation of apple pomace towards extraction of triterpenic acids, antioxidant potential, cytotoxic effects, and inhibition of clinically important enzymes","volume":"131","author":"Nile","year":"2019","journal-title":"Food Chem. Toxicol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Berga, M., Logviss, K., Lauberte, L., Paulausks, A., and Mohylyuk, V. (2023). Flavonoids in the Spotlight: Bridging the Gap between Physicochemical Properties and Formulation Strategies. Pharmaceuticals, 16.","DOI":"10.3390\/ph16101407"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.foodres.2019.03.062","article-title":"Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms","volume":"122","author":"Golden","year":"2019","journal-title":"Food Res. Int."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"100857","DOI":"10.1016\/j.landig.2025.01.013","article-title":"Importance of sample size on the quality and utility of AI-based prediction models for healthcare","volume":"7","author":"Riley","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Everett, J.D., Baird, M.E., Buchanan, P., Bulman, C., Davies, C., Downie, R., Griffiths, C., Heneghan, R., Kloser, R.J., and Laiolo, L. (2017). Modeling what we sample and sampling what we model: Challenges for zooplankton model assessment. Front. Mar. Sci., 4.","DOI":"10.3389\/fmars.2017.00077"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e15625","DOI":"10.1111\/jfpp.15625","article-title":"Multi-objective optimization of drying conditions for the Olea europaea L. leaves with NSGA-II","volume":"45","author":"Vural","year":"2021","journal-title":"J. Food Process. Preserv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Song, C., Shen, H., Amireddy, S., and Zhuang, J. (2024). Food risk assessment based on NSGA-II algorithm: A case study of alcoholic beverages. Front. Sustain. Food Syst., 8.","DOI":"10.3389\/fsufs.2024.1449964"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Arteaga-Cabrera, E., Ram\u00edrez-M\u00e1rquez, C., S\u00e1nchez-Ram\u00edrez, E., Segovia-Hern\u00e1ndez, J.G., Osorio-Mora, O., and G\u00f3mez-Salazar, J.A. (2025). Advancing Optimization Strategies in the Food Industry: From Traditional Approaches to Multi-Objective and Technology-Integrated Solutions. Appl. Sci., 15.","DOI":"10.3390\/app15073846"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.fbp.2024.12.016","article-title":"Comparative analysis of RSM and ANN-GA based modeling for protein extraction from cotton seed meal: Effect of extraction parameters on amino acid profile and nutritional characteristics","volume":"150","author":"Ware","year":"2025","journal-title":"Food Bioprod. Process."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Babor, M., Pedersen, L., Kidmose, U., Paquet-Durand, O., and Hitzmann, B. (2022). Application of non-dominated sorting genetic algorithm (NSGA-II) to increase the efficiency of bakery production: A case study. Processes, 10.","DOI":"10.3390\/pr10081623"},{"key":"ref_72","first-page":"137","article-title":"Multi-objective optimization of the green extraction conditions of bio-active compounds from a levisticum officinale WDJ Koch: Pareto optimality and compromise solutions for process management","volume":"28","author":"Plawgo","year":"2024","journal-title":"Agric. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"283","DOI":"10.17221\/26\/2022-HORTSCI","article-title":"Comparative analysis of phenolic, flavonoid and antioxidant content in root vegetables from organic and conventional production","volume":"50","year":"2023","journal-title":"Hortic. Sci."}],"container-title":["Antioxidants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3921\/15\/9\/1215\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T04:53:12Z","timestamp":1790139192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3921\/15\/9\/1215"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":73,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["antiox15091215"],"URL":"https:\/\/doi.org\/10.3390\/antiox15091215","relation":{},"ISSN":["2076-3921"],"issn-type":[{"value":"2076-3921","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]}}}