---
_id: '11495'
abstract:
- lang: eng
  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. '
article_number: '3520'
article_type: original
author:
- first_name: Julia
  full_name: Kruse, Julia
  id: '82298'
  last_name: Kruse
- first_name: Julius
  full_name: Wörner, Julius
  id: '79011'
  last_name: Wörner
- first_name: Jan
  full_name: Schneider, Jan
  id: '13209'
  last_name: Schneider
  orcid: 0000-0001-6401-8873
- first_name: Helene
  full_name: Dörksen, Helene
  id: '46416'
  last_name: Dörksen
- first_name: Miriam
  full_name: Pein-Hackelbusch, Miriam
  id: '64952'
  last_name: Pein-Hackelbusch
  orcid: 0000-0002-7920-0595
citation:
  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>
  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>
  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>.
  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>.
  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>,
    .
  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'
  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).
  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>.'
  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>.
  short: J. Kruse, J. Wörner, J. Schneider, H. Dörksen, M. Pein-Hackelbusch, Sensors
    24 (2024).
  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.'
  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).
date_created: 2024-06-03T07:43:48Z
date_updated: 2025-06-25T13:00:14Z
department:
- _id: DEP4028
doi: 10.3390/s24113520
external_id:
  isi:
  - '001245424000001'
  pmid:
  - '38894312'
intvolume: '        24'
isi: '1'
issue: '11'
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
main_file_link:
- open_access: '1'
  url: https://www.mdpi.com/1424-8220/24/11/3520
oa: '1'
pmid: '1'
publication: Sensors
publication_identifier:
  issn:
  - '1424-8220 '
publication_status: published
publisher: MDPI
quality_controlled: '1'
status: public
title: 'Methods for Estimating the Detection and Quantification Limits of Key Substances
  in Beer Maturation with Electronic Noses '
type: scientific_journal_article
user_id: '83781'
volume: 24
year: '2024'
...
---
_id: '5425'
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.
author:
- first_name: Imke
  full_name: Weishaupt, Imke
  id: '58425'
  last_name: Weishaupt
- first_name: Peter
  full_name: Neubauer, Peter
  last_name: Neubauer
- first_name: Jan
  full_name: Schneider, Jan
  id: '13209'
  last_name: Schneider
  orcid: 0000-0001-6401-8873
citation:
  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>
  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>
  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.
  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>.'
  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>, .'
  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'
  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.
  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>.'
  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>.
  short: I. Weishaupt, P. Neubauer, J. Schneider, Food Science &#38; Nutrition 10
    (2022) 800–812.
  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.
date_created: 2021-04-08T06:37:30Z
date_updated: 2025-06-26T13:32:36Z
department:
- _id: DEP4000
- _id: DEP1308
doi: ' https://doi.org/10.1002/fsn3.2709'
external_id:
  isi:
  - '000739093400001'
  pmid:
  - '35311170'
intvolume: '        10'
isi: '1'
issue: '3'
keyword:
- flash pasteurization
- fruit juice characterization and classification
- inline near-infrared spectroscopy
- multivariate data analysis
language:
- iso: eng
page: 800-812
pmid: '1'
publication: Food Science & Nutrition
publication_identifier:
  issn:
  - 2048-7177
publication_status: published
publisher: Wiley
status: public
title: Near-infrared spectroscopy for the inline classification and characterization
  of fruit juices for a product-customized flash pasteurization
type: journal_article
user_id: '83781'
volume: 10
year: '2022'
...
---
_id: '5424'
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.
author:
- first_name: Imke
  full_name: Weishaupt, Imke
  id: '58425'
  last_name: Weishaupt
- first_name: Manuel
  full_name: Zimmer, Manuel
  id: '71613'
  last_name: Zimmer
  orcid: 0000-0002-9974-2543
- first_name: Peter
  full_name: Neubauer, Peter
  last_name: Neubauer
- first_name: Jan
  full_name: Schneider, Jan
  id: '13209'
  last_name: Schneider
  orcid: 0000-0001-6401-8873
citation:
  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>
  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>
  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>.'
  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'
  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.
  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>.'
  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>.
  short: I. Weishaupt, M. Zimmer, P. Neubauer, J. Schneider, Journal of Food Science
    85 (2020) 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.'
  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.
date_created: 2021-04-08T06:37:30Z
date_updated: 2025-06-26T13:30:26Z
department:
- _id: DEP1308
- _id: DEP4018
doi: 10.1111/1750-3841.15307
external_id:
  isi:
  - '000543977000001'
  pmid:
  - '32602154'
intvolume: '        85'
isi: '1'
issue: '7'
keyword:
- flash pasteurization
- inline near infrared spectroscopy
- multivariate data analysis
- process condition influences
- sugar-water-solution model beverage
language:
- iso: eng
page: 2020 - 2031
pmid: '1'
publication: Journal of Food Science
publication_identifier:
  eissn:
  - 1750-3841
  isbn:
  - 0022-1147
publication_status: published
status: public
title: Model based optimization of transflection near infrared spectroscopy as a process
  analytical tool in a continuous flash pasteurizer
type: journal_article
user_id: '83781'
volume: 85
year: '2020'
...
---
_id: '2068'
abstract:
- lang: eng
  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.
author:
- first_name: Walter
  full_name: Dyck, Walter
  last_name: Dyck
- first_name: Thomas
  full_name: Türke, Thomas
  last_name: Türke
- first_name: Johannes
  full_name: Schaede, Johannes
  id: '2128'
  last_name: Schaede
- first_name: Volker
  full_name: Lohweg, Volker
  id: '1804'
  last_name: Lohweg
  orcid: 0000-0002-3325-7887
citation:
  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>'
  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>'
  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: '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>.'
  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>
    .'
  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'
  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.'
  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.
  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>.
  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.'
  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.'
  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.'
date_created: 2019-11-29T13:50:28Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: 10.1109/MLSP.2007.4414320
keyword:
- Sensor fusion
- Inspection
- Optical sensors
- Printing machinery
- Data security
- Data analysis
- Production
- Degradation
- Principal component analysis
- Karhunen-Loeve transforms
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4414320&tag=1
page: accepted for publication
place: Thessaloniki, Greece
publication_identifier:
  eisbn:
  - 978-1-4244-1566-3
  isbn:
  - 978-1-4244-1565-6
  issn:
  - '1551-2541 '
  unknown:
  - '2378-928X '
publication_status: published
publisher: MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING
status: public
title: A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion
type: conference
user_id: '45673'
year: 2007
...