Kıyma Kokuşma Analizi İçin Düşük Maliyetli Elektronik Burun Geliştirilmesi

Kıyma örneklerinin bozulmasını belirlemek için düşük maliyetli, kullanımı kolay bir elektronik burun geliştirilmiştir. E-burun donanım, yazılım ve veri işleme bileşenlerinden oluşmaktadır. Donanım bileşeninin ana unsurları, hidrojen sülfür (H2S) ve amonyağa (NH3) duyarlı yarı iletken gaz sensörleridir. MIT App Inventor 2 kullanılarak, donanım bileşenini çalıştırmak, verileri almak, ön işlemeye tabi tutmak ve Google Sheets'e göndermek için bir Android uygulaması geliştirilmiştir. Google Colab ve Google Script kullanılarak sınıflandırma modeli geliştirilmiş ve veri yönetimi gerçekleştirilmiştir. Toplanan sensör sinyallerinden sınıflandırma modelleri geliştirmek için lojistik regresyon metodu kullanılmıştır. Model, gaz konsantrasyonlarına dayalı olarak kıyma örneklerini "bozulmuş" ve "taze" olarak sınıflandırmıştır. Nessler çözeltisi gerçek bozulma durumunu belirlemek için kullanılmıştır. Buzdolabında ve oda sıcaklığında saklanan dana kıyma örnekleri, lojistik regresyon modeli geliştirmek için bozulmuş ve taze örneklerin elde edilmesi için kullanılmıştır. Model geliştirmek için toplam 36 örnek kullanılmıştır. Model ve prototip cihaz performansını test etmek için başka bir 24 numune seti kullanılmıştır. Test aşamasında kullanılan tüm örneklerin doğru bir şekilde sınıflandırıldığı görülmüştür. Sistemin maliyeti Ocak 2021 kur değerleri dikkate alındığında yaklaşık 100 $ olarak belirlenmiştir.

Anahtar Kelimeler:

Gıda güvenliği, yapay zeka, makine öğrenmesi, lojistik regresyon, elektronik burun

DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF

A low-cost, easy-to-use e-nose is developed to detect the spoilage of ground meat. E-nose consists of hardware, software and data processing components. The main elements of hardware component are gas sensors sensitive to hydrogen sulfide (H2S) and ammonia (NH3). Using MIT App Inventor 2 an Android application is developed to run the hardware component, retrieve the data, preprocess and send it to Google Sheets. Classification model is developed, and data management is carried out in Google Colab and Google Script. Logistic regression method is used to develop classification models from the collected signals. The model classified the samples as "spoiled" and "fresh" based on the gas concentrations. The Nessler solution is used to determine the actual spoilage state. Ground beef samples stored in the refrigerator and at room temperature are used to obtain spoiled and fresh samples to develop a logistic regression model. A total of 36 samples are used to develop model. Another set of 24 samples is used to test model and prototype device performance. It is observed that all samples used in the testing phase were classified correctly. The cost of the system has been determined as approximately $100 considering January 2021 exchange rates.

Keywords:

Food safety, Artificial intelligence, Machine learning, Logistic regression, Electronic nose,

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