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

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

doi:10.3390/su12125050

Katarzyna Szwedziak, Wiktoria Wojtkiewicz + Show 3 more

Open Access

https://doi.org/10.3390/su12125050

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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.

Highlights

It is a growing challenge and problem for mankind to meet the growing demand and need for adequate food supplies that result from the growing world population
On the basis of the conducted tests, we can conclude that the water, protein and gluten content of wheat during storage was within the standard
Mutlu et al [41,42,43] used near infrared spectrometry (NIR) combined with an artificial neural network to predict flour quality parameters such as the protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, dough softening degree, tenacity, extensibility, strength and baking test [41]

Summary

Introduction

It is a growing challenge and problem for mankind to meet the growing demand and need for adequate food supplies that result from the growing world population. By 2050, the population is projected to grow to 9.1 billion and food production will have to increase by about 70% to cover all food needs [1,2,3]. Maize, rye, oats and rice grains are an important source of nutrients and energy. They have to be transported and stored in a manner that ensures their parameters and appropriate quality indicators [8,9]. Cereals need to be transported and stored correctly in order to maintain their quality, because the production of cereal plants is seasonal in nature and, depending on the location, takes place under different conditions

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