[{"publication_identifier":{"issn":["1866-5195 "],"eissn":["1613-2041"]},"volume":75,"intvolume":"        75","external_id":{"isi":["000858906600001"]},"title":"Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker","status":"public","publication":"Brewing science","issue":"1/2","publisher":"Carl","author":[{"last_name":"Weishaupt","first_name":"Imke","full_name":"Weishaupt, Imke","id":"58425"},{"last_name":"Neubauer","full_name":"Neubauer, Peter","first_name":"Peter"},{"orcid":"0000-0001-6401-8873","last_name":"Schneider","first_name":"Jan","full_name":"Schneider, Jan","id":"13209"}],"publication_status":"published","abstract":[{"lang":"eng","text":"The conventional method for the determination of the lethal heat load during pasteurisation (expressed in so-called pasteurisation units (PU)) by measuring temperature and flow rate provides known inaccuracies and requires safety margins in terms of a planned over-pasteurisation to the detriment of the product quality. Based on the hypothesis that chemical conversions correlate with applied heat input, despite the differences in reaction kinetics between chemical conversion and microbiological inactivation, inline near infrared spectroscopy (NIRS) was investigated to identify and quantify applied PU. Acid hydrolytic sucrose degradation was confirmed a favourable marker reaction. In a first step by still using offline analytics (HPLC) and a calculation the feasibility and plausibility in principle could be proved. Compared with conventional PU deviation of only 0.3% were found when using the chemical marker reaction. However, the inline application using NIRS showed too high variations. The too low accuracy of the NIRS model for the sucrose measurement was identified of being the cause for failing the overall goal. Improvements in the inline determination seem to be promising."}],"year":"2022","isi":"1","oa":"1","type":"journal_article","citation":{"havard":"I. Weishaupt, P. Neubauer, J. Schneider, Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker, Brewing Science. 75 (2022) 1–8.","ufg":"<b>Weishaupt, Imke/Neubauer, Peter/Schneider, Jan</b>: Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker, in: <i>Brewing science</i> 75 (2022), H. 1/2,  S. 1–8.","chicago-de":"Weishaupt, Imke, Peter Neubauer und Jan Schneider. 2022. Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker. <i>Brewing science</i> 75, Nr. 1/2: 1–8. doi:<a href=\"https://doi.org/10.23763/BrSc21-20weishaupt\">10.23763/BrSc21-20weishaupt</a>, .","bjps":"<b>Weishaupt I, Neubauer P and Schneider J</b> (2022) Approach to an Inline Monitoring of the Heat Impact in a High Temperature Short Time Treatment (HTST) of Juice with the Help of a Chemical Marker. <i>Brewing science</i> <b>75</b>, 1–8.","ama":"Weishaupt I, Neubauer P, Schneider J. Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker. <i>Brewing science</i>. 2022;75(1/2):1-8. doi:<a href=\"https://doi.org/10.23763/BrSc21-20weishaupt\">10.23763/BrSc21-20weishaupt</a>","short":"I. Weishaupt, P. Neubauer, J. Schneider, Brewing Science 75 (2022) 1–8.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Weishaupt, Imke</span> ; <span style=\"font-variant:small-caps;\">Neubauer, Peter</span> ; <span style=\"font-variant:small-caps;\">Schneider, Jan</span>: Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker. In: <i>Brewing science</i> Bd. 75, Carl (2022), Nr. 1/2, S. 1–8","apa":"Weishaupt, I., Neubauer, P., &#38; Schneider, J. (2022). Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker. <i>Brewing Science</i>, <i>75</i>(1/2), 1–8. <a href=\"https://doi.org/10.23763/BrSc21-20weishaupt\">https://doi.org/10.23763/BrSc21-20weishaupt</a>","mla":"Weishaupt, Imke, et al. “Approach to an Inline Monitoring of the Heat Impact in a High Temperature Short Time Treatment (HTST) of Juice with the Help of a Chemical Marker.” <i>Brewing Science</i>, vol. 75, no. 1/2, 2022, pp. 1–8, <a href=\"https://doi.org/10.23763/BrSc21-20weishaupt\">https://doi.org/10.23763/BrSc21-20weishaupt</a>.","chicago":"Weishaupt, Imke, Peter Neubauer, and Jan Schneider. “Approach to an Inline Monitoring of the Heat Impact in a High Temperature Short Time Treatment (HTST) of Juice with the Help of a Chemical Marker.” <i>Brewing Science</i> 75, no. 1/2 (2022): 1–8. <a href=\"https://doi.org/10.23763/BrSc21-20weishaupt\">https://doi.org/10.23763/BrSc21-20weishaupt</a>.","ieee":"I. Weishaupt, P. Neubauer, and J. Schneider, “Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker,” <i>Brewing science</i>, vol. 75, no. 1/2, pp. 1–8, 2022, doi: <a href=\"https://doi.org/10.23763/BrSc21-20weishaupt\">10.23763/BrSc21-20weishaupt</a>.","van":"Weishaupt I, Neubauer P, Schneider J. Approach to an inline monitoring of the heat impact in a high temperature short time treatment (HTST) of juice with the help of a chemical marker. Brewing science. 2022;75(1/2):1–8."},"user_id":"83778","main_file_link":[{"url":"https://www.researchgate.net/publication/360806853_Approach_to_an_inline_monitoring_of_the_heat_impact_in_a_high_temperature_short_time_treatment_HTST_of_juice_with_the_help_of_a_chemical_marker","open_access":"1"}],"date_updated":"2026-03-12T11:52:14Z","date_created":"2022-02-28T10:50:48Z","department":[{"_id":"DEP4018"},{"_id":"DEP1308"},{"_id":"DEP4028"}],"doi":"10.23763/BrSc21-20weishaupt","keyword":["near infrared spectroscopy","apple juice","pasteurisation","acid hydrolytic sucrose degradation","inline measure-ment of heat input","pasteurisation units"],"page":"1-8","_id":"7090","language":[{"iso":"eng"}]},{"title":"A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion","publication_identifier":{"eisbn":["978-1-4244-1566-3"],"isbn":["978-1-4244-1565-6"],"issn":["1551-2541 "],"unknown":["2378-928X "]},"abstract":[{"text":"The production of printing goods is laborious. Furthermore, the print quality, especially in banknotes, must be assured. It is accepted, that print defects are generated because printing parameters, also machine parameters can change unnoticed. Therefore, a combined concept for a multi-sensory learning and classification model based on new adaptive fuzzy-pattern-classifiers for data inspection is proposed. This inspection concept, which combines optical, acoustical and other machine information, comes up with a large amount of data, which leads to multivariate methods for data analysis. Multivariate methods are useful for analysis of large and complex data sets that consist of many variables measured on large numbers of physical data.","lang":"eng"}],"publication_status":"published","publisher":"MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING","author":[{"last_name":"Dyck","first_name":"Walter","full_name":"Dyck, Walter"},{"full_name":"Türke, Thomas","first_name":"Thomas","last_name":"Türke"},{"last_name":"Schaede","full_name":"Schaede, Johannes","first_name":"Johannes","id":"2128"},{"id":"1804","orcid":"0000-0002-3325-7887","last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker"}],"status":"public","doi":"10.1109/MLSP.2007.4414320","date_created":"2019-11-29T13:50:28Z","department":[{"_id":"DEP5023"}],"date_updated":"2023-03-15T13:49:38Z","main_file_link":[{"url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4414320&tag=1"}],"user_id":"45673","type":"conference","citation":{"havard":"W. Dyck, T. Türke, J. Schaede, V. Lohweg, A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion, in: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, Thessaloniki, Greece, 2007: p. accepted for publication.","bjps":"<b>Dyck W <i>et al.</i></b> (2007) A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion. Thessaloniki, Greece: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, p. accepted for publication.","chicago-de":"Dyck, Walter, Thomas Türke, Johannes Schaede und Volker Lohweg. 2007. A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion. In: , accepted for publication. Thessaloniki, Greece: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. doi:<a href=\"https://doi.org/10.1109/MLSP.2007.4414320,\">10.1109/MLSP.2007.4414320,</a> .","ufg":"<b>Dyck, Walter et. al. (2007)</b>: A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion, in: , Thessaloniki, Greece, S. accepted for publication.","ama":"Dyck W, Türke T, Schaede J, Lohweg V. A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion. In: Thessaloniki, Greece: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING; 2007:accepted for publication. doi:<a href=\"https://doi.org/10.1109/MLSP.2007.4414320\">10.1109/MLSP.2007.4414320</a>","short":"W. Dyck, T. Türke, J. Schaede, V. Lohweg, in: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, Thessaloniki, Greece, 2007, p. accepted for publication.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Dyck, Walter</span> ; <span style=\"font-variant:small-caps;\">Türke, Thomas</span> ; <span style=\"font-variant:small-caps;\">Schaede, Johannes</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion. In: . Thessaloniki, Greece : MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, 2007, S. accepted for publication","apa":"Dyck, W., Türke, T., Schaede, J., &#38; Lohweg, V. (2007). A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion (p. accepted for publication). Thessaloniki, Greece: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. <a href=\"https://doi.org/10.1109/MLSP.2007.4414320\">https://doi.org/10.1109/MLSP.2007.4414320</a>","chicago":"Dyck, Walter, Thomas Türke, Johannes Schaede, and Volker Lohweg. “A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion,” accepted for publication. Thessaloniki, Greece: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, 2007. <a href=\"https://doi.org/10.1109/MLSP.2007.4414320\">https://doi.org/10.1109/MLSP.2007.4414320</a>.","mla":"Dyck, Walter, et al. <i>A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion</i>. MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, 2007, p. accepted for publication, doi:<a href=\"https://doi.org/10.1109/MLSP.2007.4414320\">10.1109/MLSP.2007.4414320</a>.","ieee":"W. Dyck, T. Türke, J. Schaede, and V. Lohweg, “A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion,” 2007, p. accepted for publication.","van":"Dyck W, Türke T, Schaede J, Lohweg V. A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion. In Thessaloniki, Greece: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING; 2007. p. accepted for publication."},"year":2007,"_id":"2068","language":[{"iso":"eng"}],"place":"Thessaloniki, Greece","page":"accepted for publication","keyword":["Sensor fusion","Inspection","Optical sensors","Printing machinery","Data security","Data analysis","Production","Degradation","Principal component analysis","Karhunen-Loeve transforms"]}]