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Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model

For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the...

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Autores principales: Kaya, Aydin, Keçeli, Ali Seydi, Catal, Cagatay, Tekinerdogan, Bedir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309019/
https://www.ncbi.nlm.nih.gov/pubmed/32503198
http://dx.doi.org/10.3390/s20113173
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author Kaya, Aydin
Keçeli, Ali Seydi
Catal, Cagatay
Tekinerdogan, Bedir
author_facet Kaya, Aydin
Keçeli, Ali Seydi
Catal, Cagatay
Tekinerdogan, Bedir
author_sort Kaya, Aydin
collection PubMed
description For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general.
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spelling pubmed-73090192020-06-25 Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model Kaya, Aydin Keçeli, Ali Seydi Catal, Cagatay Tekinerdogan, Bedir Sensors (Basel) Article For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general. MDPI 2020-06-03 /pmc/articles/PMC7309019/ /pubmed/32503198 http://dx.doi.org/10.3390/s20113173 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kaya, Aydin
Keçeli, Ali Seydi
Catal, Cagatay
Tekinerdogan, Bedir
Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
title Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
title_full Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
title_fullStr Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
title_full_unstemmed Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
title_short Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
title_sort sensor failure tolerable machine learning-based food quality prediction model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309019/
https://www.ncbi.nlm.nih.gov/pubmed/32503198
http://dx.doi.org/10.3390/s20113173
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