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Author: search.filters.author.Tangtisanon, PikulkaewSubject: search.filters.subject.electronic nose

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    A Portable Electronic Nose for Real-time Monitoring of Food Spoilage Using Multiple Machine Learning Models
    (2025-01-01)
    Tangtisanon, Pikulkaew
    ;
    Grodniyomchai, Boonyawee
    In this study, we present the design and development of a portable electronic nose (E-nose) system for detecting and classifying spoiled household food through the application of machine learning (ML) techniques. The targeted odors include fungi from bread, spoiled rice, spoiled milk, yoghurt, rotten egg, rotten boiled egg, rotten pork, and rotten beef, totaling eight odor classes. A total of 1800 samples were collected using three gas sensors and one temperature sensor. After outlier removal with Isolation Forest, 1000 samples remained. Multiple ML models were trained and evaluated over ten iterations, comparing classification accuracy and processing time. Among all the models, the k-nearest neighbor (KNN) achieved the highest performance, with an average accuracy of 99.889% and an average processing time of 0.167477 s. The decision tree (DT) model followed closely with an accuracy of 99.843% and required a significantly less processing time of 0.012080 s. Although DT has a slightly lower accuracy than KNN, its processing time is 13.86 times faster. For our scenario that requires real-time results, DT is a better choice than KNN. The proposed portable E-nose demonstrates strong potential for realworld applications such as food spoilage detection, environmental monitoring, and health diagnostics.
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    Enhancing Odor Classification of Essential Oils with Electronic Nose Data
    (2024-01-01)
    Grodniyomchai, Boonyawee
    ;
    Satcharoen, Kleddao
    ;
    Tangtisanon, Pikulkaew
    In the current business landscape, the fragrance industry has gained substantial prominence. In this context, there is a requirement to create the most compact and sufficiently accurate model possible, suitable for deployment on a portable device. The aim is to develop a model capable of effectively classifying various fragrance types based on data pertaining to air properties and fragrance component attributes. This paper presents the feature extraction from the dataset electronic node to classify odor types using a machine learning model compared before and after the feature extraction of the dataset. In our investigation, we employed datasets of varying sizes, including small datasets (composed of 1000 samples), large datasets (composed of 10000 samples), and raw datasets (composed of 21000 samples). This methodology was employed to discern disparities in model performance, average accuracy, and computational runtime across these different dataset sizes. We observed that the decision tree model, post-training with principal component analysis, showed a performance improvement when compared to the basic machine learning model. Specifically, the decision tree model achieved accuracy rates of 100.00%, 99.97%, and 97.00% respectively.