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

Katarzyna Szwedziak, Ewa Polańczyk, Żaneta Grzywacz, Gniewko Niedbała and Wiktoria Wojtkiewicz
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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

Sustainability, 2020, vol. 12, issue 12, 1-14

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.

Keywords: neural networks; regression analysis; wheat; gluten; protein; near infrared spectrometry; correlation; storage (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:12:p:5050-:d:374258

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