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Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

Author

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  • Katarzyna Szwedziak

    (Department of Biosystems Engineering, Faculty of Production Engineering and Logistic, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland)

  • Ewa Polańczyk

    (Department of Biosystems Engineering, Faculty of Production Engineering and Logistic, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland)

  • Żaneta Grzywacz

    (Department of Biosystems Engineering, Faculty of Production Engineering and Logistic, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland)

  • Gniewko Niedbała

    (Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland;)

  • Wiktoria Wojtkiewicz

    (Department of Biosystems Engineering, Faculty of Production Engineering and Logistic, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland)

Abstract

An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 ± 0.15), water (10.63 ± 0.16) or gluten (30.56 ± 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain.

Suggested Citation

  • Katarzyna Szwedziak & Ewa Polańczyk & Żaneta Grzywacz & Gniewko Niedbała & Wiktoria Wojtkiewicz, 2020. "Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage," Sustainability, MDPI, vol. 12(12), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:5050-:d:374258
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    References listed on IDEAS

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    1. Gniewko Niedbała & Danuta Kurasiak-Popowska & Kinga Stuper-Szablewska & Jerzy Nawracała, 2020. "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain," Agriculture, MDPI, vol. 10(4), pages 1-12, April.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    Cited by:

    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    2. Dominika Sieracka & Maciej Zaborowicz & Jakub Frankowski, 2023. "Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp ( Cannabis sativa L.) Using Artificial Intelligence Methods," Agriculture, MDPI, vol. 13(5), pages 1-11, May.
    3. Mohammad Rokhafrouz & Hooman Latifi & Ali A. Abkar & Tomasz Wojciechowski & Mirosław Czechlowski & Ali Sadeghi Naieni & Yasser Maghsoudi & Gniewko Niedbała, 2021. "Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat," Agriculture, MDPI, vol. 11(11), pages 1-24, November.
    4. Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.

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