[{"intvolume":"        10","keyword":["flash pasteurization","fruit juice characterization and classification","inline near-infrared spectroscopy","multivariate data analysis"],"date_updated":"2025-06-26T13:32:36Z","date_created":"2021-04-08T06:37:30Z","external_id":{"pmid":["35311170"],"isi":["000739093400001"]},"issue":"3","_id":"5425","language":[{"iso":"eng"}],"department":[{"_id":"DEP4000"},{"_id":"DEP1308"}],"page":"800-812","publication":"Food Science & Nutrition","status":"public","publisher":"Wiley","doi":" https://doi.org/10.1002/fsn3.2709","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."}],"publication_identifier":{"issn":["2048-7177"]},"year":"2022","author":[{"full_name":"Weishaupt, Imke","id":"58425","last_name":"Weishaupt","first_name":"Imke"},{"full_name":"Neubauer, Peter","first_name":"Peter","last_name":"Neubauer"},{"last_name":"Schneider","orcid":"0000-0001-6401-8873","first_name":"Jan","full_name":"Schneider, Jan","id":"13209"}],"type":"journal_article","citation":{"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>, .","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>.","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.","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.","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","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>.","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>","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>","short":"I. Weishaupt, P. Neubauer, J. Schneider, Food Science &#38; Nutrition 10 (2022) 800–812.","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>.","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.","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."},"isi":"1","volume":10,"title":"Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization","pmid":"1","user_id":"83781","publication_status":"published"},{"volume":85,"isi":"1","author":[{"first_name":"Imke","last_name":"Weishaupt","full_name":"Weishaupt, Imke","id":"58425"},{"orcid":"0000-0002-9974-2543","last_name":"Zimmer","first_name":"Manuel","full_name":"Zimmer, Manuel","id":"71613"},{"last_name":"Neubauer","first_name":"Peter","full_name":"Neubauer, Peter"},{"full_name":"Schneider, Jan","id":"13209","last_name":"Schneider","orcid":"0000-0001-6401-8873","first_name":"Jan"}],"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>.","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.","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>.","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.","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>, .","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>","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>","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>.","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.","short":"I. Weishaupt, M. Zimmer, P. Neubauer, J. Schneider, Journal of Food Science 85 (2020) 2020–2031.","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."},"type":"journal_article","user_id":"83781","publication_status":"published","title":"Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer","pmid":"1","publication":"Journal of Food Science","status":"public","page":"2020 - 2031","doi":"10.1111/1750-3841.15307","publication_identifier":{"isbn":["0022-1147"],"eissn":["1750-3841"]},"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"}],"year":"2020","department":[{"_id":"DEP1308"},{"_id":"DEP4018"}],"date_updated":"2025-06-26T13:30:26Z","keyword":["flash pasteurization","inline near infrared spectroscopy","multivariate data analysis","process condition influences","sugar-water-solution model beverage"],"intvolume":"        85","issue":"7","_id":"5424","language":[{"iso":"eng"}],"external_id":{"pmid":["32602154"],"isi":["000543977000001"]},"date_created":"2021-04-08T06:37:30Z"}]