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

Yıl 2025, Cilt: 8 Sayı: 1, 182 - 199, 17.01.2025
https://doi.org/10.47495/okufbed.1457033

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.

Kaynakça

  • Acevedo-Fani A., Soliva-Fortuny R., Martín-Belloso O. Photo-protection and controlled release of folic acid using edible alginate/chitosan nanolaminates. Journal of Food Engineering 2018; 229: 72–82.
  • Aghbashlo M., Hosseinpour S., Ghasemi-Varnamkhasti M. Computer vision technology for real-time food quality assurance during the drying process. Trends in Food Science & Technology 2014; 39(1): 76–84.
  • Alp D., Bulantekin Ö. The microbiological quality of various foods dried by applying different drying methods: A review. European Food Research and Technology 2021; 247: 1333–1343.
  • Boz M., Durgun Y. Parkinson hastalarının aktivitelerinin tanınmasında TinyML tabanlı uç bilişim sistemi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 2023; 12(1): 317-323.
  • Cen H., He Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology 2021; 18(2): 72–83.
  • Chakravartula SSN., Bandiera A., Nardella M., Bedini G., Ibba P., Massantini R., Moscetti R. Computer vision-based smart monitoring and control system for food drying: A study on carrot slices. Computers and Electronics in Agriculture 2023; 206: 107654.
  • Dai A., Zhou X., Liu X., Liu J., Zhang C. Intelligent control of a grain drying system using a GA-SVM-IMPC controller. Drying Technology 2018; 36(12): 1407938.
  • Durgun Y. Classification of starch adulteration in milk using spectroscopic data and machine learning. International Journal of Engineering Research and Development, 16(1): 221-226.
  • Durgun Y., Durgun M. Kenar hesaplama tabanlı, mikrodenetleyici entegreli, çok amaçlı ve düşük maliyetli modül geliştirilmesi: Bakteriyel koloni sayımı örneği. Journal of the Institute of Science and Technology 2024; 14(2): 531-543.
  • Gunathilake DMCC., Senanayaka DP., Adiletta G., Senadeera W. Drying of agricultural crops. In Advances in Agricultural Machinery and Technologies 2018; CRC Press: 331-365.
  • Hosseinpour S., Rafiee S., Mohtasebi SS., Aghbashlo M. Application of computer vision technique for online monitoring of shrimp color changes during drying. Journal of Food Engineering 2013; 115(1): 99-114.
  • Kamboj S., Gupta N., Bandral JD., Gandotra G., Anjum N. Food safety and hygiene: A review. International Journal of Chemical Studies 2020; 8(2): 358-368.
  • Karim A., Fawzia S., Rahman MM. Advanced Micro-level experimental techniques for food drying and processing applications. CRC Press; 2021.
  • Kaveh M., Çetin N., Khalife E., Abbaspour-Gilandeh Y., Sabouri M., Sharifian F. Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers. Journal of Food Process Engineering 2023; 46(12): e14475.
  • Khan MIH., Sablani S., Joardder M., Karim MA. Application of machine learning-based approach in food drying: opportunities and challenges. Drying Technology 2022; 40(6), 1051-1067.
  • Lightsciencetech. Visible Wavelength Range for Plant Growth. Retrieved from https://lightsciencetech.com/visible-wavelength-range-plant-growth/ (Acces date: 16.09.2023).
  • Liu Z., Zhang R., Yang C., Hu B., Luo X., Li Y., Dong C. Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2022; 120921.
  • Manzocco L., Nicoli MC. Surface processing: existing and potential applications of ultraviolet light. Critical Reviews in Food Science and Nutrition 2015; 55(4): 469–484.
  • Moses JA., Norton T., Alagusundaram K., Tiwari BK. Novel drying techniques for the food industry. Food Engineering Reviews 2014; 6: 43–55.
  • Mousakhani-Ganjeh A., Amiri A., Nasrollahzadeh F., Wiktor A., Nilghaz A., Pratap-Singh A., Khaneghah AM. Electro-based technologies in food drying - A comprehensive review. LWT - Food Science and Technology 2021; 145, 111315.
  • Nagy M., Wang S., Farag MA. Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications. Trends in Food Science & Technology 2022; 123: 290-309.
  • Nguyen D., Nguyen VD., Tran TTH., Le KH. Artificial neural network modeling of microwave-assisted heat pump drying process. IOP Conference Series: Earth and Environmental Science 2022; 1121.
  • Okpala COR., Korzeniowska M. Understanding the relevance of quality management in agro-food product industry: From ethical considerations to assuring food hygiene quality safety standards and its associated processes. Food Reviews International 2021: 1–74.
  • Qu H., Masud MH., Islam M., Khan MIH., Ananno AA., Karim A. Sustainable food drying technologies based on renewable energy sources. Critical Reviews in Food Science and Nutrition 2022; 62(25): 6872–6886.
  • Reis FR., Marques C., de Moraes ACS., Masson ML. Trends in quality assessment and drying methods used for fruits and vegetables. Food Control 2022; 109254.
  • Ren Y., Sun DW. Monitoring of moisture contents and rehydration rates of microwave vacuum and hot air dehydrated beef slices and splits using hyperspectral imaging. Food Chemistry 2022; 382: 132346.
  • Shin DJ., Andini N., Hsieh K., Yang S., Wang TH. Emerging analytical techniques for rapid pathogen identification and susceptibility testing. Annual Review of Analytical Chemistry 2019; 12: 41–67.
  • Şen Arslan H., Cabi A., Yerlikaya S., Sariçoban C. Antibacterial and antioxidant activity of peach leaf extract prepared by air and microwave drying. Journal of Food Processing and Preservation 2021; 45(10): e15847.
  • United Nations. About the Sustainable Development Goals. (Acces date: 11.09.2023).
  • Xin H., Mujumdar A. Spray drying and its application in food processing. Taylor & Francis Group 2015.
  • Yerlikaya S., Arslan HŞ. Comparison some microbiological and physicochemical properties of freeze dryed and spray dryed milk powder. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 2019; 8(2): 677-687.
  • Yerlikaya S., Arslan HŞ. Propolis katkılı liyofilize yumurta tozu üretimi. Harran Tarım ve Gıda Bilimleri Dergisi 2023; 27(1): 125-136.
  • Zambrano MV., Dutta B., Mercer DG., MacLean HL., Touchie MF. Assessment of moisture content measurement methods of dried food products in small-scale operations in developing countries: A review. Trends in Food Science & Technology 2019; 88: 484-496

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

Yıl 2025, Cilt: 8 Sayı: 1, 182 - 199, 17.01.2025
https://doi.org/10.47495/okufbed.1457033

Öz

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.

Kaynakça

  • Acevedo-Fani A., Soliva-Fortuny R., Martín-Belloso O. Photo-protection and controlled release of folic acid using edible alginate/chitosan nanolaminates. Journal of Food Engineering 2018; 229: 72–82.
  • Aghbashlo M., Hosseinpour S., Ghasemi-Varnamkhasti M. Computer vision technology for real-time food quality assurance during the drying process. Trends in Food Science & Technology 2014; 39(1): 76–84.
  • Alp D., Bulantekin Ö. The microbiological quality of various foods dried by applying different drying methods: A review. European Food Research and Technology 2021; 247: 1333–1343.
  • Boz M., Durgun Y. Parkinson hastalarının aktivitelerinin tanınmasında TinyML tabanlı uç bilişim sistemi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 2023; 12(1): 317-323.
  • Cen H., He Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology 2021; 18(2): 72–83.
  • Chakravartula SSN., Bandiera A., Nardella M., Bedini G., Ibba P., Massantini R., Moscetti R. Computer vision-based smart monitoring and control system for food drying: A study on carrot slices. Computers and Electronics in Agriculture 2023; 206: 107654.
  • Dai A., Zhou X., Liu X., Liu J., Zhang C. Intelligent control of a grain drying system using a GA-SVM-IMPC controller. Drying Technology 2018; 36(12): 1407938.
  • Durgun Y. Classification of starch adulteration in milk using spectroscopic data and machine learning. International Journal of Engineering Research and Development, 16(1): 221-226.
  • Durgun Y., Durgun M. Kenar hesaplama tabanlı, mikrodenetleyici entegreli, çok amaçlı ve düşük maliyetli modül geliştirilmesi: Bakteriyel koloni sayımı örneği. Journal of the Institute of Science and Technology 2024; 14(2): 531-543.
  • Gunathilake DMCC., Senanayaka DP., Adiletta G., Senadeera W. Drying of agricultural crops. In Advances in Agricultural Machinery and Technologies 2018; CRC Press: 331-365.
  • Hosseinpour S., Rafiee S., Mohtasebi SS., Aghbashlo M. Application of computer vision technique for online monitoring of shrimp color changes during drying. Journal of Food Engineering 2013; 115(1): 99-114.
  • Kamboj S., Gupta N., Bandral JD., Gandotra G., Anjum N. Food safety and hygiene: A review. International Journal of Chemical Studies 2020; 8(2): 358-368.
  • Karim A., Fawzia S., Rahman MM. Advanced Micro-level experimental techniques for food drying and processing applications. CRC Press; 2021.
  • Kaveh M., Çetin N., Khalife E., Abbaspour-Gilandeh Y., Sabouri M., Sharifian F. Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers. Journal of Food Process Engineering 2023; 46(12): e14475.
  • Khan MIH., Sablani S., Joardder M., Karim MA. Application of machine learning-based approach in food drying: opportunities and challenges. Drying Technology 2022; 40(6), 1051-1067.
  • Lightsciencetech. Visible Wavelength Range for Plant Growth. Retrieved from https://lightsciencetech.com/visible-wavelength-range-plant-growth/ (Acces date: 16.09.2023).
  • Liu Z., Zhang R., Yang C., Hu B., Luo X., Li Y., Dong C. Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2022; 120921.
  • Manzocco L., Nicoli MC. Surface processing: existing and potential applications of ultraviolet light. Critical Reviews in Food Science and Nutrition 2015; 55(4): 469–484.
  • Moses JA., Norton T., Alagusundaram K., Tiwari BK. Novel drying techniques for the food industry. Food Engineering Reviews 2014; 6: 43–55.
  • Mousakhani-Ganjeh A., Amiri A., Nasrollahzadeh F., Wiktor A., Nilghaz A., Pratap-Singh A., Khaneghah AM. Electro-based technologies in food drying - A comprehensive review. LWT - Food Science and Technology 2021; 145, 111315.
  • Nagy M., Wang S., Farag MA. Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications. Trends in Food Science & Technology 2022; 123: 290-309.
  • Nguyen D., Nguyen VD., Tran TTH., Le KH. Artificial neural network modeling of microwave-assisted heat pump drying process. IOP Conference Series: Earth and Environmental Science 2022; 1121.
  • Okpala COR., Korzeniowska M. Understanding the relevance of quality management in agro-food product industry: From ethical considerations to assuring food hygiene quality safety standards and its associated processes. Food Reviews International 2021: 1–74.
  • Qu H., Masud MH., Islam M., Khan MIH., Ananno AA., Karim A. Sustainable food drying technologies based on renewable energy sources. Critical Reviews in Food Science and Nutrition 2022; 62(25): 6872–6886.
  • Reis FR., Marques C., de Moraes ACS., Masson ML. Trends in quality assessment and drying methods used for fruits and vegetables. Food Control 2022; 109254.
  • Ren Y., Sun DW. Monitoring of moisture contents and rehydration rates of microwave vacuum and hot air dehydrated beef slices and splits using hyperspectral imaging. Food Chemistry 2022; 382: 132346.
  • Shin DJ., Andini N., Hsieh K., Yang S., Wang TH. Emerging analytical techniques for rapid pathogen identification and susceptibility testing. Annual Review of Analytical Chemistry 2019; 12: 41–67.
  • Şen Arslan H., Cabi A., Yerlikaya S., Sariçoban C. Antibacterial and antioxidant activity of peach leaf extract prepared by air and microwave drying. Journal of Food Processing and Preservation 2021; 45(10): e15847.
  • United Nations. About the Sustainable Development Goals. (Acces date: 11.09.2023).
  • Xin H., Mujumdar A. Spray drying and its application in food processing. Taylor & Francis Group 2015.
  • Yerlikaya S., Arslan HŞ. Comparison some microbiological and physicochemical properties of freeze dryed and spray dryed milk powder. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 2019; 8(2): 677-687.
  • Yerlikaya S., Arslan HŞ. Propolis katkılı liyofilize yumurta tozu üretimi. Harran Tarım ve Gıda Bilimleri Dergisi 2023; 27(1): 125-136.
  • Zambrano MV., Dutta B., Mercer DG., MacLean HL., Touchie MF. Assessment of moisture content measurement methods of dried food products in small-scale operations in developing countries: A review. Trends in Food Science & Technology 2019; 88: 484-496
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenme (Diğer), Endüstriyel Biyoteknoloji (Diğer), Gıda Mühendisliği
Bölüm Araştırma Makaleleri (RESEARCH ARTICLES)
Yazarlar

Yeliz Durgun 0000-0003-3834-5533

Mahmut Durgun 0000-0002-5010-687X

Erken Görünüm Tarihi 15 Ocak 2025
Yayımlanma Tarihi 17 Ocak 2025
Gönderilme Tarihi 22 Mart 2024
Kabul Tarihi 26 Ağustos 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 1

Kaynak Göster

APA Durgun, Y., & Durgun, M. (2025). Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 8(1), 182-199. https://doi.org/10.47495/okufbed.1457033
AMA Durgun Y, Durgun M. Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices. Osmaniye Korkut Ata University Journal of The Institute of Science and Techno. Ocak 2025;8(1):182-199. doi:10.47495/okufbed.1457033
Chicago Durgun, Yeliz, ve Mahmut Durgun. “Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8, sy. 1 (Ocak 2025): 182-99. https://doi.org/10.47495/okufbed.1457033.
EndNote Durgun Y, Durgun M (01 Ocak 2025) Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8 1 182–199.
IEEE Y. Durgun ve M. Durgun, “Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices”, Osmaniye Korkut Ata University Journal of The Institute of Science and Techno, c. 8, sy. 1, ss. 182–199, 2025, doi: 10.47495/okufbed.1457033.
ISNAD Durgun, Yeliz - Durgun, Mahmut. “Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8/1 (Ocak 2025), 182-199. https://doi.org/10.47495/okufbed.1457033.
JAMA Durgun Y, Durgun M. Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices. Osmaniye Korkut Ata University Journal of The Institute of Science and Techno. 2025;8:182–199.
MLA Durgun, Yeliz ve Mahmut Durgun. “Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 8, sy. 1, 2025, ss. 182-99, doi:10.47495/okufbed.1457033.
Vancouver Durgun Y, Durgun M. Hot-Air Drying Using Near UV Visible NIR Radiation Spectroscopy and Edge Computing Control System : A Study On Apple Slices. Osmaniye Korkut Ata University Journal of The Institute of Science and Techno. 2025;8(1):182-99.

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