Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
Aydin Kaya1, Ali Seydi Keçeli2, Cagatay Catal3
1Department of Computer Engineering, Cankaya University, Ankara 06790, Turkey.
Sensors (Basel, Switzerland)
|June 7, 2020
Summary
This study introduces a machine learning approach for food quality assessment using electronic noses. It proposes a novel failure tolerance method that ignores faulty sensors, enhancing overall prediction accuracy for products like beef.
Area of Science:
- Agricultural Science
- Sensor Technology
- Machine Learning
Background:
- Food quality assessment is crucial for human health and economic value in agriculture.
- Electronic noses (e-noses) simulate smell using sensors to detect odor compounds for quality assessment.
- Sensor failures in e-noses can compromise accurate food quality evaluation.
Purpose of the Study:
- To propose a machine learning-based failure tolerance strategy for e-nose sensor systems.
- To develop a method that ignores data from failed sensors instead of correcting it.
- To enhance the reliability and accuracy of food quality assessment despite sensor malfunctions.
Main Methods:
- A Single Plurality Voting System (SPVS) classification approach is proposed for failure tolerance.
- Individual classifiers (kNN, Decision Tree, LDA) are trained for each sensor feature.
- A composite classifier is built based on the outcomes of individual classifiers.
Main Results:
- The SPVS approach effectively tolerates sensor failures by ignoring problematic data.
- The proposed method maintains acceptable prediction accuracy even with sensor malfunctions.
- Promising results were demonstrated using a case study on beef cut quality assessment.
Conclusions:
- The developed machine learning-based failure tolerance method enhances food quality assessment reliability.
- Ignoring failed sensors offers an advantageous alternative to traditional data correction techniques.
- The approach shows significant potential for improving e-nose applications in food quality control.
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