Abstract
Bu çalışmanın amacı, elektronik burun ve makine öğrenimi yöntemlerini kullanarak farklı gıda aromalarından elde edilen kimyasal koku verilerinden aromalara ait sınıfların belirlenmesidir. Gıda aromaları içinde bulunan uçucu ve uçucu olmayan bileşenler arasındaki ilişki yoğun bir şekilde araştırılmaktadır. İşlenmiş gıdalarda düzenlenmiş kimyasal oluşumunu izlemek için normalleştirilmiş analitik protokoller uygulanır. Bu yöntemler sağlam olmasına karşın, son derece uzmanlaşmış enstrümantasyon, zaman alıcı ve pahalı prosedürler içerir. Elektronik burun, gıda tatlarını ayırt etmek için hızlı ölçümler yapabilen, uygun maliyetli ve güçlü bir elektronik cihazdır. Gıda işleme sırasında açığa çıkan aromatik bileşiklerinden sorumlu moleküller insan burnu tarafından tanınabildiğinden, işlenmiş gıda ürünlerinde meydana gelen değişiklikleri tespit etmek için elektronik burun kullanmak mümkün olabilir. Bu araştırma çalışmasında hesaplanan sınıflandırma sonuçları, duyarlılık (≥90.00%) ve özgüllük (≥90.00%) ile ≥90.00%'in üzerinde doğrulukla tahmin edilmiştir.
Keywords
Supporting Institution
Bandırma Onyedi Eylül Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi
Project Number
BAP-21-1004-001
Thanks
Bu çalışma, Bandırma Onyedi Eylül Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından BAP-21-1004-001 numaralı proje kapsamında desteklenmiştir.
References
- D. Ma, C. Liu, F. Wu, Z. Li, X. Wu, J. Gao, H. Zhao, Z. Zhang, “The Quality Detection and Recognition for Food Seasoning Based on an Artificial Olfactory System”, IEEE Instrum. Meas. Mag, vol:25 vo:9, pp. 42-52, 2022.
- Y. Durmuş, A.F. Atasoy, “Application of multivariate machine learning methods to investigate organic compound content of different pepper spices”, Food Biosci, vol:51, pp. 102216, 2023.
- P.-A. Chen, C.-I. Liu, K.-R. Chen, “Determining the Relationship between Aroma and Quality of Bao-Chung Tea by Solid-Phase Microextraction (SPME) and Electronic Nose Analyses”, Horticulturae, vol:9, pp. 930, 2023.
- M. Mesías, J.D. Barea-Ramos, J. Lozano, F.J. Morales, D. Martín-Vertedor, “Application of an Electronic Nose Technology for the Prediction of Chemical Process Contaminants in Roasted Almonds”, Chemosensors, vol:11, 2023.
- A. Ren, A. Zahid, A. Zoha, S.A. Shah, M.A. Imran, A. Alomainy, Q.H. Abbasi, “Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing”, IEEE Sens. J., vol:20, pp. 2075–2083, 2020.
- M. Pardo, G. Sberveglieri, “Coffee analysis with an electronic nose”, IEEE Trans. Instrum. Meas, vol:51, pp. 1334–1339, 2002.
- P.K. Kundu, A. Chatterjee, P.C. Panchariya, “Electronic Tongue System for Water Sample Authentication: A Slantlet-Transform-Based Approach”, IEEE Trans. Instrum. Meas., vol:60, pp. 1959–1966, 2011.
- M. Anly Antony, R.S. Kumar, “A Comparative Study on Predicting Food Quality using Machine Learning Techniques”, in: 2021 7th Int. Conf. Adv. Comput. Commun. Syst., IEEE, pp. 1771–1776, 2021.
- J.X. Leon-Medina, M. Anaya, D.A. Tibaduiza, “New Electronic Tongue Sensor Array System for Accurate Liquor Beverage Classification”, Sensors, vol:23, pp. 6178, 2023.
- H. Wang, Y. Sui, J. Liu, B. Kong, H. Li, L. Qin, Q. Chen, Analysis and comparison of the quality and flavour of traditional and conventional dry sausages collected from northeast China”, Food Chem. X., vol:20, pp. 100979, 2023.
- Y. Luo, R. Wang, H. Han, S. Wang, J. Ma, C. Yuan, Y. Ren, “Effects of dry-salting and brine-pickling on physicochemical properties and flavor of spaghetti squash shreds”, Food Biosci., vol: 56, pp. 103268, 2023.
- T. Feng, W. Cai, D. Chen, S. Song, L. Yao, M. Sun, H. Wang, C. Yu, Q. Liu, Y. Dang, “Analysis of umami taste and their contributing compounds in edible fungi based on electronic tongue, sensory evaluation, and chemical analysis”, J. Food Sci., 2023.
- H. Ji, D. Pu, W. Yan, Q. Zhang, M. Zuo, Y. Zhang, “Recent advances and application of machine learning in food flavor prediction and regulation”, Trends Food Sci. Technol, vol:138, pp. 738–751, 2023.
- M. Masuda, Y. Terada, R. Tsuji, S. Nakano, K. Ito, “Time-Series Sensory Analysis Provided Important TI Parameters for Masking the Beany Flavor of Soymilk”, Foods, vol: 12, pp. 2752, 2023.
- L. Wu, X. Wang, J. Hao, N. Zhu, M. Wang, “Geographical Indication Characteristics of Aroma and Phenolic Acids of the Changping Strawberry”, Foods, vol:12, pp. 3889, 2023.
- X. Zeng, R. Cao, Y. Xi, X. Li, M. Yu, J. Zhao, J. Cheng, J. Li, “Food flavor analysis 4.0: A cross-domain application of machine learning”, Trends Food Sci. Technol. Vol: 138, pp: 116–125, 2023.
- S. Wang, Q. Zhang, C. Liu, Z. Wang, J. Gao, X. Yang, Y. Lan, “Synergetic application of an E-tongue, E-nose and E-eye combined with CNN models and an attention mechanism to detect the origin of black pepper”, Sensors Actuators A Phys, vol:357, pp. 114417, 2023.
- N. Yuan, X. Chi, Q. Ye, H. Liu, N. Zheng, “Analysis of Volatile Organic Compounds in Milk during Heat Treatment Based on E-Nose”, E-Tongue and HS-SPME-GC-MS, Foods, vol:12, pp. 1071, 2023.
- E. Alpaydın, “Introduction to Machine Learning”, MIT Presss, Second Edi, 2010.
Abstract
The aim of this study is to determine the classes of flavours from chemical odor data obtained from different food flavours using electronic nose and machine learning methods. The relationship between volatile and non-volatile components in food flavors has been intensively researched. Normalized analytical protocols are applied to monitor the occurrence of regulated chemicals in processed foods. Although these methods are robust, they involve highly specialized instrumentation and time-consuming and expensive procedures. The electronic nose is a cost-effective and powerful electronic device that can make rapid measurements to distinguish food flavours. Since the molecules responsible for aromatic compounds released during food processing can be detected by the human nose, it may be possible to use electronic nose to detect changes in processed food products. In this research study, the calculated classification outcomes were estimated above ≥90.00% accuracy with sensitivity (≥90.00) and specificity (≥90.00) scores.
Keywords
Project Number
BAP-21-1004-001
References
- D. Ma, C. Liu, F. Wu, Z. Li, X. Wu, J. Gao, H. Zhao, Z. Zhang, “The Quality Detection and Recognition for Food Seasoning Based on an Artificial Olfactory System”, IEEE Instrum. Meas. Mag, vol:25 vo:9, pp. 42-52, 2022.
- Y. Durmuş, A.F. Atasoy, “Application of multivariate machine learning methods to investigate organic compound content of different pepper spices”, Food Biosci, vol:51, pp. 102216, 2023.
- P.-A. Chen, C.-I. Liu, K.-R. Chen, “Determining the Relationship between Aroma and Quality of Bao-Chung Tea by Solid-Phase Microextraction (SPME) and Electronic Nose Analyses”, Horticulturae, vol:9, pp. 930, 2023.
- M. Mesías, J.D. Barea-Ramos, J. Lozano, F.J. Morales, D. Martín-Vertedor, “Application of an Electronic Nose Technology for the Prediction of Chemical Process Contaminants in Roasted Almonds”, Chemosensors, vol:11, 2023.
- A. Ren, A. Zahid, A. Zoha, S.A. Shah, M.A. Imran, A. Alomainy, Q.H. Abbasi, “Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing”, IEEE Sens. J., vol:20, pp. 2075–2083, 2020.
- M. Pardo, G. Sberveglieri, “Coffee analysis with an electronic nose”, IEEE Trans. Instrum. Meas, vol:51, pp. 1334–1339, 2002.
- P.K. Kundu, A. Chatterjee, P.C. Panchariya, “Electronic Tongue System for Water Sample Authentication: A Slantlet-Transform-Based Approach”, IEEE Trans. Instrum. Meas., vol:60, pp. 1959–1966, 2011.
- M. Anly Antony, R.S. Kumar, “A Comparative Study on Predicting Food Quality using Machine Learning Techniques”, in: 2021 7th Int. Conf. Adv. Comput. Commun. Syst., IEEE, pp. 1771–1776, 2021.
- J.X. Leon-Medina, M. Anaya, D.A. Tibaduiza, “New Electronic Tongue Sensor Array System for Accurate Liquor Beverage Classification”, Sensors, vol:23, pp. 6178, 2023.
- H. Wang, Y. Sui, J. Liu, B. Kong, H. Li, L. Qin, Q. Chen, Analysis and comparison of the quality and flavour of traditional and conventional dry sausages collected from northeast China”, Food Chem. X., vol:20, pp. 100979, 2023.
- Y. Luo, R. Wang, H. Han, S. Wang, J. Ma, C. Yuan, Y. Ren, “Effects of dry-salting and brine-pickling on physicochemical properties and flavor of spaghetti squash shreds”, Food Biosci., vol: 56, pp. 103268, 2023.
- T. Feng, W. Cai, D. Chen, S. Song, L. Yao, M. Sun, H. Wang, C. Yu, Q. Liu, Y. Dang, “Analysis of umami taste and their contributing compounds in edible fungi based on electronic tongue, sensory evaluation, and chemical analysis”, J. Food Sci., 2023.
- H. Ji, D. Pu, W. Yan, Q. Zhang, M. Zuo, Y. Zhang, “Recent advances and application of machine learning in food flavor prediction and regulation”, Trends Food Sci. Technol, vol:138, pp. 738–751, 2023.
- M. Masuda, Y. Terada, R. Tsuji, S. Nakano, K. Ito, “Time-Series Sensory Analysis Provided Important TI Parameters for Masking the Beany Flavor of Soymilk”, Foods, vol: 12, pp. 2752, 2023.
- L. Wu, X. Wang, J. Hao, N. Zhu, M. Wang, “Geographical Indication Characteristics of Aroma and Phenolic Acids of the Changping Strawberry”, Foods, vol:12, pp. 3889, 2023.
- X. Zeng, R. Cao, Y. Xi, X. Li, M. Yu, J. Zhao, J. Cheng, J. Li, “Food flavor analysis 4.0: A cross-domain application of machine learning”, Trends Food Sci. Technol. Vol: 138, pp: 116–125, 2023.
- S. Wang, Q. Zhang, C. Liu, Z. Wang, J. Gao, X. Yang, Y. Lan, “Synergetic application of an E-tongue, E-nose and E-eye combined with CNN models and an attention mechanism to detect the origin of black pepper”, Sensors Actuators A Phys, vol:357, pp. 114417, 2023.
- N. Yuan, X. Chi, Q. Ye, H. Liu, N. Zheng, “Analysis of Volatile Organic Compounds in Milk during Heat Treatment Based on E-Nose”, E-Tongue and HS-SPME-GC-MS, Foods, vol:12, pp. 1071, 2023.
- E. Alpaydın, “Introduction to Machine Learning”, MIT Presss, Second Edi, 2010.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Machine Learning (Other) |
| Journal Section | Research Articles |
| Authors | |
| Project Number | BAP-21-1004-001 |
| Early Pub Date | April 27, 2024 |
| Publication Date | April 30, 2024 |
| Submission Date | December 15, 2023 |
| Acceptance Date | January 29, 2024 |
| Published in Issue | Year 2024 Volume: 6 Issue: 1 |
