[{"year":"2024","user_id":"81304","department":[{"_id":"DEP4028"},{"_id":"DEP5023"},{"_id":"DEP4000"},{"_id":"DEP1308"}],"status":"public","_id":"12021","keyword":["sourdough","fermentation","near-infrared spectroscopy","support vector machine"],"conference":{"start_date":"2024-10-10","end_date":"2024-10-12","name":"10. GDL Kongress Lebensmitteltechnologie 2024","location":"Lemgo"},"author":[{"first_name":"Jan","full_name":"Segermann, Jan","last_name":"Segermann","id":"81965"},{"first_name":"Mario","full_name":"Luttmann, Mario","last_name":"Luttmann","id":"63215"},{"first_name":"André","id":"62117","last_name":"Blome","full_name":"Blome, André"},{"last_name":"Feldt","id":"61469","full_name":"Feldt, Sebastian","first_name":"Sebastian"},{"last_name":"Sivanesan","full_name":"Sivanesan, Sujee","first_name":"Sujee"},{"first_name":"Christoph-Alexander","full_name":"Holst, Christoph-Alexander","last_name":"Holst","id":"64782"},{"orcid":"0000-0002-3325-7887","first_name":"Volker","last_name":"Lohweg","id":"1804","full_name":"Lohweg, Volker"},{"first_name":"Björn","id":"45666","last_name":"Frahm","full_name":"Frahm, Björn"},{"last_name":"Müller","id":"12119","full_name":"Müller, Ulrich","first_name":"Ulrich"}],"type":"conference_speech","citation":{"ieee":"J. Segermann <i>et al.</i>, <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>. 2024.","ama":"Segermann J, Luttmann M, Blome A, et al. <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>.; 2024.","van":"Segermann J, Luttmann M, Blome A, Feldt S, Sivanesan S, Holst CA, et al. Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie. 2024.","din1505-2-1":"<span style=\"font-variant:small-caps;\"><span style=\"font-variant:small-caps;\">Segermann, Jan</span> ; <span style=\"font-variant:small-caps;\">Luttmann, Mario</span> ; <span style=\"font-variant:small-caps;\">Blome, André</span> ; <span style=\"font-variant:small-caps;\">Feldt, Sebastian</span> ; <span style=\"font-variant:small-caps;\">Sivanesan, Sujee</span> ; <span style=\"font-variant:small-caps;\">Holst, Christoph-Alexander</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span> ; u. a.</span>: <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>, 2024","bjps":"<b>Segermann J <i>et al.</i></b> (2024) <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>. .","chicago":"Segermann, Jan, Mario Luttmann, André Blome, Sebastian Feldt, Sujee Sivanesan, Christoph-Alexander Holst, Volker Lohweg, Björn Frahm, and Ulrich Müller. <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>, 2024.","havard":"J. Segermann, M. Luttmann, A. Blome, S. Feldt, S. Sivanesan, C.-A. Holst, V. Lohweg, B. Frahm, U. Müller, Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie, 2024.","apa":"Segermann, J., Luttmann, M., Blome, A., Feldt, S., Sivanesan, S., Holst, C.-A., Lohweg, V., Frahm, B., &#38; Müller, U. (2024). <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>. 10. GDL Kongress Lebensmitteltechnologie 2024, Lemgo.","mla":"Segermann, Jan, et al. <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>. 2024.","short":"J. Segermann, M. Luttmann, A. Blome, S. Feldt, S. Sivanesan, C.-A. Holst, V. Lohweg, B. Frahm, U. Müller, Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie, 2024.","chicago-de":"Segermann, Jan, Mario Luttmann, André Blome, Sebastian Feldt, Sujee Sivanesan, Christoph-Alexander Holst, Volker Lohweg, Björn Frahm und Ulrich Müller. 2024. <i>Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie</i>.","ufg":"<b>Segermann, Jan u. a.</b>: Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie, o. O. 2024."},"language":[{"iso":"ger"}],"date_created":"2024-11-06T16:53:13Z","publication_status":"published","date_updated":"2025-10-17T18:22:39Z","title":"Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie"},{"_id":"13335","title":"Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods","author":[{"first_name":"Imke","full_name":"Weishaupt, Imke","last_name":"Weishaupt","id":"58425"}],"language":[{"iso":"eng"}],"date_created":"2026-01-09T11:56:40Z","publication_status":"published","place":"Berlin","citation":{"bjps":"<b>Weishaupt I</b> (2023) <i>Near Infrared Spectroscopy as Inline Analytical Tool to Optimize the Pasteurization Process of Liquid Foods</i>. Berlin: Technische Universität Berlin.","short":"I. Weishaupt, Near Infrared Spectroscopy as Inline Analytical Tool to Optimize the Pasteurization Process of Liquid Foods, Technische Universität Berlin, Berlin, 2023.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Weishaupt, Imke</span>: <i>Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods</i>. Berlin : Technische Universität Berlin, 2023","apa":"Weishaupt, I. (2023). <i>Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods</i>. Technische Universität Berlin. <a href=\"https://doi.org/10.14279/depositonce-17804\">https://doi.org/10.14279/depositonce-17804</a>","mla":"Weishaupt, Imke. <i>Near Infrared Spectroscopy as Inline Analytical Tool to Optimize the Pasteurization Process of Liquid Foods</i>. Technische Universität Berlin, 2023, <a href=\"https://doi.org/10.14279/depositonce-17804\">https://doi.org/10.14279/depositonce-17804</a>.","ufg":"<b>Weishaupt, Imke</b>: Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods, Berlin 2023.","chicago-de":"Weishaupt, Imke. 2023. <i>Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods</i>. Berlin: Technische Universität Berlin. doi:<a href=\"https://doi.org/10.14279/depositonce-17804\">https://doi.org/10.14279/depositonce-17804</a>, .","chicago":"Weishaupt, Imke. <i>Near Infrared Spectroscopy as Inline Analytical Tool to Optimize the Pasteurization Process of Liquid Foods</i>. Berlin: Technische Universität Berlin, 2023. <a href=\"https://doi.org/10.14279/depositonce-17804\">https://doi.org/10.14279/depositonce-17804</a>.","ieee":"I. Weishaupt, <i>Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods</i>. Berlin: Technische Universität Berlin, 2023. doi: <a href=\"https://doi.org/10.14279/depositonce-17804\">https://doi.org/10.14279/depositonce-17804</a>.","havard":"I. Weishaupt, Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods, Technische Universität Berlin, Berlin, 2023.","van":"Weishaupt I. Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods. Berlin: Technische Universität Berlin; 2023. 144 p.","ama":"Weishaupt I. <i>Near Infrared Spectroscopy as Inline Analytical Tool to Optimize the Pasteurization Process of Liquid Foods</i>. Technische Universität Berlin; 2023. doi:<a href=\"https://doi.org/10.14279/depositonce-17804\">https://doi.org/10.14279/depositonce-17804</a>"},"keyword":["fruit juice pasteurization","near infrared spectroscopy","process optimization","multivariate statistics","inline process analytics","Fruchtsaftpasteurisation","Prozessoptimierung","Nahinfrarotspektroskopie","multivariate Statistik","Inline-Prozessanalytik"],"supervisor":[{"first_name":"Juri ","last_name":"Rappsilber","full_name":"Rappsilber, Juri "},{"last_name":"Neubauer","full_name":"Neubauer, Peter ","first_name":"Peter "},{"orcid":"0000-0001-6401-8873","last_name":"Schneider","id":"13209","full_name":"Schneider, Jan","first_name":"Jan"},{"first_name":"Leif-Alexander ","full_name":"Garbe, Leif-Alexander ","last_name":"Garbe"}],"page":"144","abstract":[{"text":"The process of thermal preservation of liquid foods is a safety-relevant process step in the processing of products such as fruit juices and is associated with a high-energy expenditure and safety margin. There are already various approaches to improve this conventionally managed process step in terms of product and resource preservation. Compared to these novel technologies, the use of real-time process analytics offers great potential to improve already existing process plants by implementing inline capable process analytical tools. This allows direct control of the reactions taking place and changes during the running process. Instead of post process, random product control, quality control during the process can be made rendered. The chemical and pharmaceutical industry serves as a reference industry for the use of process analytical tools, although the reactions and product matrices are less complex. In the food industry, on the other hand, there is a greater variation in raw materials and intermediate products. In addition, a large number of reactions can take place in parallel within a process, and the physical states and properties of the individual components can vary. A uniform set of rules for the use of process analytical tools does not exist here. Each product, each process provides its own research potential, so that a large research gap opens up in the area of the food industry.\r\nIn order to contribute to closing this gap, this thesis presents a novel approach to improve the process of pasteurization of liquid food. For fruit juices as an application, near infrared spectroscopy in combination with chemometric methods was applied to make the process more product specific. Based on known weaknesses of the process, the relevant aspects for a product-specific treatment were identified. In the further course, the suitability of near infrared spectroscopy as a process analytical tool in the process of pasteurization was verified. Moreover, it was investigated whether a sufficiently accurate identification of the product type as well as the microbiologically relevant properties can be achieved by the application of chemometric methods. In the course of this, the suitability of the measurement methodology was confirmed and solutions were established for any process influences. The product classification and description of the microbiologically relevant parameters extract content and pH value were also implemented with sufficient accuracy. Knowing the destruction kinetics of relevant microorganisms, the product-specific determination of target values for the necessary lethal heat input could be realized. In addition, an analysis of the actual values was carried out on the basis of a chemometric regression method by inferring the microbiological pasteurization effect through the chemical reaction of acid hydrolytic sucrose degradation by means of the indirect approach. This required knowledge of the chemical reaction kinetics and mahematical modeling of the degradation behavior. The novel approach could be confirmed by calculations using results from off-line analysis, whereas the use of near infrared spectroscopy as an inline method still revealed potential for optimization with respect to measurement accuracies.\r\nIn summary, the results of this work provide a promising opportunity to make conventional processes for the preservation of liquid foods more product-specific by using near-infrared spectroscopy as an inline-capable and multimodal sensor technique, leading to an increase in process efficiency and product quality.","lang":"eng"},{"lang":"ger","text":"Der Prozess der thermischen Haltbarmachung von flüssigen Lebensmitteln ist ein sicherheitsrelevanter Prozessschritt in der Verarbeitung von Produkten wie Fruchtsäften und ist mit einem hohen energetischen Aufwand und Sicherheitszuschlag verbunden. Um diesen konventionell geführten Prozessschritt hinsichtlich Produkt- und Ressourcenschonung zu optimieren, gibt es bereits verschiedene Ansätze. Im Vergleich zu neuartigen Technologien bietet der Einsatz von Echtzeit-Prozessanalytik großes Potential bereits bestehende Prozessanlagen durch die Implementierung inlinefähiger Prozessanalysetools zu verbessern. Dies ermöglicht eine Kontrolle ablaufender Reaktionen im laufenden Prozess. Anstelle nachgelagerter, stichprobenartiger Produktkontrolle kann eine Qualitätslenkung im Prozess erfolgen. Vorbildindustrie bezüglich des Einsatzes von Prozessanalysetools ist die pharmazeutische Industrie, wo Reaktionen und Produktmatrices allerdings weniger komplex sind. In der Lebensmittelindustrie gibt es eine größere Schwankungsbreite der Rohstoffe und Zwischenprodukte. Zudem können innerhalb eines Prozesses eine Vielzahl an Reaktionen parallel ablaufen und die Zustandsformen und Eigenschaften der einzelnen Komponenten variieren. Ein einheitliches Regelwerk zum Einsatz der Prozessanalysetools existiert nicht. Jedes Produkt, jeder Prozess liefert eigenes Forschungspotential, sodass sich im Bereich der Lebensmittelindustrie eine große Forschungslücke auftut.\r\nUm einen Beitrag zur Schließung dieser Lücke zu leisten, zeigt diese Arbeit einen neuartigen Lösungsansatz auf, um den Prozess der Pasteurisation flüssiger Lebensmittel zu verbessern. Am Anwendungsfall von Fruchtsäften wurde der Einsatz von Nahinfrarotspektroskopie in Kombination mit chemometrischen Methoden genutzt, um den Prozess produktspezifischer zu gestalten. Ausgehend von bekannten Schwachstellen des Prozesses wurden die relevanten Aspekte identifiziert, deren Kenntnis für eine produktspezifische Behandlung relevant sind. Angefangen mit der Überprüfung der Eignung von Nahinfrarotspektroskopie als Prozessanalysetool im Prozess der Pasteurisation, wurde untersucht, ob eine Identifikation des Produkttyps sowie der mikrobiologisch relevanten Eigenschaften durch die Anwendung chemometrischer Methoden erfolgen kann. In diesem Zuge konnte eine Eignung der Messmethodik bestätigt werden und für etwaige Prozesseinflüsse Lösungsansätze etabliert werden. Auch die Produktklassifizierung und Beschreibung der mikrobiologisch relevanten Parameter Extraktgehalt und pH-Wert wurden hinreichend genau umgesetzt. Unter Kenntnis der Abtötungskinetiken entsprechend relevanter Mikroorganismen konnte die produktspezifische Ermittlung für Sollwerte des notwendigen letalen Hitzeeintrags realisiert werden. Im Weiteren wurde eine Analyse der Ist-Werte auf Basis einer chemometrischen Regressionsmethode durchgeführt, indem mittels des indirekten Ansatzes über die chemische Reaktion der säurehydrolytischen Saccharosedegradation auf den mikrobiologischen Pasteurisationseffekt geschlossen wurde. Dazu war die Kenntnis der chemischen Reaktionskinetik notwendig und die mathematische Modellierung des Abbauverhaltens. Der neuartige Ansatz konnte mittels Berechnungen unter Verwendung von Ergebnissen der Offline-Analytik bestätigt werden. Der Einsatz von Nahinfrarotspektroskopie als Inline- Methode hingegen offenbarte Optimierungspotenzial hinsichtlich der Messgenauigkeiten.\r\nZusammenfassend liefern die Ergebnisse dieser Arbeit eine vielversprechende Möglichkeit herkömmliche Prozesse zur Haltbarmachung von flüssigen Lebensmitteln durch den Einsatz von Nahinfrarotspektroskopie als inlinefähige, multimodale Sensortechnik produktspezifischer zu gestalten, was zur Steigerung der Prozesseffizienz und Produktqualität führt."}],"department":[{"_id":"DEP4028"}],"year":"2023","user_id":"83781","status":"public","defense_date":"2023-05-05","date_updated":"2026-01-12T08:47:34Z","doi":"https://doi.org/10.14279/depositonce-17804","type":"dissertation","publisher":"Technische Universität Berlin"},{"oa":"1","_id":"7090","publication":"Brewing science","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"}],"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","isi":"1","intvolume":"        75","author":[{"full_name":"Weishaupt, Imke","last_name":"Weishaupt","id":"58425","first_name":"Imke"},{"first_name":"Peter","full_name":"Neubauer, Peter","last_name":"Neubauer"},{"orcid":"0000-0001-6401-8873","first_name":"Jan","full_name":"Schneider, Jan","last_name":"Schneider","id":"13209"}],"publication_identifier":{"eissn":["1613-2041"],"issn":["1866-5195 "]},"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.","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>, .","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.","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>.","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>","short":"I. Weishaupt, P. Neubauer, J. Schneider, Brewing Science 75 (2022) 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>","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.","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>.","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>.","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","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."},"date_created":"2022-02-28T10:50:48Z","language":[{"iso":"eng"}],"volume":75,"publication_status":"published","keyword":["near infrared spectroscopy","apple juice","pasteurisation","acid hydrolytic sucrose degradation","inline measure-ment of heat input","pasteurisation units"],"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."}],"page":"1-8","user_id":"83778","year":"2022","department":[{"_id":"DEP4018"},{"_id":"DEP1308"},{"_id":"DEP4028"}],"status":"public","issue":"1/2","doi":"10.23763/BrSc21-20weishaupt","date_updated":"2026-03-12T11:52:14Z","external_id":{"isi":["000858906600001"]},"type":"journal_article","publisher":"Carl"},{"publication":"Food Science & Nutrition","_id":"5425","intvolume":"        10","author":[{"first_name":"Imke","full_name":"Weishaupt, Imke","last_name":"Weishaupt","id":"58425"},{"full_name":"Neubauer, Peter","last_name":"Neubauer","first_name":"Peter"},{"first_name":"Jan","last_name":"Schneider","id":"13209","full_name":"Schneider, Jan","orcid":"0000-0001-6401-8873"}],"isi":"1","publication_identifier":{"issn":["2048-7177"]},"citation":{"havard":"I. Weishaupt, P. Neubauer, J. Schneider, Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization, Food Science &#38; Nutrition. 10 (2022) 800–812.","short":"I. Weishaupt, P. Neubauer, J. Schneider, Food Science &#38; Nutrition 10 (2022) 800–812.","apa":"Weishaupt, I., Neubauer, P., &#38; Schneider, J. (2022). Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization. <i>Food Science &#38; Nutrition</i>, <i>10</i>(3), 800–812. <a href=\"https://doi.org/ https://doi.org/10.1002/fsn3.2709\">https://doi.org/ https://doi.org/10.1002/fsn3.2709</a>","mla":"Weishaupt, Imke, et al. “Near-Infrared Spectroscopy for the Inline Classification and Characterization of Fruit Juices for a Product-Customized Flash Pasteurization.” <i>Food Science &#38; Nutrition</i>, vol. 10, no. 3, 2022, pp. 800–12, <a href=\"https://doi.org/ https://doi.org/10.1002/fsn3.2709\">https://doi.org/ https://doi.org/10.1002/fsn3.2709</a>.","ufg":"<b>Weishaupt, Imke/Neubauer, Peter/Schneider, Jan</b>: Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization, in: <i>Food Science &#38; Nutrition</i> 10 (2022), H. 3,  S. 800–812.","chicago-de":"Weishaupt, Imke, Peter Neubauer und Jan Schneider. 2022. Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization. <i>Food Science &#38; Nutrition</i> 10, Nr. 3: 800–812. doi:<a href=\"https://doi.org/ https://doi.org/10.1002/fsn3.2709\"> https://doi.org/10.1002/fsn3.2709</a>, .","ieee":"I. Weishaupt, P. Neubauer, and J. Schneider, “Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization,” <i>Food Science &#38; Nutrition</i>, vol. 10, no. 3, pp. 800–812, 2022, doi: <a href=\"https://doi.org/ https://doi.org/10.1002/fsn3.2709\"> https://doi.org/10.1002/fsn3.2709</a>.","van":"Weishaupt I, Neubauer P, Schneider J. Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization. Food Science &#38; Nutrition. 2022;10(3):800–12.","ama":"Weishaupt I, Neubauer P, Schneider J. Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization. <i>Food Science &#38; Nutrition</i>. 2022;10(3):800-812. doi:<a href=\"https://doi.org/ https://doi.org/10.1002/fsn3.2709\"> https://doi.org/10.1002/fsn3.2709</a>","bjps":"<b>Weishaupt I, Neubauer P and Schneider J</b> (2022) Near-Infrared Spectroscopy for the Inline Classification and Characterization of Fruit Juices for a Product-Customized Flash Pasteurization. <i>Food Science &#38; Nutrition</i> <b>10</b>, 800–812.","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>: Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization. In: <i>Food Science &#38; Nutrition</i> Bd. 10, Wiley (2022), Nr. 3, S. 800–812","chicago":"Weishaupt, Imke, Peter Neubauer, and Jan Schneider. “Near-Infrared Spectroscopy for the Inline Classification and Characterization of Fruit Juices for a Product-Customized Flash Pasteurization.” <i>Food Science &#38; Nutrition</i> 10, no. 3 (2022): 800–812. <a href=\"https://doi.org/ https://doi.org/10.1002/fsn3.2709\">https://doi.org/ https://doi.org/10.1002/fsn3.2709</a>."},"volume":10,"publication_status":"published","pmid":"1","date_created":"2021-04-08T06:37:30Z","language":[{"iso":"eng"}],"title":"Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization","user_id":"83781","year":"2022","department":[{"_id":"DEP4000"},{"_id":"DEP1308"}],"status":"public","keyword":["flash pasteurization","fruit juice characterization and classification","inline near-infrared spectroscopy","multivariate data analysis"],"abstract":[{"lang":"eng","text":"The feasibility of inline classification and characterization of seven fruit juice varieties was investigated by the application of near-infrared spectroscopy (NIRS) combined with chemometrics. The findings are intended to be used to optimize the flash pasteurization of liquid foods. More precise information of the kind of product in real time had to be achieved to enable a more product-specific process. Using the method of partial least squares discriminant analysis, the fruit juice varieties were classified, showing a classification rate of 100% regarding an internal and 69% regarding an external test sets. A characterization by the extract content, pH value, turbidity, and viscosity was made by fitting a partial least squares regression model. The percentage prediction error of the pH value was <3% for internal and external test sets, and for the Brix value prediction errors were about 4% (internal) and 20% (external). The parameters viscosity and turbidity were found to be unsuitable. Despite this, the strategy applied to gain more product-specific information in real time showed to be feasible. By linking the results to a database containing potentially harmful microorganisms for various types of fruit juices, a more product-specific calculation of the necessary heat input can be performed. To demonstrate the practical relevance, a comparison between conventional and product-adapted process control was performed using two fruit varieties as examples in case of Alicyclobacillus acidoterrestris. Thus, with more accurate product information, achieved through the use of NIRS with chemometrics, a more precise calculation of the heat input can be achieved."}],"page":"800-812","type":"journal_article","publisher":"Wiley","issue":"3","doi":" https://doi.org/10.1002/fsn3.2709","date_updated":"2025-06-26T13:32:36Z","external_id":{"isi":["000739093400001"],"pmid":["35311170"]}},{"_id":"5424","publication":"Journal of Food Science","title":"Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer","publication_identifier":{"eissn":["1750-3841"],"isbn":["0022-1147"]},"intvolume":"        85","author":[{"first_name":"Imke","id":"58425","last_name":"Weishaupt","full_name":"Weishaupt, Imke"},{"orcid":"0000-0002-9974-2543","first_name":"Manuel","full_name":"Zimmer, Manuel","id":"71613","last_name":"Zimmer"},{"first_name":"Peter","full_name":"Neubauer, Peter","last_name":"Neubauer"},{"last_name":"Schneider","id":"13209","full_name":"Schneider, Jan","first_name":"Jan","orcid":"0000-0001-6401-8873"}],"isi":"1","volume":85,"pmid":"1","publication_status":"published","language":[{"iso":"eng"}],"date_created":"2021-04-08T06:37:30Z","citation":{"ieee":"I. Weishaupt, M. Zimmer, P. Neubauer, and J. Schneider, “Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer,” <i>Journal of Food Science</i>, vol. 85, no. 7, pp. 2020–2031, 2020, doi: <a href=\"https://doi.org/10.1111/1750-3841.15307\">10.1111/1750-3841.15307</a>.","ama":"Weishaupt I, Zimmer M, Neubauer P, Schneider J. Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer. <i>Journal of Food Science</i>. 2020;85(7):2020-2031. doi:<a href=\"https://doi.org/10.1111/1750-3841.15307\">10.1111/1750-3841.15307</a>","van":"Weishaupt I, Zimmer M, Neubauer P, Schneider J. Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer. Journal of Food Science. 2020;85(7):2020–31.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Weishaupt, Imke</span> ; <span style=\"font-variant:small-caps;\">Zimmer, Manuel</span> ; <span style=\"font-variant:small-caps;\">Neubauer, Peter</span> ; <span style=\"font-variant:small-caps;\">Schneider, Jan</span>: Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer. In: <i>Journal of Food Science</i> Bd. 85 (2020), Nr. 7, S. 2020–2031","bjps":"<b>Weishaupt I <i>et al.</i></b> (2020) Model Based Optimization of Transflection near Infrared Spectroscopy as a Process Analytical Tool in a Continuous Flash Pasteurizer. <i>Journal of Food Science</i> <b>85</b>, 2020–2031.","chicago":"Weishaupt, Imke, Manuel Zimmer, Peter Neubauer, and Jan Schneider. “Model Based Optimization of Transflection near Infrared Spectroscopy as a Process Analytical Tool in a Continuous Flash Pasteurizer.” <i>Journal of Food Science</i> 85, no. 7 (2020): 2020–31. <a href=\"https://doi.org/10.1111/1750-3841.15307\">https://doi.org/10.1111/1750-3841.15307</a>.","havard":"I. Weishaupt, M. Zimmer, P. Neubauer, J. Schneider, Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer, Journal of Food Science. 85 (2020) 2020–2031.","mla":"Weishaupt, Imke, et al. “Model Based Optimization of Transflection near Infrared Spectroscopy as a Process Analytical Tool in a Continuous Flash Pasteurizer.” <i>Journal of Food Science</i>, vol. 85, no. 7, 2020, pp. 2020–31, <a href=\"https://doi.org/10.1111/1750-3841.15307\">https://doi.org/10.1111/1750-3841.15307</a>.","apa":"Weishaupt, I., Zimmer, M., Neubauer, P., &#38; Schneider, J. (2020). Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer. <i>Journal of Food Science</i>, <i>85</i>(7), 2020–2031. <a href=\"https://doi.org/10.1111/1750-3841.15307\">https://doi.org/10.1111/1750-3841.15307</a>","short":"I. Weishaupt, M. Zimmer, P. Neubauer, J. Schneider, Journal of Food Science 85 (2020) 2020–2031.","chicago-de":"Weishaupt, Imke, Manuel Zimmer, Peter Neubauer und Jan Schneider. 2020. Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer. <i>Journal of Food Science</i> 85, Nr. 7: 2020–2031. doi:<a href=\"https://doi.org/10.1111/1750-3841.15307\">10.1111/1750-3841.15307</a>, .","ufg":"<b>Weishaupt, Imke u. a.</b>: Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer, in: <i>Journal of Food Science</i> 85 (2020), H. 7,  S. 2020–2031."},"keyword":["flash pasteurization","inline near infrared spectroscopy","multivariate data analysis","process condition influences","sugar-water-solution model beverage"],"page":"2020 - 2031","abstract":[{"text":"Near infrared spectroscopy in combination with a transflection probe was investigated as inline measurement in a continuous flash pasteurizer system with a sugar-water model solution. Robustness and reproducibility of fluctuations of recorded spectra as well as trueness of the chemometric analysis were compared under different process parameter settings. Variable parameters were the flow rate (from laminar flow at 30 L/h to turbulent flow at 90 L/h), temperature (20 to 100 degrees C) and the path length of the transflection probe (2 and 4 mm) while the pressure was kept constant at 2.5 bar. Temperature and path length were identified as the most affecting parameters, in case of homogenous test medium. In case of particle containing systems, the flow rate could have an impact as well. However, the application of a PLS model, which includes a broad temperature range, and the correction of prediction results by applying a polynomial regression function for prediction errors, was able to compensate these effects. Also, a path length of 2 mm leads to a higher accuracy. The applied strategy shows that by the identification of relevant process parameters and settings as well as the establishment of a compensation strategy, near infrared spectroscopy is a powerful process analytical tool for continuous flash pasteurization systems.","lang":"eng"}],"department":[{"_id":"DEP1308"},{"_id":"DEP4018"}],"year":"2020","user_id":"83781","status":"public","issue":"7","external_id":{"isi":["000543977000001"],"pmid":["32602154"]},"date_updated":"2025-06-26T13:30:26Z","doi":"10.1111/1750-3841.15307","type":"journal_article"}]