Cork taint, primarily caused by Trichloroanisole (TCA) contamination in wine corks, is a significant issue in the wine industry, affecting wine quality and causing financial losses. The development of an advanced detection system for TCA in wine corks based on machine learning is crucial. This thesis proposal outlines a comprehensive research plan to design and implement a machine learning-based system for the accurate detection of TCA in wine corks.
The primary objectives of this research are as follows:
To develop a machine learning-based system that can identify TCA-contaminated wine corks with high accuracy.
To evaluate the system's performance in terms of sensitivity, specificity, and reliability.
To optimize the detection process for practical application in the wine cork production industry.
To assess the economic and environmental impact of implementing the machine learning-based TCA detection system.
A thorough review of existing literature will be conducted to establish the current state of knowledge regarding TCA contamination in wine corks and the application of machine learning techniques for TCA detection. This section will cover relevant technological advancements, case studies, and gaps in existing research related to machine learning-based TCA detection in cork materials.
Data Collection: Gather a dataset of TCA-contaminated and uncontaminated wine corks with associated sensory data and spectral signatures.
Feature Engineering: Extract relevant features from the spectral data and sensory information that are indicative of TCA presence.
Model Development: Develop machine learning models (e.g., deep learning, random forests, or support vector machines) for TCA detection based on the extracted features.
Training and Validation: Train and validate the machine learning models using the collected dataset to assess their accuracy and reliability.
System Integration: Integrate the trained machine learning models into a practical TCA detection system for wine cork production facilities.
This research is expected to yield the following results:
Development of a machine learning-based TCA detection system with high accuracy and reliability.
Validation of the system's ability to differentiate between TCA-contaminated and uncontaminated wine corks.
Recommendations for integrating the machine learning-based TCA detection system into the wine cork production process.
Insights into the economic and environmental benefits of using the system in the wine industry.
The development of a machine learning-based TCA detection system for wine corks has the potential to significantly improve wine quality and reduce financial losses for winemakers. Additionally, it can contribute to sustainability efforts by reducing the need for excessive cork wastage due to TCA contamination.
The research will be conducted over a period of 5m, with the following approximate timeline:
This thesis proposal outlines a comprehensive research plan to develop a machine learning-based system for the accurate detection of TCA in wine corks, addressing a critical issue in the wine industry. The research aims to provide a reliable and efficient solution to detect TCA contamination early in the production process, ultimately enhancing wine quality and reducing economic losses. Furthermore, it contributes to sustainable practices by minimizing cork wastage.