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All Journal Jurnal Rekayasa Mesin Dental Journal (Majalah Kedokteran Gigi) Emerging Science Journal
Syahrom, Ardiyansyah
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Dentistry Environmental Science Mechanical Engineering

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Journal : Emerging Science Journal

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Odor Profiling of Blood Shells Using TGS Gas Sensor and PCA-SVM Analysis Astuti, Suryani Dyah; Funabiki, Nobuo; Soelistiono, Soegianto; Winarno; Arifianto, Deny; Ramadhani, Nadia Nur; Permatasari, Perwira Annissa Dyah; Yaqubi, Ahmad Khalil; Susilo, Yunus; Syahrom, Ardiyansyah
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-017

Abstract

Blood cockles (Andara granulosa) are among the most popular animal protein sources due to their rich nutritional content and high economic value. The storage period and temperature are two critical factors that significantly influence the freshness of blood cockles. One key indicator of blood cockle quality is the odor they emit. An unpleasant or inappropriate odor can indicate contamination or a decline in quality, posing potential food safety risks. However, conventional methods of odor quality testing are often subjective, require specialized skills, and may not always be reliable. To address the limitations of human olfaction, advancements in gas sensor technology, specifically gas array sensors (also known as the electronic nose), have been developed. This research aims to profile the freshness of blood cockles by identifying their odor under different storage conditions using electronic nose technology. The study used fresh blood cockle meat, which was stored under varying temperature conditions: at room temperature, in a cooler, and in a freezer. The storage periods for the samples were 1, 2, 3, 4, and 5 days. The samples were placed in sealed bottles and tested using a gas array sensor. The data collected from this process were in the form of voltage readings, which were analyzed using machine learning techniques, specifically Principal Component Analysis (PCA). The data were then classified using a Support Vector Machine (SVM) model. The study results showed that the gas array sensor successfully classified the odor profiles, with PCA explaining 93.83% of the variance in the data. The SVM model achieved an accuracy of 89.66% for PCA-reduced data and 91.44% for non-PCA data.
Co-Authors Ammarullah, Muhammad Imam Anita Yuliati Arifianto, Deny Asti Meizarini Devi Rianti Elly Munadziroh Faradillah Hasan Basri J. Jamari Nobuo Funabiki, Nobuo Permatasari, Perwira Annissa Dyah Purnamasari, Alqomariyah Eka Putri, Rifayinqa Ruyani Ramadhani, Nadia Nur Saad, Amir Putra Md Salsabilla, Noor Zain Soegianto Soelistiono Suryani Dyah Astuti Susilo, Yunus Titien Hary Agustantina WINARNO Yaqubi, Ahmad Khalil