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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) ILKOM Jurnal Ilmiah Jurnal Teknologi Terapan Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pusat Inovasi Masyarakat PENITI BANGSA (Pemanfaatan Ilmu Pengetahuan dan Teknologi bagi Masyarakat)
Radi Radi
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Implementation of an electronic nose for classification of synthetic flavors Radi Radi; Barokah Barokah; Dwi Noor Rohmah; Eka Wahyudi; Muhammad Danu Adhityamurti; Joko Purwo Leksono Yuroto Putro
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3018

Abstract

Classification and identification of synthetic flavor become routine activities in the flavor and food industry due to its application. As a modern olfactory technology, electronic nose (e-nose) has the possibility to be applied in these activities. This study aimed to evaluate an e-nose for classifying synthetic flavors. In this study, an e-nose was designed with an array of gases sensors as the main sensing component and principal component analysis (PCA) for the pattern recognition software. This research was started with preparation of the hardware, continued with preparation of sample, data collection, and analysis. There were nine samples of synthetic flavors with different aroma, namely: grapes, strawberry, mocha, pandanus, mango, jackfruit, orange, melon, and durian. The data collection process includes three stages, i.e. flushing, collecting, and purging of 2 min, 3 min, 2 min respectively. These sensor responses were then analyzed for forming aroma patterns. Four pre-treatment methods were applied for the aroma pattern formation: absolute data, normalize of absolute data, relative data, and normalize of relative data. With the PCA for evaluation, the results showed that the absolute data treatment provided the best results, indicated from the distribution of aroma patterns that were grouped according to the type of samples.
Freshness assessment of tilapia fish in traditional market based on an electronic nose Radi Radi; Eka Wahyudi; Muhammad Danu Adhityamurti; Joko Purwo Leksono Yuroto Putro; Barokah Barokah; Dwi Noor Rohmah
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3111

Abstract

This study evaluates an e-nose based on gas sensors to measure the freshness of tilapia. The device consists of a series of semiconductor sensors as detector, a combination of valve-vial-oxygen as sample delivery system, a microcontroller as interface and controller, and a computer for data recording and processing. The e-nose was firstly used to classify the fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared and measured using the e-nose through three stages, namely: flushing, collecting, and purging. The sensor responses were processed into aroma patterns, then classified by two pattern classification softwares of principal component analysis (PCA) and neural network (NN). There were four methods for aroma patterns formation being evaluated: absolute data, normalized absolute data, relative data, normalized relative data. The results showed that the normalized absolute data method provides the best classification with the accuracy level of 93.88%. With this method, the trained NN was used to predict the freshness of 15 tilapia samples collected from a traditional market. The result showed that 60.0% of the samples are classified into fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories.
Co-Authors Anang Kukuh Adisusilo Andi Muh. Saldan Andi Setiawan Andri Prima Nugroho Arifin Dwi Saputro, Arifin Dwi Aryanis Mutia Zahra Bambang Purwantana Barokah Barokah Dwi Noor Rohmah Eka Wahyudi Eko Mulyanto Yuniarno Emmy Wahyuningtyas Icha Syahrotul Anam Isran Mohamad Pakaya Joko Nugroho Wahyu Karyadi Joko Purwo Leksono Yuroto Putro Lilik Sutiarso Luthfi Fadillah Zamzami Makbul Hajad Marheriyanto Marheriyanto Mochamad Hariadi Muhammad Danu Adhityamurti Muhammad Hafidz Syahputra Ngadisih Ngadisih Nia Saurina Nia Saurina Prieskarinda Lestari Priyanto Triwitono Raditya Yulianta Redika Ardi Kusuma Rudiati Evi Masithoh Sri Markumningsih, Sri Yuwan Nanda Adyatma