[{"citation":{"van":"Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. Brewing science . 2021;74(9/10):107–21.","mla":"Wefing, Patrick, et al. “Determination of Free Amino Nitrogen in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms.” <i>Brewing Science </i>, vol. 74, no. 9/10, 2021, pp. 107–21, <a href=\"https://doi.org/10.23763/BrSc21-10wefing\">https://doi.org/10.23763/BrSc21-10wefing</a>.","bjps":"<b>Wefing P <i>et al.</i></b> (2021) Determination of Free Amino Nitrogen in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms. <i>Brewing science </i> <b>74</b>, 107–121.","havard":"P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms, Brewing Science . 74 (2021) 107–121.","ufg":"<b>Wefing, Patrick u. a.</b>: Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms, in: <i>Brewing science </i> 74 (2021), H. 9/10,  S. 107–121.","apa":"Wefing, P., Conradi, F., Rämisch, J., Neubauer, P., &#38; Schneider, J. (2021). Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. <i>Brewing Science </i>, <i>74</i>(9/10), 107–121. <a href=\"https://doi.org/10.23763/BrSc21-10wefing\">https://doi.org/10.23763/BrSc21-10wefing</a>","chicago":"Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer, and Jan Schneider. “Determination of Free Amino Nitrogen in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms.” <i>Brewing Science </i> 74, no. 9/10 (2021): 107–21. <a href=\"https://doi.org/10.23763/BrSc21-10wefing\">https://doi.org/10.23763/BrSc21-10wefing</a>.","short":"P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Brewing Science  74 (2021) 107–121.","chicago-de":"Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer und Jan Schneider. 2021. Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. <i>Brewing science </i> 74, Nr. 9/10: 107–121. doi:<a href=\"https://doi.org/10.23763/BrSc21-10wefing\">https://doi.org/10.23763/BrSc21-10wefing</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Wefing, Patrick</span> ; <span style=\"font-variant:small-caps;\">Conradi, Florian</span> ; <span style=\"font-variant:small-caps;\">Rämisch, Johannes</span> ; <span style=\"font-variant:small-caps;\">Neubauer, Peter</span> ; <span style=\"font-variant:small-caps;\">Schneider, Jan</span>: Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. In: <i>Brewing science </i> Bd. 74, Carl (2021), Nr. 9/10, S. 107–121","ama":"Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. <i>Brewing science </i>. 2021;74(9/10):107-121. doi:<a href=\"https://doi.org/10.23763/BrSc21-10wefing\">https://doi.org/10.23763/BrSc21-10wefing</a>","ieee":"P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, and J. Schneider, “Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms,” <i>Brewing science </i>, vol. 74, no. 9/10, pp. 107–121, 2021, doi: <a href=\"https://doi.org/10.23763/BrSc21-10wefing\">https://doi.org/10.23763/BrSc21-10wefing</a>."},"volume":74,"title":"Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms","status":"public","page":"107 - 121","main_file_link":[{"url":"https://www.researchgate.net/publication/355735532_Determination_of_free_amino_nitrogen_in_beer_mash_with_an_inline_NIR_transflectance_probe_and_data_evaluation_by_machine_learning_algorithms","open_access":"1"}],"year":"2021","author":[{"full_name":"Wefing, Patrick","first_name":"Patrick","last_name":"Wefing","id":"68976"},{"first_name":"Florian","full_name":"Conradi, Florian","last_name":"Conradi","id":"68967"},{"full_name":"Rämisch, Johannes","first_name":"Johannes","last_name":"Rämisch"},{"full_name":"Neubauer, Peter","first_name":"Peter","last_name":"Neubauer"},{"orcid":"0000-0001-6401-8873","first_name":"Jan","last_name":"Schneider","full_name":"Schneider, Jan","id":"13209"}],"date_updated":"2025-01-30T15:43:53Z","publication":"Brewing science ","publication_identifier":{"eissn":["0723-1520"],"issn":["1866-5195"]},"date_created":"2021-11-02T10:06:04Z","keyword":["mashing","NIR","machine learning","FAN"],"language":[{"iso":"eng"}],"oa":"1","publication_status":"published","article_type":"original","publisher":"Carl","quality_controlled":"1","issue":"9/10","abstract":[{"lang":"eng","text":"Free amino nitrogen (FAN) concentrations in beer mash can be determined with machine learning algorithms\r\nfrom near-infrared (NIR) spectra. NIR spectroscopy is an alternative to a classical chemical analysis and\r\nallows for the application of inline process quality control. This study investigates the capabilities of\r\ndifferent machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision Tree\r\nRegressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR), K-nearest neighbours (KNN)\r\nregression as well as Support Vector Regression (SVR) to predict the FAN content in beer mash from NIR\r\nspectra. Various pre-processing strategies such as principal component analysis (PCA) and data\r\nstandardization were used to process NIR data that were used to train the machine learning algorithms.\r\nAlgorithm training was conducted with NIR data obtained from 16 beer mashes with varying FAN\r\nconcentrations. The trained models were then validated with 4 beer mashes that were not used for model\r\ntraining. Machine learning algorithms based on linear regression showed the highest prediction accuracy on\r\nunpre-processed data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L (R2 = 0.96)\r\nand a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The FAN concentration range of the investigated\r\nsamples was between approx. 180 and 220 mg/L. Machine learning based NIR spectra analysis is an alternative\r\nto classical chemical FAN level determination methods and can also be used as inline sensor system."}],"department":[{"_id":"DEP1308"},{"_id":"DEP4028"}],"intvolume":"        74","user_id":"83781","type":"journal_article","_id":"6689","doi":"https://doi.org/10.23763/BrSc21-10wefing"},{"doi":"10.1016/j.xphs.2020.09.007","_id":"12835","place":"Amsterdam [u.a.]","type":"scientific_journal_article","intvolume":"       109","user_id":"83781","abstract":[{"lang":"eng","text":"Delayed-release dosage forms are mainly manufactured as batch processes and include coated tablets, pellets, or particles with gastric resistant polymers. Authors propose a novel approach using the hot-melt extrusion technique to prepare delayed release dosage forms via a continuous manufacturing process, a new trend in the pharmaceutical industry. A full factorial design was employed to correlate input variables, including stearic acid (SA) content, drug content, and pellet size with drug release properties of the pellets. PLS fit method suitably elaborated the relationship between input and output variables with reasonably good fit and goodness of prediction. All three input factors influenced drug release in enzyme-free simulated gastric fluid (SGF) after 120 min; however, SA content did not significantly affect drug dissolution in the enzyme-free simulated intestinal fluid (SIF). An optimized formulation and design space were determined by overlaying multiple contours established from regression equations. The continuous manufacturing process was successfully monitored using inline near-infrared (NIR) and inline particle size analysis, with drug load and pellet size being well-controlled within the design space. The obtained pellets released less than 5% after 120 min in SGF and more than 85% and 95% after 30 min and 45 min, respectively, after switching to SIF. (C) 2020 American Pharmacists Association (R). Published by Elsevier Inc. All rights reserved."}],"issue":"12","department":[{"_id":"DEP4028"}],"publisher":"Elsevier BV","isi":"1","publication_status":"published","language":[{"iso":"eng"}],"keyword":["Continuous manufacturing","Delayed-release","FT-NIR","Inline particle size analysis","Hot melt extrusion"],"date_created":"2025-04-23T08:40:12Z","publication":"Journal of Pharmaceutical Sciences","publication_identifier":{"issn":["0022-3549"],"eissn":["1520-6017"]},"date_updated":"2025-06-26T13:25:32Z","external_id":{"isi":["000590406100010"]},"author":[{"first_name":"Anh Q.","last_name":"Vo","full_name":"Vo, Anh Q."},{"first_name":"Gerd","id":"12015","full_name":"Kutz, Gerd","last_name":"Kutz"},{"last_name":"He","first_name":"Herman","full_name":"He, Herman"},{"first_name":"Sagar","last_name":"Narala","full_name":"Narala, Sagar"},{"first_name":"Suresh","full_name":"Bandari, Suresh","last_name":"Bandari"},{"last_name":"Repka","full_name":"Repka, Michael A.","first_name":"Michael A."}],"year":"2020","page":"3598-3607","status":"public","title":"Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis","citation":{"van":"Vo AQ, Kutz G, He H, Narala S, Bandari S, Repka MA. Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis. Journal of Pharmaceutical Sciences. 2020;109(12):3598–607.","bjps":"<b>Vo AQ <i>et al.</i></b> (2020) Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis. <i>Journal of Pharmaceutical Sciences</i> <b>109</b>, 3598–3607.","mla":"Vo, Anh Q., et al. “Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis.” <i>Journal of Pharmaceutical Sciences</i>, vol. 109, no. 12, 2020, pp. 3598–607, <a href=\"https://doi.org/10.1016/j.xphs.2020.09.007\">https://doi.org/10.1016/j.xphs.2020.09.007</a>.","havard":"A.Q. Vo, G. Kutz, H. He, S. Narala, S. Bandari, M.A. Repka, Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis, Journal of Pharmaceutical Sciences. 109 (2020) 3598–3607.","ufg":"<b>Vo, Anh Q. u. a.</b>: Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis, in: <i>Journal of Pharmaceutical Sciences</i> 109 (2020), H. 12,  S. 3598–3607.","apa":"Vo, A. Q., Kutz, G., He, H., Narala, S., Bandari, S., &#38; Repka, M. A. (2020). Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis. <i>Journal of Pharmaceutical Sciences</i>, <i>109</i>(12), 3598–3607. <a href=\"https://doi.org/10.1016/j.xphs.2020.09.007\">https://doi.org/10.1016/j.xphs.2020.09.007</a>","chicago":"Vo, Anh Q., Gerd Kutz, Herman He, Sagar Narala, Suresh Bandari, and Michael A. Repka. “Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis.” <i>Journal of Pharmaceutical Sciences</i> 109, no. 12 (2020): 3598–3607. <a href=\"https://doi.org/10.1016/j.xphs.2020.09.007\">https://doi.org/10.1016/j.xphs.2020.09.007</a>.","short":"A.Q. Vo, G. Kutz, H. He, S. Narala, S. Bandari, M.A. Repka, Journal of Pharmaceutical Sciences 109 (2020) 3598–3607.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Vo, Anh Q.</span> ; <span style=\"font-variant:small-caps;\">Kutz, Gerd</span> ; <span style=\"font-variant:small-caps;\">He, Herman</span> ; <span style=\"font-variant:small-caps;\">Narala, Sagar</span> ; <span style=\"font-variant:small-caps;\">Bandari, Suresh</span> ; <span style=\"font-variant:small-caps;\">Repka, Michael A.</span>: Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis. In: <i>Journal of Pharmaceutical Sciences</i> Bd. 109. Amsterdam [u.a.], Elsevier BV (2020), Nr. 12, S. 3598–3607","chicago-de":"Vo, Anh Q., Gerd Kutz, Herman He, Sagar Narala, Suresh Bandari und Michael A. Repka. 2020. Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis. <i>Journal of Pharmaceutical Sciences</i> 109, Nr. 12: 3598–3607. doi:<a href=\"https://doi.org/10.1016/j.xphs.2020.09.007\">10.1016/j.xphs.2020.09.007</a>, .","ama":"Vo AQ, Kutz G, He H, Narala S, Bandari S, Repka MA. Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis. <i>Journal of Pharmaceutical Sciences</i>. 2020;109(12):3598-3607. doi:<a href=\"https://doi.org/10.1016/j.xphs.2020.09.007\">10.1016/j.xphs.2020.09.007</a>","ieee":"A. Q. Vo, G. Kutz, H. He, S. Narala, S. Bandari, and M. A. Repka, “Continuous Manufacturing of Ketoprofen Delayed Release Pellets Using Melt Extrusion Technology: Application of QbD Design Space, Inline Near Infrared, and Inline Pellet Size Analysis,” <i>Journal of Pharmaceutical Sciences</i>, vol. 109, no. 12, pp. 3598–3607, 2020, doi: <a href=\"https://doi.org/10.1016/j.xphs.2020.09.007\">10.1016/j.xphs.2020.09.007</a>."},"volume":109}]