BALIK TAZELİĞİNİN ELEKTRONİK BURUN VE MAKİNE ÖĞRENMESİ İLE TESPİTİ ÜZERİNE LİTERATÜR ÇALIŞMASI

Yıl 2020, Cilt: 16 Sayı: 2, 161 - 170, 30.12.2020

Kutlucan Görür , Onursal Çetin , İlyas Özer , Feyzullah Temurtaş

Öz

Balık tazelik kontrolü, gıda endüstrisindeki önemi nedeniyle son yıllarda büyük bir ilgi görmektedir. Balık tazeliği çalışmalarında farklı yaklaşımlar olsa da en önemlisi elektronik sensör dizilerinden oluşan elektronik burun yaklaşımıdır. Balıkta kalite özelliklerinden en önemlisi olan tazelik, balığın ilk sudan çıktığı andan tüketicilere kadar olan sürece ve depolama prosedürlerine bağlıdır. Pratik olarak, balıklarda ölüm sonrası meydana gelen fiziksel, kimyasal, biyokimyasal ve mikrobiyolojik değişiklikler, tat ve genel bir kalite kavramı açısından gıda özelliklerinde aşamalı bir kayıpla sonuçlanır. Bu açıdan geçen süre ve sıcaklık ürünün nihai kalitesi için anahtar faktörlerdir. Sinyal işleme ve makine öğrenmesi yaklaşımları balık tazeliğine ait elektronik burun tarafından ölçülen kokunun örüntüsünün tanınmasında oldukça önemli yöntemleri içermektedir. İlgili bu literatür çalışmasında özellikle balık tazeliği, elektronik burun ve sinyal işleme-makine öğrenmesi yaklaşımları açısından değerlendirilmiştir.

Anahtar Kelimeler

Balık Tazeliği, Elektronik Burun, Sinyal İşleme, Makine Öğrenmesi

Teşekkür

İlginiz için teşekkür ederim.

LITERATURE REVIEW ON DETERMINING FISH FRESHNESS BY ELECTRONIC NOSE AND MACHINE LEARNING

Öz

Fish freshness control has gained a lot of attention in recent years due to its importance in the food industry. Although there are different approaches in fish freshness studies, the most important one is the electronic nose approach consisting of electronic sensor arrays. Freshness, which is the most important quality feature in fish, depends on the process and storage procedures from the moment the fish first emerges to the consumers. Practically, physical, chemical, biochemical and microbiological changes that occur after death in fish result in a gradual loss of food properties in terms of taste and a general concept of quality. In this respect, elapsed time and temperature are key factors for the final quality of the product. Signal processing and machine learning approaches include very important methods in recognizing the pattern of odor measured by the electronic nose of fish freshness. In this related literature study, especially fish freshness was evaluated in terms of electronic nose and signal processing-machine learning approaches.

Anahtar Kelimeler

Fish Freshness, Electronic Nose, Signal Processing, Machine Learning

Kaynakça

• [1] Vorobioff J. et al.,”Development of an Electronic Nose for Determining the Freshness of Fish by the Desorption Constants of Sensors”, Sensor Letters, 11, 1-3, 2013.
• [2] Macagnano A. et al., “A model to predict fish quality from instrumental features”, Sensors and Actuators B, 111-112, 293-298, 2005.
• [3] GholamHosseini H. et al., “Intelligent Processing of E-nose Information for Fish Freshness Assessment”, 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, 173-177, 2007.
• [4] Weng X., et al., “A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies”, Journal of Sensors, 1-14, 2020.
• [5] Alimelli A. et al., “Fish freshness detection by a computer screen photoassisted based gas sensor array”, Analytica Chimica Acta, 582, 320-328, 2007.
• [6] Karakaya D. et al., “Electronic Nose and Its Applications: A Survey”, International Journal of Automation and Computing, 17, 179-209, 2020. [7] Zhou L. et al. “Application of Deep Learning in Food: A Review”, Comprehensive Reviews in Food Science and Food Safety, 18, 1793-1811, 2019.
• [8] Pasuba K. et al., “A comparison between shallow and deep architecture classifiers on small dataset”, 8th International Conference on Information Technology and Electrical Engineering, 1-6, 2016.
• [9] Güney S. et al. “Study of fish species discrimination via electronic nose”, Computers and Electronics in Agriculture, 119, 83-91, 2019. [10] O. Connell M. et al., “A practical approach for fish freshness determinations using a portable electronic nose”, Sensors and Actuators B, 80, 149-154, 2001.
• [11] Qin J. et al., “Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques”, Food Control , 114, 107234, 2020.
• [12] Gorur K. et al., “Glossokinetic potential based tongue–machine interface for 1-D extraction”, Australasian physical & engineering sciences in medicine, 41-2, 379-391, 2018.
• [13] Gorur K. et al., “Glossokinetic potential based tongue–machine interface for 1-D extraction using neural networks”, Biocybernetics and Biomedical Engineering, 38-3, 745-759, 2018.
• [14] Gorur K. et al., “GKP signal processing using deep CNN and SVM for tongue-machine interface”, Traitement Du Signal, 36, 319-329, 2019.
• [15] Schmidhuber J., “Deep Learning in Neural Networks:An Overview”, Neural Networks, 61, 85-117, 2015.
• [16] Bernardo YAA. et al., “Fish Quality Index Method: Principles, weaknesses, validation, and alternatives—A review”, Comprehensive Reviews In Food Science And Food Safety,19, 2657-2676, 2020.
• [17] Taheri-Garavand A. et al., “Real-time nondestructive monitoring of Common Carp Fish freshness using robust vision-based intelligent modeling approaches”, Computers and Electronics in Agriculture, 159, 16-27, 2019.
• [18] Guney S. et. al, ”Fish Freshness Assessment by Using Electronic Nose”, 36th International Conference on Telecommunications and Signal Processing, 742-746, 2013.
• [19] Rivai M. et al., “Fish Quality Recognition using Electrochemical Gas Sensor Array and Neural Network”, International Conference of Computer Engineering, Network, and Intelligent Multimedia, 1-5, 2019.
• [20] Guney S. et al., “Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure”, International Journal of Pattern Recognition and Artificial Intelligence, 34-3, 1-17, 2020.
• [21] Grassi S. et al., “Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)”, Sensors, 19-14, 1-15, 2019.
• [22] Vajdi M. et al., “Using electronic nose to recognize fish spoilage with an optimum classifier”, Journal of Food Measurement and Characterization, 13, 1205-1217, 2019.
• [23] Li P., et al., “Fish meal freshness detection by GBDT based on a portable electronic nose system and HS‑SPME–GC–MS”, European Food Research and Technology, 246, 1129- 1140, 2020.
• [24] Khoshnoudi-Nia S. et al., “Prediction of various freshness indicators in fsh fllets by one multispectral imaging system”, Nature, Scientific Reports, 9, 14704, 2019.
• [25] Huang X. et al., “A novel technique for rapid evaluation of fish freshness using colorimetric sensor array”, Journal of Food Engineering, 105, 632-637, 2011.
• [26] Taheri-Garavand A., “Smart deep learning-based approach for non-destructive freshness diagnosis of common carp fish”, Journal of Food Engineering, 278, 109930, 2020. [27] Gulbag A. et al.,” A study on transient and steady state sensor data for identification of individual gas concentrations in their gas mixtures”, Sensors and Actuators B, 121, 590-599, 2007.
• [28] Gulbag A. et al., “Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks”, Sensors and Actuators B, 131, 196-204, 2008.