[{"publication":"Sensors","publication_identifier":{"issn":["1424-8220 "]},"date_created":"2024-06-03T07:43:48Z","date_updated":"2025-06-25T13:00:14Z","oa":"1","keyword":["multidimensional sensor arrays","MOS sensors","beer fermentation","process control","gas analysis","metal oxide semiconductors","intentional data analysis","chemometrics","PLSR","PCA","first-order calibration"],"language":[{"iso":"eng"}],"title":"Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses ","volume":24,"citation":{"ieee":"J. Kruse, J. Wörner, J. Schneider, H. Dörksen, and M. Pein-Hackelbusch, “Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses ,” <i>Sensors</i>, vol. 24, no. 11, Art. no. 3520, 2024, doi: <a href=\"https://doi.org/10.3390/s24113520\">10.3390/s24113520</a>.","ama":"Kruse J, Wörner J, Schneider J, Dörksen H, Pein-Hackelbusch M. Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses . <i>Sensors</i>. 2024;24(11). doi:<a href=\"https://doi.org/10.3390/s24113520\">10.3390/s24113520</a>","chicago":"Kruse, Julia, Julius Wörner, Jan Schneider, Helene Dörksen, and Miriam Pein-Hackelbusch. “Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses .” <i>Sensors</i> 24, no. 11 (2024). <a href=\"https://doi.org/10.3390/s24113520\">https://doi.org/10.3390/s24113520</a>.","apa":"Kruse, J., Wörner, J., Schneider, J., Dörksen, H., &#38; Pein-Hackelbusch, M. (2024). Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses . <i>Sensors</i>, <i>24</i>(11), Article 3520. <a href=\"https://doi.org/10.3390/s24113520\">https://doi.org/10.3390/s24113520</a>","ufg":"<b>Kruse, Julia u. a.</b>: Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses , in: <i>Sensors</i> 24 (2024), H. 11.","havard":"J. Kruse, J. Wörner, J. Schneider, H. Dörksen, M. Pein-Hackelbusch, Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses , Sensors. 24 (2024).","bjps":"<b>Kruse J <i>et al.</i></b> (2024) Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses . <i>Sensors</i> <b>24</b>.","mla":"Kruse, Julia, et al. “Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses .” <i>Sensors</i>, vol. 24, no. 11, 3520, 2024, <a href=\"https://doi.org/10.3390/s24113520\">https://doi.org/10.3390/s24113520</a>.","van":"Kruse J, Wörner J, Schneider J, Dörksen H, Pein-Hackelbusch M. Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses . Sensors. 2024;24(11).","din1505-2-1":"<span style=\"font-variant:small-caps;\">Kruse, Julia</span> ; <span style=\"font-variant:small-caps;\">Wörner, Julius</span> ; <span style=\"font-variant:small-caps;\">Schneider, Jan</span> ; <span style=\"font-variant:small-caps;\">Dörksen, Helene</span> ; <span style=\"font-variant:small-caps;\">Pein-Hackelbusch, Miriam</span>: Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses . In: <i>Sensors</i> Bd. 24, MDPI (2024), Nr. 11","chicago-de":"Kruse, Julia, Julius Wörner, Jan Schneider, Helene Dörksen und Miriam Pein-Hackelbusch. 2024. Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses . <i>Sensors</i> 24, Nr. 11. doi:<a href=\"https://doi.org/10.3390/s24113520\">10.3390/s24113520</a>, .","short":"J. Kruse, J. Wörner, J. Schneider, H. Dörksen, M. Pein-Hackelbusch, Sensors 24 (2024)."},"author":[{"first_name":"Julia","id":"82298","full_name":"Kruse, Julia","last_name":"Kruse"},{"first_name":"Julius","full_name":"Wörner, Julius","last_name":"Wörner","id":"79011"},{"last_name":"Schneider","full_name":"Schneider, Jan","id":"13209","first_name":"Jan","orcid":"0000-0001-6401-8873"},{"first_name":"Helene","last_name":"Dörksen","full_name":"Dörksen, Helene","id":"46416"},{"orcid":"0000-0002-7920-0595","first_name":"Miriam","full_name":"Pein-Hackelbusch, Miriam","id":"64952","last_name":"Pein-Hackelbusch"}],"main_file_link":[{"url":"https://www.mdpi.com/1424-8220/24/11/3520","open_access":"1"}],"year":"2024","external_id":{"pmid":["38894312"],"isi":["001245424000001"]},"status":"public","pmid":"1","type":"scientific_journal_article","user_id":"83781","intvolume":"        24","_id":"11495","article_number":"3520","doi":"10.3390/s24113520","isi":"1","publication_status":"published","article_type":"original","department":[{"_id":"DEP4028"}],"abstract":[{"text":"To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose. ","lang":"eng"}],"issue":"11","publisher":"MDPI","quality_controlled":"1"},{"doi":" https://doi.org/10.1002/fsn3.2709","_id":"5425","user_id":"83781","intvolume":"        10","type":"journal_article","publisher":"Wiley","department":[{"_id":"DEP4000"},{"_id":"DEP1308"}],"abstract":[{"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.","lang":"eng"}],"issue":"3","publication_status":"published","isi":"1","language":[{"iso":"eng"}],"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","publication_identifier":{"issn":["2048-7177"]},"publication":"Food Science & Nutrition","pmid":"1","page":"800-812","status":"public","author":[{"full_name":"Weishaupt, Imke","id":"58425","last_name":"Weishaupt","first_name":"Imke"},{"first_name":"Peter","last_name":"Neubauer","full_name":"Neubauer, Peter"},{"orcid":"0000-0001-6401-8873","id":"13209","first_name":"Jan","last_name":"Schneider","full_name":"Schneider, Jan"}],"year":"2022","external_id":{"pmid":["35311170"],"isi":["000739093400001"]},"citation":{"short":"I. Weishaupt, P. Neubauer, J. Schneider, Food Science &#38; Nutrition 10 (2022) 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-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>, .","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.","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.","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.","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>.","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.","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>","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>.","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>","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>."},"volume":10,"title":"Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization"},{"type":"journal_article","intvolume":"        85","user_id":"83781","doi":"10.1111/1750-3841.15307","_id":"5424","isi":"1","publication_status":"published","issue":"7","abstract":[{"lang":"eng","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."}],"department":[{"_id":"DEP1308"},{"_id":"DEP4018"}],"date_created":"2021-04-08T06:37:30Z","publication_identifier":{"eissn":["1750-3841"],"isbn":["0022-1147"]},"publication":"Journal of Food Science","date_updated":"2025-06-26T13:30:26Z","language":[{"iso":"eng"}],"keyword":["flash pasteurization","inline near infrared spectroscopy","multivariate data analysis","process condition influences","sugar-water-solution model beverage"],"title":"Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer","volume":85,"citation":{"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>, .","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","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>.","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>","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>.","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.","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>.","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.","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."},"external_id":{"isi":["000543977000001"],"pmid":["32602154"]},"author":[{"full_name":"Weishaupt, Imke","last_name":"Weishaupt","id":"58425","first_name":"Imke"},{"orcid":"0000-0002-9974-2543","first_name":"Manuel","last_name":"Zimmer","id":"71613","full_name":"Zimmer, Manuel"},{"first_name":"Peter","full_name":"Neubauer, Peter","last_name":"Neubauer"},{"id":"13209","last_name":"Schneider","full_name":"Schneider, Jan","first_name":"Jan","orcid":"0000-0001-6401-8873"}],"year":"2020","page":"2020 - 2031","pmid":"1","status":"public"},{"citation":{"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","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> .","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>.","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.","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.","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.","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.","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>"},"publication_status":"published","title":"A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion","status":"public","page":"accepted for publication","publisher":"MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING","year":2007,"author":[{"last_name":"Dyck","full_name":"Dyck, Walter","first_name":"Walter"},{"first_name":"Thomas","full_name":"Türke, Thomas","last_name":"Türke"},{"first_name":"Johannes","id":"2128","last_name":"Schaede","full_name":"Schaede, Johannes"},{"first_name":"Volker","full_name":"Lohweg, Volker","id":"1804","last_name":"Lohweg","orcid":"0000-0002-3325-7887"}],"main_file_link":[{"url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4414320&tag=1"}],"department":[{"_id":"DEP5023"}],"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"}],"user_id":"45673","date_updated":"2023-03-15T13:49:38Z","publication_identifier":{"isbn":["978-1-4244-1565-6"],"eisbn":["978-1-4244-1566-3"],"issn":["1551-2541 "],"unknown":["2378-928X "]},"date_created":"2019-11-29T13:50:28Z","type":"conference","place":"Thessaloniki, Greece","keyword":["Sensor fusion","Inspection","Optical sensors","Printing machinery","Data security","Data analysis","Production","Degradation","Principal component analysis","Karhunen-Loeve transforms"],"language":[{"iso":"eng"}],"_id":"2068","doi":"10.1109/MLSP.2007.4414320"}]