---
_id: '10326'
abstract:
- lang: eng
text: In the food industry, and especially in wines as products thereof, ethanol
and sulfur dioxide play an equally important role. Both substances are important
wine quality characteristics as they influence the taste and odor. As both substances
comprise volatile matter, electronic noses should be applicable to discriminate
the different qualities of wines. Our study investigates the influence of alcohol
and sulfur dioxide on the discrimination ability of wines (especially those of
the same grape variety) using two different electronic nose systems. One system
is equipped with metal oxide sensors and the other with quartz crystal microbalance
sensors. Contrary to indications in literature, where the alcohol content is discussed
to have a large influence on e-nose results, it was shown that a difference of
1 % ethanol was not sufficient to allow accurate discrimination using Linear Discriminant
Analysis by any system. On the positive side, the analyzed concentrations of ethanol
(about 12 %) did not superimpose other volatile information. So difference in
sulfur dioxide content gave an accuracy for sample discrimination of up to 90.6
% with MOS nose. Thus, we are so far partially able to discriminate wines with
electronic noses based on their volatile imprint.
author:
- first_name: Julius
full_name: Wörner, Julius
id: '79011'
last_name: Wörner
- 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: Wörner J, Dörksen H, Pein-Hackelbusch M. Key Indicators for the Discrimination
of Wines by Electronic Noses.; 2023. doi:https://doi.org/10.1109/INDIN51400.2023.10217912
apa: Wörner, J., Dörksen, H., & Pein-Hackelbusch, M. (2023). Key Indicators
for the Discrimination of Wines by Electronic Noses. In 2023 IEEE 21st International
Conference on Industrial Informatics (INDIN). 21st International Conference
on Industrial Informatics (INDIN), Lemgo. https://doi.org/10.1109/INDIN51400.2023.10217912
bjps: Wörner J, Dörksen H and Pein-Hackelbusch M (2023) Key Indicators
for the Discrimination of Wines by Electronic Noses. .
chicago: Wörner, Julius, Helene Dörksen, and Miriam Pein-Hackelbusch. Key Indicators
for the Discrimination of Wines by Electronic Noses. 2023 IEEE 21st International
Conference on Industrial Informatics (INDIN), 2023. https://doi.org/10.1109/INDIN51400.2023.10217912.
chicago-de: Wörner, Julius, Helene Dörksen und Miriam Pein-Hackelbusch. 2023. Key
Indicators for the Discrimination of Wines by Electronic Noses. 2023 IEEE
21st International Conference on Industrial Informatics (INDIN). doi:https://doi.org/10.1109/INDIN51400.2023.10217912,
.
din1505-2-1: 'Wörner, Julius ; Dörksen, Helene ; Pein-Hackelbusch,
Miriam: Key Indicators for the Discrimination of Wines by Electronic
Noses, 2023'
havard: J. Wörner, H. Dörksen, M. Pein-Hackelbusch, Key Indicators for the Discrimination
of Wines by Electronic Noses, 2023.
ieee: 'J. Wörner, H. Dörksen, and M. Pein-Hackelbusch, Key Indicators for the
Discrimination of Wines by Electronic Noses. 2023. doi: https://doi.org/10.1109/INDIN51400.2023.10217912.'
mla: Wörner, Julius, et al. “Key Indicators for the Discrimination of Wines by Electronic
Noses.” 2023 IEEE 21st International Conference on Industrial Informatics (INDIN),
2023, https://doi.org/10.1109/INDIN51400.2023.10217912.
short: J. Wörner, H. Dörksen, M. Pein-Hackelbusch, Key Indicators for the Discrimination
of Wines by Electronic Noses, 2023.
ufg: 'Wörner, Julius/Dörksen, Helene/Pein-Hackelbusch, Miriam: Key Indicators
for the Discrimination of Wines by Electronic Noses, o. O. 2023.'
van: Wörner J, Dörksen H, Pein-Hackelbusch M. Key Indicators for the Discrimination
of Wines by Electronic Noses. 2023 IEEE 21st International Conference on Industrial
Informatics (INDIN). 2023.
conference:
end_date: 2023-07-20
location: Lemgo
name: 21st International Conference on Industrial Informatics (INDIN)
start_date: 2023-07-18
date_created: 2023-09-14T05:50:47Z
date_updated: 2025-03-13T13:48:05Z
department:
- _id: DEP4000
- _id: DEP4028
doi: https://doi.org/10.1109/INDIN51400.2023.10217912
keyword:
- Ethanol
- Pipelines
- Metals
- Nose
- Electronic noses
- Sensor systems
- Sensors
- Quartz crystals
- Linear discriminant analysis
- Sulfur
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/document/10217912
oa: '1'
publication: 2023 IEEE 21st International Conference on Industrial Informatics (INDIN)
publication_status: published
status: public
title: Key Indicators for the Discrimination of Wines by Electronic Noses
type: conference_speech
user_id: '64952'
year: '2023'
...
---
_id: '2005'
abstract:
- lang: eng
text: We present a method for the fast and robust linear classification of badly
conditioned data. In our considerations, badly conditioned data are such data
which are numerically difficult to handle. Due to, e.g. a large number of features
or a large number of objects representing classes as well as noise, outliers or
incompleteness, the common software computation of the discriminating linear combination
of features between classes fails or is extremely time consuming. The theoretical
foundations of our approach are based on the single feature ranking, which allows
fast calculation of the approximative initial classification boundary. For the
increasing of classification accuracy of this boundary, the refinement is performed
in the lower dimensional space. Our approach is tested on several datasets from
UCI Reposi-tiory. Experimental results indicate high classification accuracy of
the approach. For the modern real industrial applications such a method is especially
suitable in the Cyber-Physical-System environments and provides a part of the
workflow for the automated classifier design
author:
- first_name: Helene
full_name: Dörksen, Helene
id: '46416'
last_name: Dörksen
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Dörksen H, Lohweg V. Linear Classification of Badly Conditioned Data. . In:
23rd IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA). Torino, Italy; 2018. doi:10.1109/ETFA.2018.8502485'
apa: Dörksen, H., & Lohweg, V. (2018). Linear Classification of Badly Conditioned
Data. . In 23rd IEEE International Conference on Emerging Technologies and
Factory Automation (ETFA). Torino, Italy. https://doi.org/10.1109/ETFA.2018.8502485
bjps: Dörksen H and Lohweg V (2018) Linear Classification of Badly Conditioned
Data. . 23rd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA). Torino, Italy.
chicago: Dörksen, Helene, and Volker Lohweg. “Linear Classification of Badly Conditioned
Data. .” In 23rd IEEE International Conference on Emerging Technologies and
Factory Automation (ETFA). Torino, Italy, 2018. https://doi.org/10.1109/ETFA.2018.8502485.
chicago-de: 'Dörksen, Helene und Volker Lohweg. 2018. Linear Classification of Badly
Conditioned Data. . In: 23rd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA). Torino, Italy. doi:10.1109/ETFA.2018.8502485,
.'
din1505-2-1: 'Dörksen, Helene ; Lohweg, Volker: Linear Classification
of Badly Conditioned Data. . In: 23rd IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA). Torino, Italy, 2018'
havard: 'H. Dörksen, V. Lohweg, Linear Classification of Badly Conditioned Data.
, in: 23rd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA), Torino, Italy, 2018.'
ieee: H. Dörksen and V. Lohweg, “Linear Classification of Badly Conditioned Data.
,” in 23rd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA), Turin, Italy , 2018.
mla: Dörksen, Helene, and Volker Lohweg. “Linear Classification of Badly Conditioned
Data. .” 23rd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA), 2018, doi:10.1109/ETFA.2018.8502485.
short: 'H. Dörksen, V. Lohweg, in: 23rd IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA), Torino, Italy, 2018.'
ufg: 'Dörksen, Helene/Lohweg, Volker (2018): Linear Classification of Badly
Conditioned Data. , in: 23rd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA), Torino, Italy.'
van: 'Dörksen H, Lohweg V. Linear Classification of Badly Conditioned Data. . In:
23rd IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA). Torino, Italy; 2018.'
conference:
end_date: 2018-09-07
location: ' Turin, Italy '
name: ' IEEE 23rd International Conference on Emerging Technologies and Factory
Automation (ETFA) 2018'
start_date: 2018-09-04
date_created: 2019-11-25T08:35:44Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: 10.1109/ETFA.2018.8502485
keyword:
- Task analysis
- Software
- Linear discriminant analysis
- Dimensionality reduction
- Mathematical model
- Covariance matrices
- Measurement
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8502485
oa: '1'
place: Torino, Italy
publication: 23rd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA)
status: public
title: 'Linear Classification of Badly Conditioned Data. '
type: conference
user_id: '15514'
year: 2018
...
---
_id: '2008'
abstract:
- lang: eng
text: "We concentrate our research activities on the multivariate feature selection,
which is one important part of many machine learning tasks. In partucular, Linear
Discriminant Analysis [1] belongs to the state-of-the-art methods for the multivariate
analysis. From the theoretical point of view, it is the well-known fact that LDA
is best suitable in the case the features are Gaussian distributed.\r\nIn the
theoretical part of the presented paper, we analyse the properties of the multivariate
discriminant analysis with respect to the feature selection. In this context,
we consider a binary supervised learning task and assume that the features are
Gaussian distributed. The discriminant analysis solves the mentioned supervised
learning task by maximising of the discriminant value, calculated for the linear
combination of the features.\r\nThe initial LDA solution a 2 Rd is considered
for all given features from the feature space X \x1A Rd. The corresponding discriminant
is calculated by the formula:\r\nd(a; x1, . . . , xd) := (μ+ − μ−)2\r\n\e2+\r\n+
\e2−\r\n,\r\nwhere μ+/− are projected class means and \e2 +/− are projected class
variances (with respect to a). We proof several propositions with the aim to find
subsets of the features having higher discriminant value as original d(a; x1,
. . . , xd). For the suitability in the real world settings, here we are interested
in fast searching for such subsets.\r\nThe performance of the mentioned propositions
is examined experimentally on datasets from UCI repository [2]. Several application
scenarien will be discussed and tested on the datasets. In addition, tests show
that the performance can be achieved also in the case the features are not Gaussian
distributed."
author:
- first_name: Helene
full_name: Dörksen, Helene
id: '46416'
last_name: Dörksen
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: European
Conference on Data Analysis (ECDA2018). Paderborn, Germany; 2018.'
apa: Dörksen, H., & Lohweg, V. (2018). Multivariate Gaussian Feature Selection.
. In European Conference on Data Analysis (ECDA2018). Paderborn, Germany.
bjps: Dörksen H and Lohweg V (2018) Multivariate Gaussian Feature Selection.
. European Conference on Data Analysis (ECDA2018). Paderborn, Germany.
chicago: Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection.
.” In European Conference on Data Analysis (ECDA2018). Paderborn, Germany,
2018.
chicago-de: 'Dörksen, Helene und Volker Lohweg. 2018. Multivariate Gaussian Feature
Selection. . In: European Conference on Data Analysis (ECDA2018). Paderborn,
Germany.'
din1505-2-1: 'Dörksen, Helene ; Lohweg, Volker: Multivariate Gaussian
Feature Selection. . In: European Conference on Data Analysis (ECDA2018).
Paderborn, Germany, 2018'
havard: 'H. Dörksen, V. Lohweg, Multivariate Gaussian Feature Selection. , in: European
Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018.'
ieee: H. Dörksen and V. Lohweg, “Multivariate Gaussian Feature Selection. ,” in
European Conference on Data Analysis (ECDA2018), Paderborn, 2018.
mla: Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection.
.” European Conference on Data Analysis (ECDA2018), 2018.
short: 'H. Dörksen, V. Lohweg, in: European Conference on Data Analysis (ECDA2018),
Paderborn, Germany, 2018.'
ufg: 'Dörksen, Helene/Lohweg, Volker (2018): Multivariate Gaussian Feature
Selection. , in: European Conference on Data Analysis (ECDA2018), Paderborn,
Germany.'
van: 'Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: European
Conference on Data Analysis (ECDA2018). Paderborn, Germany; 2018.'
conference:
end_date: 2018-07-06
location: Paderborn
name: European Conference on Data Analysis
start_date: 2018-07-04
date_created: 2019-11-25T08:35:48Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
keyword:
- multivariate feature selection
- Gaussian distribution
- linear discriminant analysis
language:
- iso: eng
place: Paderborn, Germany
publication: European Conference on Data Analysis (ECDA2018)
status: public
title: 'Multivariate Gaussian Feature Selection. '
type: conference
user_id: '15514'
year: 2018
...