Yakın UV Görünür NIR Radyasyon Spektroskopisi ve Kenar Hesaplama Kontrol Sistemi Kullanılarak Sıcak Hava Kurutma: Elma Dilimleri Üzerine Bir Çalışma

Öz

Yakın UV, Görünür Işık ve Yakın-Kızılötesi Radyasyon, enerji, tarım, tıp ve gıda endüstrisi araştırmaları için büyük ilgi gören ışık dalga boyları bölgelerinden bazılarıdır. Işık spektrumunun, görüntüleme, gıda kalitesi ve güvenliği değerlendirmesi için yıkıcı olmayan, gerçek zamanlı algılama kullanımı, tüm bu alanlarda giderek daha fazla önem kazanmaktadır. Cihazların gerçek zamanlı izlenmesini ve makine öğrenimi yöntemleri kullanarak kontrolünü sağlayan kenar hesaplama, sistem stabilitesini artırmak, hataları en aza indirmek ve robotik müdahaleyi kolaylaştıran araçlar geliştirmek için gereklidir. Bu çalışmada, gıda kurutmasında yakın UV-vis-NIR radyasyon ölçümü kullanılarak kurutma sisteminin etkisi ve performansı kenar hesaplama kullanılarak sunulmaktadır. Sistem, 18 farklı ölçüm yapabilen üç çok spektral sensör içermektedir. Nesnelerin ağırlığını, kabin içindeki sıcaklık ve nem ölçmek için sensörler de yerleştirilmiştir. Elde edilen veriler, makine öğrenimi algoritmalarını gerçekleştirebilen ve kabini kontrol edebilen bir mikrodenetleyici (Arduino Nano 33 BLE) kullanılarak gerçek zamanlı olarak işlenir. Kenar hesaplama, veri işleme ve analitik işlemlerin cihazda gerçekleştirilmesini sağlayarak, gerçek zamanlı sonuçlar ve kontrol işlemleri sunar. Bu çalışmada, elma dilimlerinin kurutma işlemi sırasında radyasyon seviyelerindeki değişim ve kurutma kalitesi üzerindeki etki araştırılmaktadır. Sonuçlar, kenar hesaplama teknolojisi kullanılarak yapılan ölçümlerin, elma dilimlerinin kurutma işlemi sırasında etkili bir şekilde gerçekleştirilebileceğini göstermektedir.

Anahtar Kelimeler

• Kenar hesaplama
• Gıda kurutma
• IoT (Nesnelerin İnterneti
• Makine öğrenimi

Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices

Abstract

Near-UV, Visible Light, and Near-Infrared Radiation are some of the light wavelength regions that are of great interest for energy, agriculture, medical, and food industry research. The use of light spectrum for non-destructive, real-time sensing for imaging, food quality, and safety evaluation is becoming increasingly important in all these fields. Edge computing, which enables real-time monitoring of devices and control using machine learning methods, is necessary to improve system stability, minimize errors and develop tools that facilitate robotic intervention. In this study, the effect and performance of a drying system using Near-UV-vis-NIR radiation measurement in food drying using edge computing is presented. The system comprises three multi-spectral sensors that allow 18 different measurements. Sensors are also placed to measure the weight of the objects, temperature, and humidity inside the cabin. The data acquired is processed in real-time using a microcontroller (Arduino Nano 33 BLE) that can perform machine learning algorithms and control the cabin. Edge computing enables data processing and analytic operations to be performed on the device, thus providing real-time results and control operations. In this study, the change in radiation levels and the effect on drying quality during the drying process of apple slices are investigated. The results show that measurements performed using edge computing technology can effectively be performed during the drying process of apple slices.

Keywords

• Edge computing
• Food drying
• IoT
• Machine learning

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