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Conventional firmness determination methods are generally destructive, time\u2010consuming, and unsuitable for rapid food quality monitoring systems. This study investigated the relationship between physicochemical composition and TEF and developed machine learning\u2010based models for data\u2010driven firmness prediction. A total of 163 beef tomato samples were analyzed using 11 physicochemical and mineral nutrient variables, including total soluble solids (TSS), dry matter, titratable acidity, lycopene (LYC), vitamin C, calcium, potassium, magnesium, manganese, sodium, and phosphorus. Deep learning (DL), artificial neural networks (ANNs), and multiple linear regression (MLR) models were developed and evaluated using coefficient of determination (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      ), root mean square error (RMSE), and mean absolute error (MAE). Correlation analysis demonstrated strong positive relationships between exocarp firmness and vitamin C (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.677), LYC (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.668), and manganese (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.622), whereas phosphorus (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a0\u22120.660) and TSSs (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a0\u22120.635) showed strong negative correlations. Among the predictive approaches, the DL model achieved the best performance with an\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      value of 0.709, RMSE of 0.158, and MAE of 0.132. ANNs and MLR produced lower predictive accuracy with\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      values of 0.673 and 0.605, respectively. The findings demonstrate that physicochemical and mineral nutrient profiles can support data\u2010driven prediction of TEF. However, because the predictors were obtained through laboratory analyses, further validation using independently collected data and nondestructive sensing technologies is required before practical industrial application.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Practical Applications<\/jats:title>\n                    <jats:p>The developed models demonstrate the potential of physicochemical and mineral nutrient profiles for predicting tomato exocarp firmness. The current framework is proof of concept because its predictors were obtained through laboratory analyses. Future integration with nondestructive sensing technologies may support rapid firmness assessment in grading, packaging, transportation, and postharvest management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1750-3841.71558","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:11:26Z","timestamp":1791558686000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2013Based Prediction of Beef Tomato Exocarp Firmness Using Physicochemical and Mineral Nutrient Profiles"],"prefix":"10.1111","volume":"91","author":[{"given":"Aylin","family":"Kaba\u015f","sequence":"first","affiliation":[{"name":"Department of Organic Farming, Manavgat Vocational School Akdeniz University  Antalya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0394-5862","authenticated-orcid":false,"given":"Mehmet","family":"Kayaku\u015f","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering Akdeniz University  Antalya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d6nder","family":"Kaba\u015f","sequence":"additional","affiliation":[{"name":"Department of Machine, Technical Science Vocational School Akdeniz University  Antalya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/su16229681"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpa.2024.100698"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.110354"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.postharvbio.2024.113379"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.33407\/itlt.v82i2.4178"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sjbs.2020.07.004"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/jxb\/erf109"},{"key":"e_1_2_10_9_1","unstructured":"Anonymous.1983.G\u0131da Maddeleri Muayene ve Analiz Y\u00f6ntemleri.T.C. 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