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Journal : Emerging Science Journal

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 Abd Razak, Nasrul Anuar Abdul Rahman Abror, Ghulam Muhammad Adi Prasetyo Hutomo Agesti, Dyah Ahmad Faizin Alma Ahmad Shofy Mubarak Ahmad Taufiq Mukti Aisya, Rohadatul Aji , Angger Krisna Akhmad Muzamil Akhmad Muzamil Alma, Ahmad Faizin Ama, Fadli Amillia Kartika Sari Amruloh, Yazid Muhammad Andi Hamim Zaidan Anem Alimdam Annisa Mayzealuna Arifa Mustika Arifianto, Deni Arifianto, Deny Ario Imandiri, Ario Bambang Haris Suharmono Betty Purnamasari, Betty Carissa Alfreda Assyarahil David Buntoro Kamandjaja Destiani, Reza Djoni Izak R. Dwi May Lestari, Dwi May Dwi setiani Edith Frederika Puruhito, Edith Frederika Endah Purwanti Farhah, Ghinaa Rihadatul Aisy Franky Arisgraha, Franky Hilmaniyya Hilmaniyya Ika Yuni Anggraini Kadafi, Muhammad Syaekar Katherine Katili, Rifany Humairah Purnama Khusnul Ain Khusnul Ain Khusnul Ain Lellen Novia Hariono Luthfiyah, Sari M. Zainuddin Moh. Yasin Mohammad Zulkarnaen Muhamad, Alfian Baggraf Myrna Adianti Myrtati Dyah Artaria, Myrtati Dyah Nidaul Fauziah Ni’matuzahroh Nobuo Funabiki, Nobuo Nur Vita Indri Nur Vita Indri Astutik Nurdin, Dezy Zahrotul Istiqomah Nurul Fitriyah Nurul Fitriyah Patmadevi, Maulia Permatasari, Perwira Annissa Dyah Prihartini Widiyanti Purbandini Soeparman, Purbandini R Arif Wibowo Rahmatillah, Akif Ramadhani, Nadia Nur Rania Basalamah Reza Destiani Rini Hamsidi Riries Rulaningtyas Rohman, Muchammad Nurur Rozykulyyeva, Lale Salwa, Umaimah Mitsalia Ummi Samatha, Syifa Candiki Samian Samy Bazher Septriana, Maya Siska Arianti Siswanto Soegianto Soelistiono Suhariningsih Suhariningsih Supadi Supadi Susilo, Yunus Syahrom, Ardiyansyah Syarahiel Hamdani Tri Anggono Prijo Tri Anggono Prijo, Tri Anggono Vita Arinda Ayu Putri Nata Widya Retnaning Puspita WINARNO Winarno Winarno Winarno Winarno, Winarno Yaqubi, Ahmad Khalil Yonatan Zain, Nuril Jannah