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To enable real\u2010time monitoring, this study proposes a process analytical technology (PAT) based on Raman spectroscopy and an innovative deep learning algorithm to achieve real\u2010time prediction of glucose, lactate, viable cell density, and viral titer during LV production. To address the limitations of existing Raman spectral analysis methods, this study integrated data augmentation, self\u2010supervised learning, and time\u2010series modeling strategies. A regression model integrating a one\u2010dimensional Convolutional Neural Network (1D\u2010CNN) with Squeeze\u2010and\u2010Excitation (SE) attention, enhanced by relative positional encoding (RPE), multi\u2010query attention (MQA), and Top\u2010K feature selection (termed ACNN), was constructed. This approach utilized vast amounts of unlabeled spectral data to precisely extract feature information from complex components, including viral particles. Experimental results demonstrated that the ACNN model significantly outperformed the traditional Partial Least Squares (PLS) model in predicting viral titer, cell density, glucose, and lactate concentrations (paired\n                    <jats:italic>t<\/jats:italic>\n                    \u2010test,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05 for all parameters). Notably, titer prediction error was substantially reduced. Statistical analysis showed that the RMSE of ACNN for titer prediction was significantly lower than that of PLS (\n                    <jats:italic>t<\/jats:italic>\n                    \u2009=\u20093.82,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01), and the\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    was significantly higher (\n                    <jats:italic>t<\/jats:italic>\n                    \u2009=\u20094.15,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). This study overcomes technical bottlenecks in real\u2010time quality monitoring and process optimization for LV production, providing a feasible transition from \u201cblack box operations\u201d to transparent and intelligent monitoring throughout the entire process and establishing a robust foundation for large\u2010scale intelligent production of other gene therapy vectors.\n                  <\/jats:p>","DOI":"10.1002\/bit.70332","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T18:19:33Z","timestamp":1786385973000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Algorithm\u2010Guided Raman Spectroscopy for Real\u2010Time Correlation of Critical Process Parameters and Titers in Lentiviral Vector"],"prefix":"10.1002","author":[{"given":"Meiqi","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Engineering, School of Biomedicine and Health Sciences Beijing Institute of Petrochemical Technology Beijing Beijing China"},{"name":"Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Microelectronics Science and Technology Sun Yat\u2010Sen University Zhuhai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Engineering, School of Biomedicine and Health Sciences Beijing Institute of Petrochemical Technology Beijing Beijing China"},{"name":"Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Engineering, School of Biomedicine and Health Sciences Beijing Institute of Petrochemical Technology Beijing Beijing China"},{"name":"Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Engineering, School of Biomedicine and Health Sciences Beijing Institute of Petrochemical Technology Beijing Beijing China"},{"name":"Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Engineering, School of Biomedicine and Health Sciences Beijing Institute of Petrochemical Technology Beijing Beijing China"},{"name":"Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuxing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Engineering, School of Biomedicine and Health Sciences Beijing Institute of Petrochemical Technology Beijing Beijing China"},{"name":"Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Engineering, School of Biomedicine and Health Sciences Beijing Institute of Petrochemical Technology Beijing Beijing China"},{"name":"Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"Turing Process Intelligence Co. 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