Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection
The objective of this work is to report the improvements obtained in the discrimination of complex aroma samples with subtle differences in odor pattern, by the use of a fast procedure suitable for the cases of measurements in the field demanding decision-making in real time using a portable electro...
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2014
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19369751_v7_n10_p2042_Rodriguez http://hdl.handle.net/20.500.12110/paper_19369751_v7_n10_p2042_Rodriguez |
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paper:paper_19369751_v7_n10_p2042_Rodriguez2023-06-08T16:32:09Z Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection Aroma discrimination Electronic nose Food quality assessment Time-window selection Unfolded cluster analysis Chemical sensors Cluster analysis Data handling Decision making Electronic equipment Odors Vehicle routing Aroma discrimination Electronic NOSE Food quality Food quality controls Parallel factor analysis Portable electronic nose Three-dimensional data Time windows Quality control The objective of this work is to report the improvements obtained in the discrimination of complex aroma samples with subtle differences in odor pattern, by the use of a fast procedure suitable for the cases of measurements in the field demanding decision-making in real time using a portable electronic nose. This device consists of a sensor array which records changes in conductivity as a function of time when aroma molecules reach the sensors. The core of the method consists of applying unfolded cluster analysis to selected time windows (UCATW) within the temporal evolution of the aroma profile recorded by the gas sensors, yielding an efficient, fast, and reliable data analysis tool that is easy to perform for electronic nose users. The performance of this data handling was tested in two case studies of food adulteration. The results demonstrated that this methodology enables to discriminate highly similar samples, herewith reducing the probability of achieving a wrong grouping due to the use of flawed data. The automation of this type of analysis is simple and improves the efficiency of the device significantly, herewith reducing the time of sensor’s signal recording that is necessary for a reliable assessment of the studied system. The results were validated by clustering the sample component scores that are obtained by applying parallel factor analysis (PARAFAC) to the original three-dimensional data array. An additional validation was obtained by means of a leave-one-out resampling procedure. © 2014, Springer Science+Business Media New York. 2014 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19369751_v7_n10_p2042_Rodriguez http://hdl.handle.net/20.500.12110/paper_19369751_v7_n10_p2042_Rodriguez |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Aroma discrimination Electronic nose Food quality assessment Time-window selection Unfolded cluster analysis Chemical sensors Cluster analysis Data handling Decision making Electronic equipment Odors Vehicle routing Aroma discrimination Electronic NOSE Food quality Food quality controls Parallel factor analysis Portable electronic nose Three-dimensional data Time windows Quality control |
| spellingShingle |
Aroma discrimination Electronic nose Food quality assessment Time-window selection Unfolded cluster analysis Chemical sensors Cluster analysis Data handling Decision making Electronic equipment Odors Vehicle routing Aroma discrimination Electronic NOSE Food quality Food quality controls Parallel factor analysis Portable electronic nose Three-dimensional data Time windows Quality control Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection |
| topic_facet |
Aroma discrimination Electronic nose Food quality assessment Time-window selection Unfolded cluster analysis Chemical sensors Cluster analysis Data handling Decision making Electronic equipment Odors Vehicle routing Aroma discrimination Electronic NOSE Food quality Food quality controls Parallel factor analysis Portable electronic nose Three-dimensional data Time windows Quality control |
| description |
The objective of this work is to report the improvements obtained in the discrimination of complex aroma samples with subtle differences in odor pattern, by the use of a fast procedure suitable for the cases of measurements in the field demanding decision-making in real time using a portable electronic nose. This device consists of a sensor array which records changes in conductivity as a function of time when aroma molecules reach the sensors. The core of the method consists of applying unfolded cluster analysis to selected time windows (UCATW) within the temporal evolution of the aroma profile recorded by the gas sensors, yielding an efficient, fast, and reliable data analysis tool that is easy to perform for electronic nose users. The performance of this data handling was tested in two case studies of food adulteration. The results demonstrated that this methodology enables to discriminate highly similar samples, herewith reducing the probability of achieving a wrong grouping due to the use of flawed data. The automation of this type of analysis is simple and improves the efficiency of the device significantly, herewith reducing the time of sensor’s signal recording that is necessary for a reliable assessment of the studied system. The results were validated by clustering the sample component scores that are obtained by applying parallel factor analysis (PARAFAC) to the original three-dimensional data array. An additional validation was obtained by means of a leave-one-out resampling procedure. © 2014, Springer Science+Business Media New York. |
| title |
Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection |
| title_short |
Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection |
| title_full |
Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection |
| title_fullStr |
Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection |
| title_full_unstemmed |
Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection |
| title_sort |
fast and efficient food quality control using electronic noses: adulteration detection achieved by unfolded cluster analysis coupled with time-window selection |
| publishDate |
2014 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19369751_v7_n10_p2042_Rodriguez http://hdl.handle.net/20.500.12110/paper_19369751_v7_n10_p2042_Rodriguez |
| _version_ |
1768546323940769792 |