Main Article Content

Abstract

This paper presents an overview of the global vanilla industry, emphasizing vanilla’s status as the second most costly spice and the most extensively used flavoring worldwide. To satisfy global demand, there is an increasing reliance on synthetic methods for flavor extraction, raising concerns about quality and health risks due to widespread adulteration with cheaper synthetic vanillin, often misrepresented as "pure." To tackle adulteration effectively and economically, this study proposes employing a single-stage classification model trained using the transient response of an electronic nose (e-nose) equipped with four metal oxide semiconductor (MOS) gas sensors with Principal Component Analysis (PCA) and machine learning classification models to sample vanilla from various countries (Indonesia & Madagascar) and grades (Grade A & B). 33 classifiers were trained and compared based on classification and validation accuracy. Through trial and error, it was determined that the sensor response times at the 20s, 60s, and 90s marks, using Weighted KNN, contributed to 100% classification accuracy and 80% validation accuracy. A second analysis method was attempted where the sensor transient response was processed using the Wavelet Time-Frequency Analyzer. When training classification models using the processed data, the bilayer neural network yielded the highest classification accuracy of 100% and validation accuracy of 70%.

Keywords

Classification, Electronic nose Gas sensors Machine learning Metal oxide semiconductor (MOS) Principal component analysis (PCA)

Article Details

How to Cite
Wee, S. C. M. X. ., Lee, H. E. ., & Chua, H. S. . (2024). Classification of vanilla by quality using MOS gas sensors: assessing the effectiveness of wavelet time-frequency analyzer. Future Technology, 3(3), 12–21. Retrieved from https://fupubco.com/futech/article/view/184
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