Development of a portable electronic nose for the classification of tea quality based on tea dregs aroma

Author:

Guritno Adi Djoko1,Harjoko Agus2,Tanuputri Megita Ryanjani1,Putri Diyah Utami Kusumaning2,Putro Nur Achmad Sulistyo2

Affiliation:

1. Department of Agroindustrial Technology, Faculty of Agricultural Technology , Universitas Gadjah Mada , Yogyakarta , Indonesia

2. Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences , Universitas Gadjah Mada , Yogyakarta , Indonesia

Abstract

Abstract The current assessment of tea quality is considered subjective. This study aims to develop a portable electronic nose to assess the aroma of tea dregs objectively by relying on the aromatic capture process through sensors and using multilayer perceptron (MLP). A MLP with some hyperparameter variations is used and compared with five machine-learning classifiers. The classification using MLP model with ReLU activation function and 3 hidden layers with 100 hidden nodes resulted in the highest accuracy of 0.8750 ± 0.0241. The MLP model using ReLU activation function is better than Sigmoid while increasing the number of hidden layers and hidden nodes does not necessarily enhance its performance. In the future, this research can be improved by adding sensors to the portable electronic nose, increasing the number of datasets used, and using ensemble learning or deep learning models.

Publisher

Walter de Gruyter GmbH

Reference36 articles.

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